108 items tagged "big data"

  • 'Mkb kan nog veel winnen op gebied van big data'

    mkb kan nog veel winnen gebied van big data Werken met big data, grote en ongestructureerde gegevensbestanden, is voor veel mkb'ers nog een ver-van-mijn-bed-show. Het ontbreekt ondernemers aan tijd en geld om zich in data te verdiepen, maar ook aan kennis en kunde.

    Dat blijkt uit het onderzoek Ondernemen met (big) data door het mkb van de Kamer van Koophandel en de Jheronimus Academy of Data Science (JADS) onder 1.710 leden van het KvK Ondernemerspanel. JADS is een initiatief van de universiteiten van Eindhoven en Tilburg, de provincie Noord-Brabant en de gemeente Den Bosch.

    Bijna de helft van de ondervraagden (44 procent) ziet een kans in big data, terwijl de andere helft (49 procent) geen relevantie ziet voor het eigen bedrijf. Vier van de tien ondernemers zien werken met data als kostenpost en niet als strategisch middel.

    Van alle ondernemers schenkt 37 procent nauwelijks en 25 procent niet structureel aandacht aan databeheer. Bij 40 procent staat databeheer wel structureel op de radar. Het inzetten van data als een dagelijkse, strategische activiteit gebeurt nauwelijks.

    Hekkensluiters

    De sectoren ICT en industrie zijn het meest ontwikkeld, gevolgd door logistiek en financiën. Landbouw, cultuur en sport, bouw en onderwijs zijn de hekkensluiters, terwijl big data in die sectoren juist een steeds grotere rol spelen.

    Verder speelt de omzet van een bedrijf een rol bij hoe belangrijk big data worden gevonden. Bedrijven die minder dan 250.000 euro per jaar omzetten, zien vaak de relevantie niet van big data. Bij een omzet van meer dan een miljoen euro is die relevantie boven elke twijfel verheven.

    Bijna de helft van de mkb'ers zegt graag hulp te krijgen bij ondernemen met big data. Zo willen ze raad bij het bepalen van concrete ondernemingskansen.

    10 procent van het mkb bestaat uit "koplopers" in big data. Deze koplopers groeien gemiddeld meer dan 5 procent per jaar en hebben een gemiddelde omzet van bijna twee miljoen euro. Ook zijn ze vergeleken met andere mkb'ers innovatiever en exporteren ze vaker.

    Source: nu.nl, 10 januari 2017

  • ‘Als je wilt overleven als bedrijf móét je ruimte voor Big Data inruimen’

    shutterstock 139983571De toekomst van bedrijven hangt af van Big Data aldus Emile Aarts, rector magnificus van de Tilburg University. In een interview met Management Team vertelt de rector magnificus over de typen toepassingen die hij als kansrijk ziet.
    Beeld ‘Als je wilt overleven als bedrijf móét je ruimte voor Big Data inruimen’
     
    Zoals Copernicus, Darwin en Freud ons wereldbeeld hebben laten kantelen, zo gaan Big Data dat ook doen. De invloed van Big Data wordt groot, zeer groot, aldus Aarts in het interview.
     
    Process Mining
    Een van de voorbeelden die hij noemt is Process Mining. Daarmee is het mogelijk om met ‘event logs’ en andere data in het bedrijf processen in een organisatie in kaart te brengen. Hiermee is te zien hoe het een en ander werkelijk verloopt, in hoeverre men afwijkt van de processen op papier. Procesverbeteringen kunnen daardoor nauwkeuriger en sneller worden doorgevoerd.
     
    Scenario’s
    Op de vraag of managers wel leiding kunnen geven aan data deskundigen antwoordt Aarts dat ondernemers en managers wel enig verstand moeten hebben van Big Data en kunstmatige intelligentie. Maar met verstand van Big Data zijn ze er nog niet. “De informatie op basis waarvan zij beslissingen moeten, zal steeds meer worden gevisualiseerd in scenario’s. Daarmee moeten ze dus ook kunnen omgaan, wat voor de manager die het liefste informatie in een spreadsheet heeft nog lastig kan zijn,” aldus Aarts.
     
    Soft skills
    Ook worden soft skills voor managers steeds belangrijker. “Mensen kunnen zich onderscheiden door hun empathische vermogen, hun communicatieve vaardigheden en de manier waarop ze anderen kunnen inspireren. Zeker als ze aan jongere mensen leiding willen geven, die wars zijn van hiërarchische structuren en graag werken in bedrijven waar beslissingen in sterke mate collectief worden genomen,” aldus Aarts.
     
    Bron: hrpraktijk.nl, 14 november 2016
  • ‘Van big data profiteren kan pas als belangrijke zorgen zijn weggenomen’

    546400Big data kan duizenden banen scheppen en miljarden euro’s aan omzet genereren. Van de kansen die big data biedt kan echter niet worden geprofiteerd zolang belangrijke zorgen op het gebied van privacy en beveiligen niet zijn verholpen.

    Dit meldt het Science and Technology Committee van het Britse Lagerhuis in het rapport ‘The Big Data Dilemma’ (pdf), waar onder andere technologie experts, onderzoekers, privacyspecialisten en open data experts aan hebben meegewerkt. Naar verwachting kan Big Data in alleen al het Verenigd Koninkrijk in de komende vijf jaar voor 200 miljard dollar omzet zorgen. Big data biedt dan ook enorme kansen.

    Misbruik van persoonlijke informatie
    Het rapport waarschuwt dat van deze kansen echter niet geprofiteerd kan worden indien bedrijven misbruik maken van persoonlijke informatie. Het is dan ook noodzakelijk dat er voldoende maatregelen worden genomen om de privacy van gebruikers en de veiligheid van verzamelde data te waarborgen. Zo adviseert het Science and Technology Committee het misbruik van big data strafbaar te stellen. De Britse overheid zou hiermee niet moeten wachten op de introductie van Europese regelgeving op dit gebied, maar juist vooruit lopen door nu alvast dergelijke wetgeving te introduceren. Dit kan zorgen van burgers over privacy en de veiligheid van hun data wegnemen.

    Om de groeiende juridische en ethische uitdagingen rond big data aan te pakken wil het Lagerhuis daarom een Council of Data Ethics opzetten. Deze raad zou onderdeel moeten worden van het Alan Turing Institute, het nationale instituut in het VK op het gebied van datawetenschappen.

    Bedrijven analyseren slechts 12% van hun data
    Op het gebied van big data kunnen bedrijven zich over het algemeen nog flink ontwikkelen.Bedrijven die big data inzetten zouden 10% productiever zijn dan bedrijven hun grote datasets niet analyseren. Desondanks schatten de meeste bedrijven slechts 12% van hun data daadwerkelijk te analyseren. Naar schatting zal big data in de komende vijf jaar pakweg 58.000 banen opleveren in het VK.

    Rapport The Big data Dilemma

    Source: Executive People

  • ‘Vooruitgang in BI, maar let op ROI’

    5601405Business intelligence (bi) werd door Gartner al benoemd tot hoogste prioriteit voor de cio in 2016. Ook de Computable-experts voorspellen dat er veel en grote stappen genomen gaan worden binnen de bi. Tegelijkertijd moeten managers ook terug kijken en nadenken over hun businessmodel bij de inzet van big data: hoe rechtvaardig je de investeringen in big data?

    Kurt de Koning, oprichter van Dutch Offshore ICT Management
    Business intelligence/analytics is door Gartner op nummer één gezet voor 2016 op de prioriteitenlijst voor de cio. Gebruikers zullen in 2016 hun beslissingen steeds meer laten afhangen van stuurinformatie die uit meerdere bronnen komt. Deze bronnen zullen deels bestaan uit ongestructureerde data. De bi-tools zullen dus niet alleen visueel de informatie aantrekkelijk moeten opmaken en een goede gebruikersinterface moeten bieden. Bij het ontsluiten van de data zullen die tools zich onderscheiden , die in staat zijn om orde en overzicht te scheppen uit de vele verschijningsvormen van data.

    Laurent Koelink, senior interim BI professional bij Insight BI
    Big data-oplossingen naast traditionele bi
    Door de groei van het aantal smart devices hebben organisaties steeds meer data te verwerken. Omdat inzicht (in de breedste zin) een van de belangrijkste succesfactoren van de toekomst gaat zijn voor veel organisaties die flexibel in willen kunnen spelen op de vraag van de markt, zullen zijn ook al deze nieuwe (vormen) van informatie moeten kunnen analyseren. Ik zie big data niet als vervangen van traditionele bi-oplossingen, maar eerder als aanvulling waar het gaat om analytische verwerking van grote hoeveelheden (vooral ongestructureerde) data.

    In-memory-oplossingen
    Organisaties lopen steeds vaker aan tegen de performance-beperkingen van traditionele database systemen als het gaat om grote hoeveelheden data die ad hoc moeten kunnen worden geanalyseerd. Specifieke hybride database/hardware-oplossingen zoals die van IBM, SAP en TeraData hebben hier altijd oplossingen voor geboden. Daar komen nu steeds vaker ook in-memory-oplossingen bij. Enerzijds omdat deze steeds betaalbaarder en dus toegankelijker worden, anderzijds doordat dit soort oplossingen in de cloud beschikbaar komen, waardoor de kosten hiervan goed in de hand te houden zijn.

    Virtual data integration
    Daar waar data nu nog vaak fysiek wordt samengevoegd in aparte databases (data warehouses) zal dit, waar mogelijk, worden vervangen door slimme metadata-oplossingen, die (al dan niet met tijdelijke physieke , soms in memory opslag) tijdrovende data extractie en integratie processen overbodig maken.

    Agile BI development
    Organisaties worden meer en meer genoodzaakt om flexibel mee te bewegen in en met de keten waar ze zich in begeven. Dit betekent dat ook de inzichten om de bedrijfsvoering aan te sturen (de bi-oplossingen) flexibel moeten mee bewegen. Dit vergt een andere manier van ontwikkelen van de bi-ontwikkelteams. Meer en meer zie je dan ook dat methoden als Scrum ook voor bi-ontwikkeling worden toegepast.

    Bi voor de iedereen
    Daar waar bi toch vooral altijd het domein van organisaties is geweest zie je dat ook consumenten steeds meer en vaker gebruik maken van bi-oplossingen. Bekende voorbeelden zijn inzicht in financiën en energieverbruik. De analyse van inkomsten en uitgaven op de webportal of in de app van je bank, maar ook de analyse van de gegevens van slimme energiemeters zijn hierbij sprekende voorbeelden. Dit zal in de komende jaren alleen maar toenemen en geïntegreerd worden.

    Rein Mertens, head of analytical platform bij SAS
    Een belangrijke trend die ik tot volwassenheid zie komen in 2016 is ‘streaming analytics’. Vandaag de dag is big data niet meer weg te denken uit onze dagelijkse praktijk. De hoeveelheid data welke per seconde wordt gegenereerd blijft maar toenemen. Zowel in de persoonlijke als zakelijke sfeer. Kijk maar eens naar je dagelijkse gebruik van het internet, e-mails, tweets, blog posts, en overige sociale netwerken. En vanuit de zakelijke kant: klantinteracties, aankopen, customer service calls, promotie via sms/sociale netwerken et cetera.

    Een toename van volume, variatie en snelheid van vijf Exabytes per twee dagen wereldwijd. Dit getal is zelfs exclusief data vanuit sensoren, en overige IoT-devices. Er zit vast interessante informatie verstopt in het analyseren van al deze data, maar hoe doe je dat? Een manier is om deze data toegankelijk te maken en op te slaan in een kosteneffectief big data-platform. Onvermijdelijk komt een technologie als Hadoop dan aan de orde, om vervolgens met data visualisatie en geavanceerde analytics aan de gang te gaan om verbanden en inzichten uit die data berg te halen. Je stuurt als het ware de complexe logica naar de data toe. Zonder de data allemaal uit het Hadoop cluster te hoeven halen uiteraard.

    Maar wat nu, als je op basis van deze grote hoeveelheden data ‘real-time’ slimme beslissingen zou willen nemen? Je hebt dan geen tijd om de data eerst op te slaan, en vervolgens te gaan analyseren. Nee, je wilt de data in-stream direct kunnen beoordelen, aggregeren, bijhouden, en analyseren, zoals vreemde transactie patronen te detecteren, sentiment in teksten te analyseren en hierop direct actie te ondernemen. Eigenlijk stuur je de data langs de logica! Logica, die in-memory staat en ontwikkeld is om dat heel snel en heel slim te doen. En uiteindelijke resultaten op te slaan. Voorbeelden van meer dan honderdduizend transacties zijn geen uitzondering hier. Per seconde, welteverstaan. Stream it, score it, store it. Dat is streaming analytics!

    Minne Sluis, oprichter van Sluis Results
    Van IoT (internet of things) naar IoE (internet of everything)
    Alles wordt digitaal en connected. Meer nog dan dat we ons zelfs korte tijd geleden konden voorstellen. De toepassing van big data-methodieken en -technieken zal derhalve een nog grotere vlucht nemen.

    Roep om adequate Data Governance zal toenemen
    Hoewel het in de nieuwe wereld draait om loslaten, vertrouwen/vrijheid geven en co-creatie, zal de roep om beheersbaarheid toch toenemen. Mits vooral aangevlogen vanuit een faciliterende rol en zorgdragend voor meer eenduidigheid en betrouwbaarheid, bepaald geen slechte zaak.

    De business impact van big data & data science neemt toe
    De impact van big data & data science om business processen, diensten en producten her-uit te vinden, verregaand te digitaliseren (en intelligenter te maken), of in sommige gevallen te elimineren, zal doorzetten.

    Consumentisering van analytics zet door
    Sterk verbeterde en echt intuïtieve visualisaties, geschraagd door goede meta-modellen, dus data governance, drijft deze ontwikkeling. Democratisering en onafhankelijkheid van derden (anders dan zelfgekozen afgenomen uit de cloud) wordt daarmee steeds meer werkelijkheid.

    Big data & data science gaan helemaal doorbreken in de non-profit
    De subtiele doelstellingen van de non-profit, zoals verbetering van kwaliteit, (patiënt/cliënt/burger) veiligheid, punctualiteit en toegankelijkheid, vragen om big data toepassingen. Immers, voor die subtiliteit heb je meer goede informatie en dus data, sneller, met meer detail en schakering nodig, dan wat er nu veelal nog uit de traditionelere bi-omgevingen komt. Als de non-profit de broodnodige focus van de profit sector, op ‘winst’ en ‘omzetverbetering’, weet te vertalen naar haar eigen situatie, dan staan succesvolle big data initiatieven om de hoek! Mind you, deze voorspelling geldt uiteraard ook onverkort voor de zorg.

    Hans Geurtsen, business intelligence architect data solutions bij Info Support
    Van big data naar polyglot persistence
    In 2016 hebben we het niet meer over big, maar gewoon over data. Data van allerlei soorten en in allerlei volumes die om verschillende soorten opslag vragen: polyglot persistence. Programmeurs kennen de term polyglot al lang. Een applicatie anno 2015 wordt vaak al in meerdere talen geschreven. Maar ook aan de opslag kant van een applicatie is het niet meer alleen relationeel wat de klok zal slaan. We zullen steeds meer andere soorten databases toepassen in onze data oplossingen, zoals graph databases, document databases, etc. Naast specialisten die alles van één soort database afweten, heb je dan ook generalisten nodig die precies weten welke database zich waarvoor leent.

    De doorbraak van het moderne datawarehouse
    ‘Een polyglot is iemand met een hoge graad van taalbeheersing in verschillende talen’, aldus Wikipedia. Het gaat dan om spreektalen, maar ook in het it-vakgebied, kom je de term steeds vaker tegen. Een applicatie die in meerdere programmeertalen wordt gecodeerd en data in meerdere soorten databases opslaat. Maar ook aan de business intelligence-kant volstaat één taal, één omgeving niet meer. De dagen van het traditionele datawarehouse met een etl-straatje, een centraal datawarehouse en één of twee bi-tools zijn geteld. We zullen nieuwe soorten data-platformen gaan zien waarin allerlei gegevens uit allerlei bronnen toegankelijk worden voor informatiewerkers en data scientists die allerlei tools gebruiken.

    Business intelligence in de cloud
    Waar vooral Nederlandse bedrijven nog steeds terughoudend zijn waar het de cloud betreft, zie je langzaam maar zeker dat de beweging richting cloud ingezet wordt. Steeds meer bedrijven realiseren zich dat met name security in de cloud vaak beter geregeld is dan dat ze zelf kunnen regelen. Ook cloud leveranciers doen steeds meer om Europese bedrijven naar hun cloud te krijgen. De nieuwe data centra van Microsoft in Duitsland waarbij niet Microsoft maar Deutsche Telekom de controle en toegang tot klantgegevens regelt, is daar een voorbeeld van. 2016 kan wel eens hét jaar worden waarin de cloud écht doorbreekt en waarin we ook in Nederland steeds meer complete BI oplossingen in de cloud zullen gaan zien.

    Huub Hillege, principal data(base) management consultant bij Info-Shunt
    Big data
    De big data-hype zal zich nog zeker voortzetten in 2016 alleen het succes bij de bedrijven is op voorhand niet gegarandeerd. Bedrijven en pas afgestudeerden blijven elkaar gek maken over de toepassing. Het is onbegrijpelijk dat iedereen maar Facebook, Twitter en dergelijke data wil gaan ontsluiten terwijl de data in deze systemen hoogst onbetrouwbaar is. Op elke conferentie vraag ik waar de business case, inclusief baten en lasten is, die alle investeringen rondom big data rechtvaardigen. Zelfs bi-managers van bedrijven moedigen aan om gewoon te beginnen. Dus eigenlijk: achterom kijken naar de data die je hebt of kunt krijgen en onderzoeken of je iets vindt waar je iets aan zou kunnen hebben. Voor mij is dit de grootste valkuil, zoals het ook was met de start van Datawarehouses in 1992. Bedrijven hebben in de huidige omstandigheden beperkt geld. Zuinigheid is geboden.

    De analyse van big data moet op de toekomst zijn gericht vanuit een duidelijke business-strategie en een kosten/baten-analyse: welke data heb ik nodig om de toekomst te ondersteunen? Bepaal daarbij:

    • Waar wil ik naar toe?
    • Welke klantensegmenten wil ik erbij krijgen?
    • Gaan we met de huidige klanten meer 'Cross selling' (meer producten) uitvoeren?
    • Gaan we stappen ondernemen om onze klanten te behouden (Churn)?

    Als deze vragen met prioriteiten zijn vastgelegd moet er een analyse worden gedaan:

    • Welke data/sources hebben we hierbij nodig?
    • Hebben we zelf de data, zijn er 'gaten' of moeten we externe data inkopen?

    Databasemanagementsysteem
    Steeds meer databasemanagementsysteem (dbms)-leveranciers gaan ondersteuning geven voor big data-oplossingen zoals bijvoorbeeld Oracle/Sun Big Data Appliance, Teradata/Teradata Aster met ondersteuning voor Hadoop. De dbms-oplossingen zullen op de lange termijn het veld domineren. big data-software-oplossingen zonder dbms zullen het uiteindelijk verliezen.

    Steeds minder mensen, ook huidige dbma's, begrijpen niet meer hoe het technisch diep binnen een database/DBMS in elkaar zit. Steeds meer zie je dat fysieke databases uit logische data modelleer-tools worden gegeneerd. Formele fysieke database-stappen/-rapporten blijven achterwege. Ook ontwikkelaars die gebruik maken van etl-tools zoals Informatica, AbInitio, Infosphere, Pentaho et cetera, genereren uiteindelijk sgl-scripts die data van sources naar operationele datastores en/of datawarehouse brengen.

    Ook de bi-tools zoals Microstrategy, Business Objects, Tableau et cetera genereren sql-statements.
    Meestal zijn dergelijke tools initieel ontwikkeld voor een zeker dbms en al gauw denkt men dat het dan voor alle dbms'en toepasbaar is. Er wordt dan te weinig gebruik gemaakt van specifieke fysieke dbms-kenmerken.

    De afwezigheid van de echte kennis veroorzaakt dan performance problemen die in een te laat stadium worden ontdekt. De laatste jaren heb ik door verandering van databaseontwerp/indexen en het herstructureren van complexe/gegenereerde sql-scripts, etl-processen van zes tot acht uur naar één minuut kunnen krijgen en queries die 45 tot 48 uur liepen uiteindelijk naar 35 tot veertig minuten kunnen krijgen.

    Advies
    De benodigde data zal steeds meer groeien. Vergeet de aanschaf van allerlei hype software pakketten. Zorg dat je zeer grote, goede, technische, Database-/dbms-expertise in huis haalt om de basis van onderen goed in te richten in de kracht van je aanwezige dbms. Dan komt er tijd en geld vrij (je kan met kleinere systemen uit de voeten omdat de basis goed in elkaar zit) om, na een goede business case en ‘proof of concepts’, de juiste tools te selecteren.

  • 10 Big Data Trends for 2017

    big-dataInfogix, a leader in helping companies provide end-to-end data analysis across the enterprise, today highlighted the top 10 data trends they foresee will be strategic for most organizations in 2017.
     
    “This year’s trends examine the evolving ways enterprises can realize better business value with big data and how improving business intelligence can help transform organization processes and the customer experience (CX),” said Sumit Nijhawan, CEO and President of Infogix. “Business executives are demanding better data management for compliance and increased confidence to steer the business, more rapid adoption of big data and innovative and transformative data analytic technologies.”
     
    The top 10 data trends for 2017 are assembled by a panel of Infogix senior executives. The key trends include:
     
    1.    The Proliferation of Big Data
        Proliferation of big data has made it crucial to analyze data quickly to gain valuable insight.
        Organizations must turn the terabytes of big data that is not being used, classified as dark data, into useable data.   
        Big data has not yet yielded the substantial results that organizations require to develop new insights for new, innovative offerings to derive a competitive advantage
     
    2.    The Use of Big Data to Improve CX
        Using big data to improve CX by moving from legacy to vendor systems, during M&A, and with core system upgrades.
        Analyzing data with self-service flexibility to quickly harness insights about leading trends, along with competitive insight into new customer acquisition growth opportunities.
        Using big data to better understand customers in order to improve top line revenue through cross-sell/upsell or remove risk of lost revenue by reducing churn.
     
    3.    Wider Adoption of Hadoop
        More and more organizations will be adopting Hadoop and other big data stores, in turn, vendors will rapidly introduce new, innovative Hadoop solutions.
        With Hadoop in place, organizations will be able to crunch large amounts of data using advanced analytics to find nuggets of valuable information for making profitable decisions.
     
    4.    Hello to Predictive Analytics
        Precisely predict future behaviors and events to improve profitability.
        Make a leap in improving fraud detection rapidly to minimize revenue risk exposure and improve operational excellence.
     
    5.    More Focus on Cloud-Based Data Analytics
        Moving data analytics to the cloud accelerates adoption of the latest capabilities to turn data into action.
        Cut costs in ongoing maintenance and operations by moving data analytics to the cloud.
     
    6.    The Move toward Informatics and the Ability to Identify the Value of Data
        Use informatics to help integrate the collection, analysis and visualization of complex data to derive revenue and efficiency value from that data
        Tap an underused resource – data – to increase business performance
     
    7.    Achieving Maximum Business Intelligence with Data Virtualization
        Data virtualization unlocks what is hidden within large data sets.
        Graphic data virtualization allows organizations to retrieve and manipulate data on the fly regardless of how the data is formatted or where it is located.
     
    8.    Convergence of IoT, the Cloud, Big Data, and Cybersecurity
        The convergence of data management technologies such as data quality, data preparation, data analytics, data integration and more.
        As we continue to become more reliant on smart devices, inter-connectivity and machine learning will become even more important to protect these assets from cyber security threats.
     
    9.    Improving Digital Channel Optimization and the Omnichannel Experience
        Delivering the balance of traditional channels with digital channels to connect with the customer in their preferred channel.
        Continuously looking for innovative ways to enhance CX across channels to achieve a competitive advantage.
     
    10.    Self-Service Data Preparation and Analytics to Improve Efficiency
        Self-service data preparation tools boost time to value enabling organizations to prepare data regardless of the type of data, whether structured, semi-structured or unstructured.
        Decreased reliance on development teams to massage the data by introducing more self-service capabilities to give power to the user and, in turn, improve operational efficiency.
     
    “Every year we see more data being generated than ever before and organizations across all industries struggle with its trustworthiness and quality. We believe the technology trends of cloud, predictive analysis and big data will not only help organizations deal with the vast amount of data, but help enterprises address today’s business challenges,” said Nijhawan. “However, before these trends lead to the next wave of business, it’s critical that organizations understand that the success is predicated upon data integrity.”
     
    Source: dzone.com, November 20, 2016
  • 2016 wordt het jaar van de kunstmatige intelligentie

    Artificial-intelligence.jpg-1024x678December is traditiegetrouw de periode van het jaar om terug te blikken en oudjaarsdag is daarbij in het bijzonder natuurlijk de beste dag voor. Bij Numrush kijken we echter liever vooruit. Dat deden we begin december al met ons RUSH Magazine. In deze Gift Guide gaven we cadeautips aan de hand van een aantal thema’s waar we komend jaar veel over gaan horen.Eén onderwerp bleef bewust een beetje onderbelicht in onze Gift Guide. Aan de ene kant omdat het niet iets is wat je cadeau geeft, maar ook omdat het eigenlijk de diverse thema’s overstijgt. Ik heb het over kunstmatige intelligentie. Dat is natuurlijk niets nieuws, er is al ontzettend veel gebeurt op dat vlak, maar komend jaar zal de toepassing hiervan nog verder in een stroomversnelling raken.

  • 2017 Investment Management Outlook

    2017 investment management outlook infographic

    Several major trends will likely impact the investment management industry in the coming year. These include shifts in buyer behavior as the Millennial generation becomes a greater force in the investing marketplace; increased regulation from the Securities and Exchange Commission (SEC); and the transformative effect that blockchain, robotic process automation, and other
    emerging technologies will have on the industry.

    Economic outlook: Is a major stimulus package in the offing?

    President-elect Donald Trump may have to depend heavily on private-sector funding to proceed with his $1 trillion infrastructure spending program, considering Congress ongoing reluctance to increase spending. The US economy may be nearing full employment with the younger cohorts entering the labor market as more Baby Boomers retire. In addition, the prospects for a fiscal stimulus seem greater now than they were before the 2016 presidential election.

    Steady improvement and stability is the most likely scenario for 2017. Although weak foreign demand may continue to weigh on growth, domestic demand should be strong enough to provide employment for workers returning to the labor force, as the unemployment rate is expected to remain at approximately 5 percent. GDP annual growth is likely to hit a maximum of 2.5 percent. In the medium term, low productivity growth will likely put a ceiling on the economy, and by 2019, US GDP growth may be below 2 percent, despite the fact that the labor market might be at full employment. Inflation is expected to remain subdued. Interest rates are likely to rise in 2017, but should remain at historically low levels throughout the year. If the forecast holds, asset allocation shifts among cash, commodities, and fixed income may begin by the end of 2017.

    Investment industry outlook: Building upon last year’s performance
    Mutual funds and exchange-traded funds (ETFs) have experienced positive growth. Worldwide regulated funds grew at 9.1 percent CAGR versus 8.6 percent by US mutual funds and ETFs. Non-US investments grew at a slightly faster pace due to global demand. Both worldwide and US investments seem to show declining demand in 2016 as returns remained low.

    Hedge fund assets have experienced steady growth over the past five years, even through performance swings.

    Private equity investments continued a track record of strong asset appreciation. Private equity has continued to attract investment even with current high valuations. Fundraising increased incrementally over the past five years as investors increased allocations in the sector.

    Shifts in investor buying behavior: Here come the Millennials
    Both institutional and retail customers are expected to continue to drive change in the investment management industry. The two customer segments are voicing concerns about fee sensitivity and transparency. Firms that enhance the customer experience and position advice, insight, and expertise as components of value should have a strong chance to set themselves apart from their competitors.

    Leading firms may get out in front of these issues in 2017 by developing efficient data structures to facilitate accounting and reporting and by making client engagement a key priority. On the retail front, the SEC is acting on retail investors’ behalf with reporting modernization rule changes for mutual funds. This focus on engagement, transparency, and relationship over product sales are integral to creating a strong brand as a fiduciary, and they may prove to differentiate some firms in 2017.

    Growth in index funds and other passive investments should continue as customers react to market volatility. Investors favor the passive approach in all environments, as shown by net flows. They are using passive investments alongside active investments, rather than replacing the latter with the former. Managers will likely continue to add index share classes and index-tracking ETFs in 2017, even if profitability is challenged. In addition, the Department of Labor’s new fiduciary rule is expected to promote passive investments as firms alter their product offerings for retirement accounts.

    Members of the Millennial generation—which comprises individuals born between 1980 and 2000—often approach investing differently due to their open use of social media and interactions with people and institutions. This market segment faces different challenges than earlier generations, which influences their use of financial services.

    Millennials may be less prosperous than their parents and may need to own less in order to fully fund retirement. Many start their careers burdened by student debt. They may have a negative memory of recent stock market volatility, distrust financial institutions, favor socially conscious investments, and rely on recommendations from their friends when seeking financial advice.

    Investment managers likely need to consider several steps when targeting Millennials. These include revisiting product lines, offering socially conscious “impact investments,” assigning Millennial advisers to client service teams, and employing digital and mobile channels to reach and serve this market segment.

    Regulatory developments: Seeking greater transparency, incentive alignment, and risk control
    Even with a change in leadership in the White House and at the SEC, outgoing Chair Mary Jo White’s major initiatives are expected to endure in 2017 as they seek to enhance transparency, incentive alignment, and risk control, all of which build confidence in the markets. These changes include the following:

    Reporting modernization. Passed in October 2016, this new requirement of forms, rules, and amendments for information disclosure and standardization will require development by registered investment companies (RICs). Advisers will need technology solutions that can capture data that may not currently exist from multiple sources; perform high-frequency calculations; and file requisite forms with the SEC.

    Liquidity risk management (LRM). Passed in October 2016, this rule requires the establishment of LRM programs by open-end funds (except money market) and ETFs to reduce the risk of inability to meet redemption requirements without dilution of the interests of remaining shareholders.

    Swing pricing. Also passed in October 2016, this regulation provides an option for open-end funds (except money market and ETFs) to adjust net asset values to pass the costs stemming from purchase and redemption activity to shareholders.

    Use of derivatives. Proposed in December 2015, this requires RICs and business development companies to limit the use of derivatives and put risk management measures in place.

    Business continuity and transition plans. Proposed in June 2016, this measure requires registered investment advisers to implement written business continuity and transition plans to address operational risk arising from disruptions.

    The Dodd-Frank Act, Section 956. Reproposed in May 2016, this rule prohibits compensation structures that encourage individuals to take inappropriate risks that may result in either excessive compensation or material loss.

    The DOL’s Conflict-of-Interest Rule. In 2017, firms must comply with this major expansion of the “investment advice fiduciary” definition under the Employee Retirement Income Security Act of 1974. There are two phases to compliance:

    Phase one requires compliance with investment advice standards by April 10, 2017. Distribution firms and advisers must adhere to the impartial conduct standards, provide a notice to retirement investors that acknowledge their fiduciary status, and describes their material conflicts of interest. Firms must also designate a person responsible for addressing material conflicts of interest monitoring advisers' adherence to the impartial conduct standards.

    Phase two requires compliance with exemption requirements by January 1, 2018. Distribution firms must be in full compliance with exemptions, including contracts, disclosures, policies and procedures, and documentation showing compliance.

    Investment managers may need to create new, customized share classes driven by distributor requirements; drop distribution of certain share classes post-rule implementation, and offer more fee reductions for mutual funds.

    Financial advisers may need to take another look at fee-based models, if they are not using already them; evolve their viewpoint on share classes; consider moving to zero-revenue share lineups; and contemplate higher use of ETFs, including active ETFs with a low-cost structure and 22(b) exemption (which enables broker-dealers to set commission levels on their own).

    Retirement plan advisers may need to look for low-cost share classes (R1-R6) to be included in plan options and potentially new low-cost structures.

    Key technologies: Transforming the enterprise

    Investment management poised to become even more driven by advances in technology in 2017, as digital innovations play a greater role than ever before.

    Blockchain. A secure and effective technology for tracking transactions, blockchain should move closer to commercial implementation in 2017. Already, many blockchain-based use cases and prototypes can be found across the investment management landscape. With testing and regulatory approvals, it might take one to two years before commercial rollout becomes more widespread.

    Big data, artificial intelligence, and machine learning. Leading asset management firms are combining big data analytics along with artificial intelligence (AI) and machine learning to achieve two objectives: (1) provide insights and analysis for investment selection to generate alpha, and (2) improve cost effectiveness by leveraging expensive human analyst resources with scalable technology. Expect this trend to gain momentum in 2017.

    Robo-advisers. Fiduciary standards and regulations should drive the adoption of robo-advisers, online investment management services that provide automated, portfolio management advice. Improvements in computing power are making robo-advisers more viable for both retail and institutional investors. In addition, some cutting-edge robo-adviser firms could emerge with AI-supported investment decision and asset allocation algorithms in 2017.

    Robotic process automation. Look for more investment management firms to employ sophisticated robotic process automation (RPA) tools to streamline both front- and back-office functions in 2017. RPA can automate critical tasks that require manual intervention, are performed frequently, and consume a signifcant amount of time, such as client onboarding and regulatory compliance.


    Change, development, and opportunity
    The outlook for the investment management industry in 2017 is one of change, development, and opportunity. Investment management firms that execute plans that help them anticipate demographic shifts, improve efficiency and decision making with technology, and keep pace with regulatory changes will likely find themselves ahead of the competition.


    Download 2017 Investment management industry outlook

    Source: Deloitte.com

     

  • 4 Tips om doodbloedende Big Data projecten te voorkomen

    projectmanagers

    Investeren in big data betekent het verschil tussen aantrekken of afstoten van klanten, tussen winst of verlies. Veel retailers zien hun initiatieven op het vlak van data en analytics echter doodbloeden. Hoe creëer je daadwerkelijk waarde uit data en voorkom je een opheffingsuitverkoop? Vier tips.

    Je investeert veel tijd en geld in big data, exact volgens de boodschap die retailgoeroes al enkele jaren verkondigen. Een team van data scientists ontwikkelt complexe datamodellen, die inderdaad interessante inzichten opleveren. Met kleine ‘proofs of value’ constateert u dat die inzichten daadwerkelijk ten gelde kunnen worden gemaakt. Toch gebeurt dat vervolgens niet. Wat is er aan de hand?

    Tip 1: Pas de targets aan

    Dat waardevolle inzichten niet in praktijk worden gebracht, heeft vaak te maken met de targets die uw medewerkers hebben meegekregen. Neem als voorbeeld het versturen van mailingen aan klanten. Op basis van bestaande data en klantprofielen kunnen we goed voorspellen hoe vaak en met welke boodschap elke klant moet worden gemaild. En stiekem weet elke marketeer donders goed dat niet elke klant op een dagelijkse email zit te wachten.

    Toch trapt menigeen in de valkuil en stuurt telkens weer opnieuw een mailing uit naar het hele klantenbestand. Het resultaat: de interesse van een klant ebt snel weg en de boodschap komt niet langer aan. Waarom doen marketeers dat? Omdat ze louter en alleen worden afgerekend op de omzet die ze genereren, niet op de klanttevredenheid die ze realiseren. Dat nodigt uit om iedereen zo vaak mogelijk te mailen. Op korte termijn groeit met elk extra mailtje immers de kans op een verkoop.

    Tip 2: Plaats de analisten in de business

    Steeds weer zetten retailers het team van analisten bij elkaar in een kamer, soms zelfs als onderdeel

    van de IT-afdeling. De afstand tot de mensen uit de business die de inzichten in praktijk moeten brengen, is groot. En te vaak blijkt die afstand onoverbrugbaar. Dat leidt tot misverstanden, onbegrepen analisten en waardevolle inzichten die onbenut blijven.

    Beter is om de analisten samen met de mensen uit de business bij elkaar te zetten in multidisciplinaire teams, die werken met scrum-achtige technieken. Organisaties die succesvol zijn, beseffen dat ze continu in verandering moeten zijn en werken in dat soort teams. Dat betekent dat business managers in een vroegtijdig stadium worden betrokken bij de bouw van datamodellen, zodat analisten en de business van elkaar kunnen leren. Klantkennis zit immers in data én in mensen.

    Tip 3: Neem een business analist in dienst

    Data-analisten halen hun werkplezier vooral uit het maken van fraaie analyses en het opstellen van goede, misschien zelfs overontwikkelde datamodellen. Voor hun voldoening is het vaak niet eens nodig om de inzichten uit die modellen in praktijk te brengen. Veel analisten zijn daarom ook niet goed in het interpreteren van data en het vertalen daarvan naar de concrete impact op de retailer. 

    Het kan verstandig zijn om daarom een business analist in te zetten. Dat is iemand die voldoende affiniteit heeft met analytics en enigszins snapt hoe datamodellen tot stand komen, maar ook weet wat de uitdagingen van de business managers zijn. Hij kan de kloof tussen analytics en business overbruggen door vragen uit de business te concretiseren en door inzichten uit datamodellen te vertalen naar kansen voor de retailer.

    Tip 4: Analytics is een proces, geen project

    Nog te veel retailers kijken naar alle inspanningen op het gebied van data en analytics alsof het een project met een kop en een staart betreft. Een project waarvan vooraf duidelijk moet zijn wat het gaat opleveren. Dat is vooral het geval bij retailorganisaties die worden geleid door managers uit de ‘oude generatie’ die onvoldoende gevoel en affiniteit met de nieuwe wereld hebben Het commitment van deze managers neemt snel af als investeringen in data en analytics niet snel genoeg resultaat opleveren.

    Analytics is echter geen project, maar een proces waarin retailers met vallen en opstaan steeds handiger en slimmer worden. Een proces waarvan de uitkomst vooraf onduidelijk is, maar dat wel moet worden opgestart om vooruit te komen. Want alle ontwikkelingen in de retailmarkt maken één ding duidelijk: stilstand is achteruitgang.

    Auteur: EY, Simon van Ulden, 5 oktober 2016

  • 50% NL'ers wil prive data delen voor gratis online diensten

    ANP-Data-CenterNederlanders zijn goed op de hoogte van hoe bedrijven en instellingen persoonlijke gegevens verzamelen en gebruiken. Ook zijn Nederlanders van alle Europeanen het meest bereid om persoonlijke gegevens te verstrekken in ruil voor gratis online diensten. Dat blijkt uit een onderzoek door TNS in opdracht van het Vodafone Institute.

    Deze Berlijnse denktank van Vodafone heeft 8.000 Europanen in acht landen laten ondervragen over hun kennis inzake datagebruik. Daaruit blijkt dat de helft van de Nederlanders bereid is om persoonlijke gegevens te verstrekken in ruil voor het gratis gebruik van online diensten. Bijna even veel Fransen (48%) zijn hiertoe bereid.

    Aan de andere kant van het spectrum staan Italianen en Engelsen, waarvan 66 procent liever betaalt voor online diensten dan dat zij persoonlijke gegevens verstrekken voor gratis gebruik ervan. Consumenten uit deze twee Europese landen zijn volgens de studie van het Vodafone Institute ook het minst goed geïnformeerd over de verzameling en het gebruik van persoonlijke gegevens door bedrijven en instellingen.

    Het Vodafone Institute stelt dat de studie inzicht biedt in de uitdagingen die overheid en bedrijfsleven nog hebben bij gebruik van klantgegevens voor Big Data projecten. Europeanen zijn volgens het Institute bereid hun gegevens te delen, zo lang ze een duidelijk persoonlijk of maatschappelijk belang zien. Maar wanneer organisaties te kort schieten in hun uitleg over hoe en waarom ze gegevens willen analyseren, wordt de kans veel kleiner dat mensen mee doen aan Big Data initiatieven.

    Wantrouwen over bedrijven groot, minder bij overheden

    35 procent van de Europeanen vindt bestaande wet- en regelgeving over privacy passend en proportioneel. Nederland scoort hier gemiddeld. 26 procent van de Europeanen denkt dat bedrijven hun privacy respecteren, Nederlanders scoren nog lager (22%). Nederlanders hebben wel meer vertrouwen in de overheid dan andere Europeanen: 49% denkt dat de overheid hun privacy respecteert (versus gemiddeld 36% in Europa).

    Duidelijkheid over datagebruik helpt

    Meer mensen blijken bereid te zijn om gegevens te delen als duidelijk is hoe dit hen of de samenleving helpt. Zo zou 70 procent van de Nederlanders verzameling van grote hoeveelheden anonieme data door gezondheidsorganisaties steunen, versus gemiddeld 65 procent in Europa. 67 procent van de Nederlanders zou deze instanties zelfs toegang geven tot zijn of haar gegevens (tegen 62% gemiddeld in Europa).

    Verder vindt 50 procent van de Nederlanders het krijgen van verkeersadvies op basis van verzamelde gegevens door navigatiebedrijven niet bezwaarlijk (versus 55% gemiddeld in Europa). 41 procent van de Nederlanders vindt het ook goed als deze gegevens met lokale overheden gedeeld worden om het wegennet en de doorstroming te verbeteren (45% gemiddeld in Europa).

    66 procent van de Nederlanders (68% gemiddeld in Europa) staat positief tegen ‘smart meters’ van energiebedrijven, omdat dergelijke meters kunnen helpen bij het analyseren en beperken van energieverbruik. Ook vindt 45 procent van de Nederlanders het goed als online shops verzamelde data gebruiken om producten of diensten te verbeteren, een gemiddelde score. Nederlanders staan iets minder open voor persoonlijke aanbiedingen op basis van shopgedrag (39%) dan andere Europeanen (44%).

    Doorverkopen gegevens stuit op verzet

    Het doorverkopen van persoonlijke gegevens aan derden stuit op veel weerstand. Slechts 11 procent van de Nederlanders vindt het goed als online shops hun gegevens doorverkopen voor marketing- en advertentiedoeleinden (versus 10% gemiddeld in Europa). 9 procent vindt het goed als data verkregen uit navigatieapparatuur, of over auto en/of rijgedrag geanonimiseerd en geaggregeerd worden doorverkocht (11% gemiddeld in Europa). Eveneens slechts 9 procent vindt het geen probleem als energiebedrijven geanonimiseerde en geaggregeerde data door verkopen (13% gemiddeld in Europa).

    Op de vraag wat organisaties kunnen doen om het vertrouwen van gebruikers te vergroten bij het beheren en beschermen van gegevens, zeggen Nederlanders:

    • Wees transparant over wat verzameld wordt en waarvoor dat wordt gebruikt (73%);
    • Gebruik begrijpelijke taal en korte algemene voorwaarden (61%);
    • Biedt de mogelijkheid om persoonlijke privacy-instellingen aan te passen (55%)
    • Certificering door een onafhankelijk testinstituut (48%)

    Source: Telecompaper

  • 7 voorspellingen over IT in 2045

    HeroboticsDe kans is groot dat de wereld binnenkort niet alleen wordt bevolkt door miljarden mensen, maar ook door miljarden robots. De IT-industrie wordt het terrein voor bedrijven die programma's ontwikkelen voor deze robots. Net zoals de nu voor menselijke gebruikers ontwikkelde apps zullen deze 'robo-apps' te downloaden en te installeren zijn.

    De grenzen tussen robots en mensen vervagen. Bij transplantaties wordt gebruik gemaakt van elektronisch gestuurde kunstmatige organen en protheses. Nanorobots dringen diep in het lichaam door om medicijnen af te leveren bij zieke cellen of om microchirurgie uit te voeren. Speciaal geïnstalleerde houden toezicht op de gezondheid van mensen.

    Mensen in slimme huizen wonen, waar het meeste comfort volledig is geautomatiseerd. De software waarop het huis draait regelt het verbruik en de aanvulling van energie, water, voedsel en andere voorzieningen.

    Onze digitale alter ego's komen eindelijk volledig tot wasdom binnen een enkele, wereldwijde infrastructuur die in staat is tot zelfregulering en betrokken is bij het beheer van het leven op de planeet. Het systeem zal een beetje werken als het hedendaagse TOR; de meest actieve en effectieve gebruikers zullen moderatorrechten verdienen.

    Niet alleen saaie klusjes behoren tot het verleden – ook de productie van bepaalde artikelen zal niet langer nodig zijn. In plaats daarvan stellen 3D-printers ons in staat alles te ontwerpen en te maken wat we nodig hebben.

    De pc stond weliswaar aan de basis van de hele IT-revolutie, maar in 2045 zien we hem waarschijnlijk alleen nog in musea. De dingen om ons heen verwerken hun eigen informatie.

    Niet iedereen zal even enthousiast zijn over die mooie, nieuwe robotwereld. Waarschijnlijk zullen technofoben in opstand komen om zich te verzetten tegen de ontwikkeling van intelligente huizen, geautomatiseerde levensstijlen en robots.

    Bron: Automatiseringsgids, 22 Januari 2014

  • 8 op de 10 bedrijven slaat gevoelige data op in de cloud

    54640085% van de bedrijven slaat gevoelige data op in de cloud. Dit is een flinke stijging ten opzichte van de 54% die vorig jaar aangaf dit te doen. 70% van de bedrijven maakt zich zorgen over de veiligheid van deze data.

    Dit blijkt uit onderzoek van 451 Research in opdracht van Vormetric, leverancier van databeveiliging voor fysieke, big data, public, private en hybride cloud omgevingen. Gevoelige data staat uiteraard niet alleen in de cloud. 50% van de bedrijven geeft aan gevoelige data in big data systemen te hebben staan (tegenover 31% vorig jaar), en 33% heeft dergelijke data in Internet of Things (IoT) omgevingen opgeslagen.

    Zorgen over de cloud
    451 Research heeft respondenten ook gevraagd naar de zorgen die zij hebben over de veiligheid van hun gevoelige data die in de cloud staat. De belangrijkste zorgenpunten zijn:

    • Cyberaanvallen en -inbraken bij een service provider (70%)
    • De kwetsbaarheid van een gedeelde infrastructuur (66%)
    • Een gebrek aan controle over de locatie waar data is opgeslagen (66%)
    • Een gebrek aan een data privacy beleid of privacy SLA (65%)

    Ook is respondenten gevraagd welke wijzigingen hun bereidheid data in de cloud onder te brengen zullen vergroten. De belangrijkste wijzigingen waar respondenten behoefte aan hebben zijn:

    • Encryptie van data, waarbij de encryptiesleutel wordt beheerd op de eigen infrastructuur van het bedrijf (48%)
    • Gedetaileerde informatie over de fysieke en IT-beveiliging (36%)
    • Het zelf kunnen kiezen voor encryptie van data die is opgeslagen op de infrastructuur van een service provider (35%)

    Zorgen over big data systemen
    Ook de opslag van gevoelige data in big data systemen baart respondenten zorgen. De belangrijkste zorgenpunten zijn:

    • De veiligheid van rapporten die met big data systemen worden gecreëerd, aangezien deze gevoelige data kunnen bevatten (42%)
    • Het feit dat data op iedere locatie binnen deze omgeving kan zijn ondergebracht (41%)
    • Privacyschendingen door data die uit verschillende landen afkomstig is (40%)Toegang door gebruikers met ‘superrechten’ tot beschermde data (37%)
    • Een gebrek aan een security raamwerk en beheermogelijkheden binnen de omgeving (33%)

    Ook merkt 451 Research op dat big data systemen vaak in de cloud draaien. Zorgen over de opslag van gevoelige data van de cloud zijn hierdoor ook van toepassing op data die in big data omgevingen is opgeslagen.

    Ook data in IoT omgevingen leidt tot zorgen
    Tot slot kijkt 451 Research naar de zorgen die bedrijven hebben over de opslag van data in IoT omgevingen. De belangrijkste zorgen op dit gebied zijn:

    • Het beschermen van data die door IoT wordt gecreëerd (35%)
    • Privacyschendingen (30%)
    • Identificeren welke data gevoelig is (29%)
    • Toegang van gebruikers met ‘superrechten’ tot IoT data en apparaten (28%)
    • Aanvallen op IoT-apparaten die een impact kunnen hebben op de kritieke bedrijfsvoering (27%)

    Het gehele onderzoek lees je HIER

    Source: Executive People

  • A new quantum approach to big data

    MIT-Quantum-Big-Data 0From gene mapping to space exploration, humanity continues to generate ever-larger sets of data — far more information than people can actually process, manage, or understand.
    Machine learning systems can help researchers deal with this ever-growing flood of information. Some of the most powerful of these analytical tools are based on a strange branch of geometry called topology, which deals with properties that stay the same even when something is bent and stretched every which way.


    Such topological systems are especially useful for analyzing the connections in complex networks, such as the internal wiring of the brain, the U.S. power grid, or the global interconnections of the Internet. But even with the most powerful modern supercomputers, such problems remain daunting and impractical to solve. Now, a new approach that would use quantum computers to streamline these problems has been developed by researchers at MIT, the University of Waterloo, and the University of Southern California.
    The team describes their theoretical proposal this week in the journal Nature Communications. Seth Lloyd, the paper’s lead author and the Nam P. Suh Professor of Mechanical Engineering, explains that algebraic topology is key to the new method. This approach, he says, helps to reduce the impact of the inevitable distortions that arise every time someone collects data about the real world.


    In a topological description, basic features of the data (How many holes does it have? How are the different parts connected?) are considered the same no matter how much they are stretched, compressed, or distorted. Lloyd explains that it is often these fundamental topological attributes “that are important in trying to reconstruct the underlying patterns in the real world that the data are supposed to represent.”


    It doesn’t matter what kind of dataset is being analyzed, he says. The topological approach to looking for connections and holes “works whether it’s an actual physical hole, or the data represents a logical argument and there’s a hole in the argument. This will find both kinds of holes.”
    Using conventional computers, that approach is too demanding for all but the simplest situations. Topological analysis “represents a crucial way of getting at the significant features of the data, but it’s computationally very expensive,” Lloyd says. “This is where quantum mechanics kicks in.” The new quantum-based approach, he says, could exponentially speed up such calculations.


    Lloyd offers an example to illustrate that potential speedup: If you have a dataset with 300 points, a conventional approach to analyzing all the topological features in that system would require “a computer the size of the universe,” he says. That is, it would take 2300 (two to the 300th power) processing units — approximately the number of all the particles in the universe. In other words, the problem is simply not solvable in that way.
    “That’s where our algorithm kicks in,” he says. Solving the same problem with the new system, using a quantum computer, would require just 300 quantum bits — and a device this size may be achieved in the next few years, according to Lloyd.


    “Our algorithm shows that you don’t need a big quantum computer to kick some serious topological butt,” he says.
    There are many important kinds of huge datasets where the quantum-topological approach could be useful, Lloyd says, for example understanding interconnections in the brain. “By applying topological analysis to datasets gleaned by electroencephalography or functional MRI, you can reveal the complex connectivity and topology of the sequences of firing neurons that underlie our thought processes,” he says.


    The same approach could be used for analyzing many other kinds of information. “You could apply it to the world’s economy, or to social networks, or almost any system that involves long-range transport of goods or information,” says Lloyd, who holds a joint appointment as a professor of physics. But the limits of classical computation have prevented such approaches from being applied before.


    While this work is theoretical, “experimentalists have already contacted us about trying prototypes,” he says. “You could find the topology of simple structures on a very simple quantum computer. People are trying proof-of-concept experiments.”


    Ignacio Cirac, a professor at the Max Planck Institute of Quantum Optics in Munich, Germany, who was not involved in this research, calls it “a very original idea, and I think that it has a great potential.” He adds “I guess that it has to be further developed and adapted to particular problems. In any case, I think that this is top-quality research.”
    The team also included Silvano Garnerone of the University of Waterloo in Ontario, Canada, and Paolo Zanardi of the Center for Quantum Information Science and Technology at the University of Southern California. The work was supported by the Army Research Office, Air Force Office of Scientific Research, Defense Advanced Research Projects Agency, Multidisciplinary University Research Initiative of the Office of Naval Research, and the National Science Foundation.

    Source:MIT news

  • A Shortcut Guide to Machine Learning and AI in The Enterprise

    advanced-predictive-proactive-etc-Two-men-fighting

    Predictive analytics / machine learning / artificial intelligence is a hot topic – what’s it about?

    Using algorithms to help make better decisions has been the “next big thing in analytics” for over 25 years. It has been used in key areas such as fraud the entire time. But it’s now become a full-throated mainstream business meme that features in every enterprise software keynote — although the industry is battling with what to call it.

    It appears that terms like Data Mining, Predictive Analytics, and Advanced Analytics are considered too geeky or old for industry marketers and headline writers. The term Cognitive Computing seemed to be poised to win, but IBM’s strong association with the term may have backfired — journalists and analysts want to use language that is independent of any particular company. Currently, the growing consensus seems to be to use Machine Learning when talking about the technology and Artificial Intelligence when talking about the business uses.

    Whatever we call it, it’s generally proposed in two different forms: either as an extension to existing platforms for data analysts; or as new embedded functionality in diverse business applications such as sales lead scoring, marketing optimization, sorting HR resumes, or financial invoice matching.

    Why is it taking off now, and what’s changing?

    Artificial intelligence is now taking off because there’s a lot more data available and affordable, powerful systems to crunch through it all. It’s also much easier to get access to powerful algorithm-based software in the form of open-source products or embedded as a service in enterprise platforms.

    Organizations today have also more comfortable with manipulating business data, with a new generation of business analysts aspiring to become “citizen data scientists.” Enterprises can take their traditional analytics to the next level using these new tools.

    However, we’re now at the “Peak of Inflated Expectations” for these technologies according to Gartner’s Hype Cycle — we will soon see articles pushing back on the more exaggerated claims. Over the next few years, we will find out the limitations of these technologies even as they start bringing real-world benefits.

    What are the longer-term implications?

    First, easier-to-use predictive analytics engines are blurring the gap between “everyday analytics” and the data science team. A “factory” approach to creating, deploying, and maintaining predictive models means data scientists can have greater impact. And sophisticated business users can now access some the power of these algorithms without having to become data scientists themselves.

    Second, every business application will include some predictive functionality, automating any areas where there are “repeatable decisions.” It is hard to think of a business process that could not be improved in this way, with big implications in terms of both efficiency and white-collar employment.

    Third, applications will use these algorithms on themselves to create “self-improving” platforms that get easier to use and more powerful over time (akin to how each new semi-autonomous-driving Tesla car can learn something new and pass it onto the rest of the fleet).

    Fourth, over time, business processes, applications, and workflows may have to be rethought. If algorithms are available as a core part of business platforms, we can provide people with new paths through typical business questions such as “What’s happening now? What do I need to know? What do you recommend? What should I always do? What can I expect to happen? What can I avoid? What do I need to do right now?”

    Fifth, implementing all the above will involve deep and worrying moral questions in terms of data privacy and allowing algorithms to make decisions that affect people and society. There will undoubtedly be many scandals and missteps before the right rules and practices are in place.

    What first steps should companies be taking in this area?
    As usual, the barriers to business benefit are more likely to be cultural than technical.

    Above all, organizations need to make sure they have the right technical expertise to be able to navigate the confusion of new vendors offers, the right business knowledge to know where best to apply them, and the awareness that their technology choices may have unforeseen moral implications.

    Source: timoelliot.com, October 24, 2016

     

  • About how Uber and Netflex turn Big Data into real business value

    client-logo-netflix-logo-png-netflix-logo-png-netflix-logo-qlHSS6-clipart

    From the way we go about our daily lives to the way we treat cancer and protect our society from threats, big data will transform every industry, every aspect of our lives. We can say this with authority because it is already happening.

    Some believe big data is a fad, but they could not be more wrong. The hype will fade, and even the name may disappear, but the implications will resonate and the phenomenon will only gather momentum. What we currently call big data today will simply be the norm in just a few years’ time.

    Big data refers generally to the collection and utilization of large or diverse volumes of data. In my work as a consultant, I work every day with companies and government organizations on big data projects that allow them to collect, store, and analyze the ever-increasing volumes of data to help improve what they do.

    In the course of that work, I’ve seen many companies doing things wrong — and a few getting big data very right, including Netflix and Uber.

    Netflix: Changing the way we watch TV and movies

    The streaming movie and TV service Netflix are said to account for one-third of peak-time Internet traffic in the US, and the service now have 65 million members in over 50 countries enjoying more than 100 million hours of TV shows and movies a day. Data from these millions of subscribers is collected and monitored in an attempt to understand our viewing habits. But Netflix’s data isn’t just “big” in the literal sense. It is the combination of this data with cutting-edge analytical techniques that makes Netflix a true Big Data company.

    Although Big Data is used across every aspect of the Netflix business, their holy grail has always been to predict what customers will enjoy watching. Big Data analytics is the fuel that fires the “recommendation engines” designed to serve this purpose.

    At first, analysts were limited by the lack of information they had on their customers. As soon as streaming became the primary delivery method, many new data points on their customers became accessible. This new data enabled Netflix to build models to predict the perfect storm situation of customers consistently being served with movies they would enjoy.

    Happy customers, after all, are far more likely to continue their subscriptions.

    Another central element to Netflix’s attempt to give us films we will enjoy is tagging. The company pay people to watch movies and then tag them with elements the movies contain. They will then suggest you watch other productions that were tagged similarly to those you enjoyed. 

    Netflix’s letter to shareholders in April 2015 shows their Big Data strategy was paying off. They added 4.9 million new subscribers in Q1 2015, compared to four million in the same period in 2014. In Q1 2015 alone, Netflix members streamed 10 billion hours of content. If Netflix’s Big Data strategy continues to evolve, that number is set to increase.

    Uber: Disrupting car services in the sharing economy

    Uber is a smartphone app-based taxi booking service which connects users who need to get somewhere with drivers willing to give them a ride. 

    Uber’s entire business model is based on the very Big Data principle of crowdsourcing: anyone with a car who is willing to help someone get to where they want to go can offer to help get them there. This gives greater choice for those who live in areas where there is little public transport, and helps to cut the number of cars on our busy streets by pooling journeys.

    Uber stores and monitors data on every journey their users take, and use it to determine demand, allocate resources and set fares. The company also carry out in-depth analysis of public transport networks in the cities they serve, so they can focus coverage in poorly served areas and provide links to buses and trains.

    Uber holds a vast database of drivers in all of the cities they cover, so when a passenger asks for a ride, they can instantly match you with the most suitable drivers. The company have developed algorithms to monitor traffic conditions and journey times in real time, meaning prices can be adjusted as demand for rides changes, and traffic conditions mean journeys are likely to take longer. This encourages more drivers to get behind the wheel when they are needed – and stay at home when demand is low. 

    The company have applied for a patent on this method of Big Data-informed pricing, which they call “surge pricing”. This is an implementation of “dynamic pricing” – similar to that used by hotel chains and airlines to adjust price to meet demand – although rather than simply increasing prices at weekends or during public holidays it uses predictive modelling to estimate demand in real time.

    Data also drives (pardon the pun) the company’s UberPool service. According to Uber’s blog, introducing this service became a no-brainer when their data told them the “vast majority of [Uber trips in New York] have a look-a-like trip – a trip that starts near, ends near and is happening around the same time as another trip”. 

    Other initiatives either trialed or due to launch in the future include UberChopper, offering helicopter rides to the wealthy, Uber-Fresh for grocery deliveries and Uber Rush, a package courier service.

    These are just two companies using Big Data to generate a very real advantage and disrupt their markets in incredible ways. I’ve compiled dozens more examples of Big Data in practice in my new book of the same name, in the hope that it will inspire and motivate more companies to similarly innovate and take their fields into the future. 

    Thank you for reading my post. Here at LinkedIn and at Forbes I regularly write about management, technology and Big Data. If you would like to read my future posts then please click 'Follow' and feel free to also connect via TwitterFacebookSlideshare, and The Advanced Performance Institute.

    You might also be interested in my new and free ebook on Big Data in Practice, which includes 3 Amazing use cases from NASA, Dominos Pizza and the NFL. You can download the ebook from here: Big Data in Practice eBook.

    Author: Bernard Marr

    Source: Linkedin Blog

  • Artificial intelligence: Can Watson save IBM?

    160104-Cloud-800x445The history of artificial intelligence has been marked by seemingly revolutionary moments — breakthroughs that promised to bring what had until then been regarded as human-like capabilities to machines. The AI highlights reel includes the “expert systems” of the 1980s and Deep Blue, IBM’s world champion-defeating chess computer of the 1990s, as well as more recent feats like the Google system that taught itself what cats look like by watching YouTube videos.

    But turning these clever party tricks into practical systems has never been easy. Most were developed to showcase a new computing technique by tackling only a very narrow set of problems, says Oren Etzioni, head of the AI lab set up by Microsoft co-founder Paul Allen. Putting them to work on a broader set of issues presents a much deeper set of challenges.
    Few technologies have attracted the sort of claims that IBM has made for Watson, the computer system on which it has pinned its hopes for carrying AI into the general business world. Named after Thomas Watson Sr, the chief executive who built the modern IBM, the system first saw the light of day five years ago, when it beat two human champions on an American question-and-answer TV game show, Jeopardy!
    But turning Watson into a practical tool in business has not been straightforward. After setting out to use it to solve hard problems beyond the scope of other computers, IBM in 2014 adapted its approach.
    Rather than just selling Watson as a single system, its capabilities were broken down into different components: each of these can now be rented to solve a particular business problem, a set of 40 different products such as language-recognition services that amount to a less ambitious but more pragmatic application of an expanding set of technologies.
    Though it does not disclose the performance of Watson separately, IBM says the idea has caught fire. John Kelly, an IBM senior vice-president and head of research, says the system has become “the biggest, most important thing I’ve seen in my career” and is IBM’s fastest growing new business in terms of revenues.
    But critics say that what IBM now sells under the Watson name has little to do with the original Jeopardy!-playing computer, and that the brand is being used to create a halo effect for a set of technologies that are not as revolutionary as claimed.

    “Their approach is bound to backfire,” says Mr Etzioni. “A more responsible approach is to be upfront about what a system can and can’t do, rather than surround it with a cloud of hype.”
    Nothing that IBM has done in the past five years shows it has succeeded in using the core technology behind the original Watson demonstration to crack real-world problems, he says.

    Watson’s case
    The debate over Watson’s capabilities is more than just an academic exercise. With much of IBM’s traditional IT business shrinking as customers move to newer cloud technologies, Watson has come to play an outsized role in the company’s efforts to prove that it is still relevant in the modern business world. That has made it key to the survival of Ginni Rometty, the chief executive who, four years after taking over, is struggling to turn round the company.
    Watson’s renown is still closely tied to its success on Jeopardy! “It’s something everybody thought was ridiculously impossible,” says Kris Hammond, a computer science professor at Northwestern University. “What it’s doing is counter to what we think of as machines. It’s doing something that’s remarkably human.”

    By divining the meaning of cryptically worded questions and finding answers in its general knowledge database, Watson showed an ability to understand natural language, one of the hardest problems for a computer to crack. The demonstration seemed to point to a time when computers would “understand” complex information and converse with people about it, replicating and eventually surpassing most forms of human expertise.
    The biggest challenge for IBM has been to apply this ability to complex bodies of information beyond the narrow confines of the game show and come up with meaningful answers. For some customers, this has turned out to be much harder than expected.
    The University of Texas’s MD Anderson Cancer Center began trying to train the system three years ago to discern patients’ symptoms so that doctors could make better diagnoses and plan treatments.
    “It’s not where I thought it would go. We’re nowhere near the end,” says Lynda Chin, head of innovation at the University of Texas’ medical system. “This is very, very difficult.” Turning a word game-playing computer into an expert on oncology overnight is as unlikely as it sounds, she says.

    Part of the problem lies in digesting real-world information: reading and understanding reams of doctors’ notes that are hard for a computer to ingest and organise. But there is also a deeper epistemological problem. “On Jeopardy! there’s a right answer to the question,” says Ms Chin but, in the
    medical world, there are often just well-informed opinions.
    Mr Kelly denies IBM underestimated how hard challenges like this would be and says a number of medical organisations are on the brink of bringing similar diagnostic systems online.


    Applying the technology
    IBM’s initial plan was to apply Watson to extremely hard problems, announcing in early press releases “moonshot” projects to “end cancer” and accelerate the development of Africa. Some of the promises evaporated almost as soon as the ink on the press releases had dried. For instance, a far-reaching partnership with Citibank to explore using Watson across a wide range of the bank’s activities, quickly came to nothing.
    Since adapting in 2014, IBM now sells some services under the Watson brand. Available through APIs, or programming “hooks” that make them available as individual computing components, they include sentiment analysis — trawling information like a collection of tweets to assess mood — and personality tracking, which measures a person’s online output using 52 different characteristics to come up with a verdict.

    At the back of their minds, most customers still have some ambitious “moonshot” project they hope that the full power of Watson will one day be able to solve, says Mr Kelly; but they are motivated in the short term by making improvements to their business, which he says can still be significant.
    This more pragmatic formula, which puts off solving the really big problems to another day, is starting to pay dividends for IBM. Companies like Australian energy group Woodside are using Watson’s language capabilities as a form of advanced search engine to trawl their internal “knowledge bases”. After feeding more than 20,000 documents from 30 years of projects into the system, the company’s engineers can now use it to draw on past expertise, like calculating the maximum pressure that can be used in a particular pipeline.
    To critics in the AI world, the new, componentised Watson has little to do with the original breakthrough and waters down the technology. “It feels like they’re putting a lot of things under the Watson brand name — but it isn’t Watson,” says Mr Hammond.
    Mr Etzioni goes further, claiming that IBM has done nothing to show that its original Jeopardy!-playing breakthrough can yield results in the real world. “We have no evidence that IBM is able to take that narrow success and replicate it in broader settings,” he says. Of the box of tricks that is now sold under the Watson name, he adds: “I’m not aware of a single, super-exciting app.”

    To IBM, though, such complaints are beside the point. “Everything we brand Watson analytics is very high-end AI,” says Mr Kelly, involving “machine learning and high-speed unstructured data”. Five years after Jeopardy! the system has evolved far beyond its original set of tricks, adding capabilities such as image recognition to expand greatly the range of real-world information it can consume and process.


    Adopting the system
    This argument may not matter much if the Watson brand lives up to its promise. It could be self-fulfilling if a number of early customers adopt the technology and put in the work to train the system to work in their industries, something that would progressively extend its capabilities.

    Another challenge for early users of Watson has been knowing how much trust to put in the answers the system produces. Its probabilistic approach makes it very human-like, says Ms Chin at MD Anderson. Having been trained by experts, it tends to make the kind of judgments that a human would, with the biases that implies.
    In the business world, a brilliant machine that throws out an answer
    to a problem but cannot explain itself will be of little use, says Mr Hammond. “If you walk into a CEO’s office and say we need to shut down three factories and sack people, the first thing the CEO will say is: ‘Why?’” He adds: “Just producing a result isn’t enough.”
    IBM’s attempts to make the system more transparent, for instance by using a visualisation tool called WatsonPaths to give a sense of how it reached a conclusion, have not gone far enough, he adds.
    Mr Kelly says a full audit trail of Watson’s decision-making is embedded in the system, even if it takes a sophisticated user to understand it. “We can go back and figure out what data points Watson connected” to reach its answer, he says.

    He also contrasts IBM with other technology companies like Google and Facebook, which are using AI to enhance their own services or make their advertising systems more effective. IBM is alone in trying to make the technology more transparent to the business world, he argues: “We’re probably the only ones to open up the black box.”
    Even after the frustrations of wrestling with Watson, customers like MD Anderson still believe it is better to be in at the beginning of a new technology.
    “I am still convinced that the capability can be developed to what we thought,” says Ms Chin. Using the technology to put the reasoning capabilities of the world’s oncology experts into the hands of other doctors could be far-reaching: “The way Amazon did for retail and shopping, it will change what care delivery looks like.”
    Ms Chin adds that Watson will not be the only reasoning engine that is deployed in the transformation of healthcare information. Other technologies will be needed to complement it, she says.
    Five years after Watson’s game show gimmick, IBM has finally succeeded in stirring up hopes of an AI revolution in business. Now, it just has to live up to the promises.

    Source: Financial Times

  • Bedrijven verwachten veel van Big Data

    Uit onderzoek van Forrester in opdracht van Xerox komt naar voren dat bijna driekwart van de Europese ondernemingen veel rendement verwacht van Big Data en analytics.

    big-data-1

    Voor het onderzoek werden gesprekken gevoerd met 330 senior business- (CEO, HR, Finance en Marketing) en IT-beslissers in Retail, Hightech, industriële en financiële dienstverlenende organisaties in België, Frankrijk, Duitsland, Nederland en het Verenigd Koninkrijk. Forrester concludeert dat 74 procent van de West-Europese bedrijven verwacht door inzichten verkregen met big data een return on investment (ROI) te realiseren binnen 12 maanden na implementatie. Meer dan de helft (56 procent) ervaart momenteel al de voordelen van big data.ondernemingen veel rendement verwacht van Big Data en analytics.

    Niet van een leien dakje
    Het simpelweg aanschaffen van een analysepakket voor grote hoeveelheden data is echter niet voldoende. Slechte datakwaliteit en het gebrek aan expertise belemmeren de transformatie die organisaties kunnen doormaken door met big data te werken. Er zal voldoende gekwalificeerd personeel moeten komen, om ervoor te zorgen dat op de juiste manier met de juiste data wordt gewerkt.

    Onderbuikgevoel
    Big data is essentieel bij het nemen van beslissingen in 2015: 61 procent van de organisaties zegt beslissingen steeds meer te baseren op data-driven intelligence, dan op factoren zoals onderbuikgevoel, mening of ervaring.

    Onjuiste data
    Onjuiste data blijken kostbaar: 70 procent van de organisaties heeft nog steeds onjuiste data in hun systemen en 46 procent van de respondenten is van mening dat dit zelfs een negatieve invloed heeft op de bedrijfsvoering.

    Veiligheid
    Van de respondenten beoordeelt 37 procent gegevensbeveiliging en privacy als de grootste uitdagingen bij het implementeren van big data-strategieën. Nederlandse organisaties zien het gebrek aan toegang tot interne data vanwege technische bottlenecks als grootste uitdaging bij de implementatie van big data (36 procent).

    Bron: Automatiseringsgids, 1 mei 2015

     

  • BI and Big Data: Same or Different?

    BI and Big Data: Same or Different?

    Webster dictionary defines a synonym as "a word having the same or nearly the same meaning" or as "a word or expression accepted as another name for something." This is so true for popular definitions of BI and big data. Forrester defines BI as:

    A set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making.

    While BI has been a thriving market for decades and will continue to flourish for the foreseeable future, the world doesn't stand still and:

    • Recognizes a need for more innovation. Some of the approaches in earlier generation BI applications and platforms started to hit a ceiling a few years ago. For example, SQL and SQL-based database management systems (DBMS), while mature, scalable, and robust, are not agile and flexible enough in the modern world where change is the only constant.
    • Needs to addresses some of the limitations of earlier generation BI. In order to address some of the limitations of more traditional and established BI technologies, big data offers more agile and flexible alternatives to democratize all data, such as NoSQL, among many others.

    Forrester defines big data as:

     The practices and technologies that close the gap between the data available and the ability to turn that data into business insight.

    But at the end of the day, while new terms are important to emphasize the need to evolve, change, and innovate, what's infinitely more imperative is that both strive to achieve the same goal: transform data into information and insight. Alas, while many developers are beginning to recognize the synergies and overlaps between BI and big data, quite a few still consider and run both in individual silos.

    Contrary to some of the market hype, data democratization and big data do not eliminate the need for the "BI 101" basics, such as data governance, data quality, master data management, data modeling, well thought out data architecture, and many others. If anything, big data makes these tasks and processes more challenging because more data is available to more people, which in turn may cause new mistakes and drive wrong conclusions. All of the typical end-to-end steps necessary to transform raw data into insights still have to happen; now they just happen in different places and at different times in the process.

    To address this challenge in a "let's have the cake and eat it too" approach, Forrester suggests integrating the worlds of BI and big data in flexible hub-and-spoke data platform. Our hub-and-spoke BI/Big Data architecture defines such components as

    • Hadoop based data hubs/lakes to store and process majority of the enterprise data
    • Data discovery accelerators to help profile and discover definitions and meanings in data sources
    • Data governance that differentiates the processes you need to perform at the ingest, move, use, and monitor stages
    • BI that becomes one of many spokes of the Hadoop based data hub
    • A knowledge management portal to front end multiple BI spokes
    • Integrated metadata for data lineage and impact analysis

    Our research also recommends considering architecting the hub-and-spoke environment around the three following key areas:

    • A "cold" layer based on Hadoop where processes my run slower than in DBMS but the total cost of ownership is much lower. This is where the majority of your enterprise data should end up
    •  A "warm" are based on DBMS where queries run faster, but at a price. Forrester typically sees <30% of enterprise data stored and processed in data warehouses and data marts
    • A "hot" area based on in-memory technology for real time low latency interactive data exploration. While this area requires the most expensive software/hardware investments, real time data interactivity produces tangible business benefits.

    Auteur: Boris Evelson

    Bron: Information Management

  • Big data can’t bring objectivity to a subjective world

    justiceIt seems everyone is interested in big data these days. From social scientists to advertisers, professionals from all walks of life are singing the praises of 21st-century data science.
     
    In the social sciences, many scholars apparently believe it will lend their subject a previously elusive objectivity and clarity. Sociology books like An End to the Crisis of Empirical Sociology? and work from bestselling authors are now talking about the superiority of “Dataism” over other ways of understanding humanity. Professionals are stumbling over themselves to line up and proclaim that big data analytics will enable people to finally see themselves clearly through their own fog.
     
    However, when it comes to the social sciences, big data is a false idol. In contrast to its use in the hard sciences, the application of big data to the social, political and economic realms won’t make these area much clearer or more certain.
     
    Yes, it might allow for the processing of a greater volume of raw information, but it will do little or nothing to alter the inherent subjectivity of the concepts used to divide this information into objects and relations. That’s because these concepts — be they the idea of a “war” or even that of an “adult” — are essentially constructs, contrivances liable to change their definitions with every change to the societies and groups who propagate them.
     
    This might not be news to those already familiar with the social sciences, yet there are nonetheless some people who seem to believe that the simple injection of big data into these “sciences” should somehow make them less subjective, if not objective. This was made plain by a recent article published in the September 30 issue of Science.
     
    Authored by researchers from the likes of Virginia Tech and Harvard, “Growing pains for global monitoring of societal events” showed just how off the mark is the assumption that big data will bring exactitude to the large-scale study of civilization.
     
    The systematic recording of masses of data alone won’t be enough to ensure the reproducibility and objectivity of social studies.
    More precisely, it reported on the workings of four systems used to build supposedly comprehensive databases of significant events: Lockheed Martin’s International Crisis Early Warning System (ICEWS), Georgetown University’s Global Data on Events Language and Tone (GDELT), the University of Illinois’ Social, Political, and Economic Event Database (SPEED) and the Gold Standard Report (GSR) maintained by the not-for-profit MITRE Corporation.
     
    Its authors tested the “reliability” of these systems by measuring the extent to which they registered the same protests in Latin America. If they or anyone else were hoping for a high degree of duplication, they were sorely disappointed, because they found that the records of ICEWS and SPEED, for example, overlapped on only 10.3 percent of these protests. Similarly, GDELT and ICEWS hardly ever agreed on the same events, suggesting that, far from offering a complete and authoritative representation of the world, these systems are as partial and fallible as the humans who designed them.
     
    Even more discouraging was the paper’s examination of the “validity” of the four systems. For this test, its authors simply checked whether the reported protests actually occurred. Here, they discovered that 79 percent of GDELT’s recorded events had never happened, and that ICEWS had gone so far as entering the same protests more than once. In both cases, the respective systems had essentially identified occurrences that had never, in fact, occurred.
     
    They had mined troves and troves of news articles with the aim of creating a definitive record of what had happened in Latin America protest-wise, but in the process they’d attributed the concept “protest” to things that — as far as the researchers could tell — weren’t protests.
     
    For the most part, the researchers in question put this unreliability and inaccuracy down to how “Automated systems can misclassify words.” They concluded that the examined systems had an inability to notice when a word they associated with protests was being used in a secondary sense unrelated to political demonstrations. As such, they classified as protests events in which someone “protested” to her neighbor about an overgrown hedge, or in which someone “demonstrated” the latest gadget. They operated according to a set of rules that were much too rigid, and as a result they failed to make the kinds of distinctions we take for granted.
     
    As plausible as this explanation is, it misses the more fundamental reason as to why the systems failed on both the reliability and validity fronts. That is, it misses the fact that definitions of what constitutes a “protest” or any other social event are necessarily fluid and vague. They change from person to person and from society to society. Hence, the systems failed so abjectly to agree on the same protests, since their parameters on what is or isn’t a political demonstration were set differently from each other by their operators.
     
    Make no mistake, the basic reason as to why they were set differently from each other was not because there were various technical flaws in their coding, but because people often differ on social categories. To take a blunt example, what may be the systematic genocide of Armenians for some can be unsystematic wartime killings for others. This is why no amount of fine-tuning would ever make such databases as GDELT and ICEWS significantly less fallible, at least not without going to the extreme step of enforcing a single worldview on the people who engineer them.
     
    It’s unlikely that big data will bring about a fundamental change to the study of people and society.
    Much the same could be said for the systems’ shortcomings in the validity department. While the paper’s authors stated that the fabrication of nonexistent protests was the result of the misclassification of words, and that what’s needed is “more reliable event data,” the deeper issue is the inevitable variation in how people classify these words themselves.
     
    It’s because of this variation that, even if big data researchers make their systems better able to recognize subtleties of meaning, these systems will still produce results with which other researchers find issue. Once again, this is because a system might perform a very good job of classifying newspaper stories according to how one group of people might classify them, but not according to how another would classify them.
     
    In other words, the systematic recording of masses of data alone won’t be enough to ensure the reproducibility and objectivity of social studies, because these studies need to use often controversial social concepts to make their data significant. They use them to organize “raw” data into objects, categories and events, and in doing so they infect even the most “reliable event data” with their partiality and subjectivity.
     
    What’s more, the implications of this weakness extend far beyond the social sciences. There are some, for instance, who think that big data will “revolutionize” advertising and marketing, allowing these two interlinked fields to reach their “ultimate goal: targeting personalized ads to the right person at the right time.” According to figures in the advertising industry “[t]here is a spectacular change occurring,” as masses of data enable firms to profile people and know who they are, down to the smallest preference.
     
    Yet even if big data might enable advertisers to collect more info on any given customer, this won’t remove the need for such info to be interpreted by models, concepts and theories on what people want and why they want it. And because these things are still necessary, and because they’re ultimately informed by the societies and interests out of which they emerge, they maintain the scope for error and disagreement.
     
    Advertisers aren’t the only ones who’ll see certain things (e.g. people, demographics, tastes) that aren’t seen by their peers.
     
    If you ask the likes of Professor Sandy Pentland from MIT, big data will be applied to everything social, and as such will “end up reinventing what it means to have a human society.” Because it provides “information about people’s behavior instead of information about their beliefs,” it will allow us to “really understand the systems that make our technological society” and allow us to “make our future social systems stable and safe.”
     
    That’s a fairly grandiose ambition, yet the possibility of these realizations will be undermined by the inescapable need to conceptualize information about behavior using the very beliefs Pentland hopes to remove from the equation. When it comes to determining what kinds of objects and events his collected data are meant to represent, there will always be the need for us to employ our subjective, biased and partial social constructs.
     
    Consequently, it’s unlikely that big data will bring about a fundamental change to the study of people and society. It will admittedly improve the relative reliability of sociological, political and economic models, yet since these models rest on socially and politically interested theories, this improvement will be a matter of degree rather than kind. The potential for divergence between separate models won’t be erased, and so, no matter how accurate one model becomes relative to the preconceptions that birthed it, there will always remain the likelihood that it will clash with others.
     
    So there’s little chance of a big data revolution in the humanities, only the continued evolution of the field.
  • Big Data changes CI fast!

    Few professions are left unchanged by technology. From healthcare to retail to professional sports, professionals of every stripe make use of technology to do what they do better through the application of technology’s most prolific output: data. So it would make sense that an entire industry based on the analysis of data and information also is undergoing a revision.

    Although it’s one of those industries few people think about, the competitive intelligence (CI) field has been gaining

    CI-BIG-DATA

     ground steadily since it was officially recognized as a discipline soon after World War II. The industry really did not get its official CI designation until the early 1980s, when its trade association, The Strategic and Competitive Intelligence Professionals (SCIP), was established.

    There are a few variations on the CI theme, customer intelligence and market intelligence being the most widely recognized outside of the profession. Because of the explosion of data sources, cheap processing power, and many analytics vendors today, “integrated intelligence” is taking over as the umbrella term under which all data collection, analysis, and interpretation and dissemination activities take place.

    “CI has expanded far beyond human intelligence and primary data collection at this point,” said SCIP Executive Director Nannette Bulger. “CI professionals are handling market sizing, segmentation, strategic analysis to support mergers and acquisitions, and so on.”

    As one would expect based on its name, CI is all about providing businesses with insights so they can best the competition. But with the rise of the Internet and the widespread dissemination of information and data, what was once an ivory tower type of pursuit involving high

    ly trained specialists is now, in many respects, the job of everyone in an organization. Data are everywhere. The ability to make sense of it all is still a rare skill, but its collection and organization are no longer the primary job of CI professionals.

    And this is challenging many in the business to take fresh look at what they do, said Bulger. Where once there were just a handful of players claiming CI leadership, now the industry is under siege by a growing host of data-focused startups as well as business intelligence software vendors, marketing automation players, and anyone else who analyzes data to understand business better.

    Bulger trots out example after example of companies that have figured out new business-focused uses for analytics tools developed for other purposes.

    “Before, it was just a lot of data dissemination,” she said. “Now, you have people coming out of MIT’s Media Lab working with cosmetics vendors, for example. There’s a tidal wave coming that the established vendors are trying to ignore.”

    The crest of that wave is companies coming into the CI market that may not be seen as threats by current vendors. There is a supply chain mapping company, for example, that is now doing analytics on their data to help companies avoid disruptions to their operations if a source of raw materials suddenly goes away. Providing a company with the knowledge it needs to switch suppliers quickly and continue operations is a serious competitive advantage in times of scarcity.

    While not CI directly but a great example of unlooked-for-uses of technology, the Formula One racing team McLaren is helping doctors monitor infants in intensive care units, said Bulger. Auto racing teams use real-time monitoring from sensors all over their racecars. As it turns out, this same technology is ideal for monitoring people’s vital signs.

    It is this type of disruption that has the profession in a state of flux, making even deciding who is an “intelligence” vendor and who is not problematic. Tech industry research house IDC has gone so far as to rebrand the entire industry, calling any business that provides other businesses with intelligence “value-added content” (VAC) providers:

    “Big Data and analytics (BDA) solutions are fueling a demand for more and wider varieties of data every day,” IDC wrote in a 2014 opinion brief about HGdata, a big data analytics company. “A raft of new companies that provide a range of data types – from wind speed data to data about what people are watching, reading, or listening to – are emerging to coexist with and sometimes replace more traditional data vendors in the information industry. What’s more is that organizations in many industries are curating and adding value to that content, in some cases transforming it completely and finding new ways of deriving economic value from the data. Value-added content (VAC) is an emerging market. Social media, blog posts, Web transactions, industrial data, and many other types of data are being aggregated, curated, enhanced, and sold to organizations hungry to understand their customers and products as well as the markets in which they exist.”

    At the end of the day, the big data revolution is not about data. It’s about doing what we do better. Whether improving a process, finding a cure for a rare disease, taking over market share from a competitor, or just understanding how things really work, all of these things can be done better through the analysis of data – the more data, the better. Like the world when viewed through the lens of an ultra-slow motion camera or at the tip of an electron microscope, big data gives people the ability to see things they otherwise would not be able to see.

    “I believe this is a huge growth area,” said Heather Cole, president of business intelligence solutions company Lodestar Solutions. “Companies are beginning to feel the effects of ‘digital disruption.’ They must be innovative to thrive. Customer intelligence is a valuable part of innovation. Companies that identify why their clients buy from them find new clients to serve or a new product to serve their existing clients, and will find it is much easier to hold margins and market share even in a highly competitive market.”

  • Big data defeats dengue

    mosquito-aedes-albopictusNumbers have always intrigued Wilson Chua, a big data analyst hailing from Dagupan, Pangasinan and currently residing in Singapore. An accountant by training, he crunches numbers for a living, practically eats them for breakfast, and scans through rows and rows of excel files like a madman.
     
    About 30 years ago, just when computer science was beginning to take off, Wilson stumbled upon the idea of big data. And then he swiftly fell in love. He came across the story of John Snow, the English physician who solved the cholera outbreak in London in 1854, which fascinated him with the idea even further. “You can say he’s one of the first to use data analysis to come out with insight,” he says.
     
    In 1850s-London, everybody thought cholera was airborne. Nobody had any inkling, not one entertained the possibility that the sickness was spread through water. “And so what John Snow did was, he went door to door and made a survey. He plotted the survey scores and out came a cluster that centered around Broad Street in the Soho District of London.
     
    “In the middle of Broad Street was a water pump. Some of you already know the story, but to summarize it even further, he took the lever of the water pump so nobody could extract water from that anymore. The next day,” he pauses for effect, “no cholera.”
     
    The story had stuck with him ever since, but never did he think he could do something similar. For Wilson, it was just amazing how making sense of numbers saved lives.
     
    A litany of data
     
    In 2015 the province of Pangasinan, from where Wilson hails, struggled with rising cases of dengue fever. There were enough dengue infections in the province—2,940 cases were reported in the first nine months of 2015 alone—for it to be considered an epidemic, had Pangasinan chosen to declare it.
     
    Wilson sat comfortably away in Singapore while all this was happening. But when two of his employees caught the bug—he had business interests in Dagupan—the dengue outbreak suddenly became a personal concern. It became his problem to solve.
     
    “I don’t know if Pangasinan had the highest number of dengue cases in the Philippines,” he begins, “but it was my home province so my interests lay there,” he says. He learned from the initial data released by the government that Dagupan had the highest incident of all of Pangasinan. Wilson, remembering John Snow, wanted to dig deeper.
     
    Using his credentials as a technology writer for Manila Bulletin, he wrote the Philippine Integrated Diseases Surveillance and Response team (PIDSR) of the Department of Health, requesting for three years worth of data on Pangasinan.
     
    The DOH acquiesced and sent him back a litany of data on an Excel sheet: 81,000 rows of numbers or around 27,000 rows of data per year. It’s an intimidating number but one “that can fit in a hard disk,” Wilson says.
     
    He then set out to work. Using tools that converted massive data into understandable patterns—graphs, charts, the like—he looked for two things: When dengue infections spiked and where those spikes happened.
     
    “We first determined that dengue was highly related to the rainy season. It struck Pangasinan between August and November,” Wilson narrates. “And then we drilled down the data to uncover the locations, which specific barangays were hardest hit.”
     
    The Bonuan district of the city of Dagupan, which covers the barangays of Bonuan Gueset, Bonuan Boquig, and Bonuan Binloc, accounted for a whopping 29.55 percent—a third of all the cases in Dagupan for the year 2015.
     
    The charts showed that among the 30 barangays, Bonuan Gueset was number 1 in all three years. “It means to me that Bonuan Gueset was the ground zero, the focus of infection.”
     
    But here’s the cool thing: After running the data on analytics, Wilson learned that the PIDS sent more than they had hoped for. They also included the age of those affected. According to the data, dengue in Bonuan was prevalent among school children aged 5-15 years old.
     
    “Now given the background of Aedes aegypti, the dengue-carrying mosquito—they bite after sunrise and a few hours before sunset. So it’s easily to can surmise that the kids were bitten while in school.”
     
    It excited him so much he fired up Google Maps and switched it to satellite image. Starting with Barangay Bonuan Boquig, he looked for places that had schools that had stagnant pools of water nearby. “Lo and behold, we found it,” he says.
     
    Sitting smack in the middle of Lomboy Elementary School and Bonuan Boquig National High School were large pools of stagnant water.
    Like hitting jackpot, Wilson quickly posted his findings on Facebook, hoping someone would take up the information and make something out of it. Two people hit him up immediately: Professor Nicanor Melecio, the project director of the e-Smart Operation Center of Dagupan City Government, and Wesley Rosario, director at the Bureau of Fisheries and Aquatic Resources, a fellow Dagupeño.
     
    A social network
     
    Unbeknownst to Wilson, back in Dagupan, the good professor had been busy, conducting studies on his own. The e-Smart Center, tasked with crisis, flooding, disaster-type of situation, had been looking into the district’s topography vis-a-vis rainfall in Bonuan district. “We wanted to detect the catch basins of the rainfall,” he says, “the elevation of the area, the landscape. Basically, we wanted to know the deeper areas where rainfall could possibly stagnate.”
     
    Like teenage boys, the two excitedly messaged each other on Facebook. “Professor Nick had lieder maps of Dagupan, and when he showed me those, it confirmed that these areas, where we see the stagnant water, during rainfall, are those very areas that would accumulate rainfall without exit points,” Wilson says. With no sewage system, the water just sat there and accumulated.
     
    With Wilson still operating remotely in Singapore, Professor Melecio took it upon himself to do the necessary fieldwork. He went to the sites, scooped up water from the stagnant pools, and confirmed they were infested with kiti-kiti or wriggling mosquito larvae.
     
    Professor Melecio quickly coordinated with Bonuan Boquig Barangay Captain Joseph Maramba to involve the local government of Bonuan Boquig on their plan to conduct vector control measures.
     
    A one-two punch
     
    Back in Singapore, Wilson found inspiration from the Tiger City’s solution to its own mosquito problem. “They used mosquito dunks that contained BTI, the bacteria that infects mosquitoes and kills its eggs,” he says.
     
    He used his own money to buy a few of those dunks, imported them to Dagupan, and on Oct. 6, had his team scatter them around the stagnant pools of Bonuan Boquig. The solution was great, dream-like even, except it had a validity period. Beyond 30 days, the bacteria is useless.
     
    Before he even had a chance to even worry about the solution’s sustainability, BFAR director Wesley Rosario pinged him on Facebook saying the department had 500 mosquito fish for disposal. “Would we want to send somebody to his office, get the fish, and release them into the pools?”
     
    The Gambezi earned its nickname because it eats, among other things, mosquito larvae. In Wilson’s and Wesley’s mind, the mosquito fish can easily make a home out of the stagnant pools and feast on the very many eggs present. When the dry season comes, the fish will be left to die. Except, here’s the catch: mosquito fish is edible.
     
    “The mosquito fish solution was met with a few detractors,” Wilson admits. “There are those who say every time you introduce a new species, it might become invasive. But it’s not really new as it is already endemic to the Philippines. Besides we are releasing them in a landlocked area, so wala namang ibang ma-a-apektuhan.”
     
    The critics, however, were silenced quickly. Four days after deploying the fish, the mosquito larvae were either eaten or dead. Twenty days into the experiment, with the one-two punch of the dunks and the fish, Barangay Boquig reported no new infections of dengue.
     
    “You know, we were really only expecting the infections to drop 50 percent,” Wilson says, rather pleased. More than 30 days into the study and Barangay Bonuan Boquig still has no reports of new cases. “We’re floored,” he added.
     
    At the moment, nearby barangays are already replicating what Wilson, Professor Melecio, and Wesley Rosario have done with Bonuan Boquig. Michelle Lioanag of the non-profit Inner Wheel Club of Dagupan has already taken up the cause to do the same for Bonuan Gueset, the ground zero for dengue in Dagupan.
     
    According to Wilson, what they did in Bonuan Boquig is just a proof of concept, a cheap demonstration of what big data can do. “It was so easy to do,” he said. “Everything went smoothly,” adding all it needed was cooperative and open-minded community leaders who had nothing more than sincere public service in their agenda.
     
    “You know, big data is multi-domain and multi-functional. We can use it for a lot of industries, like traffic for example. I was talking with the country manager of Waze…” he fires off rapidly, excited at what else his big data can solve next.
     
    Source: news.mb.com, November 21, 2016
  • Big Data en privacy kunnen zeker goed samengaan

    fd-big-dataBig Data wordt gezien als de toekomst van onderzoek en dienstverlening. De economische belofte is groot. Vanuit die optiek proclameren consultancybedrijven regelmatig dat je als bedrijf nu op de Big Data-trein moet springen, of anders over 5 jaar 'out of business' bent. Niet meedoen is dus geen optie. Tegelijkertijd zijn veel bedrijven huiverig, omdat Big Data problematisch kan zijn in het kader van privacy. Met nieuwe, strengere privacyregelgeving op komst kan dat betekenen dat meedoen met Big Data betekent dat je over twee jaar juist 'out of business' bent. Tijd om privacy innovatie expliciet onderdeel te maken van Big Data ontwikkelingen. Zo kunnen de economische vruchten van Big Data geplukt worden, terwijl privacy van gebruikers gerespecteerd wordt.

    De verwachte economische omvang van Big Data zal volgens Forbes groeien naar een wereldwijde markt van $ 122 mrd in 2025. Vanzelfsprekend is dit een interessant gebied voor de EU, die toch van origine een economische samenwerking is. Vanuit diezelfde EU kwam in december de definitieve tekst van de Algemene Verordening Gegevensbescherming, die de huidige regelgeving over privacy en bescherming van persoonsgegevens gaat vervangen. Als het Europees Parlement de tekst goedkeurt, deze maand of februari, wordt de nieuwe wetgeving in 2017 van kracht. Wie daarna niet voldoet maakt kans op torenhoge boetes (4% van de wereldwijde jaaromzet). Bedrijven zullen dan activiteiten moeten stopzetten als ze zich niet aan de regels uit die Verordening houden.

    Het verwerken van grote hoeveelheden persoonsgegevens, zonder vooraf duidelijk vastgesteld doel, zal niet zomaar toegestaan zijn. En was het hele idee van Big Data nu niet juist dat je enorme hoeveelheden gegevens verwerkt? En is het analyseren van gegevens met behulp van algoritmen, waarbij je de uitkomst (en het mogelijke doel) niet vooraf kúnt voorspellen, niet juist één van de kroonjuwelen van Big Data? Want hoe handig is het als een supermarktketen op basis van je aankoopgedrag als eerste weet dat je zwanger bent, of wanneer een verzekeraar op basis van Big Data kan voorspellen of je een risico bent qua gezondheid of rijgedrag zodat je wellicht meer premie moet betalen?

    Het is duidelijk dat privacybescherming bij Big Data toepassingen niet vanzelfsprekend is. En dat Big Data ook echt voordelen oplevert, natuurlijk. Gelukkig biedt de Verordening ook uitkomst. Het principe van Data Protection by Design zal een belangrijke rol spelen. Dat betekent dat organisaties de vereisten voor bescherming van persoonsgegevens in moeten bedden in de ontwikkeling van nieuwe diensten. En als je dat bij Big Data toepassingen goed doet hoeft de Verordening dus niet te betekenen dat je 'out of business' bent. Zeker niet wanneer je als organisatie je klanten echt centraal stelt. Het faciliteren van privacy kan immers ook een nicheproduct zijn, en een kansrijk product bovendien. Denk aan toepassingen waarbij eerst op basis van geaggregeerde en geanonimiseerde gegevens analyses worden gedaan.

    Vervolgens kun je op basis van de kennis die daaruit voortvloeit gerichte producten of diensten aanbieden door op individueel niveau de koppeling te maken, met de toestemming van de gebruiker uiteraard. Want die ziet dan ook de toegevoegde waarde en weet welk product of dienst hij krijgt. Innovatieve benaderingen kunnen Big Data faciliteren, mogelijk verbeteren, en met inachtneming van privacy een belangrijk dienstensegment ontsluiten. En als je het goed doet word je ook geen slachtoffer van kritiek van consumenten en onjuiste beeldvorming in de media. Daardoor zijn immers in het recente verleden al enkele initiatieven de das omgedaan.

    Organisaties moeten dus zeker op de Big Data trein springen. Maar als ze met die Big Data nog langer dan een paar jaar vooruit willen moeten ze wel eerste klas reizen, in de Data Protection by Design wagonnetjes. Ze moeten vooral ontdekken wat mogelijk is onder de nieuwe Verordening en wat ze daarvoor moeten doen, in plaats van alleen hindernissen te zien. Want Big Data en privacy kunnen prima samengaan, als je er bij het ontwerp van je diensten maar aan denkt.

    Mr.dr. Arnold Roosendaal is onderzoeker Strategie en Beleid voor de Informatiemaatschappij bij TNO en tevens verbonden aan het PI.lab.

    Source Financieel Dagblad

  • Big Data Facts: How Many Companies Are Really Making Money From Their Data?

    data monetizationMore and more businesses are waking up to the importance of data as a strategic resource. Yesterday, research released by the Economist Intelligence Unit reported that 60% of the professionals they quizzed feel that data is generating revenue within their organizations and 83% say it is making existing services and products more profitable.

    After surveying 476 executives from around the world, it found that those based in Asia are leading the way – where 63% said they are routinely generating value from data. In the US, the figure was 58%, and in Europe, 56%.

    This makes it clear that businesses are finding more and more ways to turn data into value, but at the same time, the report found, many are hitting stumbling blocks which are frustrating those efforts. Just 34% of respondents said that they feel their organizations are “very effective” at being transparent with customers about how data is used. And 9% say they feel that they are “totally ineffective” in this area, which can be very detrimental to building the all-important customer trust.

    For businesses that are built around customer data (or those which are repurposing to be so), customer trust is absolutely essential. We have seen that people are becoming increasingly willing to hand over personal data in return for products and services that make their lives easier. However that goodwill can evaporate in an instant if customers feel their data is being used improperly, or not effectively protected.

    The report states that ‘Big Data analysis, or the mining of extremely large data sets to identify trends and patterns, is fast becoming standard business practice.

    “Global technology infrastructure, too, has matured to an extent that reliability, speed and security are all typically robust enough to support the seamless flow of massive volumes of data, and consequently encourage adoption.”

    It also goes on to suggest that more and more businesses, taking cues from online giants such as Facebook and Google, are positioning themselves as data-first operations, built entirely around data collection, analysis and redistribution – as opposed to simply using it as a source of business intelligence.

    59% of respondents said that they consider data and analytics to be “vital” to the running of their organizations, with a further 29% deeming it “very important”.

    The increasing availability of cloud processing, analytics and storage services has undoubtedly opened the floodgates in terms of making Big Data driven analytics accessible to businesses of all sizes across many industries. But I feel this survey also backs up warnings that I, and others, have been voicing for some time. Data, particularly Big Data, is an almost infinitely empowering asset – but its use can be limited, or it can even become a liability if it isn’t backed up by a robust (and regulator-compliant) strategy.

    Interestingly, just under half of those surveyed (47%) say that their data analytics is limited to data they have collected themselves – through internal processes, commercial activity and customer services. I would expect this number to shrink in coming years, as more and more organizations become accustomed to adding data provided by third parties such as data wholesalers and governments into the mix.

    Another statistic which stood out to me was that 69% feel there is a business case with their companies to set up a dedicated internal data and analytics unit, with the purpose of exploring new ways to add value through data projects. This is probably driven by the fact that 48% feel that their organizations have, in the past, failed to take advantage of opportunities to capitalize on their data. I fully expect to see dedicated data teams and working groups become an increasingly vital piece of corporate infrastructure over the next few years, well beyond industries such as tech and finance where they are already commonplace.

    Overall, it seems businesses are fairly confident about their ability to keep our data safe – with 82% saying that their data protection procedures are “very” or “somewhat” effective. However, we know that large scale theft of customer data from corporations is an ever-growing problem. Executives at organizations hit by this type of crime recently – such as Anthem, Talk Talk and the US Government – were presumably fairly confident that their systems were safe too – until they discovered that they weren’t. The report also makes it clear that data breaches are certainly not limited to the high profile incidents that receive coverage in the media. In fact, fairly shockingly, 34% of respondents said that their businesses had suffered “significant” data breaches within the past 12 months.

    The EIU report, which can be read in full here, makes it clear that adoption of Big Data driven strategies has come on in leaps and bounds during the last year. However it is also equally clear that there is still a long way to go until every business is secure enough in its infrastructure to transition to a fully data driven business model.

    Source: Forbes, January 14th, 2016

     

     

  • Big Data gaat onze zorg verbeteren

    Hij is een man met een missie. En geen geringe: hij wil samen met patiënten, de zorgverleners en verzekeraars een omslag in de gezondheidszorg bewerkstelligen, waarbij de focus verlegd wordt van het managen van ziekte naar het managen van gezondheid. Jeroen Tas, CEO Philips Connected Care & Health Informatics, over de toekomst van de zorg.

    big-data-healthcare-2Wat is er mis met het huidige systeem?

    “In de ontwikkelde wereld wordt gemiddeld 80 procent van het budget voor zorg besteed aan het behandelen van chronische ziektes, zoals hart- en vaatziektes, longziektes, diabetes en verschillende vormen van kanker. Slechts 3 procent van dat budget wordt besteed aan preventie, aan het voorkomen van die ziektes. Terwijl we weten dat 80 procent van hart- en vaatziekten, 90 procent van diabetes type 2 en 50 procent van kanker te voorkomen zijn. Daarbij spelen sociaaleconomische factoren mee, maar ook voeding, wel of niet roken en drinken, hoeveel beweging je dagelijks krijgt en of je medicatie goed gebruikt. We sturen dus met het huidige systeem lang niet altijd op op de juiste drivers om de gezondheid van mensen te bevorderen en hun leven daarmee beter te maken. 50 procent van de patiënten neemt hun medicatie niet of niet op tijd in. Daar liggen mogelijkheden voor verbetering.”

    Dat systeem bestaat al jaren - waarom is het juist nu een probleem?
    “De redenen zijn denk ik alom bekend. In veel landen, waaronder Nederland, vergrijst de bevolking en neemt daarmee het aantal chronisch zieken toe, en dus ook de druk op de zorg. Daarbij verandert ook de houding van de burger ten aanzien van zorg: beter toegankelijk, geïntegreerd en 24/7, dat zijn de grote wensen. Tot slot nemen de technologische mogelijkheden sterk toe. Mensen kunnen en willen steeds vaker zelf actieve rol spelen in hun gezondheid: zelfmeting, persoonlijke informatie en terugkoppeling over voortgang. Met Big Data zijn we nu voor het eerst in staat om grote hoeveelheden data snel te analyseren, om daarin patronen te ontdekken en meer te weten te komen over ziektes voorspellen en voorkomen. Kortom, we leven in een tijd waarin er binnen korte tijd heel veel kan en gaat veranderen. Dan is het belangrijk om op de juiste koers te sturen.”

    Wat moet er volgens jou veranderen?
    “De zorg is nog steeds ingericht rond (acute) gebeurtenissen. Gezondheid is echter een continu proces en begint met gezond leven en preventie. Als mensen toch ziek worden, volgt er diagnose en behandeling. Vervolgens worden mensen beter, maar hebben ze misschien nog wel thuis ondersteuning nodig. En hoop je dat ze weer verder gaan met gezond leven. Als verslechtering optreedt is tijdige interventie wenselijk. De focus van ons huidige systeem ligt vrijwel volledig op diagnose en behandeling. Daarop is ook het vergoedingssysteem gericht: een radioloog wordt niet afgerekend op zijn bijdrage aan de behandeling van een patiënt maar op de hoeveelheid beelden die hij maakt en beoordeelt. Terwijl we weten dat er heel veel winst in termen van tijd, welzijn en geld te behalen valt als we juist meer op gezond leven en preventie focussen. 

    Er moeten ook veel meer verbanden komen tussen de verschillende pijlers in het systeem en terugkoppeling over de effectiviteit van diagnose en behandeling. Dat kan bijvoorbeeld door het delen van informatie te stimuleren. Als een cardioloog meer gegevens heeft over de thuissituatie van een patiënt, bijvoorbeeld over hoe hij zijn medicatie inneemt, eet en beweegt, dan kan hij een veel beter behandelplan opstellen, toegesneden op de specifieke situatie van de patiënt. Als de thuiszorg na behandeling van die patiënt ook de beschikking heeft over zijn data, weet men waarop er extra gelet moet worden voor optimaal herstel. En last maar zeker not least, de patiënt moet ook over die data beschikken, om zo gezond mogelijk te blijven. Zo ontstaat een patiëntgericht systeem gericht op een optimale gezondheid.”

    Dat klinkt heel logisch. Waarom gebeurt het dan nog niet?
    “Alle verandering is lastig – en zeker verandering in een sector als de zorg, die om begrijpelijke redenen conservatief is en waarin er complexe processen spelen. Het is geen kwestie van technologie: alle technologie die we nodig hebben om de omslag tot stand te brengen, is er. We hebben sensoren om data automatisch te generen, die in de omgeving van de patiënt kunnen worden geïnstalleerd, die hij kan dragen – denk aan een Smarthorloge – en die zelfs in zijn lichaam kunnen zitten, in het geval van slimme geneesmiddelen. Daarmee komt de mens centraal te staan in het systeem, en dat is waar we naartoe willen.
    Er moet een zorgnetwork om ieder persoon komen, waarin onderling data wordt gedeeld ten behoeve van de persoonlijke gezondheid. Dankzij de technologie kunnen veel behandelingen ook op afstand gebeuren, via eHealth oplossingen. Dat is veelal sneller en vooral efficiënter dan mensen standaard doorsturen naar het ziekenhuis. Denk aan thuismonitoring, een draagbaar echo apparaat bij de huisarts of beeldbellen met een zorgverlener. We kunnen overigens al hartslag, ademhaling en SPo2 meten van een videobeeld. 

    De technologie is er. We moeten het alleen nog combineren, integreren en vooral: implementeren. Implementatie hangt af van de bereidheid van alle betrokkenen om het juiste vergoedingsstelsel en samenwerkingsverband te vinden: overheid, zorgverzekeraars, ziekenhuis, artsen, zorgverleners en de patiënt zelf. Daarover ben ik overigens wel positief gestemd: ik zie de houding langzaam maar zeker veranderen. Er is steeds meer bereidheid om te veranderen.”

    Is die bereidheid de enige beperkende factor?
    “We moeten ook een aantal zaken regelen op het gebied van data. Data moet zonder belemmeringen kunnen worden uitgewisseld, zodat alle gegevens van een patiënt altijd en overal beschikbaar zijn. Dat betekent uiteraard ook dat we ervoor moeten zorgen dat die gegevens goed beveiligd zijn. We moeten ervoor zorgen dat we dat blijvend kunnen garanderen. En tot slot moeten we werken aan het vertrouwen dat nodig is om gegevens te standaardiseren en te delen, bij zorgverleners en vooral bij de patiënt.Dat klinkt heel zwaar en ingewikkeld maar we hebben het eerder gedaan. Als iemand je twintig jaar geleden had verteld dat je via internet al je bankzaken zou regelen, zou je hem voor gek hebben versleten: veel te onveilig. Inmiddels doen we vrijwel niet anders.
    De shift in de zorg nu vraagt net als de shift in de financiële wereld toen om een andere mindset. De urgentie is er, de technologie is er, de bereidheid ook steeds meer – daarom zie ik de toekomst van de zorg heel positief in.”

     Bron: NRC
  • Big Data in 2016: Cloudy, with a Chance of Disappointment, Disillusionment, and Disruption

    Chris Surdak NEWLike last year, I thought that I’d wrap up my writing calendar with some prognostications on Big Data in 2016. I doubt any of these six will come as a surprise to most readers, but what may be a surprise is how emphatically our worlds will have changed twelve months from now, when I take a crack at predicting the world of 2017. Happy New Year!

    1. Welcome to the Trough
    As Big Data moves through the Gartner hype cycle for technology adoption, we will naturally progress into the “trough of Disillusionment.” Organizations have been whipped into a frenzied pitch by the promise of Big Data, and nearly all organizations have been attempting to use Big Data to transform their business, or at least the results that they produce.

    Because Big Data has been the latest “Big Thing” and “Shiny, New Object” in the business world, it has been ever so slightly over-sold; particularly over the last year or so. Organizations have been told that all they need to do is buy and implement this or that new technology and magically, they’ll have amazing new results from their businesses. Unfortunately, like every technology innovation that preceded it, Big Data is merely an enabler, enhancer and amplifier. If your business processes or management approaches are garbage, Big Data will make them much more so.
    Expect to see many organizations become deeply disillusioned by Big Data in 2016 because they had hoped to get different results from their business, without using Big Data to actually change how they operated. Those who used Big Data to make substantive changes to how they operate will dramatically out-compete those who used Big Data to produce merely-more-detailed reports, but little actual change.

    2. The Cloudy Future of Analytics
    For years, Big Data has been too big, too expensive and too complicated for anyone outside of the Fortune 500. After all, the technologies were new, unproven and not even close to ready for prime time, and “real” data scientists were tied up in Universities, large companies, government agencies or any number of tiny, disruptive startup companies. Hence, many small- and mid-sized companies were left on the sidelines of this revolution.
    This year, you will see an explosion of cloud-based analytics solutions designed to embrace the mid-market. Some may merely provide storage and compute capacity while others will provide full-blown analytics platforms, complete with DIY training. The best will also provide on-demand expertise from data-Jedi-for-hire, which will explain why such a large number of big company data scientists will change jobs in the next 12 months.

    3. Open Warfare Online
    Unfortunately, issues related to information security will escalate beyond data breaches, hacking attacks, and identity theft. In 2016, we will see open warfare on the internet between digital have’s and have-not’s. Whether it is nations attacking one another for state secrets and political leverage, Anonymous escalating their fight with ISIS, or cyber criminals holding people and organizations hostage for millions of dollars in ransom, you can expect an ever-increasing amount of online conflict in the coming year.
    Not only will the volume of attacks grow, the techniques, the numbers of victims and the consequences to all of us will also grow; probably dramatically. Last year’s attacks against Ashley Madison, Sony, United Airlines, Excellus BCBS, Experian and the IRS will seem trivial compared to those that will likely come in 2016. Don’t be surprised by attacks against the power grid, the global financial infrastructure, the military, mass-media and other “pillars of our society.”
    This may sound rather dystopian, but the trends are all pointing in this direction. While their techniques, technologies, and approaches will become increasingly sophisticated, the goals of the attackers will be rather simple: social disruption, political change, and good old fashioned profit motive. In an increasingly-interconnected and automated world, brought on by Big Data, you’re as likely to have your power or water cut off for a week as you are in having your credit card number stolen.

    4. Persuasive Analytics Becomes Normal and Expected
    If, in 2015, you haven’t had a creepy experience with persuasive analytics, you either live in a cave, or you likely weren’t pay attention. Whether it’s instant coupons delivered one click after shopping for something online, getting an invite to a “flash sale” on a favored app, or having a friend or family member receive a notice of your browsing history or physical location, persuasive analytics is the big news in Big Data.
    No doubt your organization played around with predictive analytics over the last couple of years; nearly everyone has. But, you probably also came to the same conclusion as everyone else: predictive analytics is a waste of time and money. Knowing what MIGHT happen in the future has no value if you don’t benefit from the insight. CHANGING the future, so that you CAN benefit from it is how you monetize Big Data. This is the distinction between predictive and persuasive analytics. In the former you spend money, in the latter you make money.
    The revolution of predictive analytics is driven by the Digital Trinity of mobility, social media, and data analytics. Leverage this trio correctly and your business will thrive. Do so incorrectly, and you’ll wonder why your business is dying before your eyes.

    5. Privacy Comes to the Fore
    While personal privacy has been all but surrendered in the United States, there has been a growing trend towards personal privacy and commercial restraint in other countries. The last two years have seen major moves in the privacy arena by the European Union, including the judgment against Google in the right to be forgotten and the nullification of the Safe Harbor provision between the EU and the US.
    Similar actions in jurisdictions around the globe demonstrate a growing awareness of just how valuable our individual information has become and how important it is that we take an active role in managing our data.
    You should expect to see greater governmental action against the unfair, undisclosed, uncontrolled collection and use of end-user data, even as the use of such information becomes a commercial and governmental imperative. As both consumers and citizens, we will expect organizations to meet our needs predictively, while at the same time we will want to be able to control the unfettered access that these organizations have to our most intimate details. This is a huge privacy paradox, and all organizations pursuing a Big Data strategy should have information governance and privacy as central themes in all of their efforts.

    6. Introducing the iPresident
    While many people may not realize it, the last two presidential elections in the United States were heavily influenced by the Digital Trinity. In next-year’s election, the White House will be won by whomever uses Digital Trinity most effectively. In the past, the use of the Trinity to sway voters was fairly rudimentary, and not obvious to the public at-large.
    Next year, the impact of the Trinity won’t be nearly so subtle, or passive. Persuasive analytics will be used to drive new voters to the polls, push very targeted and specific political agendas to the fore and drive the mass media at least as much as the mass media tries to drive society. As events in Syria, Libya, France, Greece, Ferguson Missouri, Baltimore and Hong Kong have shown us, the Digital Trinity is an enabler of dramatic social change. Many of these changes will be positive, others will be decidedly less so. Either way, expect significant disruption to the same old same old in our society.
    American politics is about to be fundamentally, comprehensively and permanently changed by the full application of Big Data and many of those who have held power in our country for a very long time will no longer have a seat at the table. This process will be front and center in 2016 as the presidential election unfolds before our eyes. If you’ve paid any attention to the run up to the election in the second-half of 2015 you’ve noted the degree to which things seem different this time. Trust me you haven’t seen anything yet! Next year, pop some popcorn, tune into the election coverage, and settle in for some great entertainment, because this will be the year that the real power of the Digital Trinity will take center-stage.

    Source: Inside Bigdata

  • Big Data loses its Zing

    9 Juli 2015

    Big data isn’t what it used to be. Not because firms are disillusioned with the technology, but rather because the term is no longer helpful. With nearly two-thirds of firms having implemented or planning to implement some big data capability by the end of 2015, the wave has definitely hit. People have bought in.

    But that doesn’t mean we find many firms extolling the benefits they should be seeing by now; even early adopters still have problems across the customer lifecycle. Can your firm understand customers as individuals, not segments? Are analytics driving consistent, insightful experiences across channels? Does all that customer insight developed by marketing make a bit of difference to your contact center agents? If you are like most firms the answer is, “Not yet but we are working on it.”

  • Big Data nog weinig ingezet voor real-time of voorspellingen

    Big DataDatagedreven opereren? Bij de meeste bedrijven zijn de datatoepassingen nog relatief simpel en vooral gericht op analyse in plaats van real-time en voorspellingen. Een gemiste kans én risico voor de lange-termijnkoers van een organisatie.

    Nu al zegt 22 procent van de bedrijven achter te lopen op de concurrentie terwijl ruim 81 procent van de respondenten aangeeft dat de mogelijkheden van Big Data voor de eigen organisatie groot zijn.

    Dat blijkt uit de Big Data Survey 2015 van data-consultancybureau GoDataDriven en vakbeurs Big Data Expo. Bijna 200 bedrijven werden onderzocht om inzicht te geven in de actuele rol van big data, de mate van adoptie, intenties en mogelijke valkuilen.

    Data uit voor de hand liggende bronnen
    Wat blijkt? De data die gebruikt wordt is over het algemeen numeriek en komt vaak uit voor de hand liggende bronnen, zoals CRM en klantendatabase (18 procent), websitestatistieken (18 procent), externe bronnen (14 procent) en marketingdata vanuit e-mailstatistieken (14 procent) en transactionele data (13 procent). Toepassingen met data uit rijkere bronnen zoals tekst, beeld en geluid zijn er nog zeer weinig, terwijl hier grote winst te behalen is.

    GoDataDriven

    Meer budget voor datagedreven toepassingen
    De meeste bedrijven maken komend jaar meer budget vrij voor datagedreven toepassingen en zijn van plan te investeren in de kennisontwikkeling binnen het team. Een klein deel van de bedrijven is momenteel al bezig met het toepassen van kunstmatige intelligentie, machine learning, voorspellende modellen en deep learning.

    Maar dat verandert in hoog tempo. Binnen drie jaar verwacht 50 procent van de respondenten de eerste toepassingen met geavanceerde technologie ontwikkeld te hebben.

    Visie het belangrijkst voor succesvolle implementatie
    Wat de belangrijkste factoren zijn voor een succesvolle implementatie van een Big Data-strategie? Visie, aldus 28 procent van de ondervraagden, en ondersteuning vanuit de directie (19 procent). Maar ook ondersteunende systemen en processen (18 procent), budget (14 procent), talent (11 procent) en training (10 procent) spelen een belangrijke rol.

    GoDataDriven2

    Data als strategische pijler
    Tegelijkertijd geeft een opvallend groot deel van de ondervraagden aan dat het binnen het eigen bedrijf wel goed zit met de strategische rol van data. 37 procent vult in dat de bedrijfsdirectie data als een strategische pijler ziet, terwijl 27 procent het hier gedeeltelijk mee eens is. Bij bijna een kwart van de bedrijven (23 procent) is er binnen de bedrijfsdirectie op dit vlak juist een grote winst te halen.

    Ruim 67 procent van de bedrijven zegt dan ook dat de mogelijkheden van big data voor de eigen organisatie groot zijn. Nog eens 14,5 procent is het hier gedeeltelijk mee eens. Slechts 9 procent is het in meer of mindere mate oneens met deze stelling.

    Meer highlights:

    • Hadoop is het meest populaire dataplatform: 21 procent heeft een of andere Hadoop-implementatie (Hadoop, Horton, Cloudera).
    • Terwijl bij de licensed software SAP (8 procent), SPSS (7 procent) en SAS (6 procent) het beste scoren.
    • Datatoepassingen worden het vaakst gebruikt binnen marketing (19 procent).
    • Informatietechnologie is bij 13 procent een toepassing, terwijl fraudedetectie (6 procent) en riskmanagement (6 procent) ook regelmatig met behulp van data wordt uitgevoerd.
  • Big Data on the cloud makes economic sense

    With Big Data analytics solutions increasingly being made available to enterprises in the cloud, more and more companies will be able to afford and use them for agility, efficiency and competitiveness

    google
    For almost 10 years, only the biggest of technology firms such as Alphabet Inc.’s Google and Amazon.com Inc.
    used data analytics on a scale that justified the idea of ‘big’ in Big Data. Now more and more firms are
    warming up to the concept. Photo: Bloomberg

    On 27 September, enterprise software company SAP SE completed the acquisition of Altiscale Inc.—a provider of Big Data as-a-Service (BDaaS). The news came close on the heels of data management and analytics company Cloudera Inc. and data and communication services provider CenturyLink Inc. jointly announcing BDaaS services. Another BDaaS vendor, Qubole Inc., said it would offer a big data service solution for the Oracle Cloud Platform.

    These are cases in point of the growing trend to offer big data analytics using a cloud model. Cloud computing allows enterprises to pay for software modules or services used over a network, typically the Internet, on a monthly or periodical basis. It helps firms save relatively larger upfront costs for licences and infrastructure. Big Data analytics solutions enable companies to analyse multiple data sources, especially large data sets, to take more informed decisions.

    According to research firm International Data Corporation (IDC), the global big data technology and services market is expected to grow at a compound annual growth rate (CAGR) of 23.1% over 2014-2019, and annual spending is estimated to reach $48.6 billion in 2019.

    With Big Data analytics solutions increasingly being made available to enterprises in the cloud, more and more companies will be able to afford and use them for agility, efficiency and competitiveness.

    MarketsandMarkets, a research firm, estimates the BDaaS segment will grow from $1.8 billion in 2015 to $7 billion in 2020. There are other, even more optimistic estimates: research firm Technavio, for instance, forecasts this segment to grow at a CAGR of 60% from 2016 to 2020.

    Where does this optimism stem from?

    For almost 10 years, it was only the biggest of technology firms such as Alphabet Inc.’s Google and Amazon.com Inc., that used data analytics on a scale that justified the idea of ‘big’ in Big Data. In industry parlance, three key attributes are often used to understand the concept of Big Data. These are volume, velocity and variety of data—collectively called the 3Vs.

    Increasingly, not just Google and its rivals, but a much wider swathe of enterprises are storing, accessing and analysing a mountain of structured and unstructured data. The trend is necessitated by growing connectivity, falling cost of storage, proliferation of smartphones and huge popularity of social media platforms—enabling data-intensive interactions not only among ‘social friends’ but also among employers and employees, manufacturers and suppliers, retailers and consumers—virtually all sorts of connected communities of people.

    g tech web
     
    A November 2015 IDC report predicts that by 2020, organisations that are able to analyse all relevant data and deliver actionable information will achieve an extra $430 billion in productivity benefits over their less analytically oriented peers.

    The nascent nature of BDaaS, however, is causing some confusion in the market. In a 6 September article onNextplatform.com, Prat Moghe, founder and chief executive of Cazena—a services vendor—wrote that there is confusion regarding the availability of “canned analytics or reports”. According to him, vendors (solutions providers) should be carefully evaluated and aspects such as moving data sets between different cloud and on-premises systems, ease of configuration of the platform, etc., need to be kept in mind before making a purchase decision.

    “Some BDaaS providers make it easy to move datasets between different engines; others require building your own integrations. Some BDaaS vendors have their own analytics interfaces; others support industry-standard visualization tools (Tableau, Spotfire, etc.) or programming languages like R and Python. BDaaS vendors have different approaches, which should be carefully evaluated,” he wrote.

    Nevertheless, the teething troubles are likely to be far outweighed by the benefits that BDaaS brings to the table. The key drivers, according to the IDC report cited above, include digital transformation initiatives being undertaken by a lot of enterprises; the merging of real life with digital identity as all forms of personal data becomes available in the cloud; availability of multiple payment and usage options for BDaaS; and the ability of BDaaS to put more analytics power in the hands of business users.

    Another factor that will ensure growth of BDaaS is the scarcity of skills in cloud as well as analytics technologies. Compared to individual enterprises, cloud service providers such as Google, Microsoft Corp., Amazon Web Services and International Businsess Machines Corp. (IBM) can attract and retain talent more easily and for longer durations.

    Manish Mittal, managing principal and head of global delivery at Axtria, a medium-sized Big Data analytics solutions provider, says the adoption of BDaaS in India is often driven by business users. While the need is felt by both chief information officers and business leaders, he believes that the latter often drive adoption as they feel more empowered in the organisation.

    The potential for BDaaS in India can be gauged from Axtria’s year-on-year business growth of 60% for the past few years—and there are several niche big data analytics vendors currently operating in the country (besides large software companies).

    Mittal says that the growth of BDaaS adoption will depend on how quickly companies tackle the issue of improving data quality.

    Source: livemint.com, October 10, 2016
     

     

  • Big data privacy must be fixed before the revolution can begin

    all-you-need-to-know-about-big-dataThere won't be a 'big data revolution' until the public can be reassured that their data won't be misused.

    Big data is an asset which can create tens of thousands of jobs and generate hundreds of billions for the economy, but the opportunity can't be taken until concerns about privacy and security have been overcome.

    That's according to the newly released The Big Data Dilemma report which is based on evidence from technologists, open data enthusiasts, medical research organisations and privacy campaigners.

    It warns that a big data revolution is coming - something it's suggested will generate over £200bn for the UK economy alone over the next five years - but personal data must not be exploited by corporations and that "well-founded" concerns surrounding privacy must be addressed.

    The answer to this, the report suggests, is the formation of a 'Council of Data Ethics' which will be tasked with explicitly addressing concerns about consent and trust in the area of data collection and retention. It's only then, the report argues, that analysis of big data will truly be able to make a positive impact to society as a whole.

    The report recommends that in order to address the growing legal and ethical challenges associated with balancing privacy, anonymisation, security and public benefit, the Council of Data Ethics should be established within the Alan Turing Institute, the UK's national institute for data science.

    "There is often well-founded distrust about this and about privacy which must be resolved by industry and Government," said Nicola Blackwood MP, chair of the House of Commons Science and Technology Committee, which published the report.

    "A 'Council of Data Ethics' should be created to explicitly address these consent and trust issues head on. And the government must signal that it is serious about protecting people's privacy by making the identifying of individuals by de-anonymising data a criminal offence," she added.

    Nonetheless, the report cites high-technology science projects like the Large Hadron Collider at CERN and the Square Kilometre Array - the world's largest radio telescope, set to be run from the UK's Jodrell Bank Observatory - as examples of how benefits can be gained from analysis of vast datasets.

    "Properly exploited, this data should be transformative, increasing efficiency, unlocking new avenues in life-saving research and creating as yet unimagined opportunities for innovation," the report says.

    However, it also warns that existing big data is nowhere near being fully taken advantage of, with figures suggesting that companies are analysing just 12% of the data available to them.

    Making use of this, the committee claims, "could create 58,000 new jobs over five years, and contribute £216bn to the UK economy" and could be especially effective at boosting efficiency in the public sector.

    The committee also suggests that in order for government to address public concerns around big data, it shouldn't wait for European Union regulations take effect, but rather address the issue head on by introducing criminal penalties for misuse of data.

    "We do not share the government's view that current UK data protections can simply be left until the Data Protection Act will have to be revised to take account of the new EU Regulation. Some areas need to be addressed straightaway -- introducing the Information Commissioner's kitemark and introducing criminal penalties," the report says.

    "Such clarity is needed to give big data users the confidence they need to drive forward an increasingly big data economy, and individuals that their personal data will be respected," it adds, and the document's conclusion puts a strong emphasis on the need for data protection.

    "Given the scale and place of data gathering and sharing, district arising from concerns about privacy and security is often well founded and must be resolved by industry and government is full value of big data is to be realised," it argues.

    Privacy advocates have praised the report, but have also warned that the government still needs to do more on data protection issues.

    "It's admirable that the Committee called out the government for dragging its feet waiting for the new EU Data Protection Regulation. Now the government must take the Regulation and make it true and real to protect our data," says Matthew Rice, advocacy officer at Privacy International.

    "The recommendations in the report provide some practical, small steps that the government should take to better prepare not only for future regulation but for the future understanding of the issue of personal data protection," he adds.

    Source: ZDnet

  • Big data vendors see the internet of things (IoT) opportunity, pivot tech and message to compete

    waterfall-stream-over-bouldersOpen source big data technologies like Hadoop have done much to begin the transformation of analytics. We're moving from expensive and specialist analytics teams towards an environment in which processes, workflows, and decision-making throughout an organisation can - in theory at least - become usefully data-driven. Established providers of analytics, BI and data warehouse technologies liberally sprinkle Hadoop, Spark and other cool project names throughout their products, delivering real advantages and real cost-savings, as well as grabbing some of the Hadoop glow for themselves. Startups, often closely associated with shepherding one of the newer open source projects, also compete for mindshare and custom.

    And the opportunity is big. Hortonworks, for example, has described the global big data market as a $50 billion opportunity. But that pales into insignificance next to what Hortonworks (again) describes as a $1.7 trillion opportunity. Other companies and analysts have their own numbers, which do differ, but the step-change is clear and significant. Hadoop, and the vendors gravitating to that community, mostly address 'data at rest'; data that has already been collected from some process or interaction or query. The bigger opportunity relates to 'data in motion,' and to the internet of things that will be responsible for generating so much of this.

    My latest report, Streaming Data From The Internet Of Things Will Be The Big Data World’s Bigger Second Act, explores some of the ways that big data vendors are acquiring new skills and new stories with which to chase this new opportunity.

    For CIOs embarking on their IoT journey, it may be time to take a fresh look at companies previously so easily dismissed as just 'doing the Hadoop thing.' 

    Source: Forrester.com, 

  • Big data: laten we niet vervallen in oude fouten

    4a0527affdaec1c2a3dc5f0f95a51416Als we niet oppassen gaat het opnieuw helemaal verkeerd. Vendoren ontwikkelen big data-producten vaak als extra stap in het proces, wat dat proces er alleen maar complexer op maakt. Terwijl producten het proces nu juist zouden moeten versoepelen.

    Vijftien jaar geleden werd de revolutie in de besluitvormingsondersteuning in de kiem gesmoord doordat de datamanagementbranche inflexibele data warehouse-systemen bouwde en onbruikbare business intelligence-tools ontwikkelde. Organisaties werden gedwongen om hun bedrijfsprocessen aan te passen aan onze eigen productagenda's. Vandaag de dag, nu het big data-tijdperk opkomt, gaan we weer precies dezelfde kant op.

    Er moet een verandering in het denken komen. We moeten ons niet meer richten op het product, maar op het proces!

    Volg de processen

    Het ideale flow diagram voor een proces heeft zo min mogelijk tussenstappen. Maar in de praktijk zien we dit vrijwel nooit en dat heeft talloze redenen. De belangrijkste daarvan is dat softwareproducenten zich richten op het product en niet op het proces. Zij hanteren een strategie die probeert een product in te voegen als een van de vele tussenstappen in de processtroom. Eigenlijk ontwerpen ze zichzelf een proces binnen.

    De reactie van de datamanagementbranche op big data is tot nu toe van hetzelfde laken een pak. In de meeste gevallen betekent dit een ratjetoe van propriëtaire, op de stack gerichte big data-"oplossingen", technologische of architectonische voorschriften die alleen het eigenbelang dienen en front-end tools die eigenlijk nog niet helemaal klaar zijn.

    Maar big data is anders, omdat het onmiskenbaar multidisciplinair is: het impliceert onderlinge verbondenheid, interoperabiliteit en uitwisseling tussen verschillende domeinen. Big data wil zeggen dat je alles met alles verbindt, en wat dat betreft is big data precies het tegenovergestelde van databeheer.

    Vanuit productperspectief moet een big data-bewuste tool functioneren in een context waarbinnen problemen, praktijken en processen multidisciplinair zijn. Geen enkel product is volledig onafhankelijk of werkt volledig geïsoleerd. Dit betekent overigens niet dat er geen big data-geörienteerde producten kunnen bestaan die zich richten op uiterst specifieke toepassingen, of meer generalistische big data-geörienteerde producten die bedoeld zijn voor bepaalde proces-, domein- of functie-activiteiten. En het betekent ook niet automatisch dat een volledig cohort bestaande producten ineens "pre-big data" wordt.

    Meer van hetzelfde is de verkeerde aanpak

    Toch ontwikkelen en verkopen de meeste aanbieders "big data-in-a-platform"-producten. En deze "oplossingen" hebben één ding met elkaar gemeen, hun productgerichte model: ze zijn er helemaal op gericht om zichzelf - als een tussenstap - in een proces te nestelen. Terwijl elke tussenstap zorgt voor vertraging en een vergroting van de complexiteit en de kwetsbaarheid.

    Of erger nog: elke tussenstap heeft zijn eigen infrastructuur. Die bestaat voor elke afzonderlijke fabrikant uit eigen ondersteunend personeel met een eigen interne knowledge-base. In het beste geval betekent dit legers Java- of Pig Latin-programmeurs werven, of DBA's en SQL-programmeurs de fijne kneepjes van HQL bijbrengen. In het ergste geval betekent dit aanzienlijke hoeveelheden tijd en geld investeren in de ontwikkeling van platformspecifieke kennisbanken.

    Automatisering is de oplossing

    De manier om iets te doen aan deze scheve verhouding is focussen op het automatiseren van de processen van een datawarehouse-omgeving, zoals scoping, het opzetten van warehouses, continu beheer en periodieke refactoring. Je zou zelfs het aanmaken en beheren van documentatie, schema's en lineage-informatie voor warehouses kunnen automatiseren door het helemaal elimineren van handmatig programmeren in SQL of in interne, alleen voor bepaalde tools bestemde talen.

    Big data-producten hebben namelijk helemaal geen eigen infrastructuur nodig. Zij moeten de taal speken van en ondersteuning bieden aan de specifieke onderdelen van OLTP-systemen, warehouseplatforms, analytische databases, NoSQL- of big data-archieven, BI-tools en alle overige 'stappen' die samen een ecosysteem voor informatie vormen.

    Producten moeten zich richten op de punten in het proces tussen geïsoleerde systemen, waar een processtroom wordt geblokkeerd. Dit type blokkade is het onvermijdelijke gevolg van een productgerichte ontwikkelings- en verkoopstrategie. En zoals het er nu naar uitziet gaan we veel van dit soort blokkades krijgen op het gebied van big data.

    We moeten big data gaan zien als een soort vrijhandelszone waarbij 'handel' gelijk staat aan 'proces': gegevens worden verplaatst van de ene tussenstap naar de andere, met minimale beperking of belemmering en zonder platformspecifieke embargo's van onnodige tussenstappen.

    Het antwoord ligt mijns inziens in automatisering. En dan niet automatisering omwille van de automatisering, maar als integrale processtroom ter voorkoming van blokkades, verhoging van responsiviteit, verlaging van kosten en om IT de gelegenheid te bieden om zich te richten op het creëren van waarde.

    Laten we er met z'n allen voor zorgen dat het deze keer niet wéér misgaat!

    Source: CIO

  • Big data's big problem? Most companies don't realize they're already using it

    bigdatahCompanies are already actively using big data. They just don't call it that. While the phrase has problems, the technology is becoming more intrinsic to business.

    It turns out that no one knows what the heck big data is, and about the same number of companies are actually doing anything meaningful with it, according to a new study from Dresner Advisory Services. Surprised? More about the study? Click here.

    You shouldn't be. After all, despite years of big data prognostication, most companies still struggle to even put little data to use.

    This isn't to suggest that big data isn't a big deal, or that companies aren't deriving massive value today from their data. It is and they are. But, to get value from big data, companies first need to get real.

    Who needs it?

    As Datamation's James Maguire captures, Dresner Advisory Services doesn't see much adoption of big data.

    bd1

    Just 17% of companies acknowledge using big data today, with another 47% putting it off into an indeterminate future. No wonder, then, that the report's authors conclude, "Despite an extended period of awareness building and hype, actual deployment of big data analytics is not broadly applicable to most organizations at the present time."

    Big data, big nothing?

    Well, no. After all, 59% of the report's respondents also claim big data is "critically important," despite not doing anything with it (apparently). Something is clearly going on here....

    That "something," I suspect, is just definitional.

    You keep using that word...

    Way back in the prehistoric world of 2012, NewVantage Partners upended the prevailing wisdom of what the "big" in big data actually meant. Despite tons of hype around petabyte-scale data problems, largely fueled by Hadoop and its ecosystem vendors, the reality was (and is) that most companies don't have petabyte-scale problems.

    The primary problems most companies struggle with involve variety and velocity of data, as the survey uncovered.

    The market is finally starting to grok this, investing increasing amounts of money in technologies that more easily manage diverse data types (e.g. NoSQL databases like MongoDB and DataStax-sponsored Cassandra), and handle streaming data (e.g. Apache Spark).

    At the same time, enterprises continue to turn to more traditional data infrastructure like Oracle. As DB-Engines found in its 2015 year-end review, Oracle was the biggest gainer in terms of overall popularity last year (measured in terms of job postings, tech forum mentions, Google searches, etc.).

    More than sexy-cool NoSQL. More than cloud-first Amazon. More than anything.

    Of course, some of this increased Oracle usage has nothing to do with big data, and everything to do with managing neat-and-tidy rows-and-column data. But, based on NewVantage Partners' survey data, this comparatively "small" data is still where most of the big data analytics action resides.

    Moving beyond this structured data, too, I suspect many companies still don't think of varied, high-velocity data as "big data." This may be one reason so few companies claim to be doing much of anything with big data. As MySQL database engineer Justin Swanhart put it, "Big data is meaningless. You might as well ask people what color database they want."

    In short, big data is alive and well, but companies don't necessarily think of it as "big."

    So what?

    For enterprises wondering if they're being left behind by big data, it's time to stop worrying. As Gartner analyst Nick Heudecker posits, "big data" has migrated into more familiar categories:

    • Advanced analytics and data science
    • Business intelligence and analytics
    • Enterprise information management
    • In-memory computing technology
    • Information infrastructure

    Most enterprises are already engaged in projects that put big data to use. They just don't call it that. Even so, there's still a lot of work to do. As Michael Schrage, a research fellow at MIT Sloan School's Center for Digital Business, puts it:

    "[The] most enduring impact of predictive analytics...comes less from quantitatively improving the quality of prediction than from dramatically changing how organizations think about problems and opportunities."

    In other words, companies may already own the requisite technologies to put big data to work. What they lack is a desire to fundamentally change how they put that data to work. It's one thing to have a group of analysts decipher data, and quite another to use that data analysis to fuel real-time changes in one's business.

    That's not the sort of thing you can buy from a vendor. It's something that has to change within the DNA of an enterprise. Between a more accurate understanding of big data, and actually doing something with it, enterprises have their work cut out for them.

    Source: techRepublic

     

     

  • Bol.com: machine learning om vraag en aanbod beter bij elkaar te brengen

    0cd4fbcf0a4f81814f388a75109da149ca643f45Een online marktplaats is een concept dat e-commerce in toenemende mate blijft adopteren. Naast consumer-to-consumer marktplaatsen zoals Marktplaats.nl, zijn er uiteraard ook business-to-consumer marktplaatse waarbij een online platform de vraag van consumenten en het aanbod van leveranciers bij elkaar brengt.

    Sommige marktplaatsen hebben geen eigen assortiment: hun aanbod bestaat voor 100 procent uit aangesloten leveranciers, denk bijvoorbeeld aan Alibaba. Bij Amazon bedraagt het aandeel van eigen producten 50 procent. Ook bol.com heeft een eigen marktplaatsen: ’Verkopen via Bol.com’. Deze draagt bij aan miljoenen extra artikelen in het assortiment van Bol.com.

    Bewaken van contentkwaliteit

    Er komt veel kijken bij het managen van zo’n marktplaats. Het doel is duidelijk: ervoor zorgen dat de vraag en het aanbod zo snel mogelijk bij elkaar komen, zodat de klant direct een aantal producten krijgt aangeboden die voor hem relevant zijn. En met miljoenen klanten aan de ene kant en miljoenen producten van duizenden leveranciers aan de andere kant, is dat natuurlijk een hele klus.

    Jens legt uit: “Het begint bij de standaardisatie van informatie aan zowel de vraag- als de aanbodkant. Bijvoorbeeld, als je als leverancier een cd van Tsjaikovski of een bril van Dolce & Gabbana bij bol.com wilt aanbieden, dan zijn er vele schrijfwijzen mogelijk. Voor een verkoopplatform als ‘Verkopen via bol.com’ is de kwaliteit van de data cruciaal. Het in stand houden van de kwaliteit van de content is dus een van de uitdagingen.

    Aan de andere kant van de transactie zijn er natuurlijk klanten van bol.com die ook allerlei variaties van termen, zoals de namen van merken, in het zoekveld intypen. Daarnaast wordt er in toenemende mate gezocht op generieke termen als ‘cadeau voor huwelijk’ of ‘spullen voor een feestje’.

    Vraag en aanbod bij elkaar brengen

    Naarmate het assortiment groter wordt, wat het geval is, en de klanten steeds ‘generieker’ gaan zoeken, is het steeds uitdagender om een match te maken en relevantie hoog te houden. Door het volume van deze ongestructureerde data en het feit dat ze realtime geanalyseerd moeten worden, kun je die match niet met de hand maken. Je moet hiervoor de data slim kunnen inzetten. En dat is een van de activiteiten waar het customer intelligence team van bol.com, een onderdeel van customer centric selling-afdeling, mee bezig is.

    Jens: “De truc is om het gedrag van klanten op de website te vertalen naar contentverbeteringen. Door de woorden (en woordcombinaties) die klanten gebruiken om artikelen te zoeken en producten die uiteindelijk gekocht zijn te analyseren en met elkaar te matchen, kunnen synoniemen voor desbetreffende producten worden gecreëerd. Dankzij deze synoniemen gaat de relevantie van de zoekresultaten omhoog en help je dus de klant om het product sneller te vinden. Bovendien snijdt het mes snijdt aan twee kanten, omdat tegelijkertijd de kwaliteit van de productcatalogus wordt verbeterd. Denk hierbij aan verfijning van verschillende kleurbeschrijvingen (WIT, Wit, witte, white, etc.).

    Algoritmes worden steeds slimmer

    Het bovenstaande proces verloopt nog semi-automatisch (met terugwerkende kracht), maar de ambitie is om het in de toekomst volledig geautomatiseerd plaats te laten vinden. Om dat te kunnen doen worden er op dit moment stap voor stap machinelearningtechnieken geïmplementeerd. Als eerste is er geïnvesteerd in technologieën om grote volumes van ongestructureerde data zeer snel te kunnen verwerken. Bol.com bezit twee eigen datacenters met tientallen clusters.

    “Nu wordt er volop geëxperimenteerd om deze clusters in te zetten voor het verbeteren van het zoekalgoritme, het verrijken van de content en standaardisatie”, geeft Jens aan. “En dat levert uitdagingen op. Immers, als je doorslaat in standaardisatie, dan kom je in een selffulfilling prophecy terecht. Maar gelukkig nemen de algoritmes het beetje bij beetje over en worden ze steeds slimmer. Nu probeert het algoritme de zoekterm zelf aan een product te koppelen en legt het deze aan diverse interne specialisten voor. Concreet geformuleerd: de specialisten krijgen te zien dat ‘de kans 75 procent is dat de klant dit bedoelt’. Die koppeling wordt vervolgens handmatig gevalideerd. De terugkoppeling van deze specialisten over een voorgestelde verbetering levert belangrijke input voor algoritmes om informatie nog beter te kunnen verwerken. Je ziet dat de algoritmes steeds beter hun werk doen.”

    Toch levert dit voor Jens en zijn team een volgende kwestie op: waar leg je de grens waarbij het algoritme zelf de beslissing kan nemen? Is dat bij 75 procent? Of moet alles onder de 95 procent door menselijk inzicht gevalideerd worden?

    Een betere winkel maken voor onze klanten met big data

    Drie jaar geleden was big data een onderwerp waarover voornamelijk in PowerPoint‑slides gesproken werd. Tegenwoordig hebben vele (grotere) e-commercebedrijven een eigen Hadoop-cluster. Het is de volgende stap om met big data de winkel écht beter te maken voor klanten en bij bol.com wordt daar hard aan gewerkt. In 2010 is bij het bedrijf overgestapt van ‘massamediale’ naar ‘persoonlijk relevante’ campagnevoering, waarbij er in toenemende mate gepoogd wordt om op basis van diverse ‘triggers’ een persoonlijke boodschap aan de klant te bieden, real-time.

    Die triggers (zoals bezochte pagina’s of bekeken producten) wegen steeds zwaarder dan historische gegevens (wie is de klant en wat heeft deze in verleden gekocht).

    “Als je inzicht krijgt in relevante triggers en niet‑relevante weglaat”, stelt Jens, “dan kun je de consument beter bedienen door bijvoorbeeld de meest relevante review te tonen, een aanbieding te doen of een selectie vergelijkbare producten te maken. Op deze manier sluit je beter aan bij de klantreis en is de kans steeds groter dat de klant bij je vind wat hij zoekt.”

    En dat doet bol.com door eerst, op basis van het gedrag op de website, maar ook op basis van de beschikbare voorkeuren van de klant, op zoek te gaan naar de relevante triggers. Nadat deze aan de content zijn gekoppeld, zet bol.com A/B‑testen in om de conversie te analyseren om het uiteindelijk wel of niet definitief door te voeren. Immers, elke wijziging moet resulteren in hogere relevantie.

    Er komen uiteraard verschillende technieken bij kijken om ongestructureerde data te kunnen analyseren en hier zijn zowel slimme algoritmes als menselijk inzicht voor nodig. Jens: “Gelukkig zijn bij ons niet alleen de algoritmes zelflerend, maar ook het bedrijf, dus het proces gaat steeds sneller en beter.”

    Data-scientists

    Outsourcen of alles in-house doen is een strategische beslissing. Bol.com koos voor het laatste. Uiteraard wordt er nog op ad-hocbasis gebruikgemaakt van de kennis uit de markt als dat helpt om processen te versnellen. Data-analisten en data scientists zijn een belangrijk onderdeel van het groeiende customer centric selling team.

    Het verschil spreekt voor zich: data-analisten zijn geschoold in ‘traditionele’ tools als SPSS en SQL en doen analysewerk. Data scientists hebben een grotere conceptuele flexibiliteit en kunnen daarnaast programmeren in onder andere Java, Python en Hive. Uiteraard zijn er doorgroeimogelijkheden voor ambitieuze data-analisten, maar toch wordt het steeds lastiger om data scientists te vinden.

    Hoewel er in de markt keihard gewerkt wordt om het aanbod uit te breiden; hebben we hier vooralsnog met een kleine, selecte groep professionals te maken. Bol.com doet er alles aan om de juiste mensen te werven en op te leiden. Eerst wordt een medewerker met het juiste profiel binnengehaald; denk aan iemand die net is afgestudeerd in artificial intelligence, technische natuurkunde of een andere exacte wetenschap. Vervolgens wordt deze kersverse data scientist onder de vleugels van een van de ervaren experts uit het opleidingsteam van bol.com genomen. Training in computertalen is hier een belangrijk onderdeel van en verder is het vooral learning-by-doing.

    Mens versus machine

    Naarmate de algoritmes steeds slimmer worden en artificial‑intelligencetechnologieën steeds geavanceerder, zou je denken dat het tekort aan data scientists tijdelijk is: de computers nemen het over.

    Dat is volgens Jens niet het geval: “Je zult altijd behoefte blijven houden aan menselijk inzicht. Alleen, omdat de machines steeds meer routinematig en gestandaardiseerd analysewerk overnemen, kun je steeds meer gaan doen. Bijvoorbeeld, niet de top 10.000 zoektermen verwerken, maar allemaal. Feitelijk kun je veel meer de diepte én de breedte in. En dus is de impact van jouw werk op de organisatie vele malen groter. Het resultaat? De klant wordt beter geholpen en hij bespaart tijd omdat hij steeds relevantere informatie krijgt en daarom meer engaged is. En brengt ons ook steeds verder in onze ambitie om onze klanten de beste winkel te bieden die er bestaat.”

    Klik hiervoor het hele rapport.

    Source: Marketingfacts

  • Business Data Scientist leergang nu ook in België

     

    De Radboud Management Academy heeft haar in Nederland zo succesvolle Business Data Scientist leergang nu ook in Belgie op de markt gebracht. In samenwerking met Business & Decision werd afgelopen week in het kantoor van Axa in Brussel een verkorte leergang gegeven aan mensen uit het Belgische bedrijfsleven. Ook vertegenwoordigers van ministeries en andere overheidsinstellingen waren vertegenwoordigd.BDS

    De opleiding speelt in op de behoefte van bedrijven meer waarde te halen uit de bij hen beschikbare data. Daarbij richt de opleiding zich niet alleen op de ontwikkeling van individuele competenties maar ook op organisatiestructuren en instrumenten die organisaties helpen meer datagestuurd te werken.

    Het 3D model dat centraal staat in de leergang wordt door cursisten als een belangrijke toevoeging gezien op de technische competenties die men vaak reeds bezit. Meer en meer wordt onderkend dat eigenschappen die de interfacing met de ‘business’ kunnen verbeteren uiteindelijk bepalend zijn voor het tot waarde brengen van de inzichten die uiteindelijk met data kunnen worden gegenereerd. De toolbox van de Data Scientist wordt in de leergang op een significante wijze uitgebreid met zowel functionele, sociale als technische eigenschappen.

    Meer weten? Ga naar http://www.ru.nl/rma/leergangen/bds/

  • Business Intelligence in 3PL: Mining the Value of Data

    data-mining-techniques-create-business-value 1In today’s business world, “information” is a renewable resource and virtually a product in itself. Business intelligence technology enables businesses to capture historical, current and predictive views of their operations, incorporating such functions as reporting, real-time analytics, data and process mining, performance management, predictive analytics, and more. Thus, information in its various forms and locations possesses genuine inherent value.
     
    In the real world of warehousing, the availability of detailed, up-to-the minute information on virtually every item in the operators’ custody, from inbound dock to delivery site, leads to greater efficiency in every area it touches. Logic would offer that greater profitability ensues.
     
    Three areas of 3PL operations seem to be most benefitted through savings opportunities identified through business intelligence solutions: labor, inventory, and analytics.
    In the first case, business intelligence tools can help determine the best use of the workforce, monitoring its activity in order to assure maximum effective deployment. The result: potentially major jumps in efficiency, dramatic reductions in downtime, and healthy increases in productivity and billable labor.
     
    In terms of inventory management, the metrics obtainable through business intelligence can stem inventory inaccuracies that would have resulted in thousands of dollars in annual losses, while also reducing write-offs.
     
    Analytics through business intelligence tools can also accelerate the availability of information, as well as provide the optimal means of presentation relative to the type of user. One such example is the tracking of real-time status of work load by room or warehouse areas; supervisors can leverage real-time data to re-assign resources to where they are needed in order to balance workloads and meet shipping times. A well-conceived business intelligence tool can locate and report on a single item within seconds and a couple of clicks.
     
    Extending the Value
    The value of business intelligence tools is definitely not confined to the product storage areas.
     
    With automatically analyzed information available in a dashboard presentation, users – whether in the office or on the warehouse floor – can view the results of their queries/searches in a variety of selectable formats, choosing the presentation based on its usefulness for a given purpose. Examples:
    • Status checks can help identify operational choke points, such as if/when/where an order has been held up too long; if carrier wait-times are too long; and/or if certain employees have been inactive for too long.
    • Order fulfillment dashboards can monitor orders as they progress through the picking, staging and loading processes, while also identifying problem areas in case of stalled processes.
    • Supervisors walking the floor with handheld devices can both encourage team performance and, at the same time, help assure efficient dock-side activity. Office and operations management are able to monitor key metrics in real-time, as well as track budget projections against actual performance data.
    • Customer service personnel can call up business intelligence information to assure that service levels are being maintained or, if not, institute measures to restore them.
    • And beyond the warehouse walls, sales representatives in the field can access mined and interpreted data via mobile devices in order to provide their customers with detailed information on such matters as order fill rates, on-time shipments, sales and order volumes, inventory turnover, and more.
    Thus, well-designed business intelligence tools not only can assemble and process both structured and unstructured information from sources across the logistics enterprise, but can deliver it “intelligently” – that is, optimized for the person(s) consuming it. These might include frontline operators (warehouse and clerical personnel), front line management (supervisors and managers), and executives.
     
    The Power of Necessity
    Chris Brennan, Director of Innovation at Halls Warehouse Corp., South Plainfield N.J., deals with all of these issues as he helps manage the information environment for the company’s eight facilities. Moreover, as president of the HighJump 3PL User Group, he strives to foster collective industry efforts to cope with the trends and issues of the information age as it applies to warehousing and distribution.
     
    “Even as little as 25 years ago, business intelligence was a completely different art,” Brennan has noted. “The tools of the trade were essentially networks of relationships through which members kept each other apprised of trends and happenings. Still today, the power of mutual benefit drives information flow, but now the enormous volume of data available to provide intelligence and drive decision making forces the question: Where do I begin?”
     
    Brennan has taken a leading role in answering his own question, drawing on the experience and insights of peers as well as the support of HighJump’s Enterprise 3PL division to bring Big Data down to size:
     
    “Business intelligence isn’t just about gathering the data,” he noted, “it’s about getting a group of people with varying levels of background and comfort to understand the data and act upon it. Some managers can glance at a dashboard and glean everything they need to know, but others may recoil at a large amount of data. An ideal BI solution has to relay information to a diverse group of people and present challenges for them to think through.”
     
    source: logisticviewpoints.com, December 6, 2016
  • Business Intelligence nog steeds hot….

    Business Intelligence outdated? Niets is minder waar zo bewees het Heliview congres ‘Decision making by smart technologies’ dat afgelopen dinsdag in de Brabanthallen in Den Bosch werd georganiseerd.

    200 Klantorganisaties luisterden naar presentaties van o.a. Rick van der Lans, Peter Jager, Frank de Nijs en Arent van ‘t Spijker. Naast het bekende geluid was er ook veel nieuws te beluisteren in Den Bosch.

    Nieuwe technologieën maken heel veel meer mogelijk. Social media en, moderne, big data technologie stellen organisaties in staat veel meer waarde uit data te halen. Hoe organisaties dat moeten doen is veelal nog een uitdaging. Toepassing van de technologie is geen doel op zich zelf. Het gaat erom toegevoegde waarde voor organisaties te produceren. Of door optimalisatie van processen. Dan wel door het beter bedienen van de klant door productontwikkeling. In extremis kan data zelfs de motor achter nieuwe business concepten of –modellen zijn. Voorwaarde is wel een heldere bedrijfsvisie (al dan niet geproduceerd met intelligent gebruik van data en informatie). Belangrijk om te voorkomen dat we ongericht miljoenen stuk slaan op nieuwe technologie.

    Voor de aanwezigen was het gehoorde geluid soms bekend, maar soms ook een confrontatie met zichzelf. Een ding is zeker: De rol van data en informatie bij het intelligent zaken doen is nog niet uitgespeeld. Business Intelligence leeft.

    30 JANUARI 2015

  • CIO's Adjust BI Strategy for Big Data

     
    The CIO focus on business intelligence (BI) and analytics will likely continue through 2017, according to Gartner Inc. The research firm says the benefits of fact-based decision-making are clear to business managers in a broad range of disciplines, including marketing, sales, supply chain management, manufacturing, engineering, risk management, finance and human resources.

    "Major changes are imminent to the world of BI and analytics, including the dominance of data discovery techniques, wider use of real-time streaming event data and the eventual acceleration in BI and analytics spending when big data finally matures," Roy Schulte, vice president and distinguished analyst at Gartner, said in a statement. "As the cost of acquiring, storing and managing data continues to fall, companies are finding it practical to apply BI and analytics in a far wider range of situations."

    Gartner outlined four key predictions for BI and analytics:

    • By 2015, the majority of BI vendors will make data discovery their prime BI platform offering, shifting BI emphasis from reporting-centric to analysis-centric.
    • By 2017, more than 50% of analytics implementations will make use of event data streams generated from instrumented machines, applications and/or individuals.
    • By 2017, analytic applications offered by software vendors will be indistinguishable from analytic applications offered by service providers.
    • Until 2016, big data confusion will constrain spending on BI and analytics software to single-digit growth.

    Recent Gartner surveys show that only 30% of organizations have invested in big data, of which only a quarter (or 8% of the total) have made it into production. This leaves room for substantial future growth in big data initiatives, the firm says.

    By: Bob Violino

  • Data als ingrediënt op weg naar digitale volwassenheid

    0cd4fbcf0a4f81814f388a75109da149ca643f45Stéphane Hamel deed op 21 januari de High Tech Campus in Eindhoven aan: dé kans voor een flinke dosis inspiratie door één van ’s wereld meest vooraanstaande denkers in digital analytics. Hamel lichtte op digital maturity day 2016 (#DMD2016) het Digital Analytics Maturity-model toe.

    Imperfecte data

    Volgens Stéphane Hamel is het verschil tussen een goede en een excellente analyst het volgende: de excellente analyst weet ook bij imperfecte data te komen tot beslissingen of zinvol advies. “Data will never be perfect, know how bad the data is is essential. If you know 5 or 10% is bad, there is no problem”, aldus Hamel.

    Analytics = Context + Data + Creativity

    Analytics klinkt als een vakgebied voor datageeks en nerds. Dat beeld klopt niet: buiten de data is het onderkennen van de context waarbinnen de data zijn verzameld en creativiteit bij het interpreteren ervan essentieel. Om data te begrijpen moet je vanachter je laptop of PC vandaan komen. Alleen door de wereld ‘daarbuiten’ mee te nemen in je analyse kun je als data-analist tot zinvolle inzichten en aanbevelingen komen.

    Hamel geeft een voorbeeld uit de collegebanken: toen een groep studenten de dataset van Save the Children uit 2010 te zien kreeg, dachten sommigen dat de factor 10 toename in websiteverkeer te danken was aan een campagne of toeval. De werkelijke oorzaak was de aardbeving in Haïti.

    Digital Maturity Assessment

    Het Digital Maturity Assessment-model is ontwikkeld aan de hand van de digitale transformatie van honderden bedrijven wereldwijd. Op basis van deze ervaringen weet Stéphane welke uitdagingen bedrijven moeten overwinnen op weg naar digital leadership.

    Digital Analytics Maturity SHamel

    Dit model kun je natuurlijk gebruiken om de eigen organisatie te benchmarken tegen andere bedrijven. De meerwaarde volgens Hamel zit echter in het ‘benchmarken van jezelf versus jezelf’. Het helpt kortom om het gesprek intern aan te gaan. Als je voor de derde keer van tooling switcht, ben je zelf het probleem, niet de technologie.

    Hamel geeft de voorkeur aan een consistente score op de vijf criteria van dit Digital Maturity Assessment-model: liever een twee overall dan uitschieters naar boven of beneden. De factor die meestal het zwakst scoort is ‘process’.

    Dit criterium staat voor de werkwijze om te komen tot dataverzameling, -analyse en -interpretatie. Vaak zit dit proces zelf helemaal niet zo slecht in elkaar, maar worstelen data-analisten om aan collega’s of het managementteam uit te leggen welke stappen ze hebben gezet. Hamel benadrukt daarom: “you need a digital culture, not a digital strategy”.

    Omhels de jongens van IT

    Geef IT de kans om jou echt te helpen. Niet door te zeggen ‘voer dit uit of fix dat’. Wel door IT te vragen om samen met jullie een probleem op te lossen. Hamel ziet digitale analisten daarom vooral als change-agents, niet als stoffige dataprofessionals. Juist die shift in benadering en rol betekent dat we binnenkort niet meer spreken over digital analytics, maar over ‘analytics’.

    Data is the raw material of my craft

    Hamel’s favoriete motto “data is the raw material of my craft” verwijst naar het vakmanschap en de passie die Stéphane Hamel graag aan het vakgebied digital analytics toevoegt. Stéphane’s honger om het verschil te maken in digital analytics werd ooit tijdens een directievergadering aangewakkerd. Hamel zat in die vergadering erbij als de ‘IT guy’ en werd niet serieus genomen toen hij met data de business problemen en kansen wilde benoemen.

    Dit prikkelde Hamel om, met steun van zijn baas, een MBA te gaan doen. En met resultaat: hij rondde de MBA af behorende tot de top 5 procent van alle studenten. Sindsdien opereert hij op het snijvlak van data en bedrijfsprocessen, ondermeer in het beurswezen en in de verzekeringsbranche.

    Digital is de grote afwezige in het onderwijs

    Hamel’s zeer indrukwekkende loopbaan tonen ondermeer een erkenning als een van ’s werelds weinige Certified Web Analysts, ‘Most Influential Industry Contributor’ door de Digital Analytics Association en mede-beheerder van de grootste community op Google+ over Google Analytics. Toch vindt Hamel zijn allergrootste prestatie het afwerpen van het stempel ‘IT’er’.

    Zijn grootste ambitie voor de nabije toekomst is het schrijven van een tekstboek over digital analytics. Er is veel informatie digitaal beschikbaar, maar er mist nog veel content in offline formaat. Juist omdat ook andere sprekers op #DMD16 wezen naar het achterblijvend niveau van onze HBO- en WO-opleidingen in digitale vaardigheden vroeg ik Hamel welke tips hij heeft voor het Nederlands onderwijs.

    In de basis dient volgens Hamel de component ‘digital’ veel meer als rode draad in het curriculum te worden opgenomen. Studenten dienen daarbij gestimuleerd te worden om de content zelf te verrijken met eigen voorbeelden. Zo komt er in cocreatie tussen docenten, auteurs en studenten steeds betere content tot stand.

    De belofte van big data en marketingautomatisering

    Hamel ziet zeker in B2B de toegevoegde waarde van marketing automation. Je relatie met klant en prospect is immers meer persoonlijk. Marketingautomatisering wordt echter soms foutief ingezet waarbij email wordt ingezet om de indruk te wekken van een persoonlijke, menselijke dialoog. Hamel: “I still believe in genuine, human interaction. There is a limit to how you can leverage marketingautomation.”

    Digital Maturity bron PREZI Joeri Verbossen

    Het grootste probleem bij de succesvolle introductie van marketingautomatisering is dan ook ook de maturiteit van de organisatie. Zolang deze niet voldoende is, zal een softwarepakket altijd vooral een kostenpost zijn. Een cultuuromslag moet plaatsvinden zodat de organisatie de software als noodzakelijke randvoorwaarde beschouwt voor het kunnen uitvoeren van de strategie.

    Dezelfde nuchtere woorden gebruikt Hamel over de belofte van big data. Al te vaak hoort hij in bedrijven: “We need Big Data!” Zijn antwoord is dan: “No, you don’t big data, you need solutions. As long as it does the job, I’m happy.”

    Source: Marketingfacts

  • Data lakes, don't confuse them with data warehouses, warns Gartner

    LOIIn mid-2014, a pair of Gartner analysts levied some trenchant criticisms at the increasingly hyped concept of data lakes.

    "The fundamental issue with the data lake is that it makes certain assumptions about the users of information," said Gartner research director Nick Heudecker.

    "It assumes that users recognize or understand the contextual bias of how data is captured, that they know how to merge and reconcile different data sources without 'a priori knowledge' and that they understand the incomplete nature of datasets, regardless of structure."

    A year and a half later, Gartner's concerns do not appear to have eased. While there are successful projects, there are also failures -- and the key success factor appears to be a strong understanding of the different roles of a data lake and a data warehouse.

    Heudecker said a data lake, often marketed as a means of tackling big data challenges, is a great place to figure out new questions to ask of your data, "provided you have the skills".

    "If that's what you want to do, I'm less concerned about a data lake implementation. However, a higher risk scenario is if your intent is to reimplement your data warehousing service level agreements (SLAs) on the data lake."

    Heudecker said a data lake is typically optimised for different uses cases, levels of concurrency and multi-tenancy.

    "In other words, don't use a data lake for data warehousing in anger."

    It's perfectly reasonable to need both, he said, because each is optimised for different SLAs, users and skills.

    Data lakes are, broadly, enterprise-wide platforms for analysing disparate data sources in native format to eliminate the cost and data transformation complexity of data ingestion. And herein lies the challenge: data lakes lack semantic consistency and governed metadata putting a great deal of the analytical onus on skilled users.

    Heudecker said there is some developing maturity in understanding, but the data lake hype is still rampant.

    The maturity of the technology is harder to get a handle on because the technology options to implement data lakes continue to change rapidly.

    "For example, Spark is a popular data processing framework and it averages a new release every 43 days," Heudecker said.

    The success factors for data lake projects, he said, come down to metadata management, the availability of skills and enforcing the right levels of governance.

    "I've spoken with companies that built a data lake, put a bunch of data into it and simply couldn't find anything. Others have no idea which datasets are inaccurate and which are high quality. Like everything else in IT, there is no silver bullet."

    Data lakes are an architectural concept, not a specific implementation, he said.

    "Like any new concept, or technology for that matter, there will be accompanying hype followed by a period of disillusionment before becoming an understood practice.

    "Data lakes will continue to be a reflection of the data scientists that use them.

    "The technology may change and improve, perhaps taking advantage of things like GPUs or FPGAs, but the overall intent will be to uncover new uses and opportunities in data. Taking those insights to production will likely occur elsewhere."

  • Data science en de groei naar volwassenheid

    Data strategyIn 2015 ben ik samen met de Radboud Management Academy en Business & Decision gestart met de leergang Business Data Scientist. In deze blog ga ik in op één van de belangrijke doelstellingen van de leergang: bedrijven helpen een passend groeipad in big data gebruik te ontwikkelen en doorlopen.

    Data Science is een beetje als de buurman die je zijn nieuwe auto met het allernieuwste snufje laat zien. Bijna onmiddellijk bekruipt je het gevoel dat je dat ook nodig hebt. Veel ondernemingen ervaren hetzelfde gevoel wanneer het om data science gaat.

    Data is door talrijke technische en sociale ontwikkelingen (Connected economy, mobility, internet of things, willingness to share data) in overvloed aanwezig. Bedrijven herkennen ook dat data meer is dan een bijproduct van operationele processen. Ze zoeken daarom, meer of minder gedreven door de successen van de buurman, naar mogelijkheden om hun eigen bedrijfsvoering te verbeteren. Daarbij gebruikmakend van data als een primaire bron of asset.

    Veel ondernemingen vragen zich echter af: (Waar) moet ik beginnen? Wat moet ik ambiëren? Wie heb ik nodig om dit te organiseren? Is het voldoende als ik een stel hard core data scientists in dienst neem? Vanzelfsprekend is er geen ‘one fits all’ antwoord op deze vragen. Deze vragen zijn uitsluitend te beantwoorden wanneer de onderneming een helder beeld heeft op een haar passende data science strategie en bijbehorend groeipad. Wanneer deze ontbreken dreigt mislukking. En frustratie! Niet in de laatste plaats bij de aangenomen data scientists die de oplossing voor bijna alles zouden zijn. Het is immers moeilijk te voldoen aan onbepaalde en oneindige verwachtingen.

    Bedrijven kunnen verschillende keuzes maken in hun data science strategie. Deze zijn afhankelijk van hun eigen positie en bedrijfsstrategie. De uitgangspunten voor het laten aansluiten van de data science strategie op de bedrijfsstrategie kunnen verschillen. In de ene groep data-science strategieën (‘executie-strategie’, ‘transformatie-strategie’ en ‘service-strategie’) staat de bedrijfsstrategie niet ter discussie en is het doel van data science de bedrijfsvoering te ondersteunen en optimaliseren. In de andere data-science strategie is het doel juist de ondernemingsstrategie te veranderen. Data is dan een enabler voor fundamentele verandering van de business.

    De ene data science strategie is niet beter dan de andere. Bovendien zijn er mengvormen mogelijk en kan de ene strategie de andere in een later stadium volgen. Belangrijker is dat organisaties een expliciete keuze maken en een passende roadmap naar volwassenheid opstellen. De ontwikkeling van de data science afdeling wordt daar vervolgens op afgestemd. De ene data science competentie is namelijk de andere niet. Voor een executie strategie heb je bijvoorbeeld andere mensen en technologieën nodig dan voor een enabler strategie.

    Zo kiezen organisaties bewust een eigen data science strategie en groeipad naar volwassenheid. Vanuit dat kader kunnen technologische competenties en tools worden beoordeeld op hun noodzaak en bruikbaarheid. En maakt het gevoel ‘het nieuwste snufje van de buurman ook te moeten hebben’ plaats voor een bewust afweging op basis van ‘behoefte , bruikbaarheid en ontwikkelstadium’.

    Op zoek naar meer informatie? Indien je geinteresseerd bent kun je hier een nieuwe brochure aanvragen.

    Egbert Philips, https://www.linkedin.com/in/egbert-philips-9548bb2?trk=nav_responsive_tab_profile

  • Data voor apps, apps voor data

    5023657Data en apps hebben niets met elkaar te maken. En alles. Maar dan natuurlijk precies omgekeerd als veelal wordt gedacht.

    Verwarde mannen. Nergens kom je die zo vaak tegen als in de ict. Althans, dat denk ik wanneer ik intelligente mensen opvattingen hoor verkondigen die met minder hypegevoeligheid en meer gezond verstand niet hadden bestaan. Zo’n ervaring had ik vier jaar terug. Ik mocht advies uitbrengen over een reorganisatie van een organisatie met veel ict. Met veel ict kwamen vele data-eilanden en datalogistiek en de bekende problemen van hoge kosten en matige datakwaliteit. Bij een aantal betrokkenen bleek het beeld te bestaan dat die problemen grotendeels zouden gaan verdwijnen door legacy-applicaties te ver-appen. Ook rekening houdend met het feit dat apps nog maar net aan hun opmars waren begonnen, was dat een merkwaardige opvatting.

    Het is niet moeilijk om verwarde vakmensen te ont-hypen. Wat doen apps? Worden foute data goed? Worden corrupte data integer? Worden inconsistente data samenhangend? Worden verouderde data actueel? Nee, nee, nee en nee. Wat wel gebeurt is dat data ontsluiten voor de gebruiker gemakkelijker wordt dan met een browser en dat je programmatuur en lokale data op een mobiele client kunt installeren en bijhouden. Natuurlijk kun je in een app de halve datawereld ontsluiten, waar je ook bent, maar de credits daarvoor gaan naar open data(bases), Java, JDBC, 4G, Android/iOS, et cetera; niet naar het verschijnsel ‘app’ als zodanig.

    Ondertussen heeft de combinatie van al deze zaken wel degelijk geleid tot een totaal andere relatie van mensen en data: zowel gestructureerde als ongestructureerde data, zowel klassieke text & number data als grafische, audio- en videodata. Maar de app is slechts de zichtbare toegangspoort tot al die rijkdom, de toegangspoort naar die wereld, even low-tech en high-concept als de hyperlink.

    De opkomst van de app heeft ondertussen een wereld van data opengelegd. Niet zozeer om wat de app vermag maar vanwege het verbijsterende aanbod van apps, alle met hun eigen databronnen. In de praktijk leidt de app eerder tot versplintering van functionaliteit dan tot integratie van databronnen, al kan dat meer te maken hebben met het meer vluchtige gebruik van mobiele devices dan met de aard van apps. Maar hoe lang je er ook over doorpraat, apps als zodanig hebben geen effect op hoe een gegevenshuishouding van een organisatie moet worden opgezet of hoe bedrijfsdata moeten worden gemanaged.

    Tot hier heb ik de app bekeken als een middel om data te ontsluiten, de conventionele kijk. Er is echter ook aan andere kijk, die vooral wordt aangetroffen onder privacyadepten en databasemarketeers. Voor deze lieden is de app een data collection device, een superkrachtig middel om toegang te krijgen tot een wereld aan gedragsdata. Locatie, contacten, foto’s, in potentie alles dat een mobiel apparaat aan data kan verzamelen; de app als de primaire provider van big data.

    Zodra we de relatie tussen apps en data omkeren, zien we wel degelijk een impact op de gegevenshuishouding en een enorme uitdaging voor dataspecialisten. Wat te doen met al die ruwe sensordata die niet worden verzameld en gefilterd door data entry-mensen, ondersteund door elektronische formulieren met handige classificatiehulpmiddelen als drop-down listboxes, radio buttons en invoercontroles? Apps breiden de gegevenshuishouding van organisaties uit met bergen gedragsdata die van een totaal andere aard zijn dan de klassieke administratieve data.

    Voor app-gebruikers, met name consumenten die gratis apps gebruiken, is de grootste uitdaging het behoud van een acceptabel minimum aan privacy. Voor organisaties bestaat de uitdaging in het integreren van de klassieke administratieve data met de nieuwe gedragsdata. Zo bezien hebben apps inderdaad een enorm effect op de gegevenshuishouding van organisaties, maar dan aanvullend. De data die apps toevoegen aan de gegevenshuishouding van organisaties vormen veelal nieuwe informatie-eilanden, aanvullend aan en relatief incompatibel met de archipel van data toebehorend aan (legacy-)systemen en datawarehouses. Het perspectief van integratie van administratieve data en door apps verzamelde gedragsdata in één samenhangende gegevenshuishouding is nog groter dan de integratie van eilanden van administratieve data, juist omdat de aard van deze data zo verschillend en aanvullend is. Helaas voorspelt de geschiedenis van data-integratie niet veel goeds. We staan vermoedelijk aan het begin van een groot aantal hele en halve mislukkingen en sowieso van grote ict-investeringen. We leven weer eens in interessante tijden.

    Is dat een droevige conclusie? Ja en nee. Ja, want extra complexiteit en kosten zijn uiteindelijk in niemands belang. En nee, want ict en de manier waarop organisaties daarmee omgaan zal meer dan ooit een vitaal concurrentiemiddel worden. Jazeker, dat dachten we in de jaren ’80 ook en dat pakte anders uit, maar deze keer is het voor veel organisaties – natuurlijk lang niet alle – anders.

    Vraag niet wat uw data kunnen doen voor uw apps, maar wat uw apps kunnen doen voor uw data. Dát is de vraag.

    Source: Computable

  • Data Warehousing Lessons for A Data Lake World

     

     

    Over the past 2 decades, we have spent considerable time and effort trying to perfect the world of data warehousing. We took the technology that we were given and the data that would fit into that technology, and tried to provide our business constituents with the reports and dashboards necessary to run the businesses.

    It was a lot of hard work and we had to do many “unnatural” acts to get these OLTP (Online Transaction Processing)-centric technologies to work; aggregated tables, plethora of indices, user defined functions (UDF) in PL/SQL, and materialized views just to name a few. Kudos to us!!

    Now as we get ready for the full onslaught of the data lake, what lessons can we take away from our data warehousing experiences? I don’t have all the insights, but I offer this blog in hopes that others will comment and contribute. In the end, we want to learn from our data warehousing mistakes, but we don’t want to throw out those valuable learnings.

    Special thanks to Joe DosSantos (@JoeDosSantos) for his help on this blog.

    Why Did Data Warehousing Fail?

    Below is the list of areas where data warehousing struggled or outright failed. Again, this list is not comprehensive, and I encourage your contributions.

    • Adding New Data Takes Too Long. It took too long to load new data into the data warehouse. The general rule to add new data to a data warehouse was 3 months and $1 million. Because of the need to pre-build a schema before loading data into the data warehouse, the addition of new data sources to the data warehouse was a major effort. We had to conduct weeks of interviews with every potential user to capture every question they might ever want to ask in order to build a schema that handled all of their query and reporting requirements. This greatly hindered our ability to quickly explore new data sources, so organizations resorted to other options, which leads to…
    • Data Silos. Because it took so long to add new data sources to the data warehouse, organizations found it more expedient to build their own data marts, spreadmarts[1] or Access databases. Very quickly there was a wide-spread proliferation of these purpose built data stores across the organization. The result: no single version of the truth and lots of executive meetings wasting time debating whose version of the data was most accurate, which leads to…
    • Lack of Business Confidence. Because there was this proliferation of data across the organization and the resulting executive debates around whose data was most accurate, business leaders’ confidence in the data (and the data warehouse) quickly faded. This became especially true when the data being used to run a business unit was redefined for corporate use in such a way that it was not useful to the business. Take, for instance, a sales manager looking to assign a quota to his rep that manages the GE account and wants a report of historical sales. For him, sales might be Gross and GE might include Synchrony, whereas the corporate division might look at sales as Net or Adjusted and GE as its legal entities. It’s not so much a question of right and wrong as much as it is the enterprise introducing definitions that undermines confidence, which leads to…
    • Underinvestment In Metadata. No business leader had the time to verify the accuracy of the data, and no IT person knew the business well enough to make those data accuracy decisions. Plus, spending the money to hire consultants to do our job for us was always a hard internal sell, which leads to the metadata management denial cycle:
      • IT: “You business users need to own the data.”
      • Business: “We don’t have time to do that.”
      • IT: “Okay, let’s hire consultants.”
      • Business: “Shouldn’t we know our data better than consultants?”
      • IT: “Okay, you business users need to own the data”
      • And so forth…
    • Inability to Easily Share Data. The data warehouse lacked the ability to quickly ingest and consequently easily share data across different business functions and use cases. The data warehouse failed to become that single repository for the storage of the organization’s data assets because of the complexity, difficulty and slowness to add new data to the data warehouse, which leads to…
    • Shadow IT Spend. Nothing confirms the failure of the data warehouse more than shadow IT spend. Business users did not have confidence in how the data warehouse could help them address urgent business needs. Consequently, many line of business leaders pursued their own one-off IT initiatives (call center operations, sales force automation, campaign marketing, logistics planning, financial planning, etc.), which also further contributed to the unmanageable proliferation of data across the organizational data silos.
    • Inability to Handle Unstructured Data. Data warehouses cannot handle unstructured data. Unfortunately the bulk of the world’s data is now found in semi-structured data (log files, sensors, beacons, routers, MAC addresses) and unstructured data (text files, social media postings, audio files, photos, video files). Organizations who wanted a holistic view of the business had to make do with only 10 to 20% of the available organizational data. Hard to provide a holistic view with a 80% to 90% hole in that view.
    • No Predictive Analytic Capabilities. Business Intelligence solutions provide the summarized data necessary to support the organization’s operational and management reporting needs (descriptive analytics). However, most data warehouses lacked the detailed data across a wide variety of structured and unstructured data sources to support the organization’s predictive and prescriptive analytic needs.
    • Too Damned Expensive. Data science is about creating behavioral analytics at the individual levels (e.g., customers, employees, jet engine, train engine, truck, wind turbine, etc.). To uncover these behavioral analytics at the individual level, data scientists need the complete history of detailed transactional, operational and engagement data. The data scientists don’t want 13 months of aggregated data; they want 17 years of detailed transactions, even if that data is now located on mag tape. Trying to gather all of the voluminous data on a data warehouse is a recipe for organizational bankruptcy.
    • Inadequate Processing Power. Let’s face it; data warehouses lacked the economical processing power necessary to analyze petabytes of customer and machine data to uncover behavioral patterns and propensities. The data lake is built on modern, big data scale-out environments using open source software built on commodity servers are game changers in allowing organizations to store and analyze data volumes magnitudes bigger than one could ever economically fit into a data warehouse.

    What Did Data Warehousing Get Right?

    Okay, I was pretty harsh on the data warehouse world in which I grew up. But again, it was amazing what we were able to do with technology designed to deal with single records (insert, update, delete). I have never constructed analytics that uses only a single record. Analytics requires a massive number of records in order to uncover individual behaviors, propensities, tendencies, patterns, etc.

    So what did we get right, and what should we preserve as we move into the modern data lake world?

    • Data Governance. Data governance, into which I also group things like data accuracy, data lineage and data traceability, is as important now as it was in the data warehouse world. Having a process that allows the data science team to quickly ingest and explore the data unencumbered by data governance is a good practice. However you will need data governance rules, policies and procedures once you have determined that there is value in that data to support key decisions. If the business users do not have confidence in the data, then all is lost.
    • Metadata Management. The importance of metadata only becomes clearer as we begin to integrate data and analytics into the organization’s key business processes. The more metadata that we have about the data, the easier it is to get value from that data. Investing in the associated metadata carries the same economic value as investing it the data itself, IMHO. We want to enrich the data as much as possible, and a solid metadata management strategy is key for making that happen.
    • Conformed Dimensions. Having a single master file – or conformed dimension – for key business entities (e.g., products, customers, employees, physicians, teachers, stores, jet engines, locomotives, delivery trucks, etc.) is critical. It is these conformed dimensions that allow the data science team to tie together the wide variety of data sources to create the detailed analytic and behavioral profiles. Maintaining these conformed dimensions is hard work, but without them, there is no way to turn all this valuable data (and metadata) into actionable insights.
    • Single Version of The Truth. While I have always hated the term “single version of the truth,” operationally it is important to have all the data about your key business entities in a single (physical or logical) location. Also, in the Big Data world, the notion of data that is fit for purpose becomes critical. There may not be one truth, but there should be clarity as to how numbers are produced to provide transparency and trust.
    • Analytics Self-service. The idea of creating a self-service environment around analytics is very powerful. How do I pull IT out of the middle of the analytics request and provisioning process? If I truly want to create an environment where the analysts can quickly spin up an analytics sandbox and populate with data, I can’t have heavy manual processes in the middle of that process.
    • Reports Starting Point. The many reports and dashboards that have been built upon your data warehouse are a great starting point for your data lake journey. Business users have requested those reports for a reason. Instead of focusing time and effort to create yet more reports, first try to understand what questions and decisions the business users hoped to address with those reports, and what additional predictive and prescriptive insights do they need from those reports.
    • Yeah, SQL is still the query language of choice and we need to embrace how we help SQL-trained analysts to use that tool on the data lake. Open-source tools like Hive, HBase, and HAWQ are all designed to enable that army of SQL-trained business users and analysts to have access to the wealth of data in the data lake.

    Summary

    There is much that can be learned from our data warehousing experiences. The key is to understand what to keep and what to throw out. That means a single data lake (not data lakes). That means data governance. That means metadata management, and even more that we learned from our data warehousing experiences. We must learn from our experiences, otherwise…

    “Those who do not learn history are doomed to repeat it.”

    [1] Spreadmart (short for “spreadsheet data mart”) is a business intelligence term that refers to the propensity of some organizations or departments within organizations to use individual, desktop-based databases like spreadsheets as a primary means of data organization, storage, and dissemination.

     

     

  • Data, Analytics & Fuel Innovation at Celgene

    Williams-Richard-CelgeneCIO Richard Williams leads a global IT organization that’s harnessing digital, data, and analytics to support R&D innovation, drive operational excellence, and help Celgene achieve first-mover advantage in the shift to value-based, personalized health care intended to help patients live longer and healthier lives.
     
     
    An explosion of electronic health information is rocking the entire health care ecosystem, threatening to transform or disrupt every aspect of the industry. In the biopharmaceutical sector, that includes everything from the way breakthrough scientific innovations and insights occur to clinical development, regulatory approvals, and reimbursement for innovations. Celgene, the $11 billion integrated global biopharmaceutical company, is no exception.
     
    Indeed, Celgene, whose mission is to discover, develop, and commercialize innovative therapies for the treatment of cancer, immune-inflammatory, and other diseases, is aggressively working to leverage the information being generated across the health care system, applying advanced analytics to derive insights that power its core business and the functions that surround and support it. Long known for its commitment to external scientific collaboration as a source of innovation, Celgene is investing to harness not only the data it generates across the enterprise, but also the real-world health care data generated by its expanding network of partners. Combined, this network of networks is powering tremendous value.
     
    CIO Richard Williams sees his mission—and that of the IT organization he leads—as providing the platforms, data management, and analytics capabilities to support Celgene through the broader industry transition to value-based, personalized health care. At Celgene, this transformation is enabled by a focus on the seamless integration of information and technology. A cloud-first platform strategy, coupled with enterprise information management, serves as the foundation for leveraging the data generated and the corresponding insights from internal and external health care data.
     
    Williams recently shared his perspective on the changes wrought by enormous data volumes in health care, the role of IT at Celgene, and the ways IT supports life sciences innovation.
     
    Can you describe the environment in which Celgene is currently operating?
     
    Williams: We are living in an exciting era of scientific breakthroughs coupled with technology convergence. This creates both disruption and opportunity. The explosion and availability of data, the cloud, analytics, mobility, artificial intelligence, cognitive computing, and other technologies are accelerating data collection and insight generation, opening new pathways for collaboration and innovation. At Celgene, we’re able to apply technology as never before—in protein homeostasis, epigenetics, immuno-oncology, immuno-inflammation, informatics, and other fields of study—to better understand disease and develop targeted therapies and treatments for people who desperately need them.
     
    How does IT support scientific and business innovation at Celgene?
     
    At its core, Celgene IT is business aligned and value focused. Rather than looking at technology for technology’s sake, we view information and technology as essential to achieving our mission and business objectives. As an integrated function, we have end-to-end visibility across the value chain. This enables us to identify opportunities to leverage technology investments to connect processes and platforms across all functions. As a result, we’re able to support improvements in R&D productivity, product launch effectiveness, and overall operational excellence.
     
    This joint emphasis on business alignment and business value, which informs everything we do, is manifest in three important ways:
     
    First is our emphasis on a core set of enterprise platforms, which enable us to provide end-to-end visibility rather than a narrower functional view. We established a dual information- and cloud-first strategy to provide more comprehensive platforms of capabilities that can be shared across Celgene’s businesses. The cloud—especially with recent advances in security and analytics—provides tremendous scale, agility, and value because it allows us to standardize and create both consistency and agility across the entire organization regardless of device or access method. It’s our first choice for applications, compute power, and storage.
     
    Second is our focus on digital and the proliferation of patient, consumer, scientific, and it is creating. Health care data is growing exponentially—from something like 500 petabytes (PB) of data in 2013 to 25,000 PB by 2020, according to one study.
     
    To address this opportunity, we’ve initiated an enterprise information management (EIM) strategy through which we are targeting important data domains across our business and applying definitions, standards, taxonomies, and governance to data we capture internally and from our external partners. Establishing that consistency is critically important. It drives not only innovation, but also insight into our science, operations, and, ultimately, patient outcomes. Celgene is at the forefront in leveraging technologies that offer on-demand compute and analytic services. By establishing data consistency and influencing and setting standards, we will support our own objectives while also benefiting the broader industry.
     
    Third is our support for collaboration—the network of networks—and the appropriate sharing of information across organizational boundaries. We want to harness the capabilities and data assets of our partners to generate insights that improve our science and our ability to get better therapies to patients faster. Celgene is well-known in the industry for external innovation—how we partner scientifically—and we are now extending this approach to data and technology collaboration. One recent example is our alliance with Medidata Solutions, whose Clinical Cloud will serve as our enterprise technology and data platform for Celgene clinical trials worldwide. Celgene is also a founding commercial member of the Oncology Research Information Exchange Network, a collaboration of cancer centers spearheaded by M2Gen, a health informatics solution company. And we have teamed with ConvergeHEALTH by Deloitte and several other organizations for advanced analytics around real-world evidence and knowledge management, which will also be integrated into our data platform.
     
    You’re building this network-enabled, data-rich environment. But are your users prepared to take advantage of it?
     
    That’s an important aspect of the transformation and disruption taking place across multiple industries. Sure, IT can make information, technology, and insights available for improved decision-making, but the growing complexity of the data—whether it’s molecular structures, genomics, electronic medical records, or payment information—demands different skill sets.
     
    Data scientists are in high demand. We need to embed individuals with those specialized skills in functions from R&D to supply chain and commercial. At the same time, many more roles will require analytics acumen as part of the basic job description.
     
    As you build out your platform and data strategies, are you likely to extend those to your external alliances and partners?
     
    External collaboration enabled by shared data and analytics platforms is absolutely part of our collaboration strategy. If our informatics platforms can help our academic or commercial biotech collaborators advance the pace of their scientific evaluations, clinical studies, and commercialization, or they can help us with ours, that’s a win-win situation—and a differentiator for Celgene. We are already collaborating with Sage Bionetworks, leveraging Apple ResearchKit to develop an app that engages patients directly in innovation aimed at improving treatments for their diseases. We’re also working with IBM Watson to increase patient safety using cognitive computing to improve drug monitoring. As the power of collaborative innovation continues, collaboration will become more commonplace and lead to some amazing results.
     
    As you look out 12 to 18 months, what technologies might you want to bolt onto this platform or embed in your EIM strategy?
     
    The importance of cognitive computing, including machine learning and artificial intelligence, will continue to grow, helping us to make sense of the increasing volumes of data. The continued convergence of these technologies with the internet of things and analytics is another area to watch. It will result in operational insights as well as new, more intelligent ways to improve treatments for disease.
     
    What advice do you have for CIOs in health care or other industries who may not be as far along in their cloud, data, and analytics journeys?
    A digital enterprise is a knowledge- and information-driven enterprise, so CIOs should first focus on providing technologies and platforms that support seamless information sharing. In the process, CIOs should constantly be looking at information flows through an enterprise lens—real value is created when information is connected across all functions. Next, it’s increasingly important for CIOs to help build a technology ecosystem that allows the seamless exchange of information internally and externally because transformation and insight will occur in both places. Last, CIOs need to recognize that every job description will include data and information skills. This is an especially exciting time to be in IT because the digital capabilities we provide increasingly affect every function and role. We need to help people develop the skills they need to take advantage ofwhat we can offer now and in the future.
    Source: deloitte.wsj.com, November 14, 2016
  • De 5 beloftes van big data

    4921077Big data is een fenomeen dat zichzelf moeilijk laat definiëren. Velen zullen gehoord hebben van de 3 V’s: volume, velocity en variety. Kortgezegd gaat big data over grote volumes, veel snelheid (realtime) en gevarieerde/ongestructureerde data. Afhankelijk van de organisatie kent big data echter vele gezichten.

    Om te analyseren hoe big data het beste in een bedrijf geïntegreerd kan worden, is het van belang eerst duidelijk in beeld te hebben wat big data precies biedt. Dit is het beste samen te vatten in de volgende viif beloftes:


    1. Predictive: Big data genereert voorspellende resultaten die iets zeggen over de toekomst van uw organisatie of resultaat van een concrete actie;
    2. Actionable results: Big data levert mogelijkheden op voor directe acties op gevonden resultaten, zonder menselijke interventie;
    3. Realtime: De nieuwe snelheidsnormen zorgen dat je direct kunt reageren op nieuwe situaties;
    4. Adaptive: Een goed ontworpen model past zich constant automatisch aan wanneer situaties en relaties veranderen;
    5. Scalable: Verwerking en opslagcapaciteit is lineair schaalbaar, waardoor u flexibel kunt inspelen op nieuwe eisen.

    Deze vijf big data beloftes kunnen alleen worden gerealiseerd met inzet van drie big data disciplines/rollen: De big data scientist, de big data engineer en de big data infrastructuur specialist.

    Predictive

    In een klassieke Business Intelligence omgeving worden rapportages gegenereerd over de huidige status van het bedrijf. In het geval van big data praat men echter niet over het verleden of de huidige situatie, maar over predictive analytics.

    Voorspellende rapportages worden mogelijk gemaakt doordat de data scientist patroonherkenningstechnieken toepast op historische data en de gevonden patronen uitwerkt in een model. Het model kan vervolgens de historie inladen en op basis van actuele events/transacties de patronen doortrekken naar de toekomst. Op deze manier kan een manager schakelen van reactief management naar anticiperend management.

    Actionable results

    Actionable results ontstaan wanneer gevonden resultaten uit de modellen van de data scientist direct worden vertaald naar beslissingen in bedrijfsprocessen. Hierbij maakt de data engineer de koppeling en zorgt de data scientist dat het model de output in het juiste formaat aanbiedt. De belofte van actionable results wordt zodoende deels ingelost door de big data-specialisten, echter komt het grootste deel voor rekening van de attitude van het management team.

    Het management heeft de taak om een nieuwe manier van sturing aan te wenden. Er wordt niet meer gestuurd op de micro-processen zelf, maar op de modellen die deze processen automatiseren. Zo wordt er bijvoorbeeld niet meer gestuurd op wanneer welke machine onderhouden moet worden, maar welke risicomarges het beslissende model mag hanteren om de onderhoudskosten te optimaliseren.

    Realtime

    Bij big data wordt vaak gedacht aan grote volumes van terabytes aan data die verwerkt moeten worden. De 'big' van big data is echter geheel afhankelijk van de dimensie van snelheid. Zo is 10 TB aan data verwerken in een uur big data, maar 500 MB verwerken is ook big data als de eis is dat dit in tweehonderd milliseconde moet gebeuren. Realtime verwerking ligt in dat laatste hogesnelheidsdomein van verwerking. Er is geen gouden regel, maar men spreek vaak van realtime wanneer de reactiesnelheid binnen vijfhonderd milliseconde is. Om deze hoge snelheden te realiseren is een combinatie van alle drie de big data disciplines nodig.

    De big data infrastructuur specialist heeft de taak om het opslaan en inlezen van data te optimaliseren. Snelheidsoptimalisatie vind je door de data geheel te structureren op de manier waarop het door het model wordt ingelezen. Zo laten we alle flexibiliteit in de data los om deze vanuit één perspectief zo snel mogelijk te consumeren.

    De big data engineer realiseert dit door de snelheid van de koppelingen tussen de databronnen en de afnemers te optimaliseren, door de koppelingen in een gedistribueerd format aan te bieden. Zo kunnen een theoretisch oneindig aantal resources worden aangeschakeld om de data gedistribueerd te krijgen en elke verdubbeling van resources zorgt voor een verdubbeling van capaciteit. Ook is het aan de big data engineer om de modellen die de data scientist ontwikkelt om te zetten in een format dat alle sub-analyses van het model isoleert - en zoveel mogelijk distribueert over de beschikbare resources. Data scientists werken vaak in programmeertalen als R en Matlab, die ideaal zijn voor het exploreren van de data en de verschillende mogelijke modellen. Deze talen lenen zich echter niet goed voor distributed processing en de big data engineer moet daarom vaak in samenwerking met de data scientist een vertaling van het prototype model verwezenlijken in een productiewaardige programmeertaal als Java of Scala.

    De data scientist verzorgt zoals besproken de modellen en daarmee de logica van de dataverwerking. Om realtime te kunnen opereren is het de taak aan deze persoon om de complexiteit van de dataverwerking in te perken tot een niveau beneden exponentieel. Zodoende is een samenwerking van de drie disciplines vereist om tot een optimaal resultaat te komen.

    Adaptive

    We kunnen spreken van een adaptive omgeving - ook wel machine learning of artificial intelligence genoemd - wanneer de intelligentie van deze omgeving zich autonoom aanpast aan nieuwe ontwikkelingen binnen het te modelleren domein. Om dit mogelijk te maken is het belangrijk dat het model genoeg ervaring heeft opgedaan om zelf te kunnen leren. Hoe meer informatie er beschikbaar is over het model door de geschiedenis heen, hoe breder de ervaring is die we op kunnen doen.

    Scalable

    Schaalbaarheid wordt bereikt wanneer er een theoretisch oneindige verwerkingscapaciteit is als oneindig veel computers worden bijgeschakeld. Dit betekent wanneer je vier keer zoveel capaciteit nodig hebt, vier keer zoveel computers worden bijgeschakeld - en wanneer je duizend keer meer nodig hebt er duizend computers worden toegevoegd. Dit lijkt eenvoudig, maar tot voorheen was deze samenwerking tussen computers een zeer complexe taak.

    Iedere discipline heeft een rol in het schaalbaar maken en schaalbaar houden van big data-oplossingen. Zo verzorgt de big data infrastructuur specialist de schaalbaarheid van het lezen, schrijven en opslaan van data. De big data engineer verzorgt de schaalbaarheid van het consumeren en produceren van data en de big data scientist verzorgt de schaalbaarheid van de intelligente verwerking van de data.

    Big data, big deal?

    Om van de volledige mogelijkheden van big data gebruik te maken is het dus van groot belang een multidisciplinair team in te schakelen. Dit klinkt wellicht alsof er direct zeer grote investeringen gedaan moeten worden, echter biedt big data ook de mogelijkheid om klein te beginnen. Dit kan door een data scientist de verschillende analyses te laten doen op een laptop of een lokale server, om zo met een minimale investering een aantal ‘short-term wins’ voor je organisatie te creëren. Wanneer je de toegevoegde waarde van big data inzichtelijk hebt, is het een relatief kleine stap om een big data omgeving in productie te zetten en zodoende ook jouw organisatie op een data-gedreven manier te kunnen sturen.

    Source: Computable

  • De blik vooruit: digitaal transformeren met Big Data

    blik vooruitHet ‘Big’ in Big Data staat voor velen alleen voor de grote hoeveelheden data die organisaties in huis hebben. Die data lijken in veel praktijkvoorbeelden vooral voedingsbodem voor aanscherping van verdienmodellen – zoals in de advertentie-industrie – of totale disruptie zoals de kentering die eHealth op dit moment veroorzaakt.

    Een eenzijdig beeld misschien, want data leveren net zo goed inzichten voor interne procesverbetering of een betere klantbenadering met digitale oplossingen. Het gesprek over Big Data zou vooral moet gaan over de enorme potentie ervan: over de impact die de data kunnen hebben op bestaande producten en organisatievraagstukken en hoe die helpen bij een eerste stap richting digitale transformatie.

    Big Data in de huidige praktijk
    De praktijk leert dat er voor ieder bedrijf vier manieren zijn om data in te zetten. Terug naar het realisme in Big Data: de mogelijkheden, voorbeelden en natuurlijke grenzen.

    Excellentie: onderzoek toont aan dat de manager die zich puur door zijn gevoel of intuïtie laat leiden, kwalitatief slechtere beslissingen neemt en dus aanstuurt op een minder goede dienst of experience. Door de harde feiten uit gecombineerde data te benutten, zijn bottlenecks in processen te herkennen en zelfs te voorspellen.

    Zo kunnen Amerikaanse politiekorpsen sinds eind vorig jaar op basis van realtime data schatten waar misdrijven gepleegd zullen worden. Hoewel het al decennia gebruikelijk is om op willekeurige wijze door de stad te patrouilleren – de crimineel weet dan immers niet waar agenten zich bevinden en zou daardoor gehinderd worden – wordt die werkwijze nu losgelaten.

    Het algoritme van de analyticsoplossing PredPol belooft aan de hand van plaats, tijd en soort criminaliteit te voorspellen waar agenten met hun aanwezigheid een misdrijf kunnen voorkomen. In de film Minority Report werd het nog weggezet als voorspelling voor het jaar 2054, nu blijkt dat excelleren met voorspellende data in 2015 al de normaalste zaak van de wereld is.

    Productleiderschap: niet ieder bedrijf hoeft het nieuwe Spotify of Airbnb te worden. Wel moet er rekening worden gehouden met de disruptieve kracht van deze spelers en hun verdienmodellen.

    De leidende positie die Netflix heeft ingenomen heeft het bedrijf deels te danken aan het slimme gebruik van Big Data. De enorme hoeveelheid content die online beschikbaar is, maakt het mogelijk te grasduinen in films en series. Dat surf- en kijkgedrag vertaalt Netflix in dataprofielen waarmee het eigen product zichtbaar verbetert. De data resulteren in aanbevelingen die – voor de kijker – onverwachts bij iemand zijn smaak passen en waarmee het bedrijf je als klant verder bindt.

    De video on demand-dienst staat er bij gebruikers inmiddels om bekend te weten wat de kijker boeit en is in staat je meer te laten afnemen dan je van plan bent. Met de data die het platform continu verbeteren, heeft Netflix de entertainmentindustrie en de groeiende markt van video op afroep opgeschud.

    Het bedrijf heeft zelfs gezorgd voor het nieuwe fenomeen van binge watching, waarbij de kijker urenlang aan de hand van aanbevelingen van de ene aflevering in de andere wordt gezogen. De algoritmes die hiervoor zorgen zijn zo belangrijk dat Netflix een miljoen dollar belooft aan diegene die met een beter alternatief komt.

    Intimiteit: meer te weten komen over de klant is misschien wel de bekendste kans van Big Data. Sociale media, online tracking via cookies en open databronnen maken dat iedere organisatie diensten op maat kan bieden: generieke homepages maken plaats voor met gebruikersdata gepersonaliseerd portals, de serviceverlening verbetert nu er op de juiste plekken in de organisatie een completer plaatje is van de klant.

    Hoe ver die intimiteit gaat, bewijst Amazon. Het bedrijf zegt producten te kunnen bezorgen nog voor deze zijn besteld. Amazon kan op basis van locatie, eerdere bestellingen, zoekopdrachten, opgeslagen verlanglijsten en ander online gedrag voorspellen welke behoefte een klant heeft. De gegevens zouden al zo accuraat zijn dat het winkelbedrijf eerder weet welke producten daarvoor moeten worden besteld dan de klant zelf. Zo nauwkeurig kunnen data soms zijn.

    Risicobeheersing: als data eindelijk goed worden ontgonnen en gecombineerd, maken ze duidelijk welke risico’s bedrijven realtime lopen. Met dank aan Big Data zijn audits sneller en doelgerichter in te zetten.

    Schoolvoorbeeld daarin zijn financiële instellingen. De tienduizenden financiële transacties die deze bedrijven iedere seconde verwerken, genereren zoveel data dat door middel van patroonherkenning fraude snel kan worden opgespoord. Bestel je online een artikel waar bijvoorbeeld een ongebruikelijk prijskaartje aan hangt, rinkelt binnen no-time de telefoon om de transactie te verifiëren.

    En hoewel het kostenbesparende aspect geen verdere toelichting behoeft, is een groot deel van de bedrijven onvoldoende voorbereid. Volgens wereldwijd onderzoek van EY erkent 72 procent van de bedrijven dat Big Data belangrijk zijn voor risicobeheersing, maar zegt 60 procent ook dat zij daarin nog belangrijke stappen moeten zetten.

    De verzekeraar: Big Data als middel om te transformeren
    Van uitlatingen op sociale media tot aan sociaal demografische gegevens, van eigen data over koopgedrag tot aan openbare data als temperatuurschommelingen: hoe rijker de data hoe scherper het inzicht. Wanneer er een organisatievraagstuk ligt, is de kans groot dat data een middel zijn om tot de benodigde transformatie te komen. Met de aanpak wordt daartoe een belangrijke stap gezet.

    • Detectie – Op basis van een Big Data-analyse kan er zicht ontstaan op eventuele behoeften onder de doelgroep. Bijvoorbeeld: Maakt een zorgverzekeraar een digitale transformatie door, dan is het raadzaam profielen van klanten uit een specifieke generatie met onderzoeksdata te verfijnen. Hoe begeven zij zich door de customer journey en welke digitale oplossingen verwachten zij gedurende een contactmoment?
    • Doel- en vraagstelling – Creëer potentiële scenario’s op basis van de data. Bijvoorbeeld: de jongste doelgroep van de verzekeraar groeit op in een stedelijke omgeving. Welk (mobiel) gedrag is specifiek voor deze groep en hoe beïnvloedt dit de de digitale oplossingen waar deze jongeren om vragen? Bepaal waar de databronnen zich bevinden – welke interne en externe databronnen zijn benodigd voor beantwoording van de vragen? Denk daarbij aan interne klantprofielen, maar ook aan open data-projecten van de overheid. Sociale media – uitingen en connecties – in combinatie met demografische kenmerken en postcodegebieden verreiken de profielen. De gegevens vertellen meer over de voorkeuren en invloed die directe omgeving en online media hebben.
    • Controleer de data – Vergeet niet te kijken naar wet- en regelgeving en met name wat privacyregels verbieden. De Wet Bescherming Persoonsgegevens gaat behoorlijk ver: de wet is zelfs van toepassing op gegevens die je tijdelijk binnenhaalt ter verwerking.
    • Analyse – De data worden geïnterpreteerd door analisten. Zo wordt duidelijk dat er een aantoonbaar verband is tussen leeftijd, woonomgeving en gebruik van digitale oplossingen. Bijvoorbeeld: jonge stedelingen zijn digital native en willen een online portal met eHealth-oplossing. Hierin willen zij eigen Big Data uit apps kunnen koppelen voor een beter beeld van de gezondheid.
    • Verankering – Door klantprofielen te blijven monitoren wordt snel duidelijk of er toekomstig afwijkingen optreden. Indien nodig is de transformatie bij te sturen.

    Grenzen aan Big Data
    Het is vooral belangrijk te waken over realisme in het denken over Big Data. Want hoewel data veel antwoorden geven, zit er ook een grens aan de mogelijkheden. Zodra databronnen naast elkaar lopen, kan het zijn dat analyses elkaar beïnvloeden: uitkomsten die correleren blijken achteraf louter op toeval te berusten.

    De mens blijft ook in de toekomst een belangrijke schakel in de kansen die Big Data bieden. Cruciale kennis moet op persoonsniveau behouden blijven, alleen de mens is in staat de juiste interpretatie te leveren. Wat te doen met inzichten is aan hen voorbehouden: een onverwachts dwarsverband kan aantonen dat een groep consumenten een verhoogt bedrijfsrisico oplevert. Wat te doen met deze inzichten als die een groep mensen stigmatiseren? De ethische grens moet altijd worden bewaakt.

    In veel organisaties betekent de komst van data een ware cultuurverandering. Behalve dat managers te weten komen dát er iets verandert, weten zij door die data ook hoe en in welke richting zich iets in de toekomst ontwikkelt. Met Big Data kan de blik weer vooruit.

    Source: Emerce

  • De computeranalyse bepaalt straks of je perspectief hebt bij een bedrijf

    Moet de afdeling personeelszaken ook opletten wat de medewerkers allemaal zeggen en registreren op sociale media als LinkedIn, Facebook en Twitter? Moet dat opgeslagen worden voor de eeuwigheid? Het kunstmatig intelligente softwareprogramma Crunchr blijft daar nu nog allemaal ver vandaan, stelt oprichter Dirk Jonker van Focus Orange, de eigenaar van Crunchr, meerdere malen nadrukkelijk.

    Bedrijven zijn in de ervaring van Jonker ook zeer conservatief in het napluizen van sociale media. ‘Ze doen het nog niet, maar die discussie gaat er komen. Het lijken immers openbare data. Mensen geven heel veel data weg. Kijk naar de bonuskaart van AH: in ruil voor een beetje korting geven ze heel veel informatie aan het bedrijf.’

    Net als bij AH moet de werknemer er volgens Jonker ook beter van worden als hij bijvoorbeeld data over zijn arbeidsverleden ter beschikking stelt. ‘We moeten de werknemer in ruil daarvoor ook een dienst kunnen aanbieden. Daarnaast is transparantie over wat je doet, en waarom, het belangrijkste. Maar voorlopig moeten bedrijven eerst de data gaan gebruiken die ze al hebben.’

    Anoniem onderzoek

    Een tussenstap die bij verschillende klanten al wel heel veel kwalitatieve informatie oplevert, is een jaarlijks anoniem onderzoek onder het personeel. 'Stel ieder jaar veertig vragen over beloning, sfeer, management, chefs, trainingen', zegt Jonker. 'Daar haal je een schat aan informatie uit. En doe consequent exitinterviews als mensen vertrekken.’

    Focus Orange adviseert ook regelmatig bij het ontwerpen van cao’s. ‘Daar zetten we Crunchr ook in. Het blijkt namelijk dat ondernemingen vaak onvoldoende weten wat het personeel echt wil. Als je dat meet, kun je een pakket voorstellen dat effectief en aantrekkelijk is.'

    Consortium

    In mei heeft Jonker het People Analytics Consortium opgericht om het vakgebied verder door te ontwikkelen. De TU Delft, het Centrum voor Wiskunde en Informatie (VU/UvA), Randstad, Wolters Kluwer en ASML nemen hieraan deel. Binnen het consortium worden technieken ontwikkeld — nadrukkelijk niet met data van de betrokkenen bedrijven — om nieuwe vraagstukken te kunnen beantwoorden. Welke vragen moet een bedrijf stellen om patronen te kunnen herkennen en wat zijn de beste technieken om de vragen te laten beantwoorden?

    ‘Er worden bijvoorbeeld 40.000 lappen tekst in het systeem gestopt. Dat moet dan technisch ontleed worden. We trainen ons algoritme bijvoorbeeld op de hele correspondentie rond het Enron-schandaal. Dat is allemaal openbaar.’

    Naast deze vrij ingewikkelde techniek kan Crunchr ook worden ingezet om inconsistenties in bedrijfsdata te vinden. Bijvoorbeeld of iemand teveel verdient voor zijn functie. Bij grote bedrijven die in alle delen van de wereld werken, is zo’n 'uitschieter’ geen uitzondering. ‘Daar komen soms dingen uit die je met het blote oog niet ziet’, zegt Jonker. ‘Wij kunnen in zeven seconden 40.000 records doorakkeren. En met de uitschieters die we vinden kunnen we ons systeem weer kalibreren.’

    Opvolging

    Bij opvolgingsplanning maakt personeelszaken voor senior management en kritieke posities een plan wie deze personen kunnen opvolgen als de positie vacant komt. Voorselectie ligt gevoelig, het kost veel tijd en de plannen zijn beperkt houdbaar. Zodra een belangrijk persoon vertrekt, is de helft van de kandidaten ook al weer in een andere positie, of inmiddels ongeschikt gebleken.

    Crunchr gebruikt de opvolgingsplannen als input voor het algoritme, dat zelf moet leren wat iemand tot een goede opvolger maakt. Als er in de toekomt een positie vrijkomt, kijkt het netwerk in het hele bedrijf naar opvolgers. Het bedrijf is flexibel en niet meer afhankelijk van verouderde plannen. Iedereen komt in beeld en als een bedrijf meer vrouwen in de top wil, dan kan het daar op sturen. Door dit eindeloos systematisch te oefenen is Crunchr getraind.

    Een grote internationale onderneming maakt al snel decentraal een paar duizend plannen, die het regionaal bespreekt en waar personeelszaken op corporate niveau op stuurt. Een getraind algoritme geeft binnen enkele seconden elk plan een realiteitsscore. Een pas afgestudeerde Nederlander opvolger laten zijn voor een senior managementpositie in de VS is bijvoorbeeld onwaarschijnlijk.

    Risico's mitigeren

    Met de bekende ‘grafentheorie’ uit de wiskunde laat Crunchr zien of binnen het management iedereen denkt een aantal opvolgers te hebben, maar als dit veelal dezelfde toppotentials zijn, is het risico niet afgezwakt. Daarnaast kan het bedrijf de opvolgingsplannen gebruiken voor het voorspellen van ‘global mobility’. Hoe bewegen toekomstige leiders over de wereld, waar zitten leiders nu en waarheen verhuizen ze. Beide inzichten zijn volgens Jonker bijzonder waardevol voor de top van het bedrijf.

    FD, 3 oktober 2016

  • DeepMind gaat algoritmes gebruiken om blindheid te voorspellen

    118628 c2f7304fDeepMind, een van de dochterbedrijven van zoekgigant Google, die onderzoek doet naar zelflerende computers, gaat helpen bij onderzoek naar blindheid. DeepMind gaat samenwerken met de Britse gezondheidsorganisatie NHS om zijn technologie te leren de eerste tekenen van blindheid op te sporen.

    Daartoe krijgt DeepMind 1 miljoen geanonimiseerde oogscans aangeleverd. De software gaat die scannen en op basis van meegeleverde informatie zou het moeten weten welke scans een oogziekte vertonen en welke niet. De bedoeling is dat de software uiteindelijk uit zichzelf de eerste tekenen van oogziektes leert te herkennen.

    Het gaat op dit moment om twee vormen van blindheid die relatief veel voorkomen: leeftijdsgebonden maculadegeneratie en diabetische retinopathie. Mensen met diabetes hebben bijvoorbeeld 25 keer zoveel kans om blind te worden als mensen zonder diabetes. Het vroeg herkennen van dit soort gevallen zou kunnen helpen blindheid te voorkomen.

    Het hoofd van de oogafdeling in het ziekenhuis, Professor Peng Tee Khaw, vertelt dat het kan helpen om snel oogziektes op te sporen bij patiënten. "Deze scans zijn ongelofelijk gedetailleerd, gedetailleerder zelfs dan alle andere scans die we van het lichaam hebben. We zien beelden op celniveau. Maar het probleem is tegelijkertijd juist dat het zoveel data biedt."

    Daar komt dan ook de oplossing om DeepMind te gebruiken vandaan. "Ik heb er alle ervaring uit mijn hele leven voor nodig om de geschiedenis van een patiënt te kunnen volgen. Maar patiënten vertrouwen op mijn ervaring om hun toekomst te voorspellen. Als we zelflerende technologie kunnen gebruiken, zouden we dit veel beter kunnen doen, want dan zou ik de ervaring van wel 10.000 levens hebben."

    Bron: Techzine.nl

  • Digitale technologieën leveren Europees bedrijfsleven komende twee jaar 545 miljard euro op

    925609982sEuropese bedrijven kunnen dankzij het toepassen van digitale tools en technologieën een omzetstijging van 545 miljard euro behalen in de komende twee jaar. Voor Nederlandse bedrijven ligt dit bedrag op 23,5 miljard euro. Dat blijkt uit een onderzoek van Cognizant in samenwerking met Roubini Global Economics onder ruim 800 Europese bedrijven.
     
    Het onderzoek The Work Ahead – Europe’s Digital Imperative maakt onderdeel uit van een wereldwijd onderzoek waarin het veranderende karakter van werk in het digitale tijdperk wordt onderzocht. De resultaten tonen aan dat organisaties die het meest proactief zijn in het dichter bij elkaar brengen van de fysieke en virtuele wereld, de grootste kans hebben om meer omzet te behalen.
     
    Omzetpotentieel benutten
    Leidinggevenden geven aan dat technologieën als Artificial Intelligence (AI), Big Data en blockchain een bron kunnen zijn voor nieuwe businessmodellen en inkomststromen, veranderende klantrelaties en lagere kosten. Sterker nog, de ondervraagden verwachten dat digitale technologieën een positief effect van 8,4 procent zullen hebben op de omzet tussen nu en 2018.
     
    Digitalisering kan voor zowel kostenefficiëntie als omzetstijging zorgen. Door bijvoorbeeld intelligent process automation (IPA) toe te passen – waarbij software-robots routinetaken overnemen – kunnen bedrijven kosten besparen in de middle en backoffice. Uit de analyse blijkt dat de impact van digitale transformatie op omzet en kostenbesparing in de onderzochte industrieën (retail, financiële diensten, verzekeringen1, maakindustrie en life sciences) uitkomt op 876 miljoen euro in 2018.
     
    Nog steeds achterblijvers op digitaal gebied
    Europese executives verwachten dat een digitale economie gestimuleerd zal worden door een combinatie van data, algoritmes, software-robots en connected devices. Gevraagd naar welke technologie de grootste invloed zal hebben op het werk in 2020, komt Big Data als winnaar naar voren. Maar liefst 99 procent van de respondenten noemt deze technologie. Opvallend is dat AI vlak daarna met 97 procent op een tweede plek eindigt; respondenten beschouwen AI als meer dan een hype. Sterker nog, de verwachting is dat AI een centrale plek zal innemen in het toekomstige werk in Europa.
     
    Aan de andere kant kunnen late adopters een gezamenlijk verlies van 761 miljard euro verwachten in 2018, zo blijkt uit het onderzoek.
    Een derde van de ondervraagde managers geeft aan dat hun werkgever in hun ogen niet beschikt over de kennis en kwaliteiten om de juiste digitale strategie in te voeren of zelfs geen idee heeft van wat er gedaan moet worden. 30 procent van de ondervraagden is van mening dat hun leidinggevenden te weinig investeren in nieuwe technologieën, terwijl 29 procent terughoudendheid ondervindt in het toepassen van nieuwe manieren van werken.
     
    De belangrijkste obstakels voor bedrijven om de overstap te maken naar digitaal zijn angst voor beveiligings-issues (24%), budgetbeperkingen (21%) en een gebrek aan talent (14%).
     
    Euan Davis, European Head of the Centre for the Future of Work bij Cognizant, licht toe: “Om de noodzakelijke stap te kunnen maken naar digitaal, moet het management proactief zijn en hun organisatie voorbereiden op toekomstig werk. Langzame innovatierondes en onwil om te experimenteren zijn de doodsteek voor organisaties om digitale mogelijkheden goed te kunnen benutten. Het beheren van de digitale economie is een absolute noodzaak voor organisaties. Bedrijven die geen prioriteit geven aan het verdiepen, verbreden, versterken of verbeteren van hun digitale voetafdruk, spelen bij voorbaat een verloren wedstrijd.”
     
    Over het onderzoek
    Uitkomsten zijn gebaseerd op een wereldwijd onderzoek onder 2.000 executives in verschillende industrieën, 250 middenmanagers verantwoordelijk voor andere werknemers, 150 MBA-studenten van grote universiteiten wereldwijd en 50 futuristen (journalisten, academici en auteurs). Het onderzoek onder executives en managers is in 18 landen uitgevoerd in het Engels, Arabisch, Frans, Duits, Japans en Chinees. Executives zijn daarbij telefonisch geïnterviewd, managers via een online vragenlijst. De MBA-studenten en futuristen zijn in het Engels ondervraagd via telefonische interviews (MBA studenten in 15 landen, futuristen in 10 landen). The Work Ahead – Europe’s Digital Imperative bevat de 800 reacties van het Europese onderzoek onder executives en managers. Meer details zijn te vinden in Work Ahead: Insights to Master the Digital Economy.
     
    Source: emerce.nl, 28 november 2016
  • Do you know what a data driven company is?

    Most companies today claim to be fluent in data, but as with most trends, these claims tend to be exaggerated. Com

    Data driven company 1396308504-data-driven-means-never-having-say-sorry

    panies are high on data, but what does it mean to be a data-driven company? I went ahead and asked a number of business leaders.

    According to Amir Orad, CEO of Sisense, a business intelligence software provider, true data-driven companies understand that data should be omnipresent and accessible.

    "A data-driven company is an organization where every person who can use data to make better decisions, has access to the data they need when they need it. being data-driven is not about seeing a few canned reports at the beginning of every day or week; it's about giving the business decision makers the power to explore data independently, even if they're working with big or disparate data sources."

    Asaf Yigal, the co-Founder of Logz.io, ELK as a service cloud platform, agrees, but emphasized the importance of measurability.

    "Data-driven complains are companies that relentlessly measure and monitor the pulse of the business and are doing so in a continuous and often automated manner."

    Companies often proudly talk about data-driven marketing, but forget that the company itself should be driven by data, internally and externally. It's also important to remember that internal data might help produce information that can be used for marketing and sales purposes.

    "There's a lot of talk about data-driven marketing and sales, etc., but not a lot about a company as a whole becoming data-driven," said Manish Sood, the founder and CEO of Reltio.

    Bryan Harwood from Outsell sets says a company needs to meet the following three objectives to qualify.

     

    1. It should be able to not only draw data from a variety of internal and external sources, but also be able to blend that data in an analytics engine and distill it down to actionable insights.

    2. These insights should drive real-time decision making that infuses every level of the organization.

    3. The data should yield quantifiable results downstream that in turn, inform the organization about which data sources are yielding results.

    Considering the increasing complexity of data growing larger in size, changing rapidly and spread between many disparate sources, accessibility alone is not enough.

    "Being data-driven is not about seeing a few canned reports at the beginning of every day or week; it's about giving the business decision makers the power to explore data independently, even if they're working with big or disparate data sources. They need to be able to ask questions and receive answers that are based on data before the decision is actually made -- today in many places the executive makes a 'gut-instinct' decision and then looks for the data to justify it. But if data is readily available and easy to analyze and to present in visual form, it becomes an inherent part of the decision-making process -- and that's what makes an organization truly data-driven," said Orad.

    The surge of a data-driven culture has also had a significant impact on how companies are structured. The complexity of data forces companies to merge different department to harness their individual strengths to make the most of data. Being data-driven means making use of massive quantities of unstructured data – text, video, voice.  In the past this belonged to the IT department which had a tough time

    extracting insights from it.

     

    From darkness to light: how to become data-driven

    According to most experts, the road to data fluency is not easy or glamorous.

    "To become a data-driven company the belief in the importance of the integrity and quality of information needs to perme

    ate the culture of the company at all levels. It is not enough to start a formal data governance program, becoming data-driven requires a disciplined shift in the mindset of all employees towards maintaining the integrity and quality of their data," said Chris Jennings, vice president of technology services at Collaborative Consulting.

    Yigal from Logz.io asks companies to treat data as religion.

    "Companies need to be religious with demanding to see the data before and after changes are being made. Especially in fast moving start-up companies where changes are easily made it's prudent to track the impact of every change."

    To make sure data is not only in the hands of IT and other data enthusiasts, organizations need to embrace a switch in culture. 

    Most experts agree that business intelligence needs to be in the hands of every decision maker in the company to make sure the entire staff is aligned and fighting the same battles.

    "This way, there are no 'different versions of the truth' floating around in disparate spreadsheets, and every user has a consistent experience across platforms," said Ulrik Pederson, CTO of TARGIT.

     

    Once the organization is prepared for the switch, there are three key components of becoming a data-driven organization.

    • Build a data warehouse
    • Connect the most critical data sources
    • Share data through dashboards, analyses, mobile business intelligence, storyboards, and report

    As data volume, variety, and velocity increase, so does the organization's ability to make use of it, especially if the cultural and technical elements are in place. Analytics, resources, and skills should not be limited to a few departments, and everyone, from sales to marketing and IT to finance, should leverage the benefits of data.

    Source: InfoWorld. This article is published as part of the IDG Contributor Network.

  • Een datagedreven organisatiecultuur: waar te beginnen?

    3jzi1jporwq1besspdljn34iuj7oxblBedrijven met een datagedreven organisatiecultuur plukken daar de vruchten van. De voordelen van data inzet zijn bij de meeste bedrijven wel bekend, echter blijft de implementatie vaak achter. Op zich niet verrassend: de overgang op verschillende organisatieniveaus is een hele uitdaging en cultuurverandering kost tijd.

    Begrippen als big data, business intelligence, analytics en data science zijn voor veel organisaties nog behoorlijk abstract. Met alle beschikbare data heb je feitelijk goud in handen, maar hoe ga je slim om met data, hoeveel waarde zit er in en welke aanpak is de sleutel tot succes? Organisaties met veel data in hun bezit behalen namelijk lang niet altijd direct concurrentievoordeel. Het zijn organisaties die data gebruiken als basis voor het nemen van beslissingen die het meest profiteren.


    Mindset
    Voor bedrijven die (big) data effectief willen inzetten, is het van belang dat er een cultuur aanwezig is waarin de besluitvorming wordt gebaseerd op data. Zonder een datagedreven cultuur worden medewerkers namelijk niet gestimuleerd om de technologieën ook daadwerkelijk te gebruiken. Verandering van de mindset is hierbij dus cruciaal. Maar hoe kom je tot een cultuuromslag? En hoe zorg je ervoor dat uiteindelijk iedereen binnen je organisatie data gedreven beslissingen omarmt? Hieronder vind je een aantal direct toepasbare tips om zelf invloed uit te oefen op je organisatiecultuur.


    Start met het bepalen en meten van KPI's
    Wat je niet meet, kun je ook niet optimaliseren. De juiste KPI's zijn het fundament van data gedreven beslissingen. Zorg dat je helder in kaart hebt welke data gerelateerd zijn aan het succes van je organisatie. Welke datapunten zijn belangrijk en hoe verhouden deze zich tot je business doelstellingen? Meet wat gekwantificeerd kan worden. Is kwantificeren niet mogelijk? Zorg dan voor kwalitatieve data. Wanneer je de KPI's scherp hebt, kun je goede en gegronde beslissingen/aanbevelingen maken. Hier zal ook je omgeving de meerwaarde van zien.


    Wees zelf ambassadeur
    Maak je eigen beslissingen datagedreven en zet anderen aan om dit ook te doen. Het is van belang dat je zoveel mogelijk data van je organisatie inzet om datagedreven beslissingen te nemen. Wanneer data namelijk onderbuikgevoelens versterkt, of nog beter, deze tegenspreekt, zal het belang van een datagedreven aanpak vanzelf op de voorgrond treden.


    Start vandaag
    De weg naar een datagedreven organisatie is lang, maar je kunt wel vandaag de eerste stappen zetten. Begin bijvoorbeeld met het zoeken en uitvoeren van een aantal datahefbomen waarmee je datagedreven resultaten kunt boeken en groei van daaruit door. Maak de boodschap duidelijk naar je collega’s, toon hun het belang van datagedreven beslissingen aan. Faciliteer het gebruik van data. En laat zien dat het werkt.

    Source: Twinkle

  • Eerste Big Data Hub van Nederland geopend

    arenaDe Amsterdam ArenA opent vanmiddag haar deuren voor de eerste zogenoemde Big Data Hub van Nederland. Ondernemers kunnen daar met overheden en wetenschappers data delen en data-gedreven innovaties ontwikkelen. Op allerlei terrein: van entertainment tot mobiliteit.

    Het Data Hub richt zich grotendeels op toepassingen in en rond de ArenA. Daarbij kan worden gedacht aan het sturen van bezoekersstromen, het optimaliseren van  bezoekerservaringen, het vergroten van de veiligheid, het beter benutten van water en elektriciteit en het sneller en effectiever informeren van hulpdiensten.

    Het Big Data Value Center maakt deel uit van de al eerder gelanceerde Amsterdam Innovation ArenA. Eén van de faciliteiten wordt een zogenoemde integrated control room waarin alle data van mobiliteitsstromen, nutsvoorzieningen en veiligheidscamera’s worden gecombineerd ten behoeve van misdaadpreventie en het reguleren van optimale evacuatieroutes via LED-verlichting in de vloer.

    Vervoersbedrijven kunnen mobiliteit tijdens pieken beter stroomlijnen. Energie-, afval- en waterbedrijven gebruiken de data om preventief onderhoud te kunnen plegen of hun processen te verduurzamen.

    Tijdens evenementen kunnen de data worden gebruikt om de beleving te vergroten via social media en smartphones door spelers op het veld te volgen of zelf beelden te laten maken en te delen. Deelnemers aan de Hub kunnen deze en eigen concepten en datasets testen in deze operationele omgeving.

    De belangrijkste partners zijn KPMG, Kamer van Koophandel, TNO en Commit2Data. Deze initiatiefnemers hopen dat de oplossingen door de samenwerkende partijen worden overgenomen naar (andere) speelsteden van het EK. Of naar vergelijkbare locaties als Schiphol of de Floriade.

    De Big Data Hub Metropoolregio Amsterdam is de eerste hub van vier en heeft een belangrijke focus op de creatieve industrie. Eind dit jaar volgt de Big Data Hub voor o.a. Security en Logistiek in de metropoolregio Den Haag/Rotterdam. Eind 2017 moeten ook hubs in Noord-Nederland (Energie) en Noord-Brabant (Maakindustrie) van start zijn.

    Bron: Emerce, 12 september 2016

     

     

  • European Union to Scrutinize Usage of Big Data by Large Internet Companies

    Competition Commissioner Margrethe VestagerThe European Union is considering whether the way large Internet companies, such as Alphabet Inc.’s Google or Facebook Inc., collect vast quantities of data is in breach of antitrust rules, the bloc’s competition chief said Sunday.

    “If a company’s use of data is so bad for competition that it outweighs the benefits, we may have to step in to restore a level playing field,” said Margrethe Vestager, European Commissioner for Competition, according to a text of her speech delivered at the Digital Life Design conference in Munich, Germany.

    “We continue to look carefully at this issue,” she said, adding that while no competition problems have yet been found in this area, “this certainly doesn’t mean we never will” find them in the future.

    Her comments highlight the increased focus that regulators give to the use of so-called big data—large sets of personal information that are increasingly important for digital businesses, even though people generally hand over the information voluntarily when they use free services.

    The data can help firms target ways to make business operations more efficient. Companies increasingly are also collecting more data as a greater range of devices—from fitness trackers, smoke detectors to home-heating meters—are being connected to the Web, a phenomenon known as the “Internet of Things.”

    “But if just a few companies control the data you need to satisfy customers and cut costs, that could give them the power to drive their rivals out of the market,” Ms. Vestager said.

    The concern is that huge data sets compiled by large Internet firms could give these companies an unfair advantage by essentially erecting barriers to new competition, some experts say. Incumbent firms would amass detailed profiles of their consumers that would allow them to target advertising with precision, while new rivals could find themselves too far behind to compete.

    This isn’t the first time Ms. Vestager has expressed interest into how companies use big data. On Sunday, she laid out some details about how the European Commission is looking into the issue.

    Ms. Vestager said the commission would be careful to differentiate between different types of data, since some forms of information can become obsolete quickly, making concerns of market dominance moot.

    She also said the EU would look into why some companies can’t acquire information that is as useful as the data that other competing firms have.

    “What’s to stop them [companies] from collecting the same data from their customers, or buying it from a data-analytics company?” she said.

    Lawyers representing U.S. tech firms have said previously that competition concerns over data are misguided. They said data isn’t exclusive since many different companies can hold the same information on people’s names, addresses and credit-card details, for example. It is also easy for consumers to switch between platforms, they said.

    As for how companies protect their consumers’ data, Ms. Vestager said that was beyond her scope and pointed to the new EU-wide data-privacy rules agreed late last year.

    Ms. Vestager also said she would publish a preliminary report in the middle of the year, as the next formal step in an investigation into whether Internet commerce companies, such as Amazon.com Inc., are violating antitrust rules by restricting cross-border trade.

    “With so much at stake, we need to act quickly when we discover problems,” she said, in reference to business contracts that aim to keep national markets separate.

    To start that debate, the commissioner said she would publish a paper before Easter outlining the views of relevant parties affected or involved in geo-blocking, a practice to discriminate via price or the range of goods a company offers based on a customer’s location.

    The commission in September launched a public questionnaire to gather more information about the practice of geo-blocking.

    Source: The Wall Street Journal

  • Forrester’s Top Trends For Customer Service In 2016

    It’s a no-brainer that good customer service experiences boost satisfaction, loyalty, and can influence top line revenue. Good service — whether it’s to answer a customer’s question prior to purchase, or help a customer resolve an issue post-purchase should be easy, effective, and strive to create an emotional bond between the customer and the company. Here are 5 top trends – out of a total of 10 – that I am keeping my eye on. A full report highlighting all trends can be found here:

    Trend 1: Companies Will Make Self Service Easier. In 2015, we found that web and mobile self-service interactions exceeded interactions over live-assist channels, which are increasingly used by customers as escalation paths to answer harder questions whose answers they can’t find online. In 2016, customer service organizations will make self-service easier for customers to use by shoring up its foundations and solidifying their knowledge-management strategy. They will start to explore virtual agents and communities to extend the reach of curated content. They will start embedding knowledge into devices — like Xerox does with its printers — or delivering it via wearables to a remote service technician.

    Trend 2: Field Service Will Empower Customers To Control Their Time. 73% of consumers say that valuing their time is the most important thing a company can do to provide them with good service — whether on a call, in a chat, or while waiting for a service technician to troubleshoot and fix their product. In 2016, customer service organizations will better support customer journeys that start with an agent-assisted service interaction and end with a service call. They will explore lighter-weight field service management capabilities, which give customers self-service appointment management capabilities and allow agents to efficiently dispatch technicians and optimize their schedules.

    Trend 3: Prescriptive Advice Will Power Offers, Decisions, And Connections. Decisioning — automatically deciding a customer’s or system’s next action — is starting to be heavily leveraged in customer service. In 2016, organizations will use analytics in a much more prescriptive manner – for example to prescribe the right set of steps for customers or agents to more effectively service customers; to correlate online behavior with requests for service and prescribe changes to agent schedules and forecasts. Analytics will be used to better route a customer to an agent who can most effectively answer a question based on skills and behavior data, or to better understand customer call patterns and preempt future calls.

    Trend 4: Insights From Connected Devices Will Trigger Preemptive Service and Turn Companies Into Services-Based Ones. Companies use support automation to preemptively diagnose and fix issues for connected devices. For example, Tesla Motors pushes software patches to connected cars. Nintendo monitors devices to understand customer actions right before the point of failure. In 2016, the Internet of Things (IoT) will continue to transform companies from being products-based to services-based . Examples abound where companies are starting to monitor the state of equipment via IoT, and realizing new streams of revenue because of their customer-centric focus. To make the business model of IoT work, companies must keep a close eye on emerging interoperability standards: device-to-network connectivity, data messaging formats that work under constrained network conditions, and data models to aggregate, connect with contact center solutions, and act on the data via triggers, alerts to service personnel or automated actions.

    Trend 5: The Customer Service Technology Ecosystem Will Consolidate. The customer service process involves complex software that falls into three main categories: queuing and routing technologies, customer relationship management (CRM) customer service technologies, and workforce optimization technologies. You need to use solutions from each of these three software categories, which you must integrate to deliver quality customer service. We believe that the combination of: 1) mature software categories in which vendors are struggling with growth opportunities; 2) the rise of robust software-as-a-service (SaaS) solutions in each category; 3) rising buyer frustration; and 4) the increasing importance of delivering simpler and smarter customer service makes for ripe conditions for further consolidation to happen in the marketplace, This consolidation will make it easier for buyers to support the end-to-end customer service experience with a single set of vendor solutions.

    Source: customer think

  • From traditional Business to Smart Big Data leader

    In this post I outline how US agricultural manufacturer John Deere has transformed itself from a traditional manufacturing company to a big data leader. The post was first published in my column for Data Science Central

    John Deere 2

    John Deere has always been a pioneering company. Its eponymous founder personally designed, built and sold some of the first commercial steel ploughs. These made the lives of settlers moving into the Midwest during the middle of the 19th century much easier and established the company as an American legend.

    Often at the forefront of innovation, it is no surprise that it has embraced Big Data enthusiastically – assisting pioneers with the taming of the virtual wild frontier just as it did with the real one.

    In recent years, it has focused efforts on providing Big Data and Internet of Thingssolutions to let farmers (and in the case of their industrial division with the black and yellow logo, builders) to make informed decisions based on real-time analysis of captured data.

    So in this post I want to take a look at some of John Deere’s innovations in the virtual realm, and how they are leading to change which is said to be “revolutionizing” the world of farming.

    Smart farms

    The world’s population is growing rapidly, which means there is always going to be an increasing demand for more food. With the idea of genetically modified food still not appealing to public appetites, increasing the efficiency of production of standard crops is key to this. To this end, John Deere has launched several Big Data-enabled services which let farmers benefit from crowdsourced, real-time monitoring of data collected from its thousands of users.

    They are designed by the company’s Intelligent Solutions Group, and the vision is that one day even large farms will be manageable by a small team of humans working alongside a fleet of robotic tools, all connected and communicating with each other.

    To this end, they are working on a suite of services to allow everything from land preparation to seeding, fertilizing and harvesting to be controlled from a central hub.

    The total land available can be split into sections and “Prescriptions” issued with precise instructions for seed density, depth and fertilization. These decisions are informed by Big Data – aggregated data from thousands of users feeding their own data back to the service for analysis.

    Crowd sourced agriculture

    Myjohndeere.com is an online portal which allows farmers to access data gathered from sensors attached to their own machinery as they work the fields, as well as aggregated data from other users around the world. It is also connected to external datasets including weather and financial data.

    These services allow farmers to make better informed decisions about how to use their equipment, where they will get the best results from, and what return on their investment they are providing.

    For example, fuel usage of different combines can be monitored and correlated with their productivity levels. By analyzing the data from thousands of farms, working with many different crops in many different conditions, it is possible to fine-tune operations for optimum levels of production.

    The system also helps to minimize downtime by predicting, based on crowdsourced data, when and where equipment is likely to fail. This data can be shared with engineers who will stand ready to supply new parts and service machinery as and when it is needed – cutting down on waste caused by expensive machinery sitting idle.

    Another service is Farmsight, launched in 2011. It allows farmers to make proactive decisions about what crops to plant where, based on information gathered in their own fields and those of other users. This is where the “prescriptions” can be assigned to individual fields, or sections of fields, and machinery remotely reprogrammed to alter their behavior according to the “best practice” suggested by the analytics.

    As well as increasing farmers’ profits and hopefully creating cheaper, more abundant food for the world, there are potential environmental gains, too.

    Pesticides and fertilizer can often cause pollution of air and waterways, so having more information on the precise levels needed for optimum production means that no more than is necessary will be used.

    Who owns your agricultural data?

    Of course, with all of this data being generated and shared – there is one question which needs answering – who owns it?

    Deere offers what it calls its Deere Open Data Platform, which lets farmers share data with each other (or choose not to, if they wish) and also with third party application developers, who use can the APIs to connect equipment by other manufacturers, or to offer their own data analysis services.

    But this has not stopped many farmers asking why they should effectively pay for their own data, and asking why John Deere and other companies providing similar services shouldn’t pay them – according to American Farm Bureau Federation director Mary Kay Thatcher.

    Talks are currently ongoing between the AFBF and companies including John Deere, Monsanto and DuPont over how these concerns should be addressed. As well as privacy worries, there are concerns that having too much information could allow traders in financial markets to manipulate prices.

    Farming is one of the fundamental activities which makes us human and distinguishes us from animals. Once we developed farms, we no longer needed to constantly be on the move in the pursuit of food and fertile foraging spots, leading to the development of towns, cities and civilization.

    The future of farming?

    With the development of automation and Big Data, we are starting to delegate those responsibilities to robots – not because farmers are lazy (they really aren’t, as anyone who lives in an area where agricultural activity goes on will tell you!) but because they can often do it better.

    Sure, John Deere’s vision of vast areas of farmland managed by a man sitting at a computer terminal with a small team of helpers will lead to less employment opportunities for humans working the land, but that has been the trend for at least the last century, regardless.

    And the potential for huge positive change– in a world facing overpopulation and insufficient production of food – particularly in the developing nations, is something that has the potential to benefit everyone on the planet.

    I hope you found this post interesting. I am always keen to hear your views on the topic and invite you to comment with any thoughts you might have.

     

    Author: Bernard Marr

     

  • Geopolitieke spanningen bedreigen digitale veiligheid Nederland

    Geopolitieke ontwikkelingen, zoals internationale conflicten of politieke gevoeligheden, hebben een grote invloed op de digitale veiligheid in Nederland. Dat stelt staatssecretaris Klaas Dijkhoff in een rapportage die hij gisteren naar de Tweede kamer zond.

    Het rapport 'Cyber Securitybeeld Nederland' (CSBN) laat zien dat de eerder al gesignaleerde trends doorzetten in 2015. Een aanpak waarbij publieke en private partijen nationaal en internationaal samenwerken om de cybersecurity te verbeteren, wordt dan ook noodzakelijk geacht. Staatssecretaris Dijkhoff laat weten dat hij tijdens het aanstaande EU-voorzitterschap van Nederland hier aandacht voor wil vragen bij andere Lidstaten: "Alleen als we samenwerken, kunnen we ons digitale leven beschermen tegen criminaliteit en spionage."

    Werkprogramma

    Tegelijk met het CSBN is de voortgang van het werkprogramma van de Nationale Cyber Security Strategie 2 (NCSS 2) naar de Kamer verzonden. Het NCSS2, gestart in 2013, heeft als doel de Nederlandse digitale weerbaarheid te verbeteren. Het werkprogramma zou 'op hoofdlijnen' op schema liggen, meldt Dijkhoff aan de Tweede Kamer.

    In zijn beleidsreactie benadrukt de Staatssecretaris dat publiek-private samenwerking is cruciaal is in de aanpak van cybercrime en digitale spionage. De snelle ontwikkeling van cyberdreigingen in combinatie met een geopolitieke omgeving die steeds stabiel wordt, vraagt om 'voortdurende aandacht'. Daarom wil Dijkhoff alle relevante publieke en private partijen betrekken bij het doorontwikkelen van de 'cybersecurity-visie'. Uitgangspunt daarbij zal zijn dat cybersecurity een balans is tussen vrijheid, veiligheid en economische groei.

    Alert Online

    Bewustwording over online veiligheid is een belangrijk onderdeel van het digitale veiligheidsbeleid van de overheid. Daarom wordt ook dit jaar weer de campagne 'Alert Online' gehouden. Deze campagne is een gezamenlijk initiatief van overheid, bedrijfsleven en wetenschap en vindt dit jaar plaats van 26 oktober tot 6 november 2015. Er wordt aandacht besteed aan cybercrime die mensen en bedrijven treft, zoals phishing en cryptoware. 

     

    Bron: Automatiseringsgids, 15 Otober 2015

     

  • Growth Stories: Change Everything

    mobile-uiInterview by Alastair Dryburgh

    What do you do with a small technology company which has an interesting product but is stuck in a crowded, noisy market where larger competitors have locked up many of the distribution channels? You could keep struggling on, or you could make a bold move; re-engineer the product to meet a different purpose for a different market. That's what Pentaho did, leading to 6-times growth over 5 years and a successful sale to a large organisation.

    In this interview their CEO Quentin Gallivan describes how they did it.

    Alastair Dryburgh: Quentin, welcome. This series is about that period of a company's evolution when it has to go through the rather dangerous territory that lies between being and exciting new start up and being an established profitable business. I'm told that you've got a very, very interesting story to tell about that with Pentaho. I'm looking forward to hearing that.

    Quentin Gallivan: Okay, great.

    Dryburgh: What would be useful would be if you could give us a very quick background sketch of Pentaho. What it does and how it's evolved in the last few years.

    Gallivan: So Pentaho, the company is approximately 12 years old. There were five founders, and they all came from a business intelligence technology background. What they were looking for was a different way to innovate around the business intelligence market place.

    One of the things I saw going on with that company was that the biggest challenge in companies doing data mining or predictive analytics on unstructured data or big data, was how do you get all this unstructured data, and unstructured data being clickstream data from websites, or weather data, or now what's very popular is machine data from Internet of Things devices.

    I wondered, is there a company out there that can actually make it easier to get all this different data into these big data analytical platforms? Because that was the biggest problem we had.

    When I looked at Pentaho, at the time it was not that company. It was not the new, sexy, next generation company, but I knew the venture capitalist behind Pentaho. We spent about a month just talking about what could the company be. Version one of the company was really a business analytics software product sold to the mid-market. They got some initial traction there, but that was a very cluttered market - very busy, a lot of noise, lots of large incumbents with channel dominance and then lots of small companies. It was hard to get above the din. I was not interested in Pentaho as the company was, right? I didn't see that as very interesting, very compelling.

    What interested me though, was when you dug deeper on the technology I thought it could be repurposed to address the big data problem. That was a big leap of faith, right? Because at the time, Pentaho wasn't doing any big data, didn't have any big data capabilities. The customers were all mid-market, small companies and it was known as a business intelligence company.

    Dryburgh: Pretty substantial change of vision really, isn't it?

    Gallivan: Massive, massive change, and I looked at it and I spoke to the VC's and said, "I would be interested in taking the CEO role, but not for the company that you've invested in, but for a very, very different company and I think we can do it. I don't know if we're going to do it. It's a long shot, but if you're willing to bankroll me, and allow me to build a team and support the vision, I'll give it a go."

    Dryburgh: Could I stop you there a moment to see if I could put a little bit of a frame around this? You've got a pretty fundamental change here.There's probably, very crudely, three different elements you've got to look after. First is obviously the technology and I guess that must have needed to evolve and develop. Then you've got what you might call the harder side of the organisational change, the strategy, definition of who the customer is, the organisation, the roles, the people you need, that's the second one. Then the third element which I think is particularly interesting is the softer side which is the culture. I'd be really interested to hear which of those was the biggest issue for you?

    Gallivan: That's a great question. I like the way you framed it, I would add a fourth dimension, which is the market perception of you. How do you get people to stop thinking about you as Open Source BI company for small and medium size businesses and think about you as leading, big data analytics platform for a large companies, for the large enterprise. Those are the four vectors that we needed to cross that chasm.

    The hardest one was not the culture because at the time, the company was very small. It had 75 employees and we are going to be over 500 employees this year, right? At the time it was really an open book from a culture... The founders were very open to a change in the business. For most startups, less than 100 employees, the culture is generally driven by the founder or founders and so there was no resistance.

    Dryburgh: Okay, good. So what were the biggest things you had to do to make the transformation work?

    Gallivan: If you look at those, just think about the transformation in those four key areas, you look at the metrics. Five years ago we were known as a commercial open source BI company selling to midsize companies. What we wanted to do was to be known as a big data analytics company selling to large enterprises because for big data that's where the dollars are being spent right now.. What we did was we set down the mission, we set down the strategy and then the other piece, and this is sort of from my GE days when it comes to strategic execution, that we employed was you've got to have metrics that drive milestones in the journey.

    What we started to do was we tracked what percentage of our business came from mid-sized small companies versus large. Five years ago 0% came from large. Last quarter it was 75%. Then over this journey we would track that percentage of our business that came from these larger enterprises. The other thing we would track was in that fourth vector, the brand. How do you change the brand from being known as an open source BI company to being known as a big data analytics company? There we had again, at the best marketing organisation I've ever worked with that had a share of a voice metric. Not a feel-good, hey we had so many press releases, but a quantifiable metric about our brand that we tracked four years ago and it was what position do we play and what share of voice do we have when people talk about big data versus non big data.

    That was where our marketing team was very aggressive and had these metrics. When we first started out, since we launched ourselves as a big data analytics company we had a pretty good penetration in terms of the brand, but over the last couple years we've been tracking, we've been number one or two versus our competitors as the most identifiable brand in big data. That's a metric we track every month. Very, very quantifiable, but it's part of the journey. It took us a while to get there.

    Then the other piece, the other key metric for us is really the R and D investment and that was, we basically had to transform or re-engineer the project to really meet the needs of the large enterprise from a security standpoint, a scalability standpoint. Making sure that we integrate with all the key technologies that the large enterprise have and so that was again, when we did prioritization around out R and D we would prioritize and we'd have metrics around large enterprise and then we would sacrifice the needs of the small/medium in the product road map. That again was an evolution.

    Five years ago 10% of our R and D investment went into large enterprise features. Now that's the majority, it's something didn't happen overnight but we tracked and we shared with the company and sort of made it work.

  • Harnessing the value of Big Data

    big dataTo stay competitive and grow in today’s market, it becomes necessary for organizations to closely correlate both internal and external data, and draw meaningful insights out of it.

    During the last decade a tremendous amount of data has been produced by internal and external sources in the form of structured, semi-structured and unstructured data. These are large quantities of human or machine generated data produced by heterogeneous sources like social media, field devices, call centers, enterprise applications, point of sale etc., in the form of text, image, video, PDF and more.

    The “Volume”, “Varity” and “Velocity” of data have posed a big challenge to the enterprise. The evolution of “Big Data” technology has been a boon to the enterprise towards effective management of large volumes of structured and unstructured data. Big data analytics is expected to correlate this data and draw meaningful insights out of it.

    However, it has been seen that, a siloed big data initiative has failed to provide ROI to the enterprise. A large volume of unstructured data can be more a burden than a benefit. That is the reason that several organizations struggle to turn data into dollars.

    On the other hand, an immature MDM program limits an organization’s ability to extract meaningful insights from big data. It is therefore of utmost importance for the organization to improve the maturity of the MDM program to harness the value of big data.

    MDM helps towards the effective management of master information coming from big data sources, by standardizing and storing in a central repository that is accessible to business units.

    MDM and Big Data are closely coupled applications complementing each other. There are many ways in which MDM can enhance big data applications, and vice versa. These two types of data pertain to the context offered by big data and the trust provided by master data.

    MDM and big data – A matched pair

    At first hand, it appears that MDM and big data are two mutually exclusive systems with a degree of mismatch. Enterprise MDM initiative is all about solving business issues and improving data trustworthiness through the effective and seamless integration of master information with business processes. Its intent is to create a central trusted repository of structured master information accessible by enterprise applications.

    The big data system deals with large volumes of data coming in unstructured or semi-structured format from heterogeneous sources like social media, field devises, log files and machine generated data.  The big data initiative is intended to support specific analytics tasks within a given span of time after that it is taken down. In Figure 1 we see the characteristics of MDM and big data.  

     

    MDM

    Big Data

    Business Objective

      Provides a single version of trust of Master and Reference information.

      Acts as a system of record / system of reference for enterprise.

      Provides cutting edge analytics and offer a competitive advantage

    Volume of Data and Growth

      Deals with Master Data sets which are smaller in volume

      Grow with relatively slower rate.

      Deal with enormous large volumes of data, so large that current databases struggle to handle it.

      The growth of Big Data is very fast.

    Nature of Data

      Permanent and long lasting

      Ephemeral in nature; disposable if not useful.

    Types of Data (Structure and Data Model)

      It is more towards containing structured data in a definite format with a pre-defined data model.

      Majority of Big Data is either semi-structured or unstructured, lacking in a fixed data model.

    Source of Data

      Oriented around internal enterprise centric data.

      Platform to integrate the data coming from multiple internal and external sources including social media, cloud, mobile, machine generated data etc.

    Orientation

      Supports both analytical and operational environment.

      Fully analytical oriented

    Despite apparent differences there are many ways in which MDM and big data complement each other.

    Big data offers context to MDM

    Big data can act as an external source of master information for the MDM hub and can help enrich internal Master Data in the context of the external world.  MDM can help aggregate the required and useful information coming from big data sources with  internal master records.

    An aggregated view and profile of master information can help  link the customer correctly and in turn help perform effective analytics and campaign. MDM can act as a hub between the system of records and system of engagement.

    However, not all data coming from big data sources will be relevant for MDM. There should be a mechanism to process the unstructured data and distinguish the relevant master information and the associated context. NoSQL offering, Natural Language Processing, and other semantic technologies can be leveraged towards distilling the relevant master information from a pool of unstructured/semi-structured data.

    MDM offers trust to big data

    MDM brings a single integrated view of master and reference information with unique representations for an enterprise. An organization can leverage MDM system to gauge the trustworthiness of data coming from big data sources.

    Dimensional data residing in the MDM system can be leveraged towards linking the facts of big data. Another way is to leverage the MDM data model backbone (optimized for entity resolution) and governance processes to bind big data facts.

    The other MDM processes like data cleansing, standardization, matching and duplicate suspect processing can be additionally leveraged towards increasing the uniqueness and trustworthiness of big data.

    MDM system can support big data by:

    • Holding the “attribute level” data coming from big data sources e.g. social media Ids, alias, device Id, IP address etc.
    • Maintaining the code and mapping of reference information. 
    • Extracting and maintaining the context of transactional data like comments, remarks, conversations, social profile and status etc. 
    • Facilitating entity resolution.
    • Maintaining unique, cleansed golden master records
    • Managing the hierarchies and structure of the information along with linkages and traceability. E.g. linkages of existing customer with his/her Facebook id linked-in Id, blog alias etc.
    • MDM for big data analytics – Key considerations

    Traditional MDM implementation, in many cases, is not sufficient to accommodate big data sources. There is a need for the next generation MDM system to incorporate master information coming from big data systems. An organization needs to take the following points into consideration while defining Next Gen MDM for big data:

    Redefine information strategy and topology

    The overall information strategy needs to get reviewed and redefined in the context of big data and MDM. The impact of changes in topology needs to get accessed thoroughly. It is necessary to define the linkages between these two systems (MDM and big data), and how they operate with internal and external data. For example, the data coming from social media needs to get linked with internal customer and prospect data to provide an integrated view at the enterprise level.

    Information strategy should address following:

    Integration point between MDM and big data - How big data and MDM systems are going to interact with each other.
    Management of master data from different sources - How the master data from internal and external sources is going to be managed.
     Definition and classification of master data - How the master data coming from big data sources gets defined and classified.
    Process of unstructured and semi-structured master data - How master data from big data sources in the form of unstructured and semi-structured data is going to be processed.
    Usage of master data - How the MDM environment are going to support big data analytics and other enterprise applications.

    Revise data architecture and strategy

    The overall data architecture and strategy needs to be revised to accommodate changes with respect to the big data. The MDM data model needs to get enhanced to accommodate big data specific master attributes. For example the data model should accommodate social media and / or IoT specific attributes such as social media Ids, aliases, contacts, preferences, hierarchies, device Ids, device locations, on-off period etc. Data strategy should get defined towards effective storage and management of internal and external master data.

    The revised data architecture strategy should ensure that:

    • The MDM data model accommodates all big data specific master attributes
    • The local and global master data attributes should get classified and managed as per the business needs
    • The data model should have necessary provision to interlink the external (big data specifics) and internal master data elements. The necessary provisions should be made to accommodate code tables and reference data.

     Define advanced data governance and stewardship

     A significant amount of challenges are associated towards governing Master Data coming from big data sources because of the unstructured nature and data flowing from various external sources. The organization needs to define advance policy, processes and stewardship structure that enable big data specifics governance.

    Data governance process for MDM should ensure that:

    Right level of data security, privacy and confidentiality to be maintained for customer and other confidential master data.
    Right level of data integrity to be maintained between internal master data and master data from big data sources. 
    Right level of linkages between reference data and master data to exist.
    Policies and processes need to be redefined/enhanced to support big data and related business transformation rules and control access for data sharing and distribution, establishing the ongoing monitoring and measurement mechanisms and change.
    A dedicated group of big data stewards available for master data review, monitoring and conflict management.

    Enhance integration architecture

     The data integration architecture needs to be enhanced to accommodate the master data coming from big data sources. The MDM hub should have the right level of integration capabilities to integrate with big data using Ids, reference keys and other unique identifiers.

    The unstructured, semi-structured and multi-structured data will get parsed using big data parser in the form of logical data objects. This data will get processed further, matched, merged and get loaded with the appropriate master information to the MDM hub.

    The enhanced integration architecture should ensure that:

    The MDM environment has the ability to parse, transform and integrate the data coming from the big data platform.
    The MDM environment has the intelligence built to analyze the relevance of master data coming from big data environment, and accept or reject accordingly.

    Enhance match and merge engine

     MDM system should enhance the “Match & Merge” engine so that master information coming from big data sources can correctly be identified and integrated into the MDM hub. A blend of probabilistic and deterministic matching algorithm can be adopted.

    For example, the successful identification of the social profile of existing customers and making it interlinked with existing data in the MDM hub. The context of data quality will be more around the information utility for the consumer of the data than objective “quality”.

    The enhanced match and merge engine should ensure that:

    • The master data coming from big data sources get effectively matched with internal data residing in the MDM Hub.
    • The “Duplicate Suspect” master records get identified and processed effectively.
    • The engine should recommend the “Accept”, “Reject”, “Merge” or “Split” of the master records coming from big data sources.

     

    In this competitive era, organizations are striving hard to retain their customers.  It is of utmost importance for an enterprise to keep a global view of customers and understand their needs, preferences and expectations.

    Big data analytics coupled with MDM backbone is going to offer the cutting edge advantage to enterprise towards managing the customer-centric functions and increasing profitability. However, the pairing of MDM and big data is not free of complications. The enterprise needs to work diligently on the interface points so to best harness these two technologies.

    Traditional MDM systems needs to get enhanced to accommodate the information coming from big data sources, and draw a meaningful context. The big data system should leverage MDM backbone to interlink data and draw meaningful insights.

    Bron: Information Management, 2017, Sunjay Kumar

  • Het kabinet gaat inzetten op Big Data

    free-vector-wetenschappelijke-raad-voor-het-regeringsbeleid 028926 wetenschappelijke-raad-voor-het-regeringsbeleidZo blijkt uit de kabinetsreactie op het eerder dit jaar verschenen rapport “Big Data in een vrije en veilige samenleving” van de Wetenschappelijke Raad voor het Regeringsbeleid (“WRR”). In mei 2014 diende de regering een adviesaanvraag over het thema ‘Big Data, privacy en veiligheid’ bij het WRR in. Volgens het WRR ligt het zwaartepunt in de huidige regelgeving te zeer bij het reguleren van het verzamelen en delen van data. De WRR adviseert daarom ook de regulering aan te vullen met toezicht op fases van analyses en het gebruik van Big Data.
     
    In het rapport wordt ingegaan op Big Data-analyses door het ‘veiligheidsdomein’ (zijnde politie, justitie, inlichtingen- en veiligheidsdiensten en organisaties en samenwerkingsverbanden omtrent fraudebestrijding.) In deze sector liggen veel mogelijkheden. Echter vragen deze mogelijkheden om corresponderende waarborgen voor de vrijheidsrechten van burgers. Enkele Big Data-mogelijkheden die het rapport noemt zijn het reconstrueren van aanslagen, het in kaart brengen van terroristische netwerken, het realtime volgen van ontwikkelingen in crisissituaties en crowd control bij evenementen. Ook benoemt het rapport de vervaging van de grens tussen data uit publieke en private bronnen, zoals collega Micha Schimmel al beschreef in een blog.
     
    Het kabinet spreekt het voornemen uit te onderzoeken of de wettelijke basis omtrent data-analyses versteviging nodig heeft. Zij onderzoekt tevens welke waarborgen daarbij gehanteerd moeten worden, waarbij het vergroten van transparantie het aandachtspunt is. Met ‘transparantie’ bedoelt het kabinet; inzicht in informatie aan burgers over aangewende bestanden, toelaatbare foutmarges, gehanteerde logica en doeleinden van de analyses. Het doel hiervan is het waarborgen van deugdelijke besluitvorming waar Big Data aan ten grondslag ligt.
     
    Op dit moment is er een verbod voor besluitvorming zonder menselijke tussenkomst, waarbij geldt dat dit niet is toegestaan indien er een aanmerkelijke impact is voor burgers (artikel 42 Wbp). De situatie waarin een computerprogramma persoonsgegevens analyseert en op basis daarvan een verzoek van een betrokkene afwijst, is hierdoor bijvoorbeeld niet toegestaan.
     
    Het kabinet gaat onderzoeken of situaties waarbij de menselijke goedkeuring van Big Data-analyses buiten dit verbod valt. Eveneens gaat onderzocht worden hoe voldoende inzicht in analysemethoden gegeven kan worden zodat rechters betere afwegingen kunnen maken bij geschillen.
     
    “Analyse en gebruik vormen het hart van Big Data-processen en dat mag niet oncontroleerbaar verlopen: er moet onafhankelijk toezicht zijn op algoritmen en profielen – en dat vereist bevoegdheden, middelen en expertise voor toezichthouders.” Concludeert de WRR.
     
    De Minister van Veiligheid en Justitie noemde in zijn toespraak bij ontvangst van het rapport een aanstaande mogelijkheid om de strafmaat van daders in de Amerikaanse staat Pennsylvania te baseren op te verwachten vergrijpen in de toekomst. Hoewel hij dit afdeed als dystopische  praktijken, liet hij wel duidelijk merken dat Big Data de toekomst is en wij niet moeten kijken naar de nadelen maar ons juist moeten focussen op de voordelen. De toekomst zal ons leren wat voor impact de toepassingen van Big Data op onze samenleving hebben.
     
    source: solv.nl, 5 december 2016
  • Hoe groot is ‘the next big thing’?

    iotWat als IoT gewoon een overkoepelende term zou zijn voor manieren om iets bruikbaars te maken uit machine-gegenereerde data? Bijvoorbeeld, een bus vertelt mijn telefoon hoe ver mijn bushalte is en mijn fietsverhuur vertelt me ​​hoeveel fietsen beschikbaar zijn?

    In 2014 vroeg IDC 400 C-suite professionals wat volgens hen IoT was. De antwoorden varieerden van soorten apparaten (thermostaten, auto's, home security-systemen) tot uitdagingen (beveiliging, data management, connectiviteit). Dezelfde analist benadrukt ook dat de wereldwijde markt voor IoT oplossingen zal groeien van 1,9 biljoen in 2013 tot 7,1 biljoen dollar in 2020. Dit optimisme wordt ondersteund door Gartner’s inschatting: 4,9 miljard gekoppelde 'dingen' zullen in 2016 in gebruik zijn. In 2020 zullen dat er 25 miljard zijn.
    Met andere woorden: IoT is zeer divers en het potentieel is enorm. De waarde ligt niet alleen in de kosten van de sensoren. Het is veel meer dan dat.

    Wanneer IoT begint te vertellen
    Het IoT is niet iets dat op zichzelf staat. Het rijpt naast big data. Het uitrusten van miljarden objecten met sensoren is van beperkte waarde als het niet mogelijk is miljarden datastromen te genereren, verzenden, opslaan en te analyseren.
    De datawetenschapper is de menselijke choreograaf van dit IoT. Zij zijn essentieel voor het identificeren van de waarde van de enorme hoeveelheid data die al deze apparaten genereren. En dat is de reden waarom connectiviteit en opslag zo belangrijk zijn. Kleine geïsoleerde apparaten zonder opslag en weinig rekenkracht vertellen ons weinig. Alleen door naar grote verzamelingen data te kijken kunnen we correlaties ontdekken en wordt het mogelijk trends te herkennen en voorspellingen te doen.
    In elke zakelijke omgeving, is het scenario identiek: de CxO zal de informatie die er vandaag is bekijken ten opzichte van informatie die er was in het verleden om een voorspelbaar inzicht te krijgen in wat er gaat gebeuren in de toekomst.

    Sneller inzicht leidt tot concurrentievoordeel
    CxO’s willen tegenwoordig een ander soort bedrijf. Ze willen dat het in een snel tempo opereert en reageert op de markt, maar ze willen ook beslissingen nemen op basis van intelligentie verzameld via big data. En ze willen de beste producten maken, gebaseerd op klantinzicht. Bedrijven zijn op zoek naar een disruptief business model waardoor ze steeds meer in kunnen spelen op trends in de markt en daarmee een voorsprong hebben op de concurrentie.

    Start-up gedrag
    Het antwoord ligt in de volgende vraag aan bedrijven: "Waarom kunnen ondernemingen zich niet meer als start-ups gedragen?" Dit gaat niet over het maken van overhaaste beslissingen met weinig of geen overzicht. Het gaat over het aannemen van een slank business model dat onzekerheid en uitgerekte budgetten tolereert. En nog belangrijker, het gaat over hoe het management van het bedrijf een cultuur van slagvaardigheid neerzet.
    De organisaties die zullen winnen in het big data spel zijn niet degenen die de meeste of de beste toegang ertoe hebben. De winnaars omschrijven duidelijk hun doelen, zetten de nodige operationele grenzen en stellen vast wat de uitrusting is die nodig is om de klus te klaren.

    Leidende rol CIO's
    CxO’s hebben de zakelijke waarde van IT erkend, en willen dat CIO's meer een leidende rol nemen en in kaart brengen wat de toekomst is van het bedrijf. IT kan een enorme rol spelen in de bouw van die toekomst door samen te werken met de business en de tools te verschaffen die nodig zijn om productief te zijn. Technologie kan voortdurende innovatie op elk niveau vergemakkelijken, waardoor het bedrijf niet alleen kan overleven maar floreren.
    Het is niet niks om deze wens van bedrijven te bereiken. Maar samenwerken met technologie maakt het veel haalbaarder omdat het bedrijven in staat stelt tot een wendbare, innovatieve, data-gedreven toekomst te komen.

    Source: ManagersOnline

  • Hoe waarde creatie met predictive analysis en datamining

    De groeiende hoeveelheid data brengt een stortvloed aan vragen met zich mee. De hoofdvraag is wat we met die data kunnen Data miningen betere diensten aangeboden kunnen worden en risico’s vermeden? Helaas blijft bij de meeste bedrijven die vraag onbeantwoord. Hoe kunnen bedrijven waarde aan data toevoegen en overgaan tot predictive analytics, machine learning en decision management?

    Predictive analytics: de glazen bol voor de business

    Via data mining worden verborgen patronen in gegevens zichtbaar waardoor de toekomst voorspeld kan worden. Bedrijven, wetenschappers en overheden gebruiken al tientallen jaren dit soort methoden om vanuit data inzichten voor toekomstige situaties te verkrijgen. Moderne bedrijven gebruiken data data mining en predictive analytics om onder andere fraude op te sporen, cybersecurity te voorkomen en voorraadbeheer te optimaliseren. Dankzij een iteratief analytisch proces brengen zij data, verkenning van de data en de inzet van de nieuwe inzichten uit de data samen.

    Data mining: business in de lead

    Decision management zorgt dat deze inzichten worden omgezet in acties in het operationele proces. De vraag is hoe dit proces binnen een bedrijf vorm te geven. Het begint altijd bij een vraag vanuit de business en eindigt bij een evaluatie van de acties. Hoe deze Analytical Life Cycle eruit ziet en welke vragen relevant zijn per branche, leest u in de Data Mining From A to Z: How to Discover Insights and Drive Better Opportunities.

     

    Naast dit model waarin duidelijk wordt hoe uw bedrijf dit proces kan inzetten, wordt dieper ingegaan op de rol van data mining in het stadium van onderzoek. Door dit verder uit te diepen via het onderstaande stappenplan kan nog meer waarde uit data worden gehaald.

    1. Business-vraag omvormen tot een analytische hypothese

    2. Data gereedmaken voor data mining

    3. Data verkennen

    4. Data in een model plaatsen

    Wilt u weten hoe uw bedrijf ook data in kan zetten om de vragen van morgen te kunnen beantwoorden en een betere service kan verlenen? Download dan “Data Mining From A to Z: How to Discover Insights and Drive Better Opportunities.”

  • How a Video Game Helped People Make Better Decisions

     

    oct15 14 games aResearchers in recent years have exhaustively catalogued and chronicled the biases that affect our decisions. We all know the havoc that biased decisions can wreak. From misguided beliefs about the side effects of vaccinating our children, to failures in analysis by our intelligence community, biases in decision making contribute to problems in business, public policy, medicine, law, education, and private life.

    Researchers have also long searched for ways to train people to reduce bias and improve their general decision making ability – with little success. Traditional training, designed to debias and improve decision-making, is effective in specific domains such as firefighting, chess, or weather forecasting. But even experts in such areas fail to apply what they’ve learned to new areas. Weather forecasters, for instance, are highly accurate when predicting the chance of rain, but they are just as likely as untrained novices to show bias when making other kinds of probability estimates, such as estimating how many of their answers to basic trivia questions are correct.

    Because training designed to improve general decision making abilities has not previously been effective, most efforts to debias people have focused on two techniques. The first is changing the incentives that influence a decision. Taxing soda, for example, in the hopes that the increased cost will dissuade people from buying it. The second approach involves changing the way information for various choices is presented or choices are made, such as adding calorie information to fast-food menus or offering salad as the default side order to entrées instead of French fries. However, these methods are often not always effective, and when effective, only affect specific decisions, not decision-makers’ ability to make less biased decisions in other situations.

    My research collaborators and I wondered if an interactive training exercise might effectively debias decision-makers. (The team included Boston University’s Haewon Yoon, City University London’s Irene Scopelliti, Leidos’ Carl W. Symborski, Creative Technologies, Inc.’s James H. Korris and Karim Kassam, a former assistant professor at Carnegie Mellon University.) So we spent the past four years developing two interactive, “serious” computer games to see if they might substantially reduce game players’ susceptibility to cognitive bias.

    There was scant evidence that this kind of one-shot training intervention could be effective, and we thought our chances of success were slim. But, as we report in a paper just published in Policy Insights in the Behavioral and Brain Sciences,the interactive games not only reduced game players’ susceptibility to biases immediately, those reductions persisted for several weeks. Participants who played one of our games, each of which took about 60 minutes to complete, showed a large immediate reduction in their commission of the biases (by more than 31%), and showed a large reduction (by more than 23%) at least two months later.

    The games target six well-known cognitive biases. Though these biases were chosen for their relevance to intelligence analysis, they affect all kinds of decisions made by professionals in business, policy, medicine, and education as well. They include:

    • Bias blind spot – seeing yourself as less susceptible to biases than other people
    • Confirmation bias – collecting and evaluating evidence that confirms the theory you are testing
    • Fundamental attribution error – unduly attributing someone’s behavior to enduring aspects of that person’s disposition rather than to the circumstance in which the person was placed
    • Anchoring – relying too heavily on the first piece of information considered when making a judgment
    • Projection – assuming that other people think the same way we do
    • Representativeness – relying on some simple and often misleading rules when estimating the probability of uncertain events

    We ran two experiments. In the first experiment, involving 243 adult participants, one group watched a 30-minute video, “Unbiasing Your Biases,” commissioned by the program sponsor, the Intelligence Advanced Research Projects Activity (IARPA), a U.S. research agency under the Director of National Intelligence. The video first defined heuristics – information-processing shortcuts that produce fast and efficient, though not necessarily accurate, decisions. The video then explained how heuristics can sometimes lead to incorrect inferences. Then, bias blind spot, confirmation bias, and fundamental attribution error were described and strategies to mitigate them were presented.

    Another group played a computer game, “Missing: The Pursuit of Terry Hughes,” designed by our research team to elicit and mitigate the same three cognitive biases. Game players make decisions and judgments throughout the game as they search for Terry Hughes – their missing neighbor. At the end of each level of the game, participants received personalized feedback about how biased they were during game play. They were given a chance to practice and they were taught strategies to reduce their propensity to commit each of the biases.

    We measured how much each participant committed the three biases before and after the game or the video. In the first experiment, both the game and the video were effective, but the game was more effective than the video. Playing the game reduced the three biases by about 46% immediately and 35% over the long term. Watching the video reduced the three biases by about 19% immediately and 20% over the long term.

    In a second experiment, involving 238 adult participants, one group watched the video “Unbiasing Your Biases 2” to address anchoring, projection, and representativeness. Another group played the computer detective game“Missing: The Final Secret,” in which they were to exonerate their employer of a criminal charge and uncover criminal activity of her accusers. Along the way, players made decisions that tested their propensity to commit anchoring, projection, and representativeness. After each level of the game, their commission of those biases was measured and players were provided with personalized feedback, practice, and mitigation strategies.

    Again, the game was more effective than the video. Playing the game reduced the three biases by about 32% immediately and 24% over the long term. Watching the video reduced the three biases by about 25% immediately and 19% over the long term.

    The games, which were specifically designed to debias intelligence analysts, are being deployed in training academies in the U.S. intelligence services. But because this approach affects the decision maker rather than specific decisions, such games can be effective in many contexts and decisions – and with lasting effect. (A commercial version of the games is in production.)

    Games are also attractive because once such approaches are developed, the marginal costs of debiasing many additional people are minimal. As this and other recent work suggests, such interactive training is a promising addition to the growing suite of techniques that improve judgment and reduce the costly mistakes that result from biased decision making.

    Source: http://www.scoop.it/t/strategy-and-competitive-intelligencebig

     

  • How Big Data is changing the business landscape

    jpgBig Data is increasingly being used by prominent companies to outpace the competition. Be it established companies or start-ups, they are embracing data-focussed strategies to outpace the competition.

    In healthcare, clinical data can be reviewed treatment decisions based on big data algorithms that work on aggregate individual data sets to detect nuances in subpopulations that are so rare that they are not readily apparent in small samples.

    Banking and retail have been early adopters of Big Data-based strategies. Increasingly, other industries are utilizing Big Data like that from sensors embedded in their products to determine how they are actually used in the real world.

    Big Data is useful not just for its scale but also for its real-time and high-frequency nature that enables real-time testing of business strategies. While creating new growth opportunities for existing companies, it is also creating entirely new categories of companies that capture and analyse industry data about products and services, buyers and suppliers, consumer preferences and intent.

     

    What can Big Data analytics do for you?

    *Optimise Operations

    The advent of advanced analytics, coupled with high-end computing hardware, has made it possible for organizations to analyse data more comprehensively and frequently.

    Analytics can help organisations answer new questions about business operations and advance decision-making, mitigate risks and uncover insights that may prove to be valuable to the organisation. Most organisations are sitting upon heaps of transactional data. Increasingly, they are discovering and developing the capability to collect and utilise this mass of data to conduct controlled experiments to make better management decisions.

    * React faster

    Big Data analytics allows organisations to make and execute better business decisions in very little time. Big Data and analytics tools allow users to work with data without going through complicated technical steps. This kind of abstraction allows data to be mined for specific purposes.

    * Improve the quality of services

    Big Data analytics leads to generation of real business value by combining analysis, data and processing. The ability to include more data, run deeper analysis on it and deliver faster answers has the potential to improve services. Big Data allows ever-narrower segmentation of customers and, therefore, much more precisely tailored products or services. Big Data analytics helps organizations capitalize on a wider array of new data sources, capture data in flight, analyse all the data instead of sample subsets, apply more sophisticated analytics to it and get answers in minutes that formerly took hours or days.

    * Deliver relevant, focussed customer communications

    Mobile technologies tracks can now track where customers are at any point of time, if they're surfing mobile websites and what they're looking at or buying. Marketers can now serve customised messaging to their customers. They can also inform just a sample of people who responded to an ad in the past or run test strategies on a small sample.

    Where is the gap?

    Data is more than merely figures in a database. Data in the form of text, audio and video files can deliver valuable insights when analysed with the right tools. Much of this happens using natural language processing tools, which are vital to text mining, sentiment analysis, clinical language and name entity recognition efforts. As Big Data analytics tools continue to mature, more and more organisations are realizing the competitive advantage of being a data-driven enterprise.

    Social media sites have identified opportunities to generate revenue from the data they collect by selling ads based on an individual user's interests. This lets companies target specific sets of individuals that fit an ideal client or prospect profile. The breakthrough technology of our time is undeniably Big Data and building a data science and analytics capability is imperative for every enterprise.

    A successful Big Data initiative, then, can require a significant cultural transformation in an organisation. In addition to building the right infrastructure, recruiting the right talent ranks among the most important investments an organization can make in its Big Data initiative. Having the right people in place will ensure that the right questions are asked - and that the right insights are extracted from the data that's available. Data professionals are in short supply and are being quickly snapped up by top firms.

    Source: The Economic Times

  • How big data is having a 'mind-blowing' impact on medicine

    istock000016682100doubleDell Services chief medical officer Dr. Nick van Terheyden explains the 'mind blowing' impact big data is having on the healthcare sector in both developing and developed countries.

    For a long time, doctors have been able to diagnose people with diabetes—one of the world's fastest growing chronic diseases—by testing a patient's insulin levels and looking at other common symptoms, as well as laboratory results.

    While there has been great accuracy in their diagnoses in the past, the real opportunity in healthcare at the moment, according to Dell Services chief medical officer Dr. Nick van Terheyden, is the role big data can play in taking the accuracy of that diagnosis a step further by examining a person's microbiome, which changes as people develop diabetes.

    "We can come up with a definitive diagnosis and say you have it based on these criteria. But now, interestingly, that starts to open up opportunities to say 'could you treat that?'" Terheyden said.

    He described these new advancements as "mind-blowing."

    "So, there is now the potential to say 'I happen to know you're developing diabetes, but I'm going to give you therapy that changes your biome and reverses that process, and to me that's just mind-blowing as I continue to see these examples," Terheyden said.

    He pinned a major contributor to the "explosion" of data to genomics, saying having additional data will increase the opportunity for clinicians to identify correlations that have previously been poorly understood or gone unnoticed, and improve the development and understanding of causation.

    "When the first human was sequenced back in the early 2000s, it was billions of dollars, and many years and multiple peoples' work and effort. We're now down to sequencing people in under 24 hours and for essentially less than US$1,000. That creates this enormous block of data that we can now look at," he said.

    Increasingly, Terheyden believes the healthcare sector will see the entry of data experts, who will be there to help and support clinicians with the growing influx of the need to analyse data.

    When asked about the impact technology has had on healthcare in developing countries, Terheyden said he believes medical advances will overtake the pace of developed countries, much like how the uptake of telephonic communication has "leapfrogged" in those countries.

    He said despite the lack of resources in Africa, for instance, the uptake of mobile devices is strong and networks are everywhere, which he says is having a knock-on effect on the medical sector as it is helping those living in remote areas gain access to clinicians through telehealth.

    Research by Ericsson predicted that, while currently only 27% of the population in Africa has access to the internet, data traffic is already predicted to increase 20-fold by 2019—double the growth rate compared to the rest of the world.

    Terheyden explained while infrastructure may be rather basic in places such as Africa, and some improvements still need to be made around issues such as bandwidth, telehealth has already begun to open up new opportunities, so much so that when compared to the way medicine is practiced in developed countries, it appears archaic.

    "I know there are still some challenges with bandwidth...but that to me is a very short term problem," he said. "I think we've started to see some of the infrastructure that people are advocating that would completely blow that out of the water.

    "So, now you remove that barrier and suddenly instead of saying, 'hey you need go to a hospital and see a doctor to have a test', we're saying, 'why would you?'"

    Despite the benefits, Terheyden expects clinicians, particularly in the western world, will be faced with the challenge of coping with how their roles are changing. He pointed out that they are increasingly becoming more of a "guide, an orchestrator, and conductor," versus the person that previously "played all the instruments, as well as being the conductor."

    He highlighted given how much medical information is out there, believing it doubles every 18-24 months, it would require clinicians to be reading 80-90 hours per week to keep up to date.

    "There's this change in behaviour to longer be the expert," he said. "You're not the Wizard of Oz. People don't come to you and you dispense knowledge; you're there as the guide."

    Source: Techrepublic.com

     

  • How Nike And Under Armour Became Big Data Businesses

    960x0Like the Yankees vs the Mets, Arsenal vs Tottenham, or Michigan vs Ohio State, Nike and Under Armour are some of the biggest rivals in sports.
     
    But the ways in which they compete — and will ultimately win or lose — are changing.
     
    Nike and Under Armour are both companies selling physical sports apparel and accessories products, yet both are investing heavily in apps, wearables, and big data.  Both are looking to go beyond physical products and create lifestyle brands athletes don’t want to run without.
     
    Nike
     
    Nike is the world leader in multiple athletic shoe categories and holds an overall leadership position in the global sports apparel market. It also boasts a strong commitment to technology, in design, manufacturing, marketing, and retailing.
     
    It has 13 different lines, in more than 180 countries, but how it segments and serves those markets is its real differentiator. Nike calls it “category offense,” and divides the world into sporting endeavors rather than just geography. The theory is that people who play golf, for example, have more in common than people who simply happen to live near one another.
     
    And that philosophy has worked, with sales reportedly rising more than 70% since the company shifted to this strategy in 2008. This retail and marketing strategy is largely driven by big data.
     
    Another place the company has invested big in data is with wearables and technology.  Although it discontinued its own FuelBand fitness wearable in 2014, Nike continues to integrate with many other brands of wearables including Apple which has recently announced the Apple Watch Nike+.How Nike And Under Armour Became Big Data Businesses
     
    But the company clearly has big plans for its big data as well. In a 2015 call with investors about Nike’s partnership with the NBA, Nike CEO Mark Parker said, “I’ve talked with commissioner Adam Silver about our role enriching the fan experience. What can we do to digitally connect the fan to the action they see on the court? How can we learn more about the athlete, real-time?”
     
    Under Armour
     
    Upstart Under Armour is betting heavily that big data will help it overtake Nike. The company has recently invested $710 million in acquiring three fitness app companies, including MyFitnessPal, and their combined community of more than 120 million athletes — and their data.
     
    While it’s clear that both Under Armour and Nike see themselves as lifestyle brands more than simply apparel brands, the question is how this shift will play out.
     
    Under Armour CEO Kevin Plank has explained that, along with a partnership with a wearables company, these acquisitions will drive a strategy that puts Under Armour directly in the path of where big data is headed: wearable tech that goes way beyond watches
     
    In the not-too-distant future, wearables won’t just refer to bracelets or sensors you clip on your shoes, but rather apparel with sensors built in that can report more data more accurately about your movements, your performance, your route and location, and more.
     
    “At the end of the day we kept coming back to the same thing. This will help drive our core business,” Plank said in a call with investors. “Brands that do not evolve and offer the consumer something more than a product will be hard-pressed to compete in 2015 and beyond.”
     
    The company plans to provide a full suite of activity and nutritional tracking and expertise in order to help athletes improve, with the assumption that athletes who are improving buy more gear.
     
    If it has any chance of unseating Nike, Under Armour has to innovate, and that seems to be exactly where this company is planning to go. But it will have to connect its data to its innovations lab and ultimately to the products it sells for this investment to pay off.
     
     
    Source: forbes.com, November 15, 2016
  • How to Benchmark Your Marketing Performance Against Your Competition's

    160225-Man-Painting-Coloured-Arrows-115378220In today's digital marketing world, competitive intelligence often takes a back seat to all the key performance indicators (KPIs) on which marketers are focused—open rates, social engagement metrics, lead-to-sales opportunity conversion rates, etc.

    That inward focus on how well you are doing with your revenue-driving marketing tactics is critical. But it can lead you to celebrate the wrong things. Don't let your KPIs overshadow the importance of knowing exactly how your digital marketing strategies are performing in relation to your peers who are competing against you in the market.

    If you forget to look at the bigger picture, you'll miss a perspective that, well, separates the best marketers from the mediocre ones.

    You can easily keep tabs on how your campaigns measure up against others in your industry without hiring an expensive third-party research firm. Of course, there may be times when you do need customer research and use a fancy detailed matrix of your competitors for in-depth analysis for identifying new products or for market sizing.

    But I'm talking about a quick and easy dashboard that measures you, the marketer, against your competitors.

    Why Spy?

    Competitive intelligence helps you...

    • Increase your chances of winning in the marketplace
    • Shape the development of your digital marketing strategy
    • Create a strategy for new product launches
    • Uncover threats and opportunities
    • Establish benchmarking for your analytics
      Most businesses do not have the luxury of having a dedicated employee, let alone a dedicated team, to gather and analyze gobs of data. However, you can easily track basic KPIs to inform decision-making at your company.

    Having analyzed the digital marketing strategies of numerous companies of various size and in various industries, including e-commerce, SaaS, and travel companies—and their competitors—I suggest the following for benchmarking.

    Website Performance Metrics

    To track the performance of a website, gather data from sites such as SEMRush, Pingdom, Similarweb, and Alexa. While that data is not always accurate when you compare three or four competitors at once, you can spot trends.

    Important metrics to monitor include the following:

    • Website visits: The average number of visitors per month can easily size up how popular you and your competitors are.
    • Bounce rate and site speed: Correlate these two metrics. That's how you can determine whether you need to make changes to your own website. For example, if your website has a high page-load time compared with your competitors, that will impact your page rankings, bounce rate, and overall customer satisfaction.
    • Geographic sources of traffic: Look at what percentage of visitors comes from what regions. That's critical if your company plans to expand beyond its current geographical presence. It will also allow you to spot global opportunities by finding gaps in distribution when looking at all competitors.
    • Website traffic by channel: See where your competitors choose to spend their time and money. For example, a company that has a higher percentage of visitors from email probably has a large prospect database. If you look at their website, you can examine how they collect data for their email marketing programs. Are they getting website visitors to sign up for newsletters or special offers? If not, they may be purchasing prospect data from a data provider. You can adjust your own strategy to ramp up marketing campaigns in areas where your competitors are not actively engaging prospects, or to increase spending in areas where they are outperforming you.

    Benchmarking reports from industry research reports are also helpful for tracking average open, click-through, and conversion rates.

    By putting together your newly found competitor insight and your own metrics, including your past performance, you can establish your own benchmarking.

    Mining for More Data

    Where are your competitors spending their advertising budgets? How are they using social media and PR? What jobs are they posting? Those answers are not hard to find, and they provide powerful insights.

    • SEO/PPC research: Tools are available to help you determine what ads your competitors are running and how they rank for particular keywords. Check out SEMRush, SpyFu, and WhatRunsWhere. You can also look at their overall spending for PPC campaigns. Depending on the source, however, the accuracy of this data can be as low as 50%. So use it for gauging overall direction, but don't rely on it entirely.
    • Social media: This is probably the hottest area of marketing and the hardest to assess. Mining data on social channels is especially tough when tracking consumer brands. It's best to monitor your competitors' activities monthly, and make sure to look at the posts ad promotions that companies generate. When updating or changing your strategy, you should have a solid understanding of what social media channels your competitors are using, types of posts they are making, how frequently they are using social media, and how successful they are (including number of users and levels of engagement).
    • PR: Press releases, financial reports, and thought-leadership blog posts distributed by your competitors provide great insight into their partnerships, possible marketing spending, and other initiatives.
    • Job postings: From time to time, take a look at LinkedIn or other job sites and you can get a good idea of where and how the company plans to expand.

    Frequency of Competitive Analysis

    The answer depends on the type of business that you have and the competitive landscape.

    For example, if you are selling a product in the SaaS Cloud space where you have 10 competitors, most of which are leading innovators, it makes sense to track their every move. However, if you are a B2B company and you have only one or two competitors in the manufacturing sector, you probably can get away with doing some basic benchmarking once every quarter.

    It is advisable to do a competitive analysis prior to changing strategy, launching a new product, or making tactical plans for the next quarter or year.

    Don't Be Afraid: Know Where You Stand

    Here's the bottom line: Don't get too excited about your 5% jump in email open rates, or passing a "likes" milestone on Facebook. Have the courage to see whether you are really a marketing rock star by benchmarking yourself against your competitors. Your business needs to know what your competition is doing. And I don't mean just knowing your competitors' products and pricing.

    With the insights you'll get from these tips and tools, you will be able to create a solid strategy, spot-on tactical plans, and (at the very least) a fantastic presentation to your executives or board.

    Source: MarketingProfs

  • How to Do Big Data on a Budget?

    2016-02-11-1455188997-848612-shutterstock 274038974-thumbTo really make the most of big data, most businesses need to invest in some tools or services - software, hardware, maybe even new staff - and there's no doubt that the costs can add up. The good news is that big data doesn't have to cost the Earth and a small budget needn't prevent companies from stepping into the world of big data. Here are some tips and ideas to help keep costs down:

    Think about your business objectives
    Too many businesses focus on collecting as much data as possible which, in my view, misses the whole point of big data. The objective should be to focus on the data that helps you achieve your strategic objectives. The whole point of big data should be to learn something from your data, take action based on what you've learned and grow your business as a result. Limiting the scope of your data projects so they tightly match your business goals should help keep costs down, as you can focus only on the data you really need.

    Make use of the resources you already have
    Before you splash out on any new technology, it's worth looking at what you're already using in your business. Some of your existing infrastructure could have a role to play. Go through each of the four key infrastructure elements (data sources, data storage, data analysis and data output) and note what related technology or skills you already have in-house that could prove useful. For example, you may already be collecting useful customer data through your website or customer service department. Or you very likely have a wealth of financial and sales data that could provide insights. Just be aware that you may already have some very useful data that could help you achieve your business objectives, saving you time and money.

    Look for savings on software
    Open source (free) software, like Hadoop, exists for most of the essential big data tasks. And distributed storage systems are designed to run on cheap, off-the-shelf hardware. The popularity of Hadoop has really opened big data up to the masses - it allows anyone to use cheap off-the-shelf hardware and open source software to analyse data, providing they invest time in learning how. That's the trade-off: it will take some time and technical skill to get free software set up and working the way you want. So unless you have the expertise (or are willing to spend time developing it) it might be worth paying for professional technical help, or 'enterprise' versions of the software. These are generally customised versions of the free packages, designed to be easier to use, or specifically targeted at various industries.

    Take advantage of big data as a service (BDaaS)
    In the last few years many businesses have sprung up offering cloud-based big data services to help other companies and organisations solve their data dilemmas. This makes big data a possibility for even the smallest company, allowing them to harness external resources and skills very easily. At the moment, BDaaS is a somewhat vague term often used to describe a wide variety of outsourcing of various big data functions to the cloud. This can range from the supply of data, to the supply of analytical tools which interrogate the data (often through a web dashboard or control panel) to carrying out the actual analysis and providing reports. Some BDaaS providers also include consulting and advisory services within their BDaaS packages.

    BDaaS removes many of the hurdles associated with implementing a big data strategy and vastly lowers the barrier of entry. When you use BDaaS, all of the techy 'nuts and bolts' are, in theory, out of sight and out of mind, leaving you free to concentrate on business issues. BDaaS providers generally take this on for the customer - they have everything set up and ready to go - and you simply rent the use of their cloud-based storage and analytics engines and pay either for the time you use them or the amount of data crunched. Another great advantage is that BDaaS providers often take on the cost of compliance and data protection - something which can be a real burden for small businesses. When the data is stored on the BDaaS provider's servers, they are (generally) responsible for it.

    It's not just new BDaaS companies which are getting in on the act; some of the big corporations like IBM and HP are also offering their own versions of BDaaS. HP have made their big data analytics platform, Haven, available entirely through the cloud. This means that everything from storage to analytics and reporting is handled on HP systems which are leased to the customer via a monthly subscription - entirely eliminating infrastructure costs. And IBM's Analytics for Twitter service provides businesses with access to data and analytics on Twitter's 500 million tweets per day and 280 million monthly active users. The service provides analytical tools and applications for making sense of that messy, unstructured data and has trained 4,000 consultants to help businesses put plans into action to profit from them.

    As more and more companies realise the value of big data, more services will emerge to support them. And competition between suppliers should help keep subscription prices low, which is another advantage for those on a tight budget. I've already seen that BDaaS is making big data projects viable for many businesses that previously would have considered them out of reach - and I think it's something we'll see and hear a lot more about in the near future.

    Source: HuffPost

  • How to Optimize Analytics for Growing Data Stores

    Every minute of every day, mind-blowing amounts of data are generated. Twitter users send 347,222 tweets, YouTube users upload 300 hours of video, and Google receives more than four million search queries. And in a single hour, Walmart processes more than a million customer transactions. With the Internet of Things accelerating at lightning speed – to the tune of 6.4 billion connected devices in 2016 (up 30 percent from 2015) – this already staggering amount of data is about to explode. By 2020, IDC estimates there will be 40 zettabytes of data. That’s 5,200 GB for every person on the planet.

    This data is a gold mine for businesses. Or, at least, it can be. On its own, data has zero value. To turn it into a valuable asset, one that delivers the actionable intelligence needed to transform business, you need to know how to apply analytics to that treasure trove. To set yourself up for success, start out by answering these questions:

    What Is the Size, Volume, Type and Velocity of your Data?

    The answers to this will help you determine the best kind of database to store your data and fuel your analysis. For instance, some databases handle structured data, and others are focused on semi-structured or unstructured data. Some are better with high-velocity and high-volume data.

      RDMS Adaptive NoSQL Specialty In-Memory NewSQL Distributed
    Example DB2, Oracle, MySQL Deep Information Sciences Cloudera, MonoDB, Cassandra Graphing, Column Store, time-series MemSQL, VoltDB NuoDB Hadoop
    Data Type Structured Structured Un/semi-structured Multiple Structured Structured Structured
    Qualities Rich features, ACID compliant, scale issues Fast read/ write, strong scale, ACID, flexible Fast ingest, not ACID compliant Good reading, no writing, ETL delays Fast speed, less scale, ETL delays for analytics Good scale and replication, high overhead Distributed, document-based database, slow batch-based queries

     Which Analytics Use Cases will You Be Supporting?

    The type of use cases will drive the business intelligence capabilities you’ll require (Figure 1).

    • Analyst-driven BI. Operator seeking insights across a range of business data to find cross-group efficiencies, profit leakage, cost challenges, etc.
    • Workgroup-driven BI. Small teams focused on a sub-section of the overall strategy and reporting on KPIs for specific tasks.
    • Strategy-driven BI. Insights mapped against a particular strategy with the dashboard becoming the “single source of truth” for business performance.
    • Process-driven BI. Business automation and workflow built as an autonomic process based on outside events.

    Figure-1-1024x449

    Where Do You Want your Data and Analytics to Live?

    The main choices are on-premises or in the cloud. Until recently, for many companies – particularly those concerned about security – on-prem won out. However, that’s changing significantly as cloud-based solutions have proven to be solidly secure. In fact, a recent survey found that 40 percent of big data practitioners use cloud services for analytics and that number is growing.

    The cloud is attractive for many reasons. The biggest is fast time-to-impact. With cloud-based services you can get up and running immediately. This means you can accelerate insights, actions, and business outcomes. There’s no waiting three to four months for deployment and no risk of development issues.

    There’s also no need to purchase and install infrastructure. This is particularly critical for companies that don’t have the financial resources or skills to set up and maintain database and analytics environments on-premises. Without cloud, these companies would be unable to do the kind of analyses required to thrive in our on-demand economy. However, even companies that do have the resources benefit by freeing up people and budget for more strategic projects.

    With data and analytics in the cloud, collaboration also becomes much easier. Your employees, partners, and customers can instantly access business intelligence and performance management.

    Cloud Options

    There are a number of cloud options you can employ. Here’s a quick look at them:

    Infrastructure as a Service (IaaS) for generalized compute, network, and storage clusters. IaaS is great for flexibility and scale, and will support any software. You will be required to install and manage the software.

    Database as a Service (DBaaS), where multi-tenant or dedicated database instances are hosted by the service provider. DBaaS also is great for flexibility and scale, and it offloads backups and data management to the provider. Your data is locked into the provider’s database solution.

    Analytics as a Service (AaaS) provides complex analytics engines that are ready for use and scale as needed, with pre-canned reports.

    Platform as a Service (PaaS) is similar to DBaaS in that it scales easily and that application backups and data management are handled by the provider. Data solutions themselves are often add-ons.

    Software as a Service (SaaS) is when back office software is abstracted through a hosted application with data made available through APIs. Remote analytics are performed “over the wire” and can be limiting.

    How you leverage data can make or break your business. If you decide to go the cloud route, make sure your service provider’s database and analytics applications fit your current and evolving needs. Make sure the provider has the expertise, infrastructure, and proven ability to handle data ebbs and flows in a way that’s cost-effective for you and, equally important, ensures that your performance won’t be compromised when the data tsunami hits. Your business depends on it.

     Source: DataInformed

  • Information Is Now The Core Of Your Business

    DataData is at the very core of the business models of the future – and this means wrenching change for some organizations.

    We tend to think of our information systems as a foundation layer that support the “real” business of the organization – for example, by providing the information executives need to steer the business and make the right decisions.

    But information is rapidly becoming much more than that: it’s turning into an essential component of the products and services we sell.

    Information-augmented products

    In an age of social media transparency, products “speak for themselves”– if you have a great product, your customers will tell their friends. If you have a terrible product, they’ll tell the world. Your marketing and sales teams have less room for maneuver, because prospects can easily ask existing customers if your product lives up to the promises.

    And customer expectations have risen. We all now expect to be treated as VIPs, with a “luxury” experience. When we make a purchase, we expect to be recognized. We expect our suppliers to know what we’ve bought in the past. And we expect personalized product recommendations, based on our profile, the purchases of other people like us, and the overall context of what’s happening right now.

    This type of customer experience doesn’t just require information systems; the information is an element of the experience itself, part of what we’re purchasing, and what differentiates products and services in the market.

    New ways of selling

    New technologies like 3D printing and the internet of things are allowing companies to rethink existing products.

    Products can be more easily customized and personalized for every customer. Pricing can be more variable to address new customer niches. And products can be turned into services, with customers paying on a per-usage basis.

    Again, information isn’t just supporting the manufacturing and sale of the product – it’s part of what makes it a “product” in the first place.

    Information as a product

    In many industries, the information collected by business is now more valuable than the products being sold – indeed, it’s the foundation for most of the free consumer internet. Traditional industries are now realizing that the data stored in their systems, once suitably augmented or anonymized, can be sold directly. See this article on the Digitalist magazine, The Hidden Treasure Inside Your Business, for more information about the four main information business models.

    A culture change for “traditional IT”

    Traditional IT systems were about efficiency, effectiveness, and integrity. These new context-based experiences and more sophisticated products use information to generate growth, innovation, and market differentiation. But these changes lead to a difficult cultural challenge inside the organization.

    Today’s customer-facing business and product teams don’t just need reliable information infrastructures. They need to be able to experiment, using information to test new product options and ways of selling. This requires not only much more flexibility and agility than in the past, but also new ways of working, new forms of IT organization, and new sharing of responsibilities.

    The majority of today’s CIOs grew up in an era of “IT industrialization,” with the implementation of company-wide ERP systems. But what made them successful in the past won’t necessarily help them win in the new digital era.

    Gartner believes that the role of the “CIO” has already split into two distinct functions: Chief Infrastructure Officers whose job is to “keep the lights on”; and Chief Innovation Offers, who collaborate closely with the business to build the business models of the future.

    IT has to help lead

    Today’s business leaders know that digital is the future, but typically only have a hazy idea of the possibilities. They know technology is important, but often don’t have a concrete plan for moving forward: 90% of CEOs believe the digital economy will have a major impact on their industry. But only 25% have a plan in place, and less than 15% are funding and executing a digital transformation plan.

    Business people want help from IT to explain what’s possible. Today, only 7% of executives say that IT leads their organization’s attempts to identify opportunities to innovate, 35% believe that it should. After decades of complaints from CIOs that businesses aren’t being strategic enough about technology, this is a fantastic new opportunity.

    Design Thinking and prototyping

    Today’s CIOs have to step up to digital innovation. The problem is that it can be very hard to understand — history is packed with examples of business leaders that just didn’t “get” the new big thing.  Instead of vague notions of “disruption,” IT can help by explaining to business people how to add information into a company’s future product experiences.

    The best way to do this is through methodologies such as Design Thinking, and agile prototyping using technologies should as Build.me, a cloud platform that allows pioneers to create and test the viability of new applications with staff and customers long before any actual coding.

    Conclusion

    The bottom line is that digital innovation is less about the technology, and more about the transformation — but IT has an essential role to play in demonstrating what’s possible, and needs to step up to new leadership roles.

     

    Source: timoelliot.com, November 14, 2016

  • Insights from Dresner Advisory Services’ 2016 The Internet of Things and Business Intelligence Market Study

    • Sales and strategic planning teams see IoT as the most valuable.
    • IoT advocates are 3X as likely to consider big data critical to the success of their initiatives & programs.
    • Amazon and Cloudera are the highest ranked big data distributions followed by Hortonworks and Map/R.
    • Apache Spark MLib is the most known technology on the nascent machine learning landscape today.

    These and many other excellent insights are from Dresner Advisory Services’ 2016 The Internet of Things and Business Intelligence Market Study published last month. What makes this study noteworthy is the depth of analysis and insights the Dresner analyst team delivers regarding the intersection of big data and the Internet of Things (IoT), big data adoption, analytics, and big data distributions. The report also provides an analysis of Cloud Business Intelligence (BI) feature requirements, architecture, and security insights. IoT adoption is thoroughly covered in the study, with a key finding being that large organizations or enterprises are the strongest catalyst of IoT adoption and use. Mature BI programs are also strong advocates or adopters of IoT and as a result experience greater BI success. IoT advocates are defined as those respondents that rated IoT as either critical or very important to their initiatives and strategies.

    Key takeaways of the study include the following:

    • Sales and strategic planning see IoT as the most valuable today.The combined rankings of IoT as critical and very important are highest for sales, strategic planning and the Business Intelligence (BI) Competency Centers. Sales ranking IoT so highly is indicative of how a wide spectrum of companies, from start-ups to large-scale enterprises, is attempting to launch business models and derive revenue from IoT. Strategic planning’s prioritization of IoT is also driven by a long-term focus on how to capitalize on the technology’s inherent strengths in providing greater contextual intelligence, insight, and potential data-as-a-service business models.

    IoT-Importance-by-Function-cp

    • Biotechnology, consulting, and advertising are the industries that believe IoT is the most important to their industries.Adoption of IoT across a wide variety of industries is happening today, with significant results being delivered in manufacturing, distribution including asset management, logistics, supply chain management, and marketing. The study found that the majority of industries see IoT as not important today, with the exception of biotechnology.

    IOT-Importance-by-Industry-cp

    • Location intelligence, mobile device support, in-memory analysis, and integration with operational systems are the four areas that most differentiate IoT advocates’ interests and focus.Compared to the overall sample of respondents, IoT advocates have significantly more in-depth areas of focus than the broader respondent base. The four areas of location intelligence, mobile device support, in-memory analysis, and integration with operational systems show they have a practical, pragmatic mindset regarding how IoT can contribute greater process efficiency, revenue and integrate with existing systems effectively.

    IoT-Advocates-Circle-cp1

    • An organization’s ability to manage big data analytics is critically important to their success or failure with IoT. IoT advocates are 3X as likely to consider big data critical, and 2X as likely to consider big data very important. The study also found that IoT advocates see IoT as a core justification for investing in and implementing big data analytics and architectures.

    importance-of-big-data-cp

    • Data warehouse optimization, customer/social analysis, and IoT are the top three big data uses cases organizations are pursuing today according to the study. Data warehouse optimization is considered critical or very important to 50% of respondents, making this use case the most dominant in the study. Large-scale organizations are adopting big data to better aggregate, analyze and take action on the massive amount of data they generate daily to drive better decisions. One of the foundational findings of the study is that large-scale enterprises are driving the adoption of IoT, which is consistent with the use case analysis provided in the graphic below.

    big-data-use-cases-with-cp

    • IoT advocates are significantly above average in their use of advanced and predictive analytics today. The group of IoT advocates identified in the survey is 50% more likely to be current users of advanced and predictive analytics apps as well. The study also found that advanced analytics users tend to be the most sophisticated and confident BI audience in an organization and see IoT data as ideal for interpretation using advanced analytics apps and techniques.

    advanced-and-predictive-analytics-cp

    • Business intelligence experts, business analysts and statisticians/data scientists are the greatest early adopters of advanced and predictive analytics. More than 60% of each of these three groups of professionals is using analytics often, which could be interpreted as more than 50% of their working time.

    users-of-advanced-and-predictive-analytics-cp

    • Relational database support, open client connectors (ODBC, JDBC) and automatic upgrades are the three most important architectural features for cloud BI apps today. Connectors and integration options for on-premises applications and data (ERP, CRM, and SCM) are considered more important than cloud application and database connection options. Multitenancy is considered unimportant to the majority of respondents. One factor contributing to the unimportance of multi-tenancy is the assumption that this is managed as part of the enterprise cloud platform.

    Cloud-BI-Architectural-Requirements-cp

    • MapReduce and Spark are the two most known and important big data infrastructure technologies according to respondents today. 48% believe that MapReduce is important and 42% believe Spark is. The study also found that all other categories of big data infrastructure are considered less important as the graphic below illustrates.

    big-data-infrastructure-cp

     Forbes, 4 oktober 2016

  • Just Using Big Data Isn’t Enough Anymore

    feb16-09-603756761-1024x576Big Data has quickly become an established fact for Fortune 1000 firms — such is the conclusion of a Big Data executive survey that my firm has conducted for the past four years.

    The survey gathers perspectives from a small but influential group of executives — chief information officers, chief data officers, and senior business and technology leaders of Fortune 1000 firms. Key industry segments are heavily represented — financial services, where data is plentiful and data investments are substantial, and life sciences, where data usage is rapidly emerging. Among the findings:

     

     

    • 63% of firms now report having Big Data in production in 2015, up from just 5% in 2012
    • 63% of firms reported that they expect to invest greater than $10 million in Big Data by 2017, up from 24% in 2012
    • 54% of firms say they have appointed a Chief Data Officer, up from 12% in 2012
    • 70% of firms report that Big Data is of critical importance to their firms, up from 21% in 2012
    • At the top end of the investment scale, 27% of firms say they will invest greater than $50 million in Big Data by 2017, up from 5% of firms that invested this amount in 2015

    Four years ago, organizations and executives were struggling to understand the opportunity and business impact of Big Data. While many executives loathed the term, others were apostles of the belief that data-driven analysis could transform business decision-making. Now, we have arrived at a new juncture: Big Data is emerging as a corporate standard, and the focus is rapidly shifting to the results it produces and the business capabilities it enables. When the internet was a new phenomenon, we’d say “I am going to surf the World Wide Web” – now, we just do it. We are entering that same phase of maturity with Big Data.

    So, how can executives prepare to realize value from their Big Data investments?

    Develop the right metrics.

    While a majority of Fortune 1000 firms report implementing Big Data capabilities, few firms have shown how they will derive business value over time from these often substantial investments. When I discuss this with executives, they often point out that the lack of highly developed metrics is both a function of the relative immaturity of Big Data implementations, as well as a function of where in the organization sponsorship for Big Data originated and where it currently reports. Organizations that have the executive responsible for data report to the Chief Financial Officer are more likely to have developed precise financial measurements early on.

    Another issue with measuring the effectiveness of Big Data initiatives has been the difficulty of defining and isolating their costs. Big Data has been praised for the agility it brings to organizations, because of the iterative process by which they can load data, identify correlations and patterns, and then load more data that appears to be highly indicative. By following this approach, organizations can learn through trial and error. This poses a challenge to early measurement because most organizations have engaged in at least a few false starts while honing Big Data environments to suit their needs. Due to immature processes and inefficiencies, initial investments of time and effort have sometimes been larger than anticipated. These costs can be expected to level off as experience and efficiencies are brought to bear.

    Identify opportunities for innovation.

    Innovation continues to be a source of promise for Big Data. The speed and agility it permits lend themselves to discovery environments such as life sciences R&D and target marketing activities within financial services. Success stories of Big-Data-enabled innovation remain relatively few at this stage. To date, most Big Data accomplishments have involved operational cost savings or allowing the analysis of larger and more diverse sets of data.

    For example, financial firms have been able to enhance credit risk capabilities through the ability to process seven years of customer credit transactions in the same amount of time that it previously took to process a single year, resulting in much greater credit precision and lower risk of credit fraud. Yet, these remain largely back-office operations; they don’t change the customer experience or disrupt traditional ways of doing business. A few forward-thinking financial services firms have made a commitment to funding Big Data Labs and Centers of Excellence. Companies across industry segments would benefit from making similar investments. But funding won’t be enough; innovating with Big Data will require boldness and imagination as well.

    Prepare for cultural and business change.

    Though some large firms have invested in optimizing existing infrastructure to match the speed and cost benefits offered by Big Data, new tools and approaches are displacing whole data ecosystems. A new generation of data professionals is now emerging. They have grown up using statistical techniques and languages like Hadoop and R, and as they enter the workplace in greater numbers, traditional approaches to data management and analytics will give way to these new techniques.

    When I began advising Fortune 1000 firms on data and analytics strategies nearly two decades ago, I assumed that 95% of what was needed would be technical advice. The reality has been the opposite. The vast majority of the challenges companies struggle as they operationalize Big Data are related to people, not technology: issues like organizational alignment, business process and adoption, and change management. Companies must take the long view and recognize that businesses cannot successfully adopt Big Data without cultural change.

    Source: Harvard Business review

  • Lessons From The U.S. Election On Big Data And Algorithms

    The failure to accurately predict the outcome of the elections has caused some backlash against big data and algorithms. This is misguided. The real issue is failure to build unbiased models that will identify trends that do not fit neatly into our present understanding. This is one of the most urgent challenges for big data, advanced analytics and algorithms.  When speaking with retailers on this subject I focus on two important considerations.  The first is that convergence of what we believe to be true and what is actually true is getting smaller.

    things-you-know-300x179

    This is because people, consumers, have more personal control than ever before.  They source opinions from the web, social media, groups and associations that in the past where not available to them.  For retailers this is critical because the historical view that the merchandising or marketing group holds about consumers is likely growing increasingly out of date.  Yet well meaning business people performing these tasks continue to disregard indicators and repeat the same actions.  Before consumers had so many options this was not a huge problem since change happened more slowly.  Today if you fail to catch a trend there are tens or hundreds of other companies out there ready to capitalize on the opportunity.  While it is difficult to accept, business people must learn a new skill, leveraging analytics to improve their instincts.

    The second is closely related to the first but with an important distinction; go where the data leads. I describe this as the KISS that connects big data to decisions.
    The KISS is about extracting knowledge, testing innovations, developing strategies, and doing all this at high speed. The KISS is what allows the organization to safely travel down the path of discovery – going where the data leads – without falling down a rabbit hole.
    KISS1-300x164
    Getting back to the election prognosticators, there were a few that did identify the trend.  They were repeatedly laughed at and disregarded. This is the foundation of the problem, organizations must foster environments where new ideas are embraced and safely explored.  This is how we will grow the convergence of things we know. 
     
    Source: Gartner, November 10, 2016
  • Localization uses Big Data to Drive Big Business

    There’s growing interest in using big data for business localization now, although the use of customer data for optimal orientation of busi

    localization

    ness locations and promotions has been around for at least a decade.

    There’s growing interest in using big data for business localization now, although the use of customer data for optimal orientation of business locations and promotions has been around for at least a decade.

    In 2006, the Harvard Business Review declared the endof big-box retail standardization in favor of catering to customers’ local and regional tastes, fostering innovation, and – not incidentally – making it harder for competitors to copy their store formats by changing up the one-size-fits-all approach. A decade later, analytics are affordable for businesses of all sizes, giving smaller players in a variety of industries the ability to localize as well.

    An example of early localization of items sold comes from Macy’s. Executive search firm Caldwell Partners describes the department-store chain’s vast localization project, which began in the mid-2000s to differentiate store inventories for customer preferences, beginning in markets such as Miami, Columbus, and Atlanta. This strategy has helped Macy’s remain profitable despite ongoing major declines in department-store sales in recent years.

    Localization for stronger consumer appeal, better product offerings

    In hospitality, hotel chains now use localization strategies to compete with locally owned boutique hotels and with Airbnb rentals that promise a “live like a local” experience.

    Visual News reports that Millennials’ tastes and preferences are driving this trend. These younger travel enthusiasts want a unique experience at each destination, even if they’re staying in properties owned by the same hotel brand.

    Hospitality Technology notes that today’s customer profile data gives hotel chains a “360 degree view of customer spending behavior across industries, channels, and over time,” for more precise location orientation and targeted marketing.

    In fact, any consumer-facing business can benefit from using local-market data. GIS firm ESRI has described how individual bank branches can orient their loan offerings to match the needs and risk profiles of customers in the immediate area. Other elements that can be localized to suit area customers’ tastes and spending power include product prices, menu items, location hours, staffing levels, décor, and product displays.

    Localization for more effective marketing

    Outside the store itself, localization is a powerful tool for improving the return on marketing. By using detailed data about local customer behavior, retailers, restaurants and other businesses can move from overly broad promotions to segmented offers that closely align with each segment’s preferences.

    In some cases, this type of marketing localization can reduce expenses (for example, by lowering the total number of direct-mail pieces required for a campaign) while generating higher redemption rates.

    Localization of marketing efforts goes beyond cost savings to the establishment of customer loyalty and competitive advantage. Study after study shows that consumers expect and respond well to offers based on their preferences, but companies have been slow to provide what customers want.

    An international study reported by Retailing Today in June found that 78% of consumers make repeat purchases when they receive a personalized promotion, and 74% buy something new. Despite this, the study found that less than 30% of the companies surveyed were investing heavily in personalization.

    A similar 2015 study focusing on North American consumers, described by eMarketer, found that more than half of the consumers surveyed wanted promotions tailored to their product preferences, age range, personal style, and geographic location. That study found that although 71% of the regional retailers in the survey say they localize and personalize promotional emails, half the consumers said they got promotional emails that didn’t align with their preferences.

    Clearly, there’s room for improvement in the execution of localized marketing, and businesses that get it right will have an advantage with customers whose expectations are going unmet right now.

    Smart localization and orientation involve understanding the available data and knowing how to use it in cost-effective ways to give customers the information they want. It also involves rethinking the way businesses and consumers interact, and the role geography plays in business.

    Localization and careful audience targeting may be the keys to business survival. A 2013 Forrester report proclaimed that in the digital age, “the only sustainable competitive advantage is knowledge of and engagement with customers.”

    With so much power of choice in the hands of consumers, it’s up to retailers, restaurants and other businesses to earn their loyalty by delivering what they want in real time, no matter where they’re located.

    Author: Charles Hogan

    Charles Hogan is co-founder and CEO at Tranzlogic. He has over 20 years of experience in fintech, data analytics, retail services and payment processing industries. Follow on twitter @Tranzlogic)

  • Modern Information Management: Understanding Big Data at Rest and in Motion

    Big data is the buzzword of the century, it seems. But, why is everyone so obsessed with it? Here’s what it’s all about, how companies are gathering it, and how it’s stored and used.

    7979558647 6c822e698d o YO

    What is it?

    Big data is simply large data sets that need to be analyzed computationally in order to reveal patterns, associations, or trends. This data is usually collected by governments and businesses on citizens and customers, respectively.

    The IT industry has had to shift its focus to big data over the last few years because of the sheer amount of interest being generated by big business. By collecting massive amounts of data, companies, like Amazon.com, Google, Walmart, Target, and others, are able to track buying behaviors of specific customers.

    Once enough data is collected, these companies then use the data to help shape advertising initiatives. For example, Target has used its big data collection initiative to help target (no pun intended) its customers with products it thought would be most beneficial given their past purchases.

    How Companies Store and Use It

    There are two ways that companies can use big data. The first way is to use the data at rest. The second way is to use it in motion.

    At Rest Data – Data at rest refers to information that’s collected and analyzed after the fact. It tells businesses what’s already happened. The analysis is done separately and distinctly from any actions that are taken upon conclusion of said analysis.

    For example, if a retailer wanted to analyze the previous month’s sales data. It would use data at rest to look over the previous month’s sales totals. Then, it would take those sales totals and make strategic decisions about how to move forward given what’s already happened.

    In essence, the company is using past data to guide future business activities. The data might drive the retailer to create new marketing initiatives, customize coupons, increase or decrease inventory, or to otherwise adjust merchandise pricing.

    Some companies might use this data to determine just how much of a discount is needed on promotions to spur sales growth.

    Some companies may use it to figure out how much they are able to discount in the spring and summer without creating a revenue problem later on in the year. Or, a company may use it to predict large sales events, like Black Friday or Cyber Monday.

    This type of data is batch processed since there’s no need to have the data instantly accessible or “streaming live.” There is a need, however, for storage of large amounts of data and for processing unstructured data. Companies often use a public cloud infrastructure due to the costs involved in storage and retrieval.

    Data In Motion – Data in motion refers to data that’s analyzed in real-time. Like data at rest, data may be captured at the point of sale, or at a contact point with a customer along the sales cycle. The difference between data in motion and data at rest is how the data is analyzed.

    Instead of batch processing and analyzation after the fact, data in motion uses a bare metal cloud environment because this type of infrastructure uses dedicated servers offering cloud-like features without virtualization.

    This allows for real-time processing of large amounts of data. Latency is also a concern for large companies because they need to be able to manage and use the data quickly. This is why many companies send their IT professionals to Simplilearn Hadoop admin training and then subsequently load them up on cloud-based training and other database training like NoSQL.

    9427663067 713fa3e786 o

    Big Data For The Future

    Some awesome, and potentially frightening, uses for big data are on the horizon. For example, in February 2014, the Chicago Police Department sent uniformed officers to make notification visits to targeted individuals they had identified as potential criminals. They used a computer-generated list which gathered data about those individuals’ backgrounds.

    Another possible use for big data is development of hiring algorithms. More and more companies are trying to figure out ways to hire candidates without trusting slick resume writing skills. New algorithms may eliminate job prospects based on statistics, rather than skillsets, however. For example, some algorithms find that people with shorter commutes are more likely to stay in a job longer.

    So, people who have long commutes are filtered out of the hiring process quickly.

    Finally, some insurance companies might use big data to analyze your driving habits and adjust your insurance premium accordingly. That might sound nice if you’re a good driver, but insurers know that driving late at night increases the risk for getting into an accident. Problem is, poorer people tend to work late shifts and overnights or second jobs just to make ends meet. The people who are least able to afford insurance hikes may be the ones that have to pay them.

    Source: Mobilemag

  • Nederland wordt innovatiever

    unnamedNederland wordt innovatiever. Gedurende het afgelopen jaar hebben bedrijven 2,9 procent meer radicale innovaties gerealiseerd, het hoogste niveau sinds jaren, zo blijkt uit de Erasmus Concurrentie en Innovatie Monitor in 2006 die vandaag wordt gepresenteerd.
     
    De sterk gestegen innovatie hangt samen met het feit dat bedrijven steeds meer worden geconfronteerd met disruptieve technologieën en nieuwe  zakelijke modellen. Hierbij valt te denken aan Big Data, Internet of things, 3D-printing, cloud technologie en robotisering. Ook de aantrekkende economie helpt een handje mee.
     
    Niet alle bedrijfstakken profiteren van de ontwikkelingen. De werkgelegenheid in de financiële sector en verzekeringen is relatief hard getroffen. Consumenten regelen steeds meer bankzaken online en daarvoor is minder personeel nodig. In de logistieke sector verwacht de helft van de ondervraagde bedrijven dat de vierde industriële revolutie leidt tot minder werkgelegenheid bij hun bedrijf.
     
    Startups zijn het meest positief over de ontwikkeling van de werkgelegenheid als gevolg van de die komende revolutie. Startups scoren in vergelijking met andere leeftijdsgroepen van bedrijven ook bovengemiddeld op tal van prestatie-indicatoren: disruptieve innovatie, medewerkerstevredenheid, enthousiasme, gevoel van geluk en lage stressniveaus.
     
    De regio Eindhoven voert de ranglijst aan van regio’s op radicale innovaties met een score van 9,8 procent boven het landelijk gemiddelde. De regio Twente scoort het meest positief op werkbeleving.
     
    De Erasmus Concurrentie en Innovatie Monitor wordt jaarlijks uitgevoerd door onderzoeksinstituut INSCOPE van Rotterdam School of Management, Erasmus University (RSM).
     
    Bron: Emerce, 24 november 2016
  • New kid on the block in Market Intel

    radar ontvangers

    Market intelligence neemt een vlucht. Nu ondernemingen hun interne informatiehuishouding in toenemend mate in orde hebben

    gaat de aandacht (opnieuw?) uit naar de informatievoorziening met betrekking tot de markt van ondernemingen. Opnieuw? Ja, opnieuw!

    Als sinds de ’60er jaren staat het onderwerp midden in de belangstelling maar onder invloed van informatietechnologische ontwikkelingen werd het steeds naar de achtergrond gedrongen door aandacht voor de interne optimalisering van de informatiehuishouding. Executive informatiesystemen (een term uit de jaren ’80) leidde tot BI en BI tot DWH, ETL, Reporting en score carding. De toename van data op social media, het net en de mogelijkheden op het gebied van ongestructureerde data – data mining maar ook machine learning voedden nu opnieuw de aandacht voor toepassing van technologie bij het beter kennen van de bedrijfsomgeving. Het belang daarvan is dus niet veranderd maar de mogelijkheden nemen wel toe.

    Drie jaar geleden werd Hammer, market intelligence opgericht met als doel bedrijven van market intel te voorzien met gebruikmaking van moderne data technologie. Egbert Philips (Director van het in Arnhem gevestigde Hammer); “Wat betreft management informatie zou er minstens zo veel aandacht moeten zijn voor het kennen en doorgronden van markt en bedrijfsomgeving als voor de interne prestaties. Dit zou niet afhankelijk moeten zijn van technologische mogelijkheden. De ontwikkeling van data science en big data technologieën maken het wel mogelijk market intelligence beter en efficiënter in te richten. Daar richten we ons met Hammer op. We willen een partner zijn voor bedrijven die hun markten structureel willen kennen en doorgronden. Informatie technologie blijft daarbij een middel maar wel een heel belangrijk middel.”

    Hammer is weliswaar een jonge onderneming maar bepaald niet nieuw in het veld. De oprichters zijn reeds jarenlang actief in market intel en de toepassing daarvan in onder ander strategische planning vraagstukken. Hammer ondersteunt echter ook meer tactische beslissingen. Vraagstukken met betrekking tot pricing, sourcing/inkoop, het kiezen van distributiepartners, productontwikkeling en business development kunnen niet goed worden beantwoord zonder input van marktinformatie.

    Eind november organiseert Hammer een klantevent. Wanneer u geïnteresseerd bent stuur dan een e-mail naar info@hammer-intel.com

    http://www.hammer-intel.com

     

  • Noord-Nederland bundelt krachten in unieke opleiding Data Science

    HanzeHogeschool logo-300x169Op 7 maart start de opleiding Data Science in Noord-Nederland. Om de al maar groeiende hoeveelheid data te managen leidt IT Academy Noord-Nederland professionals uit het Noorden op tot data scientist. Met geaccrediteerde vakken van de Hanzehogeschool Groningen en de Rijksuniversiteit Groningen slaat de opleiding een brug tussen toegepast en wetenschappelijk onderwijs. De opleiding is opgezet in samenwerking met het bedrijfsleven.

    Er liggen steeds meer kansen voor bedrijven en instellingen om met enorme hoeveelheden data op innovatieve wijze nieuwe producten en diensten aan te bieden. Hoe kunnen bedrijven omgaan met deze data en hoe zit het met privacy en het eigendom van data? Het verzamelen van data is stap één, maar het kunnen ordenen en analyseren creëert waarde. Een bekend voorbeeld is Uber die door het gebruik van Big Data een compleet nieuw (disruptive) business model voor de vervoerssector heeft gecreëerd.


    De vraag naar data scientists neemt toe. De opleiding Data Science is de eerste van zijn soort in Noord-Nederland. Het RDW speelde met haar data-intensieve bedrijfsvoering en roep om een opleiding op het gebied van Big Data een cruciale rol in de ontwikkelfase van de opleiding. Om het programma met de juiste elementen te laden bundelde de IT Academy de krachten van de Hanzehogeschool en de RUG. Hoogleraren en docenten van beide instellingen zullen delen van het programma verzorgen. Daarnaast zorgen gastsprekers van andere kennisinstellingen en het bedrijfsleven voor casuïstiek uit de praktijk om de opgedane kennis gelijk toe te passen.

    IT Academy Noord-Nederland
    IT Academy Noord-Nederland biedt state-of-the-art onderwijs, doet onderzoek door middel van open samenwerking tussen bedrijven, kennisinstellingen en organisaties om zo in Noord-Nederland het innovatief vermogen te versterken, werkgelegenheid in ICT te stimuleren en een aantrekkelijke landingsplaats voor talent te zijn. IT Academy Noord-Nederland is een initiatief van de Hanzehogeschool Groningen, Rijksuniversiteit, Samenwerking Noord en IBM Client Innovation Center.

    Source: Groninger krant

  • Potentieel van toepassing Big Data Analytics in supply chain nog onbenut!

    Volgens een Onderzoek van Accenture hebben bedrijven hoge verwachtingen van de toegevoegde waarde die Big Data analytics kan hebben voor het optimaliseren van hun supply chain. Volgens dat zelfde onderzoek heeft echter slechts 17% van de onderzochte bedrijven technologie of systemen geïmplementeerd die daaraan moeten bijdragen. Hier ligt dus een enorm verbeter potentieel.

    De mogelijkheden voor optimalisatie van de value chain zijn talrijk. Zo kan met behulp van Big Data technologie claims en claim/klachten informatie worden doorzocht die waardevolle inzichten biedt voor verbetering in R&D en gewenste functionaliteiten en voorwaarden van in te kopen goederen. Ook kan gedacht worden aan verhoging van de reactietijd bij calamiteiten, verkorting van de order to delivery time en identificatie van leveringsrisico’s op basis van patroonherkenning, Daarnaast kunnen mogelijkheden worden geïdentificeerd voor verbetering van de integratie en efficiency van de supply chain.

    Er zijn een aantal belangrijke redenen waarom,  ondanks de duidelijke toegevoegde waarde, de penetratiegraad van Big Data technologie verre van hoog is. Dit hangt vooral  samen met de hoogte van de investeringen en beveiligingsissues. Daarnaast ontbreekt het veelal aan een eenduidige business case of spelen privacy redenen een rol.

    Verder blijkt (volgens het onderzoek) dat toepassing van Big data technologie specifieke competenties en vaardigheden vereist. Om toegevoegde waarde uit data te genereren hebben slechts weinig bedrijven een team van volwaardige data scientists tot hun beschikking. Dit kan wijzen op het ontbreken van het juiste talent maar zou ook kunnen wijzen op een geringe prioriteit bij bedrijven.

    Bron: BI-kring redactie

  • PwC's 18th Annual Global CEO Survey: Mobile, Data Mining & Analysis Most Strategically Important Technologies

     

    81% of CEOs see mobile technologies for customer engagement as the most strategically important in 2015. 80% see data mining and analysis technologies as the most strategically important, followed by cybersecurity (78%), Internet of Things (IoT) (65%), socially-enabled business processes (61%) and cloud computing (60%).

    These and many other research findings are part of PwC’s 18thAnnual Global CEO Survey. Released earlier today at the opening of the World Economic Forum Annual Meeting in Davos, Switzerland,the study provides insights into CEO’s current priorities and future plans across a wide spectrum of areas.
    PwC interviewed 1,322 CEOs in 77 countries, with 28% of the interviews conducted by telephone, 59% online and 13% via mailed questionnaires. PwC’s sample is selected based on the percentage of the total GDP of countries included in the survey, to ensure CEOs’ views are fairly represented across all major countries and regions of the world. Please see page 40 of the study for additional details of the methodology. The free PDF of PwC’s 18th Annual Global CEO Survey is pwc-18th-annual-global-ceo-survey-jan-2015.pdf here.
    Key Take-Aways
    CEOs are more confident in their enterprises’ business prospects in 2015 than they are about global economic growth. 39% of CEOs are very confident in their business prospects for 2014, surpassing the 37% confident in global economic growth. The following graphic compares the trending of these two metrics:

    unnamed 4


    Over-regulation (78%), availability of key skills (73%) and government response to fiscal deficit and debt burden (72%) are the top three threats CEOs are most concerned about. It’s interesting to note that five of the top then threats are technology-related. Availability of key skills, cyber threats including lack of data security, shift in consumer spending and behaviors, speed of technology change, and new market entrants are five threat areas that are being addressed by scalable, secure enterprise technologies today.

    unnamed 3


    Mobile technologies for customer engagement is the most strategically important series of technology CEOs are focusing on today. The report states that “the number of mobile phone users globally was expected to total 4.55B in 2014 – nearly 70% of the world’s population – with smartphone users totaling 1.75B. The volume of mobile traffic generated by smartphones is now about twice that of PCs, tablets and routers – despite having only surpassed them in 2013 – and is predicted to grow ten-fold by 2019”. The following graphic compares the strategic importance of key technologies.

    unnamed 2


    The majority of CEOs think that digital technologies have created high value for their organizations. CEOs cited areas including data and data analytics, customer experience, digital trust and innovation capacity as key areas where digital technologies are delivering value. Operational efficiency (88%), data and data analytics (84%) and customer experience (77%) are the top three priorities that CEOs are concentrating today. The first graphic compares the level of value being gained from each digital investment area, and the second provides an expanded analysis of selected digital technologies’ value.

     

    unnamed 1

     

    unnamed

    86% of CEOs realize they need to champion the use of digital technologies for their enterprises’ digital investments to succeed. Taking control and driving change management deep into their organizations by championing digital technologies is the most effective strategy for making digital investments pay off. The following graphic shows that CEOs realize how critical their roles are in overcoming resistance to the change that technologies bring into an organization. CEOs in 2015 will champion mobility, data and analysis to strengthen their organizations’ ability to compete in increasingly turbulent markets.

    86

     

  • Retailers are using big data for better marketing

    Durjoy-Patranabish-Blueocean-Market-IntelligenceToday, the customers’ expectations are growing by leaps and bounds and the credit goes to the technology that has given ample choices to them. Retailers are leaving no stone unturned to provide better shopping experience by adapting to analytical tools to catch up with the changing expectations of the consumers. Durjoy Patranabish, Senior Vice President, Blueocean Market Intelligence divulged Dataquest about the role of analytics in retail sector. 

    How retailers are using big data analytics to drive real business value?
    The idea of data creating business value is not new; however, the effective use of data is
    becoming the basis of competition. Retailers are using big data analytics to make variety of intelligent decisions to help delight customers and increase sales.

    These decisions range from assessing the market, targeting the right segment, forecasting demand to product planning, and localizing promotions. Advanced analytics
    solutions such as inventory analysis, price point optimization, market basket analysis, cross-sell/ up-sell analytics, real-time sales analytics, etc, can be achieved using
    techniques like clustering, segmentation, and forecasting. Retailers have now realized the importance of big data and are using it to draw useful insights and managing the customer journey.

    How advanced clustering techniques can be used to predict better purchasing behaviors in targeted marketing campaigns?
    Advanced clustering techniques can be used to group customers based on their historical purchase behavior, providing retailers with a better definition of customer segmentation on the basis of similar purchases. The resulting clusters can be used to characterize different customer groups, which enable retailers to advertise and offer promotions to these targeted groups. In addition to characterization, clustering allows retailers to predict the buying patterns of new customers based on the profiles generated. Advanced clustering techniques can build a 3D-model of the clusters based on key business metrics,

    such as orders placed, frequency of orders, items ordered or variation in prices. This business relevance makes it easier for decision makers to identify the problematic clusters that force the retailers to use more resources to attain a targeted outcome. They can then focus their marketing and operational efforts on the right clusters to enable optimum utilization of resources.

    What trends are boosting big data analytics space?

    Some of the trends in the analytics space are:


    „„1. The need for an integrated, scalable, and distributed data store as a single repository will give rise to the growth of data lakes. This will also increase the need for data governance.
    „„2. Cloud-based big data analytics solutions are expected to grow three times more quickly than spending on on-premises solutions.
    „„3. Deep learning which combines machine learning and artificial intelligence to uncover relationships and patterns within various data sources without needing specific models or programming instructions will emerge
    4. „„ The explosion of data coming from the Internet of Things will accelerate real-time and streaming analytics, requiring data scientists to sift through data in search of repeatable patterns that can be developed into event processing models
    „„5. Analytics industry will become data agnostic, primarily having analytics solutions focused around people and machine rather than on structured and unstructured data
    6. „„ Data will become an asset which organizations can monetize by selling or providing value added content.

    What are your views on ‘Big Data for Better Marketing’. How retailers can use analytics tools to be ahead of their competitors?

    Whether it is to provide a smarter shopping experience that influences the purchase decisions of customers to drive additional revenue, or to deliver tailor made relevant real-time offers to customers, big data offers a lot of opportunities for retailers to stay ahead of the competition.


    Personalized Shopping Experience: Data can be analyzed to create detailed customer profiles that can be used for micro-segmentation and offer a personalized shopping experience. A 360 degrees customer view will inform retailers how to best contact their customers and recommend products to them based on their liking and shopping pattern.
    Sentiment analysis can tell retailers how customers perceive their actions, commercials, and products they have on offer. The analysis of what is being said online will provide retailers with additional insights into what customers are really looking for and it will enable retailers to optimize their assortments to local needs and wishes.
    Demand Forecast: Retailers can predict future demand using various data sets such as web browsing patterns, buying patterns, enterprise data, social media sentiment, weather data, news and event information, etc, to predict the next hot items in coming seasons. Using this information, retailers can stock up and deliver the right products
    and the right amount to the right channels and regions. An accurate demand forecast will not only help retailers to optimize their inventory and improve just-in-time delivery but
    also optimize in-store staffing, thus bringing down the cost.
    Innovative Optimization: Customer demand, competitor activity, and relevant news & events can be used to create models that automatically synchronize pricing with inventory levels, demand and the competition. Big data can also enable retailers to optimize floor plans and find revenue optimization possibilities.

    Source: DataQuest

  • Routinebanen worden opgeslokt door robots en artificial intelligence

    Robots en artificial intelligence zijn anno 2016 al ver genoeg ontwikkeld om een relatief groot deel van het fysieke voorspelbare werk en dataverwerkingstaken van mensen over te nemen. Bovendien zal technologische vooruitgang ervoor zorgen dat steeds meer taken van mensen worden overgenomen, wat ofwel leidt tot meer tijd voor andere taken, of een vermindering van het aantal menselijke werknemers.

    Automatisering en robotisering bieden de mensheid de mogelijkheid om zich te bevrijden van repetitief, fysiek werk, dat vaak als onplezierig of saai wordt ervaren. Hoewel het verdwijnen van dit werk zal zorgen voor positieve effecten op aspecten als gezondheid en werkkwaliteit, heeft de ontwikkeling ook negatieve effecten op de werkgelegenheid – zeker in banen waarvoor weinig vaardigheden gevraagd worden. De afgelopen jaren is er veel gesproken over de omvang van de bedreiging die robots vormen voor de banen van menselijke werknemers en een recent onderzoek van McKinsey & Company gooit nog meer olie op het vuur. Volgens schattingen van het Amerikaanse consultancykantoor zal op korte termijn tot wel 51% van al het werk in de Verenigde Staten zwaar worden getroffen door robotisering en AI-technologie. 

    Analyzing work activities

    Het onderzoek, dat is gebaseerd op een analyse van meer dan 2.000 werk-gerelateerde activiteiten in de VS in meer dan 800 arbeidsfuncties, suggereert dat voorspelbaar fysiek werk in relatief stabiele omgevingen de grootste kans loopt om te worden overgenomen door robots of een andere vorm van automatisering. Voorbeelden van dit soort omgevingen zijn onder meer de accommodatie en horecabranche, de maakindustrie en de retailsector. Vooral in de maakindustrie zijn de mogelijkheden voor robotisering groot – ongeveer een derde van al het werk in de sector kan als voorspelbaar worden beschouwd. Kijkend naar de huidige automatiseringstechnologie zou tot wel 78% van dit werk kunnen worden geautomatiseerd.

    Maar het is echter niet alleen simpel productiewerk dat kan worden geautomatiseerd, aangezien ook werk op het gebied van dataverwerking en dataverzameling met de huidige technologie al kan worden gerobotiseerd. Volgens berekeningen van McKinsey kan tot wel 47% van de taken van een retail salesmedewerker op dit gebied worden geautomatiseerd – al ligt dit nog altijd veel lager dan de 86% automatiseringspotentie in het data-gerelateerde werk van boekhouders, accountants en auditors. 

    Automation is technically feasible

    In het onderzoek werd ook in kaart gebracht welke functies de meeste potentie voor automatisering hebben. Onderwijsdiensten en management lijken, kijkend naar de huidige technologie, de vakgebieden die het minst getroffen zullen worden door robotisering en AI-technologie. Vooral in het onderwijs zijn de percentages automatiseerbare taken laag, met weinig dataverzameling, -verwerking en voorspelbaar fysiek werk. Managers kunnen wel enige automatisering verwachten in hun werk, vooral op het gebied van dataverwerking en verzameling. In de bouw en landbouwsector is er sprake van veel werk dat als onvoorspelbaar kan worden beschouwd. De onvoorspelbare aard van deze werkzaamheden beschermt arbeiders in deze segmenten, omdat deze taken minder eenvoudig te automatiseren zijn.

    McKinsey benadrukt dat de analyse zich richt op het vermogen van de huidige technologieën om taken van mensen over te nemen. Dat dit technologisch mogelijk is, betekent volgens het consultancybureau niet dat deze werkzaamheden ook daadwerkelijk zullen worden overgenomen door robots of intelligente technologie. In het onderzoek wordt namelijk geen rekening gehouden met de implementatiekosten van deze technologie, of naar de grenzen van automatisering. Daardoor zullen werknemers in bepaalde gevallen goedkoper en beter beschikbaar blijven dan een gerobotiseerd systeem.

    Met het oog op de toekomst, voorspellen de onderzoekers dat met de komst van nieuwe technologieën op het gebied van robotisering en kunstmatige intelligentie er ook meer taken geautomatiseerd kunnen worden. Vooral technologie die het mogelijk maakt om natuurlijke gesprekken te voeren met robots, waarbij de machines menselijke taal kunnen begrijpen en automatisch kunnen antwoorden, zal volgens de onderzoekers een grote impact hebben op de mogelijkheden voor verdere robotisering.

    Bron: Consultancy.nl, 3 oktober 2016

     

  • Security Concerns Grow As Big Data Moves to Cloud

    red-hacked-symbol-200x133Despite exponential increases in data storage in the cloud along with databases and the emerging Internet of Things (IoT), IT security executives remain worried about security breaches as well as vulnerabilities introduced via shared infrastructure.

    A cloud security survey released Wednesday (Feb. 24) by enterprise data security vendor Vormetric and 451 Research found that 85 percent of respondents use sensitive data stored in the cloud, up from 54 percent last year. Meanwhile, half of those surveyed said they are using sensitive data within big data deployments, up from 31 percent last year. One-third of respondents said they are accessing sensitive data via IoT deployments.

    The upshot is that well over half of those IT executive surveyed are worried about data security as cloud usage grows, citing the possibility of attacks on service providers, exposure to vulnerabilities on shared public cloud infrastructure and a lack of control over where data is stored.

    Those fears are well founded, the security survey notes: “To a large extent both security vendors and enterprises are like generals fighting the last war. While the storm of data breaches continues to crest, many remain focused on traditional defenses like network and endpoint security that are clearly no longer sufficient on their own to respond to new security challenges.”

    Control and management of encryption keys is widely seen as critical to securing data stored in the cloud, the survey found. IT executives were divided on the question of managing encryption keys, with roughly half previously saying that keys should be managed by cloud service providers. That view has shifted in the past year, the survey found, with 65 percent now favoring on-premise management of encryption keys.

    In response to security concerns, public cloud vendors like Amazon Web Services, Google, Microsoft and Salesforce have moved to tighten data security through internal development, partnerships and acquisitions in an attempt to reduce vulnerabilities. Big data vendors have lagged behind, but the survey noted that acquisitions by Cloudera and Hortonworks represent concrete steps toward securing big data.

    Cloudera acquired encryption and key management developer Gazzang in 2014 to boost Hadoop security. Among Hortonworks’ recent acquisitions is XA Secure, a developer of security tools for Hadoop.

    Still, the survey warned, IoT security remains problematic.

    When asked which data resources were most at risk, 54 percent of respondents to the Vormetric survey cited databases while 41 percent said file servers. Indeed, when linked to the open Internet, these machines can be exposed vulnerabilities similar to recent “man-in-the-middle” attacks on an open source library.

    (Security specialist SentinelOne released an endpoint platform this week designed to protect enterprise datacenters and cloud providers from emerging threats that target Linux servers.)

    Meanwhile, the top security concerns for big data implementations were: the security of reports that include sensitive information; sensitive data spread across big data deployments; and privacy violations related to data originating in multiple countries. Privacy worries have been complications by delays in replacing a 15-year-old “safe harbor” agreement struck down last year that governed trans-Atlantic data transfers. A proposed E.U.-U.S. Privacy Shield deal has yet to be implemented.

    Despite these uncertainties and continuing security worries, respondents said they would continue shifting more sensitive data to the cloud, databases and IoT implementations as they move computing resources closer to data. For example, half of all survey respondents said they would store sensitive information in big data environments.

    Source: Datanami

  • Software kiest de beste sollicitant

    hh-6379374Sollicitanten interviewen is tijdverspilling. Wie beschikt over voldoende historische data en de juiste rekenmodellen, kan uit een stapel cv’s haarfijn destilleren wie er het meest geschikt is voor een bepaalde vacature. Sterker nog: als een wervingsspecialist maar voldoende gegevens heeft, kan hij voorspellen hoe goed iemand zal worden in zijn baan zonder diegene ooit gezien te hebben.

    Geraffineerd rekenmodel

    Voor de meeste bedrijven is het bovenstaande een verre toekomstschets, maar de technologie is er al, betoogt wetenschapper Colin Lee in zijn proefschrift. Hij promoveerde deze maand aan de Rotterdam School of Management (Erasmus Universiteit) op onderzoek waarin hij een geraffineerd rekenmodel gebruikt om patronen in meer dan 440.000 bestaande cv’s en sollicitaties te analyseren. Het model blijkt met 70% nauwkeurigheid te kunnen voorspellen wie er uiteindelijk werkelijk wordt uitgenodigd op gesprek, op basis van zaken als werkervaring, opleidingsniveau en vaardigheden.

    Intuïtie

    ‘Belangrijke voorspellers zijn relevantie van de werkervaring en het aantal dienstjaren. Je kunt die samenvoegen in een formule, en zo de beste match bepalen’, zegt Lee. Hoewel werkervaring bepalend is, zijn recruiters verder niet erg consequent in wat zij de doorslag laten geven, zo concludeert hij uit de patronen. ‘We kunnen daar wel een rode draad in herkennen, maar veel lijkt op basis van intuïtie te gebeuren.’

    Argwaan

    Waar Nederlandse bedrijven huiverig zijn om de analyse van 'big data' een centrale rol te geven bij werving en selectie, is die praktijk al jaren gemeengoed in Silicon Valley. Voorlopers als Google baseren hun aannamebeleid in de eerste plaats op harde data en algoritmen, gebaseerd op succesvolle wervingen uit het verleden. ‘Bedrijven zijn vaak extreem slecht in werving en mensen interviewen. Ze varen op gevoel en ongefundeerde theorieën’, zei directeur human resources Laszlo Bock van Google vorig jaar in een interview met het FD.

    Kan een bedrijf zich met louter data een weg banen naar de perfecte kandidaat? In Nederland heerst de nodige argwaan, en niet alleen over de nog onbewezen technologie. Ook ethische vraagstukken spelen een rol, zegt Lee. ‘De toekomst is dat je exact kunt becijferen hoe iemand gaat presteren op basis van de parameters in zijn cv. Dat is eng omdat je mensen op voorhand uitvlakt.’

    Optimale match

    Wervingssoftware wordt wel al langer in minder extreme vormen toegepast, bijvoorbeeld door grote uitzenders als Randstad, USG en Adecco. Die maken met speciale software een eerste voorselectie uit honderden, of zelfs duizenden cv’s. Dat gebeurt met behulp van zogenaamde 'applicant tracking systemen' (ATS). Dat zijn filters die zowel openbare gegevens op sociale media als interne databases van klanten gebruikt om te werven, of om te bepalen of een werknemer wel de optimale ‘match’ is in zijn huidige functie.

    ‘Vaak kunnen wij beter zien of iedereen binnen een bedrijf tot zijn recht komt dan dat bedrijf zelf’, zegt Jan van Goch van Connexys, een maker van wervingssoftware. De belangrijkste barrière voor verdere ontwikkeling van dit soort toepassingen is volgens hem niet zozeer de technologie, als wel de angst van klanten voor privacyinbreuk en aansprakelijkheid. Zij zitten vaak op bergen aan waardevolle historische informatie over hun sollicitanten, maar weigeren die te ontsluiten voor gebruik in grotere databases.

    Wetgeving

    Van Goch: ‘Als al die informatie bij elkaar komt, kunnen we nog veel slimmer matchen en werven. Klanten willen dat wel, maar ze geven zelf niet altijd toestemming om eigen gegevens te gebruiken en blijven er dus op zitten en dat is doodzonde. Een deel is bang om aangeklaagd te worden op het moment dat het op straat komt te liggen, des te meer sinds de wetgeving voor dataopslag is aangescherpt.’

    Source: FD

  • Strong Innovators Mine Big Data: Insights From BCG's 50 Most Innovative Companies

    tDu62fzJBCG’s 11th Annual Survey underscores how the world’s most innovative companies have learned how to create entirely new business models faster than competitors, translating that speed into global scale. Two new entrants to BCG’s annual list, Uber, and Airbnb, are examples of companies capitalizing on their unique multisided platforms to grow fast and scale globally. An excellent book on how multisided platforms are proliferating is Matchmakers: The New Economics of Multisided Platforms by David S. Evans and Rochard Schmalensee.

    Key insights from the Boston Consulting Group annual study of innovative companies include the following:

    • Four of the top 10 most innovative companies are cloud-based businesses. These include Google, Amazon, Netflix and Facebook. Two of the top 10 are investing heavily in cloud technologies, platforms, services and apps (Microsoft and IBM). The following graphic lists BCG;s 2016 most innovative companies.

    most innovative companies

    • Strong innovators are 4.5X more likely to be adept at leveraging Big Data and analytics for providing support for inputs to ideation. 65% of strong innovators mine big data or social networks for ideas. The following graphic compares how strong versus weak innovations rate their company’s skill at leveraging Big Data and advanced analytics for each phase of the innovation process. Being able to gain insights faster than competitors and act on them is a topic David S. Evans and Rochard Schmalensee often mention in their book.

    big data and analytics

    • BCG found that the most innovative companies have exceptional skills and insights into how to make the most of the multiple data sources within and outside their organization. The study highlights how companies who ranked high in innovation are adept at making the most of global patents, scientific literature, semantic networks and venture funding databases.  Strong innovators also can use external data throughout each phase of the innovation process, supporting new ideas to providing insights into investment decisions.  The following two figures provide insights into the differences between strong and weak innovators regarding their sources of new projects and ideas.

    strong innovators cast a wide net

    mutiple sources of information

    Source: forbes.com, January 21, 2017

  • Succes with big data starts with asking the right questions

    stellen van vragenMany organizations complain that they aren’t achieving the success with big data projects they hoped for. At the recent Strata & Hadoop World conference in New York, Information Management spoke with Tara Prakriya, chief product officer at Maana, about why that is, and what can be done about it.

    What are the most common themes that you heard among conference attendees?

    Tara Prakriya: There seemed to be a lot of disappointment over the results of big data projects, with value falling short of expectations. At Strata there appeared to be a lack of process, tools and platforms for subject-matter experts and data science collaboration. There is an inability to combine models and on top of that the right questions aren’t being asked to achieve very specific optimization for enterprises.

    What does your company view as the top data issues or challenges this year?

    Prakriya: We see two buckets:

    First, we start from a precise question and develop a model to answer. We then back into appropriate data within what exists in the enterprise and NO more. When getting the data, we are more surgical about the data we are looking at and only use data we need.

    Second, Maana is usually deployed into environments in which all users do not have equal access to all data under its management. We have more focus on knowledge management. We also have the affordance, with our platform, for sharing the knowledge without having to have to share the data itself.

    How do these themes and challenges relate to our company’s market strategy this year? 

    Prakriya: Our theme is about utilizing information in the form of knowledge to get to the ROI for business processes optimization. There is a clear link between the information for the Q&A and what that takes to move the needle. We see Fortune 500 companies going through digital transformation and existing technologies are falling short of getting business value

    Source: Information Management, David Weldon, October 19th, 2016

  • Tekort aan data scientists wordt belangrijke trend in BI

    2 April 2015

    Data Is geen informatie en informatie is nog geen kennis. Het op creatieve doch methodisch verantwoorde manier verwerken van data blijft mensenwerk. Onderzoeksbureau Passioned geeft bijvoorbeeld aan dat de exponentiële aanwas én beschikbaarheid van data en de almaar groeiende behoefte aan analytics ervoor zorgen dat het al bestaande tekort aan data scientists steeds groter zal worden (op basis van een artikel dat al in 2013 in NRC verscheen). De vraag is echter gerechtvaardigd wat een data scientist tot een goede data scientist maakt. Een goede data scientist heeft niet alleen verstand van statistiek, data blending en data visualisatie. Een goede data scientist kan meer. Hij of zij is in staat om data op bedrijfsrelevantie te beoordelen en door middel van analyse toegevoegde waarde voor de business te creëren. En niet in de laatste plaats is hij of zij in staat de gegenereerde inzichten actionable te maken. Dat vereist sociaal communicatieve vermogens. De data scientist kan wel de meest gewilde werknemer van de 21e eeuw zijn. Het is nog niet zo eenvoudig aan het profiel te voldoen.

    BI-kring redactie

  • Ten ways to drive value from big data

    bigstock-Big-data-concept-in-word-tag-c-49922318Just think what $48 billion could buy. In the private sector, that could buy a lot of R&D and innovation; the lifeblood of a successful and growing economy. In the public sector, think of the boost it could give to education, healthcare and defence.

    What is that figure and why is it relevant?

    According to a new report from PricewaterhouseCoopers, that is the sum of money the Australian economy left on the table last year and wasted due to its inability to fully leverage the potential of data-driven innovation.

    $48bn: that’s the equivalent of 4.4 per cent of gross domestic product and about the same as the entire (and struggling) Australian retail industry.

    Little wonder that big data is top of the CIO priority list on almost every report you read. So why then do people still question the value of big data?

    The challenge is that, while the principle of getting value out of all the data that is now available in the world in order to gain new insight, better serve customers and find new markets seems simple, doing so is actually hard in practice.

    How do you realise commercial value from your data and not end up on a hunt for fool’s gold?

    1. Realise you’re on a journey

    When you begin to seek value from big data you will need a healthy degree of realism. It is hard to get to a revenue stream right away. More likely it will be a journey, starting with projects that add value to the existing business before arriving at opportunities that create whole new revenue streams.

    A good starting point is to leverage how you already make money. Consider your current revenue streams and how you could approach them differently to add new value, and what insights will be required to achieve that.

    2. Do you have the right team

    It is common to get caught up in the excitement of big ideas, but are you ready for it? It’s not to say the concept won’t become reality, but in many instances, it may become apparent that you are six-12 months away from it becoming viable.

    From experience, those companies that have gone at projects too early have failed to realise the value, and then stalled in their big data activities as a whole due to the failure, or have given the idea away in their attempts. A key element of being ready is having the right team in place; that is tenacious, will persist in the face of various obstacles, brave enough to take the initiative, and inventive being able to create new value from data.

    3. Selling your data could be like selling your soul

    As big data skills are in short supply, some companies decide to sell their data and achieve revenue from it that way. Unless you understand how you could monetise your data yourself, you are at risk of commoditising your own information.

    Before you go this route, focus on figuring out how your data can augment your unique value proposition, and don’t give up on creating new value from your data just because the first experiment doesn’t work. Also be aware of the potential risks of selling your data or the insights into it. These include issues around the security of the data once it is no longer under your control, the chance of re-identification of anonymised information, and the potential impact on your reputation if it is used for unintended purposes.

    4. Is there a wider ecosystem you could be part of?

    There are, however, increasing instances of companies and industries collaborating around, or selling data. For example, in the pharmaceutical industry we have seen organisations working as a consortium around the creation of new data sets from clinical research, as a way to overcome the prohibitive cost they would have faced doing it alone.

    The advice here would be not to rule options out — especially if they might enable you to do things with data that you couldn’t do before, and as a result, move up the value chain or closer to the end consumer.

    5. Don’t chase fool’s gold

    Use data, and especially social data, wisely. While providing great insight into the digital DNA of customer decision making, developing accurate models for sentiment analysis is hard, due to the large amount of false positives that exist.

    The nature of social means that many companies, at any time of the day or night, can have somebody saying something negative about them. How do you know when is this out of the norm?

    This level of understanding is something that often develops over time, and is an enrichment and maturity process in your analytics. Get it right and you can make money by developing an understanding of the soft signals, but unless you have a historic wealth of data in that area, gaining that sort of intelligence is hard to come by.

    6. Understand the customer contract

    You need to know what your trust relationship is with your audience. At the advent of the concept of 1-1 marketing, the customer understood the idea that by giving a bit more information they could get a more personalised treatment — and they didn’t mind it.

    However, with the arrival of big data, some customers feel that companies are going too far in collecting and using intimate details gathered without their prior permission. It also depends on the company.

    Customers expect the likes of Google and Shazam to use data to make recommendations. You just need to understand where the line needs to be drawn.

    7. Realise the goalposts are always shifting

    Change is inevitable and rapid. Your key competitors today may well be superseded by companies you never dreamt would fit in your market. However, it’s not all about the big guys. The internet of Things is going to be a great leveller, particularly in the field of ‘controls’ which is enabling new, smaller players to nip in and seize customer data and ownership. This is giving them the power to disintermediate traditional providers.

    8.Be prepared to unlearn

    In some cases you might find that the data shows that your assumptions are incorrect or that your activity is not the success you hoped it would be.

    For example, many companies seek to make money from content, but analysis shows it is a highly crowded market and there is little money in it other than for the likes of Google and Facebook.

    Companies need to understand what the implication is for their business. Does content add value to your customers, is it expected by your customers? Rather than there being money in the content itself, is there value that can be derived from better understanding the digital signature of your end users in being able to see what caused someone to click on an ad or what sort of people are visiting your website?

    9. Don’t confuse perfection with monetisation

    This is extremely important. When programmers and IT people talk about data, they often talk about perfection because we are very deterministic. We want to say ‘if this, then that’; if you get this data, then you will achieve exactly that result.

    It is hard to achieve perfection. Consider s case where you find you can cut the cost of a process by 30 per cent in a relatively short time frame. Or you might have the potential to cut your costs by double that, but it would most likely take you several years. Is the wait worth the lost opportunity cost? In a big data world, the best practice approach would be to experiment — try something and iteratively improve it instead of trying to get perfection out of the gate.

    10. Remember, David doesn’t always beat Goliath

    while new entrants can outmanoeuvre established organisations, most often David doesn’t beat Goliath. In fact, as often, the incumbent can use data to create barriers to entry, due to the fact that they have a significant advantage from the large volumes of historic customer information and transactional data they hold.

    However, to realise the benefit, established operations need to digitise or datify all of this information before their rivals do, and potentially seek out new data streams to compliment what they already have.

    For new entrants, many of the key business opportunities exist where there is a breakdown in a process or supply chain in an area that really matters to the customer — think Uber or Airbnb. Look for what is ‘broken’ in areas that are not already heavily digitised.

    The reality is that there is a whole range of data out there, offering new ways to get insights, drive value and compete. It is essential that you understand the potential and get excited about the opportunity. Think really broadly about the data that is out there, both inside and outside of the organisation, see what there is that could add value, without ending up on a hunt for fool’s gold.

    Source: The Australian Business review

  • The big data race reaches the City

    coloured-high-end-data-cables-large transEduPGWXTgvtbFyMaMlYatm4ovIMMP 5WSTNAIgCzTy4

    Vast amounts of information are being sifted for the good of commercial interests as never before

    IBM’s Watson supercomputer, once known for winning the television quiz show Jeopardy! in 2011, is now sold to wealth management companies as an affordable way to dispense investment advice. Twitter has introduced “cashtags” to its stream of social chatter so that investors can track what is said about stocks. Hedge funds are sending up satellites to monitor crop yields before even the farmers know how they’re doing.

    The world is awash with information as never before. According to IBM, 90pc of all existing data was created in the past two years. Once the preserve of academics and the geekiest hedge fund managers, the ability to harness huge amounts of noise and turn it into trading signals is now reaching the core of the financial industry.

    Last year was one of the toughest since the financial crisis for asset managers, according to BCG partner Ben Sheridan, yet they have continued to spend on data management in the hope of finding an edge in subdued markets.

     
    “It’s to bring new data assets to bear on some of the questions that asset managers have always asked, like macroeconomic movements,” he said.

    “Historically, these quantitative data aspects have been the domain of a small sector of hedge funds. Now it’s going to a much more mainstream side of asset managers.”

     
    59823675 The headquarters of HSBC Holdings Plc left No 1 Canada Square or Canary Wharf Tower cen-large transgsaO8O78rhmZrDxTlQBjdEbgHFEZVI1Pljic pW9c90 
    Banks are among the biggest investors in big data

    Even Goldman Sachs has entered the race for data, leading a $15m investment round in Kensho, which stockpiles data around major world events and lets clients apply the lessons it learns to new situations. Say there’s a hurricane striking the Gulf of Mexico: Kensho might have ideas on what this means for US jobs data six months afterwards, and how that affects the S&P stock index.

    Many businesses are using computing firepower to supercharge old techniques. Hedge funds such as Winton Capital already collate obscure data sets such as wheat prices going back nearly 1,000 years, in the hope of finding patterns that will inform the future value of commodities.

    Others are paying companies such as Planet Labs to monitor crops via satellite almost in real time, offering a hint of the yields to come. Spotting traffic jams outside Wal-Marts can help traders looking to bet on the success of Black Friday sales each year – and it’s easier to do this from space than sending analysts to car parks.

    Some funds, including Eagle Alpha, have been feeding transcripts of calls with company executives into a natural language processor – an area of artificial intelligence that the Turing test foresaw – to figure out if they have gained or lost confidence in their business. Trades might have had gut feelings about this before, but now they can get graphs.

    biggest spenders
     
     

    There is inevitably a lot of noise among these potential trading signals, which experts are trying to weed out.

    “Most of the breakthroughs in machine-learning aren’t in finance. The signal-to-noise ratio is a problem compared to something like recognising dogs in a photograph,” said Dr Anthony Ledford, chief scientist for the computer-driven hedge fund Man AHL.

    “There is no golden indicator of what’s going to happen tomorrow. What we’re doing is trying to harness a very small edge and doing it over a long period in a large number of markets.”

    The statistics expert said the plunging cost of computer power and data storage, crossed with a “quite extraordinary” proliferation of recorded data, have helped breathe life into concepts like artificial intelligence for big investors.

    “The trading phase at the moment is making better use of the signals we already know about. But the next research stage is, can we use machine learning to identify new features?”

    AHL’s systematic funds comb through 2bn price updates on their busiest days, up from 800m during last year’s peak.

    Developments in disciplines such as engineering and computer science have contributed to the field, according to the former academic based in Oxford, where Man Group this week jointly sponsored a new research professorship in machine learning at the university.

    google-driverless 3147440b 1-large transpJliwavx4coWFCaEkEsb3kvxIt-lGGWCWqwLa RXJU8
    The artificial intelligence used in driverless cars could have applications in finance

    Dr Ledford said the technology has applications in driverless cars, which must learn how to drive in novel conditions, and identifying stars from telescope images. Indeed, he has adapted the methods used in the Zooniverse project, which asked thousands of volunteers to help teach a computer to spot supernovae, to build a new way of spotting useful trends in the City’s daily avalanche of analyst research.

    “The core use is being able to extract patterns from data without specifically telling the algorithms what patterns we are looking for. Previously, you would define the shape of the model and apply it to the data,” he said.

    These technologies are not just been put to work in the financial markets. Several law firms are using natural language processing to carry out some of the drudgery, including poring over repetitive contracts.

    Slaughter & May has recently adopted Luminance, a due diligence programme that is backed by Mike Lynch, former boss of the computing group Autonomy.

    Freshfields has spent a year teaching a customised system known as Kira to understand the nuances of contract terms that often occur in its business.

    Its lawyers have fed the computer documents they are reading, highlighting the parts they think are crucial. Kira can now parse a contract and find the relevant paragraphs between 40pc and 70pc faster than a human lawyer reviewing it by hand.

    “It kicks out strange things sometimes, irrelevancies that lawyers then need to clean up. We’re used to seeing perfect results, so we’ve had to teach people that you can’t just set the machine running and leave it alone,” said Isabel Parker, head of innovations at the firm.

    “I don’t think it will ever be a standalone product. It’s a tool to be used to enhance our productivity, rather than replace individuals.”

    The system is built to learn any Latin script, and Freshfields’ lawyers are now teaching it to work on other languages. “I think our lawyers are becoming more and more used to it as they understand its possibilities,” she added.

    Insurers are also spending heavily on big data fed by new products such as telematics, which track a customer’s driving style in minute detail, to help give a fair price to each customer. “The main driver of this is the customer experience,” said Darren Price, group chief information officer at RSA.

    The insurer is keeping its technology work largely in-house, unlike rival Aviva, which has made much of its partnerships with start-up companies in its “digital garage”. Allianz recently acquired the robo-adviser Moneyfarm, and Axa’s venture fund has invested in a chat-robot named Gasolead.

    EY, the professional services firm, is also investing in analytics tools that can flag red flags for its clients in particular countries or businesses, enabling managers to react before an accounting problem spreads.

    Even the Financial Conduct Authority is getting in on the act. Having given its blessing to the insurance sector’s use of big data, it is also experimenting with a “sandbox”, or a digital safe space where their tech experts and outside start-ups can use real-life data to play with new ideas.

    The advances that catch on throughout the financial world could create a more efficient industry – and with that tends to come job cuts. The Bank of England warned a year ago that as many as 15m UK jobs were at risk from smart machines, with sales staff and accountants especially vulnerable.

    “Financial services are playing catch-up compared to some of the retail-focused businesses. They are having to do so rapidly, partly due to client demand but also because there are new challengers and disruptors in the industry,” said Amanda Foster, head of financial services at the recruiter Russell Reynolds Associates.

    But City firms, for all their cost pressures, are not ready to replace their fund managers with robots, she said. “There’s still the art of making an investment decision, but it’s about using analytics and data to inform those decisions.”

    Source: Telegraph.co.uk, October 8, 2016

     

     

  • The Chief Data Officer - Who Are They and Why Companies Need one

    CDOData has become one of the core areas that companies are investing in at the moment, whether they are mature, on the journey or just embarking on data projects. Every mainstream magazine presented articles about data, big data, customer data etc. Citing that data is at the heart of every or most initiative whether it's to do with the customer experience, creating or enhancing a new product, streamlining the operational processes or getting more laser focused on their marketing. 

    With this new (or should I say ongoing) trend, there has emerged a new power hitter, the data supremo, the knight in shining data (that’s enough now this isn’t a boxing match!) – please welcome to the ring the new kid on the block in the C-Suite – the Chief Data Officer or CDO. 

    Let’s take a look under the covers at who the CDO is, and what they are tasked with in this brave new world.

    The New Frontier

    The characteristics of the CDO:

    1. A Leader (yes with a capital L) that knows their “true North” and can guide companies and corral people on a journey from data immaturity to being a data competitor
    2. An advocate for all things data from: data governance, to data analytics, to data architecture, to data insights and actions
    3. An individual that has the tenure of business, and can quickly understand business models and strategy and align these to the data / information strategy – supporting the strategy to ensure the business evolves into the vision that has been set
    4. An individual who has the knowledge of technical concepts, and the ability to set the technical data strategy and collaborate with technology colleagues (namely the CIO / CDO) to ensure alignment with the overall technology strategy
    5. An individual that can lead an organisation through deep change, rethinking the way they look at decisions and encourage a data-driven approach
    6. Most importantly, and this is purely my view – a delivery and execution focus with the confidence to try things in a small way and if they fail then rethink and attempt again. This is about failing fast – none of us have the true answer to this and without a little bit of experimentation the CDO will almost definitely fail!

    I am sure there are for more characteristics that may be focused around privacy, data security, agile etc. All of these are important, and certainly up there with the responsibilities. In some industries, such as the financial world and heavily regulated ones, there may well be Chief Information Security officers or Compliance personnel that can support the CDO. That leads me onto my next point. 

    A Seat at the Top Table

    I have a sneaky little feeling that most of you think the CDO should be sitting somewhere below the CIO / CTO or even CFO – wrong! In my humble opinion (well actually experience of working with a number of CDOs across different industries) – they should be reporting directly to the CEO and should be on the Board. Being able to set the strategic direction, and the ability to sit at the top table permits that level of confidence and authority to be voice of data within the company. Spelling out just what and where the company needs to go with data and how it’s going to get there – a roadmap if you will.  This is the guiding true north of the CDO – sitting at the top and being able to provide input at the top – down will ensure that agreement on data projects and the force behind them prevail – and not just fall down some miserable drain somewhere never to be seen again or for someone to say – “oh no we just weren’t ready for a CDO”. Once a CDO has been employed and onboarded, the company has to ensure that data is seen as a strategic business asset and with the intelligent use of data comes profitability, deeper customer understanding, better product outcomes, a better and slicker operations.

    If the CDO has the opportunity to be on par with the C-Suite then they can work with their counterparts more effectively to complement the IT Strategy (working with the CIO / CTO); understand the issues that they CMO is having working the marketing funnel and customer propensities to buy products etc.; being able to support the CFO by providing deeper insights into regulatory reporting and getting it faster out of the blocks – ensuring governance of data is permeated throughout.

    Gartner predicts that 90% of large organisations will have a Chief Data Officer role by 2019 

    The Journey Starts…in the first 100 days

    Meet and listen to your key Stakeholders: Just like presidents and prime ministers when they come into office, they have a 100-day plan (or you hope they do!). This is when they need to be at their most thoughtful, observant, curious and in deep listening mode. It isn’t the bulldozer effect here – this is where humility must be on offer – a stance of “I want to know where you have been” and “I want to listen to where you want to go” and most of all “I want to support you to get there”.  No assumptions or judgements should be in place here – leave your EGO at the door. 

    Where is the PAIN?: While meeting with key and wider stakeholders, start to understand their key issues / pain points / challenges with data. Understanding where companies haven’t had a good experience with data or have invested in technology for the sake of it and of little or no value etc. With this, the CDO can start to think about how their role can support in the objectives that others have been set – be it departmentally / locally / regionally / globally. With one of the CDOs we are working with now, they were set a very difficult task of supporting the company with very little resource and budget. Cobbling together what they could from project to project. As this grew, the issue became that business units would get frustrated by the lack of impetus or push from the CDO, that led to scepticism and frustration throughout the business. From all sides. This is really key for most organisations – empower the CDO – don’t give them a job that doesn’t have a mandate – what I’m really saying is prepare to win and not fail. The CDO in this case, thankfully, is now turning heads and being able to deliver real change and benefits in a business that is going through deep change. 

    Measures / KPIs and all things glossy: This is the real icing on the cake, the sponge and creamy bits of the cake that everyone wants. How to take the pulse of the organisation and start to understand what needs to be measured in the various departments, what are they measuring now, how are they getting to that data, which business questions are being asked, which aren’t being answered and which by their very nature have been put to the side as people don’t know how to answer them. Initiatives will also emerge from the multitude of conversations – some people will talk about a Single Customer view as being the holy grail, others will want operational efficiency, some will want to take products to market quicker, others will want to know how they can predict who will buy their products in the wild west! All of these are great places to start to think and build a plan of attack. 

    Roadmap time: This is where the plan starts to shape! The culmination of all the conversations, have started to sink in, there has been a ton of data to pick through both technical and business in nature. Supported as a high-level picture or document or even powerpoint deck (go easy on the number of slides) - these are the areas that the CDO needs to focus on after all of the conversations:

    Business Drivers & Goals – a deep understanding of the business strategy and where that particular roadmap is headed and how everything the CDO does, needs to align with that. 

    Business Intelligence /Analytical Maturity – a stake in the ground as to where the company is on their journey – the five areas being (just one of the many frameworks that can be used):

    • Stage 1 – BI / Analytically impaired
    • Stage 2 – Localised BI / Analytics
    • Stage 3 – BI / Analytics Aspirations
    • Stage 4 – BI / Analytics Company
    • Stage 5 – BI / Analytics Competitor

    Data Governance – are there standards, policies, processes and data owners across the organisation. The CDO needs to think about how to define these areas, and take the organisation on a journey where they can adopt principles of Master Data & Reference Data Management (being just one of the issues). Adopting a maturity metre to understand the structures that need to be put in place, will help the business understand the action that is needed – the maturity needs to focus on the following five areas and how to get there:

    • Stage 1 – No Data Governance
    • Stage 2 – Application Based Standards
    • Stage 3 – Data Stewardship
    • Stage 4 – Data Governance Council
    • Stage 5 – Enterprise Data Governance Program

    Resources & Organisation – we recently worked with an Insurance company that worked with data in isolation, and depended on a few key people to churn reports week in, week out! By churn I really do mean hand cranking them, by extracting and manipulating hundreds of spreadsheets – nightmare city! By taking a measured view across the organisation, we were able to find some astute data evangelists, analysts as well as consulting on who they should hire, the insurance company was able to centralise a team that would provide data and MI (as they liked to call it) to the business. Thereby, taking off the pressure of the few that had been sweating a lot! In this case they didn’t hire a CDO, however, the CDO when appointed needs to sniff around the business and dig out those people that can form what might become an Analytics Centre of Excellence, or a Business Intelligence Competency Centre. In another company, we were able to find these people in their business units and over time, helped the CDO to bring them into the CDO office – this helps as they have the business knowledge and can perform data analysis that someone from the outside wouldn’t be able to do as quick – true insights supported by their data analysts and then able to take action as they have the credibility of knowing the business.  The main idea here is to move to a point of true centralised or hub and spoke model to support the business, so that data is talked about at the coffee machine- I mean what else would you talk about at the coffee machine right? Grin

    Tools & technology – last and by no means least – the various investments or divestments that need to be made. What is working, what isn’t, what isn’t aligned to the overall strategy, how many BI applications are being used across the organisation, what platforms are required and so on. This is the cool stuff that centres around visualisation to support data storytelling, predictive analytics and all things that will drive those initiatives and give people the freedom to enjoy data and being able to self-serve all they want. This could take into account if the company needed to look at Hadoop or next generation AI / machine learning and to have that on the roadmap or whether a data warehouse will be suffice to being with, and how they integrate key data to answer those business questions.

    As stated the main artefacts that are produced – the Data or Information Strategy that outlines what will be done to reach the 100-days mark. A communications document / slideware that puts the roadmap on a page and explains the high-level.  The evangelical movement begins.

    Once the roadmap has been accepted, the CDO needs to start walking the walk, taking that action and measuring the results. There need to be regular updates with the key stakeholders, to ensure they know what has been delivered, what will be delivered, when it will be delivered and how much value is being delivered by all the initiatives – this is the real rub – the impact that the strategy has had. 

    "There is no division where you can't add value by using data." Davide Cervellin, eBay Head of EU Analytics 

    It’s a Brave New World

    Not every company out there will need a CDO, but the tide is turning and without the focus on data that is crucial to every business now – companies that don’t appoint this role may well be left behind. The competitive advantage that a CDO brings to the top table is one that is gaining ground and is much needed in times of differentiation and innovation. 

    More and more companies will continue to generate more and more data from all sources – customers, sensors, products, social etc. and the difference could be the CDO, the data strategy and aligning with the business strategy. Being able to transform, equip the business with the data they need, creating value by experimentation, not being afraid to fail, breaking through the scepticism, will win the CDO a lot of friends, but most of all, the CEO will be blessed with a C-Suite that can break the mould of the old – “how things have always been done!”

    Author: Samir Sharma

     

     

  • The Cloud’s Biggest Threat Are Data Sovereignty Laws

    CloudThe beauty of the cloud is the promise of simplification and standardization — without regard to physical or geographic boundaries. It’s this “any time, any place, any device” flexibility that is driving rapid adoption.

    However, new government regulations on data sovereignty threaten to complicate the delivery model that has made cloud computing attractive, presenting new concerns for companies with operations in multiple countries.

    While the strike down this fall of the United States-European Union “Safe Harbor” agreement made most of the headlines, I see the recent localization law in Russia (which went into effect in September) as a more significant development. The law mandates that personal data on Russian citizens must be stored in databases physically located within the country itself.

    With this ruling, companies that capture, use and store data must abide by specific laws or face the consequences of falling out of compliance. Russia is a warning bell. With currently 20+ countries also considering similar privacy laws, the landscape will grow increasingly complex for cloud providers, and more costly for customers, thus chipping away at the beauty of the cloud.

    To make the point clear, let’s take a look at what the Russian law portends.

    Your business could likely store data in numerous locations across the globe, but because the software is cloud-based, it’s up to you to re-architect the way it operates, ensuring Russian data lives in Russia.

    There is, however, some leeway that allows a business to process data at runtime in a different country from where the data is persistently stored. For example, your business can have a runtime in Germany, but the Russian employee data gets stored based on local rules in Russia.

    So your cloud provider must have data centers in multiple countries. At SAP, we do, including in Russia, but I don’t think a data center in every country across the globe is a long-term answer.

    So how do you calculate the risk of falling out of compliance? Here are three considerations for preparing for a worsening regulatory climate:

    Create A Roadmap. Know where your company does business, and where it plans to. If expansion is on the horizon, start monitoring legislation in those countries to estimate costs and restrictions early to minimize compliance risk.

    Know Your Cloud. Understand from your cloud providers where the data resides. What is their roadmap? What are the costs?

    Realize How Mission-Critical Compliance Is. Every industry and enterprise is different. Maintaining compliance may be critical to your business, or it may be an afterthought. Understand how your company prioritizes these regulations and how much of your resources you should dedicate. You may decide that it makes more sense to manage data sovereignty on your own, or you may decide to hire a vendor.

    In the post-Safe Harbor era, updates to data sovereignty legislation are likely to occur with greater frequency. If you manage data outside of legislated parameters, you may be fine for a period of time — but the truth is that you will face significant challenges if there is a data breach that can be traced back to your company.

    There’s no United Nations of data; each country is looking at its own specific types of data. The only way to deal with it is to store specific data in-country. It’s expensive. The cloud is helping businesses move forward at an astounding pace by reducing complexity. The demand for simplification will continue to drive this journey.

    Source: TechCrunch

  • The Data Digest: Sports, Spectators, And Screen Time

    Sports fans around the world are having a heyday: From the Copa America soccer tournament in the US to the European Champions Cup across the pond, and from live Wimbledon matches to the imminent Summer Olympic Games, there is no lack of global sports entertainment at this moment.

    Sports teams have always brought people together as much as divided them — and in today’s age, technology amplifies the drama of fandom. Personal devices play a critical role in how people come together around sports, when fans watch the action unfold, and how they discuss the results.

    For example, Forrester’s latest Consumer Technographics® survey data reveals that consumers worldwide have recently accessed sports applications on their mobile phones and tablets: 

    data-digest 6.30.16 0

    Our previous research shows that consumers often prefer tablets for longer, more engaging, media-rich experiences — and in fact, Forrester’s mobile behavioral data indicates that consumers spend more time on sports apps when using their tablet rather than their mobile phone. However, technology doesn’t only enable sports enthusiasts to get into the game — oftentimes, it also allows more casual fans to multitask.

    In an earlier research report, my colleagues Nicole Dvorak and Kris Arcand note that as personal devices become integral to the sports spectator experience, marketers should design mobile experiences to engage audiences in new ways. They say that “smartphones [and tablets] are the perfect devices to provide newer, more creative extensions.” Marketers can leverage these screens to “appeal to ad fans [and] game fans, engage [the multitaskers], and tempt the party crowds with delicious offers.”

    Source: Forrestor.com

     

  • The Datafication of Value

    The business model for asset centric organizations is increasingly dependent on the provisioning of services that are bundled with products. These services rely increasingly on the data that originates from the production and use of the product. However, both technological developments and customer demand enable the free availability of this data to all, including third parties. This undermines the very business model that supports the production of the product and its data. Value in the value chain shifts from the assets required to produce goods to the data used to create value added services. This whitepaper discusses how modern corporations can achieve continuous data driven innovation, on par with the agility of disruptive startups, whilst retaining a competitive advantage by leveraging their traditional asset-base.
     
    Interesting? For more information: http://www.blinklane.com/profile/arent-van-t-spijker or to download the report click here.
  • The role of big data in financial inclusion

    Data-driven insights can be invaluable for businesses – and improve financial inclusion in a world where 2.5 billion people still have no access to financial services

    data drivenRelevance and personalisation are delivered by data-driven insights; this is one of the new truths of business. As a mathematician and statistician this appeals to my inner geek; I enjoy numbers, patterns and formulas. For many years it wasn’t a “cool” thing to admit but now in the 21st century the geeks have inherited the earth and technology has penetrated every part of our lives. Thanks to the ubiquity of technology those same numbers, patterns and formulas that I learnt to love are opening new doors and delivering data insights that are changing the face of business and commerce.

    Patterns and predictions can influence every part of our lives. What particularly interests me is how insights can deliver impact and influence in real world applications. I see the power of data every day, because we use it to inform our own business decisions at MasterCard. We use it as a route to identify areas for growth, address concerns, to understand our audiences and to drive social good on a global scale.

    Of course, we are not alone; the use of data to inform business decisions is nothing new. What is different today is that ubiquitous technology I referenced earlier. Whether it’s looking for the best deal on your next holiday at home on your tablet, buying a coffee with your smartphone or simply keeping abreast of news in other parts of the world via Twitter or Reddit, the world has changed and this has opened an opportunity to connect with people and businesses globally, in a far more relevant way. This new deeper understanding is driving growth – and more businesses need to seize the moment and be part of the new data driven opportunity.

    Post-recession there has been a seismic shift in consumer spending habits and for businesses; this has amplified competition across all sectors. Insights into consumer spending, derived from data, are increasingly valued by businesses in order to understand their competitors, differentiate themselves and ultimately drive growth. We no longer live in a world where business can create products, services, destinations and experiences that are driven by an identification of demand. Make it matter to me, make it work for me, make it for me is the mind set of our global consumer and in return we not only need to listen but we need to help make this a reality. While it is valuable to look at broad demographics, to see an audience by geography or age group; in today’s world we are all a “segment of one” or smaller and to engage me you have to listen, learn, personalise and reinvent; while balancing this with my right to privacy.

    We can do that at MasterCard; every day our network handles payments for two billion cardholders and tens of millions of merchants in over 210 countries around the world. These transactions or payments generate real-time data on a global scale, available faster than regular government statistics. The data we see is not personally identifiable data but by understanding what it tells us, we can create highly sophisticated macro-economic indicators that can inform business, retailers, governments and more can be used to help inform their business decisions.

    One of the fastest growing areas of our business is how we use aggregated data on spending patterns found in the payments we process to help our partners and customers build more relevant and tailored solutions, products and experiences. For businesses, who excel at capturing and analysing their own customer data, our macro-level data insights applied to their data can illustrate what happens next, outside of their company, across the market. By sharing these insights, we can give them an edge over their competitors, helping them to provide the optimum experience for their customers.

    A good example is some research MasterCard recently commissioned into travel trends. As a globally connected business, we wanted to look at how consumers spend across 135 cities around the world. MasterCard’s fourth annual Global Destination Cities Index found that London was the world’s top travel destination for consumer spending, with a projected 18.7 million international visitors in 2014 which equates to an £11bn injection into London from the tourism industry alone. Our report showed us the countries from where these visitors were coming, identified the travel corridors that link cities and much more. This kind of information is valuable to a huge range of businesses as well as local authorities and governments who can plan for an influx of tourists within a city as well as enabling business and government to plan for the year ahead.

    Alongside this we can also look at how global events give us an opportunity to identify insights. This year the World Cup took Brazil by storm and we were able to draw some particularly interesting trends and analysis. In advance of the big events many expected all spending to rise, particularly for items connected to the event. Instead, through MasterCard’s data and analytics we were able to see a spike in spending in Brazil on groceries and a drop in spending on luxury goods. This kind of economic insight can be of significant value to brands with larger single items costs, by enabling them to better plan promotional windows.

    Data-driven insights are also vital for our own commercial success and they inform how we drive growth. MasterCard’s vision of a world beyond cash is well publicised and the benefits for business, government and all of us are endless our data and insights help us understand the way we pay and the barriers around the shift from cash and paper to not only deliver against this vision but also to enhance the customer experience. One of the many things we have identified is that while there are unifying themes, like safety and security and ease and speed there are also local considerations that we are able to take into consideration when building next generation products and solutions around the world.

    Insights generated from data demonstrate an opportunity to grow our business among small and medium-sized enterprises (SMEs) too. Globally, some 80% of SMEs don’t have access to full banking services, even in developed economies. Our simplify commerce platform can help an SME set up from scratch in 15 minutes, enabling them to start selling over the internet and accept card payments. Without big data on the needs of SMEs we may never have identified this opportunity for growth.

    Data-driven insights are not just valuable for business; it also has an exciting role in driving social good. As president of international markets, I manage around 60% of MasterCard’s business worldwide, so the expansion of e-commerce, m-commerce and innovative payment technology driving growth in emerging markets is extremely important to me. We use data-driven insights to understand how people spend money around the world and identify where people are falling through the gaps in the financial system. For example, we know that 2.5 billion people are still without access to financial services – this is unacceptable.

    There is a perception that this is just a developing world issue, but that is not the case. Our data shows us there are 93 million people in Europe that don’t have access to basic bank accounts or financial products. Take Italy for example, where 25% of Italians are underserved and don’t have access to basic electronic payment. Families are left crippled in a world without access to sustainable financial services whether it is savings, social credits or insurance. Our data insights help us determine how we support people that are experiencing different aspects of financial exclusion in every market that we operate. We can work to engage these marginalised audiences in each market, helping people improve their lives by providing opportunities for access to essential financial services. Governments in these countries can also use this information to build better financial systems and encourage people to become more included in these systems. This marks a step forward for good in the way big data is used.

    The value of data-driven insights as a significant source of competitive differentiation to MasterCard will come as no surprise. But, I believe big data has the potential to transform people’s lives for good globally and the capability to drive social change, supporting transformational innovation and growth in developing countries. There is a need to drive financial inclusion across the world and data can play a part in making a real difference, the opportunity is there for the taking we just need to embrace the change.

    Ann Cairns is president of international markets at MasterCard

    Source: The Guardian, January 20, 2015

  • The Tech Industry Is in the Middle of a Data Engineering Talent Shortage

    MOD-54199 growthindataengineersA New Report From Stitch and Galvanize, The State of Data Engineering, Reveals a Shortage of Data Engineering Talent

    PHILADELPHIA, PA--(Marketwired - Sep 12, 2016) -  A new study, The State of Data Engineering released today by Stitch, an ETL service; and Galvanize, a learning community for technology, reveals a shortage in data engineering talent. There are only 6,500 self-reported data engineers on LinkedIn, but the San Francisco Bay area alone has 6,600 job listings for that same title.

    "Companies are increasingly viewing data as a competitive advantage," said Jake Stein, CEO of Stitch. "The Ubers and Airbnbs of tech have mastered using data to build better and smarter technology products. Now other tech companies are looking to do the same, and this is causing a major talent shortage."

    The report delves deep into three core trends around data engineering:

    • Growth in the data engineering discipline
      While there's a shortage of this talent today, the numbers of data engineers are growing rapidly. From 2013-2015, the number of data engineers increased 122%. For context, the number of data scientists increased 47% in that same time period. Jonathan Coveney, data engineer at Stripe, says this rise in data engineering talent reflects a new sophistication in how companies think about data and the people who manage it, "There's a growing sense today that data isn't just a byproduct, but rather it's a core part of what a company does." 
    • Where data engineers are coming from
      42% of all data engineers were formerly working as a software engineer, making it above and beyond the most common background. Data engineers are coming from other disciplines as well, but in much smaller numbers. A few other backgrounds include analyst (7%), consultant (6%), business analyst (3%), and data architect (3%). 
    • The skill sets of data engineers
      The top five skills of data engineer are: SQL, Java, python, Hadoop, and Linux. But these skills are notably different depending on the size of the company. For data engineers working at large companies (1000+ employees), top skills are: data warehousing, business intelligence, Oracle, ETL, and Unix. For data engineers working at small companies (1-200 employees), top skills are: python, Java, javascript, MySQL, and machine learning.

    Working with massive data sets requires specialized data engineering talent, and the race is on to get it. "The need for data science skills has become dramatic because companies realize the value and growth potential in their data assets," said Jim Deters, co-founder and CEO of Galvanize. "That's why companies of all sizes are starting to send their software engineers to Galvanize to learn how to work with big data and even the companies that aren't re-training or re-investing in their talent are telling us they need more people who can do this work -- it's a great opportunity for those with aptitude and ambition to learn these skills and to take some of these jobs."

    Bron:marketwired.com, 12 september 2016

     

  • The Top 5 Trends in Big Data for 2017

    Last year the big data market centered squarely on technology around the Hadoop ecosystem. Since then, it’s been all about ‘putting big data to work’ thro

    top 5ugh use cases shown to generate ROI from increased revenue and productivity and lower risk.

    Now, big data continues its march beyond the crater. Next year we can expect to see more mainstream companies adopting big data and IoT, with traditionally conservative and skeptic organizations starting to take the plunge.

    Data blending will be more important compared to a few years ago when we were just getting started with Hadoop. The combination of social data, mobile apps, CRM records and purchase histories via advanced analytics platforms allow marketers a glimpse into the future by bringing hidden patterns and valuable insights on current and future buying behaviors into light.

    The spread of self-service data analytics, along with widespread adoption of the cloud and Hadoop, are creating industry-wide change that businesses will either take advantage of or ignore at their peril. The reality is that the tools are still emerging, and the promise of the (Hadoop) platform is not at the level it needs to be for business to rely on it.

    As we move forward, there will be five key trends shaping the world of big -Data:

    The Internet of Things (IoT)

    Businesses are increasingly looking to derive value from all data; large industrial companies that make, move, sell and support physical things are plugging sensors attached to their ‘things’ into the Internet. Organizations will have to adapt technologies to map with IoT data. This presents countless new challenges and opportunities in the areas of data governance, standards, health and safety, security and supply chain, to name a few.

    IoT and big data are two sides of the same coin; billions of internet-connected 'things' will generate massive amounts of data. However, that in itself won't usher in another industrial revolution, transform day-to-day digital living, or deliver a planet-saving early warning system. Data from outside the device is the way enterprises can differentiate themselves. Capturing and analyzing this type of data in context can unlock new possibilities for businesses.

    Research has indicated that predictive maintenance can generate savings of up to 12 percent over scheduled repairs, leading to a 30 percent reduction in maintenance costs and a 70 percent cut in downtime from equipment breakdowns. For a manufacturing plant or a transport company, achieving these results from data-driven decisions can add up to significant operational improvements and savings opportunities.

    Deep Learning

    Deep learning, a set of machine-learning techniques based on neural networking, is still evolving, but shows great potential for solving business problems. It enables computers to recognize items of interest in large quantities of unstructured and binary data, and to deduce relationships without needing specific models or programming instructions.

    These algorithms are largely motivated by the field of artificial intelligence, which has the general goal of emulating the human brain’s ability to observe, analyze, learn, and make decisions, especially for extremely complex problems. A key concept underlying deep learning methods is distributed representations of the data, in which a large number of possible configurations of the abstract features of the input data are feasible, allowing for a compact representation of each sample and leading to a richer generalization.

    Deep learning is primarily useful for learning from large amounts of unlabeled/unsupervised data, making it attractive for extracting meaningful representations and patterns from Big Data. For example, it could be used to recognize many different kinds of data, such as the shapes, colors and objects in a video — or even the presence of a cat within images, as a neural network built by Google famously did in 2012.

    As a result, the enterprise will likely see more attention placed on semi-supervised or unsupervised training algorithms to handle the large influx of data.

    In-Memory Analytics

    Unlike conventional business intelligence (BI) software that runs queries against data stored on server hard drives, in-memory technology queries information loaded into RAM, which can significantly accelerate analytical performance by reducing or even eliminating disk I/O bottlenecks. With big data, it is the availability of terabyte systems and massive parallel processing that makes in-memory more interesting.

    At this stage of the game, big data analytics is really about discovery. Running iterations to see correlations between data points doesn't happen without millisec

    onds of latency, multiplied by millions/billions of iterations. Working in memory is at three orders of magnitude faster than going to disk.

    In 2014, Gartner coined the term HTAP - Hybrid Transaction/Analytic Processing, to describe a new technology that allows transactions and analytic processing to reside in the same in-memory database. It allows application leaders to innovate via greater situation awareness and improved business agility, however entails an upheaval in the established architectures, technologies and skills driven by use of in-memory computing technologies as enablers.

    Many businesses are already leveraging hybrid transaction/analytical processing (HTAP); for example, retailers are able to quickly identify items that are trending as bestsellers within the past hour and immediately create customized offers for that item.

    But there’s a lot of hype around HTAP, and businesses have been overusing it. For systems where the user needs to see the same data in the same way many times during the day, and there’s no significant change in the data, in-memory is a waste of money. And while you can perform analytics faster with HTAP, all of the transactions must reside within the same database. The problem is, that most analytics efforts today are about putting transactions from many different systems together.

    It’s all on Cloud

    Hybrid and public cloud services continue to rise in popularity, with investors claiming their stakes. The key to big data success is in running the (Hadoop) platform on an elastic infrastructure.

    We will see the convergence of data storage and analytics, resulting in new smarter storage systems that will be optimized for storing, managing and sorting massive petabytes of data sets. Going forward, we can expect to see the cloud-based big data ecosystem continue its momentum in the overall market at more than just the “early adopter” margin.

    Companies want a platform that allows them to scale, something that cannot be delivered through a heavy investment on a data center that is frozen in time. For example, the Human Genome Project started as a gigabyte-scale project but quickly got into terabyte and petabyte scale. Some of the leading enterprises have already begun to split workloads in a bi-modal fashion and run some data workloads in the cloud. Many expect this to accelerate strongly as these solutions move further along the adoption cycle.

     

    There is a big emphasis on APIs to unlock data and capabilities in a reusable way, with many companies looking to run their APIs in the cloud and in the data center. On-premises APIs offer a seamless way to unlock legacy systems and connect them with cloud applications, which is crucial for businesses that want to make a cloud-first strategy a reality.

    More businesses will run their APIs in the cloud, providing elasticity to better cope with spikes in demand and make efficient connections, enabling them to adopt and innovate faster than competition.

    Apache Spark

    Apache Spark is lighting up big data. The popular Apache Spark project provides Spark Streaming to handle processing in near real time through a mostly in-memory, micro-batching approach. It has moved from being a component of the Hadoop ecosystem to the big data platform of choice for a number of enterprises.

    Now the largest big data open source project, Spark provides dramatically increased data processing speed compared to Hadoop, and as a result, is much more natural, mathematical, and convenient for programmers. It provides an efficient, general-purpose framework for parallel execution.

    Spark Streaming, which is the prime part of Spark, is used to stream large chunks of data with help from the core by breaking the large data into smaller packets and then transforming them, thereby accelerating the creation of the RDD. This is very useful in today’s world where data analysis often requires the resources of a fleet of machines working together.

    However, it’s important to note that Spark is meant to enhance, not replace, the Hadoop stack. In order to gain even greater value from big data, companies consider using Hadoop and Spark together for better analytics and storage capabilities.

    Increasingly sophisticated big data demands means the pressure to innovate will remain high. If they haven’t already, businesses will begin to see that cus

    tomer success is a data job. Companies that are not capitalizing on data analytics will start to go out of business, with successful enterprises realizing that the key to growth is data refinement and predictive analytics.

    Information Management, 2016; Brad Chivukala

  • Tips for Creating a Winning Data Scientist Team

    Finding the right mix of support to do more with your data is no easy task. Data scientist teamData scientists remain in high-demand, and fetch top dollar. Here are some tips on how to assemble a winning team.

    So much data, so little time

    Organizations continue to struggle with how to get more out of their data. “It’s not a new challenge, but the problem is only exacerbated as more data is exchanged and created at petabyte scale,” confirms Dermot O’Connor, cofounder and vice president at Boxever. “The proliferation of data and the pressure for organizations to turn data into business value has increased demand for data science professionals.” Approximately 10 percent of the workforce at Boxever is data scientists, and O’Connor shared his views on how to best assemble a data science team.

    Seeking the ‘total package’

    “When a company seeks to hire a data scientist, it's typically seeking someone with skills in advanced programming and statistical analysis, along with expertise in a particular industry segment,” O’Connor explains. “The need is great, and the skills gap is widening: A study by McKinsey predicts that ‘by 2018, the U.S. alone may face a 50 percent to 60 percent gap between supply and requisite demand of deep analytic talent.’ Good data scientists are often referred to as ‘unicorns’ because it is so rare to find professionals who possess all the right skills to meet today’s requirements.”

    Still the top job in America

    “As the ‘top job in America in 2016,’ data scientists don’t come cheap,” O'Connor confirms. “How can today’s organizations harness the brains behind data science to get the most out of their investment, whether in talent or technology? Here are some things to consider when building your data science team…”

    Data science is a team sport

    “There are many facets to creating successful data science teams in a practical, operational sense,” O’Connor says. “It’s rare to hire just one or two on staff, so remember that for data scientists as much as any other role, strength comes in numbers.”

    Outsource to innovate

    “If you do the math, a team of seasoned data scientists – let’s say only five – will cost you well over $1 million annually in fixed costs,” O’Connor notes. “And like many in IT functions, they’re likely to be pulled in many directions. Having a dedicated resource to optimize your systems with networks getting increasingly smarter with every interaction via machine learning is one way to ensure that projects are efficient while blending technology platform costs with the costs for data science talent that drives them.”

    Balance functional and strategic tasks

    “Part of the reason data scientists are so in demand is because they have concrete skills in predictive analytics that others – in IT and business roles – lack,” O’Connor explains. “That being said, you’ll need sufficient talent and resources to both write and maintain software and algorithms while also gathering insights from internal teams and customers to customize and optimize the logic behind them.”

    Set data scientists up for success with the right data management systems

    “High volume, omni-channel systems are very complex – and time consuming – to manage,” says O’Connor. “Having a hub where data at the individual customer level is aggregated helps set the foundation for data scientists to really shine. Finding ways to automate processes so that the right data is available on demand will make any data scientist’s life easier and will make more possible under their strategic guidance.”

    Expect to ‘see inside the black box’ of AI

    “A data scientist should be tasked with explaining the process of machine learning and artificial intel

    ligence in layman’s terms to bring in others into their realm throughout the enterprise,” O’Connor explains. “This is essential for gathering insights that make predictions stronger and actions more focused by design. And as marketers take on greater oversight of data, it’s important that CMOs and other decision-makers find complementary talent and technology to help them see the big picture to explore all that’s possible with their data.”

    Bron: Information Management, 2016

  • TNO: ‘Amsterdam blijft bereikbaar dankzij big data’

    1000Innovatieorganisatie TNO ziet kansen voor big data en Internet of Things-technologie (IoT) om de bereikbaarheid van de metropoolregio Amsterdam te vergroten. “Met big data kunnen we meerdere oplossingen aan elkaar koppelen om de infrastructuur van een stad optimaal te benutten”, zegt Leo Kusters, Managing Director Urbanisation bij TNO.

    Binnen enkele decennia woont 70 procent van de wereldbevolking in grote steden of in sterk verstedelijkte regio’s. Het economische en culturele succes van regio’s als de Randstad trekt veel mensen. De infrastructuur van deze steden wordt daardoor steeds meer belast. Infrastructuur en mobiliteit zijn daarom bepalende factoren voor het succes van de grootstedelijke regio’s.

    Slimme mobiliteit

    Kusters wijst op het project Praktijkproef Amsterdam (PPA), waarin TNO samenwerkt met ARS Traffic & Transport Technology aan het verminderen van files in de regio Amsterdam. “Aan dit project zijn 15.000 automobilisten verbonden”, zegt Kusters. Door weggebruikers beter te informeren over de verkeerssituatie in de stad, verwacht TNO dat het aantal files in de regio Amsterdam afneemt.

    De deelnemers hebben de beschikking over een app waarmee ze op individueel niveau geïnformeerd worden over de beste reiskeuzes die ze kunnen maken. Daarnaast kunnen gebruikers via de app ook zelf incidenten en vertragingen op de weg melden. Hierdoor komen de automobilisten sneller op hun bestemming en kunnen ze rekenen op een betrouwbare reistijd.

    Bijzonder aan dit project is volgens Kusters dat de app ook advies geeft op basis van verkeerslichten die op rood staan. Vervolgens houdt het systeem rekening met deze verkeerslichten om een opstopping op de weg te voorkomen.

    TNO voert een vergelijkbaar project uit met vrachtverkeer in Helmond. Kusters: “Door de stad Helmond loopt een snelweg waar veel vrachtauto’s overheen rijden. Hierdoor is er in de stad veel belasting voor het milieu en de luchtkwaliteit.” In dit project experimenteert TNO met data-analyse om de doorstroming voor de betrokken vrachtwagens te optimaliseren. De chauffeurs krijgen doorlopend snelheidsadviezen om de doorstroming in de stad te verbeteren. Hierdoor hoeven chauffeurs minder te stoppen in de stad. Vrachtwagens verbruiken daardoor minder brandstof.

    Twee vliegen in één klap

    Een grote kans van big data en de toepassing van IoT-technologie ligt volgens Kusters in het combineren van meerdere oplossingen voor optimale benutting van bestaande infrastructuur. Big data kan ook bijdragen aan besparingen in het onderhoud van de infrastructuur, waar Nederland jaarlijks € 6 mrd aan uitgeeft.

    TNO richt zich bijvoorbeeld op het verlengen van de levensduur van bruggen. ”Een essentieel onderdeel van de infrastructuur”, zegt Kusters. “Als bruggen niet werken, staat alles stil.” TNO meet met sensoren de haarscheurtjes in bruggen. “Zo kunnen we precies weten wanneer een brug onderhoud nodig heeft of moet worden vervangen. Dit maakt het mogelijk om de levensduur van de brug ‘op maat’ te verlengen. Dus precies op tijd met een minimum aan overlast voor het verkeer.”

    De levensduur van infrastructuuronderdelen wordt meestal bepaald op basis van theoretische modellen. Kusters: “Omdat de werkelijkheid altijd anders is, ontwikkelt TNO met Rijkswaterstaat nieuwe meetmethodes. Het gebruik van infrastructuur kan in de praktijk intensiever of juist minder intensief zijn in vergelijking met de inschatting uit theoretische modellen, en de schade dus ook. Door big data in te zetten, kunnen we nauwkeurige voorspellingen maken voor het onderhoud van de brug en daarmee kosten besparen.”

    De coöperatieve auto

    Bij deze projecten is de betrokkenheid van verschillende partijen van groot belang, meent Kusters. “Mobiliteit is allang niet meer het alleenrecht van de overheid. De overheid neemt een andere rol aan bij de verduurzaming van infrastructuur en mobiliteit. Ook technologiebedrijven worden steeds belangrijker. Dat zijn bedrijven als TomTom en Google, maar ook een partij als chipleverancier NXP, die kunnen bijdragen aan de ontwikkeling van technologie om voertuigen met elkaar te laten communiceren.”

    De TNO-directeur spreekt over de ‘coöperatieve auto’. “Dat betekent dat alle diensten en modaliteiten waar je als automobilist gebruik van wil maken, aan elkaar worden gekoppeld. Het systeem gaat dan als het ware met je mee denken.”

    De coöperatieve auto maakt gebruik van IoT-technologie om rechtstreeks met andere voertuigen of de infrastructuur te communiceren. Hierdoor houdt de auto continu rekening met de huidige verkeerssituatie en de voertuigen die in dezelfde omgeving rijden. Kusters: “Dat is een grote doorbraak, een efficiënte deels-zelfrijdende auto die altijd oplet en altijd wakker is. Zo kunnen we de wegcapaciteit stevig laten toenemen en een flink deel van de fileproblemen oplossen.”

    Toekomstvisie

    De Managing Director Urbanisation ziet de IoT-toepassingen voor mobiliteit in rap tempo toenemen. "De autonoom zelfrijdende auto in de stad is misschien wel minder ver weg dan we denken”, zegt Kusters. “We hebben al auto’s die zelf kunnen parkeren. In de toekomst betekent dit dat de parkeerproblemen in de grote steden ten einde lopen.”

    Naast de IoT-toepassing voor coöperatieve auto’s, ziet Kusters ook kansen voor verbeteringen aan de infrastructuur. “Het verbonden zijn van mensen en van apparaten zal ook terug te zien zijn op het straatbeeld, zoals wifi op straat, wifi voor auto’s, en slimme LED-verlichting. Dat betekent overigens niet dat al die informatie over één en hetzelfde netwerk zal gaan. De informatie die tijdkritisch is en de verkeersveiligheid beïnvloedt, zal bijvoorbeeld gebruikmaken van een apart netwerk. Dit gaan we in steden en op snelwegen binnen een paar jaar in de praktijk zien.”

    In de toekomst ziet de directeur leefomgeving van TNO ook meer veranderingen in het aanzicht van de binnenstad. “In de stad gaan we meer en meer elektrisch rijden. Dat zien we al in recente openbaar vervoersaanbestedingen.” Ook fietsersaantallen zullen volgens Kusters nog verder groeien. “In een stad als Amsterdam is er dan meer ruimte nodig voor de fiets”, zegt Kusters. “Dit is de enige vorm van mobiliteit die in Amsterdam toeneemt. Meer ruimte voor fietsers is daarom belangrijk. Dat gaat wel ten koste van de parkeerplaatsen van de auto’s, maar hoeft dan niet zomaar ten koste te gaan van de bereikbaarheid.”

    Source: DuurzaamBedrijfsleven

  • Van Business Intelligence naar Data Science

    691283Organisaties die al jaren ervaring hebben met de inzet van datawarehouses en Business Intelligence gaan steeds vaker Data Science-toepassingen ontwikkelen. Dat is logisch, want data heeft een impact op iedere organisatie; van retailer, reisorganisatie en financiële instelling tot ziekenhuis. Er wordt zelfs beweerd dat we momenteel in een vierde industriële revolutie zijn aanbeland, waarbij data als productiefactor is toegevoegd aan het lijstje mensen, kapitaal en grondstoffen. Hoe verhouden BI en Data Science zich tot elkaar en op welke manier maak je als BI-organisatie de stap naar Data Science-toepassingen?


    Algoritmes en Data
    Big Data is in een aantal jaar razendsnel opgekomen. Inmiddels zijn we van de Big Data-hype terechtgekomen in een tijd waarin het juist gaat over het voorspellen, de tijd van Data Science, waarin machine learning, artificial intelligence en deep learning een steeds grotere rol spelen. We komen terecht in een wereld waarin singularity, het moment waarop systemen intelligenter zijn dan de mens, steeds dichterbij komt. Of we dit punt ooit zullen bereiken weet niemand, wat er zal gebeuren op dat moment is nog onzekerder. Maar wat wel een feit is, is dat de wereld om ons heen steeds meer gedomineerd wordt door algoritmes en data. 
    Hadoop heeft met zijn andere manier om data op te slaan en doorzoekbaar te maken een cruciale rol gespeeld in de Big Data-revolutie. Door de toegenomen rekenkracht en de afgenomen kosten van opslagcapaciteit is het tegenwoordig mogelijk om vrijwel onbeperkte hoeveelheden data op te slaan en beschikbaar te maken, waardoor data en technologie steeds minder een belemmering zijn voor innovatie.

    Data en Technologie
    Innoveren met data draait vanzelfsprekend om data en om technologie, maar deze komen steeds meer en gemakkelijker beschikbaar. Denk aan bijvoorbeeld de opkomst van open source technologie, waardoor je de technologie kan zoeken bij de toepassing. Dit was vroeger wel anders, toen waren het de grote organisaties die zich een licentie op dure software konden veroorloven om concurrentievoordeel mee op te bouwen. Open source is natuurlijk niet gratis, maar de kosten groeien lineair naarmate je een technologie meer gebruikt en niet zoals bij licensed producten, exponentieel.

    Verdwijnt Business Intelligence?
    Zowel Business Intelligence als Data Science draaien om slim gebruik van data. Business intelligence zorgt voor rapportages, zoals financiële rapporten, die een accuraat beeld schetsen van wat er heeft plaatsgevonden. Bij Data Science draait het om vooruitkijken met het vergroten van bedrijfswaarde als doel. Vanwege het experimentele karakter van Data Science hoeven uitkomsten niet altijd raak te zijn.  
    In de praktijk dragen dashboards, visualisaties en rapporten vaak bij aan de bewustwording over de waarde van data. Het is niet ongebruikelijk dat een directie een visie en strategie gericht op datagedreven toepassingen gaat ontwikkelen op basis van datavisualisaties en dashboards. 

    Voldoen bestaande organisatiestructuren nog wel?
    Organisaties die aan de slag gaan met datagedreven toepassingen doen er goed aan hun organisatie eens goed onder de loep te nemen. Innoveren draait niet om het schrijven van een Project Initiation Document (oftewel PID), maar om het simpelweg starten. Projectresultaten leiden niet altijd tot een valide business case, bij innovatie hoort ook falen. Kijk naar Google, toch een van de meest succesvolle organisaties wat betreft datatoepassingen, daar falen ook veel projecten. Het is zaak om te experimenteren en in korte iteraties te bepalen of je verder gaat of niet. Fail fast!

    Innoveren als een startup
    Waar Google, Microsoft en Apple de technologie zelf ontwikkelden in hun garage, zijn het nu startups die vaak starten met behulp van state-of-the art technologie die beschikbaar is als open source product. Studenten leren op de universiteit te werken met open source, technologie die ze ook thuis kunnen gebruiken. Organisaties die talent willen aantrekken zullen ook open source moeten adopteren om interessant te blijven als werkgever.
    Het nadeel van bestaande organisaties is dat de werkwijze zich vaak niet goed leent voor innovatie. Bij een online retailer werd een afdeling verantwoordelijk voor conversie. Vol enthousiasme ging de afdeling ‘Conversie’ aan de slag met het ontwikkelen van productaanbevelingen. Al vrij snel bleek het succes van de afdeling afhankelijk te zijn van de prestaties van andere afdelingen die andere targets nastreefden. De inkoper kocht volgens eigen KPI’s producten in en de marketeer bepaalde op zijn eigen manier de prijzen. De engineers en front-end developers bepaalden op basis van eigen testen de gebruikerservaring. Door de afhankelijkheid van andere afdelingen en conflicterende doelen per afdeling had de afdeling ‘Conversie’ dus feitelijk geen controle over zijn eigen succes.

    De enige manier om deze kloof te slechten is door te gaan werken in multidisciplinaire teams, die verantwoordelijk zijn voor features en niet voor processen. Deze teams kennen een heel andere dynamiek doordat verschillende disciplines samenwerken en samen dezelfde verantwoordelijkheid dragen, zoals bijvoorbeeld conversie. Startups hebben het wat dat betreft gemakkelijk, zij hebben geen bestaande organisatie, zij beginnen met het aantrekken van de juiste mensen en bouwen de skills gaandeweg op. Waar vroeger de systemen het kostbaarst waren, zijn het tegenwoordig de mensen die van de grootste waarde zijn.

    De rol van de Data Scientist
    Data Science heeft een centrale rol in teams die zich richten op innovatie en de ontwikkeling van datagedreven producten. Data Science is hiermee echt een businessafdeling en zeker geen ondersteunende afdeling die voor de business werkt. Een Data Scientist heeft over het algemeen ook een ander profiel dan een BI-specialist.
    Een Data Scientist is een soort van schaap met vijf poten. Een Data Scientist beschikt over het algemeen over een statistische achtergrond, heeft kennis van machine learning en bouwt naast modellen ook applicaties. Daarnaast is een Data Scientist communicatief vaardig en van nature nieuwsgierig, waardoor hij graag experimenteert en onderzoekt. Josh Wills, destijds verantwoordelijk voor Data Science bij Cloudera omschreef het als volgt: “Een Data Scientist is iemand die beter is in statistiek dan een software engineer en beter in software engineering dan een statisticus”. 

    Van BI naar Data Scientist
    Veel datawarehouse- en Business Intelligence-specialisten hebben programmeerervaring en zouden de stap naar Data Science kunnen zetten door zich bijvoorbeeld te verdiepen in Python en R en statistiek. Het helpt ook als organisaties functies creëren voor Data Scientists, niet alleen zodat externe consultancy-organisaties kennis kunnen overdragen maar ook zodat het voor bestaande medewerkers eenvoudiger wordt om door te groeien. Zodra organisaties de waarde erkennen van Data Science zal duidelijk worden dat het de mensen zijn die het verschil maken in de razendsnelle ontwikkeling van datatoepassingen en technologische innovatie.

    Bron: biplatform.nl

     

  • Van data driven naar data-informed besluitvorming

    intuitie 855x500Veel organisaties starten net met het data driven maken van hun besluitvorming, anderen zijn al verder gevorderd. De prominentere plaats van Big Data en algoritmen in besluitvorming van organisaties lijkt op het eerste gezicht alleen maar een positieve ontwikkeling. Wie wil er nou niet de customer journey kunnen volgen, de lead time verkorten en maximaal wendbaar zijn? Wie wil er geen slimme algoritmen waardoor complex speurwerk én moeilijke beslissingen geautomatiseerd worden?

    Besluitvorming, gedreven door Big Data en algoritmen, kent echter een aantal valkuilen: beslissingen, die teveel steunen op data, bevorderen een cultuur waarin medewerkers minder kritisch zijn, minder verantwoordelijkheid nemen en minder vertrouwen op hun eigen kennis en ervaring. Deze valkuilen zijn vooral van toepassing als de data en algoritmen nog niet ver genoeg ontwikkeld zijn, wat bij veel organisaties het geval is. Daarom pleiten wij voor ‘data-informed’ besluitvorming, waarin organisaties een balans vinden tussen enerzijds data en algoritmen, en anderzijds intuïtie, gestoeld op kennis en ervaring. In deze werkwijze is de medewerker nog in control. Hij verschuilt zich niet achter data en algoritmen, maar gebruikt deze om slimmere beslissingen te nemen.

    De upside van data driven besluitvorming

    De Big Data revolutie ontstond vanuit de groeiende aanwas en rijkere data die wordt verzameld en opgeslagen. Bovendien maakt slimme tooling het onttrekken en analyseren van data steeds gemakkelijker. Organisaties als Google, Tesla en de campagneteams van Hillary Clinton en Donald Trump zijn baanbrekend met hun datagedreven besluitvorming. Zo gebruikt Google Big Data en complexe algoritmen om advertenties te optimaliseren, zodat deze zo goed mogelijk bij de doelgroep aansluiten. Tesla zet sensoren en Big Data in om technische problemen op afstand te detecteren en te verhelpen (of zelfs te voorspellen en te voorkomen), waardoor recalls tot het verleden behoren. Dergelijke toepassingen zijn niet alleen weggelegd voor hippe startups, opgeschaalde multinationals of presidentskandidaten met veel geld. Datagedreven sturen kan iedereen door bijvoorbeeld met één proces of product te starten.

    Nederlandse vervoersbedrijven bepalen aan de hand van een voorspellend model de materieel- en personeelsinzet. Dit helpt hen om de mobiliteit tijdens pieken beter te stroomlijnen en geeft hen de kans om de dienstverlening keer op keer te verbeteren. Energiebedrijven gebruiken data voor het plegen van preventief onderhoud en het verduurzamen van hun processen. Profvoetbalclubs zetten tijdens wedstrijden data in om de klantbeleving te vergroten door spelers op het veld te volgen of zelf beelden te laten maken en te delen via social media en smartphones.

    De valkuilen van data driven besluitvorming

    Wanneer organisaties puur op basis van data en algoritmen beslissingen nemen, noemen we dat ‘data driven’ of ‘data centric’. Veel processen en zelfs beslissingen zijn (deels) geautomatiseerd, het menselijk brein verdwijnt naar de achtergrond en de data staat centraal in de besluitvorming. Wanneer algoritmen en data nog onvoldoende ontwikkeld zijn, verhoogt dit de kans op de volgende valkuilen:

    • Aannames worden onvoldoende getoetst;
    • Contextkennis wordt onvoldoende ingezet;
    • De data is onbetrouwbaar.

    Aannames worden onvoldoende getoetst

    In de aanloop naar de economische crisis van 2008 stuurden veel financiële instellingen op basis van risicomodellen die bijna niemand meer begreep. Het risico van hypotheekproducten schatten zij veel te laag in. Zij stelden de modellen nauwelijks ter discussie, maar gebruikten ze als verantwoording van correct handelen. Het resultaat: een systemische miscalculatie die bijna niemand zag aankomen, met desastreuze gevolgen.

    Dit voorbeeld illustreert dat het risicovol is om aannames van algoritmen niet of minder goed te laten toetsen door de mens én wat er gebeurt als we het vertrouwen in onze eigen intuïtie kwijtraken. Intuïtie kan een waardevolle toevoeging op data zijn, want met één van beiden dek je nog zelden de relevante werkelijkheid af.

    Contextkennis wordt onvoldoende ingezet

    Het CBS stelde dat Nederlanders in 2011 meer gingen lenen. Dit baseerden zij op hogere creditcardbestedingen. Maar wat was het geval? Nederlanders bestelden meer producten online en de creditcard was vaak het enige beschikbare betaalmiddel. Het CBS telde alle creditcardtransacties als leningen, ook gewone betalingen. Oftewel: iemand die online een boek of een vliegticket met een creditcard betaalde, was volgens het CBS iemand die niet meer bij de bank kon lenen en daarom zijn creditcard gebruikte.

    Dit voorbeeld illustreert het gevaar van het blind volgen van de data zonder contextkennis. Mét contextkennis had een analist op een lager detailniveau (type creditcardbesteding) geanalyseerd en geïnterpreteerd.

    De data is onbetrouwbaar

    In de campagne voor de presidentsverkiezingen van 2016 in de VS maakten zowel de teams van Hillary Clinton en Donald Trump gretig gebruik van Big Data en algoritmen. Onder meer voor nauwkeurige peilingen en efficiënte inzet van campagnemiddelen. Trump won, ondanks het beperkte budget (slechts de helft van Clinton). Het verhaal gaat dat de data van team Clinton minder betrouwbaar waren. Deelnemers van polls durfden tegenover haar team er niet voor uit te komen dat ze op Trump gingen stemmen. Tegen team Trump waren ze eerlijker. Zij zagen – tegen alle polls in – de overwinning al vijf dagen van te voren aankomen.

    Het vertrouwen in Big Data bij verkiezingscampagnes wordt nu ter discussie gesteld. Er was echter niets mis met de ontwikkelde algoritmen en de aanpak in het algemeen, maar met onbetrouwbare data zijn deze weinig waard of zelfs schadelijk, blijkt nu. Mensen kunnen nu eenmaal liegen of sociaal wenselijke antwoorden geven. In de sociale wetenschappen worden er niet voor niets allerlei strategieën toegepast om dit te minimaliseren. Het is dus belangrijk om aannames en datakwaliteit regelmatig te toetsen.

    Onjuiste of incomplete kennis kan desastreuze én onethische gevolgen hebben

    In het Amerikaanse rechtssysteem gebruiken ze geautomatiseerde data-analyse om de kans op recidive te berekenen. Er komt geen mens meer aan te pas. Ze crunchen de data en bepalen zo of iemand wel of niet vervroegd vrijkomt. Wetenschappers spreken over het doemscenario van volledig geautomatiseerde rechtspraak. Hoogleraar recht en informatisering Corien Prins: ‘Want op een gegeven moment is het uit je handen, dan heb je er niets meer over te zeggen.’

    Het belang van intuïtie

    Intuïtie wordt vaak als iets vaags of ongrijpbaars gezien. Dat heeft vooral met de definities te maken die worden gehanteerd: “iets aanvoelen zonder er over na te denken” of “het gevoelsmatig weten, zonder erover te hoeven nadenken”. Wat vaak wordt vergeten is dat intuïtie is opgebouwd op basis van kennis en ervaring. Hoe meer kennis en ervaring, hoe beter de intuïtie is ontwikkeld. Intuïtie wordt ‘bovenrationeel’ genoemd. Het werkt immers snel, moeiteloos en onbewust, in tegenstelling tot het ‘normale’ rationele denkproces, wat langzaam, complex en bewust is. Malcolm Gladwell beschreef in zijn boek Blink: The Power of Thinking Without Thinking dat bepaalde kunstcritici in een fractie van een seconde zien of een schilderij echt of namaak is, zonder dat ze daar direct een verklaring voor hebben. De ontwikkeling van kunstmatige intelligentie is nog niet zover dat zij deze experts kunnen vervangen.

    Beslissen op basis van intuïtie of onderbuikgevoel kent echter de nodige beperkingen. We hebben nogal wat vooroordelen (bias). Sommige waarheden zijn contra-intuïtief. Je denkt dat je alleen de boodschappen koopt die je echt nodig hebt. Wat blijkt: je maakt toch regelmatig gebruik van “drie-halen-twee-betalen”, waardoor je regelmatig voedsel weggooit. ‘Confirmation bias’ (tunnelvisie) is een veel voorkomende bias: we zien alleen de datapunten die in onze visie passen en alternatieven maken geen kans. Bovendien zijn we als mens niet in staat gigantische hoeveelheden data in korte tijd zonder rekenfouten te analyseren, zoals een computer dat kan. Bij deze menselijke tekortkomingen helpen data en algoritmen voor betere beslissingen.

    Van data driven naar data-informed

    Het is zaak om als organisatie geen genoegen te nemen met alleen data of alleen intuïtie. Het zijn twee bronnen die elkaar versterken. Wat is de optimale balans? Dat wordt met name bepaald door de stand van de technologie. Op gebieden waar algoritmen en kunstmatige intelligentie intuïtie nog niet kunnen vervangen, is het verstandig om ‘data-informed’ besluitvorming (zie Figuur) te hanteren. In deze aanpak is data niet leidend – zoals bij data driven besluitvorming – maar een verrijking van onze eigen capaciteiten. We hebben namelijk zelf onvoldoende mogelijkheden om alle informatie te kennen, te combineren, toe te passen en foutloos te werken. We hebben wel de kwaliteiten om niet-meetbare factoren mee te wegen, we kennen verklaringen en kunnen betekenis geven aan de data. En bovenal: we kunnen verantwoordelijkheid nemen. Data voorziet ons van informatie, maar wij gebruiken daarnaast intuïtie om beslissingen te nemen. Ditzelfde concept wordt toegepast in het vliegverkeer. Hoe goed de automatische piloot ook werkt, de menselijke piloot blijft eindverantwoordelijk. Zijn kennis en ervaring is nodig om besluiten te nemen, op basis van wat het vliegtuig voorstelt. Zowel data driven werken als volledig op basis van intuïtie werken kent dus beperkingen. Combineer het beste van beiden om als organisatie snel en gedegen besluiten te kunnen nemen.

    data driven data informed 1024x523

    Figuur. Data driven en data-informed (illustratie door Nick Leone, geïnspireerd op Fishman (2014) “The Dangers of Data Driven Marketing”).

    Case: Datagedreven verbeteren bij de Sociale Verzekeringsbank

    De Sociale Verzekeringsbank (SVB) wil hun klanten optimaal bedienen. Daarvoor is inzicht benodigd in de klantreis. De SVB brengt de digitale klantreis in beeld op basis van data, over de klantkanalen heen, met behulp van Process Mining. Deze data wordt uiteindelijk ingezet om de klantreis te sturen en te verbeteren. De SVB formuleerde onderzoeksvragen over de te verwachten klantreis. Bijvoorbeeld “Hoeveel klanten die een transactie uiteindelijk offline regelen zijn wel in de online portal geweest?” en “Op welke webpagina haken klanten af?” Data-analisten genereerden inzicht in de daadwerkelijke klantreis. Uit de data-analyse bleek bijvoorbeeld dat meer klanten dan verwacht afhaakten van online naar offline en dat zij dit vooral deden op een specifieke webpagina in de portal. De resultaten werden geduid door domeinexperts binnen de organisatie. Zij gaven direct aan dat het afhaken zeer waarschijnlijk een gevolg was van een extra authenticatie-stap. Na verdere analyse bleek dat deze stap vrij onverwacht in het proces kwam: de klant was hier niet voorbereid, waardoor zij het niet meer begrepen en/of zij niet bereid waren een extra stap te zetten. Op basis van de gezamenlijke conclusies zijn verbetervoorstellen uitgewerkt op gebied van proces, IT en webcontent. De effectiviteit hiervan is vervolgens weer getoetst door middel van data-analyse.

    Met alleen data had de SVB weinig inzicht gekregen in de context van de customer journey en beweegredenen van klanten en was er geen verbetering gerealiseerd. En met alleen intuïtie zou er veel minder inzicht in de daadwerkelijke klantreis zijn geweest. Klanten bewegen zich vaak anders dan men verwacht. Bovendien is (nog) niet elk gedrag en elke beweegreden van de klant in data te vatten.

    De basisingrediënten van data-informed werken

    Een data-informed besluitvormingscultuur herken je – naast het optimaal inzetten van data – aan kritisch denken, vertrouwen in eigen beoordelingsvermogen en (onderling) begrip van het waarom van besluiten. Een onderdeel daarvan is een periodieke toetsing van de beslismodellen. Bijvoorbeeld door regelmatig geautomatiseerde besluitvormingsprocessen achteraf te analyseren of door de feedback van klanten en andere stakeholders te gebruiken als input voor je beslismodellen. Deze cultuur van data-informed verbeteren vraagt om een datahuishouding die op orde is en expertise op gebied van data science.

    Tot slot nog een aantal concrete tips voor data-informed besluitvorming:

    • Zorg dat je personeelsbestand met data weet om te gaan. Om als organisatie competitief te zijn moeten de medewerkers kritisch zijn, complexe analyses kunnen uitvoeren en interpreteren, en acties kunnen definiëren.
    • Zorg dat je data blijft interpreteren en toetsen met je intuïtie en andersom. Bijvoorbeeld door met hypothesen of onderzoeksvragen te werken en niet te zoeken naar willekeurige verbanden. Dit scherpt je begrip over wat de data echt betekent en wat er werkelijk gebeurt in het proces of met de klant.
    • Innoveer en exploreer met nieuwe data-oplossingen in een ‘speeltuin’, om nieuwe analyses en analysemethoden te stimuleren. Implementeer deze zodra de oplossing getoetst is en de kwaliteit van de data en het algoritme op orde is.

    Source: managementsite.nl, 23 januari 2017

  • Waarom een leergang Business Data Scientist

    data scientist

    Elke organisatie die veranderd, is op zoek. Die zoektocht heeft vaak betrekking op data: hoe kunnen we data beter toepas

    sen? Hoe kunnen we nieuwe toepassingen voor data vinden? Hebben we wel de juiste data? Wat moeten we doen met data science en big data? Hoe kunnen we data inzetten om betere besluiten te nemen en dus ook beter te presteren?

    Organisaties moeten een antwoord vinden op deze vragen. Deels onder druk van de verder ontwikkelende markt en veranderende concurrentie. Daarmee krijgt data een centrale plaats in de bedrijfsvoering en worden organisaties dus 'data driven'.

    Uiteraard heb je hier 'data science' voor nodig: de omgevingen en vaardigheden om data te ontleden, analyseren en te vertalen naar modellen, adviezen en besluiten.

    We hebben de leergang business data scientist ontworpen omdat geen bedrijf met alleen tools en technieken succesvol gaat worden. Het is juist de business data scientist die de brug vormt tussen data science en de verandering die in organisaties plaats vindt.

    Te vaak ligt bij organisaties de nadruk op de technologie (Hadoop? Spark? Data lake? Moeten we R leren?). Om succesvol te zijn met data heb je ook andere instrumenten nodig. Bedrijfskundige modellen, business analyse, strategievorming helpen om de juiste vragen te formuleren en doelen te stellen. Softskills en veranderkundige vaardigheden om die doelen zichtbaar te maken voor opdrachtgevers en stakeholders. Kennis van data science, architectuur, methoden en organisatiemodellen geeft de inzichten om data science in een organisatie in te passen. Visie en leiderschap is nodig om data sc

    ience in een organisatie te laten werken. Ons doel is om deelnemers dit te laten zien. De opleiding is ontworpen om al deze aspecten samen te laten komen en bruikbare instrumenten te geven.

    Wat ik het leukste vind van deze leergang? Steeds weer de brug maken naar: wat gaan we nu doen, hoe breng je de theorie in praktijk brengen. Elke deel theorie wordt vertaald naar een praktische toepassing in de casus. En dat is de eerste stap naar het halen van successen met data science in je eigen werk, team, afdeling, divisie of organisatie.

    Meer weten? Interesse? Op 28 november is er een thema avond over de Business Data Scientist in Utrecht. Aanmelden kan via de Radboud Management Academy!

    Deze blog is verschenen op www.businessdecision.nl.

    Auteur: Alex Aalberts

     

     
  • Werkgevers voorspellen wie er ziek wordt met big data

    ziekenhuis ANP 0Steeds meer bedrijven in de VS werken samen met zorgverzekeraars en partijen die gezondheidsdata verzamelen om erachter te komen welke werknemers risico lopen om ziek te worden.

    Daarover schrijft The Wall Street Journal. Om de ziektekosten in de hand te houden slaan sommige bedrijven de handen ineen met bedrijven die allerlei gegevens van werknemers verzamelen en verwerken. Daaruit kunnen ze dan bijvoorbeeld opmaken wie er risico loopt om diabetes te krijgen.

    Op basis van die informatie krijgen werknemers dan bericht dat ze eens naar de dokter zouden moeten gaan of hun gewoontes moeten aanpassen. ''Je kunt beter voorspellen wat het risico is dat iemand een hartaanval krijgt door te letten waar hij of zij winkelt dan door op chromosomen te letten'', zegt Harry Greenspun van Deloitte's Center for Health Solutions, tegen de Wall Street Journal.

    Uiteenlopende informatie
    De directeur van zo'n dataminingbedrijf vertelt dat ze de meest uiteenlopende informatie kunnen gebruiken om de gezondheid van werknemers te voorspellen. Dat loopt uiteen van waar je inkopen doet tot de kredietwaardigheid van werknemers. Mensen met weinig geld, zo is de redenering, zijn eerder geneigd om niet de juiste medicijnen aan te schaffen, als de dokter dat adviseert.

    Een ander bedrijf, Castlight, biedt een platform voor werknemers om zorgverzekeraars te vergelijken. Op basis van de data die zij daarmee verzamelen hebben zij onlangs zelfs een product ontwikkeld dat kan voorspellen of een vrouwelijke werknemer binnenkort zwanger wordt. Dat weten ze op basis van een aantal persoonskenmerken, die zij vervolgens weer koppelen informatie uit declaratieverzoeken bij de zorgverzekeraar. Declareer je opeens de pil niet meer, ben je 30 en heb je al een kind? Dan gaan de alarmbellen af.

    Ook zegt het veel over je als je, in de VS, gaat stemmen voor de verkiezingen voor het Huis van Afgevaardigden of de Senaat. Mensen die dit doen zijn over het algemeen mobieler en actiever en dat zegt veel over je algemene gezondheid.

    Enorme risico's of juist handig?
    Niet iedereen is enthousiast over de verzamelwoede van gegevens door verzekeraars en bedrijven. Frank Pasquale, professor aan de Universiteit van Maryland, zegt tegenover de Wall Street Journal 'enorme potentiële risico's' te zien.

    Sommige werknemers zijn onaangenaam verrast als blijkt dat ze op basis van medische tests een medisch advies van hun werkgever krijgen. Maar het kan ze wel aanzetten hun gedrag te wijzigen en zo de kans op een bepaalde aandoening te verminderen. Zo laat de krant een vrouwelijke werknemer aan het woord die gevaar liep om diabetes te krijgen. Nu is ze flink afgevallen en is het risico afgenomen.

    Source: RTL Z

  • What are key trends in Big Data in 2017


    BDThe focus on big data in 2017 will be on the value of that data, according to John Schroeder, executive chairman and founder of MapR Technologies, Inc. Schroeder offers his predictions on the 6 trends in big data we can expect.

    1.Artificial Intelligence is Back in Vogue

    “In the 1960s, Ray Solomonoff laid the foundations of a mathematical theory of artificial intelligence, introducing universal Bayesian methods for inductive inference and prediction,” Schroeder explains. “In 1980 the First National Conference of the American Association for Artificial Intelligence (AAAI) was held at Stanford and marked the application of theories in software. AI is now back in mainstream discussions and the umbrella buzzword for machine intelligence, machine learning, neural networks, and cognitive computing. Why is AI a rejuvenated trend? The three V’s come to mind: Velocity, Variety and Volume. Platforms that can process the three V’s with modern and traditional processing models that scale horizontally providing 10-20X cost efficiency over traditional platforms. Google has documented how simple algorithms executed frequently against large datasets yield better results than other approaches using smaller sets. We'll see the highest value from applying AI to high volume repetitive tasks where consistency is more effective than gaining human intuitive oversight at the expense of human error and cost.”

    2.Big Data for Governance or Competitive Advantage

    “In 2017, the governance vs. data value tug-of-war will be front and center,” Schroeder predicts. “Enterprises have a wealth of information about their customers and partners. Leading organizations will manage their data between regulated and non-regulated use cases. Regulated use cases data require governance; data quality and lineage so a regulatory body can report and track data through all transformations to originating source. This is mandatory and necessary but limiting for non-regulatory use cases like customer 360 or offer serving where higher cardinality, real-time and a mix of structured and unstructured yields more effective results.”

    3.Companies Focus on Business- Driven Applications to Avoid Data Lakes From Becoming Swamps

    “In 2017 organizations will shift from the ‘build it and they will come’ data lake approach to a business-driven data approach,” says Schroeder. “Today’s world requires analytics and operational capabilities to address customers, process claims and interface to devices in real time at an individual level. For example any ecommerce site must provide individualized recommendations and price checks in real time. Healthcare organizations must process valid claims and block fraudulent claims by combining analytics with operational systems. Media companies are now personalizing content served though set top boxes. Auto manufacturers and ride sharing companies are interoperating at scale with cars and the drivers. Delivering these use cases requires an agile platform that can provide both analytical and operational processing to increase value from additional use cases that span from back office analytics to front office operations. In 2017, organizations will push aggressively beyond an “asking questions” approach and architect to drive initial and long term business value.”

    4.Data Agility Separates Winners and Losers

    “Software development has become agile where dev ops provides continuous delivery,” Schroeder says. “In 2017, processing and analytic models evolve to provide a similar level of agility as organizations realize data agility, the ability to understand data in context and take business action, is the source of competitive advantage not simply have a large data lake. The emergence of agile processing models will enable the same instance of data to support batch analytics, interactive analytics, global messaging, database and file-based models. More agile analytic models are also enabled when a single instance of data can support a broader set of tools. The end result is an agile development and application platform that supports the broadest range of processing and analytic models.”

    5.Blockchain Transforms Select Financial Service Applications

    “In 2017 there will be select, transformational use cases in financial services that emerge with broad implications for the way data is stored and transactions processed,” Schroeder explains. “Blockchain provides a global distributed ledger that changes the way data is stored and transactions are processed. The blockchain runs on computers distributed worldwide where the chains can be viewed by anyone. Transactions are stored in blocks where each block refers to the preceding block, blocks are timestamped storing the data in a form that cannot be altered. Hackers find it impossible to hack the blockchain since the world has view of the entire blockchain. Blockchain provides obvious efficiency for consumers. For example, customers won't have to wait for that SWIFT transaction or worry about the impact of a central datacenter leak. For enterprises, blockchain presents a cost savings and opportunity for competitive advantage.”

    6.Machine Learning Maximizes Microservices Impact

    “This year we will see activity increase for the integration of machine learning and microservices,” Schroeder says. “Previously, microservices deployments have been focused on lightweight services and those that do incorporate machine learning have typically been limited to ‘fast data’ integrations that were applied to narrow bands of streaming data. In 2017, we’ll see development shift to stateful applications that leverage big data, and the incorporation of machine learning approaches that use large of amounts of historical data to better understand the context of newly arriving streaming data.”

    Bron: Informatie Manegement, Januari 2017

EasyTagCloud v2.8