⊿ Point. {R} Glossary. ◢ Keyword: D. ▰ Sources. 〓 Books [B] ◥ University. {q} PhD. ⏫ THEMES. ⏫ Big Data. [B] Big Data. ⚫ USA. ↂ EndNote. ☝️ BD Dummies. Data Cleansing. After cleansing, a data set will be consistent with other similar data sets in the system. The inconsistencies detected or removed may have been originally caused by user entry errors, by corruption in transmission or storage, or by different data dictionary definitions of similar entities in different stores. Data cleansing differs from data validation in that validation almost invariably means data is rejected from the system at entry and is performed at entry time, rather than on batches of data. The actual process of data cleansing may involve removing typographical errors or validating and correcting values against a known list of entities.
The validation may be strict (such as rejecting any address that does not have a valid postal code) or fuzzy (such as correcting records that partially match existing, known records). Some data cleansing solutions will clean data by cross checking with a validated data set. Motivation[edit] In the business world, incorrect data can be costly.