Schema-level data quality monitoring, table comparison, dataset popularity analysis, and ad-hoc column quality assessment using Snowflake Data Metric Functions (DMFs) and Access History. Use when user asks about: data quality, schema health, DMF results, quality score, trust my data, quality regression, quality trends, SLA alerting, data metric functions, failing metrics, quality issues, compare tables, data diff, validate migration, table comparison, popular tables, most used tables, unused data, dataset usage, table popularity, listing quality, listing health, listing freshness, provider data quality, consumer data quality, one-time quality check, quick quality scan, check data quality without DMFs, recommend monitors, what should I monitor, DQ coverage gaps, unmonitored tables, DMF coverage report, monitoring health, noisy monitors, silent monitors, misconfigured monitors, DMF cost optimization, investigate DQ incident, why did freshness drop, why did row count drop, correlate violation, multi-dimensional root cause, circuit breaker, pause pipeline on violation, halt bad data propagation, custom DMF, format validation DMF, email format check, value range check, referential integrity DMF, DMF expectations, set threshold, tune DMF threshold, DMF expectation management, attach DMFs, set up DMFs for first time, DMF setup wizard, accepted values, ACCEPTED_VALUES, validate column values, allowed values check, value in set, categorical validation.