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Big data tokyo (extended version)

  1. DATA AND DISILLUSIONMENT SOLVEfor INTERESTING OTHERWISE LIFE IS DULL.
  2. Volume (the “big” part) Pick any Velocity two Variety (the “fast” (the part) “anything” part)
  3. Big Data is the Third Age of computing Computing Networking Big Data Automate Interconnect Predict & change things things things (Jim Stodgill of O’Reilly Radar said this.)
  4. Enterprises expect Big Data to deliver better decisions and improved customer experiences What tangible benefits do you hope to achieve through your big data initiatives? NewVantage Partners LLC www.newvantage.com
  5. (And apparently Hadoop is winning) What data management approaches are you considering? NewVantage Partners LLC www.newvantage.com
  6. The relational database is a general- purpose tool.
  7. A library is a database optimized for retrieval Photo by cybrgrrl (http://www.flickr.com/photos/cybrgrl/1295482521/) on Flickr
  8. A change counter is a database optimized for insertion
  9. An example: eventual consistency
  10. “End of Day Balance will only appear for dates previous to the last 2 business days.” “Transactions from today are reflected in your balance, but may not be displayed on this page if you recently updated your bankbook, if a paper statement was recently issued, or if a transaction is backdated. These transactions will appear in your history the following business day.”
  11. Relational BIG Statistical
  12. http://www.flickr.com/photos/jenny-pics/3239638494/sizes/l/ Breadcrumb trail
  13. The average enterprise has 178 social media accounts (According to @setlinger and the Altimeter group.)
  14. Ward off disease. Pinpoint disasters. A force Reveal corruption. for good. Make cities smarter. Improve how we teach.
  15. Big healthcare
  16. Big philanthropy
  17. Big commuting
  18. Erode our privacy. Justify prejudices. A force Polarize groups. for bad. Leak private truths.
  19. Big prejudice
  20. “…nobody notices offers they do not get. And if these absent opportunities start following certain social patterns (for example not offering them to certain races, genders or sexual preferences) they can have a deep civil rights effect.” Anders Sandberg, Oxford University
  21. Personalization looks a lot like prejudice.
  22. Big radio
  23. Times a song in “heavy rotation” is played each day 30 Every 55m 15 Every 4h 0 2007 2012
  24. Humans are bad at data.
  25. We prefer false positives.
  26. Wooly mammoth http://www.flickr.com/photos/pong/172438102/sizes/o/
  27. Sun temple http://www.flickr.com/photos/30787002@N02/3298693694/sizes/l/
  28. Some proof.
  29. It’s really hard to find people who can think about data well How challenging is it to source data scientists? NewVantage Partners LLC www.newvantage.com
  30. Mistake correlation for causality Seek truthiness rather than fact Find patterns where they don’t exist Easily swayed by tone Side with our tribes Dig in and ignore new evidence
  31. Athenian swimming pools
  32. Volume Big Variety Data Good data Velocity Veracity
  33. 525,000 state & local officers Under 25 officers per precinct 130 million incident reports 200,000 uses of force 31% keep computer files
  34. Evidence.com
  35. Hard drive
  36. Big Data is not about data.
  37. Big Data is about truth, auditability, and the ability to analyze data on a level playing field. It’s about analysis for everyone.
  38. Alistair Croll @acroll www.solveforinteresting.com THANKS! alistair@solveforinteresting.com SOLVEfor INTERESTING OTHERWISE LIFE IS DULL.
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