Data cleansing is the process of identifying and correcting errors or inconsistencies in a dataset. This ensures that the information is accurate, complete, and reliable. For example, if a customer database contains duplicate entries or misspelled names, data cleansing helps to fix these issues, making the data more useful for analysis and decision-making. Effective data cleansing can involve various techniques, such as removing duplicates, filling in missing values, and standardizing formats. By maintaining clean data, organizations can improve their business intelligence efforts, leading to better insights and more informed choices. Ultimately, data cleansing is essential for achieving high-quality data.