Data Quality: What does poor quality data cost?
Read our new brochure “What does poor quality data cost?” to know how high costs can precisely get and how you avoid them from the beginning.
In Germany, approximately 8 million addresses alter due to relocation and 870,000 due to bereavement each year. Many of the 387,000 weddings and 180,000 divorces each year also lead to changes of name. Thousands of street names, post codes and town names change annually, and information about contact persons, legal structures and names in companies are subject to constant change. But the largest number of changes affect the postal address – the CRM is not necessarily up-to-date. Postally clean addresses are of central importance for companies. Only correct addresses ensure the deliverability of mailings, minimise postage and advertising expenses, and are an indispensable prerequisite for the identification of duplicates.
From what point do enterprises have a data quality problem? When is the time when action for optimisation need to be taken and how to improve the data quality? Often it is not clear at first sight whether an address is correct or wrong.
Inadequate data quality is noticeable at different levels
These symptoms should be taken seriously, as they provide an indication of the actual state of data quality:
- High return rate for mailing actions due to incorrect or obsolete addresses.
- Customers complain about multiple received advertising mail, since the contact data occur as duplicates.
- Titles are badly or not at all maintained, so that letters with a wrong salutation are sent to customers.
- Legal provisions such as the antiterrorism regulation can not be adhered to, since the technical possibility for the implementation of the comparison against sanction lists is missing.
- The employees complain about the complex data record management in more than one system and moan about a high manual effort in the search for specific customer groups or other relevant information.
A good data quality is also a prerequisite for data integration and data synchronisation. If this is not the case, the company customer master data can not be reasonably enriched or integrated. When two or more data sets are combined, errors can occur which distort the information in the master data record. This in turn can have fatal effects on all divisions of the company: the customer is advised incorrectly by the support, Invoices are send out to products that the customer has not purchased or the customer is advertised with products to which he has no interest or which he has already purchased. Especially in the CRM environment this can anger the customers – the business relationship is at risk in the worst case. Wrong addresses and inadequate data quality will thus become a cost factor that companies could avoid.