As data volumes explode and regulatory requirements tighten, metadata management has become a cornerstone of modern data governance. From enabling data discovery, data lineage, and compliance tracking to driving AI/ML readiness and analytics quality, metadata is the hidden engine behind effective data strategies.

In 2025, the best metadata management tools go far beyond data catalogs. They offer powerful automation, deep integrations, intuitive user experiences, and robust governance frameworks, supporting IT leaders, data stewards, and compliance teams.

In this post, we review the Top 10 Metadata Management Tools based on insights from Gartner, G2, and Forrester and compare them by pricing, licensing options, features, and integration capabilities.

What are Metadata Management Tools for Data Governance?

Metadata management tools are essential components of data governance. They provide a way to collect, organize, store, and manage metadata about data assets, helping organizations understand, use, and manage their data effectively. These tools facilitate data discovery, lineage tracking, and quality improvement, ultimately enabling more informed decision-making and compliance with regulations.

What is metadata management in data governance?

Metadata management is a cross-organizational agreement defining informational assets for converting data into an enterprise asset. As data volumes and diversity grow, metadata management becomes even more critical in determining business value from the gigantic amounts of data.

Which tool is commonly used for metadata management?

Google Cloud Data Catalog

This tool can organize and use large data sets, store metadata, and quickly access data from multiple sources.

What is data management in data governance?

Data governance puts all the policies and procedures in place, and data management executes all of these pieces to compile and use the data for decision-making. Enterprise data management enables the execution and enforcement of policies and processes that data governance creates.

Why Metadata Management Matters in 2025?

• Powers enterprise data governance programs

• Enables self-service analytics and data discovery

• Ensures data lineage, auditability, and compliance

• Supports AI readiness through contextual data intelligence

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Top 10 Metadata Management Tools for 2025

1. Collibra Data Intelligence Cloud

Overview: A market leader offering an enterprise-grade platform for data governance, cataloging, privacy, and lineage.

• Collibra Pricing: Enterprise-tier pricing upon request

• Collibra Licensing Options: Modular pricing (Governance, Catalog, Lineage)

• Best For: Enterprises prioritizing data governance + compliance

• Key Features:

• Active metadata and AI-driven workflows

• Business glossary, data quality rules

• End-to-end lineage and privacy controls

G2 Score: 4.4★ | Gartner: Visionary
Pros: Governance-rich, extensible
Cons: Steeper learning curve

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2. Alation Data Catalog

Overview: Alation pioneered the data catalog space and continues to excel in intuitive metadata search and collaboration.

• Alation Pricing: Subscription-based; quote required

• Licensing Options: Per user or per data connector

• Best For: Teams focusing on collaboration & self-service analytics

• Key Features:

• Behavioral intelligence for search

• Stewardship workflow automation

• Deep BI tool integrations (Tableau, Power BI)

G2 Score: 4.7★ | Forrester Wave: Leader
Pros: Easy adoption, rich search
Cons: Limited lineage visuals vs competitors

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3. Informatica Enterprise Data Catalog (EDC)

Overview: High-performance metadata management as part of Informatica’s IDMC cloud-native data platform.

• Informatica Pricing: Quote-based; consumption pricing available

• Licensing Options: Per data asset or user

• Best For: Large enterprises with hybrid/multi-cloud environments

• Key Features:

• AI-powered cataloging

• Domain discovery and data profiling

• Pre-built connectors for 200+ data sources

G2 Score: 4.4★ | Gartner: Leader
Pros: Broad coverage and integration
Cons: Complexity at enterprise scale

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4. Atlan

Overview: A modern, collaborative metadata platform built for data teams with embedded workflows and Slack-like UX.

• Atlan Pricing: Tiered pricing, starting from team plans

• Licensing Options: Per user, per asset tier

• Best For: Agile data teams and startups

• Key Features:

• Active metadata for orchestration

• Real-time collaboration

• Column-level lineage

G2 Score: 4.8★
Pros: Fast setup, UX-driven
Cons: Newer in regulated enterprise spaces

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5. Data.World

Overview: A knowledge graph-based platform with strong governance and semantic metadata support.

• Data.World Pricing: Free basic tier; enterprise on request

• Licensing Options: Per user or project

• Best For: Teams focusing on data context and collaboration

• Key Features:

• Knowledge graph architecture

• Policy automation

• Open metadata standards support

G2 Score: 4.6★
Pros: Open standards, linked data modeling
Cons: Steeper learning curve for ontology users

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6. Azure Purview (Microsoft Purview)

Overview: Microsoft’s unified data governance service is integrated with Azure, Microsoft 365, and Power Platform.

• Azure Purview Pricing: Pay-as-you-go, starting ~$0.003/asset/month

• Licensing Options: Per scan and data mapped

• Best For: Microsoft-heavy IT environments

• Key Features:

• Data discovery and lineage

• Integration with Microsoft compliance manager

• Scan automation

G2 Score: 4.3★ | Gartner: Visionary
Pros: Seamless M365/Azure integration
Cons: Still maturing beyond the Microsoft ecosystem

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7. IBM Watson Knowledge Catalog

Overview: IBM’s enterprise data catalog solution is embedded in Cloud Pak for Data.

• IBM WKC Pricing: Cloud subscription or enterprise deployment

• Licensing Options: Per user or capacity unit

• Best For: Data governance and privacy compliance at scale

• Key Features:

• Automated metadata harvesting

• Lineage + trust score indicators

• Integration with DataStage, Db2, Hadoop

G2 Score: 4.2★
Pros: Governance + security focus
Cons: Heavier infrastructure

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8. Octopai

Overview: Automated metadata management and data lineage tool with excellent support for BI ecosystems.

• Octopai Pricing: Subscription-based, quote required

• Licensing Options: Per environment

• Best For: BI teams managing complex reporting environments

• Key Features:

• BI lineage from source to dashboard

• Impact analysis

• Automated metadata harvesting

G2 Score: 4.7★
Pros: Great for BI governance
Cons: Less broad than general-purpose catalogs

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9. Talend Data Inventory

Overview: Talend’s integrated catalog and data profiling engine are part of the Talend Data Fabric suite.

• Talend Pricing: Starts at ~$1,170/month for cloud

• Licensing Options: Per user or workload

• Best For: SMEs looking for lightweight governance

• Key Features:

• Auto-tagging and quality scoring

• Role-based access

• Real-time inventory updates

G2 Score: 4.3★
Pros: Lightweight and fast
Cons: Less powerful for complex lineage

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10. SAP Data Intelligence

Overview: SAP’s enterprise metadata and pipeline platform integrates deeply with SAP and non-SAP landscapes.

• SAP Pricing: Cloud subscription, quote required

• Licensing Options: Per pipeline or user

• Best For: SAP-centric data governance

• Key Features:

• Metadata explorer

• Data lineage and pipeline tracing

• Integration with SAP BW/HANA

G2 Score: 4.1★
Pros: Deep SAP integration
Cons: Best for SAP-native organizations

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Feature Comparison Table

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FAQ:

What is metadata management in data governance?

Metadata management is a cross-organizational agreement defining informational assets for converting data into an enterprise asset. As data volumes and diversity grow, metadata management becomes even more critical in determining business value from the gigantic amounts of data.

What are metadata tools?

These tools provide a centralized way to catalog, track, and govern data, ensuring better accessibility, compliance, and decision-making. By implementing the right metadata management solution, teams can enhance collaboration, improve data quality, and maintain a clear lineage of their data assets.

How do you implement metadata management?

Implementing metadata management in your organization involves several steps, which can be broken down into the following steps:

1. Assessment and planning.

2. Tool selection.

3. Process design.

4. Tool implementation and integration.

5. Metadata population and maintenance.

6. Training and adoption.

7. Monitoring and continuous improvement

What is the purpose of metadata?

Metadata's primary purpose is to provide information about data, making it easier to find, understand, and use. It acts as a structured data description, offering context like its origin, format, and relationships to other datasets. This "data about data" enables users to locate relevant information, organize content, and ensure data is accessible and trustworthy.

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Metadata Governance and SaaS Management with CloudNuro.ai

While metadata tools help track what data is and where it lives, platforms like CloudNuro.ai help govern who uses it across SaaS platforms, critical for licensing, compliance, and cost control.

CloudNuro offers:

• SaaS discovery across your tech stack

• License usage and vendor risk monitoring

• Policy-based governance and alerts

👉 Book a Demo to see how CloudNuro complements your metadata governance stack for full-spectrum SaaS visibility.

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