Peter Rakers De Verstoring van Big Data

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1 Peter Rakers De Verstoring van Big Data Na globalisatie en digitalisatie komt dataficatie, het continue genereren van real time gegevens. Voor veel bedrijven werkt dit fenomeen eerder verstorend. Peter Rakers geeft voorbeelden over de opportuniteiten en valstrikken van big data. meer weten over Peter Rakers:

Voor veel bedrijven werkt dit fenomeen eerder verstorend.

2 Disruptive in Belgium Gent, 26 september 2014 Peter Rakers

3 Data has become raw material that can be used in an innovative way in order to get new products, services or insights with significant added value Cropland: Disruptive in Belgium - 10 jaar SC, Gent, Sept 26,

or insights with significant added value.

4 Cropland will look at your (big) data business case from 4 different angles Customers Products Team Channels 2014 Cropland: Disruptive in Belgium - 10 jaar SC, Gent, Sept 26,

5 and on 3 different levels LEVEL 1 INTERNAL DATA ANALYSIS Although obvious at first sight, connecting system A with system B or exchanging marketing information with financial (sales) data or consumer data appear not to be easy in practice. During the start up of a Cropland project, we always analyze the internal data at first in order to take out maximum information from structured sources Cropland: Disruptive in Belgium - 10 jaar SC, Gent, Sept 26,

6 and on 3 different levels LEVEL 2 THE COMBINATION WITH EXTERNAL DATA After mapping the internal data, we look for associations and correlations with external sources. Free data, but also paid information from other stakeholders or data providers are combined. Unexpected events such as weather, demographic patterns or others could have an important influence on the company s objectives Cropland: Disruptive in Belgium - 10 jaar SC, Gent, Sept 26,

Free data, but also paid information from other stakeholders or data providers are combined.

7 and on 3 different levels LEVEL 3 AN INTERCONNECTED NETWORK OF COMPLEMENTARY BUSINESSES Without exposing company-owned information, Cropland will look at innovative segmentation models based on a concept called the DATA POOL Cropland: Disruptive in Belgium - 10 jaar SC, Gent, Sept 26,

Cropland will look at innovative segmentation models based on a concept

8 What is foresight? Level of abstrac ction Short term Qualitative Long term Qualitative Short term Quantitative Long term Quantitative TODAY Time into the future 2014 Cropland: Disruptive in Belgium - 10 jaar SC, Gent, Sept 26,

9 For today, we can call it Level of abstrac ction War gaming g Scenarios Thinking Forecasting Predictive Analytics TODAY Time into the future 2014 Cropland: Disruptive in Belgium - 10 jaar SC, Gent, Sept 26,

Analytics TODAY Time into the future 2014 Cropland:

10 What is surprising is not the magnitude of our forecast errors, but our absence of awareness of it Nassim Nicholas Taleb Author of 'The Black Swan 2014 Cropland: Disruptive in Belgium - 10 jaar SC, Gent, Sept 26,

Nassim Nicholas Taleb Author of 'The Black Swan 2014

11 Human beings and organizations do not act in response to reality but to an internally constructed version of reality Kees van der Heijden Strategic Planning Royal Dutch / Shell 2014 Cropland: Disruptive in Belgium - 10 jaar SC, Gent, Sept 26,

Kees van der Heijden Strategic Planning Royal Dutch / Shell

12 And that s why I believe, That seemingly gyrandom events have potential to become disruptive for others Mohamed Bouazizi, a Tunisian vegetable seller, set himself alight on December 17, Cropland: Disruptive in Belgium - 10 jaar SC, Gent, Sept 26,

Tunisian vegetable seller, set himself alight on December 17,

13 Topic of the day Scenarios Thinking Predictive Analytics 2014 Cropland: Disruptive in Belgium - 10 jaar SC, Gent, Sept 26,

14 Big data definition Big Data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, organize, manage, and process the data within a tolerable elapsed time. Big data sizes are a constantly moving target, as of 2012 ranging from a few dozen terabytes to many petabytes of data in a single data set. kb (kilobyte) = =10 3 MB (megabyte) = =10 6 GB (gigabyte) g = =10 9 TB (terabyte) = =10 12 PB (petabyte) = =10 15 EB (exabyte) = = ZB (zettabyte) = =10 21 YB (yottabyte) = = Cropland: Disruptive in Belgium - 10 jaar SC, Gent, Sept 26,

Big data sizes are a constantly moving target, as of 2012 ranging from a few dozen terabytes to many petabytes of data in a single data set.

15 Big data definition Amount from big to bigger Range from structured to unstructured Speed from batch to streaming Process from Excel to Hadoop from IT/BI to data science 2014 Cropland: Disruptive in Belgium - 10 jaar SC, Gent, Sept 26,

Process from Excel to Hadoop from IT/BI to data science 2014

16 Big data definition Business Intelligence: Descriptive statistics, trends, linear (structured) data Big Data Inductive statistics, laws, nonlinear (unstructured) systems 2014 Cropland: Disruptive in Belgium - 10 jaar SC, Gent, Sept 26,

Inductive statistics, laws, nonlinear (unstructured) systems

17 Big data mind shift The ability to work directly with vast amounts of data (N =all) instead of extrapolating smaller sets The willingness to embrace real-world messiness. Only 5% of all available data is structured, meaning in a form that fits neatly into a traditional data base The fact that by machine learning ability, correlations become more powerful over time. N=all time 2014 Cropland: Disruptive in Belgium - 10 jaar SC, Gent, Sept 26,

Only 5% of all available data is structured, meaning in a form that fits neatly into a traditional data base The

18 Post hoc, ergo propter hoc After this, therefore because of this or correlation is not causality More trumps better The loss in accuracy at micro level is largely compensated by the insights we get at macro level New data scientists and strategists are needed to cope with this paradigm shift Most of our institutions were established under the presumption that human decisions are based on information that is small, exact and causal in nature. More trumps better. The obsession with exactness is an artifact of the informationdeprived analog era. -Viktor Mayer Schönberger, Big Data Cropland: Disruptive in Belgium - 10 jaar SC, Gent, Sept 26,

established under the presumption that human decisions are based on information that is small, exact and causal in nature. More trumps better.

19 Big data predictive analytics Data always speaks. It always has a story to tell and there's always something to learn from it. Data scientists t see thisover and over again across PA (predictive analytics) projects. Pull some data together and, although you can never be certain what you'll find, you can be sure you'll discover valuable connections by decoding the language g itspeaks and listening. That's the Data Effect - Eric Siegel, Predictive Analytics Cropland: Disruptive in Belgium - 10 jaar SC, Gent, Sept 26,

Pull some data together and, although you can never be certain what you'll find, you can be sure you'll discover valuable connections

20 Predictive analytics Most data is not accumulated for the purpose of prediction but PA can and will learn from this massive recording of events the same way as human beings learn from the accumulation of life experience Cropland: Disruptive in Belgium - 10 jaar SC, Gent, Sept 26,

events the same way as human beings learn from the accumulation of life

21 Consequences for the public There ain t no such thing as a free lunch Robert Heinlein: The Moon is a Harsh Mistress, SF novel, 1966 according for free for free for free for free for free for free for free for free 2014 Cropland: Disruptive in Belgium - 10 jaar SC, Gent, Sept 26,

22 Today Recent mood analysis of 2013 by Twitter (Newsmonkey, March 13, 2014) 2014 Cropland: Disruptive in Belgium - 10 jaar SC, Gent, Sept 26,

23 Today Prof. Veronique Hoste of UGent investigated the content of Twitter messages on the risk of suicide and found several key words with significant predictive signaling. The Bringham Young University connected Twitter messages with HIV sexual risk behaviour in well specified areas of the US 2014 Cropland: Disruptive in Belgium - 10 jaar SC, Gent, Sept 26,

24 Today DANIEL KATZ, MICHIGAN STATE UNIVERSITY 2014 Cropland: Disruptive in Belgium - 10 jaar SC, Gent, Sept 26,

25 Big data adoption position 2014 Cropland: Disruptive in Belgium - 10 jaar SC, Gent, Sept 26,

26 Big Data adoption position Core business BLINDSIGHTVICTIM You are attacked on your core activities by players who don t follow the established rules of the game Late BIG DATA B I G D A T A DOPTION FORESIGHT DISRUPTOR As market leader or innovative challenger, you adopted big data opportunities early in your strategy to stay in or enter the game Early I HINDSIGHT SURVIVOR M P INSIGHT EXPERT A You are at the end of your marketing era. C You keep your core activities up to speed Your expertise declines in valued as the T and in parallel, you develop new services game is changing gquickly based on big data insights Parallel business

27 New kids on the block Core business B I G late D A T A I M P A C T Parallel business early

28 Conclusion for now Big data is here and it is here to stay It will become an (new) strategic tool to look at the future Access to big data (in real time) offers an important knowledge and decision taking advance It deals with larger sense of group behavior but business derivates focus on an individual approach Companies with datafication of human behavior as core business (e.g. Google, Amazon, Facebook) will only increase their dominance on the global market The next step is that companies can not only predict but also prescibe our behavior without us being aware And so, although we experience mainly the benefits of big data today, there s an important risk (or dark side) in the further evolution of datafication for individuals 2014 Cropland: Disruptive in Belgium - 10 jaar SC, Gent, Sept 26,

29 The future for now 2014 Cropland: Disruptive in Belgium - 10 jaar SC, Gent, Sept 26,

Introduction to Predictive Analytics. Dr. Ronen Meiri ronen@dmway.com

Introduction to Predictive Analytics Dr. Ronen Meiri Outline From big data to predictive analytics Predictive Analytics vs. BI Intelligent platforms What can we do with it. The modeling process. Example