Introduction
Data Quality Analysis identifies grain, keys, types, and table relationships before checking missing values, duplicates, formats, ranges, consistency, referential integrity, timeliness, volume, and distribution shifts. It connects defects to affected metrics and decisions, distinguishes blockers from tolerable gaps, and produces prioritized remediation plus repeatable validation rules.
Use Cases
Use it before analysis, during migration, for vendor deliveries, metric investigations, and recurring quality monitoring. It can detect text dates, duplicate keys, category drift, invalid amounts, broken joins, or sudden data loss, and assess whether a dataset can support the intended conclusion.
Template
Upload the files or provide schemas and samples. Define what each row represents, keys, required fields, valid ranges, relationships, refresh frequency, and intended use. Add known issues, critical metrics, tolerance thresholds, audience, and whether you need an issue log, remediation plan, and automated checks.
