Data Governance Platform Evaluation Criteria: 22 Tests to Run

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Judge a data governance platform on 22 behavior tests. Each test has a passing answer, a red flag, and a note on whether a demo or a proof of concept (POC) can show it. Atlan, the context layer for AI, wrote this checklist and asks you to score it on the same scale. For regulated data, the deciding tests check two things: policy follows a tag, and AI agents see only what the asking user can see.

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Run it after you set up the governance program and shortlist data governance platforms. Use it to compare data governance tools on behavior. Weight freshness (A2) heavily. Stale catalogs are one of three reasons governance implementations fail.

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Data governance platform evaluation checklist: 22 tests

Send vendors the “How to test it” column first. To keep results hard to fake, your team drives while the vendor watches, and you reveal the real tag, columns and test users on the day. In the POC, your admin sets up from public documentation alone. In the “Run in” column, Both means you start in the demo and re-run in the POC.

A. Coverage and freshness

ID	Test	How to test it	Good answer	Red flag	Run in
A1	Connects to your actual estate	Send your top 10 sources; ask whether each connects natively, by API push or manually	Method and connector page per source	“We support it”, no connector page	Both
A2	Metadata stays current	Rename a column and add a table in one source; time both, then check the renamed column’s term, tag and owner	Both appear on the next crawl or event; links kept or flagged	Links silently lost	POC
A3	Column-level lineage across tools	Trace one real dashboard field through your transformations to source columns	Column-to-column path from your SQL by automated SQL lineage	Table-level only, or the vendor’s sample warehouse	POC

B. Business meaning and ownership

Terms count only when they are attached to columns. That link is the core of KPI definitions and a business glossary. Bulk ownership is how you scale governance across business domains.

ID	Test	How to test it	Good answer	Red flag	Run in
B1	Glossary terms bind to columns	Create “active customer”, link it to columns in two systems, search	Both columns returned	A term list with no asset links	Demo
B2	Ownership at scale	Assign owners to 500 assets by schema, tag or domain	A reversible rule or bulk assignment	One asset at a time	Demo
B3	Trust status shows where people work	Certify a table; check search and one connected tool, such as a semantic layer	Same status everywhere	Visible only in the platform UI	Both

C. Policy and access

ID	Test	How to test it	Good answer	Red flag	Run in
C1	Policy follows the data	List the enforcement points. Tag a column PII from your classification taxonomy; with no new rule, check masking in your warehouse and one BI tool	One tag-driven policy works at both (attribute-based access control, NIST SP 800-162)	A rule per tool, or one enforcement point	Both
C2	Conflicts resolve predictably	Give a user a grant through a team role and a deny through the data’s tag. Apply both live and query as that user	Documented, deterministic precedence and a view of which policy decided (zero-trust data governance)	“It depends on configuration”	Demo
C3	Tags propagate downstream	Tag a source column; check derived columns	Inherited by rule, reason visible	Every copy tagged by hand	POC
C4	Enforced in the data platform	Query the warehouse directly as the masked user, then edit their grant there by hand	Masked in your query, by the platform or by policy pushed to native warehouse masking; the edit shows as drift	Masking only in the tool’s own preview, or drift unnoticed	POC

D. Workflow and automation

ID	Test	How to test it	Good answer	Red flag	Run in
D1	Approvals change something	Request and approve a restricted dataset; check the grant	Permission provisioned or ticket audited (access requests that provision permissions)	An email, nothing else	Demo
D2	Suggestions keep a human on the loop	Generate descriptions for 50 assets; open the review state	Reviewable, with the approver recorded	AI writes straight to production metadata	POC
D3	Rule-based automation	Run “unowned tables in schema X get the domain steward”; open the run history	A per-run log that shows failures	Works only through scripts from the vendor’s services team	Both

E. Quality and trust signals

Impact analysis is how you fix data quality problems at the source and hold producers to data contracts.

ID	Test	How to test it	Good answer	Red flag	Run in
E1	Quality beside the asset	Fail one check on one table; open the asset page	Status, last run and owner, from native quality checks or your tool	A separate console	POC
E2	Impact before change	Propose dropping a column; ask what breaks	Affected assets and their owners, who can be notified	A manual lineage walk	Both

F. Audit and evidence

ID	Test	How to test it	Good answer	Red flag	Run in
F1	Who changed what, and when	Change a classification; see it in the UI, then call the audit API yourself in the POC	Actor, time, old and new value, by API	“We can pull logs on request”	Both
F2	Admin events reach your SIEM	Trigger an admin event live; watch it land in a sink you name	Streamed to storage you own	In-app only	Demo

G. AI consumption and openness

This group checks whether agents get governance rules they can read.

ID	Test	How to test it	Good answer	Red flag	Run in
G1	Agents get the asking user’s permissions	Ask how the Model Context Protocol server handles transport, credentials and identity mapping. As a low-privilege user, request a masked column and a restricted table; open the audit record	That user’s assets only, masked data, audit naming user and agent (access control for AI agents)	An admin key or shared service account. The MCP spec makes authorization optional	POC
G2	Agents read definitions	Ask “what does active customer mean, and which table do I use?”	Governed definition, owner, certified table	Column names and a guess	Both
G3	Lineage is open	Export one lineage graph live as OpenLineage events (with the column lineage facet), then import one	Documented open ingest and export	Only through vendor agents	Demo

H. Adoption and exit

ID	Test	How to test it	Good answer	Red flag	Run in
H1	Business users adopt it	Untrained business users find a certified asset, read its definition, request access	They finish unaided	Only trained stewards finish	POC
H2	You can leave with your metadata	Export one domain’s terms, tags, owners and lineage in an open format; read it with your script	A documented bulk export, no vendor help	Services-only or proprietary export	Both

Here is Atlan’s own Governance Center tour, a recorded video, so it earns a 1 at most.

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How do you score vendors on data governance evaluation criteria?

Score each test 0 to 3 on the evidence you saw.

Score	Evidence seen	Meaning
0	Not shown, or “on the roadmap”	Does not count
1	Slides, docs, a recorded video or an RFP answer	Claimed
2	Live on the vendor’s sample data, your team driving	Demonstrated
3	On your data, in your POC, by your users	Proven

A demo tops out at 2. Only your own data earns a 3.

Pick three to five knockouts before any demo. For regulated data the defaults are C1, C4, F1 and G1. Any knockout below 2 removes the vendor. Set group weights from your governance operating model.

Scores of 2 decide who earns a POC. Scope that proof of value around the tests marked POC. The 3s decide the purchase, and they build the ROI case. For the procurement document, use an RFP template with weighted criteria.

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How does Atlan answer these evaluation criteria?

Atlan wrote this checklist, so score it like any vendor. Documentation earns a 1.

On C1, an Atlan purpose “scopes access by asset tag instead of by team”, and a deny in any metadata or data policy wins over every grant, per the same page.

On C4, a data policy “grants or restricts permissions to query and preview data on the tagged assets”, and “Atlan replaces the value of tagged columns with a masked output when the user queries or previews.” The docs describe masking at query and preview in Atlan, so test masking inside the warehouse yourself in the POC.

On F1, the docs list asset audit search over POST /api/meta/entity/auditSearch as one of four customer-accessible audit surfaces.

On G1, the docs describe Atlan MCP as a hosted server on every tenant where “every action respects the permissions you’ve already set in Atlan.”

For shortlists built around context engineering for agents, see context layer evaluation criteria and evaluating AI readiness platforms.

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FAQs about data governance platform evaluation criteria

1. What are the most important data governance platform evaluation criteria?

Your knockouts. For regulated data, start with C1 (policy follows a tag), C4 (masking holds in the warehouse), F1 (change history exportable by API) and G1 (an agent sees only what its user can).

2. Should AI agents be part of a data governance platform evaluation?

Yes, as one more test user. The Model Context Protocol leaves authorization optional, so ask how the server authenticates, connect as a low-privilege user, and check the agent gets exactly what that user would.

3. How do you compare data governance vendors fairly?

Give every vendor the same 22 tests and weights before any demo, let your team drive, and reveal inputs on the day. Scores of 2 pick the POC finalists; 3s, earned on your own data, decide the purchase.

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Sources

1. Guide to Attribute Based Access Control (ABAC) Definition and Considerations, NIST SP 800-162
2. Authorization, Model Context Protocol specification (version 2026-07-28)
3. Column Level Lineage Dataset Facet, OpenLineage
4. Purposes, Atlan Documentation
5. Protect data by tag (purpose), Atlan Documentation
6. API audit usage, Atlan Documentation
7. Atlan MCP, Atlan Documentation