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BIG DATA is DEAD | Marc Weimer-Hablitzel, Etventure | DN18

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BIG DATA is DEAD | Marc Weimer-Hablitzel, Etventure | DN18

  1. 1. BIG DATA is DEAD 1 Marc Weimer-Hablitzel marcwh@etventure.com Nov 2018
  2. 2. 2 Marc Weimer-Hablitzel Principal - Data Innovations ● Corporate & Startup experience ● 15+ years Data Science Tech & Business
  3. 3. 3 THREE SIGNS THAT BIG DATA IS THESE TWO JOIN FORCES FAMOUS BEHAVIORAL ECONOMICS SCIENTIST JOKES ABOUT IT Prof. Dan Ariely COO TERADATA THINKS O. Ratzesberger
  4. 4. 4 IS DATA THE NEW OIL? Gather Store Get Rich
  5. 5. WHILE DATA HAS BECOME THE TOP CEO TOPIC STILL MOST PROJECTS FAIL 5 TOP 3 REASONS 1. Bad data quality 2. Not enough data 3. No talents understood data potential ¹ successfully using data ² 1) etventure: “Studie Digitale Transformation 2018”, 4-2018 2) I. Swanson: “Three Ways To Boost Your ROI In Data Science”, www.forbes.com, 6-2-2018
  6. 6. WHILE DATA HAS BECOME THE TOP CEO TOPIC STILL MOST PROJECTS FAIL 6 TOP 3 REAL REASONS 1. Wrong focus (technology instead of business impact) 2. Poor data strategy 3. Unattractive for talents understood data potential ¹ successfully using data ² 1) etventure: “Studie Digitale Transformation 2018”, 4-2018 2) I. Swanson: “Three Ways To Boost Your ROI In Data Science”, www.forbes.com, 6-2-2018
  7. 7. 7 ...IT’S ALL ABOUT THE USE CASES Gather Store Refine Use Cases Value Today
  8. 8. THE MOST SUCCESSFUL COMPANIES ARE... 8 ● Focus on customer needs ● Design of customer experience ● continuous improvement with qualitative and quantitative Feedback CUSTOMER CENTRIC TO THE OUTSIDE DATA CENTRIC TO THE INSIDE ● Focus on use case ● Design of data generation ● continuous improvement towards measurable baseline and human acceptance criteria Data Thinking
  9. 9. 9 ITERATE TO SUCCESS
  10. 10. ● premium car manufacturer increasing volatility and complexity in special equipment Automotive They all talk AI but we still do our planing like it’s 1999 Prove that machine learning can improve the shortcomings of current demand prediction > 1 month > working prototype Head of Planning
  11. 11. 11 Prove in one month that machine learning can improve the shortcomings of current demand prediction IDENTIFICATION (8 days) ● 7 Use Cases ● > 10 Mio € / p.a. business impact ● Baseline from historic data PROOF (14 days) ● PoC: Early detection of peak demand using external factors ● 2 Mio € / p.a. business impact ● 3 iterations / 100% client commitment
  12. 12. 12 TOOLS OF DATA THINKING
  13. 13. 13 DATA POTENTIAL - WORKSHOP Business Understanding Ideation Definition Prioritization
  14. 14. Problem (Describe the problem in one sentence) USE CASE CANVAS Impact (Describe the impact of the problem) 14 Baseline (What’s the baseline value? What’s the desired target value?) Accessibility of Data (List relevant data sets and their accessibilty) Contact Person (Who’s the ‘go to’ person for data?) Use Case Description (e.g. classification) Current Solution (Describe the current solution) Cause (Describe the cause) Acceptance Criterion (Describe the conditions under which an improvement is expected) Frequency (How often does the problem occur?) © etventure GmbH
  15. 15. 15 THE 5 PRINCIPLES OF DATA THINKING
  16. 16. 16 IMPACT FIRST, DATA SECOND
  17. 17. 17 START SMALL TEST EARLY ITERATE FAST
  18. 18. 18 DESIGN ON YOUR NEEDS, NOT ON OUR HAVES
  19. 19. 19 BETTER FAKE DATA, THAN NO DATA
  20. 20. 20 BE SOLUTION AGNOSTIC
  21. 21. KEY TAKEAWAYS 21 1. Data value can be found everywhere 2. A structured approach helps! 3. Stop wasting your time on BIG Data
  22. 22. 22 Marc Weimer-Hablitzel Principal - Data Innovations Mobil: +49 151 57539361 marcwh@etventure.com Ritterstr 24-27 10969 Berlin www.etventure.com KONTAKT THANK YOU! Want a copy of this presentation?: Post your feedback on LinkedIn and add linkedin.com/in/marcwh #DataThinking
  23. 23. www.etventure.com etventure.de © All Rights Reserved changing the game 23

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