Buying a Data Catalog in 2026: What Actually Matters

The data catalog market has a problem. There are too many tools that all claim to do the same thing, and the evaluation criteria that vendors push (number of connectors, UI polish, AI features) often have little to do with whether the tool actually gets adopted by your team.

We have talked to dozens of data teams about their catalog experiences. Some love their tools. Most do not. The difference almost never comes down to features.

What Actually Predicts Success

Time to First Value

The single best predictor of catalog adoption is how quickly it shows useful results after connecting a data source. If it takes two weeks of configuration before anyone sees anything useful, the project is already in trouble. Champions lose enthusiasm. Skeptics feel validated.

The tools that succeed tend to show a meaningful result within the first session. Connect a database, see your tables, see relationships, see some metadata. Maybe not perfect, but enough to demonstrate that the tool understands your environment.

Low Maintenance Burden

Every catalog requires some ongoing effort. Descriptions need writing. Business glossaries need curating. Ownership needs assigning. The question is how much of this is manual versus automated.

Tools that require manual tagging of every table and column eventually get abandoned. The initial enthusiasm carries the project for a few months, then the maintenance burden catches up and adoption stalls.

Look for tools that automate what they can (schema extraction, relationship discovery, lineage inference) and leave humans to add what only humans can (business context, ownership, tribal knowledge).

Integration With Existing Workflow

A catalog that requires switching to a new UI for every data question will lose to a catalog that integrates where people already work. That might mean a browser extension, a Slack bot, an IDE plugin, or API access for custom integrations.

The data teams that get the most value from catalogs are the ones where catalog data surfaces in the tools people already use, not the ones where people have to remember to go check the catalog.

What Matters Less Than Vendors Claim

Number of Connectors

Every catalog vendor has a slide showing 100+ supported data sources. This sounds impressive until you realize you probably use three to five. What matters is whether those specific connectors work well, not whether the vendor supports a database you have never heard of.

Ask about the depth of each connector, not the count. Does the PostgreSQL connector extract foreign key definitions? Does the Snowflake connector capture view definitions and materializations? Depth beats breadth.

AI-Powered Everything

"AI-powered" has become a checkbox item. AI-generated descriptions, AI-powered search, AI-driven classification. Some of this is genuinely useful. Much of it produces generic descriptions that nobody trusts.

The useful AI applications in catalogs are: suggesting relationships between tables, classifying columns as potential PII, and answering natural-language questions about the schema. The less useful ones are: auto-generating descriptions (too generic), auto-tagging (too noisy), and "AI insights" (too vague).

Collaboration Features

Every catalog has comments, likes, endorsements, and other social features. In theory, these turn the catalog into a living knowledge base. In practice, few data teams have the culture or the time to maintain active discussions in yet another tool.

The collaboration features that actually get used are ownership assignment and incident flagging. Everything else is nice to have but rarely the deciding factor.

What to Actually Evaluate

Try It Against Your Data

Not demo data. Your data. The demo always looks great. What matters is whether the tool handles your specific schema quirks, your naming conventions, your scale.

Most catalogs offer a free trial or POC period. Use it with a real database. See what it discovers automatically. See what it misses. See how long it takes before someone on your team finds something useful they did not know before.

Talk to a Similar Customer

Not the case study on the website. An actual customer with a similar data stack and team size. Ask them: How long did implementation take? What was adoption like after six months? What do people actually use it for day-to-day?

Check the Admin Burden

During evaluation, note every configuration step, every manual mapping, every approval workflow. These are the tasks that someone on your team will own indefinitely. If the setup requires two weeks of full-time work from a senior data engineer, that is a real cost.

Test Search Quality

Connect your data, wait for indexing, then have someone on your team search for a table they need regularly. If the search does not find it quickly, the catalog will not get used. Good search is the single most important UX feature in a data catalog.

The Build vs. Buy Question

Some teams build internal catalogs using tools like DataHub, Amundsen, or OpenMetadata. This makes sense if you have the engineering capacity to maintain it and specific requirements that commercial tools do not meet.

It does not make sense if you are building it because commercial tools seem expensive. The cost of maintaining an internal catalog (infrastructure, updates, bug fixes, feature requests) often exceeds the cost of a commercial tool within a year.

Where Squish Fits In the Stack

Squish is not a full data catalog. We focus specifically on the relationship discovery problem: connecting to your databases, finding all explicit and implicit relationships, and making that information available in the formats your other tools consume.

If you already have a catalog, Squish feeds it better relationship data than it can discover on its own. If you do not have a catalog yet, Squish gives you the relationship foundation that any future catalog will build on.

We export to dbt, Snowflake, and Databricks formats so the relationships we discover can flow into whatever tools you choose. We would rather be a sharp tool that does one thing well than another bloated platform that does everything adequately.