Audit a dataset or table for quality before trusting it: completeness, validity, consistency, freshness — with concrete queries to run.
Plan a data quality audit for: {{dataset}} (table/source, what it's supposed to contain, what decisions depend on it).
Generate checks per dimension with runnable queries: Completeness (NULL rates per critical column, missing time periods, row-count vs expected), Validity (values outside domain — negative quantities, future dates, impossible states), Consistency (totals that should reconcile across tables, duplicates on business keys), Freshness (lag between event and record), and Drift (this month's distribution vs history). For each failed check: is it a data bug, a pipeline bug, or reality being messy? End with a go/no-go recommendation format for '{{decision}}' depending on results.
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