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 and poor decision-making. Common tasks in data cleaning include removing duplicates, filling in missing values, and correcting typos. Effective data cleaning improves the quality of data, making it more reliable for analysis. It often involves using software tools or programming languages like Python or R to automate the cleaning process. By ensuring that data is accurate and well-organized, organizations can enhance their data-driven strategies and insights.