Data quality differs from data cleansing in that whereas many data cleansing products can help in applying data edits to name and address data or in transforming data during an ETL process, there is usually no persistence in this cleansing. Each time a data warehouse is populated or updated, the same corrections are applied to the same data.

Data cleansing is an action, whereas data quality describes the state of the data. A data cleansing process can contribute to improving the quality of data. Improved data quality is the result of a business improvement process that looks to identify and eliminate the root causes of bad data. A critical component of improving data quality is being able to distinguish between "good" (i.e., valid) data and "bad" (i.e., invalid) data. But because data values appear in many contexts, formats, and frameworks, this simple concept devolves into extremely complicated notions as to what constitutes validity. This is because the validity of a data value must be defined within the context in which that data value appears.

There are many dimensions of data quality. The ones that usually attract the most attention are dimensions that deal with data values:

• Accuracy, which refers to the degree with which data values agree with an identified source of correct information.

• Completeness, which refers to the expectation that data instances contain all the information they are supposed to. Completeness can be prescribed on a single attribute, can be dependent on the values...