Data cleaning: challenges and modern approaches

Data cleaning is a critical stage in data preprocessing, directly impacting the quality of analytics and machine learning models. This article examines key challenges in data cleaning—missing values, outliers, duplicates, and inconsistencies—and evaluates modern automated approaches, including rule-based systems, ML-based anomaly detection, and hybrid frameworks.