This eBook focuses on the evolving role of analysts in preparing data for AI and machine learning initiatives. It explains why traditional data preparation approaches struggle with the scale, complexity, and governance requirements of AI-ready data. The guide covers best practices for data cleansing, feature engineering, validation, and automation, emphasizing transparency and repeatability. It highlights the importance of embedding business logic and context into datasets so AI models produce meaningful outcomes. The eBook positions analysts as critical contributors to AI success by enabling trusted, explainable, and scalable data pipelines.

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