Data cleaning is a critical process in data analysis that involves identifying and correcting errors or inconsistencies in a dataset to ensure its accuracy and reliability. It includes techniques such as removing duplicates, addressing missing values, and correcting data entry errors. For instance, duplicates can be identified and removed to avoid skewing analysis results, while missing values can be imputed using statistical methods or replaced with default values based on context. Additionally, data can be normalized or standardized to ensure consistency in format and measurement units. Outlier detection and correction also play a key role, as extreme values can distort analysis outcomes. By applying these techniques, data scientists and analysts can enhance the quality of their datasets, leading to more reliable insights and better decision-making.