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Building a Just in Time Data Warehouse by Dan Morris and Jason Pohl

  1. 1. Building a Just-In-Time Data Warehouse Dan Morris (Viacom) JasonPohl (Databricks) February 18, 2016
  2. 2. About Viacom • LeadingGlobalEntertainmentContentCompany • 23 Brandsin 170+ Countries 2
  3. 3. Introductions Dan Morris • SeniorDirectorof ProductAnalytics • 12 Years @Viacomin a varietyof roles • Intersectionof ProductandData 3
  4. 4. About My Team • ProductAnalytics team formedone year ago • Ourmissionis to grow our globalaudiencewiththe highestquality userspossible 4
  5. 5. Key Areas of Focus • Mobilizeeffortsusing growthtargets • Uncoverdeepinsightsusing churnand cohort analysis • Treat all ideas as hypothesesand test them rigorously 5
  6. 6. Where Are We Today: App Platform 6 Make it extremelysimpleto build and deploy engaging apps around the globe FEATURES UI & ANIMATIONS CONFIGURATION SETTINGS APP BINARY
  7. 7. Disciplined Product Dev Approach is Key 7 • 23 brandsin 170+ countries • Lots of marketdynamics • Many stakeholders ... Data is a must!
  8. 8. Sound Data Management is Required 8 9 11 13 14 16 18 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Expected Data Volume Growth (TB) 2016
  9. 9. Our Data Infrastructure 9 S3 Spark + Databricks Redshift Tableau
  10. 10. Introducing the TV Land iOS App 10
  11. 11. Applying Product Analytics to TV Land 11 • Growth Targets • Dashboards • DeepDive Analyses • A/B Testing
  12. 12. Baselines Used to Set Growth Targets 12 Business ModelingETL Data Volume • 30 sites/apps • 11 TB Data Volume • 30 sites/apps • 1 TB S3 Spark + Databricks Redshift Tableau
  13. 13. Growth Targets are Monitored via Dashboards 13 1/4/16 1/11/16 1/18/16 1/25/16 New Users Returning Users Weekly Retention by Cohort 0 1 2 3 4 1/4/16 100% 53% 41% 33% 30% 1/11/16 100% 58% 51% 42% 1/18/16 100% 49% 38% 1/25/16 100% 49% Audience Growth by Cohort
  14. 14. Dashboards Spark Deep Dive Analyses 14 0% 25% 50% 75% 100% 0 1 2 3 4 5 6 7 8 9 10 11 12 Users will quickly churn if not activated within small window of time. %ofusersretained
  15. 15. Deep Dive Analysis Requires Flexibility 15 Not NeededDeep Dive Analysis S3 Spark + Databricks • Define schema on read instead of write • Work through data quality issues just-in-time. • Tease out business question iterative and interactively. • Use programming language of your choice. Redshift Tableau
  16. 16. Hypotheses Require A/B Testing 16 Statistical Analysis Data Sets • Adobe Logs • Experiment Logs S3 Spark + Databricks Tableau Not Needed Redshift Tableau
  17. 17. Summary of Our Setup 17 Just in Time Traditional Primary Audience • Product Analysts • Product Team • Business Stakeholders Tasks • Exploratory Analysis • A/B Testing • Ad Hoc Queries • Dashboards Tools • S3 • Spark • Databricks • Redshift • Tableau
  18. 18. Coming Soon… 18 • Go live with internalA/B testing platform • Continueto evolve our setup • Further scale model to support Product AnalyticsPan- Viacom
  19. 19. Questions ? 19
  20. 20. Thank you. Otherpartingwordsorcontactinformationgohere.

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