As organizations navigate an increasingly complex data landscape, those who effectively harness data, analytics, and AI capabilities will win hands down. Here’s what I learned at this year’s Gartner Data & Analytics Conference.
Did I use AI to write this article? Yes of course! But the thoughts are my own. At the end of each day I recorded a video describing my key observations, impacts and musings on what I had observed and learned. I fed these transcripts into Allata’s internal AI tool to help summarize and craft the flow of my thoughts into a form consumable by you…the reader.
Introduction: The Data Leader’s Challenge
The 2025 Gartner Data & Analytics conference opened with an interesting question: “Are you exhausted?” This simple question perfectly captured the reality facing data leaders today. We have more data—multimodal data from multiple sources—than ever before, and we’re being asked to do more with it than ever before. As data leaders, we’re expected to articulate business value clearly, develop the right talent, and lead our teams more effectively.
After three days of sessions and interacting with data leaders across industries, I’ve never been more convinced that if you want to be at the center of value creation, data and analytics (and increasingly, AI) is where you need to be. This is truly the frontier of business innovation and outcomes.
The Three Journeys of Data Leadership
In the keynote, Gartner emphasized three main journeys that data leaders must navigate simultaneously:
Journey to Business Outcomes: Quality with Purpose
The first journey focuses on achieving trustworthy data at the necessary level of quality to drive business outcomes. One of the most insightful comments came from Andrew White, who noted that you don’t actually want the “best” data quality—because the highest possible data quality is inefficient. What you actually want is the lowest level of data quality necessary to achieve your business outcomes. This means you don’t need to implement master data management for every possible field or define every data object. Instead, you need the necessary level of fidelity aligned to specific business outcomes and use cases. This perspective is reshaping how organizations approach data products and AI solutions—designing them with specific business outcomes in mind rather than pursuing perfection for its own sake.
Journey to Capabilities: Building the Right Data Ecosystem
The second journey involves developing the right capabilities through modular and open architecture approaches. This includes embracing a data product mentality with proper documentation, catalogs, contracts, and marketplaces. Walking the exhibit floor, I observed the vast ecosystem of tools available—from master data management and metadata management to catalogs and all-in-one analytics platforms. The challenge lies in understanding what each tool actually does and how it fits into your specific needs.
As one vendor conversation revealed, you need expertise to cut through marketing claims and understand how a solution differs from alternatives. This requires knowing the hard problems you’re trying to solve for your organization and ensuring that your selected tools address those specific challenges.
Journey to Behavioral Change: Creating a Data-Driven Culture
The third journey involves cultural transformation—moving from principles to habits in creating truly data-driven organizations. This requires storytelling skills and leadership at all levels to drive meaningful behavioral change.
I’ve found that helping organizations see what’s possible and aligning strategic vision with execution is critical to this journey. Data leaders must be “complex humans” operating across vision, teaching, and execution to drive this cultural transformation. These three journeys illustrate precisely why data and analytics have become the epicenter of value creation – they connect directly to business outcomes, build essential capabilities, and transform organizational behavior.
The AI Reality Check: Beyond the Hype
A joke circulating at the conference was about “AI washing”—the tendency for vendors to rebrand everything with an AI label. Curious about these claims, I engaged with several vendors promoting “AI-powered” solutions.
The reality? Most aren’t delivering what we would expect or hope for from truly agentic AI capabilities. The ability to send an agent on a task to document something, evaluate quality, or perform thoughtful assessment isn’t widely available….yet. But it will be—and that future is approaching rapidly. This gap between marketing claims and reality underscores the importance of having expertise to evaluate solutions based on your specific organizational needs rather than buzzwords.
The Balancing Act: Data Governance vs. Data Management
I attended a fascinating debate between proponents of governance-first and management-first approaches. As someone who leans toward data management—getting data together, modeling it, and driving business outcomes—I appreciated the governance perspective that without proper definitions and understanding, chaos can ensue.
The truth is that both approaches must work in conjunction. There was a helpful Venn diagram showing the overlap between data governance and data management, illustrating how these functions must collaborate to build a repeatable engine for data delivery.
This balance becomes increasingly important as data volumes and use cases continue to grow. There will never be less data available than there is today, and there will never be less demand for data-driven insights. This balance between governance and management creates the foundation upon which data-driven value creation stands – without it, organizations struggle to deliver on the potential business impact and transformation.
The Path Forward: Strategy and Operating Model
One speaker distilled data leadership into two essential components: strategy and operating model. Your strategy includes defining business outcomes, drivers, and the roadmap to get there. Your operating model is the “how”—encompassing your technical ecosystem, organizational structure, and the practices and processes needed to execute effectively.
Together, these elements enable you to deliver value through data, analytics, and AI. An effective operating model includes your architecture, data platform, and delivery approach—all aligned to drive specific business outcomes.
Conclusion: Personal Reflections
What struck me most about the conference was how the same information resonates differently with each attendee based on their experiences. My takeaways are filtered through my decades of experience in data—the successes, the scars, and the challenges I’ve witnessed both personally and with clients. I’m using this experience to synthesize insights that can help organizations move forward on their data journeys. By combining conference learnings with practical experience, we can develop approaches that address real-world challenges.
I have never been more convinced that organizations who manage to move with the right governance and agility to drive data, analytics, and AI—while transforming their cultures to be more data-driven—will win hands down. This is where the future of business value lies, and I’m excited to be part of that journey with the organizations I work with.
Call to Action
Are you navigating your organization’s data journey? I’d love to connect and discuss how these insights might apply to your specific challenges. At Allata, we’re passionate about helping organizations harness the power of data to drive meaningful business outcomes.
Reach out to discuss how we can help you navigate the frontier of data leadership and build capabilities that create lasting value for your organization.
