Turning thousands of undocumented datasets into a usable enterprise data catalog
A structured metadata enrichment solution for organizations with large and growing data estates. AI was used to analyze technical metadata, generate dataset descriptions, suggest business definitions, classify fields and explain lineage, while data owners and governance teams retained control over final approvals.
The data existed, but people could not reliably understand or find it.
As enterprise data platforms grow, technical metadata grows faster than teams can document it. Dataset names, column names and system information may exist, but the business meaning, ownership and context often remain incomplete.
What we found
- Large numbers of tables and columns had limited business descriptions.
- Different teams used different naming conventions and definitions for similar data.
- Data owners spent significant time answering basic questions about datasets.
- Catalog teams depended on manual collection and maintenance of metadata.
- Business users often searched by technical names instead of business concepts.
- Lineage existed in technical systems but was difficult for business users to interpret.
- Metadata became outdated as pipelines, schemas and ownership changed.
What the business needed
- A repeatable way to enrich metadata across a large data estate.
- Descriptions written in business language rather than only technical terminology.
- Suggested classifications, tags and definitions for datasets and fields.
- Better explanations of upstream and downstream data relationships.
- Confidence indicators and review workflows for AI generated suggestions.
- A clear ownership model so data stewards remained accountable for published metadata.
- A scalable process that could run as part of normal data platform operations.
We created an AI assisted metadata factory that enriches the catalog while keeping data owners in control.
The solution combined technical metadata, existing documentation, schema information and business context to produce structured metadata suggestions for review.
AI assisted catalog enrichment
The metadata factory automated the repetitive work involved in creating and maintaining data descriptions and classifications.
- Scanned approved technical metadata from Databricks, Snowflake, Fabric and connected data sources.
- Analyzed table names, column names, data types, sample metadata and existing documentation.
- Generated plain language dataset and column descriptions using available enterprise context.
- Suggested business terms, tags, classifications and relationships between related datasets.
- Identified potential sensitive fields for governance review rather than publishing classifications automatically.
- Generated explanations of lineage and dependencies from available technical metadata.
- Assigned confidence levels so low confidence suggestions could receive additional review.
- Published approved metadata back into the enterprise catalog with ownership and version history.
The enrichment layer works across the modern enterprise data estate.
The AI layer works with metadata rather than directly changing production data. Governance controls determine what information can be used and what suggestions can be published.
A six stage operating model for continuous metadata enrichment
The process is designed to start with a controlled data domain and scale across the enterprise as quality and governance controls mature.
Discover
Inventory data assets, technical metadata, owners, existing descriptions and lineage.
Profile
Assess metadata completeness, naming quality, business context and current catalog coverage.
Enrich
Generate descriptions, definitions, tags, classifications and relationship suggestions.
Validate
Apply confidence thresholds, business rules and data steward review before publication.
Publish
Write approved metadata to the catalog with ownership, lineage and version information.
Refresh
Reprocess changed schemas and datasets so the catalog remains aligned with the data estate.
The impact comes from reducing repetitive catalog work and improving how quickly users understand enterprise data.
Lower cataloguing effort
AI can automate much of the first draft work for descriptions, tags and metadata enrichment.
Faster enrichment cycles
Large metadata backlogs can be processed in repeatable batches instead of documented field by field.
Faster data discovery
Business language descriptions and searchable context help users identify relevant datasets faster.
Fewer routine data questions
Better metadata reduces repeated requests to data owners for basic dataset and field explanations.
Faster steward review
Data stewards review structured AI suggestions instead of creating every description from scratch.
Governed publishing target
AI suggestions can be routed through defined ownership and approval controls before becoming official metadata.
Better metadata makes the entire data estate easier to use, govern and scale.
The solution moves cataloguing from a manual documentation exercise toward an ongoing operating capability connected to the data platform.
Faster Data Discovery
Users can search using business terms and understand the purpose of datasets without relying on technical owners for every question.
Lower Stewardship Effort
Data stewards spend more time validating important definitions and less time writing repetitive first drafts.
Stronger Governance
Ownership, classification, lineage and approval workflows provide a more consistent foundation for enterprise data governance.
AI Ready Data Estate
Structured metadata and business context make enterprise data easier for analytics and AI applications to discover and use correctly.
Make your enterprise data easier to understand and use.
Combine metadata automation, AI assisted enrichment and human governance to turn a fragmented data catalog into a practical enterprise data discovery layer.
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