1. The Big Deal about Big Data Mads Carsten Brink Hansen | Targit A/S | Aarhus University

2. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 THE BIG DEAL ABOUT BIG DATA

3. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 The Interest in Big Data What is Big Data DEMO Takeaways Agenda

4. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 Gartner on Big Data

5. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 You (Google) on Big Data & BI

6. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 You (Google) on Big Data, BI & Analytics

7. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 WHAT IS BIG DATA?

8. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 IBM on Analytics and Big Data

9. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 Volume Variety Velocity Big Data - Definition

10. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 When thinking of Big Data Volume is likely to be the first that comes into mind Very large volumes of data might require new exciting technologies… but the capacity of “well-known” technology has improved as well Big Data - Volume Volume Variety Velocity

11. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 invented Big Data-technologies because commercial software failed to deliver [and was a strategic misfit] Technology companies created products and services based on the inventions to the extend that Big Data = Hadoop Big Data – Volume - Technology Volume Variety Velocity

12. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 Velocity is about data generation [and requirements for processing] From periodic reporting to near-real- time-insight From Batch-Processing to complex- event-processing Big Data - Velocity Volume Variety Velocity

13. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 Big Data – Velocity - CEP Volume Variety Velocity

14. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 Where does Big Data come from? Big Data - Variety Volume Variety Velocity Social Media Content Video Content E-mail Market Signals Sensors and many, many more sources

15. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 Volume, Velocity or Variety? Volume Variety Velocity

16. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 Business Problem Collect Data Process Analyze & Insight Take Action Big Data Analytical Process [Insight Discovery]

17. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 Big Data Analytical Process – in Context Insight Discovery Insight Testing Execution

18. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 Analytics is applicable to any kind of data: Big, Small, Internal, External, Structured, Unstructured The Data Analytical Process is a learning process Many organizations have unexploited, internal data Big Data Analytical Process = Analytics

19. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 A study by Ulster University and Kent University (funded by UK regional government) Provided POS/loyaltycard from Tesco to local food-producers The insight generated supplemented and sometimes superseded “established wisdom” Most challenging was the analytical capabilities and accept of the fact that “established wisdom” sometimes was mistaken Is Big Data Analytics only for Big Business?

20. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 DEMO

21. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 Well established in the metal industry Producing parts used in trucks, lorries and heavy construction vehicles by MAN, Scania, Volvo, Caterpillar and more Eberhart Metallbau GmbH

22. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 -An important customer has complained deliveries on -August 27 th, -September 15 th -October 20 th Business Problem Collect Data Process Analyze & Insight Take Action

23. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 -ERP-system (AX) -Finished goods booked as inventory once a day -Production Management System -Detailed data for one week -Aggregated data (hourly) for one year Collect Data - Data Sources 1#2 Business Problem Collect Data Process Analyze & Insight Take Action

24. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 -Workplan System -People-to-Shift-planning -Work Satisfaction System Collect Data - Data Sources 1#2 Business Problem Collect Data Process Analyze & Insight Take Action

25. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 Identification of root-cause of the quality issue (Work satisfaction on one team) Better understanding of relationship between quality, yield and work satisfaction Process + Analyze & Insight Business Problem Collect Data Process Analyze & Insight Take Action

26. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 Changes to the Production Management System and integration to ERP (AX) New Action Loop [Dashboard] to monitor Quality, Production and Work- satisfaction – in test To put the Dashboard into production Take Action! Business Problem Collect Data Process Analyze & Insight Take Action

27. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 Eberhart Metallbau GmbH - Results Insight Discovery Insight Testing Execution To-Do

28. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 TAKEAWAYS

29. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 We do not need new technology to get started There are a lot of unexploited, internal data – start here Analytics is a learning process – allow for this TAKEAWAYS

30. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 50% Believe that business analytics creates a competitive advantage for their organization 29% We are not sure how to apply the analytical insights to our business 28% Analytics is not a priority for senior management So let us do something about it!

31. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 Please download the session slides from the NAVUG Congress Community or through the Congress App Please complete your session survey in the Congress App Final reminders 31

32. #AXUGCongres16 | #NAVUGCongress16 | #CRMUGCongress16 Mads Brink Hansen Targit A/S / Aarhus University mcbh@target.commcbh@target.com / mcbh@mgmt.au.dkmcbh@mgmt.au.dk Speaker contact info 32 Headshot And/Or Company Logo 32