Govern & improve
How do I improve and govern the data?
Data stewards and data governance managers.
Once data is mastered, this is how you keep it good: clean projects fix recurring quality problems, rules apply logic automatically, enrichers fill gaps from external sources, tags and the glossary classify records, and workflows put approvals around changes.
In this section
- Work with the glossary how-to
- CleaningsectionA clean project takes a set of golden records, shows you what is wrong with them, and lets you fix it in bulk.
- EnrichmentsectionAn enricher fills gaps in your data from an external source - a company register, a mapping service, a search index.
- RulessectionRules apply logic to your data automatically: change a value, add a tag, block a change.
- TagssectionTags are labels you attach to golden records and data parts.
- AI agentssectionAI agents do stewardship work for you - proposing fixes for quality problems, suggesting duplicates to merge, drafting rules.
- WorkflowssectionA workflow puts an approval step in front of a change, so that edits to golden records are reviewed before they take effect.
- Set retention policies on data how-to
Table of contents
- Work with the glossary
- Cleaning - A clean project takes a set of golden records, shows you what is wrong with them, and lets you fix it in bulk.
- Enrichment - An enricher fills gaps in your data from an external source - a company register, a mapping service, a search index.
- Rules - Rules apply logic to your data automatically: change a value, add a tag, block a change.
- Tags - Tags are labels you attach to golden records and data parts.
- AI agents - AI agents do stewardship work for you - proposing fixes for quality problems, suggesting duplicates to merge, drafting rules.
- Workflows - A workflow puts an approval step in front of a change, so that edits to golden records are reviewed before they take effect.
- Set retention policies on data