Bring your material master data to a higher level

A high quality of material master data is of enormous importance for numerous departments and processes in a company. Incorrect, incomplete or outdated material masters have a negative impact on the processes of the entire value chain. Incorrect or incomplete information makes inquiries in the responsible departments necessary and increases the coordination effort. This results in longer process throughput times and lower efficiency.

Benefit from best practices in the introduction of master data processes

Decisive importance for smooth processes

The importance of maintained material master data becomes clear in three exemplary scenarios:

• An employee from the sales department is on site at the customer. In order to be able to create a quotation, he needs to be sure that the stored data such as classification of a material, average production time or delivery time of a product is correct.

• In logistics, incorrect material master data has considerable consequences. For example, if the dimensions or weights of the materials are not correct, trucks will drive half-full tours instead of being fully loaded. If the wrong materials are delivered, an increased number of returns is the result.

• The management uses annual reports and analyses as a basis for strategic decisions. Therefore, reports should ideally be based on correct data. The timeliness of material master data plays a decisive role in warehouse and inventory planning, for example.

What causes poor quality of material master data?

Despite their undisputed importance for the success of the organization, many companies tend to neglect the maintenance of material master data. The reasons for this are manifold. Certainly, the complexity of the topic plays a role. Numerous organizational units are involved in the creation and maintenance of material master data – from sales, purchasing and logistics to finance and controlling and quality assurance. This makes the processes complex and sometimes confusing. Often there are no clear validation rules and responsibilities, so it is not clear who is allowed to maintain which data. Changes made to a data set are difficult to track.

Especially occasional users who do not regularly work with SAP systems are unfamiliar with the user interfaces and quickly feel overstrained. In the best case they need more time for their work, in the worst case they make mistakes when creating new material master data. The spectrum ranges from incorrect to missing entries in the fields, which means that relevant information for subordinate processes is missing.

Two does not work better than one

Typical results of carefree data maintenance are multiple data sets. The fact that such duplicates can accumulate in the system is also related to the fact that neither SAP ERP nor SAP S/4HANA has a functionality that checks master data for duplicates. If duplicates exist, it is not clear which is the correct data record and whether the information is reliable. A manual duplicate check costs a lot of time because the correct data record has to be defined and duplicate data records have to be merged. However, there are a number of helpful tools that perform a duplicate check largely automatically.

In addition to duplicate data records, obsolete data as well as incorrect and incomplete information also contribute to poor quality of material master data. This results in additional work and additional costs. The business processes in materials management are severely disrupted. Insufficiently maintained material master data can lead to incorrect deliveries and material bottlenecks. The company’s customers also feel the effects of poor data quality, for example by receiving incorrect invoices. A low quality level of material master data therefore leads to frustrated employees and dissatisfied customers alike.

The path to high-quality material master data

In view of the consequences of poor data quality, it is worthwhile to analyze existing errors and information gaps in the data and to remedy the resulting problems. SAP Master Data Governance (SAP MDG) can help here. The software puts master data maintenance on a stable basis by allowing users to define their own rules and design processes to match the prerequisites and requirements in their own company.

Process-controlled workflows can be used to define exactly who is allowed to maintain which data and in which order. Users are only shown the fields that are relevant for them. In most system architectures, SAP MDG functions as the “single point of truth” and then distributes the master data to the target systems for further enrichment. In addition, the history of the master data becomes traceable. Those involved can see who made which changes and when. The definition of mandatory fields prevents incompletely filled data records from impairing and delaying operational processes. If the process of material master data maintenance runs according to clear rules, the data quality remains permanently high, so that the effort to clean up the material master will be considerably reduced in the future.

Short-term and long-term success

The company-wide introduction of SAP MDG creates the conditions for improving data quality with appropriate measures. This also includes duplicate checking and cleansing. SAP MDG can be used to determine the existence of duplicate data records. In the next step, the duplicates are cleaned up using mass change requests – an important process for optimizing data quality.

On the one hand, professional, software-supported master data management provides companies with rapid success by creating the conditions for smooth operations and high process efficiency. At the same time, companies set an important course for the future. Because only a uniform, reliable view of corporate data enables advanced data analyses and powerful reporting. In this respect, up-to-date, complete and consistent material master data is an indispensable prerequisite for companies to provide the right answers to strategic challenges throughout the supply chain and to draw the right conclusions from their data.