What Should I Look For in an Enterprise Data Catalog?

Key Takeaways

• Evaluate a catalog against six areas: governance, scale, security, lineage and quality, openness, and AI-context readiness. Not a single feature list.
• Verify vendor scale and accuracy claims with named customers and benchmark numbers, not screenshots, and treat vendor-published figures as a starting point rather than independent proof.
• Security compliance ownership differs sharply between self-hosted open source and a managed vendor. Know which one you're buying.
• Lineage and quality only function as governance when they're enforced automatically, not documented after the fact.
• AI-context readiness, meaning semantics, memory, and governed agent access, is now a checklist requirement, not a future nice-to-have.
• Cost and pricing model belong in the scorecard too. A catalog that clears all six technical areas can still be the wrong buy if its pricing model doesn't fit your asset growth.

Governance: controls, not a glossary page

Governance in a catalog demo often means "yes, we have a glossary." A glossary alone is a searchable document, not an enforcement mechanism. Require these as baseline, not as a roadmap item:

• RBAC and ABAC enforced on every read and write path. Not just at the UI layer. If someone queries the metadata API directly or an agent calls it through automation, the same rules apply.
• SSO, so access ties to your identity provider instead of a separate user table someone forgets to deprovision.
• A shared glossary and classification tags that attach to actual columns and tables, not a document living next to the tool.
• Domains and data products as first-class objects, so ownership maps to how your organization is actually structured, not a flat list of tables.
• Stewardship workflows and ownership that function as guardrails: someone is notified when a table changes owner, when a classification is missing, when access is requested outside policy.

Ask the vendor to show you an access request denied in real time, not described in a slide. If they can't demo the enforcement path, treat the control as unbuilt rather than in progress. For a broader view of what governance tooling should include beyond the catalog itself, see enterprise data governance tools.

Scale: verify the number

Every vendor will tell you they scale. Ask for specifics instead of adjectives:

• Asset count and relationship count, not just "millions of tables." A table with no lineage relationships is cheap to index. A table with 15 upstream and downstream edges takes more work to catalog and query correctly, so relationship count is a better indicator of real load than table count alone.
• Ingestion model: pull and scheduled versus event-driven. Scheduled ingestion means your catalog is stale between runs, sometimes by hours. Event-driven ingestion propagates a schema change or a new column almost immediately, which matters if an agent reads that metadata to answer a question five minutes later.
• Named benchmark numbers, checked against a real deployment, not a screenshot of a dashboard. Treat vendor-published numbers, including the ones below, as a floor for the level of detail to demand, not as a substitute for a reference call with a named customer at comparable scale.

As one example of that level of detail: we have published a benchmark at 2 million assets and 5.8 million tag relationships on EKS, Postgres 15, and OpenSearch 2.19, with the setup documented rather than left as a bare claim. On the open-source side, OpenMetadata reports more than 4,000 enterprise deployments and 130+ connectors, drawn from a community of 13,500+ members. Both sets of numbers are the vendor's own reporting, useful as a specificity bar to hold every vendor to, not as third-party-audited proof. Ask any vendor, including this one, for a reference customer at your target scale before treating a benchmark page as settled.

Another proof point worth asking vendors to explain, because it shows what scale looks like when an AI agent is the one querying metadata: OpenAI's Kepler agent cut query time from 22-plus minutes to under 90 seconds across 70,000 datasets and 580-plus petabytes processed daily, per OpenMetadata's published case study. That's a vendor-documented case study rather than an OpenAI-authored figure, but it's a concrete, named-customer example of what governed metadata access does at real volume, which is the standard to hold every vendor's scale claims to.

Security and compliance: know who holds the audit

This is where open source and enterprise-ready get confused most often. Open source doesn't come with someone else's SOC 2. If you self-host an open-source catalog, your organization owns the audit, the patching, the encryption configuration, and the incident response. If you buy a managed vendor, they hold the certification and you inherit it, but you should still ask which one and see the report.

Confirm directly:

• Encryption in transit and at rest, not assumed because "we're on AWS."
• Who is named on the SOC 2 or equivalent audit: the vendor or nobody.
• Permission-aware retrieval for agent access. If an AI agent queries the catalog, it should only see what the requesting user is allowed to see, and every query should generate an audit trail. A chatbot that answers questions using metadata the underlying user couldn't access on their own creates a security gap that a friendly interface can mask, so ask specifically how the vendor enforces this at the agent layer.

Lineage, quality, and contracts: the operational control plane

Lineage and quality tooling only function as governance when they're enforced automatically. A lineage diagram someone drew in a wiki six months ago describes a past state and goes stale as soon as the pipeline changes. Lineage that updates automatically when a dbt model changes reflects the current state and can be relied on for impact analysis.

Check for:

• column-level lineage, not just table-level. Table-level lineage tells you two tables are connected. Column-level lineage tells you which column caused a downstream metric to break.
• Native data quality, run inside the platform rather than bolted on through a separate tool that doesn't share metadata.
• Freshness SLAs with actual alerting when a table misses its expected update window.
• Incident and root-cause workflows, so a broken dashboard traces back to the failed test or the schema change that caused it, without a Slack thread and three engineers reconstructing it by hand.
• Data contracts that make agreements between producers and consumers enforceable, catching a breaking schema change before it ships rather than after a downstream report is wrong. Good metadata management supports decision-making because ownership and lineage attach directly to the data, so anyone tracing a number back to its source can do it without relying on a person's memory of how a pipeline works.

Open foundation: openness matters more the longer you plan

An Apache 2.0 license and open APIs reduce lock-in in a way that's easy to underweight during a 90-day evaluation and expensive to correct later. If your metadata lives in an open, documented schema, you can move it, extend it, or build your own tooling against it. If it lives in a proprietary format with a thin export button, renegotiating terms at renewal will be harder because switching costs are higher.

Broad connector coverage matters for the same reason: a catalog with 130+ connectors reaches your Snowflake, dbt, and Airflow stack today, and reaches whatever you adopt in three years without a custom integration project. Check connector coverage against your actual roadmap, not just your current stack, since the cost of a missing connector shows up later as an integration project you didn't budget for.

AI-context readiness: the newest line item, and now required

Two years ago, this section didn't exist on an RFP. It belongs there now because the readers of your catalog have expanded to include agents, not just analysts. The evidence for why is stark: per the Spider 2.0 benchmark as reported by Collate, GPT-4o and Sonnet 4.5 execute at close to 10% accuracy on real enterprise schemas without grounding, climbing into the high 70s once semantic context, meaning meaning and not just schema, is supplied. That gap is the reason semantic grounding needs to be treated as a requirement rather than an enhancement when evaluating agent-facing metadata.

Check for three specific things:

• Governed agent access, through an MCP or equivalent mechanism, instead of agents scraping CSV exports or hitting an ungoverned API. The governed MCP server pattern means an agent authenticates as a real user and only sees what that user is permitted to see, with the same audit trail a human query would generate.
• Semantics exposed alongside technical metadata. A schema tells an agent a column is named cust_stat. A glossary and ontology tell it that means customer account status, with three valid values. Without that layer, agents guess, and the Spider 2.0 numbers show what guessing costs in accuracy. This is what separates a catalog from a true enterprise context layer.
• Memory that captures corrections. If an analyst tells an agent that revenue excludes returns, that correction should persist for the next session and the next agent, not vanish when the conversation ends.

Gartner projects, per our research, that prioritizing semantics in AI-ready data could raise agentic AI accuracy by up to 80% and cut costs by up to 60% by 2027. For the mechanics of what counts as AI-ready data, the four pillars are semantics, quality, lineage, and context. Check a vendor against each of the four separately, since a strong search interface can sit on top of a catalog missing one or more of these pillars.

How to validate before you sign

Verify each of these claims yourself rather than taking the vendor's word for it. Run a structured proof of concept against your own sources, not a demo environment loaded with clean sample data. Test it with your worst offenders: the metric name three teams define differently, the deprecated table nobody remembered to archive, the cross-team join that breaks lineage tracking in most tools.

Score each of the six areas separately, and add cost as a seventh line on your own scorecard. A catalog that scores well on lineage and quality but has no answer for agent-level permission enforcement has a real gap, even if the rest of the demo looks strong, and a catalog that clears all six technical areas but prices per asset can become the wrong buy once you cross a growth threshold you didn't model at signing. Ask for the pricing model in writing, not just the list price: per-asset, per-seat, and compute-based pricing behave very differently as you scale from 500,000 to 2 million assets. Vendors will naturally lead with their strongest area on the call, so plan to spend deliberate time on the other five areas and to model the cost curve yourself rather than take the quoted number at face value.

For the fundamentals of what a data catalog does and how the category has evolved from technical inventory to business context layer, that companion piece covers the taxonomy this article turns into a buying checklist.

Frequently asked questions

Is an open-source catalog enterprise-ready out of the box, or does it need a managed layer?

Open source gives you the software, not the operational guarantees. Self-hosting means your team owns patching, uptime, encryption configuration, and audit compliance. A managed layer adds SOC 2 ownership, SLAs, and support, which is what most enterprise buyers actually mean when they say "enterprise-ready." Neither answer is wrong, but they're different purchases with different accountability, and typically different pricing models as well.

What's the difference between a data catalog and a full context layer for AI agents?

A data catalog inventories tables, columns, and lineage so people can find and trust data. A context layer adds the semantic and memory layer agents need: glossary terms tied to real columns, ontology relationships, and persistent corrections from past queries. A catalog can exist without being AI-context ready. A context layer requires the catalog as its foundation.

How many connectors does an enterprise catalog actually need?

Enough to cover every system your data actually moves through today, plus headroom for what you'll add in the next two to three years. 130+ connectors, as OpenMetadata reports, typically spans warehouses, lakes, BI tools, and orchestration systems without a custom integration project for common tools, though the right number for you depends on your own stack rather than any fixed threshold.

What quality signals should a catalog surface before a number reaches an executive dashboard?

Freshness against an SLA, test pass or fail status at the column level, and an active incident flag if a related table is currently broken upstream. If a dashboard number is fed by a table failing its freshness check, that should be visible before the number reaches a business user, not after someone asks why revenue looks wrong.

Does an enterprise catalog need to support MCP or agent access today?

If any team in your organization is already piloting AI agents against your data, yes. Even if not, treat it as a near-term requirement rather than a future one. The Spider 2.0 benchmark shows how badly agents perform without governed semantic grounding, and building agent access into the initial evaluation costs less than retrofitting it after agents are already in use.