How Can We Apply Agile Principles and Practices in Analytics?
summary
Claims that matter: analytics moves faster when analysts joining engineering scrums and when one engineer is dedicated data engineer for data science so the data scientist keeps focus. A Target data warehouse is built iteratively from proof‑of‑concept logic into production code with analysts drafting POCs in the same tools engineers use. Teams pilot Scrum and Kanban — Scrum helps new teams, Kanban suits maintenance — and frequent sprint demos plus quick feedback loops reveal which ideas actually deliver value (one‑in‑ten success rate cited).
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chapters (11)
- 0:00Introduction
- 2:15What is agile and why it matters
- 5:26Defining value in analytics
- 7:55Bridging business and analytics
- 10:27Cross-functional teams in analytics
- 15:06Collaboration models in analytics
- 23:19Agile planning for analytics
- 27:17Measuring efficiency with feedback
- 31:53Key takeaways
- 35:09Q&A on remote agile teams
- 39:08Q&A on collaboration
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