Building Data Cultures
summary
Argues that analytics only matter when treated as a product: define success metrics up front, put tracking plans in a single source of truth (spreadsheets, Airtable, JSON/YAML on GitHub), and include developers, product and analysts in the release cycle so events stay consistent. Cites a mobile game with 100 million users and 1 million in five days, shows a signup A/B error that drops conversion from 81% to 46%, and describes building internal pipelines and AO to stop typo‑driven failures.
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chapters (13)
- 0:00Building data cultures
- 1:26Genetics to product analytics
- 3:15QuizUp growth lessons
- 6:05Defining retention challenge
- 8:34Bad data quality costs
- 10:18Typo’s massive data impact
- 14:30Broken data release cycle
- 19:25Teams’ data workflow mistakes
- 21:48Developers and analytics
- 23:59Tooling for data collaboration
- 26:50Analytics in product design
- 30:20Summary and call to action
- 30:50Q&A
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