The causes of dirty or noisy data are many and each business domain has unique data cleanliness issues. In the enrichment phase, data, which may be obtained from an external source outside the organization, is added to the selected and cleaned data to add value to it. For example, in a retail KDD effort, customer shopping data is combined with third party demographic data, which maps customer names to addresses, number and types of cars they own, how many and what ages are their children, etc., perhaps to produce a target market group. In the data coding phase raw data is converted into a form that lends itself to be more effectively mined. It's sort of like cloud salting.
Attributes can be, and are often added to enhance the discovery of patterns within the data.
Values, such as dates, times, salaries or other data that express numerical values are normalized in such a manner that informational clusters are easily formed and associations can be more readily identified. The following record is an example of one possible form of data normalization. The focus of a coding transformation on this type of data might be to bring the salary and age fields to within the same value order as the other numeric fields. The numerical points of this record, when plotted, will fall closer to each other than if the data had been plotted in its original form. Depending on the data mining technique used on this particular kind of data set, this could prove to be an extremely valuable transformation.