What is Data Cleansing?
Data Cleansing refers to the process of detecting, correcting or removing incomplete, inaccurate or duplicated records from a dataset. The aim of cleansing a dataset is to achieve consistency with similar datasets.
Inconsistencies detected are often the result of human error or corruption caused either during initial capture, migration between systems or inconsistent data processing. The goal of data cleansing is to achieve consistent, complete, accurate and uniform data. Data cleansing is often considered to be the most important aspect of data quality management.