The purpose of data cleansing is to enhance the quality and accuracy of data by identifying and rectifying errors, inconsistencies, and inaccuracies within datasets. This process involves standardizing formats, removing duplicates, and filling in missing values to ensure that data is reliable and valid for analysis. Clean data is essential for making informed decisions, as it prevents faulty conclusions that can arise from corrupted or poorly structured information. Overall, data cleansing is vital for maintaining high standards in data management.