How to Build a Data Catalog That People Actually Use
Data catalogs sound great in theory. Create one place where everyone can find the data they need. Add definitions. Add ownership. Add lineage. Add classifications. Make it searchable. Then suddenly the entire organization understands its data and starts making better decisions.
That is usually not what happens.
I have seen organizations put a tremendous amount of effort into cataloging data only to end up with something that technically exists but gets very little use. The metadata is there. The business definitions are there. The data owners are listed. The platform may even be expensive and impressive. But people still send messages asking where to find a metric.
They still ask which table they should use. They still build duplicate reports. They still disagree about what revenue means. They still ask someone on the data team to explain a dataset that already has documentation.
The problem is usually not that the organization failed to build a data catalog. The problem is that it built a catalog without building a reason for people to use it. A useful data catalog is not really a documentation project. It is a usability project, an adoption project, and ultimately a trust project. That difference matters.
Start With the Questions People Are Already Asking
One of the biggest mistakes I have seen is starting with the technology. Organizations buy a cataloging platform, connect data sources, scan thousands of tables, and celebrate having metadata available. Technically, that is progress. Practically, it may not change anything.
I prefer to start by listening to the questions people are already asking.
Where do I find sales?
Which customer table is correct?
What is the difference between net revenue and gross revenue?
Who owns this metric?
Why does this dashboard not match that dashboard?
Can I use this field for reporting?
Is this data refreshed daily?
Where did this number come from?
Those questions tell you what the catalog actually needs to solve. If people constantly ask which table to use, then your first priority is not cataloging every table. It is identifying and clearly labeling the trusted tables. If people constantly disagree about metric definitions, then your first priority is business definitions. If people do not know where data originated, then lineage becomes more important.
The catalog should evolve around real friction. That is much more valuable than trying to document everything just because the platform makes it possible.
Do Not Catalog Everything First
This is probably one of the most important lessons I have learned. Trying to catalog everything at once sounds thorough, but it usually creates a lot of noise. Most organizations have far more data than people actually use. There may be thousands of tables, temporary datasets, staging tables, extracts, historical structures, test data, and legacy objects sitting around the environment. If all of that appears equally important inside the catalog, users have another problem. Now instead of not knowing where to find data, they have too many choices.
I prefer to think about a catalog in layers. Start with the datasets that support the most important business processes.
Sales.
Customers.
Products.
Finance.
Operations.
Employees.
Inventory.
Appointments.
Whatever matters most to the organization. Within those areas, identify the datasets that people should actually use. That distinction is critical. A catalog should not simply tell someone what exists. It should help them understand what they should trust.
Certification Matters More Than Documentation
Documentation is useful. Certification is more useful.
Imagine searching for customer data and finding twelve tables. Every table has a description. Every table lists its columns. Every table has an owner. That sounds like a well-documented environment.
But the user still has the same question. Which one should I use?
A good catalog needs to answer that clearly. This is why I like the concept of certified data. Certified datasets have been reviewed, validated, and approved for broader use. They should be clearly marked. Users should immediately understand that these are the preferred sources.
That does not mean other data should disappear. Analysts and developers may still need access to lower-level datasets. But there should be an obvious path toward the trusted version. In my experience, reducing ambiguity creates far more value than adding another paragraph of documentation.
Business Language Has to Come First
A common failure in catalog projects is writing documentation entirely from the technical perspective. A technical description might say something like:
This table joins transaction header records to transaction detail records using transaction ID and filters out deleted records.
That may be perfectly accurate. It is also probably useless to most business users. The business wants to know:
This dataset contains completed customer purchases and is the recommended source for sales reporting.
That sentence answers a much more important question.
What is this data for?
Good catalog documentation needs both perspectives. The business description explains what the data represents and when someone should use it. The technical description explains how it is constructed. The order matters. Lead with the business meaning. Then provide technical details for those who need them.
Give Metrics First Class Treatment
Tables and columns are important, but many users do not think in terms of them. They think in terms of metrics.
Revenue.
Appointments.
Conversion.
Average ticket.
Customer retention.
Inventory.
Collections.
Margin.
Labor cost.
If a user searches for a metric, the catalog should help them understand exactly what that metric means. That includes
The definition.
The calculation.
The source.
The grain.
The refresh frequency.
The owner.
Any important exclusions. And ideally the reports where that metric appears. This is where catalogs can become incredibly valuable. Instead of documenting only the physical data environment, the catalog becomes a bridge between business language and technical implementation. That is where real adoption begins.
Ownership Cannot Be Fake
I have seen catalogs where every dataset has an owner listed. At first glance, that looks like strong governance. Then you ask the owner a question. They have no idea they are the owner. That is not governance; that is decoration. Ownership needs to mean something. The person listed should understand what they are accountable for. That does not mean they need to know every technical detail.
A business owner may be responsible for approving definitions and resolving business disagreements.
A technical owner may be responsible for data quality, pipelines, or system behavior.
A data steward may help maintain definitions and metadata.
Those roles can be different. What matters is that the names in the catalog represent real responsibilities. Users lose trust quickly when ownership information is outdated or meaningless.
Search Has to Work Like People Think
Search is one of the most overlooked parts of catalog design. Technical teams often name things according to systems, schemas, or development standards. Business users search differently. A dataset may technically be called something like customer_transaction_fact.
The business user searches for sales.
Or purchases.
Or revenue.
Or transactions.
A good catalog connects those terms.
Synonyms matter.
Business terminology matters.
Abbreviations matter.
Common misspellings sometimes even matter.
If the user needs to know the technical name before they can find the data, the catalog is not really helping them. The whole point should be to reduce the amount of tribal knowledge required to navigate the environment.
Put the Catalog Where Work Happens
Another lesson I have learned is that adoption becomes much harder when the catalog feels like a separate destination. People do not wake up excited to browse metadata. They are trying to finish their work. The catalog should fit naturally into that workflow.
If analysts live in a data platform, metadata should be visible there. If business users live in reporting tools, definitions should be accessible from reports. If teams communicate through collaboration tools, links to definitions should be easy to share.
A catalog should not become another website everyone has to remember exists. The easier it is to access in the moment someone has a question, the more valuable it becomes.
Data Quality Belongs in the Catalog
Documentation tells users what data means. Data quality tells them whether they should trust it today. Those are different questions.
Imagine a dataset with perfect documentation that has not refreshed in three days. The catalog says it is trusted, but reality says otherwise. Useful catalogs should provide some visibility into operational health.
When was the data last refreshed?
Did the last pipeline complete successfully?
Are important fields unexpectedly null?
Are row counts significantly different from normal?
Are there active data quality issues?
Users do not necessarily need every technical monitoring detail. But they should have enough context to understand whether the data is reliable. Trust is not static. It changes with the data's condition.
Lineage Needs to Be Understandable
Lineage is one of those catalog features that can look impressive very quickly. Huge diagrams appear showing dozens of systems, pipelines, tables, and reports connected together. This is technically fascinating but practically overwhelming. Good lineage should answer specific questions.
Where did this metric come from?
What source system created this field?
Which reports will be affected if this table changes?
What transformations happened before the data reached this dashboard?
Different users need different levels of lineage.
A developer may want detailed technical lineage.
A business user may only need to understand that the number originated in a particular operational system and was transformed through an approved reporting dataset.
Do not confuse complexity with usefulness.
Build Feedback Into the Catalog
A catalog should not be treated as finished.
Data changes.
Definitions change.
Ownership changes.
Business processes change.
New systems arrive.
Old systems disappear.
Documentation becomes stale surprisingly quickly.
Users need an easy way to flag problems.
This definition is unclear.
This owner is wrong.
This table is no longer used.
I cannot find the metric I need.
This description is outdated.
That feedback is incredibly valuable because it tells you where the catalog is failing. I would rather have a smaller catalog that gets regularly improved than a massive catalog that nobody maintains.
Measure Usage
If you want people to use the catalog, measure whether they actually use it.
Look at searches.
Look at popular datasets.
Look at searches that produce no useful result.
Look at pages people open and immediately leave.
Look at repeated questions still being sent directly to the data team.
Those signals tell you where documentation and navigation need improvement. The goal is not simply increasing catalog traffic, but to reduce friction.
If analysts stop asking which customer table to use because they can find the answer themselves, the catalog is working.
If business leaders can look up a metric definition without scheduling a meeting, the catalog is working.
If developers can understand the downstream impact of a change before breaking a report, the catalog is working.
Governance Should Feel Helpful
One of the fastest ways to undermine catalog adoption is to turn it into a compliance exercise. If users associate the catalog with forms, approvals, and bureaucracy, they will avoid it whenever possible.
Governance is important.
Standards are important.
Ownership is important.
But the experience should still feel helpful. A good catalog should make the user's job easier before it asks the user to contribute anything.
Help them find data.
Help them understand metrics.
Help them identify trusted sources.
Help them troubleshoot problems.
Once users see value, asking them to help maintain definitions or ownership becomes much easier. Value has to come before obligation.
The Best Catalog Reduces Dependence on People
Every organization has people who know where everything is.
They know which table is correct.
They know which report has the right number.
They know which system is unreliable.
They know which field should never be used.
These people become human catalogs. That works until they are busy, on vacation, change roles, or leave the organization. A strong data catalog captures that institutional knowledge and makes it available to everyone. That does not eliminate the need for experts; it just means experts spend less time answering the same questions repeatedly.
That is one of the biggest benefits of a well-designed catalog. Tribal knowledge becomes organizational knowledge.
Final Thoughts
Building a data catalog is easy to misunderstand. The technical implementation is only part of the work. The real challenge is creating something people trust enough and find useful enough that they choose to use it.
Start with real questions.
Prioritize the data that matters.
Clearly identify trusted datasets.
Write in business language.
Treat metrics as important objects.
Create meaningful ownership.
Make search intuitive.
Show data quality.
Keep lineage understandable.
Measure adoption.
Continuously improve the experience.
Most importantly, remember that the catalog exists to help people make decisions. The goal is not to document every table in the organization. The goal is to make data easier to find, easier to understand, and easier to trust. When a catalog accomplishes those things, adoption stops feeling like something you have to force. People use it because it solves a problem and that is when a data catalog becomes more than metadata.
It becomes part of how the organization works.
