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What Developers Should Do With Data

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Developers rely on manipulating data to create an engaging product for users. But in the early stages of a product, there is a dearth of it, which can make the user experience dull. Then as the product ages, the amount of data increases, and can become too noisy if not properly organized. In this talk, Poornima Vijayashanker will provide some strategies for dealing with data life cycles, and how to understand what stage you're at to guide product development.

Published in: Software
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What Developers Should Do With Data

  1. 1. What Developers Should Do with Data Poornima Vijayashanker October 9, 2014 @poornima 1
  2. 2. Background • R&D Engineer @ Synopsys • Founding Engineer @ Mint.com • Founder of BizeeBee • Duke University • EIR @ 500 Startups • Founder of Femgineer 2
  3. 3. Agenda • Not enough data • Noisy data • Too much data • Secure data 3
  4. 4. BIG data 4
  5. 5. No Data Growth Product Process. Lots of Data Noisy Data Secured Data Launch Some Customers Traction 5
  6. 6. User Experience 6
  7. 7. make it compelling 7
  8. 8. Why would I allow a 20-something to access my financial data?! 8
  9. 9. build trust 9
  10. 10. make it frictionless 10
  11. 11. delight! 11
  12. 12. 12
  13. 13. Noisy data. Data streams Third-party User actions 13
  14. 14. No Data Growth Product Process. Lots of Data Noisy Data Secured Data Launch Some Customers Traction 14
  15. 15. parse it 15
  16. 16. aggregate it 16
  17. 17. mash it up! 17
  18. 18. 18
  19. 19. 19
  20. 20. No Data Growth Product Process. Lots of Data Noisy Data Secured Data Launch Some Customers Traction 20
  21. 21. mo’ data. mo’ problems! 21
  22. 22. Vocal minority or is it a major bug? 22
  23. 23. Analytics 23
  24. 24. Storage & Files 24
  25. 25. Hosting 25
  26. 26. Retrieval 26
  27. 27. Warehousing 27
  28. 28. No Data Growth Product Process. Lots of Data Noisy Data Secured Data Launch Some Customers Traction 28
  29. 29. Privacy 29
  30. 30. Unsecured data 30
  31. 31. Secure it! User Employee Outsider 31
  32. 32. 32
  33. 33. Various hats White hat Black hat Grey hat 33
  34. 34. responsible disclosure 34
  35. 35. Review • Not enough data • Noisy data • Too much data • Secure data 35
  36. 36. Get samples of the book: http://femgineer.com/ transform-ideas 36
  37. 37. q&a 37

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