This week, Data Culture attended the Gartner Data & Analytics Summit, the go-to conference for enterprise data leaders. Every year, the event brings together top minds in data and industry leaders like Amazon, JPMorgan Chase, Netflix, and Procter & Gamble.
With the buzz (and confusion) around AI, we were there to get some perspective on how leaders are evolving their data capabilities, what challenges still concern them, and where AI applications are making a meaningful headway.
Data Governance is priority #1, #2, and #3.
Everyone agrees: clean, secure, and well-managed data is critical for AI success. The conversation has shifted from just "adopting AI" to ensuring the right foundation is in place.
At its core, everyone recognizes the importance of secure, clean data access and the responsible management of sensitive information. But with AI, governance isn’t just good practice, it’s the gatekeeper. Get it wrong, and it could be the biggest blocker to AI adoption in your organization.
Agents & Automation are in the spotlight.
There’s a lot of momentum around AI agents, automated systems that make decisions and take action based on data. But to be effective, they need a solid foundation in decision intelligence, the practice of structuring decision-making. Without it, automation risks becoming just faster guesswork.
(aside) I’m personally very excited to see decision intelligence making a comeback. I’ve always been a big fan of upfront planning, setting clear success criteria before diving into the data. It’s a practice I often push data analysts to adopt before jumping into an open-ended data analysis.
AI/BI ("Chat with Your Data") has lost some allure.
Nearly every BI tool now has a chat interface, but there’s still a lot of skepticism. Many data teams aren’t confident they will deliver on the promise of simplicity, and, in some cases, find them counterproductive to building trust in data. Instead of making data more accessible, the general sense is that they are more likely to create confusion at this early stage.
Semantic Layers & Knowledge Graphs were less of a focus than expected.
Given their role in making AI “data-literate,” I expected more focus here. Many companies are focused on fixing basic data issues before adding these advanced tools. Plus, since interest in AI-powered “chat with your data” features has slowed down, the need for semantic layers, which support them, seems to follow.
I came in curious about trends and attitudes toward AI in data and while AI was certainly a hot topic, the underlying conversation was more familiar: getting the basics right and making a strong business case for data initiatives.
At the core, many companies are still working to gain trust in their data and ensure it's being used in ways that create real, additive value. Most of the people I spoke with see this clearly: prioritizing what matters while exploring where AI can accelerate the journey.
