Data Cleansing is the process of identifying, correcting, removing, or replacing inaccurate, incomplete, duplicated, or inconsistent data. It improves the quality of datasets before they are used for analysis, reporting, machine learning, or business decision-making.
Common cleansing activities include removing duplicate records, correcting formatting errors, handling missing values, standardizing names and addresses, and identifying invalid or outdated information. By maintaining clean and consistent datasets, organizations can improve the accuracy of analytics, reduce errors, strengthen AI model performance, and make more reliable decisions.
