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Data cleansing is the process of altering data in a given storage resource to make sure that it is accurate and correct. There are many ways to pursue data cleansing in various software and data storage architectures; most of them center on the careful review of data sets and the protocols associated with any particular data storage technology.
Data cleansing is also known as data cleaning or data scrubbing.
Data cleansing is sometimes compared to data purging, where old or useless data will be deleted from a data set. Although data cleansing can involve deleting old, incomplete or duplicated data, data cleansing is different from data purging in that data purging usually focuses on clearing space for new data, whereas data cleansing focuses on maximizing the accuracy of data in a system. A data cleansing method may use parsing or other methods to get rid of syntax errors, typographical errors or fragments of records. Careful analysis of a data set can show how merging multiple sets led to duplication, in which case data cleansing may be used to fix the problem.
Many issues involving data cleansing are similar to problems that archivists, database admin staff and others face around processes like data maintenance, targeted data mining and the extract, transform, load (ETL) methodology, where old data is reloaded into a new data set. These issues often regard the syntax and specific use of command to effect related tasks in database and server technologies like SQL or Oracle. Database administration is a highly important role in many businesses and organizations that rely on large data sets and accurate records for commerce or any other initiative.