A Practical Approach To Unlock Value From Data and Analytics
summary
Claims concrete numbers and fixes: only 5% of data is actually used and 97% of data engineers report burnout, so the focus is on measurable change not hype. Recommends metrics for data model consistency, privacy, accessibility, pipeline health and technical debt, tooling like CI/CD and data quality checks, and tracks time spent troubleshooting versus building. Describes two outcomes: a fragmented org where less than 1% can query data versus a self‑service org with roughly 80% of needed data available and staff able to run SQL.
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chapters (6)
- 0:00Introduction
- 4:24Fragmented vs consolidated data
- 8:47Speed vs quality in data products
- 13:12Key metrics for data engineering
- 18:38Building data-driven culture
- 22:38Q&A
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