Every dataset contains some errors. Data cleansing a two step process including detection and then correction of errors in a data set. Duplication of the data is a major area of concern faced by most of the organizations. The fundamental meaning of Data Cleansing Services is to improve the quality of the data by sorting out inaccurate or incomplete data. The process is completed with correcting those errors and omissions. The process of Data Cleansing involves checking of completeness, format, limit checks, reasonableness checks and review of the data, done to identify outliers such as statistical, temporal, geographic and environmental, along with other errors. The process does not end here; instead the core of the process is assessment of data by subject area of experts, like taxonomic specialists.

A proper planning is essential to ensure a good data management policy. Keeping the core idea of Data Cleansing, the process involves other aspects like data quality, vision and policy. These three aspects integrated in the process will improve the reputation of the organization among users as well as suppliers. Documentation is the key to good data quality and if the data is organized properly, it helps in the tasks like checking, validating or correcting of data, improvise their efficiency and further reduces the time and expenditure involved in data cleaning. The task of data cleaning belongs to all, whether collector, custodian, or user. However, the prime responsibility belongs to the Information Management of the organization, who is supposed to take care of the storage and management of data.