In this contributed article, engineering leader Uma Uppin emphasizes that high-quality data is fundamental to effective AI systems, as poor data quality leads to unreliable and potentially costly model outcomes. Key data attributes like accuracy, completeness, consistency, timeliness, and relevance play crucial roles in shaping AI performance and minimizing ethical risks. Uppin argues that robust data governance practices, including regular data checks and a company-wide data management culture, are essential for sustaining AI accuracy and reducing discrimination.