What data warehousing, CRM and ERP projects have in common is the integral approach. With the aid of business intelligence and analytical applications, the aim is to create a much better foundation and support for making competent and well-founded decisions.

During data integration, short-comings in the data quality often become obvious. Inconsistencies and faulty, obsolete and incomplete data or redundancies are frequently discovered. These defects necessarily have an effect on all of the aggregated or derived data. This in turn impairs the integrity of the data analyses to a great extent or even makes them unusable.

Despite many different negative examples, problem awareness concerning the data quality of customer and prospect addresses is still below average. That is surprising, since the possible negative effects are quite serious: avoidable high costs, severe impairment of customer relationships, missed opportunities.

One of the greatest challenges in constructing and utilizing data warehouse and CRM projects is the quality of the initial data and the data extraction from heterogeneous sources. Usually customer data from the most diverse applications and databases is exported in a uniform format. But by no means should one assume that the customer-relevant data from the diverse applications and areas of the company is in good condition. That is connected, among other things, with the fact that the address data and other customer data were originally recorded and used for other purposes. Particularly here the necessity of cleansing and maintaining the integrity of address data becomes obvious, in order to prevent faulty data representation and flawed data evaluation and analysis. Of course, the same applies to follow-up, quality-assurance measures in the ongoing contact with customers, whether it be per e-mail, telephone or via Internet (e-CRM).

With an array of highly qualified software solutions Uniserv helps you realize a continuous address management strategy covering all essential functionalities such as data formatting, duplicate check, data verification, data maintenance, data enhancement, data input and reaction collection, so that you stay competitive with reliable customer data.