Adaptive Machine Learning Frameworks for Data Quality Monitoring: From Anomaly Detection to Continuous Pipeline Validation

Abstract

Data quality monitoring (DQM) has become a critical requirement in modern data-driven systems, especially in machine learning (ML) pipelines where poor-quality, inconsistent, or drifting data can directly degrade model performance, reliability, interpretability, and fairness. As organizations increasingly rely on automated decision-making systems, even subtle data anomalies such as distributional shifts, missing-value spikes, schema mismatches, or feature correlation changes can propagate downstream and produce significant operational and reputational risks. Traditional rule-based validation approaches, including static thresholds, manual audits, and predefined integrity constraints, are often inadequate in dynamic, large-scale, and streaming environments where data characteristics evolve continuously. Consequently, machine learning techniques have emerged as adaptive and scalable solutions for automated data quality monitoring, enabling systems to detect complex anomalies, context-sensitive outliers, and temporal drift patterns without exhaustive manual specification. This article surveys key ML-driven approaches to DQM, including statistical anomaly detection, density-based outlier detection, isolation-based methods, and concept drift detection frameworks, while also examining their integration into continuous ML pipelines. Foundational techniques such as the Local Outlier Factor (LOF) and Isolation Forest are discussed alongside modern validation architectures that embed automated profiling, distribution comparison, and alerting mechanisms into production workflows. By synthesizing algorithmic foundations, system design principles, and operational best practices, this article presents a structured framework for implementing robust ML-based DQM systems capable of maintaining data integrity in complex, high-volume environments.