This guide explains why high-quality, trusted data is foundational for successfully deploying generative AI and advanced analytics. It explores common data quality challenges such as inconsistency, bias, duplication, and lack of governance, which can undermine AI outcomes. The playbook outlines best practices for cleansing, standardizing, and validating data at scale while maintaining transparency and auditability. It emphasizes embedding data quality checks into automated pipelines, aligning governance with business goals, and ensuring AI-ready datasets are explainable and traceable. The guide positions data quality as a continuous discipline critical to responsible AI adoption.

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