What is Data Cleansing ?
Data cleansing (or ‘data scrubbing’) is detecting and then correcting
or removing corrupt or inaccurate records from a record set.
After cleansing, a data set will be consistent with other similar
data sets in the system. The inconsistencies detected or removed may
have been caused by different data dictionary definitions of similar
entities in different stores, or caused by user entry errors or data
which was corrupted in transmission or storage. Preprocessing the data
will also guarantee that it is unambiguous, correct, and complete.
The actual process of data cleansing may involve removing typos 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 ZIP code or fuzzy such as correcting records that partially
match existing, known records. 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.
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