We are Big Data - Sander Klous

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Information about We are Big Data - Sander Klous

Published on July 4, 2016

Author: crimsonwing

Source: slideshare.net

1. We are Big Data The future of our information society prof. dr. Sander Klous Big Data Ecosystems in Business and Society University of Amsterdam Managing Director Big Data Analytics KPMG Advisory klous.sander@kpmg.nl @sanderklous http://nl.linkedin.com/in/sanderklous

2. Extreme expectations https://www.youtube.com/watch?v=2vXyx_qG6mQ 2

3. Content 1. Big, bigger, biggest 2. New possibilities 3. Shared value 4. Dealing with data 5. Reflections and Conclusions 3

4. 4

5. Content 1. Big, bigger, biggest 2. New possibilities 3. Shared value 4. Dealing with data 5. Reflections and Conclusions 5

6. Data driven business models 6

7. Jobless recovery http://www.futuretech.ox.ac.uk/sites/futuretech.ox.ac.uk/files/The_Future_of_Employment_OMS_Working_Paper_1.pdf Accountants: 95% 7

8. Big Brother? That’s us! 8

9. Content 1. Big, bigger, biggest 2. New possibilities 3. Shared value 4. Dealing with data 5. Reflections and Conclusions 9

10. From shareholder value to shared value Unilever: Lifebuoy – Soap for Africa Michael Porter & Mark Kramer 10

11. Shared value with Big Data http://www.visaeurope.com/en/newsroom/news/a rticles/2010/validsoft_fraud_solution.aspx http://www.confused.com/car-insurance/black-box 11

12. Smart cities & living labs 12

13. Content 1. Big, bigger, biggest 2. New possibilities 3. Shared value 4. Dealing with data 5. Reflections and Conclusions 13

14. Correlation or causality Apples and Pears ■ Jar A contains 10 apples and 30 pears ■ Jar B contains 20 of each Fred picks a jar, without further evidence there is a 50% chance this is jar A (or B). Fred pulls out a pear. The new probability that Fred picked bowl A is 0.75 x 0.5 / ( 0.75 x 0.5 + 0.5 x 0.5 ) = 0.6 Jar A Jar B P(Hn|E) = P(E|Hn)P(Hn) Sum1 N (P(E|Hn)) 14

15. Quantity over Quality Known symmetric statistical error • Example: Typical Gaussian distributed measurement errors • Solution to get a more accurate mean value: More data from the same source Statistical Systematically SymmetricAsymmetric Blue line: financially healthy clients Red line: clients from Fin. Health Dep. Unknown asymmetric systematically error •Example: Tidal effects in the lake of Geneva The TGV on the train track near CERN •Solution to get a more accurate results: More data from different sources 15

16. Systems determine our behavior 16

17. Content 1. Big, bigger, biggest 2. New possibilities 3. Shared value 4. Dealing with data 5. A practical guide 6. Reflections and Conclusions 17

18. Data Lakes & Platform thinking KPMG Analytics & Visualization Environment: http://kave.io The Overview  Horizontally Scalable  Open Source  Configurable  Modular  Secure 10,000 tweets on motorways in Jan. & Feb. 2013 Weather radar Characteristic transition point traffic jams Vehicle intensity vs density in 2013: dry vs wet road Predicted vehicle intensity Platform thinking in Harvard Business Review: https://hbr.org/2013/01/three-elements-of-a-successful-platform 18

19. Agile project management Insightinto digitalizationDiscovering thedigital possibilities Breakingthe rules Relatedworlds Ambition Ambition Customer perspective Powertrack planning Business value Ideasfor digitalizationVision Generating an appetite Preparation for and creating client cases using a Big Data opportunity event Proof of Concept for selected cases and hand over Transformation of the solution and business model Envisioning… uncovering client cases using a Big Data opportunities Preparing… selected client cases for Proof of Concepts Reviewing… evaluating the results and determining the way forward Developing… the Proof of Concepts of the selected client cases Coordinating the project using a phased and agile approach Continuous interaction to orchestrate Big Data strategy process and if necessary adjust business case directions. 2 weeks 10 weeks to be decided Part of this proposal Additional activities Client cases (start small and accept failures) 19

20. The data driven organization Spotify: ING: https://www.youtube.com/watch?v=Mpsn3WaI_4k (1 of 2) https://www.youtube.com/watch?v=X3rGdmoTjDc (2 of 2) https://m.youtube.com/watch?v=NcB0ZKWAPA0&feature=youtu.be 20

21. Content 1. Big, bigger, biggest 2. New possibilities 3. Shared value 4. Dealing with data 5. A practical guide 6. Reflections and Conclusions 21

22. The golden case 1. Adds value for society and clients. 2. Shows the potential of Big Data 3. Starts small but is meaningful. 4. Has the potential to scale fast. 22

23. Maybe trust is overrated 23

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