Metadata Generation
Quick WinAutomatically generating and maintaining data documentation.
The Pain
Data documentation is incomplete or outdated. Manual documentation takes time staff don't have. Finding data requires institutional knowledge. New staff struggle to understand available data.
What's Possible
AI can generate metadata descriptions and keep documentation current. Data becomes discoverable without relying on memory. Documentation stays relevant through automation.
Signals This Is Worth Exploring
Data documentation is incomplete or outdated
Finding data requires asking around
Documentation effort competes with other priorities
New staff struggle to find data
Impact
More complete data documentation
Easier data discovery
Documentation that stays current
Faster onboarding for new staff
Typical Approach
Assess
Inventory undocumented data assets. Define metadata standards.
Pilot
Generate metadata for selected datasets.
Scale
Extend to comprehensive metadata coverage.
What to Watch Out For
Generated descriptions need review
Some metadata requires human input
Documentation systems need integration
Standards may vary across teams
Questions to Think About
Before we talk, you might want to consider:
What data documentation do you have today?
How do staff find data they need?
What metadata would be most valuable?
What data catalogue systems do you use?
Build On This
Once the basics are working, you can expand:
Lineage tracking
Document where data comes from and goes
Semantic mapping
Link related data across systems
Usage tracking
Show which data is actually used
Want to explore if this fits your organization?
Book a 30-minute call to discuss your situation and whether this use case makes sense for you.
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