Data cleaning is detecting and correcting errors, inconsistencies, and inaccuracies in a dataset before analysis or model training. Tasks include handling missing values, removing duplicates, fixing formatting errors, and resolving outliers. Poor data quality is the leading cause of failed AI projects — a model trained on dirty data produces unreliable results. Automated cleaning pipelines combined with human review are the standard approach.