assess-quality

To identify data quality gaps, a read-only assessment loads domain semantics, tables, and column metadata to determine monitored and missing dimensions. It profiles data in parallel to validate gaps and outputs a prioritized action plan without modifying existing rules.

A data quality coverage assessment is needed before creating new quality rules, generating formal quality reports, or planning governance improvements. It reveals which dimensions are currently monitored and which columns are priority candidates for new rules.

The best way to analyze missing data quality dimensions is using a semantics-first gap analysis approach. This method loads a domain-specific dimension catalogue, validates gaps with parallel EDA profiling, and identifies priority columns for new rule creation.

Yes, you can assess data quality coverage for a single column, a specific table, or a full domain. The assessment scope adjusts accordingly, loading the relevant domain-specific dimension catalogue and profiling data in parallel to report monitored and missing dimensions.

No, the data quality gap analysis is strictly read-only and does not create or modify rules itself. It provides a prioritized, ready-to-action plan with optional follow-on steps like rule creation and formal reports for future implementation.

To find priority columns for new data quality rules, perform a coverage assessment that loads column metadata and profiles data in parallel. The output identifies which columns are missing monitored dimensions and prioritizes them for new rule creation.