Microsoft Purview Data Quality: Profiling, Rules, and Quality Scoring at Scale

A data team builds data quality monitoring. They profile key datasets, define quality rules, and create dashboards showing quality scores. The dashboards show that customer address completeness is 72%, email validity is 68%, and product categorization accuracy is 81%. The reports get published monthly. Nobody acts on them. The data team can't explain why quality scores matter to the business, the business teams don't know which scores affect their work, and there's no workflow connecting a declining quality score to a specific remediation action by a specific person. The quality program produces visibility without accountability — and quality scores that don't trigger action don't improve quality.

Data quality that improves data connects scoring to accountability and remediation. Quality rules that encode the specific business definition of 'quality' for each critical data asset — not generic completeness and validity checks, but the rules that matter to the business processes consuming the data. Quality scores assigned to data stewards who are accountable for the assets they own. Thresholds that trigger remediation workflows when quality degrades below the level downstream processes require. Integration with Unified Catalog so quality scores are visible alongside data discovery — analysts see quality before they use data. Integration with Fabric pipelines so quality gates prevent bad data from flowing downstream. Done this way, quality becomes a governed capability that sustains. Done as dashboards without accountability, it's a project that ends when the dashboard launches.