As we mentioned in our post-show recap, the Rocket team came away from the Gartner Data & Analytics (D&A) Summit in Orlando with great insights and ideas from the many sessions and discussions. For those who couldn’t attend, the Summit brought together IT business, and data leaders from across industries to discuss, learn, and share insights around artificial intelligence (AI), data management, architecture, governance, and more. The attendees’ top-of-mind concerns included how to use AI to its fullest potential, how data architecture can help, and what data professionals should do to prepare for the future.
With the team experiencing the event from varied perspectives, we regrouped afterward and shared our individual takeaways. As it turned out, there were two major themes: The future of AI and data architecture.
To help guide you on these important topics, I want to share our takeaways and what they mean for your roles, now and in the future.
AI: The most used (and still the most confusing) 2025 acronym
The bottom line is that companies are still working to figure out what their AI initiatives should be. The technology is evolving so rapidly that it’s hard to put a stake in the ground. But it’s clear that AI is the future of data management and corporate knowledge. The key is to fully define what you want from applying AI to your data collection and interpretation initiatives before you start.
Here are our thoughts:
- The level of investment in the data space, now driven by AI, continues to accelerate. But the business cases and results from AI initiatives are still evolving. While it’s critical to tie business outcome to AI investments, the untapped data trapped in the mainframes remain a major obstacle to building that strong business case.
- There was an interesting point made in one of the sessions: “Only one in five organizations have managed to use their data effectively for AI use cases.” With the predominance of AI-focused slogans, and companies claiming to have a good handle on anything AI-related, this quote struck a chord. It doesn’t quite fall in line—and we couldn’t agree more! The AI boom is not as simple as some people might think it is. For instance, we know that the vast majority of mainframe data is still not being leveraged (72% according to our research). Leaving this critical data out of analytics and AI initiatives means that companies are making decisions about their futures with incomplete or even inaccurate data.
- Data management, governance, and change management are essential. They should be a part of every AI project’s cost consideration. But companies should invest in the foundation first to get a handle on managing and integrating their data. AI governance should mirror the existing data governance framework, not just focus on surface level practices, and ask the question: “What could go wrong?” Metadata is the key. It’s the one component that spans all data types and formats. But to work, the core metadata needs to be clean and accurate, well organized, and easy for anyone in the organization to use.
- AI is everywhere. It was mentioned at almost every exhibitor booth and in almost every session. While there are some clearer AI use cases than even a year ago, the exact benefits remain ambiguous. We didn’t hear anyone say “10x cost savings with AI for xyz use case.” Attendees were looking for concrete answers to their specific problems. “Ready your data for AI” messaging was everywhere. But what does that mean? The answers were almost always vendor-specific. An overriding definition and thought leadership are needed to help companies understand what it means for them.
- Data security and how to safely use enterprise content with genAI was a major concern among attendees. Guidance about how to accomplish that security resonated with them, especially around mainframe data. Data and IT professionals understand that mainframes hold vast amounts of mission-critical data and that leveraging it is too often a roadblock they don’t know how to get past. Many don’t even know if they can get past it.
Data architecture was another huge area of focus. Data mesh vs. fabric vs. warehouse vs. data lake…! Continue reading our insights and takeaways here.