What Should a Developer Do With Data?

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Too often as developers we rely on data to guide our decision making when it comes to building product.  But, too often there isn’t enough data to tell a coherent story, the data that is available is just too noisy, or there maybe anecdotal evidence that is in direct opposition to the story conveyed by the data.  In this talk, Poornima Vijayashanker will provide some strategies for how to make better decisions by weighing data with feedback from customers in order to guide product development.

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What Should a Developer Do With Data?

  1. 1. What Should a Developer Do With Data? Poornima Vijayashanker poornima@femgineer.com @poornima 1
  2. 2. Background • R&D Engineer @ Synopsys • Founding Engineer @ Mint.com • Founder & CEO @ BizeeBee • Founder of Femgineer.com • Instructor @ Duke University 2
  3. 3. BIG DATA 3
  4. 4. Not about applications! 4
  5. 5. Agenda • Not enough data • Noisy data • Too much data • Secured data 5
  6. 6. Process. No Data Lots of Data Noisy DataSecured Data Growth Product Launch Some CustomersTraction 6
  7. 7. No Data or Not Enough? 7
  8. 8. User Experience. 8
  9. 9. Make it compelling. 9
  10. 10. Who’s gonna allow 20-somethings to access their finances? 10
  11. 11. 1. Build trust. 11
  12. 12. 12
  13. 13. 2. Make it frictionless. 13
  14. 14. 14
  15. 15. 3. Delight! 15
  16. 16. 16
  17. 17. 17
  18. 18. Privacy. 18
  19. 19. 19
  20. 20. Noisy Data 20
  21. 21. Process. No Data Lots of Data Noisy DataSecured Data Growth Product Launch Some CustomersTraction 21
  22. 22. • Data Streams • Third-Party Data • User Actions Noisy Data 22
  23. 23. Parse it. Aggregate it. Mash it up! 23
  24. 24. 24
  25. 25. 25
  26. 26. Process. No Data Lots of Data Noisy DataSecured Data Growth Product Launch Some CustomersTraction 26
  27. 27. Data. User data - DB. User data - Analytics. Application data - Logs. 27
  28. 28. Vocal Minority vs. Major Bug? 28
  29. 29. Mo’ data, mo’ problems! 29
  30. 30. Storage. 30
  31. 31. Retrieval. 31
  32. 32. Warehouse. 32
  33. 33. Distributed computing. 33
  34. 34. Limit the set based on frequency. 34
  35. 35. Process. No Data Lots of Data Some DataSecured Data Growth Product Launch Some CustomersTraction 35
  36. 36. Security - Access Controls. Employee access. Outsider access. User access. 36
  37. 37. 37
  38. 38. Various Hats • White Hat • Black Hat • Grey Hat 38
  39. 39. Responsible Disclosure. 39
  40. 40. Responsible Disclosure Details • Driven by social responsibility • Hardware and software makers repair vulnerabilities • e.g. Facebook, Google, Mozilla, and Barracuda Networks 40
  41. 41. Review • Not enough data • Noisy data • Too much data • Secure data 41
  42. 42. Additional Resources • Office Hours • Online Mentoring • Courses 42
  43. 43. Q&A 43

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