This guide explains why data preparation has become the most critical factor in successful AI adoption. It shows how modern AI, including generative and agentic systems, amplifies both good and bad data at unprecedented speed, making data hygiene, context, and governance essential. The guide outlines five practical steps for AI readiness: prioritizing data hygiene, establishing a data governance culture, selecting the right data catalog, beginning AI inquiries in controlled sandboxes, and keeping humans in the loop to verify results. Real-world scenarios illustrate how poor metadata, inconsistent definitions, and missing context can cascade into costly business decisions. The guide emphasizes metadata, lineage, semantic layers, and governance as the foundation that allows AI to reason accu

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