Forrester Wave Insights: How Modern Data Governance Powers AI at Enterprise Scale

As someone who works closely with industry analysts and helps shape the market discourse on data governance, I had the privilege of hosting a fascinating conversation between Alation CEO Satyen Sangani and Raluca Alexandru, Lead Analyst at Forrester Research. Their discussion around The Forrester Wave™: Data Governance Solutions, Q3 2025 revealed just how dramatically the data governance landscape is transforming.

The conversation reinforced what we've been seeing in our analyst interactions and hearing from our customers: data governance has evolved from a compliance-focused discipline into what Raluca described as "the control plane for trust, agility, and AI at enterprise scale." This transformation represents the most significant shift in our industry in decades. In this blog, I’ll recap key takeaways from the talk. Let’s dive in!

The consumer paradigm comes to enterprise data

Enterprise data interaction is mirroring consumer behavior changes. "We all have seen how generative AI has changed and transformed our personal lives and even our professional lives," Satyen noted. Today, more people are turning to tools like ChatGPT, Claude, and other LLMs to ask questions and get precise, timely answers tailored to exactly what they want to know.

This shift from searching Wikipedia pages to asking AI represents more than convenience—it's fundamentally changing how people in enterprises expect to interact with their data ecosystems. The implications for governance platforms are profound: We're moving from passive documentation systems to active, intelligent assistants that can understand intent and deliver precise outcomes.

How have the Forrester Waves for data intelligence evolved?

Through our work with analysts and customers, we've identified four distinct evolutionary phases in the data governance market:

Wave 1: Data catalog pioneering (2015-2018): Satyen reflected on Alation's early days: "We came on the scene in 2015. And at the time, metadata management was a category, but cataloging wasn't. And we really realized that the production of information was critical to be done not just by the IT audience, but by the business audience more generally speaking."

Wave 2: Data intelligence catalyzes convergence and integration (2018-2021): The market recognized that "data governance, data cataloging, data lineage, metadata management, these tools converged," as Satyen explained. Organizations needed integrated suites rather than disparate point solutions.

Wave 3: Federated governance and data products (2021-2024): We learned that "most catalogs die under the weight of the content and the use cases and the variety of users that they have." Data products have emerged as the solution, allowing governance to be right-sized to specific business outcomes.

Wave 4: Agentic intelligence (2024-now): This is where we are today—entering an era where, as Satyen put it, "agents are both important in helping do the work of data management and also in helping actually be the result of data management."

Forrester's market assessment: The shift to agentic AI

Raluca's research reveals a market in rapid transition: "Data governance has outgrown its compliance roots: In today's AI-fueled and data-saturated enterprise, it's the control plane for trust, agility, and scale."

The Forrester evaluation of 13 leading providers shows what Raluca calls a "clear market shift toward agentic AI—governance systems that actively automate policy enforcement and remediation while keeping humans in the loop." As she emphasized, "The next frontier isn't just about managing metadata — it's about activating it."

This activation manifests in several critical capabilities:

• Automated classification and intelligent rule generation

• Context-aware recommendations for data stewards

• Self-driving governance experiences that scale consistency

• AI-powered policy suggestions aligned with business outcomes

The business-led revolution: Governance gets strategic

 One of the most significant trends Raluca identified—and one we're seeing across our customer base—is business teams taking the lead in governance strategy, rather than simply reacting to IT initiatives.

"Business is not necessarily leading, not running the governance process, but inspiring and designing it with the needs of the business rather than just the old school data for the sake of data," Raluca observed.

The data supports this trend:

• 71% of enterprises now create data products (a dramatic increase from previous years)

• 28% are merging data and AI governance practices, representing a growing investment in these focuses

• 69% are increasing spending on data governance solutions

Raluca also noted the increasingly widespread adoption of federated governance models, as well as a growing focus on quality controls as AI adoption scales.

This shift represents governance's evolution from a back-office compliance function to a strategic business enabler.

Metadata activation: The technical foundation of intelligent governance

Traditional metadata management has given way to activated metadata. As Raluca explained, this means "basically giving context and not just semantic context, but understanding how that data can be used and why it should be used in a certain way."

This activation enables transformative capabilities:

• Powerful semantic search that understands intent, not just keywords

• Automated policy enforcement that adapts to business context

• Data product marketplaces where metadata creates discoverable, reusable assets

• Collaborative frameworks that connect technical and business teams

AI has a key part to play in this evolution. We believe agents are essential—not only for managing data, but for demonstrating the value of data management itself. But to make agents effective, you need critical metadata.

Numbers Station, an AI startup recently acquired by Alation, learned this through trial and error. “When they started the company, they wanted to make LLMs work on top of structured data,” Satyen shared. “And they found that it was tremendously difficult to do without agents. And in their testing, they found a 30% uplift in many cases for what metadata can do to accuracy and evaluation credibility, when you had LLMs talking to and reading from structured data.”

This isn't a theoretical benefit; it's quantifiable, reproducible.

From procedural to declarative: The governance paradigm shift

A critical transformation is underway: the move from procedural to declarative governance.

“In the future, we’ll be declarative,” Satyen said. “Data governance will be outcome-based. The shift with LLMs is that you can specify governance outcomes rather than depend on expertise to create detailed policies. LLMs help implement and adapt policies to specific data contexts.”

Satyen illustrated this with our Data Quality Agent: "We allow you to recommend the monitors that you need based upon the outcome that you're trying to specify. If you have a table with 400 columns but only need 20 of those columns to fill your churn model, you don't need to apply data quality rules to the other 380."

This shift to declarative governance represents a transformation:

• AI-assisted intelligence replaces manual expertise

• Controls align with outcomes, not blanket policies

• Governance shifts from reactive compliance to proactive enablement

• Documentation becomes dynamic and measurable

This shift sets the stage for the next generation of agent-powered governance platforms—where precision and context drive business value.

Precision agents for business outcomes

The ultimate goal of this evolution is what Satyen calls "precision agents"—AI systems that can reliably interact with mission-critical business data. "Getting these data agents to be able to read to and write from and building agents that read to and write from these mission-critical datasets is a huge problem that we're focused on," he explained.

This requires solving the fundamental challenge of making language models work accurately with structured data across different model types, data sources, and business contexts—every single time.

For organizations struggling with governance prioritization—a common theme in my analyst conversations—Satyen offered clear guidance: "You have to start off from a business outcome that you want to be able to drive, work back to the data product that you want to be able to build."

This approach transforms governance teams from insight providers into active business process improvers. As Satyen advised, "Start with one, get success, go do another." This focused methodology not only proves value but builds organizational momentum for broader transformation.

Market consolidation: The platform imperative

An important trend Raluca and I discussed is market consolidation. "There are always going to be companies that are more mature on some elements of the data governance process," she noted, but "the overall trend that we see is consolidation, and there is a vision to add as many elements from the data governance life cycle within the platforms."

This validates our platform approach at Alation—building comprehensive capabilities while maintaining the flexibility to integrate with existing enterprise investments.

Governance as a competitive advantage

Through my work with analysts and customers, I see clearly that the future of data governance isn't about better compliance—it's about enabling entirely new business capabilities. As AI becomes central to enterprise operations, governance transforms from a supporting function to a strategic differentiator.

Organizations embracing agentic AI, focusing on business outcomes, and building measurable intelligence into their governance practices are creating sustainable competitive advantages. Those remaining trapped in procedural, compliance-focused approaches will struggle to capitalize on AI's transformative potential.

The path forward

The conversation between Satyen and Raluca reinforced what we're hearing across our analyst network and from our customers: the governance transformation is accelerating. The tools and techniques exist today. The business case is proven. The market is moving.

As Raluca concluded, "Modern data governance is on its way to becoming... that control plane for trust, agility, and AI at scale." For organizations ready to embrace this future, the opportunity has never been greater.

The question isn't whether governance will evolve—it's whether your organization will lead or follow that evolution.

To learn more about how Alation is helping organizations build the future of data governance through our agentic data intelligence platform, explore our resources.