From Summit to Strategy: Inside Gartner’s D&A Summit 2026

From March 9-11, Rocket attended Gartner’s Data and Analytics Summit in Orlando, Florida. This year, the theme was “Value at AI Velocity: Navigating the Now and Next,” and the more than 3,000 vendors, data leaders, CIOs, and technologists who attended embodied it in their insights and questions.

The three days of the summit were full of thought-provoking conversations with customers, partners, and peers. Whether at the session I hosted, during lunches and coffee meetings, or in the hallways between forums, the recurring theme was about how to solve data access, trust, and control issues.

Now that the event is over, the priority is execution. Here’s how to translate the takeaways of Gartner D&A into real, near-term action in your organization’s data strategy.

Harnessing the complete power of your data

I hosted a session titled “From Pilot to Production: 5 Shifts for an AI-Ready Data Estate.” I noticed a common pattern that many experience: we get an AI pilot working well in a demo, then it hits the “pilot-to-production wall.” This is when trust is tested, data and content don’t match, controls become a barrier, and early successes stall.

I concentrated on the practical changes teams are making to overcome barriers, especially as LLMs and agent-style workflows become more widespread in real operations. My aim was to identify and clarify the issues practitioners face daily, such as the lack of measurable trust, disconnected data and content, and weak usage controls. Agents access raw tables, schemas evolve and break, automations fail, and due to the absence of guardrails, users notice failures, risking their trust and that of the enterprise.

Read here for a comprehensive breakdown of the 5 key shifts I identified to scale AI from experiment to enterprise impact.

What it all adds up to

At the event, a consistent subject was that AI amplifies bad data faster than anything before it. My main takeaway from Gartner’s D&A Summit is that if your core datasets aren’t trusted, your AI outputs won’t be either.

One concept that stood out to me from Gartner’s keynote was “policy as code” as a practical foundation for governing this new landscape. In a highly distributed, agent-driven environment, governance can’t rely solely on centralized controls. Policies need to travel with the data itself, ensuring protection, access, and compliance are enforced at the point of use, wherever that data ends up.

At the same time, data trust remains a critical pillar. As organizations push toward AI-ready data, capabilities like lineage and data quality monitoring are no longer optional—they’re essential. Without clear visibility into where data comes from and confidence in its integrity, agentic systems risk amplifying errors at scale. Organizations must formally make "trust" part of SLAs that guarantee consistent, governed data delivery across all modernization and AI initiatives.

Many teams are experimenting with GenAI, but they’re doing so in a way that’s disconnected from governance, and that’s where things fall apart. Instead of letting AI models pull from wherever or indiscriminately embed everything, we have to define standardized, governed ways for systems to retrieve information.  

What’s becoming clear is that strong data management underpins everything. The traditional boundaries between structured and unstructured data are beginning to blur, especially as agents consume and generate information across formats. Organizations are starting to think about data more holistically, focusing less on type and more on usability, context, and governance. This is where context, another hot topic at the summit, really comes into play. AI agents need not only access to data, but also context and structure. The organizations that move the fastest are those making their data both accessible and interpretable for AI.

What matters most  

While the path forward may still be evolving, the urgency to act is not. As leaders advance with initiatives, it’s crucial to clearly understand their current position and future goals, and to develop a practical strategy to reach them. This involves setting well-defined goals, establishing data management and architecture roadmaps, and partnering with the right strategic allies for success.

Rocket is designed to help you turn these insights into action, unlocking your data’s potential to achieve real, lasting success. Learn how to leverage AI successfully and optimize your entire data ecosystem for the future.