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Building a Data Driven Culture by Microsoft Sr Manager

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Product Management Event at #ProductCon Seattle on Building a Data Driven Culture by Ria Sankar, Senior Manager, Analytics for Ethics & Compliance at Microsoft.

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Building a Data Driven Culture by Microsoft Sr Manager

  1. 1. www.productschool.com Building a Data Driven Culture by Microsoft Sr Manager
  2. 2. Join 35,000+Product Managers on Free Resources Discover great job opportunities Job Portal prdct.school/PSJobPortalprdct.school/events-slack
  3. 3. C O U R S E S Product Management Learn the skills you need to land a Product Manager job
  4. 4. C O U R S E S Coding for Managers Build a website and gain the technical knowledge to lead software engineers
  5. 5. C O U R S E S Data Analytics for Managers Learn the skills to understand web analytics, SQL and machine learning concepts
  6. 6. C O U R S E S Learn how to acquire more users and convert them into clients Digital Marketing for Managers
  7. 7. C O U R S E S UX Design for Managers Gain a deeper understanding of your users and deliver an exceptional end- to-end experience
  8. 8. C O U R S E S For experienced Product Managers looking to gain strategic skills needed for top leadership roles Product Leadership
  9. 9. C O U R S E S Corporate Training Level up your team’s Product Management skills
  10. 10. Ria Sankar T O N I G H T ’ S S P E A K E R
  11. 11. Building a Data Driven Culture @SankarRia
  12. 12. First, 5 lessons learned along the way.
  13. 13. OXO Good Grips Deep customer empathy brings transformation
  14. 14. The Law of Unintended Consequences Instrument for leading indicators of failure
  15. 15. The Placebo effect The importance of hypothesis-driven testing Further Viewing: https://www.ted.com/talks/ben_goldacre_battling_bad_science?language=en#t-839994 Picture Source: https://upload.wikimedia.org/wikipedia/commons/5/55/VariousPills.jpg Source: Wikimedia Commons
  16. 16. Spurious Correlations Correlation != Causation Sources: http://bama.ua.edu https://www.tylervigen.com/spurious-correlations
  17. 17. Left-handed Bias Statistics can be sinister without reliable data Further Reading: https://marginalrevolution.com/marginalrevolution/2013/09/sinister-statistics-do-left-handed-people-die-young.html https://www.bbc.com/news/magazine-23988352 https://www.ncbi.nlm.nih.gov/pubmed/2006231 Source: http://en.wikipedia.org/wiki/Handedness If this were true, being left-handed ~= smoking 120 cigarettes a day
  18. 18. The 6 pillars of a data driven culture.
  19. 19. Focus on diversity of experience, skills & education Source: https://i.imgflip.com/1w3emg.jpg 1: Diverse Team
  20. 20. 2. Customer Empathy Understand your customer’s functional, social and emotional needs Social “Give volunteers a way to connect with like-minded friends” Emotional “Instill in veterans a new mission and sense of purpose” Functional “Find the best volunteers for disaster response” Source: TeamRubiconUSA.org
  21. 21. Understand their: 1. Circumstance 2. Desired Outcome 3. Level of Quality 4. Barriers Source: https://technofaq.org/wp-content/uploads/2016/09/happy-customer.jpg
  22. 22. • Time to appointment • Time to care • Cost of treatment • Effective treatment • Provider expertise • Complexity of care Case Study: Immediate Clinic From: Yelp.com WHEN my child is hurt, I NEED immediate care SO I CAN relax knowing that she is pain-free
  23. 23. 3: Business Consequences Source: hubspot.net Define your learning plan with a view of F.A.T.E. principles
  24. 24. Identify specific, measurable KPIs and benchmarks EffectiveSalesperson Belief Frequency Metric Result
  25. 25. Instrument for cause, effect, and response Cause EffectResponse
  26. 26. Qual & Quant effective in understanding new or sparse use cases Further reading: https://www.linkedin.com/pulse/user-research-turning-data-delightful-experiences-ria-sankar/
  27. 27. Customer Empathy High quality Data Business Problem Insight Establish a single source of truth and data dictionary 4: Data Literacy
  28. 28. Invest in data quality and tiered data access Data Ingestion Data Collection Data Storage Data Access Data Insights Further Reading: https://www.go- fair.org/fair-principles/
  29. 29. MAP CHART Graphical tools provide practical significance & isolate data issues
  30. 30. Law of the Vital Few 80% of effects come from 20% of causes Further Reading: The 7 basic quality tools: https://asq.org/quality-resources/seven-basic-quality-tools Source: https://en.wikipedia.org/wiki/Pareto_chart
  31. 31. 5: Hypotheses Testing Experiments provide an objective view of effect and significance
  32. 32. Belief Offline Test No Change Online Experiment No Change Significant Difference Ship! Significant Difference Offline metrics, Surveys, Sample data, Panel Studies Online Experimentation with AB or Multi-variate tests Lifecycle of a Belief Customer Interaction, Problem Definition, Data Collection
  33. 33. Experiment Design 101 Scientific vs. Design thinking
  34. 34. Hypothesis Testing Roadmap: 101 Source: https://sixsigmadsi.com/hypothesis-testing-cheat-sheet
  35. 35. Establish urgency with a powerful story Authenticity Insight Purpose 6: Storytelling
  36. 36. Source: https://en.wikipedia.org/wiki/Martin_Luther_King_Jr. Authenticity Purpose Insight
  37. 37. Thank you! @SankarRia
  38. 38. www.productschool.com Part-time Product Management, Coding, Data Analytics, Digital Marketing, UX Design, Product Leadership courses and Corporate Training

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