Data is the most important raw material of the 21st century. Everyday business life is full of situations in which data plays a central role. When introducing new software based on master data, business partners, customer addresses and the material master must be properly maintained so that the subsequent processes run smoothly. When transferring source data to a new system, the following applies: The fewer errors that there are in the source data, the fewer errors there will be in the new system. This aspect is all the more important when you consider that existing errors can even have a greater effect in more complex environments.
Companies should also keep an eye on data quality in migration projects to ensure that they can work with clean data in the new system. Even a complete technology change, such as from SAP ERP to SAP S/4HANA, is best achieved on a clean data basis. And when it comes to digitizing a previously manual data entry process, automated processes come to nothing if the data basis proves to be too faulty.
Clean basis for decisions
In addition to new software, migration and automation, clean data is also important as a foundation for improving performance and competitiveness. After all, when making data-based business decisions, management needs the assurance that it can rely on the validity of existing data. These many examples make it clear that a stringent quality strategy must always take data quality into account. Companies can only really benefit from their data if it is of the highest possible quality.
Obstacles to high data quality
Given the importance of high data quality, the question inevitably arises as to why companies fail in practice to ensure this quality. An important factor is the lack of human and time resources. It can be quite time-consuming to check the quality of the existing data and, if necessary, to make any necessary improvements. Meeting these tasks with internal capacities often proves to be an enormous challenge for companies.
In addition, a lack of expertise can also play a role. After all, a large number of questions need to be clarified in the course of data and address cleansing. Which tables can be used to validate international address data? Which parameters must be assigned to an appropriate program so that it can identify duplicates? How is a duplicate actually defined?
Often companies do not have suitable software for address validation and duplicate cleansing. In this case, not only the purchase of such software represents a cost factor, but also the training of employees in the operation of the program. Often the actual costs of such a measure cannot be reliably estimated at the beginning.
How can clean data be guaranteed?
There is no doubt that companies benefit greatly from maintained data and depend on it for their business success. In practice, however, it has been shown that companies for various reasons - from personnel bottlenecks to a lack of know-how and tools - have difficulties in achieving and permanently securing high data quality. They are therefore challenged to find a way to keep their data clean within the scope of their individual possibilities and thus create an indispensable prerequisite for the future success of the company.