The Forrester Wave™: Data Governance Solutions, Q3 2025

Data Governance Solutions Vendor Product Comparisons 2025

Key features and pros and cons of top Data Governance Solutions products:

Alation - Leaders

Pros:

Differentiated strategy centers on shifting governance from passive documentation to intelligent, agentic workflows
Excellent capabilities in cataloging and collaborative metadata curation
AI-powered features for data enrichment and policy enforcement
Data marketplace and product builder modules as key differentiators
Intuitive UX and flexible integration
Superior collaboration features

Cons:

Data observability is a key area for improvement
Native data quality and observability are high on customers' wish lists

Atlan - Leaders

Pros:

Strategy focuses on activating metadata as a dynamic layer for governance, collaboration, and AI enablement
Best-in-class features for policy management, stewardship, and collaborative governance
Knowledge graph and AI-powered automation support clear data ownership
Integration-first approach enables seamless alignment with existing data environments
Superior adoption across customer organizations
Intuitive UI, strong lineage and smart search capabilities
Seamless integration across the data stack

Cons:

Should improve access to granular reporting metrics and executive dashboards to better support federated teams

Collibra - Leaders

Pros:

Flexible, enterprise-grade solution with knowledge graph and customizable operating model as core differentiators
Excellent innovation efforts and strong community involvement
Excels in classification, data observability, and policy modeling
Supports complex regulatory frameworks
Strong lineage and impact analysis features for audit and compliance
Workflow engine supports granular role-based responsibilities

Cons:

Policy enforcement customization can require manual efforts, posing challenges for faster time to value
Steep learning curve among nontechnical users
Slower rollout of sought-after features like AI governance and data productization
Collaboration features remain an area for improvement
Pace of delivering anticipated enhancements has been uneven

Informatica - Leaders

Pros:

Strategy focuses on enabling trusted, AI-ready data through metadata-driven governance
Over $250 million in annual R&D spend and recent acquisitions in privacy and access management
Superior metadata management and glossary creation
Strong automated lineage with generous range of connectors and deep field-level scanning
Best-in-class quality and impact analysis
CLAIRE powers classification, policy detection, and rule generation
Unified remediation workflows with intelligent automation as key differentiators
Comprehensive cataloging, classification, and integration capabilities
Platform's scalability

Cons:

Platform's scale, complexity, and pricing models can challenge adoption by smaller teams
Customers note clearer roadmap communication, simplified adoption paths, and composable pricing models as areas for improvement
Advanced functionalities such as group-based approvals and parallel processing are among customers' enhancement requests

Ataccama - Strong Performers

Pros:

Strategy centers on convergence — offering a single platform to manage profiling, cleansing, lineage, and policy enforcement
Scores highly in data quality, observability, and profiling
Robust anomaly detection and remediation workflows
Lineage engine supports accurate cross-system tracing and impact analysis
Glossary and policy features tightly integrated with classification and access control
AI agent automates rule creation and profiling
Transparent pricing

Cons:

Broad coverage can limit depth in areas like AI governance and advanced policy modeling
Should better leverage its platform to enable effective peer-to-peer knowledge sharing, particularly within industry verticals
Broader AI governance and workflow reusability are still evolving
Less suited to those prioritizing deep AI governance or advanced policy orchestration
Some advanced features require custom configuration or scripting
Customers seek improvements in workflow reusability, AI governance maturity, and a dedicated marketplace for data products

BigID - Strong Performers

Pros:

Strategy built around agentic AI and security-first governance
Scores highly for security and privacy features
Native controls for masking, anonymization, and automated risk detection
Ensures Zero Trust access and proactive threat mitigation
Solid business glossary enriched with metadata and AI-driven suggestions
Modular architecture and deep ecosystem integrations
Trusted and resourceful partner within the ecosystem
Customers praise scanning, classification, and privacy capabilities along with professional services

Cons:

Innovation remains centered on security and compliance, with governance enhancements steadily gaining traction
Pricing transparency is an area for improvement
Lineage acquisition and visualization is an area for improvement, with limited depth in contextual mapping
Needs to add sophistication for scaling data literacy and managing enterprisewide terminology
Customers seek improved lineage visualization, scalable glossary automation, and more clarity and versatility in pricing models

data.world - Strong Performers

Pros:

Strategy centers on metadata-driven collaboration powered by semantic knowledge graph
Positioned as a modern alternative to legacy platforms
Prioritizes usability and extensibility
Differentiated by pricing model and transparency
Platform stands out for semantics, issue remediation, and intuitive stewardship support
Collectors simplify metadata ingestion across diverse sources
Role-based personalization and discussion features support superior collaborative governance
Customers consistently highlight ease of use, strong modern data stack integration, and reliable customer success teams

Cons:

Better long-term communication to manage customer expectations would strengthen the vendor's codevelopment model
Currently lacks native capabilities for observability, data masking, and anonymization
AI governance efforts are solid but less suitable for enterprises requiring sophisticated automation workflows, integrated policy orchestration, or AI governance at scale
Areas for improvement include workflow maturity, more strategic onboarding, and more versatile AI functionalities

OvalEdge - Strong Performers

Pros:

Strategy focuses on democratizing governance through simplicity and affordability
Delivers on vision to eliminate fragmented approaches and enable faster time to value
Customer-driven roadmap with frequent releases and strong support from responsive team
Practical, value-focused approach resonates with cost-conscious buyers
Scores well in stewardship workflows and security and compliance capabilities
Solid metadata crawling and advanced features like AskEdgi assistant and governance recipes
Source-code-based lineage enables streamlined workflows for wide range of roles
AI governance and advanced policy modeling are solid
Customers highlight affordability, responsive support, and ease of use

Cons:

Innovation strategy is more conservative than its peers
Advanced data quality, scalable glossary automation, and complex marketplace features are still evolving
Areas for improvement include UI polish, advanced data quality capabilities, and enhanced onboarding assistance

Precisely - Strong Performers

Pros:

Vision is to unify data governance with quality, enrichment, and spatial analytics into a single, accessible platform
Data Integrity Suite's common foundation streamlines metadata management, security, and AI-powered intelligence
Robust partner ecosystem supported by consulting expertise
Delivers solid capabilities across observability and policy enforcement
Governance metrics reporting capabilities are noteworthy differentiators
Strong semantic detection, enrichment datasets spanning various domains, and data valuation methodologies
Customers appreciate intuitive UI, strong classification engine, and responsive support
Ease of use and competitive pricing, particularly for smaller enterprises

Cons:

Innovation strategy is somewhat pragmatic and incremental
Some customers report delays in feature delivery and inconsistent communication
Lineage management capabilities are limited and often require manual effort
Lack AI-driven inference
AI governance and data masking features are underdeveloped
Key areas for improvement include lineage automation, workflow agility, and enhanced masking capabilities

Quest Software - Strong Performers

Pros:

Strategy centers on delivering a unified platform that bridges technical and business users via flexible governance framework
Delivers superior capabilities in data observability and internal marketplace functionality
Standout features like automated data value scoring and AI model certification
Platform supports structured data well and is expanding into unstructured sources
Flexible metamodel, mind map visualizations, and integrated data quality engine
Customers praise ease of use, integration with erwin Data Modeler, and responsive customer support

Cons:

Vision is shifting to a stronger AI focus but lacks clarity and has yet to be effectively communicated
Roadmap emphasizes AI data readiness, marketplace enrichment, and expanded data quality capabilities
Continues to deliver steady, customer-driven innovation
Glossary, enrichment, and collaboration tools are solid but lack the depth and automation of leading competitors
AI governance capabilities are still maturing
Customers would like to see a simpler UI, external marketplace provisioning, and more thorough documentation

IBM - Contenders

Pros:

Strategy focuses on delivering enterprise-scale governance via a modular platform
Vision emphasizes AI readiness and self-service access
Investing in watsonx to modernize governance stack and advancing AI governance capabilities
Partner ecosystem and regulatory alignment are strengths
Delivers solid cataloging, glossary, and stewardship features
On-par performance in data discovery, semantics, and observability
Enterprise-grade catalog and glossary
Customers appreciate cataloging and glossary capabilities and implementation support

Cons:

Execution lags its peers: roadmap lacks clarity and urgency in addressing usability, collaboration, and AI governance automation
Adoption support and roadmap transparency remain weak points
Scores below par in impact analysis and policy compliance due to limited automation and actionable insights
Needs to improve collaboration with high demand for customizable UI features and collaboration tool integrations
Lacks the intuitive UX and automation depth of competitors
Less suitable for those wanting a modern UX, collaboration, and AI governance automation
Customers cite challenges with usability, collaboration, and the speed of innovation, particularly in lineage, policy management, and data productization
IBM didn't provide reference customers for this evaluation

OneTrust - Contenders

Pros:

Strategy focuses on compliance-first governance, integrating privacy, consent, and AI risk management
Rich partner ecosystem is a key strength
Delivers superior policy enforcement and privacy automation
Strong ethical AI governance
Earns high marks for ability to support compliance workflows and regulatory intelligence
Classification capabilities are strong, using AI to identify and categorize sensitive data across diverse environments
Customers value privacy and data AI readiness strengths, integration flexibility, and platform support

Cons:

Vision isn't distinctly articulated and is underwhelming given the speed and expectations of the market
Roadmap emphasizes AI governance, policy orchestration, and data readiness, but prioritization and communication with customers on deployments reflect a reactive approach
Adoption beyond core compliance and privacy teams is still gaining traction
Cataloging and lineage acquisition capabilities lag, offering limited automation and depth for metadata management and visualizing data flows
Enrichment capabilities could benefit from modernization, reducing ability to enhance metadata dynamically or support advanced data quality monitoring
Customers seek improvements in pricing model flexibility, lineage visualization, and role-based UI personalization

SAS - Contenders

Pros:

Positions governance capabilities in context of its analytics heritage
Emphasizes data readiness for AI and decisioning
Supports data stewards and analysts with integrated tools for cataloging, profiling, and ensuring data quality
Enables alignment of governance with analytics workflows
Supports classification, glossary linking, and data masking
Strength lies in integration with the broader Viya ecosystem and support for analytics-driven governance
Customers appreciate data quality and profiling tools and ability to support analytics workflows

Cons:

Doesn't aim to compete directly with governance-first vendors
Roadmap includes enhancements to data quality monitoring, glossary workflows, and AI governance, but current capabilities are largely SAS-centric and focus mainly on technical users
Fails to articulate vision and innovation strategy clearly
Community and pricing transparency lag behind competitors
Gaps in advanced governance features — such as semantics, complex lineage visualization, and AI governance — limit ability to provide automation and actionable insights for complex use cases
Features like dashboards and policy enforcement are still in development or require custom configuration
Customers note gaps in collaboration features, policy management, and marketplace functionality
SAS didn't provide reference customers for this evaluation

FAQs

Q: Which vendors are Leaders in 2025?

A: Alation, Atlan, Collibra, Informatica

Q: What does "Leader" mean in Forrester Wave terms?

A: Leaders in the Forrester Wave demonstrate the strongest current offerings and strategies in the data governance solutions market. They excel across key evaluation criteria including governance automation, AI capabilities, user experience, and business value alignment. Leaders typically show superior performance in metadata management, policy enforcement, collaboration features, and have clear, well-executed roadmaps for advancing governance capabilities. They also demonstrate strong customer satisfaction and market presence.

Q: How do I interpret a Forrester Wave?

A: https://www.forrester.com/policies/forrester-wave-methodology/

Report:

Top Vendor Products for Data Governance Solutions in 2025

Data Governance Solutions Vendor Product Comparisons 2025

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FAQs

Q: Which vendors are Leaders in 2025?

Q: What does "Leader" mean in Forrester Wave terms?

Q: How do I interpret a Forrester Wave?

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