Data-Driven Transformation: Organizational Changes and Leadership Insights
summary
Panel reports concrete wins and failures: a city platform ingests 10 million camera events/day and 2 million IoT events to power transport models that reuse Google Maps crowdsourced timings; a simple worst‑case flood simulation uses height maps rather than a full PhD model and proves useful; streaming stacks center on managed Flink and Flink ML; product teams ship a Nike‑style “Answers” dashboard with 15‑minute fresh sales/inventory; common shocks are projects killed by politics, paywalls for extra data, security gaps, and the reality that ~70% of data work is cleaning.
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chapters (10)
- 0:00Introduction
- 1:54Top-down data decisions
- 7:09Data-driven failures
- 16:24AI adoption pitfalls
- 24:14Driving data adoption
- 31:59AI and data strategy
- 37:45Resolving data disagreements
- 41:37Product data challenges
- 44:29Breaking silos
- 45:53Q&A
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