Just hanging out, contemplating the complexities of data governance. Photo by author David E. Sweenor.

It’s no surprise that AI and ML are transforming how we collect and use data. But what may be surprising is the lack of attention given to AI/ML governance in the 2025 Gartner Magic Quadrant for Data and Analytics Governance Platforms, which was published at the beginning of January 2025. This raises an interesting question for me: are business leaders adequately preparing for the unique challenges these technologies bring? Probably not.

In today’s competitive landscape, data, analytics, and AI governance is more important than ever before. With regulations like GDPR, CCPA, the EU AI Act, data breaches, and the generative AI mania, organizations need a strong framework to manage and protect their data, analytics, and AI assets. This is where data, analytics, and AI governance platforms come into play. They provide a centralized system for setting policies, managing access, and ensuring things don’t go awry.

In this analysis, we’ll go beyond the usual “Leaders” list. We’ll explore the “Visionaries” quadrant, focusing on those who are making data accessible to business users without compromising security or governance.

The Big Picture – But with a Twist

As expected, familiar names like Collibra and Informatica lead the pack. Collibra earns its place with a comprehensive data catalog and strong data lineage capabilities. Informatica stands out for its broad integration with various data management tools and robust data quality management features. Interestingly, IBM is the only Leader with a dedicated AI/ML governance offering. Their watsonx.governance platform provides a suite of tools for managing and governing AI/ML models.

But let’s shift our attention to the “Visionaries.” This quadrant is where we see innovation in data governance, particularly in making data more accessible to business users while maintaining governance and security.

• Atlan: Atlan speeds up documentation and policy enforcement by integrating AI and automation into data stewardship. However, Atlan’s platform may present challenges during implementation, especially for organizations with limited technical expertise.  
• Alation: Alation provides a single source of truth for your data, enabling discovery, lineage tracking, and policy management. However, Alation currently lags in functionality in areas like data quality and observability.  
• data.world: data.world fosters collaboration by providing a platform for data scientists, analysts, and business users to share, discover, and analyze data together. However, setting up the data.world platform and managing integrations with on-premises and cloud sources may require additional effort and expertise.  

While the Magic Quadrant offers one firm’s perspective, it’s important to note some interesting omissions. The lack of emphasis on AI/ML governance is surprising, especially given the growing importance of these technologies. Also absent are several vendors with D&A governance capabilities used by organizations around the world, including:

• Microsoft Purview: This platform excels at data discovery, classification, and lifecycle management, with a focus on the Microsoft ecosystem.  
• Databricks Unity Catalog: A centralized governance solution for the Databricks Lakehouse, offering fine-grained access control and AI governance features.  
• Snowflake Horizon: Consolidates governance capabilities within the Snowflake Data Cloud, enabling organizations to govern data, applications, and AI assets.

Digging Deeper – A Critical Eye

While Gartner’s focus on “governance of data in the cloud” and “support for complex data landscapes” is relevant, we need to consider whether these criteria fully capture today’s data, analytics, and AI governance challenges.

For example, the MQ doesn’t strongly emphasize integrating data governance with data quality and observability tools, which is essential for ensuring data reliability–the fuel for analytics and AI. As noted earlier, Alation lags in this area and relies on third-party vendors to fill the gaps. The criteria could also benefit from a stronger focus on real-time data governance capabilities, as organizations increasingly need to govern data in motion. Alex Solutions seems to be ahead of the curve here, investing in real-time data activation orchestration.

Furthermore, some real-world observations challenge the MQ’s findings. While Gartner might praise a vendor’s ability to automate policy enforcement, industry reports suggest that many organizations struggle to put those policies into action. This disconnect is a potential weakness in current data governance tools. Similarly, Gartner’s assessment of Atlan revealed that some customers were not satisfied with the platform’s policy management support.

Also noteworthy, the limited emphasis on data discovery and classification in the MQ is concerning. These capabilities are critical for managing sensitive data under privacy regulations like GDPR and CCPA. It’s worth noting that vendors like data.world are addressing this by integrating generative AI into their platforms for features like guided exploration and AI-based search. This is paramount because people cannot keep up with the data sprawl across organizations–no matter what some these human-centric vendors may tell you.

However, the most glaring omission in this Magic Quadrant is the lack of focus on AI/ML-specific governance capabilities. While some vendors offer basic functionality, none specifically address the unique challenges of governing AI/ML models throughout their lifecycle. This is a significant oversight, considering the growing importance of AI/ML in data-driven decision-making. Several key players in the AI/ML space, such as Domino Data Lab, Dataiku, SAS and others, are absent from the report, raising questions about the MQ’s completeness.

CDAOs should absolutely consider AI observability and ML Ops in their data governance strategy. These disciplines are crucial for ensuring the responsible and ethical use of AI/ML. AI observability helps organizations understand and monitor the behavior of their AI models, while ML Ops provides a framework for managing the entire ML lifecycle, including model development, deployment, and monitoring.

In fact, thinking about it, I’m not sure why it’s called Data and Analytics governance, as nearly all of the criteria are related to data governance.

Here’s what they included:

• Access Management: Setting roles, permissions, and access privileges.  
• Active Metadata: Analyzing metadata to identify issues and automate actions.  
• Business Glossary: Defining and managing business terms and their relationships.  
• Connectivity/Integration: Connecting to various data sources and tools.  
• Data Catalog: Inventorying and curating data assets.  
• Data Classification: Categorizing data based on sensitivity, risk, etc.  
• Data Dictionary: Defining technical metadata about data elements.  
• Data Lineage: Tracking data origin, transformations, and movement.  
• Impact Analysis: Assessing the impact of changes on data and metadata.  
• Matching, Linking, and Merging: Identifying and resolving duplicate or related data.  
• Model Management: Reviewing, editing, and exploring data and policy models.  
• Orchestration/Automation: Automating data governance tasks.  
• Organization and Role Models: Defining organizational structures and user roles.  
• Profiling: Analyzing data to understand its characteristics and quality.  
• Rule Management: Defining and enforcing business rules on data.  
• Security: Ensuring platform security and access control.  
• Tag Management: Enriching data with tags and labels.  
• Task Management: Assigning and managing governance tasks.  
• User Interface: Providing a user-friendly interface for all governance roles.  
• Workflow Management: Designing and managing governance workflows.  

Where’s the “Analytics Governance” in Data and Analytics Governance?

Here’s what was largely missing or under-evaluated for AI/ML governance:

• Bias detection and mitigation: Identifying and reducing biases in training data and models.
• Explainability of models: Making model predictions understandable to humans.
• Model versioning and dependency management: Tracking model versions and their associated data and code.
• Privacy-preserving ML techniques: Protecting sensitive information used in model training.
• Monitoring for model drift and fairness: Tracking changes in model performance and fairness over time.
• Data Science reproducibility: Ensuring that data science experiments and results can be reliably reproduced.

And finally,

• Data Mesh Support: While data marketplaces are considered, broader support for data mesh architectures might not be fully evaluated yet.
• Real-time Data Governance: Capabilities for governing streaming data and real-time analytics are likely still emerging.

Making it Actionable

So, what can you, as CDAOs and data governance professionals, take away from all this? CDAOs and data, analytics, and AI governance professionals can glean several insights from the 2025 Gartner Magic Quadrant for Data and Analytics Governance Platforms to enhance their data governance strategies:  

• Integration is key: Data governance should be tightly integrated with your broader data management strategy, including data quality, observability, and AI/ML governance. Prioritize platforms that offer robust APIs and ecosystem integration capabilities to prevent data silos and ensure a unified governance framework.  
• AI/ML and automation is essential: Look for vendors that actively incorporate AI/ML and automation to streamline data governance processes and empower data stewards with intelligent tools.  
• Look beyond the Gorilla in the Quadrant: Don’t hesitate to explore emerging vendors, niche players, and others who are not in the MQ (e.g. Microsoft, Databricks, Snowflake, Domino, SAS, etc.) who might offer innovative solutions tailored to your specific needs. Engage with industry analysts, consult with peers, and conduct thorough proof-of-concept evaluations to make informed decisions.  

By taking these steps, you can ensure that your data governance program is comprehensive and effective.

The Future of Data, Analytics, and AI Governance: What’s Next?

We’ve covered a lot in this analysis, but the conversation doesn’t stop here. We’ve covered a lot in this analysis, but the conversation doesn’t stop here. The field of data, analytics, and AI governance is dynamic, with new vendors and solutions constantly emerging. As Matt Turk’s MAD (Modern Data & Analytics) Landscape shows, with over 2000 logos, the data and analytics space is crowded and rapidly evolving.  

Here’s a question for you: Is it just a matter of time until a “Visionary” like Atlan or data.world disrupts the established Leaders in the data governance market? Could a “sleeper” like Acryl Data, DataKitchen, Great Expectations, or Soda Data emerge as a major player? Which vendor do you see as the most likely to shake things up, and why?

We’d love to hear your thoughts! What are your biggest takeaways from the 2025 Gartner Magic Quadrant for Data and Analytics Governance Platforms? What challenges or opportunities do you see in the data governance landscape? Share your insights and experiences in the comments section below!

Did you miss the MQ? Grab one from our friends at:

• https://www.informatica.com/lp/gartner-leadership.html
• https://www.collibra.com/resources/2025-gartner-magic-quadrant-for-data-and-analytics-governance-platforms
• https://atlan.com/gartner-magic-quadrant-data-governance-2025

About the Author

Books: Artificial Intelligence | Generative AI Business Applications | The Generative AI Practitioner’s Guide | The CIO’s Guide to Adopting Generative AI | Modern B2B Marketing

Founder of TinyTechGuides, David Sweenor is a top 25 analytics thought leader and influencer, international speaker, and acclaimed author with several patents. He is a marketing leader, analytics practitioner, and specialist in the business application of AI, ML, data science, IoT, and business intelligence.

With over 25 years of hands-on business analytics experience, Sweenor has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest, in advanced analytic roles.

Follow David on Twitter @DavidSweenor and connect with him on LinkedIn https://www.linkedin.com/in/davidsweenor/.