Data governance provides the structural foundation on which effective and successful master data management can be built in a company. In day-to-day business, there are always situations in which data governance helps to solve problems, overcome challenges and achieve competitive advantages. The following best-practice examples are intended to show how data governance concretely promotes corporate success.
A pharmaceutical manufacturer produces a vaccine and ships it to its customers. When the goods arrive at the customer’s, it turns out that the vaccine was not cooled during transport to the extent that it should have been. Due to the incorrect transport conditions with a temperature that was too high, the vaccine is no longer usable and must be disposed of.
How could this happen? The required storage temperature is not explicitly stored in the master data of the vaccine; instead, a default value is used that is significantly too high for the vaccine. The damaged goods cause economic damage amounting to one million euros. In addition, the customer is annoyed because he has to incur significant additional expense and the subsequent delivery of the intact vaccine is delayed. The fact that the incident becomes public leads to considerable damage to the manufacturer’s image.
If there were governance in the “storage temperature” field, there would be a clear technical person responsible for this. This person would have ensured in the maintenance process that the correct value for the storage temperature is entered or that the corresponding specialist checks are available.
A manufacturer of smaller production parts regularly has an increased logistical effort in shipping – with the consequence that shipments arrive too late or the shipping costs are higher than planned. The cause: The dimensions or weight of the manufactured parts are not maintained at all or not properly in the master data. As a result, the required packaging sizes and the resulting transport costs can only be determined directly during shipping in the warehouse.
As a result, shipping costs are increased and delivery times are longer. The customers are annoyed because of the unexpected longer delivery times and are not willing to bear the higher shipping costs, leaving the manufacturer stranded.
It is only during a data governance project that it becomes apparent that the responsibility for maintaining the dimensions and weights in the material master data had not previously been clearly clarified. By bringing all the departments involved to the table, each area can better understand the others’ perspective. Together, they clarify when and where the data must be reliably available and who maintains it.
A health insurance company from Baden-Württemberg collects personal data as part of various competitions. For a subsequent advertising campaign, the company uses this data and actively contacts the individuals. As it turns out, the data processing is illegal, which brings the Baden-Württemberg data protection authority onto the scene.
It concludes that the consent forms used in the sweepstakes were misleading and classifies the data processing as unlawful. Although the health insurance company pledges its immediate cooperation in the proceedings, the data protection authority imposes a fine of 1.24 million euros because of the serious disregard for data protection.
Data governance can support the implementation of legal and regulatory requirements. Clear responsibilities in the governance role model, the establishment of internal control measures as part of the data processing procedures, and regular reviews minimize or eliminate corresponding risks and protect the company from fines and damage to its reputation.
The selected examples make it clear that functioning data governance and the resulting uniform, disciplined management and maintenance of data have a positive effect at many points in day-to-day business and avert damage to the company – whether in financial terms or with regard to reputation. In view of these added values, it is worthwhile for companies to place their (master) data management on the stable organizational foundation of data governance.