How to kill your Big Data project!

Doing Data Science & Big Data projects for more than 3 years now I ran into a lot of issues, situations & organizational processes that really "kill" your endeavour rather than to keep it alive and valuable. Time to tell! Time to share! Time to change! :-)

Transcript

1. 29.10.14 The unbelievable Machine Company 1 „How to kill your big data project“
2. 29.10.14 The unbelievable Machine Company 2 http://humancapitalist.com/wordpress/wp-content/uploads/2013/11/canstockphoto3422904.jpg 1. Invest in technology first!
3. 29.10.14 The unbelievable Machine Company 3 Quick win, win, win: Vendor, Business manager, C-Level.
4. 29.10.14 The unbelievable Machine Company 4 2. Think you already have enough data!
5. 29.10.14 The unbelievable Machine Company 5 3. Put your data in ONE database!
6. 29.10.14 The unbelievable Machine Company 6 Polyglot persistence…
7. 29.10.14 The unbelievable Machine Company 7 How do our future IT landscapes look like?
8. 29.10.14 The unbelievable Machine Company 8 4. Keep your data pools locked!
9. 29.10.14 The unbelievable Machine Company 9 5. Give it to your BI team! http://image.slidesharecdn.com/businessintelligenceinsights1-140211090350-phpapp02/95/business-intelligence-insights-how-to-present-visual- data-your-team-understands-1-638.jpg?cb=1392131333
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11. 29.10.14 The unbelievable Machine Company 11 6. Hire PhD. only!
12. 29.10.14 The unbelievable Machine Company 12 7. R only or: A fool with…
13. 29.10.14 The unbelievable Machine Company 13 8. Expect magic!
14. 29.10.14 The unbelievable Machine Company 14 "Learning Hadoop is easier than learning the company’s business," which means that the first place to look for Big Data expertise is in-house, not the land of magical data-science fairies.” Svetlana Sicular, Gartner Analyst
15. 29.10.14 The unbelievable Machine Company 15 9. Don´t like the first results!
16. 29.10.14 The unbelievable Machine Company 16 Ironically, the greatest value from predictive analytics typically comes more from their unexpected failures than their anticipated success. In other words, the real influence and insight come from learning exactly how and why your predictions failed. Why? Because it means the assumptions, the data, the model and/or the analyses were wrong in some meaningfully measurable way. Michael Schrage, a research fellow at MIT Sloan School’s Center for Digital Business
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18. 29.10.14 The unbelievable Machine Company 18 10. Believe everything!
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21. 29.10.14 The unbelievable Machine Company 21 11. Believe machines are made for the digital transformation!
22. 29.10.14 The unbelievable Machine Company 22 12. Don´t tell anyone!
23. 29.10.14 The unbelievable Machine Company 23 13. Don´t care about design!
24. 29.10.14 The unbelievable Machine Company 24 14. Don´t ask for help!
25. 29.10.14 The unbelievable Machine Company 25 Ideation / Storyline Data Pool Creation Explorative Analysis Automated Analysis Delivery & Storytelling Production / Data Product Business Owner Data Scientist Machine Intelligence Business Lane Data Science Lane System Engineer Infrastructure/ Cloud Technology Lane Human-Machine Interplay Data Science Process Model created 2013 by *um - The unbelievable Machine Company GmbH
26. 29.10.14 The unbelievable Machine Company 26 15. Nearly forgot this one…
27. „The real revolution is not in the machines that calculate data but in data itself and how we use it!“ From: „Big Data – A Revolution that will transform how we live, work and think“ by Mayer-Schönberger / Kenneth Cukier
28. 29.10.14 The unbelievable Machine Company 28 The unbelievable Machine Company GmbH Grolmanstr. 40 10623 Berlin Contact: Klaas Bollhoefer klaas.bollhoefer@unbelievable-machine.com Tel. +49-30-889 26 56 -22 Fax. +49-30-889 26 56 -1 Mobile +49-151-27569582