Data cleansing is a systematic process within data management and preprocessing focused on identifying and rectifying or eliminating errors, inconsistencies, and inaccuracies in datasets. This methodological approach is essential for ensuring data quality, thereby enabling robust and reliable data analysis, modeling, and subsequent research outcomes.