Introducing Matia’s enhanced Data Catalog: end-to-end visibility with actionable lineage

We built something new-ish. Something data teams have been asking for. And it lives right inside Matia.

Today, we’re launching the enhanced Matia Data Catalog, a fully integrated, metadata-rich experience that brings data discovery, governance, and observability into one unified platform. 

No extra logins. No duct tape integrations. No more switching between five tools to understand where your data came from.

Just your data. Clear, connected, and certified.

The catalog, reimagined for modern data teams

Most data catalogs offer a fragmented view of your assets. You click around a lineage diagram, maybe find a stale column description, and then hop into another tool to dive deeper, fix your pipelines.

Not here.

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Because the Data Catalog is built into the same unified data operations platform you use for ingestion, transformations with dbt, reverse ETL, and data observability, lineage is not just a picture. Dynamic lineage graphs are a source of truth. You can click into any asset in your pipeline and see:

• Table- and column-level lineage
• dbt models, sources, and tests
• Snowflake and dbt tags, pulled in automatically
• Operational metadata like run history, error logs, and freshness
• Ownership and certification so you know who to ask and what to trust

With this launch, we’re introducing Matia Tags, a way to organize, filter, and group data assets that actually makes sense for your team. Tag by project, domain, owner, business unit, or anything else.

Tags, trust, and team visibility

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With this launch, we’re introducing Matia Tags, a way to organize, filter, and group data assets that actually makes sense for your team. Tag by project, domain, owner, business unit, or anything else.

Looking for something in particular? Pairing tags with the the revamped universal search allows you to parse and locate relevant assets faster

And when a dataset is ready for prime time? Certify it. Our new Data Certification feature helps your team know what is vetted and trustworthy. This reduces decision risk and increases stakeholder confidence.

Ramp’s data team is already seeing the difference.

“Most data catalog tools show you a diagram, but what we needed was context. What ran, what failed, what changed,. Matia is unified, so lineage is not just visual. It is actionable.”

- Akshay Ajbani, Senior Analytics Engineer, Ramp

Top Use Cases: More Than Just Metadata

We’re already seeing our customers and design partners leverage catalog for various use cases.

AI Enablement
AI needs context. The Matia Catalog gives your models access to complete, trusted, and documented metadata across your stack. Whether you’re training LLMs, surfacing recommendations, or building AI-powered apps, the catalog provides the data clarity AI systems require. With operational lineage, freshness tracking, and ownership metadata all in one place, your AI tools can reason with confidence and stay grounded in reality.

Scalable Data Governance

Matia brings governance into the flow of work. Our users are setting ownership, tracking certifications, and applying Matia Tags across your entire data estate. Compliance teams get the audit trail they need. Analysts get clarity on what’s certified and who’s responsible. Business users can check data definitions. And best of all, it happens right in the platform, no separate tool (can you tell by now we really don’t like that?)... just practical control and accountability at scale.

Unlocking Productivity

With a unified catalog that connects ingestion, reverse ETL, and observability, your team no longer wastes time context-switching. Analysts can self-serve metadata, engineers can debug with full visibility, and data owners can assign and track responsibility, all from one place. The result: faster answers, fewer blockers, and a more productive data team.

Improving Data Quality

Matia's catalog doesn’t just show what data is where. Combined with the power of Matia Observability, itt reveals how it’s behaving. Freshness indicators, error logs, and run history are all embedded into lineage views, making it easy to catch and fix quality issues before stakeholders notice. And because it’s tied to observability, you can continuously monitor and improve the reliability of every dataset.

Want to see how it works?  Explore the Data Catalog 

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