10 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

Know what’s Happening Next — before everyone else does.

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Host or Publisher DAMA South Florida