Processes of data cleaning

Data cleaning has at least three different steps.

1. Normalization
The successfull data cleaning starts with recognition of the datastructure:
Data must be entered into separate fields. This is the most complex part of the whole process. 
Normalization prepares the data for the cleaning, but as a subsidiary benefits it also provides the right type of data format for personalisation.

2. Standardization, address validication

Standardization is doing the process of data validication: checkes out and correct the data provided by normalization. The recognized and cleaned data is ready to be used for postage process. In case of anomalies - when the system couldnt decide on valid data, or when several solution is possible, the data could be checked and corrected by a special interface. 

3. Deduplication

 
Contacts that are represented by several records could be identified and moved from the database which has two advantages: it is reducing postal costs, and rising the quality and the prestige of a campaign. Deduplication can be done on 'family level' as well which means that a household is not getting the same message twice.

4. Postal automation:

Besides data cleaning Modus is able to prepare deliveries by the rules of delivery companies.