Gwen Thomas, Founder, The Data Governance Institute

Are your program’s stakeholders engaged, enthusiastic, and supportive? If not, there’s a good chance they simply can’t see the actual impact your efforts have on their own programs and on the enterprise. They see tasks, details, and inconveniences, but they don’t see the value meter moving as you work.

Professionals who train to serve in Data Governance, Data Quality, and related fields learn how to DO the work. But they may not be taught that demonstrating the impact of their efforts is PART of the work. And they aren’t taught how to make that impact visible to stakeholders who simply can’t see it.

This session introduces simple practices that can be built into everyday work. Taken together, they produce what stakeholders expect to see: how your team’s contributions are moving the needle in their own success gauges and why they should speak up about the value your program provides.

Leticia Naqvi
People Analytics Research Manager, Apple

As organizations accelerate the deployment of artificial intelligence and advanced analytics, insufficient attention is often given to the foundational role of data quality, structure, and governance. Although algorithmic sophistication continues to advance, evidence from both research and practice indicates that AI outcomes are shaped primarily by the integrity of underlying data assets rather than by model design alone. Weak data foundations can introduce inconsistencies, bias, and operational risk that are subsequently amplified through automated and data driven systems. These conditions frequently erode trust in AI outputs and limit the effectiveness of decision support capabilities. As a result, organizations may experience stalled adoption and reduced value realization from AI initiatives.

This session reframes commonly observed AI implementation challenges as data foundation challenges, emphasizing the role of data governance, master data management, metadata, lineage, and standardization in enabling reliable and scalable AI use. Drawing on enterprise analytics and data governance practice, the discussion examines how foundational data capabilities influence explainability, accountability, and decision readiness within AI enabled systems. Particular attention is given to balancing governance rigor with organizational flexibility to support innovation while maintaining control. The session offers practical insights into strengthening data foundations to support responsible and effective AI deployment. Attendees will gain a clearer understanding of why sustainable AI success remains contingent upon disciplined data
management practices.