Managing Stakeholders: The Key to Successful Data Science for Business
summary
Claims that implementation is a people problem and not just technical get operationalized: use multi-level stakeholder analysis, prioritize the KPI owner as the critical entry point, and require at least bring a data sample before any on-site work. Practical techniques include in-person trust building, quick EDA, map business processes with Lucidchart, run a pre-mortem, avoid Scrum in favor of Kanban/Trello, and push back on scope creep by renegotiating priorities or requesting extra resources. The project uses an auto ml platform at the vendor.
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chapters (12)
- 0:00Stakeholder management intro
- 3:14Understanding stakeholders
- 7:07Stakeholder analysis
- 11:28Design thinking for stakeholders
- 13:58Choosing project entry
- 16:08Building trust with stakeholders
- 20:27Selecting impactful use case
- 24:24Pre-mortem for risk
- 25:06Clear communication
- 26:59Agile vs Scrum in DS
- 30:12Pushing back on scope
- 32:01Closing remarks & Q&A
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