Practical steps to establish and scale your data & ML team
summary
Builds a one-person data team into a scaling function by shipping an MVP data platform, proving value with an automated forecasting pipeline live by end of 2022, and prioritising two initial use cases rather than a huge roadmap. Uses a bought stack (mentions data bricks and a BI add‑on), hires two analysts then a product manager, reduces hands‑on time to ~10–20%, applies Team Topologies to split stream teams and avoid cognitive overload as demand and budget double.
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chapters (10)
- 0:00Scaling data and ML teams
- 2:30First year challenges
- 7:09Building data platform
- 11:41Bottom-up to top-down demand
- 13:56Stakeholder relationships
- 16:14Growing and delegating
- 19:21Managing team growth
- 22:01Scaling lessons
- 25:00Self-service analytics
- 27:12Final thoughts
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