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Data Cleansing


Prior to data integration, a process of data cleansing is required. This is the process of detecting and correcting [or deleting] corrupt or inaccurate records from a record set.

Data inconsistencies may arise when:

  • Different data dictionary definitions of similar entities are used in different data stores.
  • User entry errors
  • Data corruption during transmission or storage.

Data cleansing is also known as data scrubbing.

 

Data Cleansing Process

The data cleansing process involves:

  1. Removing typos
  2. Validating** data defintions against that of the destination data warehouse. The validation may be strict [rejecting any item that does not have a valid value] or fuzzy [correcting records that partially match existing, known records].
  3. Correcting values against a known list of entities.

Data cleansing is synonymous with the less frequently-used term data scrubbing.

**Data cleansing differs from data validation in that validation commonly means data is rejected from the system at entry and is performed at entry time, rather than on batches of data.

Next: Data Normalization

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