No Metadata? No AI: Inside Gartner’s Metadata Management MQ

Breaking down Gartner’s Magic Quadrant for Metadata Management Solutions.

Gartner just re-released its Magic Quadrant for Metadata Management Solutions after five years – and when Gartner brings a report back after a hiatus, you know there’s a real shift happening in the industry.

We’re thrilled that Atlan has been named a Leader in this year’s report, with customers specifically praising the platform’s ease of use, lineage, and search capabilities. Atlan was also the only vendor to score above average in all five category use cases, earning top rankings in categories critical to AI deployment:

• Data Usability Enablement
• Enabling Modern Data Architectures
• AI Readiness
• Data Engineering
• Data Governance

But there’s an even bigger takeaway here – one that should make data leaders and practitioners alike stop and listen:

Metadata has evolved from being a documentation checkbox into the foundational layer for enterprise AI.

That’s why this report matters now: metadata is no longer passive documentation. Gartner found that metadata management is shifting from augmented data catalogs to metadata-anywhere orchestration platforms – the driving layer for AI readiness and agentic AI.

As the report put it: “Metadata is now the indispensable foundation for modern and agentic AI systems.”

The message is clear for enterprises that are experimenting with, piloting, or deploying AI at scale: Now is the time to prioritize metadata management – or risk being left behind.

The Return of the Metadata Management MQ: Why Now?

Gartner paused the Metadata Management Magic Quadrant in 2021, when the category merged into data governance. But in four short years, AI has fundamentally changed the equation – warranting a much-needed update.

Gartner’s research found that 51% of organizations have already implemented metadata management, and 45% say it’s a top priority for the next two to three years. But the question of why now? has a simple answer: Production-ready AI requires metadata management maturity.

“Traditionally treated as a static documentation exercise involving data catalogs and repositories, metadata management is rapidly shifting to an active, AI-augmented ecosystem — one that is essential for enterprise AI readiness, efficient data governance, and real-time decision making,” the report explains.

As the pressure to deploy scalable AI agents and systems continues to grow, metadata is emerging as the key differentiator between success and failure. That’s because AI models fail when metadata is fragmented or stale.

AI outputs are only trustworthy if they’re grounded in continuous, active context. Without that, AI agents hallucinate, misinterpret business logic, or operate on stale assumptions that undermine trust. Distrust limits AI adoption and value – and when the value isn’t there, eventually the investment won’t be either, putting companies that opt out at a major competitive disadvantage.

The New Era of Metadata Management: Orchestration

Active metadata management turns metadata from static documentation into a source of fresh insights, providing a foundation for advanced analytics and AI model assurance. That requires data monitoring and automation, which give users real-time updates and workflow recommendations.

But according to Gartner, the real differentiator for modern metadata management is metadata orchestration.

“Rather than relying on stand-alone catalogs or isolated repositories, metadata orchestration implements an ‘anywhere’ approach,” says the report. “This approach enables metadata to flow effortlessly across an organization’s entire data ecosystem.”

Atlan was specifically designed for this new paradigm. Our Metadata Lakehouse provides an Iceberg-native store, real-time event streaming, and a knowledge graph for semantic understanding – creating a unified context layer for structured, semistructured, and unstructured data. These architectural capabilities make metadata analytics readily accessible and purpose-built for AI.

With this architecture, Atlan received the highest scores in Gartner’s Critical Capabilities for data lineage and impact analysis, semantics, orchestration, and automation – the exact capability set that underpins effective metadata orchestration.

What Metadata Orchestration Actually Requires

To take metadata orchestration from vision to reality, Gartner suggests integrating:

Metadata Lakehouse

According to Gartner, “The modern metadata repository is not simply a storage location, but a dynamic ‘lakehouse’ that integrates diverse metadata types…supporting a holistic view of enterprise data.”

With this context, Gartner specifically cited Atlan in the Magic Quadrant, stating: “Atlan’s vision of being the metadata control plane to capture, unify, and understand enterprises’ data estates is central to supporting all consumption and AI use cases and providing the necessary context and data for agentic solutions.”

This architectural approach – what we call the Metadata Lakehouse – breaks down silos that have traditionally existed between:

• Technical metadata, including schemas, lineage, and data types
• Business metadata, like definitions, ownership, and domain context
• Operational metadata, such as quality scores, usage patterns, and access controls

This allows organizations to not just wrangle context that lives across teams, but to feed it to AI in a way that makes clarity out of chaos. Grounding AI agents and systems in rich context makes them more accurate and reliable.

Embedded Metadata Delivery

Metadata must be able to flow wherever it’s needed, from data warehouses to BI tools to AI agents. This allows teams to automatically enforce governance, surface lineage, and provide the context that makes outputs trustworthy.

But for end users, it delivers something extra: a seamless user experience.

“Instead of having to switch to a separate catalog system, users receive context-aware metadata insights integrated directly into the applications they use,” the report explains. “This seamless integration improves productivity and ensures that data insights are always at hand.”

Atlan’s context layer ensures that metadata is fit for purpose and readily available, so accurate answers are easy to come by in a user-friendly interface.

Analytics-Driven Automation

Automation is an established factor in active metadata management – but metadata orchestration takes it a step further, using metadata analytics to proactively manage data pipelines.

This means systems are able to independently detect or predict anomalies, breakages, inconsistencies, and maintenance needs, so teams can address them without impacting efficiency or performance.

Gartner sums up the value, saying: “By unifying dispersed metadata and automating its analysis, orchestration platforms offer organizations unprecedented visibility into data lineage and quality – crucial elements for building trustworthy AI systems and compliant data governance frameworks.”

What Data Leaders Need to Know About Metadata Management

As orchestration becomes more central to metadata management, leaders need to be able to not just understand the technical capabilities, but also anticipate the challenges and ask the right questions to solve them. Here’s what to know:

The Metadata Challenge

As the report lays out, the challenge with getting AI into production isn’t just organizing metadata – it’s orchestrating it in real time across your entire data estate.

We spoke with hundreds of data and AI leaders to understand how this challenge actually feels. They told us things like:

“We have a thousand AI use cases on the roadmap, but we don’t even know what data we have.” – CDO, Fortune 500 Tech Company

“When someone says TAM here, it means Total Addressable Market. But online, it might mean something else entirely. How do I train my AI on that?” – CIO, Investment Management Firm

And at Atlan Re:Govern, Joe DosSantos, Vice President of Enterprise Data and Analytics at Workday, underscored the urgency behind metadata management, saying:

“While humans can often work around a flawed system, patching together dashboards, making educated guesses, and AI will not. It will demand clarity. This new reality will create gravity around the governed data, making the semantic context layer the single most important asset in the organization.”

Where to Start: Questions for Metadata Management

Data leaders’ perspectives on metadata management can help you understand and stay ahead of potential challenges. Your teams should be able to answer these questions:

• Do we know what data we have, where it lives, and who owns it? If your teams can’t answer this consistently, your AI systems won’t be able to either.
• Can our AI access the context it needs (e.g. lineage, semantics, governance rules) in real time? Static catalogs can’t keep up with agentic workflows that act autonomously and trigger downstream processes.
• Are we treating metadata as infrastructure, or still as documentation? The shift from catalogs to orchestration means metadata management is now about enabling AI at scale, not just organizing data assets.

The stakes are high: Gartner estimates that by 2027, companies that broadly leverage metadata analytics will deliver new data assets up to 70% faster. And in our own experiments with customers in Atlan AI Labs, embedding metadata context and context supply chains in talk-to-data applications increased accuracy by over 5x.

Evaluating Metadata Management Solutions

The landscape of metadata management solutions can be dizzying to navigate – even once you’ve asked all the questions above, you may not know where to start. Gartner outlines a set of capabilities that every metadata management platform must provide – here are those capabilities translated into practical evaluation questions:

• Metadata discovery – What data sources and asset types can the platform discover natively, and how are new systems onboarded and kept current?
• Metadata curation and analysis – What stewardship workflows exist, and how are trust signals captured and analyzed?
• Ontology and taxonomy management – How are business terms and relationships modeled, governed, and versioned at enterprise scale? How do definitions and policies propagate from glossaries?
• Data lineage – What end‑to‑end lineage coverage is supported, and how can it be used for impact analysis and change management?
• Data profiling – What profiling statistics and detections are supported, and where does compute run?
• Rule management – What rule/policy types can be defined, and how are they enforced, versioned, and monitored?
• Metadata operational support – What deployment, security, and compliance options exist, how does the platform scale and operate reliably, and what are the API limits for automation at scale?

Of course, these are a starting point; your ideal solution should also work with your existing tooling, provide a user-friendly experience, and offer support that focuses on delivering value for your specific business. Look for a platform with a product vision that puts metadata at the forefront, so that you can execute on all of your AI and agentic use cases.

What’s Ahead

We started Atlan with the belief that data without context is chaos, and context doesn’t stand still. For data and AI to be truly valuable, context and metadata need to be more than documentation.

We tried and failed three times before we got it right – but we stuck with our vision because we saw the potential of context to bridge the gaps in analytics and AI. So it’s gratifying to see Gartner validate that vision, and to be recognized alongside companies like IBM and Informatica as leaders investing heavily in advancing metadata management for AI-driven enterprises.

Customer feedback has consistently pointed to what matters most: speed of deployment, intuitive experiences, and open integration models that work with existing architectures. And that’s what we aim to deliver to our customers in every interaction, no matter where they are in their metadata maturity journey.

But this recognition is about more than validation. It’s about the moment we’ve been building toward – as a community and as an industry. And there’s still so much more work to do as the landscape evolves.

One thing is clear: Organizations that treat context as infrastructure will be the ones that scale AI successfully. Those that don’t will keep running impressive demos and pilots that never make it to production, as scrutiny on their AI initiatives grows.

This Magic Quadrant is more than just validation that metadata orchestration matters – it’s a roadmap for building the foundation that makes your AI systems trustworthy, governable, and ready to deliver real business value. Because without metadata, there’s no AI.

Access the full Gartner Magic Quadrant for Metadata Management Solutions 2025 report here.