Building The Data Dream Team
summary
Panel argues for domain-aligned, cross-functional teams that own and ship data products using a central self-serve platform and governance as policies as code. Real examples: a cloud migration paired with data mesh that produced 300 data products in three years but fired two executives because management measured cloud hydration instead of business value. Practical tooling cited includes AWS Lake Formation and cross-account sharing. Recommendation: embed data skills in product teams, run a platform COE, and measure use-case value not bytes moved.
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chapters (16)
- 0:00Introduction and welcome
- 1:28Team structure in data teams
- 6:02Fundamental roles in data teams
- 8:08Socio-technical aspects of data mesh
- 11:39Should data teams sit in IT?
- 17:23Becoming a mature data organization
- 19:51Data mesh principles and vendor challenges
- 23:27Gartner's hype cycle on data mesh
- 25:55DataOps and its role with data mesh
- 30:09Advice for struggling data mesh implementations
- 35:06Biggest challenges in data engineering
- 40:30New leadership transitions in data teams
- 44:44Pitching data teams to skeptical boards
- 46:54Retaining talent in the great resignation
- 50:03Importance of diversity in teams
- 53:40Legacy and future aspirations
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