Your Data Doesn't Agree with Itself: Why AI Needs Authoritative Context

AI is moving faster than most data foundations can support—and enterprises are hitting the same wall: their data doesn't mean the same thing across systems. When codes, classifications, and business definitions vary by team or platform, analytics become unreliable and AI amplifies the inconsistency rather than resolving it. The problem isn't just data quality. It's missing context. In this session, we will share how the Context Layer serves as a governed, semantic foundation that ensures business meaning travels with data across systems, teams, and AI workflows. You'll learn: -Why cloud data platforms, catalogs, and MDM tools still leave a critical gap -How fragmented reference data undermines AI reliability and cross-system interoperability -What a governed enterprise semantic layer looks like in practice -How to operationalize reference data as a reusable, enterprise-wide asset—without replacing existing systems Whether you're investing in AI initiatives, modernizing your data platform, or untangling MDM complexity, this session will show you the foundational layer that makes all of it work.