The optimization of the data quality is an endurance run

Master data quality has a beard, a very long one. But customer data is subject to ever-increasing dynamics, and there are always changes and updates that must be reflected in the measures to optimize data quality. Instead of one-off actions, therefore, a continuous “cycle” of measures is required that leads to a gradual and sustainable improvement in the quality of the data.

Bad, well-kept data jeopardizes business success

If customer data quality is poor, the company lacks the 360-degree view of the customer that is so important today. However, this 360-degree view is a central requirement

for the planning and execution of marketing and sales campaigns,

for support and customer service tailored to the needs of the customer

for perfectly adapted products and services

In sectors such as banking and finance, the 360-degree view is also an important basis for meeting, for example, compliance requirements and legal requirements. And the predictive analytics methods that are being used today in many sectors for strategic business decisions also fall into the void without a 360-degree view.

What is happening in Germany?

The big challenge for companies that want to ensure the high quality of their customer base data in the long term is shown by the following data:

Every year eight million people move in Germany

Every year, there are more than 800,000 deaths in Germany

Each year, 45,000 street names and almost 2,000 place names are renamed in Germany

Moreover, in the business-to-business sector, insolvencies (more than 23,000 companies in 2015), takeovers and mergers and employee changes are the main reasons for continuously changing company and address data

Another external “risk factor” for the quality of the customer master data is the customer himself. If the customer incorrectly or incompletely enters his or her data – for a request, an order or a complaint – it is difficult or even impossible for the company to promptly make that mistake to recognize and eliminate. Frequently, the customer finds an “accomplice” in the company, the employee in the call center, in the order acceptance or in customer service. Input errors are unavoidable even there, the same applies to the transfer of data from the ordering system in the accounting or ERP system.

But not only the “human factor” threatens the data quality. Another reason for the lack of data quality is the isolation of data silos, which are still common in many companies today, in which customer data is stored and processed. The CRM system in sales, the marketing automation software in marketing, the ticketing system in customer service or the ERP system in accounting or controlling.

All these systems are fed separately with customer data, the processing and updating of the data is then usually also isolated in the respective systems. Very few companies today are already able to consolidate these different databases into one central customer master database. Thus, they inevitably fail in the optimization of data quality, because it applies the rule: without consolidation no optimization.

Leave a Reply

Your email address will not be published. Required fields are marked *