Data cleaning is the process of identifying and correcting errors or inconsistencies in a dataset. This step is crucial because inaccurate data can lead to misleading results and poor decision-making. Common tasks in data cleaning include removing duplicate entries, filling in missing values, and correcting formatting issues. Effective data cleaning improves the quality of data used in analysis, making it more reliable. Tools and techniques, such as data validation and automated scripts, can help streamline this process. By ensuring that data is accurate and consistent, organizations can enhance their data analysis efforts and achieve better outcomes.