Imagine trying to find one specific document in a massive digital library without any labels or categories.
That’s the challenge enterprises face with unorganized data. Metadata management tools solve this by adding context and descriptive information to enterprise data. This extra layer makes information much easier to search, trust, and reuse across teams.
For example, technical metadata (format, structure, lineage) and business metadata (definitions, rules, ownership) together give you complete info on each data asset.
In practice, good metadata management means that even non-IT people can confidently discover and understand data in a self-service way.
But what should you look for in a great metadata management solution?
#Features that every metadata management tool should have
Data asset extraction, aggregation, and cataloging
Whenever a document, file, or other digital information asset is created, modified, or deleted, metadata is created, too. Your metadata management tool should be able to harvest metadata across your asset landscape, from both internal and external data sources.
However, extracted metadata is often incomplete or comes with missing or invalid attributes. In this case, your tool creates (aggregates) additional metadata and links it to your digital resources.
Ultimately, a capable metadata management tool creates a complete, detailed, and organized inventory of your data assets by collecting and arranging metadata descriptions. These catalogs help data consumers search and retrieve data as they help connect business context to actual data and its location.
Data context management
Context is a key to data discoverability. Even when it comes to seemingly most straightforward business terms, different contexts can lead to different perceptions of data.
For example, the concept of “client” can have different approaches, depending on the team. The sales department will use the term for the company as a whole and is primarily interested in accessing client data through their sales tools to move clients through the funnel.
On the other hand, IT would be interested in strictly technical aspects of the term, such as new clients onboard or clients who haven’t renewed their maintenance contract.
A metadata management tool must be able to discern between different data contexts to provide a seamless experience at every organizational level.
Data asset governance
This is an essential element of data management that controls the complete information lifecycle, regulates its usage, and ensures its availability, quality, security, etc. Data governance is critical for every business operation because only well-organized metadata can give a holistic view of the organization's data.
Data integration and integrity control
The solution you choose must also be capable of combining data from multiple sources into a single repository. However, the same functionalities can also be used to support decision-making, improve communication, and increase efficiency. Data integrity control, on the other hand, helps prevent data corruption and ensures the data is properly processed.
Collaborative and group functionality
The platform should enable communication and sharing channels to support connectivity between different departments and teams. Collaborative functionalities might include workflows, stewardship, version control, and audit trails.
User access control and authorization
To establish and maintain adequate data control systems that ensure the appropriate authorization, security, and accountability.
#1. Hygraph
Hygraph is a headless CMS that unifies data from multiple backend services into a single API. This means you can pull in content or metadata from distributed sources like databases, APIs, other CMSs, and manage it all in one place via Hygraph’s intuitive UI.
Non-technical users love the zero-code interface for defining and editing metadata, while developers appreciate how a single GraphQL endpoint simplifies integration on any frontend.
Hygraph takes performance and security seriously — the frontend only talks to that one endpoint, which reduces both complexity and attack surface.
In 2025, Hygraph also introduced AI features such as MCP Server and AI Assist, which help auto-enrich and organize content metadata faster.
Telenor has used Hygraph to add a metadata abstraction layer across its streaming platform. This shows how Hygraph can scale your content operations while keeping high-quality metadata governance.
Other users have noted how Hygraph “lets you work with external APIs and integrate them into your own content,” which speaks to its flexibility in bridging data silos.
#2. Collibra
Collibra is often called “the data intelligence cloud” because of its advanced enterprise data cataloging and governance capabilities. It provides a centralized platform where you can catalog data assets, define business terms, track lineage, and enforce governance policies across the enterprise.
Its workflow and role-based access features are designed so that data stewards, owners, and consumers can collaborate with clear accountability.
Users in finance, healthcare, and other regulated industries love Collibra for its flexibility to fit complex governance needs. However, Collibra’s abundance of features comes with complexity, as some users note the product’s language and terminology can feel high-end and alienating.
Collibra is a decent choice if you need an end-to-end governance solution and are willing to invest in configuring it. Having a dedicated data governance team helps a lot.
#3. Informatica
Informatica’s Enterprise Data Catalog is a metadata management suite that automatically scans and indexes data across databases, data lakes, ETL tools, and BI reports. Then, it builds a unified inventory of all data assets with technical, business, operational, and usage metadata.
This tool is great for rich metadata analytics, as it provides semantic search, lineage tracing, impact analysis, and data relationship discovery out of the box. It gives you both a business glossary view and deep technical details in one place, which makes it appealing to a wide group of users, including analysts, engineers, and data stewards.
With a strong focus on data governance, Informatica has built-in data quality scores, compliance dashboards, and policy management, which suit organizations that have strict regulatory requirements.
However, like Collibra, some users caution that Informatica’s catalog can be old-school and heavy if all you need is basic cataloging.
To conclude, Informatica is a powerful, enterprise-grade tool that has been consistently expanding its features, but it might be overkill for smaller teams.
#4. Atlan
Atlan is an active metadata platform for data teams with strong collaboration and automation features. The platform is very friendly to both engineers and business users, automatically indexing data assets and actively pushing context to where users work. For example, it can integrate with BI tools, Slack, etc., to deliver metadata in real-time.
Users praise how easy and fast Atlan is to deploy compared to legacy tools. In fact, one user noted that traditional catalogs were “expensive and riding on reputation,” while newer tools like Atlan offer better integration and simpler management.
Atlan also brings automated lineage, granular governance, and a Google-like search interface that’s intuitive for analysts.
On top of it, Atlan is Snowflake-native. So if your stack is on Snowflake or modern cloud warehouses, Atlan is optimized to provide column-level lineage for Snowflake and connectors to popular SaaS sources.
#5. Apache Atlas
Apache Atlas is an open-source metadata management framework you can use to define data assets, tags, and capture lineage and relationships among those assets.
A huge bonus with Atlas is that it integrates with other Apache projects. For example, it can plug into Hadoop, Hive, Spark, and Kafka to automatically capture metadata as data flows through those systems.
This makes it a great choice for engineering teams who are deeply invested in the Apache stack. With Atlas, they can build a central data catalog with custom metadata types and also apply data governance rules.
However, user experience is a known shortcoming. Data experts have reported that Atlas’s UI is unimpressive and not very friendly for non-technical users. It gets the job done for behind-the-scenes metadata capture, but business analysts might struggle with it.
That said, Atlas is a solid choice if you need a free, self-hosted solution and have the resources to build custom front-ends or integrations, as many companies do.
#6. erwin by Quest
Erwin (now part of Quest Software) has evolved into a broader data intelligence platform that combines data catalog, data governance, and data modeling. Such a blend of features is especially interesting to organizations that prioritize data architecture.
With erwin, you can discover and harvest metadata from a wide variety of sources, then map that technical metadata to business glossaries and process models. In other words, erwin helps answer “what data do we have and where is it?” in a deep, architectural way.
Another selling point is its data lineage and impact analysis. Since erwin began as a data modeling platform, it excels at visualizing how data flows and changes through systems. It also supports importing metadata from other integration tools to centralize all lineage in one place.
All in all, erwin is a powerful suite that might be an overkill for simple cataloging, but for those looking to tie technical metadata to business outcomes, it’s a top choice.
#7. Alation
Alation is a data catalog platform with an interface that feels a bit like a wiki combined with Google search. It encourages users not only to find data but also to contribute knowledge about it.
For example, in Alation, you can write wiki-style articles for datasets, add tags, and even discuss data right in the tool. As their tagline says, “data intelligence + human brilliance”, Alation is a good choice for organizations where analysts and domain experts actively collaborate on content.
A standout feature is Alation’s Trust Flags and popularity rankings, which show which datasets are regularly used and which are verified or deprecated, which helps guide new users to trusted data. Alation also has an AI-powered recommendation engine that suggests related datasets or queries, learning from how people use the data.
Users frequently mention that Alation is easier for business users to understand compared to competitors like Collibra.
#8. Alex Solutions
Alex Solutions is an enterprise metadata management tool known for its data catalog, business glossary, data quality, and privacy compliance features. Alex positions itself as an “active metadata fabric”. This means that besides just cataloging data, it also actively scans and monitors it for changes, quality issues, and policy violations.
The platform can harvest metadata from a wide array of databases, applications, and even BI tools.
Alex is relatively easy to deploy and use, considering its diversity of features. It presents a clean UI for data stewards to enrich data assets with business context and for analysts to search and understand data.
Among key capabilities, there’s an advanced lineage visualization and intelligent tagging that uses machine learning to classify data.
#9. IBM Watson Knowledge Catalog (IBM)
Watson Knowledge Catalog (WKC) is a modern metadata management system that has evolved from IBM’s older InfoSphere suite. This platform provides a cloud-native catalog in which you can organize data assets, analytical models, and AI assets with rich metadata.
One of the biggest strengths of IBM’s catalog is integration. For example, if your enterprise uses IBM databases, ETL, or IBM governance tools, WKC plugs in natively.
WKC offers automated metadata discovery, a business glossary for terminology, policy management for compliance, and since recently, AI capabilities. For example, you can use IBM’s AI tech to provide intelligent recommendations and data profiling to automatically classify data or suggest data to users.
A standout feature is the quality score and regulatory filters. IBM can assign trust scores to data and help filter or mask data based on privacy rules, which is important for industries like healthcare or finance.
Another thing to note is that you can deploy WKC both in the cloud and on-premises, which is interesting to companies with strict data residency requirements.
#10. MANTA
Manta is a niche metadata tool that focuses on automated data lineage. Instead of offering a full data catalog with business glossaries, Manta specializes in scanning your databases, ETL scripts, and BI reports to figure out how data flows from system to system.
For example, Manta can parse SQL code, stored procedures, or data transformation scripts and then generate a visual map showing, say, how a field in a report traces back to columns in source databases.
This lineage is incredibly useful for impact analysis, debugging data issues, and compliance, for example, in proving where a certain number in a report came from.
Manta often works in tandem with other catalog or governance tools like Collibra or Alation. In fact, Alation has used Manta under the hood for some lineage connectors.
#11. OvalEdge
OvalEdge is an all-in-one data catalog and governance tool popular with mid-sized companies and beginners in data governance. You can use it to crawl databases, data lakes, and files to create a searchable inventory of data assets.
It also has built-in data profiling and quality checks, which is a nice bonus for ensuring your metadata is paired with some understanding of the data’s content.
OvalEdge prioritizes ease of use and adoption. It comes with social features like upvoting assets, and it’s relatively easy to set up without a huge implementation project. The tool also has role-based security so you can control who sees what, and even enforce row-level security on the data through the catalog interface.
According to a financial firm user, building inventory and lineage with OvalEdge is fast, yet organizational change management is a big challenge in adopting the product.
#12. Progress Semaphore
Progress Semaphore was originally developed by SmartLogic, which merged with MarkLogic before being acquired by Progress. It’s an enterprise-grade semantic AI platform that uses knowledge graphs and ontologies to manage metadata.
Unlike traditional data catalogs that focus on listing data assets, Semaphore is built to enrich data with concepts, categories, and relationships, which essentially turns raw data into linked, contextual knowledge.
This makes it especially appealing to content-rich industries such as publishing, insurance, and healthcare, where unstructured data needs consistent tagging and classification. Semaphore provides tools to build ontologies (formal models of knowledge for a domain) and then uses NLP (Natural Language Processing) to auto-classify content against those models.
For example, a pharmaceutical company might use Semaphore to ensure all documents about a certain drug are tagged with the proper ingredient and therapeutic area metadata, which leads to better search and compliance.
#Wrapping up
Each of these enterprise metadata management tools has its own strengths, so the “best” tool depends on your organization’s priorities. One thing’s for certain — metadata management is a critical function behind self-service analytics, regulatory compliance, AI initiatives, and more.
And while the right metadata management platform will ensure everyone can find, understand, and trust the data they need, a big part of success also depends on culture and process.
So when evaluating these tools, consider not just features and tech, but also how they will fit into your data workflows.
Get in touch with Hygraph’s team to discuss your challenges and see how we can help you untap the huge potential of metadata in your organization.