Why Most Data Projects Fail & How to Avoid It
summary
Claim: 85% of data projects fail because teams lack the right people, ratios, timelines and concrete value, not because of a specific tool. Requires data scientists vs data engineers vs operations at balanced ratios, answers to who what when where how, a clear architecture choice (cloud vs on‑prem vs edge), deliverable tranches on a sane timeline, a singular focus of one to three things, and a business case targeting roughly 10x ROI so budget holders can justify ongoing spend.
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chapters (13)
- 0:00Intro
- 1:1385% of data projects fail
- 2:14The right questions
- 2:55The right people at the right ratios
- 8:21Data teams
- 10:28What is the business value?
- 11:40When will you generate value?
- 13:24Where will data be processed?
- 15:15How will the plan be executed?
- 16:47Why is data valuable?
- 18:38Not all gaps are technology
- 20:28Should you get help?
- 21:15Outro
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