Bridging Business and Tech for Data Science Success
summary
Claims that 87% of data science projects fail and pins most failures on weak cross-team communication rather than algorithms. Recommends concrete fixes: run short iterative cycles with continuous feedback, use explicit business metrics (measure business impact over accuracy), hold regular reviews and retrospectives and focus meetings, and map blockers to actionable asks (data access, timelines). Builds resilience by combining hard skills (ML, engineering, software, PM, domain) with soft skills (empathy, translation, prioritization) so projects deliver measurable production value.
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chapters (14)
- 0:00Introduction
- 1:51Why data science projects fail
- 3:46Failed data science projects
- 5:15Business versus technical teams
- 7:27Project managers' role
- 8:55Shared goals and politics
- 10:19What business should communicate
- 13:15What technical teams focus on
- 15:30Measuring progress challenges
- 16:51Agile workflows for data science
- 17:58Managing expectations and tasks
- 19:01Hard and soft skills for success
- 20:29Empathy in collaboration
- 21:12Conclusion and key takeaways
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