Ask Measure Learn
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Lutz Finger emphasizes the importance of asking the right questions and measuring the right data in his work on data analytics. He asserts that more data does not necessarily lead to more insights, and highlights the significance of actionable insights through benchmarking, predictions, and recommendations. The document ultimately advocates for a focus on relevant data rather than the volume of data in order to derive meaningful conclusions and actions.
Ask Measure Learn
- 1. Lutz Finger Ask Measure Learn Last update: March 3, 2014
- 2. The philosophy of the day is data-ism. —DAVID BROOKS (@NYTDAVIDBROOKS)
- 3. Who • • • • am I Lutz Finger Director Data Analytics at LinkedIn Author of Ask Measure Learn Co-Founder Fisheye Analytics (sold to WPP) @LutzFinger LutzFinger.com linkedin.com/in/lutzfinger ?
- 6. Who are you?
- 7. Mario Vinasco Earliest Adopter Rahul Todkar Most Popular Bruno Aziza Most Connected Ron Goncalves, MBA, PMP, Most Endorsed
- 13. Intellectually enriching – at most
- 14. We focus too much on technology Google Search on the term “Big Data” ENGINEERS DATA SCIENTISTS
- 15. ASK the right Questions. MEASURE the right data – even if it is not Big data. Take Actions and from them. LEARN ?
- 18. ASK Let’s do “Social Media”
- 19. ASK Source: IBM Institute for Business Value.
- 20. • Opinion leaders (Katz 1955) • Influentials (Merton 1968) • Law of the Few (Gladwell 2000) Source: ‘Ask Measure Learn’ by O’Reilly Media ASK Please find an “INFLUENCER”
- 21. Source: ‘Ask Measure Learn’ by O’Reilly Media ASK A few person decide what we do…
- 22. Source: ‘Ask Measure Learn’ by O’Reilly Media ASK REALLY?
- 23. ASK Dear Marketers, There is no influencer. It’s a myth.
- 24. ASK Reality 50% is Homophily • • • • 4 years 1001 Students on Facebook traditional Self-reported Data How did taste Spread Source: Kevin Lewisa, Marco Gonzaleza and Jason Kaufman (2012): PNAS Vol 109, no 1 Influence is often overestimated. It needs: § Reach § Readiness § Topic Dependence
- 25. ASK Aja Dior M.? AP News?
- 26. ASK Aja Dior M.? AP News? Aja Dior M. omgg, my aunt tiffany who work for whitney houston just found whitney houston dead in the tub. such ashamed & sad :( 45 min
- 27. ASK I want to create “REACH” … in order to “SELL”
- 29. • Click Rates – Media • Low CPO advertisers • Etc.. MEASURE The world is full of WRONG metrics
- 30. Measure MEASURE What is RIGHT? Source: WIkipedia 30
- 31. Big Data MEASURE even if it is not
- 32. Social Data Behavioral Data Identity Data Data Volume MEASURE Analytical Challenge & Complexity 277+ Million Members = a lot of Data
- 33. MEASURE More Data does not equal More Insights
- 34. MEASURE We want small data…. Data ELT and Aggregation Web API Reach Analytics Portal or No Reach 1 Petabyte 2 Terabyte 3 Gigabyte 4 Bit
- 35. MEASURE Calculating Reach via Network Data 1977: Linton C. Freeman, “Centrality based on Betweenness .”
- 36. “SELL” needs purchase intend MEASURE “REACH” creates awareness
- 38. Source: ‘Ask Measure Learn’ by O’Reilly Media MEASURE What is your behavior?
- 39. MEASURE The issue with the Correlation / Causation
- 40. MEASURE Sometimes data is not easy to get.
- 41. Source: ‘Ask Measure Learn’ by O’Reilly Media MEASURE Social Behavior is ‘unstructured’
- 42. New York Weather in April 2013 Source: ‘Ask Measure Learn’ by O’Reilly Media MEASURE It is way easier to work with ‘structured data’
- 44. MEASURE What is RIGHT? Source: ‘Ask Measure Learn’ by O’Reilly Media 44
- 45. LEARN Right Actions and learn from them.
- 46. 2 Connections 4% 4 Connections 3% 1 Connection 28% 0 Connections 59% LEARN Information vs. Action…
- 48. LEARN Three Types of actionable Insights Benchmark Predictions Recommendations & Filter
- 49. LEARN BENCHMARK
- 50. LEARN Competitive Benchmark Source: ‘Ask Measure Learn’ by O’Reilly Media 50
- 52. People You May Know Groups You May Like Ads You Be Interested in Companies you May Want to Follow Similar Profiles Puls LEARN LinkedIn Recommendation Products
- 53. More than 50% of connections LEARN Recommendations Impact More than 50% of group joins More than 50% job applications
- 54. Source: ‘Ask Measure Learn’ by O’Reilly Media LEARN Filter
- 56. LEARN Predicting the OSCAR 2013
- 58. Possible other Features: CROWDSOURCED: • Box Office Results • Movie Goer Reviews • Critics OTHER “Hard Facts” • Genre • Payment of Actors • Etc. Source: Farsite LEARN Predicting the OSCAR
- 59. Source: ‘Ask Measure Learn’ by O’Reilly Media LEARN Predicting Box Office
- 60. LEARN Predictions are not easy, especially if they are about the Future
- 61. Behavioral Score = 0.5 Identity Score = 0.3 Emily Score = 0.9 Social Score = 0.1 Behavioral Score = 0.2 Identity Score = 0.3 Steve Score = 0.9 ... With premium account, you get more search results and access to … Social Score = 0.4 ... Do you know 5 of you connections have started to use premium account … LEARN Propensity to Email Marketing
- 62. Source: ‘Ask Measure Learn’ by O’Reilly Media LEARN Which Model to use
- 63. Google’s Prediction API LEARN Which Model to use
- 64. Benchmark Recommendation & Filter Predict LEARN Many more examples…
- 65. LEARN Data Wrangling & Data Science is getting Easier
- 67. Summary
- 68. ASK the right Questions. Forget Big Data but MEASURE the right data. Take Actions and from them. LEARN ?
- 69. We are hiring!