Data Quality Manager

Defines, monitors, and remediates data quality issues; maintains quality, freshness, observability and completeness of enterprise data.

Provides training and tooling that can assess data quality during its lifecycle; often in connection with enterprise catalogues, master data management systems and/or business applications.

Pains/Downside

Without FAIR practices, Data Quality Managers spend most of their time firefighting: missing values, mislabeled fields, and duplicate or conflicting records surface late — often only when a report breaks or an audit flags them. Siloed sources make it hard to trace an error back to its origin, so root-cause analysis and remediation are slow and manual. Quality rules and thresholds are inconsistently applied across catalogues, MDM systems, and business applications, making it difficult to demonstrate freshness, completeness, or observability at an enterprise level.

Gains/Upside

FAIR metadata gives Data Quality Managers a head start: persistent identifiers and rich metadata make missing, mislabeled, or duplicate data far easier to spot and trace to its source. Quality rules can be applied consistently across catalogues, MDM systems, and business applications, turning freshness, completeness, and observability into metrics that can be monitored and reported rather than manually chased.

FAIR doesn't guarantee correctness or accuracy, but it gives the Data Quality Manager the foundation to make quality measurable: persistent identifiers and rich metadata make it possible to trace issues to their source quickly, and standardized definitions of freshness, observability, and completeness let quality rules apply consistently across catalogues, MDM systems, and business applications.