Data Poker not Data Chess - thinking in Bets for Data Teams
summary
Argues that strategic data choices are bets, not chess moves, and proposes Spotify’s DIBBs process (Data → Insight → Belief → Bet) as a practical method. Gives concrete examples: 150 hours/month lost to manual reporting leads to a three-week trial with three data engineers and two product analysts; trial cuts build time but reveals upstream quality issues, prompting a DBT uplift squad; warns about open‑source project decay and vendor feature gaps. Emphasizes calibrating bet size to uncertainty and separating skill vs luck.
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chapters (12)
- 0:00Intro
- 0:50Who are we
- 2:40The stakes
- 5:18Personas
- 6:52Probability
- 8:41Uncertainty
- 14:45Support Product Strategy
- 17:02Our Hypothesis
- 18:57Chess vs Poker
- 22:22Skill vs Luck
- 26:43Why should data teams think in bets
- 34:07Learning loops
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