Gartner Summit 2025: Alation Empowers Agentic AI with Metadata Mastery

By Steve Wooledge

Published on March 13, 2025

Alation was thrilled to participate in the Gartner Data & Analytics Summit 2025, held in sunny Orlando, Florida. As the creator of the modern data catalog, we have continually evolved the platform to meet the changing needs of data teams — first by integrating data governance, and now by leading the market in the reinvention of the data catalog as an agentic data platform.

This evolution marks a shift: Alation now offers AI agents to automate and guide data management processes, accelerating the delivery of AI, BI, and compliance initiatives. At the same time, the platform provides the trusted data foundation enterprises need to build and deploy their own AI agents faster and more confidently. With these innovations, we not only help customers govern their data more effectively — we make it possible for them to operationalize AI and scale business outcomes.

This year’s Summit provided the perfect stage to showcase these advancements. Alongside these exciting product updates, we also hosted customer-led sessions featuring Verizon, the NBA, and Burns & McDonnell, who shared their journeys building data culture, modernizing governance, and delivering data products with Alation.

In this post, we’ll cover highlights from our customer sessions, explore Gartner’s key themes, and dive deeper into how agentic capabilities and data intelligence are shaping the future of data management.

Key themes: Agentic AI, metadata, and the emergence of the “trust stack”

During this year's Summit, data leaders explored three powerful and interconnected themes: agentic AI, metadata management, and the transition from the modern data stack to what Gartner calls the “trust stack.”

Agentic AI emerged as a significant focus, with intelligent AI agents—autonomous systems designed to perform tasks and decisions using reliable, curated data—highlighted as key to operational efficiency and innovation.

Leaders discussed the need for robust metadata management as foundational to these initiatives, echoing the sentiment: “If you're serious about AI, you must get serious about metadata.” High-quality metadata directly supports the effectiveness of AI agents by providing essential context, ensuring accuracy, and building trust in AI outcomes.

Central to these conversations was the concept of the “trust stack,” shifting emphasis away from the crowded modern data stack and toward solutions cultivating trust, data quality, and comprehensive governance practices.

Gartner analysts Gareth Herschel and Carlie Idoine emphasized this during their keynote, noting that poor data quality remains a major barrier, cited by nearly half of organizations struggling to demonstrate AI's tangible value. To navigate this challenge, they urged data leaders to build adaptable ecosystems—characterized by modular architectures, proactive data governance, and clear communication of AI’s business value.

This was a timely keynote! In keeping with Gartner’s vision, Alation made key announcements designed to support this vision of trusted, AI-ready data environments:

Agentic Data Intelligence Platform: A reinvention of the data catalog as an Agentic platform, enabling AI agents to help our customers automate and guide data management processes to speed up the delivery of AI, BI, and compliance initiatives. This includes our new AI Agent SDK, which empowers the rapid development of sophisticated, adaptable AI agents with rich metadata from the data catalog.
Alation Data Quality: A new Alation-native data quality solution, powered by agentic AI, to prioritize, monitor, and remediate data quality at enterprise scale.
Alation Data Products Marketplace: A place where business users and data teams can quickly find, understand, and access trusted, reusable data products.
Documentation Agent automates data curation to enhance metadata richness.

These innovations exemplify Alation’s commitment to transforming data catalogs and governance, positioning metadata as the backbone of effective AI. Leading enterprises such as Verizon, the NBA, and Burns & McDonnell are already leveraging Alation to operationalize these strategies, turning concepts into tangible business outcomes. (More on that below!)

How Verizon builds a scalable data product economy

I had the honor of welcoming Latheef Syed, Associate VP of Data and Analytics at Verizon, to share his story on how the telecom giant is overcoming common enterprise data challenges to build a thriving data product economy.

As Verizon expanded through mergers and acquisitions, it faced significant challenges: fragmented systems, inconsistent definitions, and difficulty aligning data initiatives to measurable business outcomes. To address these challenges, Verizon adopted a data product operating model—shifting from raw, siloed data to well-defined, reusable data products that teams across the company can leverage.

Verizon's key challenges:

Data silos from legacy mergers were causing inconsistency
Aligning data initiatives with business value
Limited metadata reduced trust and usability of data assets

As Syed noted, "Without having a centralized repository where you can manage your data with the right labeling and lineage, the trust factor will be lost."

Building a scalable data marketplace

As part of its strategy, Verizon developed an enterprise-wide approach to cataloging and managing its data products via a hub-and-spoke model. By establishing a centralized system for data discovery, metadata management, and governance, the company created a trusted foundation to deliver data across the business.

Key elements of Verizon’s approach include:

Centralized discovery: A single platform where business units can find and reuse certified data products.
Metadata investment: Verizon focused on defining ontologies, tagging data assets, and tracking lineage to improve transparency.
AI-ready data: By embedding metadata and governance into its data products, Verizon ensures that future AI models have the context needed for training and performance.

Results: Verizon supports growing adoption to fuel AI innovation

Verizon has recognized the need for a centralized data marketplace after seeing widespread demand for data access across the organization. With thousands of internal users across multiple business units seeking reliable data for discovery and analytics, it became clear that a scalable platform was necessary.

This growing demand underscored the importance of a structured approach to managing and distributing data products, ultimately leading to the development of Verizon’s data marketplace. By making metadata-rich data products more accessible, Verizon has since improved data-driven decision-making – and set the stage for AI application development across the business.

Verizon’s lessons for data leaders

Verizon’s approach offers lessons for organizations looking to scale their data product strategy:

Prioritize trust: Invest in governance and metadata from the outset
Streamline discovery: A centralized approach simplifies access to trusted data
Design for AI from the start: Metadata-rich data products are critical for developers seeking to build AI models that perform

Conclusion: Pioneering a connected AI ecosystem

Verizon is shaping the future of data and AI by building a connected data ecosystem to enhance customer and employee experiences. Ultimately, the telecom leader aims to eliminate friction and create seamless interactions by integrating AI-driven insights across retail, digital, and service platforms.

To fuel these AI initiatives, Verizon is actively strengthening its data foundation—investing in metadata, governance, and accessibility to ensure data is AI-ready. The company’s build-by-design approach embeds AI into every product and service, driving smarter solutions across its many enterprise offerings.

Customer spotlight: How the NBA launched a data product operating model

At our Gartner booth, I had the pleasure of hosting Jeff Cruz, Technical Data Product Manager at the NBA, who shared insights on how the NBA has transformed its approach to data management using Alation.

Three years ago, the NBA undertook a major data warehouse migration, which demanded deep collaboration between technical and business teams. Cruz highlighted this migration as a turning point: "It led to today's collaborative cycles, where everyone—from end-users to technical stakeholders—is involved, providing continuous feedback."

Cruz views his role as data product manager as a critical bridge between business requirements and technical execution. With a sales background, he emphasizes clearly defining business problems upfront. He frequently asks stakeholders: "What are you trying to solve, really? Why do you want this product?" This approach ensures each data product has clear objectives, defined audiences, and measurable success criteria, establishing clarity from the start.

The NBA’s data strategy team operates much like a software development team, managing data products through structured agile cycles—planning, design, development, testing, deployment, and continuous maintenance. Products are never "finished"; they evolve through regular feedback loops and iterations.

The results have been impressive: faster product launches, enhanced collaboration, and increased internal trust. Cruz emphasizes, "We’ve built trust internally. When someone from my team speaks, stakeholders know they’re informed."

Leveraging Alation as its primary discovery and governance platform, the NBA ensures every data product is trusted, discoverable, and strategically aligned. Cruz concludes, "Alation helps maintain consistency in definitions and governance, reducing redundancy and accelerating innovation."

Customer spotlight: Burns & McDonnell offers secure self-service analytics

For engineering giant Burns & McDonnell, Alation has enabled secure, self-service data access without compromising data protection. David Lawson, Lead Data Steward, described their success in integrating Alation with Immuta and Databricks to implement automated, metadata-driven data masking and security policies.

This integration dramatically streamlined governance and data discovery:

Sensitive data is automatically masked based on metadata tags
Policies are transparent, visible, and intuitive for business users
Workflow-based approval processes maintain secure governance while enabling agility

“Alation’s metadata-powered approach means users find data faster and can trust its quality and security,” says Lawson. “Our data stewards are now empowered, and our business users have greater confidence in the data they access.”

Key takeaways from the CDAO boardroom

During the Chief Data & Analytics Officer (CDAO) boardroom discussion, data leaders gathered to explore pressing challenges, often returning to the evolving roles of data products, governance, and marketplaces in achieving strategic business goals.

Data leaders strongly agreed that data products represent the future, and agreed on the importance of clear ownership, robust governance frameworks, and explicit alignment with business objectives. They stressed that an effective data product strategy must be business-led, ensuring each product directly contributes to measurable outcomes.

Another significant topic was the importance of data marketplaces. Data leaders recognized marketplaces as vital tools for promoting data reuse and establishing clear accountability among data product owners. They advocated for marketplace designs that simplify data discovery, enhance transparency, and utilize gamification and rewards to encourage high-quality contributions, fostering an engaged and responsible data community.

Participants also emphasized data governance, highlighting trust and quality as essential foundations for AI-driven innovation. They discussed adopting a pragmatic approach that prioritizes business needs over rigid controls. Leaders favored incremental governance strategies that start small and scale iteratively, enabling continuous improvement. Transparency and ongoing feedback loops were identified as crucial to maintaining high-quality data, with metadata management and certification highlighted as essential components for building trust. Leaders agreed that, while governance is often viewed as burdensome, the ultimate goal is to empower teams and drive business results.

Together, these discussions confirmed that proactive governance, strategic business alignment, and effective metadata management are critical for organizations seeking to fully leverage AI and data-driven initiatives.

Conclusion: Reinventing data catalogs for the agentic AI era

Reflecting Gartner's key themes, Alation is reshaping the role of data catalogs and governance for the era of intelligent agents. Our solutions empower organizations to transition seamlessly into the “trust stack,” leveraging metadata as the cornerstone of trusted, agent-driven insights.

Whether through the new agentic platform, Alation Data Quality, or the Data Products Marketplace, new features demonstrate our commitment: empowering enterprises to harness the full potential of AI through better metadata and governance.

Ready to elevate your organization’s AI journey? To learn more: