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Bluff Your Way in Data - The Ultimate 101 for C*Os

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This talk is opinionated. You want their money, but everybody wants your money. Big data, small data, wide data, no data. How to profit from data fo' real and what should you never ever try to do. If you don't believe in experts but rather have malicious joy hearing about failures and experiences, you gonna like this. This talk is for the underdog. If you’re trying to solve data related problems with no or limited resources, be them time, money or skills don’t go no further.

Published in: Data & Analytics
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Bluff Your Way in Data - The Ultimate 101 for C*Os

  1. 1. 1
  2. 2. Bluff Your Way in Data - The Ultimate 101 for C*Os Daniel Molnar @ door2door SMART Conference 2017 2
  3. 3. tl;dr 4 there is need for snake oil, 4 false hope of mundane details lead to total predictability, 4 most data analysis goes nowhere, 4 don't fear the AI extinction nor Cambridge Analytics psychometrics. 3
  4. 4. BUSINESS 4
  5. 5. "In god we trust everybody else bring data to the table." 1 W. Edwards Deming 5
  6. 6. Approach 4 KPIs must hurt (aka no feelgood metrics), 4 you are what you measure, 4 you can run in one direction, 4 is it actionable (the Friday 1700 test). 6
  7. 7. Do 4 make definitions, 4 show direction, 4 care about data quality, 4 rule dashboards. 7
  8. 8. KPIs that matter 4 DAU, WAU, MAU, LTV, churn, 4 cohorts, segments, funnels, 4 first hour, first day. 8
  9. 9. Marketing 4 Google Analytics (sampling, off by 20%, no user granularity, no raw, 150k per year), 4 CPA, FB CPA, mobile CPA, conversion, attribution -- don't trust, 4 Net Promoter Score -- never do it. 9
  10. 10. TECHNOLOGY 10
  11. 11. "Don't reinvent the flat tyre." 1 Alan Kay 11
  12. 12. Toolset 4 Excel, 4 SQL, 4 Python, 4 MPP DWHs (Redshift et al). 12
  13. 13. Run if you hear anything like 4 Apache, 4 Hadoop, 4 Spark, 4 real-time, 4 stream. 13
  14. 14. Machine Learning 14
  15. 15. Approach 4 hybrid approaches (domain expert, vanilla ML), 4 you are a machine instructor, 4 the Mailchimp way (offline built model redeployed each quarter), 4 Tensorflow (logic to clients, handle models). 15
  16. 16. Do 4 deploy good enough fast, 4 copy the big bois (Kaggle), 4 feature engineer (domain expertise), 4 build core data/feature (augment and enhance). 16
  17. 17. Wrapup 17
  18. 18. 18
  19. 19. Want moar? Joel Spolsky: You Suck at Excel Dan McKinley: Data Driven Products Now! John Foreman: Data Smart 19
  20. 20. Thank you! We are hiring! @door2doorHQ @soobrosa visuals: ˙Cаvin 〄, thelearningcurvedotca, JD Hancock, Thomas Hawk, jonolist, Kalexanderson 20

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