Gartner Data & Analytics Summit 2025 Recap

This week, the annual Gartner Data & Analytics Summit in Orlando brought together data professionals from across industries and functions. As always, the event sparked rich discussions about the evolving role of data, the level of organizational support and investment, and the shifting career landscape in our field. I left with a wealth of insights, valuable content to share with my team, and practical lessons to apply in my daily work.

Let’s start with my favorite session from the Gartner Data & Analytics Summit: The Peculiarly Challenging Business Case for Generative AI, led by Nate Suda. Nate explored three key business case patterns for AI adoption—Defend, Extend, and Upend—offering a structured approach to evaluating investments in this rapidly evolving space. He left the audience with a powerful set of financial models and practical strategies for engaging CFOs in identifying the right investment mix for their organizations.

• Evolution of Data Products - In many organizations, data products have become an oversimplified concept—essentially curated datasets with predictable characteristics designed for easy consumption. While this approach initially addressed the need for trustworthy data, it often fails to keep pace with the speed of business. Now, the shift is underway toward active data products, driven by active metadata, enabling more dynamic, responsive, and intelligent data solutions.
• The Experience Matters - Gartner’s approach to user engagement—through synchronized music, seamless color transitions, engaging slides, and pre-session questions—was a masterclass in capturing attention. It’s a level of thoughtfulness we should apply to how we engage consumers of our data. How we present recommendations, surface outcomes, and structure categorization matters. Creating meaningful feedback loops ensures users stay engaged, interact with data purposefully, and derive real value. Thoughtful design isn’t just for conferences—it’s essential for data-driven decision-making.
• What is the definition of legacy? - One theme that stood out in technical architecture discussions was the ever-increasing velocity at which we’re expected to swap out tools and platforms. I remember a simpler time when technology was either modern or legacy—back when we were virtualizing servers on VMware and running core data platforms on mainframes. That world no longer exists. Today, technology is expected to be modular, with each component replaced as soon as a new best-of-breed tool emerges. As Mark Beyer put it in his session on active metadata, "tools and technologies evolve." The key is not to resist this change but to plan for it, accept it, and manage it effectively.
• Need for Prioritization - AI teams are quickly learning what cybersecurity and engineering teams have long understood: prioritization is the key to success and managing churn. There’s no shortage of great ideas for applying AI, but each use case comes with an endless list of data to source, transform, and analyze. Even the best-funded teams can’t do it all. The challenge isn’t just building AI solutions—it’s understanding their impact to drive smarter prioritization. Focusing on the right initiatives is what ultimately leads to meaningful outcomes.
• Separating Governance & Execution - Discussions around governance structures and best practices have gained even more traction this year. As organizations continue to refine their approach, we’re seeing a diverse range of operating models emerge. One principle remains constant: governance must be separate from execution to enable multiple teams to move in parallel. The most effective organizations embrace lightweight governance—frameworks that empower teams rather than slow them down. Avoiding bottlenecks like decision-by-committee or single-person approvals ensures governance is an enabler, not a roadblock, allowing execution to align seamlessly with pre-defined policies and objectives.
• Is the decision architect the new business relationship manager? The new black belt? - Throughout the conference, I’ve heard the title Decision Architect come up in multiple conversations. The premise is compelling—this role focuses on how people consume information and make decisions within business processes, with the goal of improving both. The need is clear, but are companies ready for such a role? For any new position to be effective, the organization must understand how to integrate it as a true partner. I also can’t help but wonder if this is a rebranding of past roles like Business Relationship Manager or Black Belt—without a fundamental shift in how organizations leverage data for decision-making will result in disappointing outcomes. No firm answers yet, but it’s an interesting evolution as businesses continue to mature in their data strategies.
• CISOs at a data conference! - Over happy hour, I had the chance to meet two CISOs—one from an insurance company and another from a manufacturing firm. It was eye-opening to see them at a data conference and hear how their roles are evolving. For two hours, we dove into the shifting skill sets their teams need, new approaches to risk management, and the realization that the tool stacks they’ve built over the years are functionally obsolete when it comes to AI models and their points of engagement with new types of engineers and functions. The intersection of cybersecurity and data is changing fast, and it’s clear that security leaders are actively rethinking their strategies to keep up.
• Red & Blue Team Evolution - Building on my conversation with the CISOs, we also touched on the growing testing needs for AI models. As AI-enabled services, chatbots, and assistants become more prevalent across enterprises, the demands on red and blue teams will increase in complexity and technical knowledge. These teams will not only focus on traditional security but will also be responsible for testing AI interfaces to ensure data remains protected as designed—shielded from manipulation & loss by both internal and external threats. As AI continues to evolve, the role of security teams in safeguarding these technologies will become even more critical.
• Risk Measures Require Revaluation - AI theft comes in two distinct forms: data leakage from AI chatbots and the theft of AI intellectual property, including algorithms, models, and training methods. Each poses a unique risk to an organization, and the investment required to protect against both forms is often uneven. For organizations that leverage industry models, the primary focus should be on reducing the risk of data leakage. In contrast, organizations that have developed proprietary AI models—their "secret sauce"—must direct their protection efforts toward safeguarding these models and the intellectual property they contain. Balancing these risks and investing in protections accordingly is critical to ensuring the security of both data and innovation.
• "AI is our number 1 priority" - While I don’t doubt that people are selecting the "AI" checkbox on their vendor surveys, the reality is that AI itself is not the objective—business outcomes are. We need to challenge each other to first articulate the story of business value and needs, then determine the right enabling methods to drive success. And for extra clarity, we should visually map out how investments are allocated—not just to AI, but across all the tools and strategies contributing to our business goals. AI is a means, not the end.

The overarching theme I took away can be summed up as: "Culture eats strategy for breakfast." You can design the most complex processes and enforcement mechanisms that your budget allows, but real, lasting impact—both in organizational velocity and outcomes—comes from transforming your people & how they work as collaborative teams.

How they work, collaborate, measure success, and recover from failure matters more than any single tool or framework. The key is to focus on enablement, skill development, and fostering a collaborative culture. Do that, and you’ll see a higher return than any new, flashy vendor solution could ever deliver.

A special thanks you Adam Rothenfluh for the invite. Looking forward to 2026 Gartner Data & Analytics Summit.