Data cleansing is a reactive measure, as opposed to data validation that proactively eliminates ‘dirty’ data during the creation phase. Data cleansing involves removing or correcting the incorrect data. For example typo errors can be corrected with a mass spell checker, or against known lists of correct possibilities, for example postal codes. Where the correct entry cannot be determined then the entries are ‘flagged’ for inspection and manual correction.