Data quality management (DQM) tools ensure data’s quality and trustworthiness for use in reports, queries, analytics, and other applications. Modern DQM tools automate profiling, parsing, standardization, matching, merging, correcting, cleansing, and enhancing data for delivery into enterprise data warehouses and other downstream repositories. They enable creating and revising data quality rules. They support workflow-based monitoring and corrective actions, both automated and manual, in response to quality issues. Stewardship workflows enable nontechnical users to identify, quarantine, assign, escalate, resolve, and monitor data quality issues. They provide visualizations and other analytics that give stewards and other stakeholders insights into data quality and assist in correcting any issues discovered.