Data Quality Manager
Defines, monitors, and remediates data quality issues; maintains quality, freshness, observability and completeness of enterprise data.
| Synonyms of Data_Quality_Manager |
|---|
| GxP Quality Lead |
| Validation Lead |
| FAIR persona related to Data_Quality_Manager |
|---|
| Subject_Matter_Expert |
| Ontologist |
| Data_Standards_and_Governance_Expert |
| Data_Owner |
| Curator |
| Master_Data_Manager |
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.
Upside
FAIR data would make it easier to spot data quality issues such as missing or mis-labelled metadata.
Downside
Managing missing data and metadata and identifying errors, esp when sources are siloed.
Data maybe FAIR but if it's of low quality it can be useless.
Fair
F1 provides persistent identifiers that ensure data quality issues can be traced back to specific sources and corrected.
F2 enriches metadata so quality dimensions (freshness, observability and completeness) can be assessed and monitored.
A1 guarantees that quality-controlled datasets remain accessible to the right users without delays.
I1 enables interoperability so quality improvements in one domain flow consistently into others; enables automated data quality protocols to assess freshness, observability and completeness measures.
R1.1 ensures reproducibility of quality checks and audits, demonstrating compliance and improvement over time.