Your Data Catalog is Only as Good as the Context Inside It

Most organizations struggle with curation: turning the metadata they already have into something people can trust.

Modern data catalogs can ingest metadata from thousands of tables, dashboards, reports, and data products. Ingestion gets the metadata into the catalog. Curation makes it useful. Someone still needs to add the business meaning, ownership, classifications, and governance that help people understand and confidently use that data.

That work often falls to data stewards.

They review assets, write descriptions, connect business terms, identify sensitive data, assign ownership, and make sure governance policies are applied consistently. Across thousands of assets, that workload quickly becomes difficult to manage.

The result is familiar: incomplete descriptions, inconsistent classifications, missing business context, and a catalog that users struggle to trust.

And now there’s even more at stake. That same governed context increasingly needs to serve AI assistants and agents, not just human data consumers.

The Stewardship Work That Slows Down Governance

Publishing a trusted data asset can involve multiple manual steps:

• Reviewing metadata: Understanding schemas, profiling results, lineage, and other available context.
• Writing descriptions and definitions: Translating technical metadata into language business users can understand.
• Connecting business context: Mapping assets to glossary terms, domains, owners, and related data.
• Applying governance: Identifying sensitive information, including PII, and making sure the appropriate governance controls are applied.

None of these tasks is unnecessary. The problem is that much of the work is repetitive and time consuming. The breakdown of that delay speaks for itself:

• Reviewing ingested metadata: 2 to 3 weeks spent reviewing uncurated tables and profiling output across thousands of assets.
• Drafting descriptions and terms: 1 to 2 months spent manually writing definitions and glossary terms.
• Mapping and domain alignment: About 1 week spent mapping assets to business domains.
• Governance and classification: 2 to 3 weeks spent assigning stewards and tagging PII.

Added together, a single batch of assets can take two to four months to move from ingested metadata to governed, trusted data, before anyone has even checked whether a definition or an owner assignment is right.

When stewards have hundreds or thousands of assets waiting for attention, governance becomes a queue. High-priority assets move forward while the long tail remains incomplete, outdated, or missing the context users need.

What if Stewards Didn’t Have to Start From a Blank Page?

The Actian Data Steward Agent gives data stewards a head start.

Once metadata is ingested, the agent analyzes the available context and generates recommendations for tasks such as drafting asset descriptions, connecting glossary terms, and identifying PII.

Data stewards can review those recommendations, refine them where needed, and approve them.

That changes the stewardship workflow from:

Research → Draft → Classify → Map → Review → Publish

to:

AI-generated recommendation → Steward review → Publish

The agent handles much of the repetitive preparation, while the steward focuses on reviewing recommendations and making governance decisions.

Human-in-the-Loop Governance Matters More in the Age of AI

AI can accelerate curation. Governance still depends on human judgment.

A description may be technically accurate while missing an important business nuance. A glossary term may appear relevant but be inappropriate for a particular domain. Sensitive-data classifications may depend on organizational context and policies.

The data steward provides that judgment.

The goal is to give stewards a better starting point: less time writing metadata, more time deciding on quality, policy, ownership, definitions, and appropriate use.

This becomes even more important as organizations adopt generative AI and AI agents. These systems depend on trusted definitions, relationships, policies, ownership, and business context to interpret enterprise data correctly.

Incomplete context can lead to technically plausible answers that lack the meaning and governance people need to act on them confidently.

From Governance Backlog to Governance at Scale

For governance leaders, the bigger opportunity is scale.

As organizations add data sources and assets, AI-assisted stewardship can help teams keep curation from becoming a growing bottleneck. Teams can work through governance backlogs faster, improve metadata consistency, and extend governance across more of the organization’s data.

For data stewards, that means spending less time filling in fields and more time applying the expertise that makes their role so valuable: understanding the business, resolving ambiguity, setting standards, and making governance decisions.

The future of data stewardship is governance that can finally keep pace with the data it’s responsible for.

See AI-assisted data stewardship in action and discover how the Actian Data Steward Agent helps accelerate curation, reduce manual work, and give stewards more time to focus on governance decisions. Start with a demo of the Actian Data Intelligence Platform.

About Author

About Alicia Felix

Alicia Felix is a Channel Sales Engineer at Actian with over eight years of experience in data management and data governance. She has worked with organizations across North America, Latin America, and Europe to transform raw data into well-governed, high-value assets for advanced analytics and AI.