Tomas Tucek | Data Track | Compass AI & Data Summit 2025
summary
Argues data quality is an organization-wide change program, not a data-team side project, and needs executive backing, budget and clear response rules. Shows quick wins: a retailer finds 60,000 customers missed weekly emails (~$3 lost per email) and turns that into funding; one team fixes 50 failing tests in 24 hours once leadership demands action. Recommends five-stage TDQ approach (define, measure, report, deliver, manage defects), use junior staff or Excel to start, iterate.
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chapters (12)
- 0:00Data quality in AI era
- 3:18Poor data quality problem
- 5:25Measuring data quality isn't enough
- 7:52Leadership support for data quality
- 11:01Data quality as change management
- 13:29Bridging data teams and business
- 16:19Soft skills in data quality
- 18:02Communicating data quality
- 20:56Iterative data quality improvement
- 22:23Begin data quality management
- 29:49Call to action
- 31:14Q&A
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