Governance programs were built to describe policy, and the catalog did that job. Then two new consumers arrived. Auditors started asking for evidence that a definition was applied, and AI agents started reading definitions at machine speed with no analyst in between.

Both need the same thing: proof that the glossary definition and the number in the report are the same definition. In most estates, three gaps stand in the way:

• Drift. The glossary says what revenue means; the Power BI measure computes something else, and no mechanism detects the divergence.
• Coverage. Catalog population depends on steward attendance, so coverage stalls and descriptions go stale.
• Reconstruction. Every audit cycle rebuilds the history of a definition from tickets, spreadsheets, and memory.

What changes with MetaKarta

Policy applied where the data is used, versioned where it changed. MetaKarta compiles governed definitions into the native artifacts of the systems that consume them: your warehouse, your BI tools, and your AI agents. The definition the steward approved is the definition the dashboard runs.

Coverage computed from the estate. 400+ native connectors harvest metadata directly from databases, ETL tools, and BI platforms, and parser-based lineage connects each asset from source to report. Stewards spend their time on the judgment calls only they can make: ownership, meaning, and approval.

The audit answer is a query. The full metadata estate sits under version and configuration management. Ask what a metric meant on any date, who changed it, and why, and the answer comes back with its lineage attached.

AI consumes the same approved definitions. MetaKarta MCP serves governed metadata and compiled context to AI agents, with per-user access tokens so an agent sees exactly what the person behind it is authorized to see. Every response returns the metadata evidence that produced it.

How it works

MetaKarta runs on one shared metadata repository, so lineage, catalog, governance, and semantics describe the same estate.

• Parser-based lineage: Column-level lineage computed from SQL, ETL mappings, and BI models, including jobs that haven't run this quarter.
• Catalog on live metadata: Descriptions and ownership tied to assets harvested from the estate, so the catalog stays current as the estate changes.
• Governance and version history: Every definition change logged, attributed, and reversible.
• Ontology Modeling: Business vocabulary live-linked to the metric that implements it, and reconciled against what BI actually computes.
• Semantic Hub: Your governed definition compiled into Power BI, Tableau, Snowflake Semantic Views, and Databricks Metric Views in one pass, so every consuming system runs the approved logic.

See it on your own estate

Three checks make a useful evaluation of any metadata platform, including ours:

1. The drift check. Take one glossary term and one BI measure that should match. Ask the vendor to show whether they do, and to compile the approved definition into the BI tool.
2. The dormant-job check. Ask for lineage on a job that hasn't run this quarter. Lineage computed from code answers it. Lineage observed from query logs covers only the jobs that ran.
3. The point-in-time check. Ask what a metric meant at last quarter-end and who has changed it since.

Built on nearly 30 years of metadata engineering

MetaKarta's metadata technology runs inside the products of Microsoft Purview, Informatica from Salesforce, IBM, Oracle, and Qlik Talend. MetaKarta brings that engine to your governance program directly, with definitions that stay portable as native artifacts in the tools you already own.

When the definition the steward approved is the definition the dashboard runs, the measures that move are drift incidents caught before deployment, enforced-definition coverage, and audit response time.

Get in touch to learn more about how MetaKarta compiles a governed glossary definition into your BI tools and warehouse and delivers the same as compiled context to AI agents.

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