Applying Machine Learning for Automated Data Quality and Anomaly Detection in Enterprise Data Pipelines

Abstract

Data quality failures including missing values, inconsistent representations, duplicate entities, and anomalous records continue to be a dominant barrier to trustworthy analytics and effective machine learning (ML) deployment, particularly as organizations scale across diverse, fast-moving data sources. Traditional rule-based validation and constraint checking, while effective in narrow domains, struggle to generalize in environments characterized by high volume, velocity, and schema heterogeneity, often requiring extensive manual maintenance and domain expertise. Recent advances in ML-based data management shift this paradigm by learning statistical, relational, and semantic patterns directly from data, enabling automated detection, diagnosis, and, in some cases, repair of quality defects. This article surveys these approaches through a structured lens, connecting foundational ideas in probabilistic modeling and anomaly detection with modern deep learning techniques and practical data-cleaning systems. By examining representative systems such as HoloClean and ActiveClean, we analyze architectural tradeoffs between accuracy, computational cost, and human-in-the-loop effort, as well as the balance between aggressive cleaning and error propagation risk. Empirical results across these systems demonstrate that ML-informed data quality pipelines can significantly improve anomaly detection accuracy, reduce manual labeling and correction effort, and produce measurable gains in downstream predictive performance, underscoring data quality as a first-class concern in end-to-end ML system design rather than a preprocessing afterthought.