This is a key part of data quality enhancement and involves identifying and rectifying issues such as missing values, duplicate entries, and formatting errors to enhance data accuracy and reliability. Data cleansing several steps, such as data auditing, workflow specification, data cleaning, data validation, and data verification. It ensures data is not only error-free, but also standardised, unique, and relevant, making it suitable for further processing or analysis. Cleansed data enables organisations to extract more accurate insights, enhance operational efficiency, and make better-informed decisions.