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The Evolution of BI in Early Stage Startups

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This is a talk about how BI develops within a startup, from simple excel files to hard core data science. The talk proposes 4 main stages that BI teams go through as a company grows.

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The Evolution of BI in Early Stage Startups

  1. 1. The Evolution of BI in Early Stage Startups Presented at to Oct 7, 2015 Montreal
  2. 2. Personal Intro Liberal Arts Product Manager + BI Dabbler =
  3. 3. What is this talk about? ● Business Intelligence (BI) from a product perspective - How does BI build tech that people use? ● Organizational dynamics - How does BI grow within a company and how does it relate to other teams?
  4. 4. What is this talk based on? ● Using BI at Yahoo and Pollenizer ● Running BI at F&O ● Running BI at Flatbook ● BI porn addiction
  5. 5. Part 1 - So WTF is BI?
  6. 6. This is BI https://eng.asana.com/2014/11/stable-accessible-data-infrastructure-startup/
  7. 7. This is also BI
  8. 8. A: BI is stage and people dependant
  9. 9. Part 2 - The 4 Stages of BI
  10. 10. Caveats ● Stages can overlap ● Stages can be skipped
  11. 11. Stage 1 - Analysis
  12. 12. Stage 1 - Analysis ● BI = Analysis ● Data availability not a major focus ● Key team member = someone who is good at excel (generally founder, then analyst) ● KPI = do analysis that helps business
  13. 13. Stage 2 - Dashboards
  14. 14. Stage 2 - Dashboards ● BI = Data Availability + Visualizations ● BI now uses tech resources ● Key team member = ○ product oriented data engineer -or- ○ product oriented analyst w/ dev support ● KPI = dashboard usage ● Analysis can stay in BI or move to biz units (product, marketing etc)
  15. 15. Stage 3 - Self Serve ETC...
  16. 16. Stage 3 - Self Serve ● BI = Self Serve Tools + Data Warehouse ● High $$$ for tools and plumbing. . . ● But, this is the only model that scales ● Key team member = BI focused engineers and product people ● KPI = tool usage + reduced analyst load ● Analysis should move to biz units
  17. 17. Stage 4 - Data Science
  18. 18. Stage 4 - Data Science ● BI = Data Science (Predictive Analytics) ● Some analysis is too hard for analysts, it needs devs and math people ● Not for everyone, but high ROI for some ● Key team member = data scientists ● KPI = direct ROI to the business
  19. 19. Part 3 - So What?
  20. 20. So what? ● Know what stage you’re at ● Know what people you have (and want) ● Grind out BI value
  21. 21. isouweine@gmail.com | @sonofsarah

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