Inconvenient Truths About Data in the Age of AI

Topics includes a real-world example * Understanding of why your data governance work IS AI governance (and how the two connect) * Real examples of how AI projects fail when data fundamentals aren't addressed * Awareness of shadow AI proliferation in your organization and why it matters * Evidence that data lineage and metadata aren't "nice to have"—they're legal requirements * Context for why MDM, data catalogs, and governance are suddenly getting executive attention * Knowledge of where bias hides in your data and your role in preventing algorithmic discrimination * Recognition of data architecture gaps (batch vs. real-time) that AI exposes