24 items tagged "intelligente organisatie"

  • 5G Technology and its possible business opportunities

    5G Technology and its possible business opportunities

    With 4G technology dominating the wireless networks of the present, a successor is emerging in the form of 5G technology. 5G is expected to replace 4G technology and dominate the wireless networks of the future. This may feel far away, but this innovation may have major implications for business. Businesses who are prepared and ready to adapt to this new technology may have an advantage compared to its competitors. Let's take a better look at 5G technology and the knowledge we already have about it.

    5G. What is it?

    4G LTE is referred to as the gold standard of wireless technology, but only until the arrival of 5G networks – the latest generation of mobile internet connectivity which provides faster speeds and more reliable connections than ever before. The improved networks will act as a catalyst of both innovation and implementation of IoT technology, providing the necessary infrastructure to carry masses of data.

    How big will it be?

    5G networks are expected to launch across the world by 2020, but will require ongoing innovation and investment. Penetration and adoption is not expected until 2025-2030. It is estimated that 5G will boost real global GDP growth by $3 trillion dollars cumulatively between 2020 and 2035.

    What are possible opportunities?

    5G is more than just faster internet. It is a more consistent, efficient, and seamless way to open doors for many cross-sector and integrated technologies. These are some of the most exciting examples:

    - Autonomous vehicles: 5G is expected to catapult the autonomous vehicle market into the mainstream. The potential for car-to-car communication through 5G technology means cars will be able to communicate with each other rather than react, making them safer and able to make immediate decisions.

    - Smart cities: 5G will transform the development of smart cities. For example, capturing data through 5G will help enable smart energy grids to provide real-time diagnosis in the event of power outages, and smart parking management systems that run on 5G will inform drivers of open parking spaces in real time and therefore reduce traffic and emissions.

    - Drones: 5G networks can be used to optimize drone operation and to integrate fleets of drones, enabling them to fight fires, deliver medical supplies and to provide other services in the case of an emergency.

    - Healthcare: 5G networks have the power to revolutionise the healthcare industry by using wearables and biosensors to remotely track health data and provide real-time diagnoses. This can benefit healthcare providers by allowing patients to be monitored at home, as well as benefit employers who can monitor employee health in high-risk and remote environments such as mines.

    Conclusion

    As we accelerate towards wide-scale adoption of 5G technologies, businesses need to start planning now if they are to take full advantage. The infrastructure put in place will transform many new and existing sectors, and allow for Industry 4.0 to become a reality.

    The ultra-fast speeds, ultra-low latency and increased connectivity means businesses will be able to use technologies such as IoT networks, artificial intelligence, automation and machine learning to gather and process masses of data from multiple sources anywhere in the world. This data can be used to maximise the efficiency of business operations and introduce new and innovative solutions to the market.

    Source: B2B international

  • A three-stage approach to make your business AI ready

    A three-stage approach to make your business AI ready

    Organizations implementing artificial intelligence (AI) have increased by 270% over the last four years, according to a recent survey by Gartner. Even though the implementation of AI is a growing trend, 63% of organizations haven’t deployed this technology. What is holding them back: cost? talent shortage? something else?

    For many organizations it is the inability to reach the desired confidence level in the algorithm itself. Data science teams often blow their budget, time and resources on AI models that never make it out of the beginning stages of testing. And even if projects make it out of the initial stage, not all projects are successful.

    One example we saw last year was Amazon’s attempt to implement AI in their HR department. Amazon received a huge number of resumes for their thousands of open positions. They hypothesized that they could use machine learning to go through all of the resumes and find the top talent. While the system was able to filter the resumes and apply scores to the candidates, it also showed gender bias. While this proof of concept was approved, they didn’t watch for bias in their training data and the project was recalled.

    Companies want to jump on the “Fourth Industrial revolution” bandwagon and prove that AI will deliver ROI for their businesses. The truth is AI is in its early stages and many companies are just now getting AI ready. For machine learning (ML) project teams that are starting a project for the first time, a deliberate, three-stage approach to project evolution will pave a shortcut to success:

    1. Test the fundamental efficacy of your model with an internal Proof of Concept (POC)

    The point of a POC is to prove that in a certain case it is possible to save money or improve a customer experience using AI. You are not attempting to get the model to the level of confidence needed to deploy it, but just to say (and show) the project can work.

    A POC like this is all about testing things to see if a given approach produces results. There is no sense in making deep investments for a POC. You can use an off-the-shelf algorithm, find open source training data, purchase a sample dataset, create your own algorithm with limited functionality, and/or label your own data. Find what works for you to prove that your project will achieve the intended corporate goal. A successful POC is what is going to get the rest of the project funded.

    In the grand scheme of your AI project, this step is the easiest part of your journey. Keep in mind, as you get further into training your algorithm, you will not be able to use sample data or prepare all of your training data yourself. The subsequent improvements in model confidence required to make your system production ready will take immense amounts of training data.

    2. Prepare the data you’ll need to train your algorithm… and keep going

    In this step the hard work really begins. Let’s say that your POC using pre-labeled data got your model to a 60% confidence. 60% is not ready for primetime. In theory, that could mean that 40 percent of the interactions your algorithm has with customers will be unsatisfactory. How to reach a higher level of confidence? More training data.

    Proving AI will work for your business is a huge step toward implementing it and actually reaping the benefits. But don’t let it lull you into thinking the next 10% confidence is going to be 6x easier than that. The ugly truth is that models have an insatiable appetite for training data and getting from 60% to 70% confidence could take more training data that it took to get to the original 60 percent. The needs become exponential. 

    3. Watch out for possible roadblocks

    Imagine: if it took tens of thousands of labeled images to prove one use case for a successful POC, it is going to take tens of thousands of images for each use case you need your algorithm to learn. How many use cases is that? Hundreds? Thousands? There are edge cases that will continually arise, and each of those will require training data. And on and on. It is understandable that data science teams often underestimate the quantity of training data they will need and attempt to do the labeling and annotating in-house. This could also partially account for why data scientists are leaving their jobs.

    While not enough training data is one common pitfall, there are others. It is essential that you are watching for and eliminating any sample, measurement, algorithm, or prejudicial bias in your training data as you go. You’ll want to implement agile practices to catch these things early and make adjustments.

    And one final thing to keep in mind,=: AI labs, data scientists, AI teams, and training data are expensive. Yet, in a Gartner report that says that AI projects are in the top three priorities, it also states that AI is thirteenth on their list of funding priorities. Yes, you’re going to need a bigger budget.

    Author: Glen Ford

    Source: Dataconomy

  • BC - Business & Competitive - Intelligence

    BC (Business & Competitive) Intelligence

    Business Intelligence is zo´n begrip dat zich nauwelijks letterlijk laat vertalen. Bedrijfsintelligentie zou in de buurt kunnen komen, maar valt net als andere vormen van intelligentie moeilijk precies te duiden. Bedrijfsinzicht of -begrip komen wellicht nader in de buurt. Andere benaderingen (van andere auteurs) voegen daar bedrijfs- of omgevingsverkenningen als alternatieve vertaling aan toe.

    Om Business en Competive Intelligence goed te begrijpen maken we hier gebruik van een analytisch schema (tabel 1.1). Daarmee wordt het mogelijk de verschillende verschijningsvormen van BI te onderscheiden en daarmee de juiste variant bij het juiste probleem toe te passen. Belangrijk is dat het hierbij gaat om stereotypen! In de praktijk komen mengvormen voor.

    Het uitgangspunt is dat BI wordt gezien als een informatieproces waarbij met behulp van data, kennis of inzicht wordt geproduceerd.

     

    Data over de interne bedrijfsvoering

    Data over de bedrijfsomgeving

    Bedrijfskundige

    benadering

    A

    B

    Technologische

    benadering

    C

    D

    Tabel 1.1

    In de tabel is een bedrijfskundige van een technologische benadering te onderscheiden. BC Intelligence behandeld BI vanuit de bedrijfskundige processen die dienen te worden ondersteund. Er bestaat ook een technologisch perspectief op BI. Het uitgangspunt van deze benadering is veeleer te profiteren van de mogelijkheden die informatietechnologie biedt om bedrijfsinzicht te verkrijgen.. Op de andere as in het schema worden data over de interne bedrijfsvoering (interne data) van data over de bedrijfsomgeving (externe data) onderscheiden. We spreken met nadruk over onderscheiden in plaats van gescheiden categorieën. In de gebruikspraktijk blijken de categorieën namelijk wel te onderscheiden maar nauwelijks te scheiden. Ze kunnen niet zonder elkaar en zijn vaak ondersteunend of complementair.

    Business Intelligence

     

    Data over de interne bedrijfsvoering

    Data over de bedrijfsomgeving

    Bedrijfskundige

    benadering

    A

    B

    Technologische

    benadering

    C

    D

    Hoewel het onderscheid arbitrair is en de term BI net zo goed voor het totale quadrant gereserveerd zou kunnen worden (met CI als deelverzameling) hebben veel BI projecten betrekking op de cellen A en C.

    BI gaat dus vaak op het optimaliseren van bedrijfsprocessen waarbij het accent ligt op het verwerwen van bedrijfsinzicht uit data over de onderneming zelf. Deze data genereren doorgaans kennis over de huidige situatie van de onderneming. Kennis die voor strategievorming en optimalisatie van bedrijfsresultaten (denk aan Performance Management) onontbeerlijk is.

    De technoloische component van BI wordt  door cel C gerepresenteerd. Helaas heeft deze invalshoek bij veel dienstverleners de overhand. Het accent ligt daarbij op de inrichting van een technologische infrastructuur die adequate kennis over de onderneming en haar prestaties mogelijk maakt. In cel C kunnen daarom zowel ETL-tools, datawarehouses als ook analytische applicaties worden gedacht.

    Redactioneel BI-kring:

    In de cel A hebben wij eigenlijk nauwelijks een categorie gedefinieerd.  Wat mij betreft zou daarPerformance Management thuis horen. Die term zou ik dus willen toevoegen. Als Key words kun je denken aan: Key Performance Indicators, Performance Process Management, Organizational Performance,  PDCA (Plan Do Check Act) Cycle, Performance Planning.

    Voor wat betreft C kunnen we verwijzen naar bovenstaande tekst:Datawarehousingen OLAPzijn daar de centrale elementen.Key words zijn dat databases, ETL (Extraction, Transformation and Load), , architecture, data dictionary, metadata, data marts.

    Met betrekking tot OLAP zijn key words:analytische applicaties, reporting, queries, multidimensionale schema’s, spreadsheet, Kubus, data mining.

    Competitive Intelligence

     

    Data over de interne bedrijfsvoering

    Data over de bedrijfsomgeving

    Bedrijfskundige

    benadering

    A

    B

    Technologische

    benadering

    C

    D

    CI is het proces waarin data over de omgeving van de onderneming in een informatieproces worden getransformeerd in ´strategisch bedrijfsinzicht´. Hoewel de term Competitor en Competitive Intelligence vanaf de tachtiger jaren wordt gebruikt heeft deze benadering ook in de jaren zeventig al aandacht gehad onder de noemer 'environmental scanning'.

    CI speelt een belangrijke rol in strategische maar ook andere bedrijfskundige processen. Prestaties van de onderneming, concurrentiepositie, mogelijke toekomstige posities als ook innovatievermogen kunnen slechts worden bepaald met behulp van kennis over de bedrijfsomgeving.

    Redactioneel BI-kring:

    Competitive Intellience heeft dus te maken met alle informatievoorziening die wordt georganiseerd om de concurrentiepositie van ondernemingen te kunnen bepalen, beoordelen en veranderen. Het raakt dus direct aan strategie, strategische intelligence, concurrentie-analyse, concurrentiepositie, en alle intelligence die nodig is om de positie van de onderneming in de omgeving goed te kunnen beoordelen.

    Het organiseren van CI is in organisaties nog steeds zwaar onderbelicht. Het blijkt moeilijk structuur aan te brengen in de noodzakelijke informatieprocessen als ook om ze uit te voeren. De inrichting van een ´systeem´ dat dit proces zou moeten realiseren staat in het middelpunt van de aandacht maar is voor veel organisaties ook nog een brug te ver. Een verantwoorde ontwikkelbenadering vergroot de succeskansen echter aanzienlijk.

    Data over de bedrijfsomgeving zijn vaak ongestructureerd van aard en in de organisatie voorhanden. De kunst is deze data beschikbaar te maken voor de besluitvorming. Wanneer de data niet in de onderneming beschikbaar is verschillen de technieken en instrumenten die moeten worden ingezet om deze data te ontsluiten van de bij BI gebruikte technieken. De technieken varieren vandocumentmanagementsystemen tot information agents die zelfstandig het internet afzoeken naar interessante bouwstenen (data!). Bij het structureren en analyseren van de ongestructureerde documenten wordt text mining gebruikt (in geval van www; web-content-mining).

    Redactioneel BI-kring

    Om competitive Intelligence adequaat te ondersteunen en met name ook primaire data beschikbaar te maken ten behoeve van het proces zijn Collaboration tools populair. Het gaat hier over kennismanagement achtige systemen en shareware toepassingen die de data-, informatie- en kennisdeling faciliteren. Key words: kennismanagagement, shareware, sharepoint, knowledge management.

    Overzicht data categorieen BI-kring

    Cel A format

    • Performance Management

    Key words:Key Performance Indicators, Performance Process Management, Organizational Performance,  PDCA (Plan Do Check Act) Cycle, Performance Planning

    Cel C format

    • Datawarehousing

    Key words:databases, ETL (Extraction, Transformation and Load), , architecture, data dictionary, metadata, data marts., Big Data

    • Online Analytical Processing

    Key words:analytische applicaties, reporting, queries, multidimensionale

     schema’s, spreadsheet, Kubus, data mining, dashboarding.

    Cel B format

    • Competitive Intelligence

    Key words:strategie, strategische intelligence, concurrentie-analyse, concurrentiepositie, competitor intelligence, technological intelligence, environmental scanning, environmental intelligence.

    • Content (Competitive Intelligence als product)

         Key words:

    Cel D format

    • Collaboration

    Key words:kennismanagagement, shareware, sharepoint, knowledge management.

    • Search methodologies

                     Key words:documentmanagement systemen, spider technologie,

    ongestructureerde informatie, information agents, text mining, content mining. Search technologies.

    Integraal tav hele schema (intelligente organisaties hebben het hele model integraal geimplementeerd)

    • Intelligente organisatie

    Key words:Management informatie, Intelligente organisatie, lerende organisatie, organisationeel leren, leren, Intelligence change management.

     

    Bron: Egbert Philips

     

  • Data management: building the bridge between IT and business

    Data management: building the bridge between IT and business

    We all know businesses are trying to do more with their data, but inaccuracy and general data management issues are getting in the way. For most businesses, the status quo for managing data is not always working. However, tnew research shows that data is moving from a knee-jerk, “must be IT’s issue” conversation, to a “how can the business better leverage this rich (and expensive) data resource we have at our fingertips” conversation.

    The emphasis is on “conversation”, business and IT need to communicate in the new age of Artificial Intelligence, Machine Learning and Interactive Analytics. Roles and responsibilities are blurring, and it is expected that a company’s data will quickly turn from a cost-center of IT infrastructure to a revenue-generator for the business. In order to address the issues of control and poor data quality, there needs to be an ever-increasing bridge between IT and the business. This bridge has two component parts. The first one is technology, which is both sophisticated enough to handle complex data issues but easy enough to provide a quick time-to-value. The second one is people who are able to bridge the gap between IT systems/storage/access items and business users need for value and results (enter data analysts and data engineers).

    This bridge needs to be built with three key components in mind:

    • Customer experience:

      For any B2C company, customer experience is the number one hot topic of the day and a primary way they are leveraging data. A new 2019 data management benchmark report found that 98% of companies use data to improve customer experience. And for good reason, between social media, digital streaming services, online retailers and others, companies are looking to show the consumer that they aren’t just a corporation, but that they are the corporation most worthy of building a relationship with. This invariably involves creating a single view of the customer (SVC), and  that view needs to be built around context and based on the needs of the specific department within the business (accounts payable, marketing, customer service, etc.).
    • Trust in data:

      Possessing data and trusting data are two completely different things. Lots of companies have lots of data, but that doesn’t mean they automatically trust it enough to make business-critical decisions with it. Research finds that on average, organizations suspect 29% of current customer/prospect data is inaccurate in some way. In addition, 95% of organizations see impacts in their organization from poor quality data. A lack of trust in the data available to business users paralyzes decisions, and even worse, impacts the ability to make the right decisions based on faulty assumptions. How often have you received a report and questioned the results? More than you’d like to admit, probably. To get around this hurdle, organizations need to drive culture change around data quality strategies and methodologies. Only by completing a full assessment of data, developing a strategy to address the existing and ongoing issues, and implementing a methodology to execute on that strategy, will companies be able to turn the corner from data suspicion to data trust.
    • Changing data ownership:

      The responsibilities between IT and the business are blurring. 70% of businesses say that not having direct control over data impacts their ability to meet strategic objectives. The reality is that the definitions of control are throwing people off. IT thinks of control as storage, systems, and security. The business thinks of control as access, actionable and accurate. The role of the CDO is helping to bridge this gap, bringing the nuts and bolts of IT in line with the visions and aspirations of the business.

    The bottom line is that for most companies data is still a shifting sea of storage, software stacks, and stakeholders. The stakeholders are key, both from IT and the business, and in how the two can combine to provide the oxygen the business needs to survive: better customer experience, more personalization, and an ongoing trust in the data they administrate to make the best decisions to grow their companies and delight their customers.

    Author: Kevin McCarthy

    Source: Dataversity

  • 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

  • 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

  • Effective data analysis methods in 10 steps

    Effective data analysis methods in 10 steps

    In this data-rich age, understanding how to analyze and extract true meaning from the digital insights available to our business is one of the primary drivers of success.

    Despite the colossal volume of data we create every day, a mere 0.5% is actually analyzed and used for data discovery, improvement, and intelligence. While that may not seem like much, considering the amount of digital information we have at our fingertips, half a percent still accounts for a huge amount of data.

    With so much data and so little time, knowing how to collect, curate, organize, and make sense of all of this potentially business-boosting information can be a minefield, but online data analysisis the solution.

    To help you understand the potential of analysis and how you can use it to enhance your business practices, we will answer a host of important analytical questions. Not only will we explore data analysis methods and techniques, but we’ll also look at different types of data analysis while demonstrating how to do data analysis in the real world with a 10-step blueprint for success.

    What is a data analysis method?

    Data analysis methods focus on strategic approaches to taking raw data, mining for insights that are relevant to a business’s primary goals, and drilling down into this information to transform metrics, facts, and figures into initiatives that benefit improvement.

    There are various methods for data analysis, largely based on two core areas: quantitative data analysis methods and data analysis methods in qualitative research.

    Gaining a better understanding of different data analysis techniques and methods, in quantitative research as well as qualitative insights, will give your information analyzing efforts a more clearly defined direction, so it’s worth taking the time to allow this particular knowledge to sink in.

    Now that we’ve answered the question, ‘what is data analysis?’, considered the different types of data analysis methods, it’s time to dig deeper into how to do data analysis by working through these 10 essential elements.

    1. Collaborate your needs

    Before you begin to analyze your data or drill down into any analysis techniques, it’s crucial to sit down collaboratively with all key stakeholders within your organization, decide on your primary campaign or strategic goals, and gain a fundamental understanding of the types of insights that will best benefit your progress or provide you with the level of vision you need to evolve your organization.

    2. Establish your questions

    Once you’ve outlined your core objectives, you should consider which questions will need answering to help you achieve your mission. This is one of the most important steps in data analytics as it will shape the very foundations of your success.

    To help you ask the right things and ensure your data works for you, you have to ask the right data analysis questions.

    3. Harvest your data

    After giving your data analytics methodology real direction and knowing which questions need answering to extract optimum value from the information available to your organization, you should decide on your most valuable data sources and start collecting your insights, the most fundamental of all data analysis techniques.

    4. Set your KPIs

    Once you’ve set your data sources, started to gather the raw data you consider to potentially offer value, and established clearcut questions you want your insights to answer, you need to set a host of key performance indicators (KPIs) that will help you track, measure, and shape your progress in a number of key areas.

    KPIs are critical to both data analysis methods in qualitative research and data analysis methods in quantitative research. This is one of the primary methods of analyzing data you certainly shouldn’t overlook.

    To help you set the best possible KPIs for your initiatives and activities, explore our collection ofkey performance indicator examples.

    5. Omit useless data

    Having defined your mission and bestowed your data analysis techniques and methods with true purpose, you should explore the raw data you’ve collected from all sources and use your KPIs as a reference for chopping out any information you deem to be useless.

    Trimming the informational fat is one of the most crucial steps of data analysis as it will allow you to focus your analytical efforts and squeeze every drop of value from the remaining ‘lean’ information.

    Any stats, facts, figures, or metrics that don’t align with your business goals or fit with your KPI management strategies should be eliminated from the equation.

    6. Conduct statistical analysis

    One of the most pivotal steps of data analysis methods is statistical analysis.

    This analysis method focuses on aspects including cluster, cohort, regression, factor, and neural networks and will ultimately give your data analysis methodology a more logical direction.

    Here is a quick glossary of these vital statistical analysis terms for your reference:

    • Cluster: The action of grouping a set of elements in a way that said elements are more similar (in a particular sense) to each other than to those in other groups, hence the term ‘cluster’.
    • Cohort: A subset of behavioral analytics that takes insights from a given data set (e.g. a web application or CMS) and instead of looking at everything as one wider unit, each element is broken down into related groups.
    • Regression: A definitive set of statistical processes centered on estimating the relationships among particular variables to gain a deeper understanding of particular trends or patterns.
    • Factor: A statistical practice utilized to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called ‘factors’. The aim here is to uncover independent latent variables.
    • Neural networks: A neural network is a form of machine learning which is far too comprehensive to summarize, but this explanation will help paint you a fairly comprehensive picture.

    7. Build a data management roadmap

    While (at this point) this particular step is optional (you will have already gained a wealth of insight and formed a fairly sound strategy by now), creating a data governance roadmap will help your data analysis methods and techniques become successful on a more sustainable basis. These roadmaps, if developed properly, are also built so they can be tweaked and scaled over time.

    Invest ample time in developing a roadmap that will help you store, manage, and handle your data internally, and you will make your analysis techniques all the more fluid and functional.

    8. Integrate technology

    There are many ways to analyze data, but one of the most vital aspects of analytical success in a business context is integrating the right  decision support software and technology.

    Robust analysis platforms will not only allow you to pull critical data from your most valuable sources while working with dynamic KPIs that will offer you actionable insights, it will also present the information in a digestible, visual, interactive format from one central, live dashboard. A data analytics methodology you can count on.

    By integrating the right technology for your statistical method data analysis and core data analytics methodology, you’ll avoid fragmenting your insights, saving you time and effort while allowing you to enjoy the maximum value from your business’s most valuable insights.

    9. Answer your questions

    By considering each of the above efforts, working with the right technology, and fostering a cohesive internal culture where everyone buys into the different ways to analyze data as well as the power of digital intelligence, you will swiftly start to answer to your most important, burning business questions. 

    10. Visualize your data

    Arguably, the best way to make your data analysis concepts accessible across the organization is through data visualization. An online data visualization is a powerful tool as it lets you tell a story with your metrics, allowing users across the business to extract meaningful insights that aid business evolution. It also covers all the different ways to analyze data.

    The purpose of data analysis is to make your entire organization more informed and intelligent, and with the right platform or dashboard, this can be simpler than you think.

    Data analysis in the big data environment

    Big data is invaluable to today’s businesses, and by using different methods for data analysis, it’s possible to view your data in a way that can help you turn insight into positive action.

    To inspire your efforts and put the importance of big data into context, here are some insights that could prove helpful. Some facts that will help shape your big data analysis techniques.

    • By 2020, around 7 megabytes of new information will be generated every second for every single person on the planet.
    • A 10% boost in data accessibility will result in more than $65 million extra net income for your average Fortune 1000 company.
    • 90% of the world’s big data was created in the past three years.
    • According to Accenture, 79% of notable business executives agree that companies that fail to embrace big data will lose their competitive position and could face extinction. Moreover, 83% of business execs have implemented big data projects to gain a competitive edge.

    Data analysis concepts may come in many forms, but fundamentally, any solid data analysis methodology will help to make your business more streamlined, cohesive, insightful and successful than ever before.

    Author: Sandra Durcevic

    Source: Datapine

  • Finance gedwongen te moderniseren door digitalisering

    Finance-Data1

    Gedreven door technologie en toenemende transparantie-eisen groeit finance uit tot het dataknooppunt van de organisatie. Hoe werkt dat in de praktijk? Drie CFO's bieden een blik achter de schermen.

     De finance-functie moderniseert. Met real-time analytics, altijd en overal beschikbare data, nieuwe samenwerkingstools en een behoorlijke dosis aanpassingsvermogen wordt de strijd aangegaan met de voortdurend veranderende omstandigheden waarbinnen de onderneming moet aantonen van toegevoegde waarde te zijn voor zijn stakeholders. 'Eigenlijk staat in elke branche het verdienmodel onder druk', zegt Robert van de Graaf, CFO met brede ervaring in de financiële sector. 'En overal ervaart men de noodzaak daar antwoorden op te formuleren. Het is immers een kwestie van 'to disrupt or to be disrupted'.' Dat finance in dat proces een leidende rol vervult, is in de ogen van Van de Graaf 'logisch'. 'Finance is per slot van rekening de hoeder over de continuïteit van de onderneming en het daarbij behorende businessmodel.'

    Data-voorsprong

    De sensationele voorbeelden - Uber en Airbnb die hele branches bedreigen - trekken uiteraard de meeste aandacht, maar de veranderingen zijn niet altijd meteen ingrijpend en zichtbaar. Wat overigens niets afdoet aan de noodzaak de confrontatie ermee te zoeken, vindt Van de Graaf. 'In vele branches heeft men nu nog een data-voorsprong op de klanten, maar over drie tot vijf jaar is daar geen sprake meer van. Denk aan de impact van The Internet of Things in de woning en scanners waarmee je de eigen gezondheid kan bepalen. Als je wacht tot het zover is, is het te laat. Je moet nú de vraag gaan beantwoorden wat die ontwikkeling gaat betekenen voor je onderneming.'

    In zijn rol van aanjager in dat proces moet finance uit zijn comfortzone stappen, vindt Van de Graaf. 'Ik zie graag dat finance het aangaan van kort cyclische projecten stimuleert. Eis dan niet een business case vooraf, maar spreek bijvoorbeeld af dat binnen drie maanden duidelijk wordt of er al dan niet een klantbehoefte is aangeboord. Houd daarbij de teams in eerste instantie klein, want dat bevordert de creativiteit.'

    Volumedaling

    Het is een proces waarmee inrichter Koninklijke Ahrend ervaring heeft. 'De vastgoedcrisis van 2008 halveerde de Europese markt voor inrichten en had daarmee een majeure impact op de omzet van dit bedrijf', zegt CFO Rolf Verspuij. Vervolgens kwamen daar de effecten van trends als digitalisering overheen. 'Flexwerken en thuiswerken zorgden voor een verdere volumedaling. De verkoop van kasten en werkplekken waren de kurk waar het bedrijf op dreef, maar dat tijdperk liep af, zoveel was duidelijk.'

    In 2012 ging daarom het roer om. 'Vanzelfsprekend' had finance een leidende rol bij die veranderingsoperatie, vindt Verspuij, die begin 2012 bij Ahrend in dienst trad. 'Uiteindelijk gaat het om het halen van financiële doelstellingen. Van alle onderdelen van het nieuwe businessmodel moet toch bepaald worden of en in welke mate ze bijdragen aan het resultaat.' Bovendien noopte de nieuwe koers tot het aanpassen van informatiesystemen voor meer inzicht in de performance.

    'Flexibiliteit en aanpassingsvermogen zijn zeer belangrijk geworden voor onze organisatie', aldus Verspuij, 'want we onderscheiden ons nu door marktgerichtheid. Vroeger was Ahrend min of meer een productiehuis: we ontwikkelden en produceerden een mooi product, om dat in hoge volumes weg te zetten. Nu is luisteren naar de markt het devies. We spelen daar vervolgens op in met nieuwe concepten en allerlei slimme inrichtingsoplossingen. '

    Registratiesysteem

    Waarbij Ahrend nu juist gebruikmaakt van digitalisering: if you can't beat them, join them. 'Zo maken we het de facility-manager gemakkelijk door meubilair te voorzien van geavanceerde technologieën waarmee registratie van gebruik en het creëren van overzicht tot de mogelijkheden behoort. Beheer, onderhoud en kostenbewaking zijn dan efficiënter uit te voeren.' Daarnaast wordt het interessant innovaties toe te passen waarbij gebruikgemaakt wordt van de mobiele telefoon.

    Door al deze veranderingen is Ahrends omzet weer gestegen, zelfs tot boven het niveau van 2007, mede door een aantal overnames vorig jaar. De doorgevoerde veranderingen dragen bovendien zichtbaar positief bij aan de resultaten. 'We zijn er nog niet', zegt Verspuij, 'maar er zijn grote stappen gezet.'

    Wisselwerking

    Ook bij de HVC Groep, een afval-, energie- en grondstoffenbedrijf, hebben marktverschuivingen geleid tot digitalisering van het product- en dienstenpakket. Zo chipt HVC de afvalbakken. De beschikbaarheid van data is een stuwende factor voor de verbreding van de informatievoorziening door de finance-functie, zo vertelt CFO Ingrid Tigchelaar. 'Waarbij er duidelijk een wisselwerking is tussen vraag en aanbod. De roep om transparantie en de technologische mogelijkheden tot dataverzameling en -analyse versterken elkaar.' HVC Groep is in handen van overheden en in die zin een 'klassiek' nutsbedrijf. 'Dat betekent dus: talrijke stakeholders die steeds meer informatie willen over de prestaties en bedrijfsvoering van de organisatie', vertelt Tigchelaar. 'Transparantie is een maatschappelijke norm geworden.'

    Ze werkt momenteel aan de omwenteling om aan die norm te voldoen. 'In de basis is de HVC Groep een volcontinu procestechnologisch bedrijf. We waren al gewend veel gegevens te verzamelen over de bedrijfsvoering, vooral met het oog op de monitoring van de continuïteit en de veiligheid van de bedrijfsprocessen. Echter, die gegevens werden altijd alleen intern gebruikt. Om ze geschikt te maken voor andere stakeholders is een kwaliteitsslag nodig; de buitenwereld stelt nu eenmaal andere kwaliteitseisen aan die informatie. Met alle gevolgen dus voor de ordening, organisatie, verslaggeving en rapportage van die gegevens.'

    Betrouwbaarheid

    Kenmerkend voor de manier waarop finance zich ontwikkelt, zo zegt Tigchelaar, is dat financiële en niet-financiële informatie steeds meer verweven raken. 'In dit type bedrijf liggen de uitdagingen niet in het proces van de verwerking van financiële gegevens, dat is wel op orde. Wel is het belangrijk dat je die financiële gegevens kunt laten aansluiten op al die andere data die belangrijk zijn voor de bedrijfsvoering. Eén bron van informatie: daarmee verhoog je de betrouwbaarheid ervan enorm.'

    Om welke gegevens gaat het dan? Tigchelaar noemt als voorbeeld de hoeveelheid gerecycled afval. 'Met het Rijk zijn daar in de zogeheten VANG-doelstellingen afspraken over gemaakt. Zo moet in 2020 75 procent van het afval gerecycled worden. Deze doelstellingen zijn overgenomen door lagere overheden en die willen verantwoording afleggen aan de burgers. Dat betekent dat wij als inzamelaar en verwerker van afval daar informatie over moeten geven; dat is ook vastgelegd in de dienstverleningsovereenkomsten met onze stakeholders.'

    Een ander voorbeeld is de onlangs afgesloten brand- en ongevallenverzekering. 'Waarbij een goede registratie van alle incidenten in het bedrijf van groot belang is. Dat deden we al, maar alleen voor intern gebruik. Laten zien aan externe stakeholders dat we in control zijn, stelt extra eisen aan de verzameling en verwerking van de betreffende data.'

     

    source: www.fd.nl

     

  • 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

     

  • How employee personas can help to improve productivity within organizations

    How employee personas can help to improve productivity within organizations

    The importance of staff engagement and morale in the workplace of an organization

    Companies are becoming more crowded with communal factors across the board, often advertising similar job benefits and perks. Competition is increasingly fierce and the cost of training new recruits is rising, this emphasizes that it is essential to retain the employees who bring value to the company. As well as retaining valuable employees, companies need to understand and maximize the potential of the diverse behaviors and attitudes of their workforce. This workforce is often multigenerational, consisting of individuals across various nationalities and backgrounds.

    What are personas?

    In simple terms, a persona is a character created based on research to represent different personalities and segments of individuals who, in this instance, are employed in the workplace of an organization. By creating employee personas employers can better understand, develop and cater to employee needs and aspirations. Whether they may be ‘the high flyer’, ‘the innovator’ or ‘the nerdy one’, should not be taken into account.

    Employee personas are created to recognise that there are different segments of people, who are not necessarily characterised by a similar age, background or experience level. Categorisation is more based on different attitudes, behaviours and needs. Once these have been identified, employers are able to better understand their employees and ultimately drive the growth of both the employees individually and the company as a whole.

    Why create employee personas?

    The key benefits of creating employee personas are as follows:

    1. Appreciation of the employee – understanding the persona perspective, what goals they have, what challenges they may face, their behaviours and attitudes on a day to day basis and how these have developed. This allows the organization to act proactively upon this, creating committed, loyal and determined personnel. It also allows the company to work collaboratively and make decisions which are mutually beneficial to both employees and organizational growth. This also leads to more effective management, which again only comes from truly understanding your employees and what they want. Appropriate mentoring and guidance are proven to have a higher chance of growing the individual’s core strengths. Combined with appreciation of the employee, this will help to retain your workforce and ensures that both you and your employees have a unified purpose.
    2. Greater efficiencies – by understanding the different customer journeys each persona undertakes, what their pain points and their core strengths may be, companies can use this information to their advantage and reduce inefficiencies by having employees play to their core strengths. These pain points and strengths will become cues to best communicate with these employees and find out what strategies and methods work most effectively for that employee group. By focusing on strengths and diminishing weaknesses efficiencies will increase.
    3. Maintaining your company culture (particularly when hiring new employees) – by creating employee personas, employers can choose individuals who have a great fit with existing employees and the organization. As the process of attracting new employees becomes increasingly fierce, advertising employee personas can help you to zero in on the ideal candidate for your company. This goes beyond the usual checklist of qualifications, work experience and skillset It can also incorporate the mindset and character of new employees and help to gain a new perspective for your company.

    How to create employee personas?

    • Map out the steps each employee takes. From the initial hiring process through to career progression, till eventually leaving the company, whether this be through retirement, a career change, a career break or anything else.
    • Gather information about the different job descriptions, aspirations and challenges different departments, experience levels and ages may face.
    • Analyse and design the data into personas based on common attitudes, needs and behaviors of employees.
    • Remember that one persona may cover those of different ages, job roles and levels of experience, depending on their approach and attitude to the workplace.
    • Use your employee persona research to design your solution and your steps going forward.
    • Revisit and maintain your employee personas every now and then (six months seems like a valid timespan) to keep them up to date. Personas may change over time from the introduction of new technologies and general working rules and regulation.

    Conclusion

    Everyone knows by now that the saying ‘one size fits all’ isn’t true, particularly in the multicultural, multigenerational and dynamic society in which we live. Without considering the needs of indiciduals, companies risk employee disengagement and lower levels of productivity. Instead, be proactive and find out what your employee personas are and utilise this to your advantage. By understanding how needs may differ you may be able to improve your organization's engagement levels, loyalty and employee wellbeing.

    Author: Natalia Jain

    Source: B2B international

  • How to act now to be successful in the future? Digital business models

    How to act now to be successful in the future? Digital business models

    Digital business models created around data are producing a winner-take-all market, not a digital divide. That’s why leaders need to “stop doing analytics for analytics’ sake, focus on the business problem, and define and ask the big questions of your data,” warns disrupting digital business author Ray Wang in 10 Enterprise Analytics Trends to Watch.

    The Constellation Research founder and principal analyst notes that digital leaders are now grabbing 70% of overall market share, and more than 75% of profits. A Harvard Business Review Analytic Services report that features insights from Wang warns brands of “an existential imperative; those companies that do not evolve into data-driven organizations will be supplanted by those that do.”

    For most, a long way to go and a short time to get ghere

    The inflection point for the data-driven enterprise report, based on a survey of 729 business leaders conducted by Harvard Business Review Analytic Services, shows that while 90% of respondents say they’re confident that their organizations will achieve its vision of a data-driven enterprise, most have an alarmingly long way to go:

    • While 86% say the ability to extract new value and insights from existing data and analytics applications is very important, only 30% say their organization is currently very effective at doing so.
    • While 78% say accessing and combining data from a variety of external data sources is very important, just 23% say their organization is currently very effective at doing so.

    And those new digital business models that are, according to Ray Wang, creating a winner-take-all market? Only 28% of respondents say that introducing new business models is a key goal of their evolution into a data-driven organization. For leaders this is key to digital transformation, says Wang. For the remaining 72% that don’t have new business model creation or business model evolution as a goal, there’s simply no time to wait.

    “This is a top-down strategic business model decision that boards have to address,” says Wang. “Boards aren’t doing their jobs because they don’t understand the problem: they’re in a data war, and data is the weapon."

    Leaders are moving further ahead, faster

    In 10 Enterprise Analytics Trends to Watch, Wang notes that you’ll also see analytics leaders applying artificial intelligence for business agility and scale. This automation and augmentation when it comes to data and insights is set to move leaders and fast followers even further ahead when it comes to digital transformation.

    “The situation in almost every market is that executives realize that they need to transform. They want to start using artificial intelligence, for example,” says Wang. “But they don’t realize that these changes happen along a continuum. It’s an intensive, multi-year process.”

    As the next decade looms, the race is on to make the most, and more than competitors, of data. Is your 2020 vision for data and analytics clear?

    ''Every board member and CEO needs to understand that data assets have to be managed the same way they manage any other asset. If they don’t, they will be disrupted.'' - Ray Wang, Constellation Research

    Source: Microstrategy

  • How To Build A Game-Changing Team For Your Business

    corporate-team-buildingGetting a successful business up and running is a key skill for entrepreneurs. Building a team that can take it to the next level is another; and some might say, one of the most difficult to master. The people they need to bring on board must be at ease with autonomy, entrepreneurial, driven, and able to apply their skills to a wide range of tasks. They also need to share the founder’s vision. Above all, to be part of a game-changing growth strategy, they have to be great team players. But where do you find these people and how do you get them on board?

    Harness the team-building power of technology

    Does it matter if your next key player is based in London, New York, Tokyo or Rome? Not if you have access to the technology that can empower key hires to elevate the team and help the business achieve its goals from anywhere in the world. In-app advertising platform Tapdaq has just closed a $6.5million Series A funding round which will be used to further expand the company by hiring the best talent from across the globe.

    “We’re not into letting geography determine who we hire – we want to find the perfect person for the role,” says CEO and cofounder Ted Nash, a serial entrepreneur who has been creating online companies since he was 12 and was the world’s first teenager to achieve 1million App Store downloads.

    He adds: “There are processes you need to put in place to make sure everyone’s doing the job that’s being asked of them and to maintain a strong company culture, but having a global approach to your workforce allows you to tap into top talent from across the world, freeing your company from geographical boundaries.”

    Target the power players

    Business intelligence company DueDil is growing rapidly, doubling in size to 80 people in 2015, which has involved building core functions, such as the sales team, from scratch. Getting the right people in place to lead those key areas is crucial to success, and the London-based firm recently appointed Nick Noyer as VP of marketing. Noyer was previously director of EMEA marketing and international strategy at Box , where he led market entry into Europe.

    DueDil cofounder and CEO Damian Kimmelman said: “We’ve found smart leaders who bring new skillsets to the company, which is important. But for me, it’s critical to look for someone who can show they are hungry to succeed, as I want people alongside me who have something to prove and are motivated by big challenges. If they have that single attribute, they tend to rise to the obstacles we face as a company as we scale.”

    Source : Forbes

  • How to improve your business processes with Artificial Intelligence?

    How to improve your business processes with Artificial Intelligence?

    In the age of digital disruption, even the world’s largest companies aren’t impervious to agile competitors that move quick, iterate fast, and have the capacity to build products faster than their peers. That’s why many legacy organizations are taking a closer look at business process management.

    Simply speaking, business process management is the practice of reengineering existing systems in your firm for better productivity and efficiency. It takes a proactive approach towards identifying business problems and the steps needed to rectify them. And while business process management has traditionally been the forte of management consultants and other functional experts, rapid advancements in artificial intelligence and big data means this sector is also undergoing a fundamental transformation.

    So it begs the question: how do you start “plugging AI” into your company’s existing data and systems?

    Where to begin?

    Artificial intelligence is exciting because it promises to introduce a totally new way to business operations. However, most traditional organizations don’t have the necessary infrastructure and/or computing power to deploy these technologies.

    Moving your data and applications to the cloud is a very popular solution to unlocking the necessary computing resources, but there's a catch. You can’t just copy-paste your files to the cloud and start using AI. Older systems weren’t built with a cloud deployment in mind, so leveraging the cloud usually requires rebuilding your existing software using a common cloud-ready platform like Kubernetes, Pivotal Cloud, and Docker Swarm.

    The point is that once you make a decision towards digital transformation, you need complete buy-in from all areas of the business and a commitment to process and technology changes. Getting that commitment typically involves showcasing the real benefits that AI can unlock. Let’s take a closer look at how artificial intelligence is actively impacting the way companies do their business.

    1. Analyzing sales calls

    When it comes to simulating business processes and operations one crucial aspect is definitely sales calls. That’s because sales, and the ensuing revenue that comes from it, are the bread and butter of your business. Top-tier sales representatives will ensure your firm keeps chugging along and reaching new boundaries.

    In the past, analyzing sales calls was a manual process. There might have been a standard sales playbook with generic questions that each individual would be expected to ask. But now, AI conversational tools like Gong are automating this process entirely.

    Gong is able to record each outbound sales call that your team makes and pick up on cues that help it determine how the call went. So, a successful sales call will probably see the prospect talking more than the sales rep, for example.

    2. Converting voicemail into text

    Have you ever heard the phrase: “Your unhappiest customers are your greatest source of learning?” These famous words were said by none other than Bill Gates. But how can you even accurately quantify customer sentiment if you don’t take the requisite steps to track it?

    It’s certainly possible that a large chunk of your customers don’t want to remain on hold while waiting for a customer support agent and prefer to leave a voicemail instead. Intelligent automation tools like Workato are making it possible to automate voicemail follow-ups, thereby ensuring that no customer falls through the cracks and each one is given an appropriate response to their concerns.

    For example, Workato was able to help automate voicemail follow-ups for a large chain of cafes. Whenever a new voicemail came into its system, the intelligent tool would use speech to text conversion to create a transcript of the voicemail. It would then take that text and add it on the service ticket, giving customer support agents a much better idea of the nature of the complaint and allowing them to resolve it quicker.

    3. Detecting fraud

    Occupational fraud causes organizations to lose about 5% of their total revenue every year with a potential total loss of $3.5 trillion. Machine learning algorithms are actively quelling this trend by spotting discrepancies and anomalies in everyday processes.

    For example, banks and financial institutions use intelligent algorithms to detect suspicious money transfers and payments. This process is also applicable in cybersecurity, tax evasion, customs clearing processes, insurance, and other fields. Large-scale organizations that are able to leverage AI are potentially looking at cost savings in the millions of dollars each year. These resources can then be spent in other critical areas of business such as research and development so companies can stay competitive and ahead of the curve.

    Conclusion

    Artificial intelligence isn’t just a fancy buzzword that people are tossing around with willful abandon. In fact, every time you take advantage of Google’s typo detection feature (when you see ‘did you mean’ in the search engine) you’re actually plugging into its DeepMind platform, an example of AI in everyday use.

    AI has the potential to promote greater efficiency, output, less interruption, and, ultimately, higher revenue across businesses of all shapes and sizes.

    Author: Santana Wilson

    Source: Oracle

  • 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

  • Implementation of AR, VR and other emerging technologies in your organisation: a huge challenge

    apple ar bril gerucht 1

    We are moving toward a digitally-enabled world, where the lines between real and digital are blurring. As the digital world becomes an increasingly detailed reflection of the physical world, it creates fertile ground for new business models and ecosystems.

    To embrace a digitally-enabled world, businesses need to digitally enable and transform every aspect of their operations. And they know this, with 55% of IT decision makers stating they have a year or less to make digital inroads before suffering financially and competitively. 

    There are a plethora of new technologies to explore, and for enterprises still in the throes of digital transformation it can be difficult to figure out what technology to embrace next. 

    What are some of these technologies that are ripe for companies to embrace? 

    1. Augmented and virtual reality.
    2. Artificial Intelligence and voice interfaces.
    3. Machine to machine interactions. 

    Each has different benefits for companies with one overarching theme - not every technology is the right fit for ev

    ery company, and certain considerations must be made before deciding to implement. 

     

    Augmented and Virtual Reality

    AR and VR are arguably the most complex to implement of these new technologies. Everyone seems to be talking about them, but few companies can currently do anything useful with either. Why? Because they’re very hard to implement at the moment as they require new hardware which is still changing rapidly.

    While major companies like Amazon and Microsoft have rolled out development platforms, the industry is still shaking out and establishing standards. Instead of rushing to leverage the newest technology, companies should first assess if it really makes sense for their business - a task that is unfortunately not always easy.

    How can companies know they’re ready? There are a few specific use cases that are good guidelines; essentially, AR/VR only makes sense in certain markets currently. 

    The first are companies that regularly need to visualize 3D objects like architects, construction firms and engineers. AR and VR can help those in the building space easily visualize and create floor and building plans.

    In the automotive industry, car body designers can also benefit from this technology as it enables them to easily integrate and see the many small parts needed to create the car design. Simply, AR and VR make moving from the planning to production stage much faster - whether it’s for a car, home or machined component.

    Industries that require complex diagnose and repairs - like energy - can benefit greatly from AR as it allows for augmented insights and repairs to very complicated systems with small parts. The one caveat, these must be high value systems to justify the cost of AR technology. Smaller car engine repairs would not justify the cost, but something which is critical to keep operating, such as an expensive and complex piece of manufacturing equipment or a wind turbine, really makes sense. 

    Another instance where AR and VR are worth the investment is training for scenarios that are difficult to train for real life. In fact, we’ve seen versions of AR being used in flight simulators for quite some time.

    The military has also begun using the technology in its training, and some areas of the private sector could learn to do the same. For example, in the medical field doctors could benefit from training for complex procedures with VR that would not otherwise be possible to prepare for. 

    The main theme among these use cases is that AR and VR currently make sense only in industries that require visualization of plans/designs, high value and complex equipment or hard to train for scenarios. Companies should ask themselves if they fit into any of these areas and then determine if the potential benefits outweigh the costs of this technology. 

    AI and Voice Interfaces

    Although AI is even more complex under the hood than AR and VR, it has a huge benefit in terms of implementation - it can use the input from users’ current devices today! 

    A specific area we are excited about in AI is the improvement in voice recognition and voice synthesis. These services can be purchased at competitive pricing from most major AI technology providers such as Microsoft, Amazon, Google and IBM. They can also be combined with animated characters to provide visual voice interfaces that companies can benefit from right now. 

    Digital agents are one of the biggest opportunities for digital transformation this year. This needs to go a step beyond the largely voice only interfaces of Alexa, to providing great visual interfaces that allow users to perform tasks like browsing, selecting and configuring.

    While digital agents don’t make sense for every industry and company, they do offer vast opportunities to any company that has customers who interact with a product or service and want to offer an improved 24/7 experience. These include use cases like digital stylists, virtual concierges and digital chefs just to name a few. 

    Digital agents give companies the ability to provide step by step instructions to customers or employees whether it is a digital chef demonstrating how to prepare a meal or a digital agent training employees on ways to complete internal tasks.

    The hospitality industry also has a huge opportunity with digital agents, which act as a virtual concierge for guests - availa

    ble 24/7 - and can be accessed anywhere by guests. Providing personalized customer service and simplifying the process of using a concierge that may encourage more guests to pay for services at the hotel. 

    Ecommerce is another industry that can reap the rewards brought by digital agents. They offer a way to compete with Amazon by offering a better and more personalized customer experiences for online shoppers and ease the sometimes frustrating and arduous process of contacting customer service. They also offer an easier way to browse or configure and order complex products - like a customized RV or vacation package. 

    Essentially, any company whose success is based on customers spending money can benefit from a digital agent as it helps to improve customer experience and encourage more spending thanks to ease of use and personalization. These agents are available 24/7, directly on a personal smartphone.

    Machine-to-Machine Interactions

    Interactions between machines is another huge opportunity for companies to digitally transform. When machines can interact and complete tasks with little human intervention using APIs, it streamlines the process and frees up resources for more complex tasks. Like the other technologies, machine-to-machine interactions are most beneficial to certain industries. 

    One example of machine-to-machine interactions widely used today is shipping. This is a process that has become completely digital for many companies. A company can order goods, and a carrier like DHL ships the package and provides tracking information that is automatically shared throughout a packages journey thanks to automated barcode scanning machines. Once the company receives the goods, they can automatically process the 

    packages without requiring an employee to review the shipment, because they trust the machine-to-machine reports that signal all packages have arrived. Three companies have interacted via API only. 

    While the shipping example is already widely implemented, there are other machine-to-machine scenarios that companies can utilize. Anytime machines can be connected using the API economy or IoT devices, processes can be sped up and simplified. Human error becomes a thing of the past and the labor force is freed up to complete more complex tasks. 

    Some considerations when implementing M2M in your business include using Swagger or the Open API Initiative format to share your API. You should certainly implement basic monitoring and analysis to ensure these API’s are being used as intended and not abused. 

    Another new technology which can be highly beneficial is using a centralized blockchain log for machine-to-machine processes that require external auditing. This means that all parties can download the log and ensure transactions are unaltered, which is a great way to provide for transparency for automated trading or bidding interactions. 

    Overall, there is a great opportunity for companies to digitally transform using technologies like AR/VR, digital agents and machine-to-machine interaction. With so many new technologies, it can be difficult for companies to determine which are worth the investment and which to pass up. The main consideration to account for are if the use cases for a technology make sense for the company and if the technology is being used successfully by others in the industry already.

    Source: David Brebner (Information Management)

  • In een intelligente organisatie is er altijd plaats voor een chatbot in HR

    In een intelligente organisatie is er altijd plaats voor een chatbot in HR

    Mensen vormen het hart van een bedrijf, en de afdeling Human Resources is er om voor die mensen te zorgen. HR is de bewaker van de cultuur en zorgt dat werknemers mogelijkheden krijgen om te groeien. Het houdt het bedrijf levendig en gezond. HR draait dus om mensen. Is een virtuele assistent, oftewel een chatbot, tussen al deze mensen wel op zijn plek?

    Hoewel HR draait om de mensen binnen een organisatie, besteden HR-medewerkers ongeveer een vierde van hun tijd aan administratieve taken. Het beantwoorden van vragen van medewerkers is bijvoorbeeld een dagelijks terugkerende taak. Vragen als ‘hoeveel vakantiedagen heb ik nog?’ of ‘wat zijn de regels rond ziekteverlof?’ komen bijna dagelijks aan bod. Een chatbot kan al die vragen van medewerkers beantwoorden. Dit ontziet niet alleen de HR-manager, maar het schept ook direct duidelijkheid voor medewerkers die de vragen stellen. Nooit meer de frustraties van lang wachten op een antwoord op een simpele vraag. Dat klinkt goed toch?

    Een chatbot kan de gestelde vragen ook nauwkeurig bijhouden, om zo knelpunten in het HR-beleid op te merken. Daarnaast wordt een chatbot met de hulp van kunstmatige intelligentie steeds slimmer, naarmate hij meer vragen krijgt. De antwoorden die hij geeft zullen elke dag beter en nauwkeuriger worden. Dit wirdt ook wel machine learning genoemd.

    Persoonlijke antwoorden voor specifieke situaties

    Vooral het aanvragen van verlof is een administratieve taak die vaak veel tijd kost. Denk aan het aanvragen van zwangerschapsverlof bijvoorbeeld. Een bot kan persoonlijke antwoorden en oplossingen geven voor deze specifieke aanvraag.

    Ook tijdens ziekte kan de chatbot een rol spelen. Een van de belangrijkste taken van HR is het zorgen voor een gemotiveerd personeel. Om hieraan bij te dragen kan een chatbot bijvoorbeeld een ‘beterschap’ boodschap sturen als iemand zich ziek meldt. De virtuele assistent kan ook vragen en bijhouden hoe het met diegene gaat, om zo het herstel in het oog te houden.

    Sollicitatieprocedures gladstrijken met een chatbot

    Gezien de huidige arbeidsmarkt is het vaak lastig om nieuw personeel te vinden. Het is daarom essentieel dat het sollicitatieproces vlekkeloos verloopt. Een chatbot kan dit optimaliseren door vragen van een sollicitant direct te beantwoorden. Na het beantwoorden van een vraag, kan de chatbot zelf waardevolle data verzamelenover de sollicitant. De bot slaat de antwoorden op zodat het eenvoudiger wordt om kandidaten te screenen. Niet alleen het leven van de recruiter wordt zo makkelijker, ook dat van de sollicitant.

    Het grootste deel, ongeveer 80%, van mensen die ergens solliciteren, overweegt ergens anders heen te gaan als ze tijdens het proces niet regelmatig updates krijgen over hun sollicitatie. Ze blijven wel aan boord als ze regelmatig op de hoogte gehouden worden over hoe het ervoor staat. Een bot kan een sollicitant op de hoogte houden en zo het proces van recruitment op een positieve noot beginnen. Nadat de sollicitant de selectieprocedure heeft doorlopen en zijn of haar proeftijd in gaat, begint de onboarding. De onboarding is een belangrijke periode om ervoor te zorgen dat een nieuwe medewerker zo snel mogelijk mee kan draaien in de organisatie. In plaats van te werken via een checklist kan de chatbot een groot deel van de onboarding van de HR overnemen en kan de medewerker snel zelf aan de slag. Doordat alle documenten en informatie klaargezet worden in de chatbot kan HR zich meer focussen op het persoonlijke aspect van de onboarding.

    Chatbot voor HR, meer ruimte voor mensen

    Ondanks de opkomst van nieuwe technologie is de wereld van HR er eentje die draait om mensen. Mensen die tijd nodig hebben om er voor elkaar te zijn, in plaats van dat ze zich constant bezig moeten houden met administratieve taken. HR moet zich kunnen richten op de ontwikkeling van medewerkers en als mentor kunnen optreden. HR moet de perfecte nieuwe collega kunnen vinden en de doelen van de organisatie nastreven. Door de inzet van een chatbot kan juist het werk uit handen genomen worden dat zoiets in de weg staat. Zo kan een bedrijf zich niet alleen richten op wat belangrijk is, maar kan het ook zijn medewerkers de ruimte geven te doen waar ze goed in zijn door altijd paraat te staan met de juiste informatie en het juiste advies. Daarom heeft een intelligente organisatie altijd plaats voor een chatbot in HR.

    Auteur: Joris Jonkman

    Bron: Emerce

  • Informatie kun je delen, kennis niet!

    Kennisdeling 2Binnen organisaties wordt veel aandacht besteedt aan kennisdeling. De gedachte is, dat door kennisdeling de concurrentiepositie van de organisatie wordt vergroot. Met de introductie van het kennisdelen op grote schaal lijken de communicatieproblemen toe te nemen. Communicatieproblemen zijn een grote bron van ergernis. Wat wordt er nu eigenlijk bedoelt met communicatieproblemen? Heeft de wijze van kennisdelen een aandeel in het toenemen van communicatieproblemen?

    Kennis kun je niet 1 dimensionaal overdragen

    Als je gaat verdiepen hoe kennisdelen werkt en hoe organisaties het uitvoeren zie je een discrepantie. Kennis is persoonsgebonden en kun je niet 1 dimensionaal overdragen. Informatie kun je daarentegen wel 1 dimensionaal overdragen. Wil je kennis overdragen dan dien je dat eerst te ontleden tot informatie. Die informatie moet dan wel aansluiten op iemands persoonlijke informatie, ervaring en vaardigheid wil die persoon de informatie kunnen transformeren tot kennis.

    Een goed verstaander zet informatie om in kennis

    Ga als proef eens naar een lezing waar je totaal niets van af weet. De kans is groot dat je weinig opsteekt omdat je de informatie niet kan verbinden met je persoonlijke informatie, ervaringen en vaardigheden. Vervolgens ga je naar een lezing met een onderwerp waar je wel veel van af weet. De kans is groot dat je er veel van opsteekt omdat je de informatie gemakkelijk verbindt met je persoonlijke informatie, ervaringen en vaardgheden. Het spreekwoord “Een goed verstaander heeft aan een half woord genoeg” is hier een voorbeeld van.

    Kennis komt procesmatig tot stand

    Kennis komt procesmatig tot stand. Een proces van feiten > gegevens > informatie > kennis > Wijsheid. Tussen de overgangen vindt er een transformatie plaats

    Wijsheid is een staat van bewustzijn waar feiten direct kunnen worden geplaatst in een context. Data en informatie kun je eenvoudig delen en wordt dan ook veel gedaan binnen organisaties. Alleen is het dan nog geen kennis. Informatie wordt pas kennis zodra het verbonden wordt met iemands persoonlijk informatie, ervaring en vaardigheid. Wordt informatie gedeeld, die niet aansluit op iemands persoonlijke informatie, ervaring en vaardigheid dan kan er ook geen kennisdeling plaats vinden.

    ‘Meester-gezel Leerling’

    Kennisdelen behelst meer dan wat er nu binnen organisaties wordt gedaan. Organisaties kunnen veel winst behalen met leren volgens het model ‘Meester-gezel Leerling’.

    “Het model ‘Meester-gezel Leerling’ is gebaseerd op de methodiek dat de ‘leerling’ meeloopt met de ‘meester’ en zich gaandeweg door afkijken, oefenen, meelopen, overnemen de beroepshandelingen eigen maakt. De Leerling wordt Gezel en mag zich na vele jaren Meester noemen. Voorbeelden kennen we bij beroepen, zoals bv. vioolbouwer, klokkengieter of glasblazer waarbij het vak in de werkkring pas echt geleerd kan worden.” Bron

    Kennisoverdracht druppelsgewijs

    Door een langdurig samenwerking van de ‘Meester-Gezel Leerling’ wordt kennis met informatie als tussenstap druppelsgewijs overgedragen die aansluit op iemands ervaring, vaardigheid en houding. Voor de Meester is het belangrijk om zijn kennis op een dusdanig manier tot informatie te verwerken zodat de leerling die informatie kan omzetten in kennis. Dit vereist veel tijd en aandacht van beiden kanten. Door kennis over te dragen aan de leerling leert ook de Meester meer over zijn expertise.

    Kortere dienstverbanden bemoeilijken kennisdelen

    Als je dat vergelijkt met hedendaagse organisaties dan staan we daar ver vanaf. Medewerkers worden bestookt met gestandaardiseerde informatie die niet aansluit op hun persoonlijke informatie, ervaring en vaardigheid. De steeds kortere dienstverbanden, groei van flexwerkers zorgt dat het delen van kennis steeds moeizamer gaat. Het is nog maar de vraag of deze werkwijze stand zal houden.

    Managers denken dat ze kennis overdragen terwijl ze dat niet doen

    Managers hebben veel kennis van de inhoud maar weinig kennis van kennisoverdracht en samenwerking 

    Het niet weten hoe je kennis over moet dragen zou geen probleem zijn als dat zou worden erkend. Managers denken dat ze kennis aan het overdragen terwijl ze dat niet doen. Die misvatting zorgt voor veel frustraties van zowel de manager als de medewerker.

    Leerlingen komen van school af met een hoofd vol informatie maar geen kennis

    Binnen het onderwijs vindt een vergelijkbaar proces plaatst. Daar wordt gedacht dat er aan kennisoverdracht wordt gewerkt terwijl ze aan informatieoverdracht doen. Schoolverlaters bezitten voornamelijk informatie wat nog niet is omgezet in kennis. Het is dan ook niet vreemd dat er een grote kloof is tussen wat organisaties nodig hebben en wat scholen leveren.

    Van informatie- naar kennisoverdracht

    De eerste stap die nodig is het erkennen dat er binnen organisaties geen kennisoverdracht plaatst vindt maar informatieoverdracht. Dat maakt de weg vrij om werkwijzen te introduceren zoals ‘Meester-Gezel Leerling’ die informatie omzet naar kennis. Waarschijnlijk zullen de steeds kortere dienstverbanden en de groei in flexwerkers worden afgeremd. Kennisoverdracht in specifieke branches verg nu eenmaal tijd en aandacht die je eenmaal verkregen niet zomaar weggooit.

    Mensen zijn geen robots

    Mensen zijn geen robots die je even snel programmeert met informatie. Mensen zijn unieke en creatieve wezens die pas tot hun recht komen als er veel tijd en aandacht besteed wordt aan het leerproces. Mensen verrijken daarbij de overgedragen kennis met hun eigen persoonlijke informatie, ervaringen en vaardigheden. Zo wordt de kennis verdiept en verbreedt. Een win win dus!

    Auteur: Sybren van de Schaar

    Bron: managementsite

  • Investing In Artificial Intelligence

    shutterstock Artificial intelligence is one of the most exciting and transformative opportunities of our time. From my vantage point as a venture investor at Playfair Capital, where I focus on investing and building community around AI, I see this as a great time for investors to help build companies in this space. There are three key reasons.

    First, with 40 percent of the world’s population now online, and more than 2 billion smartphones being used with increasing addiction every day (KPCB), we’re creating data assets, the raw material for AI, that describe our behaviors, interests, knowledge, connections and activities at a level of granularity that has never existed.

    Second, the costs of compute and storage are both plummeting by orders of magnitude, while the computational capacity of today’s processors is growing, making AI applications possible and affordable.

    Third, we’ve seen significant improvements recently in the design of learning systems, architectures and software infrastructure that, together, promise to further accelerate the speed of innovation. Indeed, we don’t fully appreciate what tomorrow will look and feel like.

    We also must realize that AI-driven products are already out in the wild, improving the performance of search engines, recommender systems (e.g., e-commerce, music), ad serving and financial trading (amongst others).

    Companies with the resources to invest in AI are already creating an impetus for others to follow suit — or risk not having a competitive seat at the table. Together, therefore, the community has a better understanding and is equipped with more capable tools with which to build learning systems for a wide range of increasingly complex tasks.

    How Might You Apply AI Technologies?

    With such a powerful and generally applicable technology, AI companies can enter the market in different ways. Here are six to consider, along with example businesses that have chosen these routes:

    • There are vast amounts of enterprise and open data available in various data silos, whether web or on-premise. Making connections between these enables a holistic view of a complex problem, from which new insights can be identified and used to make predictions (e.g., DueDil*, Premise and Enigma).
    • Leverage the domain expertise of your team and address a focused, high-value, recurring problem using a set of AI techniques that extend the shortfalls of humans (e.g., Sift Science or Ravelin* for online fraud detection).
    • Productize existing or new AI frameworks for feature engineering, hyperparameter optimization, data processing, algorithms, model training and deployment (amongst others) for a wide variety of commercial problems (e.g., H2O.ai, Seldon* and SigOpt).
    • Automate the repetitive, structured, error-prone and slow processes conducted by knowledge workers on a daily basis using contextual decision making (e.g., Gluru, x.ai and SwiftKey).
    • Endow robots and autonomous agents with the ability to sense, learn and make decisions within a physical environment (e.g., Tesla, Matternet and SkyCatch).
    • Take the long view and focus on research and development (R&D) to take risks that would otherwise be relegated to academia — but due to strict budgets, often isn’t anymore (e.g., DNN Research, DeepMind and Vicarious).

    There’s more on this discussion here. A key consideration, however, is that the open sourcing of technologies by large incumbents (Google, Microsoft, Intel, IBM) and the range of companies productizing technologies for cheap means that technical barriers are eroding fast. What ends up moving the needle are proprietary data access/creation, experienced talent and addictive products.

    Which Challenges Are Faced By Operators And Closely Considered By Investors?

    I see a range of operational, commercial and financial challenges that operators and investors closely consider when working in the AI space. Here are the main points to keep top of mind:

    Operational

    • How to balance the longer-term R&D route with monetization in the short term? While more libraries and frameworks are being released, there’s still significant upfront investment to be made before product performance is acceptable. Users will often be benchmarking against a result produced by a human, so that’s what you’re competing against.
    • The talent pool is shallow: few have the right blend of skills and experience. How will you source and retain talent?
    • Think about balancing engineering with product research and design early on. Working on aesthetics and experience as an afterthought is tantamount to slapping lipstick onto a pig. It’ll still be a pig.
    • Most AI systems need data to be useful. How do you bootstrap your system w/o much data in the early days?

     Commercial

    • AI products are still relatively new in the market. As such, buyers are likely to be non-technical (or not have enough domain knowledge to understand the guts of what you do). They might also be new buyers of the product you sell. Hence, you must closely appreciate the steps/hurdles in the sales cycle.
    • How to deliver the product? SaaS, API, open source?
    • Include chargeable consulting, set up, or support services?
    • Will you be able to use high-level learnings from client data for others?

    Financial

    • Which type of investors are in the best position to appraise your business?
    • What progress is deemed investable? MVP, publications, open source community of users or recurring revenue?
    • Should you focus on core product development or work closely on bespoke projects with clients along the way?
    • Consider buffers when raising capital to ensure that you’re not going out to market again before you’ve reached a significant milestone. 

    Build With The User In The Loop

    There are two big factors that make involving the user in an AI-driven product paramount. One, machines don’t yet recapitulate human cognition. To pick up where software falls short, we need to call on the user for help. And two, buyers/users of software products have more choice today than ever. As such, they’re often fickle (the average 90-day retention for apps is 35 percent).

    Returning expected value out of the box is key to building habits (hyperparameter optimization can help). Here are some great examples of products that prove that involving the user in the loop improves performance:

    • Search: Google uses autocomplete as a way of understanding and disambiguating language/query intent.
    • Vision: Google Translate or Mapillary traffic sign detection enable the user to correct results.
    • Translation: Unbabel community translators perfect machine transcripts.
    • Email Spam Filters: Google, again, to the rescue.

    We can even go a step further, I think, by explaining how machine-generated results are obtained. For example, IBM Watson surfaces relevant literature when supporting a patient diagnosis in the oncology clinic. Doing so improves user satisfaction and helps build confidence in the system to encourage longer-term use and investment. Remember, it’s generally hard for us to trust something we don’t truly understand.

    What’s The AI Investment Climate Like These Days?

    To put this discussion into context, let’s first look at the global VC market: Q1-Q3 2015 saw $47.2 billion invested, a volume higher than each of the full year totals for 17 of the last 20 years (NVCA).

    We’re likely to breach $55 billion by year’s end. There are roughly 900 companies working in the AI field, most of which tackle problems in business intelligence, finance and security. Q4 2014 saw a flurry of deals into AI companies started by well-respected and achieved academics: Vicarious, Scaled Inference, MetaMind and Sentient Technologies.

    So far, we’ve seen about 300 deals into AI companies (defined as businesses whose description includes such keywords as artificial intelligence, machine learning, computer vision, NLP, data science, neural network, deep learning) from January 1, 2015 through December 1, 2015 (CB Insights).

    In the U.K., companies like Ravelin*, Signal and Gluru* raised seed rounds. approximately $2 billion was invested, albeit bloated by large venture debt or credit lines for consumer/business loan providers Avant ($339 million debt+credit), ZestFinance ($150 million debt), LiftForward ($250 million credit) and Argon Credit ($75 million credit). Importantly, 80 percent of deals were < $5 million in size, and 90 percent of the cash was invested into U.S. companies versus 13 percent in Europe. Seventy-five percent of rounds were in the U.S.

     The exit market has seen 33 M&A transactions and 1 IPO. Six events were for European companies, 1 in Asia and the rest were accounted for by American companies. The largest transactions were TellApart/Twitter ($532 million; $17 million raised), Elastica/Blue Coat Systems ($280 million; $45 million raised) and SupersonicAds/IronSource ($150 million; $21 million raised), which return solid multiples of invested capital. The remaining transactions were mostly for talent, given that median team size at the time of the acquisition was 7 people.

    Altogether, AI investments will have accounted for roughly 5 percent of total VC investments for 2015. That’s higher than the 2 percent claimed in 2013, but still tracking far behind competing categories like adtech, mobile and BI software.

    The key takeaway points are a) the financing and exit markets for AI companies are still nascent, as exemplified by the small rounds and low deal volumes, and b) the vast majority of activity takes place in the U.S. Businesses must therefore have exposure to this market.

    Which Problems Remain To Be Solved?

    Healthcare

    I spent a number of summers in university and three years in grad school researching the genetic factors governing the spread of cancer around the body. A key takeaway I left with is the following: therapeutic development is very challenging, expensive, lengthy and regulated, and ultimately offers a transient solution to treating disease.

    Instead, I truly believe that what we need to improve healthcare outcomes is granular and longitudinal monitoring of physiology and lifestyle. This should enable early detection of health conditions in near real time, driving down cost of care over a patient’s lifetime while consequently improving outcomes.

    Consider the digitally connected lifestyles we lead today. The devices some of us interact with on a daily basis are able to track our movements, vital signs, exercise, sleep and even reproductive health. We’re disconnected for fewer hours of the day than we’re online, and I think we’re less apprehensive to storing various data types in the cloud (where they can be accessed, with consent, by third-parties). Sure, the news might paint a different story, but the fact is that we’re still using the web and its wealth of products.

    On a population level, therefore, we have the chance to interrogate data sets that have never before existed. From these, we could glean insights into how nature and nurture influence the genesis and development of disease. That’s huge.

    Look at today’s clinical model. A patient presents into the hospital when they feel something is wrong. The doctor must conduct a battery of tests to derive a diagnosis. These tests address a single (often late-stage) time point, at which moment little can be done to reverse damage (e.g., in the case of cancer).

    Now imagine the future. In a world of continuous, non-invasive monitoring of physiology and lifestyle, we could predict disease onset and outcome, understand which condition a patient likely suffers from and how they’ll respond to various therapeutic modalities. There are loads of applications for artificial intelligence here: intelligence sensors, signal processing, anomaly detection, multivariate classifiers, deep learning on molecular interactions...

    Some companies are already hacking away at this problem:

    • Sano: Continuously monitor biomarkers in blood using sensors and software.
    • Enlitic/MetaMind/Zebra Medical: Vision systems for decision support (MRI/CT).
    • Deep Genomics/Atomwise: Learn, model and predict how genetic variation influence health/disease and how drugs can be repurposed for new conditions.
    • Flatiron Health: Common technology infrastructure for clinics and hospitals to process oncology data generated from research.
    • Google: Filed a patent covering an invention for drawing blood without a needle. This is a small step toward wearable sampling devices.
    • A point worth noting is that the U.K. has a slight leg up on the data access front. Initiatives like the U.K. Biobank (500,000 patient records), Genomics England (100,000 genomes sequenced), HipSci (stem cells) and the NHS care.data program are leading the way in creating centralized data repositories for public health and therapeutic research.

    Enterprise Automation

    Could businesses ever conceivably run themselves? AI-enabled automation of knowledge work could cut employment costs by $9 trillion by 2020 (BAML). Coupled with the efficiency gains worth $1.9 trillion driven by robots, I reckon there’s a chance for near-complete automation of core, repetitive businesses functions in the future.

    Think of all the productized SaaS tools that are available off the shelf for CRM, marketing, billing/payments, logistics, web development, customer interactions, finance, hiring and BI. Then consider tools like Zapier or Tray.io, which help connect applications and program business logic. These could be further expanded by leveraging contextual data points that inform decision making.

    Perhaps we could eventually re-image the new eBay, where you’ll have fully automated inventory procurement, pricing, listing generation, translation, recommendations, transaction processing, customer interaction, packaging, fulfillment and shipping. Of course, this is probably a ways off.

    I’m bullish on the value to be created with artificial intelligence across our personal and professional lives. I think there’s currently low VC risk tolerance for this sector, especially given shortening investment horizons for value to be created. More support is needed for companies driving long-term innovation, especially considering that far less is occurring within universities. VC was born to fund moonshots.

    We must remember that access to technology will, over time, become commoditized. It’s therefore key to understand your use case, your user, the value you bring and how it’s experienced and assessed. This gets to the point of finding a strategy to build a sustainable advantage such that others find it hard to replicate your offering.

    Aspects of this strategy may in fact be non-AI and non-technical in nature (e.g., the user experience layer ). As such, there’s renewed focus on core principles: build a solution to an unsolved/poorly served high-value, persistent problem for consumers or businesses.

    Finally, you must have exposure to the U.S. market, where the lion’s share of value is created and realized. We have an opportunity to catalyze the growth of the AI sector in Europe, but not without keeping close tabs on what works/doesn’t work across the pond.

    Source: TechCrunch

  • Learning from the financial reports of your business: 3 important questions to ask

    Learning from the financial reports of your business: 3 important questions to ask

    Your business’s financial reports should help you make important business decisions. While there are many smart decisions you’ll be able to make with efficient reports by your side, these are three of the most important questions to ask:

    1. What should I sell more or less of?

    A key principle to help you, not only to make your business survive but to make it flourish, is to use the market intelligence you have and to provide for your market's current and future needs and wants. By using sales reportst hat show your top selling and least selling products over a certain period, you can easily identify which products you need to:

    • a) Market differently
    • b) Tweak so that they become more customer-friendly
    • c) Cut down on producing or sourcing, or
    • d) Discontinue altogether.

    Sales reports can also reveal top customers. This enables you to spend more time focusing on relation ship management with your essential customers, ensuring that these customers are well taken care of. You can then redirect the cash you generate from these customers into market aspects that need improvement.

    2. Do I need additional funding?

    There are a few internal courses of action you could look at first, like reducing costs or driving more sales to increase revenue for example. But this may not be enough. Many start-up and small businesses are self-funded, which is ideal. However, for many businesses there comes a time when cash flow is not sufficient to maintain operations and additional funding may be necessary. Whether it’s acquired through a business loan or through an external investor, financial reports are required to convince a bank or an investor that you can manage your finances. So you need to ensure that you keep track of your finances regularly and that your financial data are always up to date. Financial reports such as the income statement and can help you gauge whether you need additional funding to curb seasonal fluctuations, or whether you need to purchase additional stock to cater for a growth in demand.

    3. How and when can I expand my business?

    When your business has been profitable for some time, you don’t have to stress about cash flow, and the demand for your product exceeds your expectations, it’s a clear sign that business is going well. This means that it may be time to expand your business. As exciting as expansion is, it needs to be considered very carefully. In this consideration,  insights from financial reports and other business reports will be crucial. There is also a sum of other options you could consider before expanding. Examples are: replacing old equipment, settling debts, giving something back to shareholders, or putting money aside for a darker period. However, if your mind is made up on expansion, a big decision is to decide on what type of expansion to go with. Possible expansion options are:

    • Sell more of the same products.
    • Open a shop in another area.
    • Diversify – offer complimentary products to what you currently offer.
    • Franchise your business.
    • Merge with or acquire another business.

    Not all expansion options are suitable for all types of businesses or industries. It’s important to do thorough research into what will work for you and your business, and what resources you have at your disposal. Use all relevant intelligence that is available! Part of this research will be determining the long-term profitability of your business, considering the expansion opportunity. What does the future look like? Without having access to up-to-date financials that you can easily analyze, you could end up making rash decisions that could have negative consequences or even cost you your business. Being able to make quick decisions, based on accurate data, allows you to kunderstand whether you need to wait for the right moment to expand your business, or if your business is ready to do it immediately. Whatever the case, realizing and understanding the importance of your business’s financial reports goes a long way to growing and maintaining a successful business. We hope you keep these insights in mind and can become the champion of your financials and get them to work for you and your business.

    Author: Milentha Bisetty

    Source: Sage Intelligence

  • Rabobank: Zelfontwikkeling binnen IT door combinatie technologie en cultuur

    Rabobank: Zelfontwikkeling binnen IT door combinatie technologie en cultuur

    'We geven we de mensen een eigen verantwoordelijkheid, daar hebben we vertrouwen in'

    Rabobank stelt zijn IT-medewerkers in staat zelf hun ontwikkeling in eigen hand te nemen door de strategische inzet van technologie plus een cultuur van zelfontwikkeling en coaching.

    Talentontwikkeling is van enorm belang geworden nu het rekruteren van talenten steeds uitdagender wordt. Binnen Rabobank is er jaren gewerkt aan talentontwikkeling en hebben medewerkers zelf de hand in hun eigen ontwikkeling, ook kunnen leidinggevenden een goed beeld krijgen welke capaciteiten binnen IT-teams zijn ontwikkeld en welke ontbreken. CIO sprak met Anton Rutten, hoofd IT systems bij Rabobank, en met Sander Ettema, hoofd Development Automation bij Rabobank.

    Online leerplatform

    Voordat we dieper ingaan op de strategische talentontwikkeling bij Rabobank wil Rutten een persoonlijk verhaal kwijt waarin hij zelf werd gedwongen kritisch te kijken naar zijn eigen talenten. ´Ik zou weer bij Rabo beginnen na een sabbatical en als hoofd IT systems zou ik ook developers moeten aansturen. Het was een tijd geleden dat ik zelf code aan het kloppen was, dus ik begon eerst eens wat met Java te spelen, maar zag al snel dat dat hem niet zou worden´.

    Rutten kreeg een tip om het eens met het online leerplatform Pluralsight te proberen. Met een persoonlijk abonnement stelde hij zich op de hoogte van ontwikkelingen in talen en nieuwe technologieën als AI. ´Toen ik na mijn sabbatical aan de slag ging bij Rabobank nam ik direct contact op met Sander. Toen bleek dat Pluralsight al op kleine schaal binnen Rabo werd gebruikt. Daar hebben we verder op doorgepakt´.

    Vertrouwen in eigen verantwoordelijkheid

    Zelfontwikkeling binnen teams is hard nodig als we vervolgens naar het grotere plaatje kijken, zegt Rutten. Binnen IT systems van Rabobank werken 4000 mensen, dus is er altijd veel vraag naar mensen. Het IT landschap binnen een bank is complex, benadrukt hij, en is ook nog eens internationaal. ´We investeren dus veel in mensen en zijn meer gericht op leren en ontwikkelen dan andere werkgevers. Dat horen we vaak terug en we hopen altijd dat als onze mensen elders gaan werken, ze dat blijven verkondigen. Voor de ontwikkeling van de organisatie hebben we zelfs een aparte afdeling genaamd Transition and Change. Deze afdeling was ooit onderdeel van IT, maar is nu ondergebracht in de business´.

    Ondanks dat de teams vrij veel zelfstandigheid hebben, ook in de eigen ontwikkeling, is de verleiding groot om te veel te sturen op de meest gewenste ontwikkeling. De balans daarin vinden is belangrijk, zo blijkt. ´We communiceren op strategisch niveau waar de organisatie naartoe gaat en je ziet dat de teamleden reageren door te kijken naar cursussen die daarmee in lijn zijn´, zegt Ettema. ´Bij Rabo geven we de mensen een eigen verantwoordelijkheid, daar hebben we vertrouwen in. Wij zorgen ervoor dat duidelijk is wat onze speerpunten zijn en welke technologische keuzes we maken´.

    Veranderende organisatie

    Dat het niet voor iedereen altijd even makkelijk is om met dergelijke ontwikkelingen mee te gaan, moge duidelijk zijn. Zo is de organisatie in 2016 overgegaan naar DevOps. Daar hadden met name de oudere medewerkers en degenen die meer in beheerdersrollen zaten soms moeite mee. ´Ze dachten het niet te kunnen of wilden het liever niet´, zegt Rutten. ´Het was ook een flinke reorganisatie, waarbij ieders ontwikkeling onder de loep werd genomen. We hebben daar met de mensen ook gesprekken over gehad. Het is geen skill- maar een will-issue. We hebben gekeken wie we mee konden nemen in de nieuwe organisatie. Degenen die mee konden hebben zich binnen de kaders die we stelden goed ontwikkeld. Nu zijn ze hartstikke trots dat ze merken dat het mogelijk is om binnen een week live te gaan´.

    Doordat de organisatie is veranderd, is de strategische resource planning ook anders. Er wordt niet meer gekeken naar functies, maar naar rollen binnen teams. Talent wordt veel meer gecoacht. ´Vooral managers moeten daarin stappen maken´, zegt Rutten. ´De oude manier van mensen beoordelen werkt niet meer. En er zijn steeds andere vaardigheden nodig. Als dat tijdelijk is, kan dat worden opgelost met zzp'ers of een externe partners´.

    Steeds bijspijkeren

    Die veranderende behoeftes worden opgepakt door zogeheten 'communities', interne groepen georganiseerd rondom een gedeeld onderwerp. Ook voor die groepen is Pluralsight beschikbaar. Communities kunnen zo relevante content voor hun doelgroep samenbrengen in zogenoemde 'channels'. Rutten geeft aan dat developers zich veel breder moeten ontwikkelen, omdat door de intrede van de cloud ook de infrastructuur steeds meer codegericht is. Hiermee dient een ontwikkelaar ook kennis van deze infrastructuur tot zich te nemen. Ettema vult aan door serverless computing en voorspellende analyses te noemen als aandachtsgebieden, naast security. ´Binnen de communities wordt dat herkend en als onderwerp gepitcht in hun channels´, zegt hij.

    Medewerkers kunnen doorlopend op die platforms per gebied hun kennis testen en zich bijspijkeren op die facetten waar hij of zij nog tekortschiet. ´In 5 minuten 20 vragen die de waarde van je kennis voor de organisatie meet´, zegt Ettema. ´Zo hebben 500 man onlangs hun kennis gemeten op het gebied van security. Vervolgens krijgt elke individuele medewerker een persoonlijk advies over hun ontwikkelkansen. Daarnaast verrijkt het het beeld voor het management op de kennis die we in huis hebben en de kennis die nog verder ontwikkeld moet worden. Zo kunnen we onze mensen beter helpen en begeleiden´.

    Auteur: René Schoemaker

    Bron: CIO

  • The most wanted skills related for organizations migrating to the cloud

    The most wanted skills related for organizations migrating to the cloud

    Given the widespread move to cloud services underway today, it’s not surprising that there’s growing demand for a variety of cloud-related skills.

    Earlier this year, IT consulting and talent services firm Akraya Inc. compiled a list of the most in-demand cloud skills for 2019, let's take a look at them:

    Cloud security

    Cloud security is a shared responsibility between cloud providers and their customers. That creates a need for professionals with specialization in cloud security skills, including those who can leverage cloud security tools.

    Machine learning (ML) and artificial intelligence (AI)

    In recent years cloud vendors have developed and expanded their set of tools and services that allow organizations to reap the benefits of machine learning and artificial intelligence in the cloud. Companies need people who can leverage these new capabilities of the cloud.

    Cloud migration and deployment within multi-cloud environments

    Many organizations are looking to adopt multiple cloud services, and are looking for professionals who can contribute to their cloud migration efforts. Cloud migration has its risks and is not an easy process, and improper migration processes often lead to business downtime and data vulnerability. This means that employees with appropriate skillset are key.

    Serverless architecture

    Underlying cloud server infrastructure needs to be managed by cloud developers within a server-based architecture. But today’s cloud consists of industry standard technologies and programming languages that help move serverless applications from one cloud vendor to another, Akraya said. Companies need expertise in serverless application development.

    Author: Bob Violino

    Source: Information-management

  • Toveren met Talent Analytics?

    talentmanagementOrganisaties kunnen op tal van terreinen nog winnen aan intelligentie. Afhankelijk van de definitie van intelligentie (een intelligente organisatie is echt iets anders dan dingen gewoon  slim doen) kunnen we bijvoorbeeld ook veel intelligenter HRM bedrijven.

    Zoals bij veel functionele disciplines al het geval is, komt nu ook bij HR het gebruik van informatietechnologie in de belangstelling te staan.

    Rocket science? Gaat dit de discipline overbodig maken? Welnee. Zoals altijd gaat het om het nemen van goede of betere beslissingen. In dit geval ten aanzien van de ontwikkeling van het menselijk kapitaal in de onderneming. En de beste beslissingen worden nog altijd genomen met kennis van zaken. En kennis van zaken is op data en inzichten gebaseerd. En data is steeds meer beschikbaar. Nu ook ten aanzien van mensen en hun talent. Het gaat erom die data te gebruiken. Daarvoor moeten we de data alleen toegankelijk en toepasbaar maken. En dit is geen rocket science!

    Toch raakt ook de HR manager (net als zijn collega uit andere disciplines overigens) van slag als de ‘ICT tovenaars’ met beloften van Big Data technologie aankomen. How come? Omdat we nog steeds denken dat de wetten van de functionele discipline overbodig worden als automatisering voorbij komt? Omdat we bang zijn te zeggen dat we die technologie niet snappen of er de relevantie niet van zien? Omdat we tegen de ICT-er niet durven te zeggen dat het leuk klinkt maar dat onduidelijk blijft wat de toegevoegde waarde is voor onze discipline (waar de ICT-er doorgaans niets van snapt)? Zijn dit mogelijke oorzaken? Of speelt er iets anders?

    Het is hier in ieder geval oppassen geblazen. Big data en data mining leveren geen beslissingen maar nieuwe data en correlaties; mogelijke inzichten die tot kennis kunnen verworden als we statistisch en methodisch goed genoeg zijn onderlegd. Als bijvoorbeeld blijkt dat alle goede sales managers in Oktober zijn geboren betekent dit niet dat ‘geboren in Oktober’ een kwalificerende functie-eis moet zijn.. Om maar een potentiële valkuil te noemen. Wel kunnen in alle fasen van talent management (integrale proces van talent werving, selectie, training, ontwikkeling en promotie) met behulp van meer data en datatechnologie betere beslissingen worden genomen. Een data-infrastructuur die data genereert, analyseert en op maat  aanbiedt voor besluitvorming, kan dan veel toegevoegde waarde opleveren. Mits met kennis van zaken ingericht.

  • Transformeren doe je als complete organisatie, niet alleen digitaal

    Transformeren doe je als gehele organisatie, niet alleen digitaal

    ‘Transform or die’, klinkt het tegenwoordig. Maar slechts een klein deel, vijf procent, van de digitale transformaties slaagt. De reden: het doel van de verandering is niet duidelijk. Dat is te ondervangen door jezelf gestructueerd vragen te stellen.

    Ergens willekeurig beginnen, leidt vaak tot teleurstelling. Daarom moet je eerst begrijpen waar je vandaag staat. Pas daarna richt je je op langetermijndoelen die je wilt bereiken. Daarbij niet alleen gericht op technologie en infrastructuur, want organisatorische ondersteuning, processen, cultuur en huidige klantactiviteiten zijn net zo belangrijk. Anders gezegd wil je weten wat je mate van digitale volwassenheid is. Dat kan om te beginnen door te kijken hoe ondersteunend je organisatie is als het gaat om digitale strategie, het managen en uitvoeren van de plannen. Hoe werken teams samen en zijn die processen opgelijnd?

    Brandstof

    De belangrijkste reden dat digitale transformaties mislukken, is omdat organisaties niet duidelijk zijn over wat het voor hen en hun werknemers betekent. Dat geeft al aan dat cultuurverandering een van de grootste hindernissen is. Hoe staat je organisatie tegenover innovatie en verandering en hoe wordt het ondersteund? Dat werknemers de mogelijkheid krijgen om de vaardigheden te ontwikkelen die nodig zijn in de nieuwe realiteit wordt daarbij nog te vaak over het hoofd gezien.

    Natuurlijk kan geen transformatie worden voltooid zonder de juiste technologie om dit te ondersteunen. Die vormt immers de basis van je digitale onderneming. Kijk daarbij goed met welke huidige systemen er wordt gewerkt en hoe die je zullen helpen om beter te presteren, processen te automatiseren en uiteindelijk je klantervaring te optimaliseren. Net als de mate waarin IT-infrastructuur en de bedrijfsprocessen je doelen ondersteunen. Waarbij je er eveneens achter wil komen welke data je al beschikbaar hebt. Dat is ongetwijfeld meer dan je denkt en belangrijke brandstof voor je organisatie. Niet in de laatste plaats om goed op klantervaringen in te kunnen spelen. Breng dus ook in kaart hoe je klanten momenteel bij je processen betrekt. Plus welke kanalen je gebruikt om je marketingactiviteiten beter te laten presteren.

    Oogsten

    Dit lijkt al met al wellicht extra werk, maar zorgt er juist voor dat je voorbereid bent op dure implementaties en tijdrovende processen. Je hoeft ook niet altijd groot te beginnen. Zet eerst kleine stapjes, bewijs wat werkt en bouw daarop voort. Daarmee stel je prioriteiten. En weet je waar je moet beginnen om de resultaten vanaf dag één te kunnen oogsten. Wel zo prettig voor de toekomst.

    Auteur: Vera Engelbertink

    Bron: Emerce

  • What to keep in mind when recruiting the right data scientist

    What to keep in mind when recruiting the right data scientist

    As a relatively new role, 'data guru' is a challenging job specification to draft for. Organisations are seeking highly-skilled and well-educated individuals to fulfil the position, but the truth is, the data scientist an organisation needs is not a guru, but a colleague.

    Most organisations forget that recruiting the right talent is just as much about them as it is about the potential candidates. For example, does the organisation provide an interesting and successful environment for the data scientist to thrive in? Does it create new opportunities and positions for data scientists? Does it support its data scientists and allow them the freedom to work creatively?  

    Understanding what data scientists look for is crucial when looking to recruit and retain the right data talent. 

    So, what makes a data scientist tick?

    The fact of the matter is that the attrition rate for data scientists is very high. A recent poll by KDNuggets on data scientists revealed that more than one in three expect to stay in their job for three years of less. There are a number of reasons that can lead to a data scientist deciding to hand in their notice, and often these things are in the organisation’s control, like the company’s culture and technology available for the data scientists to use.

    If the organisation doesn’t provide access to data and the tools necessary for data scientists to do their jobs well, it will lead to frustration. More importantly, these barriers make it difficult for data scientists to achieve their goals and perform to their best level, which understandably results in shorter tenures.  

    Moreover, from a cultural perspective, many businesses aren’t quite up to speed with data. This starts with the C-suite: if senior management cannot see the value of a data-driven culture, then it will stifle efforts. A data scientist will soon feel under-appreciated and question the point of their analyses and recommendations if action isn’t being taken by the business. 

    Even if data is at the heart of the business, the data scientist is often left out of the decision-making process. Not only does this dissociate them from the hard work they have done, but it often leads to their work being misinterpreted, with the full benefits of the analyses being lost on the board.

    What will draw a data scientist to work for a business?

    1.The right challenge

    Data scientists are often drawn to innovation, they want to be a part of it, to evoke it, and to drive it. First and foremost, you will attract data talent by ensuring that your organisation is pushing the boundaries of data analytics and use. Nothing is more engaging than a challenge, and data scientists want to be challenged by your company if they’re going to consider it as a place to work. 

    2. The right tools

    This almost goes without saying. A good comparison is surgeons. You wouldn’t expect a heart surgeon to be able to carry out their job properly or effectively if they didn’t have the right tools or equipment available to them in the operating room. It’s the same for data scientists. Without the right tools in place, data professionals may only be working with partial, fragmented datasets or they may not have access to all the data they need, in order to gain the insights that will help to transform the business.

    3. The right level of empowerment

    With the right tools in place, people need to be given the space, time and trust to think and work creatively. Taking their insights on-board and actioning their suggestions will go a long way in making a data scientist feel appreciated and included in the company’s success.

    4. The right training and development

    Innovation is a constant within data analytics, from new tools and developments to learning from others’ methods and implementations. It is important your data scientists are continuously challenged and are learning new skills to keep up with this ever-developing market. Your organisation should open up a dialogue with your data professionals, so that you know what they want, what they are good at, and what they need from you. Only then can you help them develop themselves and grow into an integral role for the business.

    Conclusion: It takes two to data science

    The hiring process is not a one-way affair. While the organisations must make the decision to hire a data scientist based on their skills and experience, the data scientist must also decide whether the organisation is the right place for them to grow and develop their career.

    As soon as organisations start realising this, they can work on becoming a more attractive and exciting business to work for, providing the right challenges, tools, culture and environment for data scientists to thrive. In doing so, the pool of prospective data professionals that are applying to work for the business will inevitably increase, enabling them to hire the best people and to help the business grow and maintain data science success moving forward.

    Author: Eva Murray

    Source: Dataconomy

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