Data Products and AI: A Conversation on Success Factors and Hurdles
summary
Panelists define a data product as anything from a table or API to an ML model but say success hinges on life‑cycle management and data quality, not novelty. One automaker recounts a platform built since 2015 with automated connectors and a match‑fund (50% centrally funded) incentive to prioritize use cases; a public data lab stresses it cannot operate product maintenance. Governance needs KPIs, catalogs and cost allocation (popular assets drive cloud bills), and an MIT study warning of 95% GenAI project failure tempers agent hype.
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chapters (9)
- 0:00Introduction
- 1:03Defining data products
- 6:19Data availability challenges
- 13:24Building data product teams
- 14:49Prioritizing use cases
- 20:47Post-maturity challenges
- 25:49Governance versus innovation
- 29:43AI and agents in data products
- 35:46Future trends
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