This ebook explains what it truly means for data to be “AI-ready” and why most organizations struggle to reach that state. It outlines common data challenges such as fragmentation, poor quality, lack of context, and weak governance, emphasizing that AI success depends more on data preparation than algorithms. The guide introduces practical frameworks for analysts to contextualize, govern, and automate data pipelines so AI models produce reliable and explainable results. It highlights the analyst’s evolving role in bridging business logic and technical execution, enabling scalable, trustworthy AI use cases across the organization.

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