AI for Data Quality: Leveraging Machine Learning for Quality Assurance

Abstract

The relationship between artificial intelligence and data quality is fundamentally bidirectional: AI systems depend on high-quality data for reliable performance, while AI techniques offer powerful tools for assessing, monitoring, and improving data quality at scale. This review examines the application of machine learning and AI to data quality management, analyzing how intelligent systems can automate quality assessment, anomaly detection, data cleaning, and continuous monitoring. Drawing on literature from data management, computer science, and information systems, we review the current state of AI-driven data quality tools and their capabilities across multiple quality dimensions. We analyze the technical approaches that have proven most effective, including supervised and unsupervised learning for anomaly detection, deep learning for data imputation, and natural language processing for semantic quality assessment. We examine the gap between technological capability and organizational adoption, identifying barriers including user acceptance concerns, skill gaps, and management hesitancy. We propose a framework for integrating AI into data quality management that addresses technical, organizational, and human dimensions.