Building successful data teams in the realities of 2023
summary
Data teams shrink while expectations rise: budgets tighten, Snowflake bills shock finance, and 85% of data projects fail (Gartner), so prioritize work that shows clear ROI and track projects like 12–18 month deliverables. Start by inventorying shadow IT and data flows, set a narrow mission, distinguish ad hoc vs production grade pipelines, retrain legacy DBAs to become generalists, and choose central vs decentralized structures based on latency, cost and domain knowledge. Negotiate a seat at the table and reporting plus operational headcount to actually change outcomes.
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chapters (19)
- 0:00Building data teams intro
- 1:11Data teams as people problem
- 2:34Defining data teams 2023
- 5:23Shrinking teams market trends
- 7:10ROI scrutiny tech modernization
- 8:11LLMs and data governance
- 8:52Why data projects fail
- 10:59Improving data teams steps
- 12:04Friction and mission clarity
- 14:11Generalists over specialists
- 15:16Rethinking self-service analytics
- 16:19Collapsing data roles headcount
- 17:22Chief Data Officer role
- 19:55Central vs decentralized teams
- 22:44Domain knowledge vs efficiency
- 25:12Tech stack and org fit
- 26:15Justifying data projects
- 27:39Project management in teams
- 28:22Q&A
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