Inconvenient Truths About Data in the Age of AI

DAMA South Florida

Everyone wants AI. Few organizations are ready. The bottleneck isn't algorithms or compute power-it's data management you've done for years.

Nova Southeastern University, DeSantis Bldg, Room3000

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

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Inconvenient Truths About Data in the Age of AI

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Inconvenient Truths About Data in the Age of AI

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