The Importance of Data Quality in Artificial Intelligence Technology: Foundations, Impacts, and Governance

• Francis. D. Williams

  Author

Artificial intelligence (AI) technologies are increasingly embedded in critical decision-making processes across industries, including healthcare, finance, manufacturing, and public administration. While advances in algorithms and computing power have received significant attention, the quality of data underlying AI systems remains a fundamental yet often underestimated determinant of AI performance and trustworthiness. This paper examines the importance of data quality in AI technology by analyzing its conceptual foundations, technical implications, organizational consequences, and governance challenges. We argue that data quality directly influences model accuracy, generalizability, robustness, and fairness, and that deficiencies in data quality can propagate errors throughout the AI lifecycle. Building on interdisciplinary literature, the paper develops a structured framework linking dimensions of data quality—such as accuracy, completeness, consistency, timeliness, and representativeness—to key stages of AI system development and deployment. We further discuss managerial and policy implications, emphasizing the need for systematic data governance and quality assurance mechanisms. The study contributes to the AI and information systems literature by positioning data quality as a strategic and ethical cornerstone of responsible AI development.

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