Data Quality Management: The Good, The Bad, and The Messy
summary
Shows concrete failures like undelivered packages from wrong addresses and wrong salaries from bad timesheets then traces causes to pipeline bugs, missing validations and unknown data flows and claims five core quality dimensions and a rising “wheel of chaos.” Recommends a shift left approach, three golden rules — fix root cause, fix fast, clean historic data — and appointing a data quality manager who runs a prioritized backlog, monitoring dashboards and soda-style checks. Practical outcomes: quicker fixes when upstream issues are found but persistent cleanup and organizational buy‑in remain required.
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chapters (6)
- 0:00Introduction and speaker background
- 1:06The bad: Data quality issues
- 3:57The messy: Data lifecycle and causes
- 11:04The good: Shift left strategy
- 26:05Conclusion and next steps
- 27:35Q&A session
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