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

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

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

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

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

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

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

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

What are the longer-term implications?

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

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

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

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

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

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

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

Source: timoelliot.com, October 24, 2016

Becoming a BI Analyst: What Does It Entail?

As with many buzzwords emerging from the intersection of business and technology, the phrase “business intelligence” (BI) is often misunderstood. In a nutshell, it refers to the skill and practice of extracting insights from data to realize new goals, strategies, trends, and values. A business intelligence analyst, working with a network of other knowledge workers (such as data stewards and data governance specialists), helps an enterprise thrive.  

Business Intelligence Explained

Business intelligence refers to the perspectives gained from analyzing the business information that companies hold. Since that data may be spread across many locations and departments, business intelligence is an amalgam of analytics and mining that can empower management with the tools needed to make informed decisions that may not otherwise be apparent.  

Today’s data-driven businesses are growing at an unprecedented pace, often along unpredictable paths. Because of this, you might think that business intelligence should largely be an automated affair – even the domain of AI. However, algorithms and automation alone cannot harness the creative connections and nuanced insights required within the field. Although IT is obviously a major part of the equation, business intelligence requires human intelligence.  

Curious about what it takes to become a business intelligence analyst? Read on for the skills and education you’ll need and the responsibilities you’ll have if you follow this career path.

What Is a Business Intelligence Analyst?

As is common among data-centric professions, a business intelligence analyst (BIA) must wear many hats and have skills that fall across various areas. Still, the core of the job boils down to creating regular reports that summarize a company’s current data holdings in relation to parallel financial reports and current market intelligence. 

Typically, these reports cogently present salient trends in an identified market that could impact the goals and actionable items on a company’s agenda, plotted as a function of the various data assets at the organization’s disposal.  

Although a business intelligence analyst is much more than a glorified office assistant, the job is best understood as a support role for executive decision-makers. A BIA must provide meticulously supported analytical insights that reflect the current realities of both the enterprise and markets in question. At the end of the day, the key outcomes of the analyst’s work are to bolster the company’s place in the market, streamline the efficiency of the staff, amplify overall productivity, and even upgrade performance at the level of customer experience.   

The business intelligence analyst is a relatively new vocation but growing fast: Forbes recently tapped the BIA as one of the most sought-after positions in the greater STEM marketplace.  

Since there’s a demand for BI expertise across so many industries – healthcare and medicine, insurance, finance, e-commerce – professionals working in the U.S. can expect to command a salary of roughly $80,000 per year (with even higher figures in especially tech-heavy states).

What Skills Do Business Intelligence Analysts Need?

Just as one would expect from the job title, the lion’s share of a business intelligence analyst’s skill set involves crunching data. They need to have a strong command of data at every level, including organization, storage, mining big data, and analysis – all with a keen and responsive eye for spotting key performance indicators and business-critical priorities in a company’s data troves.  

Beyond data, a top-tier BIA will have some proficiency in tools tailored specifically for BI, programming languages, and systems analysis.  

Data and tech know-how may anchor the position, but it’s nothing without a raft of communications skills to translate data insights into actionable steps. This entails critical thinking and the ability to make presentations that speak to the needs of stakeholders in easy-to-understand language and data visualizations.

Typical skills required for business intelligence analysts:

• Extensive knowledge of software in user interface, database management, enterprise resource management (proficiency in Python, R, C#, Hadoop, and SQL)
• Presentation and reporting in a timely and cogent manner (mastery of PowerPoint and business functions of Zoom are obvious assets)
• Upper-level background in integrating software and programs into multiple tiers of data services
• A knack for problem-solving in both technical and interpersonal contexts; at least five years of engagement in analytical and critical thinking skills in a professional setting
• Ability to build rapport with both individuals in management and interdepartmental teams (especially in cases of implementing new software and tech that may result from BI recommendations)

BI Roles and Responsibilities 

As much as business intelligence can be about interpersonal action, much of an analyst’s duties are solitary ones, chief among these authoring procedures for data processing and collection. From there on, expect reporting and more reporting, including analytical reports that can be personalized for the needs of stakeholders, highlighting the most departmentally relevant findings.  

A business intelligence analyst also needs to maintain an active role in the various life cycles of data as it moves throughout the organization. After all, data reports are built upon regularly monitoring the way data is collected, looking at field reports, product summaries from third parties, and even through public record.  

As a function of this, a BIA may want to continually track burgeoning trends in tech or emerging markets that could potentially offer efficiency or value within the industry and their specific enterprise.  

Working in concert with specialists in data governance and stewardship, a BIA must oversee the integrity, security, and location of data storage. This should be performed in the organization’s computer database and may be done in conjunction with new operational protocols that make the most of the database as it evolves in tandem with updates and unique program features. Finally, BIAs benefit from taking a step back for meta-analysis, forging new methodologies that improve analysis at every step outlined above.

Required Education and Training 

There are several routes you may follow to prepare for a career in business intelligence. Most obviously, you can earn a bachelor’s degree directly in business intelligence, which incorporates a study of analytics with elements of marketing, tech, and management. Alternatively, a beginner in the field may want to proceed more obliquely, garnering a B.A. in a related field, such as computer science, accounting, finance, management, or business. A bachelor’s is enough to open the door for most entry-level positions in business intelligence, but a master’s in a more comprehensive discipline such as business analytics can make the difference in landing more competitive, elite jobs.  

Date: September 20, 2023

Author: Shauna Frenté

Source: Dataversity 

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

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

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

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

In today’s business world, “information” is a renewable resource and virtually a product in itself. Business intelligence technology enables businesses to capture historical, current and predictive views of their operations, incorporating such functions as reporting, real-time analytics, data and process mining, performance management, predictive analytics, and more. Thus, information in its various forms and locations possesses genuine inherent value.

• Status checks can help identify operational choke points, such as if/when/where an order has been held up too long; if carrier wait-times are too long; and/or if certain employees have been inactive for too long.
• Order fulfillment dashboards can monitor orders as they progress through the picking, staging and loading processes, while also identifying problem areas in case of stalled processes.
• Supervisors walking the floor with handheld devices can both encourage team performance and, at the same time, help assure efficient dock-side activity. Office and operations management are able to monitor key metrics in real-time, as well as track budget projections against actual performance data.
• Customer service personnel can call up business intelligence information to assure that service levels are being maintained or, if not, institute measures to restore them.
• And beyond the warehouse walls, sales representatives in the field can access mined and interpreted data via mobile devices in order to provide their customers with detailed information on such matters as order fill rates, on-time shipments, sales and order volumes, inventory turnover, and more.

From Patterns to Predictions: Harnessing the Potential of Data Mining in Business

Data mining techniques can be applied across various business domains such as operations, finance, sales, marketing, and supply chain management, among others. When executed effectively, data mining provides a trove of valuable information, empowering you to gain a competitive advantage through enhanced strategic decision-making.

Je hoort het vaak in bestuurskamers: we willen een data-driven organisatie zijn. We willen aan de slag met IoT, (predictive) analytics of location based services. En ja, dat zijn sexy toepassingen. Maar wat zijn de werkelijke business drivers? Die blijven vaak onderbelicht. Onderzoek laat zien op welke terreinen organisaties met een hoge ‘datavolwassenheid’ vooroplopen.

SAS ondervroeg bijna 600 beslissers en kon op basis van de antwoorden de respondenten onderverdelen in drie groepen: de koplopers, een middengroep en de achterblijvers. Zo ontstaat goed zicht op waarin de koplopers zich onderscheiden van de achterblijvers.

Het eerste wat opvalt is de proactieve houding. Koplopers maken budget vrij om oude processen en systemen te vervangen en investeren in de uitdaging van data-integratie. Er heerst bovendien een cultuur van ‘continuous improvement’. Deze bedrijven zijn voortdurend actief op zoek naar verbetermogelijkheden. Dit in tegenstelling tot de achterblijvers, die pas willen investeren in verbeteringen als ze precies weten hoe hoog de ROI is.

De koplopers vervangen hun oude systemen het vaakst door open source data platformen, waarbij Hadoop verreweg het meest populaire platform is. Behalve in technologie investeren deze bedrijven ook meer in het opschonen van data. Ze hebben goede processen ingericht om ervoor te zorgen dat data up-to-date en van de juiste kwaliteit is voor het beoogde gebruik. En ook de governance op deze processen is beter dan in de bedrijven die achterblijven (lees hier over het verhogen van de ROI op data en IT).

Ook investeren koplopers meer in talent. 73 procent van deze bedrijven heeft een dedicated datateam dat wordt bezet met eigen mensen. De achterblijvers hebben vaker ofwel helemaal geen datateam ofwel een team dat wordt ingevuld door externe mensen. Koplopers investeren ook meer in werving en selectie van gekwalificeerd personeel. Daardoor ondervindt ‘slechts’ 38 procent van de koplopers een tekort aan interne vaardigheden, tegenover 62 procent van de achterblijvers.

Dit alles leidt ertoe dat koplopers beter zijn voorbereid op de GDPR-regelgeving, die in 2018 zijn intrede doet.

Ze zijn beter in staat om de risico’s te benoemen die verbonden zijn aan een data-driven strategie en ze hebben maatregelen genomen om deze risico’s af te dekken of te verkleinen.

De komst van de GDPR is voor veel organisaties een aanleiding om te investeren in een goede datastrategie. Maar dit is niet de enige reden. Bedrijven met een hoge datavolwassenheid kunnen:

• sneller ingewikkelde vragen beantwoorden
• sneller beslissingen nemen
• sneller innoveren en groeien
• de klantervaring verbeteren
• groei realiseren in omzet en marktaandeel
• kortere time-to-market voor nieuwe producten en diensten realiseren
• business processen optimaliseren
• betere strategische plannen en rapportages maken

Alle reden dus om écht in data governance en data management te investeren en niet alleen maar te roepen dat je organisatie data-driven is. 90 procent van de ondervraagden vindt zichzelf namelijk datagedreven, maar de realiteit is helaas minder rooskleurig.

Interesse in de volledige onderzoeksresultaten?
Download hier het rapport ‘How data-driven organisations are winning’.

Bron: Rein Mertens (SAS)

In: www.Analyticstoday.nl

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

Predictive analytics: de glazen bol voor de business

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

Data mining: business in de lead

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

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

1. Business-vraag omvormen tot een analytische hypothese

2. Data gereedmaken voor data mining

3. Data verkennen

4. Data in een model plaatsen

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

Market intelligence neemt een vlucht. Nu ondernemingen hun interne informatiehuishouding in toenemend mate in orde hebben

gaat de aandacht (opnieuw?) uit naar de informatievoorziening met betrekking tot de markt van ondernemingen. Opnieuw? Ja, opnieuw!

Als sinds de ’60er jaren staat het onderwerp midden in de belangstelling maar onder invloed van informatietechnologische ontwikkelingen werd het steeds naar de achtergrond gedrongen door aandacht voor de interne optimalisering van de informatiehuishouding. Executive informatiesystemen (een term uit de jaren ’80) leidde tot BI en BI tot DWH, ETL, Reporting en score carding. De toename van data op social media, het net en de mogelijkheden op het gebied van ongestructureerde data – data mining maar ook machine learning voedden nu opnieuw de aandacht voor toepassing van technologie bij het beter kennen van de bedrijfsomgeving. Het belang daarvan is dus niet veranderd maar de mogelijkheden nemen wel toe.

Drie jaar geleden werd Hammer, market intelligence opgericht met als doel bedrijven van market intel te voorzien met gebruikmaking van moderne data technologie. Egbert Philips (Director van het in Arnhem gevestigde Hammer); “Wat betreft management informatie zou er minstens zo veel aandacht moeten zijn voor het kennen en doorgronden van markt en bedrijfsomgeving als voor de interne prestaties. Dit zou niet afhankelijk moeten zijn van technologische mogelijkheden. De ontwikkeling van data science en big data technologieën maken het wel mogelijk market intelligence beter en efficiënter in te richten. Daar richten we ons met Hammer op. We willen een partner zijn voor bedrijven die hun markten structureel willen kennen en doorgronden. Informatie technologie blijft daarbij een middel maar wel een heel belangrijk middel.”

Hammer is weliswaar een jonge onderneming maar bepaald niet nieuw in het veld. De oprichters zijn reeds jarenlang actief in market intel en de toepassing daarvan in onder ander strategische planning vraagstukken. Hammer ondersteunt echter ook meer tactische beslissingen. Vraagstukken met betrekking tot pricing, sourcing/inkoop, het kiezen van distributiepartners, productontwikkeling en business development kunnen niet goed worden beantwoord zonder input van marktinformatie.

Eind november organiseert Hammer een klantevent. Wanneer u geïnteresseerd bent stuur dan een e-mail naar info@hammer-intel.com

http://www.hammer-intel.com

81% of CEOs see mobile technologies for customer engagement as the most strategically important in 2015. 80% see data mining and analysis technologies as the most strategically important, followed by cybersecurity (78%), Internet of Things (IoT) (65%), socially-enabled business processes (61%) and cloud computing (60%).

These and many other research findings are part of PwC’s 18thAnnual Global CEO Survey. Released earlier today at the opening of the World Economic Forum Annual Meeting in Davos, Switzerland,the study provides insights into CEO’s current priorities and future plans across a wide spectrum of areas.
PwC interviewed 1,322 CEOs in 77 countries, with 28% of the interviews conducted by telephone, 59% online and 13% via mailed questionnaires. PwC’s sample is selected based on the percentage of the total GDP of countries included in the survey, to ensure CEOs’ views are fairly represented across all major countries and regions of the world. Please see page 40 of the study for additional details of the methodology. The free PDF of PwC’s 18th Annual Global CEO Survey is pwc-18th-annual-global-ceo-survey-jan-2015.pdf here.
Key Take-Aways
CEOs are more confident in their enterprises’ business prospects in 2015 than they are about global economic growth. 39% of CEOs are very confident in their business prospects for 2014, surpassing the 37% confident in global economic growth. The following graphic compares the trending of these two metrics:

Over-regulation (78%), availability of key skills (73%) and government response to fiscal deficit and debt burden (72%) are the top three threats CEOs are most concerned about. It’s interesting to note that five of the top then threats are technology-related. Availability of key skills, cyber threats including lack of data security, shift in consumer spending and behaviors, speed of technology change, and new market entrants are five threat areas that are being addressed by scalable, secure enterprise technologies today.

Mobile technologies for customer engagement is the most strategically important series of technology CEOs are focusing on today. The report states that “the number of mobile phone users globally was expected to total 4.55B in 2014 – nearly 70% of the world’s population – with smartphone users totaling 1.75B. The volume of mobile traffic generated by smartphones is now about twice that of PCs, tablets and routers – despite having only surpassed them in 2013 – and is predicted to grow ten-fold by 2019”. The following graphic compares the strategic importance of key technologies.

The majority of CEOs think that digital technologies have created high value for their organizations. CEOs cited areas including data and data analytics, customer experience, digital trust and innovation capacity as key areas where digital technologies are delivering value. Operational efficiency (88%), data and data analytics (84%) and customer experience (77%) are the top three priorities that CEOs are concentrating today. The first graphic compares the level of value being gained from each digital investment area, and the second provides an expanded analysis of selected digital technologies’ value.

86% of CEOs realize they need to champion the use of digital technologies for their enterprises’ digital investments to succeed. Taking control and driving change management deep into their organizations by championing digital technologies is the most effective strategy for making digital investments pay off. The following graphic shows that CEOs realize how critical their roles are in overcoming resistance to the change that technologies bring into an organization. CEOs in 2015 will champion mobility, data and analysis to strengthen their organizations’ ability to compete in increasingly turbulent markets.

Last year the big data market centered squarely on technology around the Hadoop ecosystem. Since then, it’s been all about ‘putting big data to work’ thro

ugh use cases shown to generate ROI from increased revenue and productivity and lower risk.

Now, big data continues its march beyond the crater. Next year we can expect to see more mainstream companies adopting big data and IoT, with traditionally conservative and skeptic organizations starting to take the plunge.

Data blending will be more important compared to a few years ago when we were just getting started with Hadoop. The combination of social data, mobile apps, CRM records and purchase histories via advanced analytics platforms allow marketers a glimpse into the future by bringing hidden patterns and valuable insights on current and future buying behaviors into light.

The spread of self-service data analytics, along with widespread adoption of the cloud and Hadoop, are creating industry-wide change that businesses will either take advantage of or ignore at their peril. The reality is that the tools are still emerging, and the promise of the (Hadoop) platform is not at the level it needs to be for business to rely on it.

As we move forward, there will be five key trends shaping the world of big -Data:

The Internet of Things (IoT)

Businesses are increasingly looking to derive value from all data; large industrial companies that make, move, sell and support physical things are plugging sensors attached to their ‘things’ into the Internet. Organizations will have to adapt technologies to map with IoT data. This presents countless new challenges and opportunities in the areas of data governance, standards, health and safety, security and supply chain, to name a few.

IoT and big data are two sides of the same coin; billions of internet-connected 'things' will generate massive amounts of data. However, that in itself won't usher in another industrial revolution, transform day-to-day digital living, or deliver a planet-saving early warning system. Data from outside the device is the way enterprises can differentiate themselves. Capturing and analyzing this type of data in context can unlock new possibilities for businesses.

Research has indicated that predictive maintenance can generate savings of up to 12 percent over scheduled repairs, leading to a 30 percent reduction in maintenance costs and a 70 percent cut in downtime from equipment breakdowns. For a manufacturing plant or a transport company, achieving these results from data-driven decisions can add up to significant operational improvements and savings opportunities.

Deep Learning

Deep learning, a set of machine-learning techniques based on neural networking, is still evolving, but shows great potential for solving business problems. It enables computers to recognize items of interest in large quantities of unstructured and binary data, and to deduce relationships without needing specific models or programming instructions.

These algorithms are largely motivated by the field of artificial intelligence, which has the general goal of emulating the human brain’s ability to observe, analyze, learn, and make decisions, especially for extremely complex problems. A key concept underlying deep learning methods is distributed representations of the data, in which a large number of possible configurations of the abstract features of the input data are feasible, allowing for a compact representation of each sample and leading to a richer generalization.

Deep learning is primarily useful for learning from large amounts of unlabeled/unsupervised data, making it attractive for extracting meaningful representations and patterns from Big Data. For example, it could be used to recognize many different kinds of data, such as the shapes, colors and objects in a video — or even the presence of a cat within images, as a neural network built by Google famously did in 2012.

As a result, the enterprise will likely see more attention placed on semi-supervised or unsupervised training algorithms to handle the large influx of data.

In-Memory Analytics

Unlike conventional business intelligence (BI) software that runs queries against data stored on server hard drives, in-memory technology queries information loaded into RAM, which can significantly accelerate analytical performance by reducing or even eliminating disk I/O bottlenecks. With big data, it is the availability of terabyte systems and massive parallel processing that makes in-memory more interesting.

At this stage of the game, big data analytics is really about discovery. Running iterations to see correlations between data points doesn't happen without millisec

onds of latency, multiplied by millions/billions of iterations. Working in memory is at three orders of magnitude faster than going to disk.

In 2014, Gartner coined the term HTAP - Hybrid Transaction/Analytic Processing, to describe a new technology that allows transactions and analytic processing to reside in the same in-memory database. It allows application leaders to innovate via greater situation awareness and improved business agility, however entails an upheaval in the established architectures, technologies and skills driven by use of in-memory computing technologies as enablers.

Many businesses are already leveraging hybrid transaction/analytical processing (HTAP); for example, retailers are able to quickly identify items that are trending as bestsellers within the past hour and immediately create customized offers for that item.

But there’s a lot of hype around HTAP, and businesses have been overusing it. For systems where the user needs to see the same data in the same way many times during the day, and there’s no significant change in the data, in-memory is a waste of money. And while you can perform analytics faster with HTAP, all of the transactions must reside within the same database. The problem is, that most analytics efforts today are about putting transactions from many different systems together.

It’s all on Cloud

Hybrid and public cloud services continue to rise in popularity, with investors claiming their stakes. The key to big data success is in running the (Hadoop) platform on an elastic infrastructure.

We will see the convergence of data storage and analytics, resulting in new smarter storage systems that will be optimized for storing, managing and sorting massive petabytes of data sets. Going forward, we can expect to see the cloud-based big data ecosystem continue its momentum in the overall market at more than just the “early adopter” margin.

Companies want a platform that allows them to scale, something that cannot be delivered through a heavy investment on a data center that is frozen in time. For example, the Human Genome Project started as a gigabyte-scale project but quickly got into terabyte and petabyte scale. Some of the leading enterprises have already begun to split workloads in a bi-modal fashion and run some data workloads in the cloud. Many expect this to accelerate strongly as these solutions move further along the adoption cycle.

There is a big emphasis on APIs to unlock data and capabilities in a reusable way, with many companies looking to run their APIs in the cloud and in the data center. On-premises APIs offer a seamless way to unlock legacy systems and connect them with cloud applications, which is crucial for businesses that want to make a cloud-first strategy a reality.

More businesses will run their APIs in the cloud, providing elasticity to better cope with spikes in demand and make efficient connections, enabling them to adopt and innovate faster than competition.

Apache Spark

Apache Spark is lighting up big data. The popular Apache Spark project provides Spark Streaming to handle processing in near real time through a mostly in-memory, micro-batching approach. It has moved from being a component of the Hadoop ecosystem to the big data platform of choice for a number of enterprises.

Now the largest big data open source project, Spark provides dramatically increased data processing speed compared to Hadoop, and as a result, is much more natural, mathematical, and convenient for programmers. It provides an efficient, general-purpose framework for parallel execution.

Spark Streaming, which is the prime part of Spark, is used to stream large chunks of data with help from the core by breaking the large data into smaller packets and then transforming them, thereby accelerating the creation of the RDD. This is very useful in today’s world where data analysis often requires the resources of a fleet of machines working together.

However, it’s important to note that Spark is meant to enhance, not replace, the Hadoop stack. In order to gain even greater value from big data, companies consider using Hadoop and Spark together for better analytics and storage capabilities.

Increasingly sophisticated big data demands means the pressure to innovate will remain high. If they haven’t already, businesses will begin to see that cus

tomer success is a data job. Companies that are not capitalizing on data analytics will start to go out of business, with successful enterprises realizing that the key to growth is data refinement and predictive analytics.

Information Management, 2016; Brad Chivukala

Elke organisatie die veranderd, is op zoek. Die zoektocht heeft vaak betrekking op data: hoe kunnen we data beter toepas

sen? Hoe kunnen we nieuwe toepassingen voor data vinden? Hebben we wel de juiste data? Wat moeten we doen met data science en big data? Hoe kunnen we data inzetten om betere besluiten te nemen en dus ook beter te presteren?

Organisaties moeten een antwoord vinden op deze vragen. Deels onder druk van de verder ontwikkelende markt en veranderende concurrentie. Daarmee krijgt data een centrale plaats in de bedrijfsvoering en worden organisaties dus 'data driven'.

Uiteraard heb je hier 'data science' voor nodig: de omgevingen en vaardigheden om data te ontleden, analyseren en te vertalen naar modellen, adviezen en besluiten.

We hebben de leergang business data scientist ontworpen omdat geen bedrijf met alleen tools en technieken succesvol gaat worden. Het is juist de business data scientist die de brug vormt tussen data science en de verandering die in organisaties plaats vindt.

Te vaak ligt bij organisaties de nadruk op de technologie (Hadoop? Spark? Data lake? Moeten we R leren?). Om succesvol te zijn met data heb je ook andere instrumenten nodig. Bedrijfskundige modellen, business analyse, strategievorming helpen om de juiste vragen te formuleren en doelen te stellen. Softskills en veranderkundige vaardigheden om die doelen zichtbaar te maken voor opdrachtgevers en stakeholders. Kennis van data science, architectuur, methoden en organisatiemodellen geeft de inzichten om data science in een organisatie in te passen. Visie en leiderschap is nodig om data sc

ience in een organisatie te laten werken. Ons doel is om deelnemers dit te laten zien. De opleiding is ontworpen om al deze aspecten samen te laten komen en bruikbare instrumenten te geven.

Wat ik het leukste vind van deze leergang? Steeds weer de brug maken naar: wat gaan we nu doen, hoe breng je de theorie in praktijk brengen. Elke deel theorie wordt vertaald naar een praktische toepassing in de casus. En dat is de eerste stap naar het halen van successen met data science in je eigen werk, team, afdeling, divisie of organisatie.

Meer weten? Interesse? Op 28 november is er een thema avond over de Business Data Scientist in Utrecht. Aanmelden kan via de Radboud Management Academy!

Deze blog is verschenen op www.businessdecision.nl.

Auteur: Alex Aalberts

Steeds meer bedrijven in de VS werken samen met zorgverzekeraars en partijen die gezondheidsdata verzamelen om erachter te komen welke werknemers risico lopen om ziek te worden.

Daarover schrijft The Wall Street Journal. Om de ziektekosten in de hand te houden slaan sommige bedrijven de handen ineen met bedrijven die allerlei gegevens van werknemers verzamelen en verwerken. Daaruit kunnen ze dan bijvoorbeeld opmaken wie er risico loopt om diabetes te krijgen.

Op basis van die informatie krijgen werknemers dan bericht dat ze eens naar de dokter zouden moeten gaan of hun gewoontes moeten aanpassen. ''Je kunt beter voorspellen wat het risico is dat iemand een hartaanval krijgt door te letten waar hij of zij winkelt dan door op chromosomen te letten'', zegt Harry Greenspun van Deloitte's Center for Health Solutions, tegen de Wall Street Journal.

Uiteenlopende informatie
De directeur van zo'n dataminingbedrijf vertelt dat ze de meest uiteenlopende informatie kunnen gebruiken om de gezondheid van werknemers te voorspellen. Dat loopt uiteen van waar je inkopen doet tot de kredietwaardigheid van werknemers. Mensen met weinig geld, zo is de redenering, zijn eerder geneigd om niet de juiste medicijnen aan te schaffen, als de dokter dat adviseert.

Een ander bedrijf, Castlight, biedt een platform voor werknemers om zorgverzekeraars te vergelijken. Op basis van de data die zij daarmee verzamelen hebben zij onlangs zelfs een product ontwikkeld dat kan voorspellen of een vrouwelijke werknemer binnenkort zwanger wordt. Dat weten ze op basis van een aantal persoonskenmerken, die zij vervolgens weer koppelen informatie uit declaratieverzoeken bij de zorgverzekeraar. Declareer je opeens de pil niet meer, ben je 30 en heb je al een kind? Dan gaan de alarmbellen af.

Ook zegt het veel over je als je, in de VS, gaat stemmen voor de verkiezingen voor het Huis van Afgevaardigden of de Senaat. Mensen die dit doen zijn over het algemeen mobieler en actiever en dat zegt veel over je algemene gezondheid.

Enorme risico's of juist handig?
Niet iedereen is enthousiast over de verzamelwoede van gegevens door verzekeraars en bedrijven. Frank Pasquale, professor aan de Universiteit van Maryland, zegt tegenover de Wall Street Journal 'enorme potentiële risico's' te zien.

Sommige werknemers zijn onaangenaam verrast als blijkt dat ze op basis van medische tests een medisch advies van hun werkgever krijgen. Maar het kan ze wel aanzetten hun gedrag te wijzigen en zo de kans op een bepaalde aandoening te verminderen. Zo laat de krant een vrouwelijke werknemer aan het woord die gevaar liep om diabetes te krijgen. Nu is ze flink afgevallen en is het risico afgenomen.

Source: RTL Z