Why data catalogues fail to gain adoption

The business case for a data catalogue is persuasive. Data is scattered across warehouses, lakes, dashboards and operational systems. People struggle to find it, understand it and know whether it can be trusted. A catalogue promises a searchable map with definitions, lineage, owners and policy.

Then the platform launches. The governance team asks people to complete descriptions. Analysts continue asking colleagues in chat. The most useful context remains in code, tickets and individual memory. Six months later, the catalogue is technically populated and operationally peripheral.

The failure is rarely search. It is that the catalogue was treated as a destination rather than part of the work.

Adoption starts with a specific user and decision

“Business users” is not a useful persona. A finance analyst validating revenue, a data engineer assessing blast radius, a privacy officer locating personal data and a marketer choosing an audience dataset need different evidence.

Before implementation, define a small number of high-value tasks:

• Find the approved dataset for a named decision.
• Understand a metric’s definition, grain and limitations.
• Identify the owner and service expectation.
• See upstream sources and downstream consumers before a change.
• Request and receive appropriate access.
• Assess whether a dataset contains restricted data.
• Diagnose whether an incident affects critical reports.

If the catalogue cannot materially improve these journeys, adding more metadata will not create adoption.

Manual metadata is an unfunded product

Catalogues often depend on teams entering descriptions, classifications, owners and quality information. That work competes with delivery, and its benefit may accrue to unknown future users. Unsurprisingly, fields remain empty or become stale.

Automate technical metadata from the systems that already know it: schemas, jobs, queries, lineage, ownership files, access policy, quality tests and deployment history. Ask humans for context only humans can provide: business meaning, appropriate use, material limitations and accountable decisions.

Even human context should live close to change. If a data product is defined in code, make description and ownership part of its reviewed specification. If a business definition changes through a governance workflow, update the catalogue as an output of that decision—not as a second administrative task someone may remember later.

A catalogue cannot manufacture trust

A “certified” badge does not make data correct. Users trust data when they can see who owns it, how recently it ran, whether quality expectations passed, how it is used and what happened when it failed.

Certification should have explicit criteria and expiry. A dataset may be approved for regulatory reporting but unsuitable for real-time personalisation. Trust is contextual, not universal.

Show evidence:

• current owner and last review;
• freshness and quality service indicators;
• incident and change history;
• active consumers and endorsed use cases;
• definition, grain and exclusions;
• source and transformation lineage; and
• security classification and access path.

A sparse record with current evidence is more valuable than a beautifully written page that nobody can verify.

Put discovery where users already work

Practitioner discussions repeatedly identify a simple adoption barrier: if people analyse data in another platform, they will not routinely visit a separate catalogue to confirm context.

Bring metadata into the warehouse interface, BI tool, notebook, IDE, pull request and access-request flow where possible. Link dashboards back to governed definitions. Surface owner and quality status beside the table. Let users report an issue without starting a new search for the support process.

The catalogue can remain the system that organizes metadata. It should not insist on being the only interface through which people experience it.

Search quality is an information-architecture problem

Indexing every technical asset creates a large result set without helping users distinguish what matters. Duplicate tables, intermediate models, abandoned dashboards and region-specific copies overwhelm search.

Create meaningful boundaries. Mark products intended for broad consumption. Hide or clearly label implementation artefacts. Use business vocabulary and synonyms. Rank endorsed, active and healthy products above unowned output. Preserve technical depth for users who need it without making every consumer navigate the physical architecture.

The goal is not to expose the estate exactly as it exists. It is to make the estate understandable without concealing important truth.

Ownership must lead somewhere

An owner field is useful only if the named group can answer questions and make changes. Avoid individuals where routine support needs continuity, but identify one accountable decision-maker for contested definitions.

Define the interaction:

• How does a user request clarification or access?
• What response time is expected?
• How are disputed definitions resolved?
• How does a consumer learn about breaking change?
• What happens when the owner is inactive?

A catalogue that identifies ownership without connecting it to service is a corporate directory with table names.

Security cannot be an afterthought

Catalogues centralise knowledge about the data estate. Even when they do not expose values, metadata can reveal sensitive domains, system names, customer attributes and lineage. Apply least privilege, audit access, classify metadata and separate broad discovery from restricted detail.

Do not solve adoption by making everything visible. Make the correct level of context discoverable to the correct audience, with a clear path to request more.

A minimum viable catalogue programme

Start with one business area and a measurable discovery problem.

1. Select 20–50 data products that support important decisions.
2. Automate their technical metadata and lineage.
3. Add owner, definition, grain, endorsed use, limitations and service indicators.
4. Integrate context into the tools consumers already use.
5. Retire or label duplicates found during the work.
6. Observe searches, zero-result queries, access requests and support questions.
7. Improve the product before expanding the inventory.

This deliberately avoids cataloguing everything. Coverage is useful only when the record can be maintained and the user journey works.

What to measure

Measure whether the catalogue changes behaviour:

• successful discovery for priority tasks;
• time from question to trusted dataset;
• zero-result and reformulated searches;
• use of endorsed products versus duplicate extracts;
• access-request completion time;
• owner response time;
• stale critical records;
• incidents caused by misunderstood or undiscoverable data; and
• catalogue context viewed inside downstream tools.

Page views alone can reward curiosity without proving value.

Adovo’s view

People do not want a catalogue. They want to answer a question without betting on data they do not understand.

Automate what systems know. Ask people only for decisions and meaning. Put trusted context in the path of work, and start with a small set of products important enough to maintain. A catalogue becomes valuable when it shortens uncertainty. Until then, it is just another place the organisation asks people to update.

Primary documentation and further reading

• Microsoft Purview data products
• Microsoft guidance on content ownership