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Data quality is the cornerstone of successful AI initiatives, underlined by the principle that even advanced AI models are only as efficient as their foundational data. The chapter delves deep into the dimensions determining data quality, from accuracy to relevance, and underscores their indispensable role in AI’s efficacy. Readers will gain insights into pivotal techniques like data auditing, cleaning, and integration, as well as the novel use of AI itself in assuring data quality. As the world of AI continues to expand, comprehending and ensuring data quality becomes not just a value-add but a crucial determinant of an organization’s AI success.