Data volume has exploded—but clarity hasn’t kept up. Most organizations still struggle to locate, trust, and use the data they need. For many teams, finding the right dataset is like searching for a needle in a haystack. Ask three different teammates to define “monthly revenue,” and you will likely get three different answers. Not because it's an ambiguous term, but because it's defined differently across teams, tools, and reports—without a shared source of truth. 

This is why data catalogs are so important. A well-designed data catalog does more than index your assets; it helps teams locate, understand, and trust the data they need. Think of it as a search engine for your organization’s data—complete with descriptions, lineage, usage stats, and business meaning. But building a catalog that actually delivers value takes more than installing software. It requires alignment, thoughtful design, and a commitment to collaboration. A data catalog can be transformative—but only if it reflects the language of the business, integrates with your modeling process, and is built for people, not just metadata.

In this article, we explore three foundational practices to ensure your data catalog becomes a meaningful asset—not just another system to manage.

Define Your Business Glossary Before You Define Your Metadata

The first and arguably one of the most common mistakes that teams make when rolling out a data catalog is jumping straight into inventorying assets. They scan their data warehouse, tag all the tables, load up the schema, and call it quits. But when a user opens the catalog and asks, “Which of these tables actually defines our active customers?” The answer isn’t clear. That’s because metadata without meaning isn’t helpful—it’s noise. 

To avoid building a confusing data catalog, you need to start with your business glossary. This is the shared language of your organization: the definitions, calculations, and concepts that underpin your most important metrics and workflows. Without a glossary, you risk building a catalog that looks complete on paper but is impossible to navigate in practice. Users will still have to ask around for clarity, teams will still debate what “churn” means and trust in the catalog will erode quickly.

By starting with a glossary, you anchor your catalog in business context. Tools like Ellie.ai empower teams to build glossaries visually, collaboratively, and alongside conceptual data models. That means terms like “active user”, or “monthly churn” aren’t just defined—they’re connected to the systems and datasets that produce them. If your teams can’t agree on the definitions, they won’t agree on the data—even if it’s cataloged. When your catalog is built on top of a strong glossary, it becomes more than a metadata registry.

Think Like a Product Manager, not a Librarian

Many data catalogs are designed by engineers—for engineers. They’re structured for completeness, not usability. Tagging systems are accurate. Lineage is exhaustive. But when business users open the catalog, they bounce. It’s too technical, too complex, and doesn’t answer the questions they came in with. To overcome this hurdle, you must shift your perspective and treat your catalog like a data product. This means designing with users in mind. Who needs the catalog, and what are they trying to do? Are they looking for definitions? Trying to trace the source of a number? Evaluating data quality before building a report?

Thinking like a product manager changes how you roll out and scale the catalog. You start with user research. You pilot one domain—say, marketing analytics—before rolling out across the company. You build in feedback loops that let users flag outdated assets, suggest better descriptions, or request new terms. You don’t just publish a catalog—you support it, enable it, and improve it.

In Ellie.ai, this principle is built into the platform. The catalog is integrated directly into the data design environment—which means business users and technical teams can collaborate in the same space. Models, terms, and metadata aren’t scattered across tools. They’re connected, accessible, and relevant. A catalog that no one uses is as bad as having no catalog at all. If it’s not intuitive, searchable, and built around real user needs, it won’t deliver the value you expect.

Align the Catalog with Your Modeling Workflow

Here’s where even well-intentioned catalog deployments start to fail; they’re built in isolation. To keep your catalog relevant, it needs to be part of your data modeling lifecycle. This starts with linking the catalog to your conceptual model. With Ellie.ai, teams begin by collaboratively modeling business entities and defining relationships. As they move from conceptual to logical modeling, the platform captures metadata, ensuring consistency from definition to deployment.

Because the modeling and cataloging happen in one environment, changes are visible, version-controlled, and traceable. Updates to a model trigger updates in the catalog. New glossary terms are reflected in real time and everyone is in alignment. This prevents the “handoff trap” where insights are lost as work moves between tools. Instead of treating modeling, glossary, and catalog as three separate systems, unify them into one flow. The result is a catalog that evolves with your business, not one that constantly plays catch-up.

Questions to Ask Before You Deploy a Data Catalog

Before you hit "go" on your catalog rollout, it’s worth asking:

• Do we have a shared business glossary that defines key metrics and entities?
• Are our data models accessible to both technical and non-technical users?
• Who will use the catalog, and do they know how to navigate it?
• How will we handle catalog updates as business logic or systems change?
• What does success look like, and how will we measure it?

If you can’t confidently answer these questions, it’s not a sign to abandon your catalog—it’s a sign to slow down and prioritize alignment. Planning upfront saves a lot of cleanup later.

Internal Launch Playbook: What Good Enablement Looks Like

Even the best-designed catalog will fall flat without proper enablement. If users don’t know how to use it—or worse, don’t know it exists—adoption will lag, and the catalog will be dismissed as just another unused tool.

Here’s what strong internal enablement looks like:

1. Start with a domain-specific pilot
   Don’t try to launch the catalog company-wide all at once. Instead, pick one business unit—such as marketing or customer support—and tailor the catalog experience to their needs. This creates a focused feedback loop and helps refine your approach before scaling.

2. Train for context, not just clicks
   Training shouldn’t just cover how to search or tag data. It should explain why the catalog matters, how it supports data-driven work, and how it connects to existing tools like dashboards or modeling platforms. Frame the catalog as a daily enabler, not a standalone utility.

3. Use internal champions to build momentum
   Identify a few early adopters within each department and give them extra visibility and training. These champions can help answer peer questions, promote adoption organically, and provide frontline feedback on what’s working—or not.

4. Establish ownership and governance early
   Make it clear who is responsible for maintaining catalog entries, updating definitions, and reviewing feedback. Without governance, catalogs go stale quickly. Ownership doesn’t have to be centralized, but it must be clear and consistent.

5. Collect feedback and iterate
   Build in regular check-ins to collect user input. Are terms missing? Are there gaps in the metadata? Is the search experience working as expected? Treat your catalog like any other product—it should evolve based on user needs.

Tip: Don’t think of catalog enablement as a one-time onboarding session. Think of it as an ongoing communications effort, baked into how you support and scale your data practice.

Catalogs Are the Front Door to Data—Design Them Like It

A data catalog isn’t just a technical tool—it’s an entry point. A place where data consumers first go to find answers, understand relationships, and build trust in what they’re using.

But trust isn’t built through tags and tables alone. It comes from context, clarity, and collaboration.

To recap:

1. Start with a business glossary to anchor meaning.
2. Treat the catalog like a product—designed for usability, not just completeness.
3. Integrate it into your modeling workflow to keep it current and connected.

When deployed thoughtfully, a data catalog becomes more than a source of metadata. It becomes a source of confidence, helping teams navigate complexity, work together, and make smarter decisions.

Ready to Rethink Your Data Catalog?

Ellie.ai helps teams move beyond static metadata to build dynamic, collaborative, and scalable data ecosystems. With support for business glossary creation, visual data modeling, and integrated catalog deployment, Ellie.ai puts clarity at the center of your data stack. Explore the Ellie.ai platform and design your catalog with confidence.