For organizations to use AI for their analytics needs, the role of metadata management has shifted from back-office hygiene to mission-critical infrastructure. Metadata is no longer just about cataloging data products, it’s about enabling AI to understand your data. Using metadata management you can declutter your data analytics, ensure that you’ve “certified” dashboards for their AI-readiness and align your metrics so that AI agents can deliver trusted results, every time.
In this post, we’ll review some of the most powerful metadata management platforms available in 2025, with a focus on tools that help make your data AI-ready, discoverable, and governed at scale.
What are metadata management tools?
As more companies invest in AI and data-driven decision making, the importance of metadata has grown. Metadata essentially tells you what a dataset means, where it came from, how it’s used, and who owns it. When managed properly, metadata helps you certify data assets for AI use (for example making sure it uses the agreed definition of “ARR”, “DAUs”, “CAC” etc) trust it, and use it to power AI-based data analytics. After all, if the data is flawed, ai-based data analytics won’t work.
With the explosion of data sources, formats, and users, manual approaches to tracking metadata no longer work. That’s where modern metadata platforms come in. These tools help teams automatically collect, organize, and enrich metadata so they can better understand and manage their data assets.
Here are some of the top metadata management tools available in 2025:
Best 8 active metadata management tools
1. Euno
Website: euno.ai
Euno is built for data teams who want to activate their metadata, not just document it. It connects lineage, usage, code, and ownership, so you can see how data flows, what’s trusted, and what needs to go. It also powers an MCP server that gives AI agents access to governance signals like certification status and metric definitions, so they stop guessing and start acting with context.
Euno’s core use cases:
- Dashboard certification: Set your own certification rules—based on lineage, SQL logic, ownership, etc.—and enforce them at scale. Euno surfaces what violates the rules and tells you exactly how to fix it.
- Metrics governance: Manage a source of truth for metrics. Find the most-used business terms in your BI layer, resolve conflicting definitions, and move what matters into a governed semantic model.
- Decluttering & cost control: Automatically flag stale dashboards, unused refresh jobs, and idle models. Clean up the noise and cut compute costs in the process.
2. Collibra
Website: collibra.com
Collibra is a long-standing leader in the space and is often used by large enterprises with strict governance or compliance needs. It provides a centralized platform for managing data policies, data quality rules, and business glossaries. Collibra works well for organizations that need tight control over data access and workflows that cross many departments and systems.
Key Features:
- End-to-end data governance and lineage tracking
- Business glossary, data marketplace, and issue management
- Deep integrations with enterprise data ecosystem
3. Atlan
Website: atlan.com
Atlan’s focus is data democratization for modern data teams. Atlan aims to be a “home” for data teams. It’s designed to help analysts, engineers, and business users work together by making data more discoverable and understandable.
Key Features:
- Active metadata – metadata that can trigger alerts, updates, and recommendations based on usage
- Git-style versioning and programmatic API access
- Slack and BI integrations for seamless workflows
4. Alation
Website: alation.com
Alation was one of the first platforms to focus on making data easy to find and trust. It uses search, machine learning, and query history to help users quickly locate relevant data. Alation also provides tools for documentation and stewardship, which are useful for regulated industries like finance and healthcare.
Key Features:
- Data stewardship dashboard
- Policy center and query log analysis
- Deep AI-assisted discovery
5. DataHub
Website: datahubproject.io
DataHub is an open source metadata platform. Originally developed at LinkedIn, DataHub is popular among engineering-focused teams that want to customize their metadata platform. It’s open source and has a strong developer community. Companies use it to build internal data catalogs, track data lineage, and manage schema changes across large-scale data systems. It is best for technical teams looking to customize and extend metadata infrastructure.
Key Features:
- Graph-based metadata model
- Real-time event streaming ingestion
- Community-driven plugins and connectors
6. Castordoc (Now catalog after being acquired by Coalesce)
Website: castordoc.com
Castor is designed for companies that want metadata management without the overhead. It provides simple, intuitive interfaces for documenting data and understanding usage, including column-level lineage. It’s a great choice for startups or teams looking to increase data literacy without heavy process.
Key Features:
- Clean UI for business and data users
- Auto-documentation and column-level lineage
- Usage analytics and adoption metrics
7. Select Star
Website: selectstar.com
Select Star focuses on making it easy to understand how data is used across the company. It automatically ranks popular datasets and shows how they’re connected to dashboards, queries, and users. This helps new team members onboard quickly and enables better decision making by showing where data comes from and how it’s being used.
Key Features:
- Popularity-based data ranking
- AI-driven documentation suggestions
- Plug-and-play integrations with Snowflake, dbt, and Looker
8. Data.world
Website: data.world
Data.world uses knowledge graph technology to link data assets with concepts, people, and policies. This makes it easier to understand not just what a dataset is, but how it fits into the business. It’s especially useful for organizations trying to align technical data with strategic goals or regulatory requirements. It is best for orgs building semantic data layers or ontologies.
Key Features:
- Knowledge graph modeling
- Linked data and RDF support
- SOC2 and HIPAA compliant
Key Takeaways
Metadata is no longer optional, it’s strategic. As AI becomes embedded across business processes, metadata that is complete, contextual, and machine-readable becomes your biggest asset.
No matter which tool you choose, investing in metadata is no longer optional, it’s the foundation for data quality, trust, and long-term AI success.
