Data cleaning is the process of identifying and correcting errors or inconsistencies in a dataset. This step is crucial because inaccurate or incomplete data can lead to misleading results in analysis. Common tasks in data cleaning include removing duplicates, filling in missing values, and correcting formatting issues. Effective data cleaning improves the quality of data, making it more reliable for decision-making. It often involves using tools and techniques to automate the process, ensuring that the dataset is accurate and ready for further analysis. Properly cleaned data enhances the performance of algorithms in fields like machine learning and data analysis.