Increase the quality of material master data with SAP DQM

Material master data is relevant for a number of business processes. If it is of poor quality, this can result in production downtimes, recalls, longer processing times or costly data cleansing activities. This inevitably leads to increased effort and unnecessary costs. What follows from this discovery? Improving the quality of material master data should definitely be given high priority. SAP Data Quality Management (DQM) plays a central role in this context.

Increase your data quality with the help of SAP DQM and SAP MDG

Four steps to optimized material master data

SAP DQM is one of the three core functionalities of SAP Master Data Governance (MDG). It enables companies to define data quality rules and store key figures in order to carry out quality analyses and thus monitor data quality on an ongoing basis. The path to high-quality material master data using SAP DQM involves four steps.

Step 1: Define quality

The first step is to define the required quality of the data and the necessary requirements based on the business processes. It is important to determine what high data quality means and which data quality rules are required. For example, standards and reports, analyses, lessons learned from the past and pattern recognition of data can be used for this.

Step 2: Enter quality

It is important to ensure quality at the data entry stage so that new and existing data meets the defined quality standards. This is made possible by establishing rule-based checks for all types of data entry, for example for change requests or mass processing.

Step 3: Monitor quality

Regular analyses and the definition of KPIs help to monitor data quality. The aim is to constantly re-evaluate the status of the data and identify potential problems before they have a negative impact. Dashboards can be used to visualize trends in data quality.

Step 4: Improve quality

Improving data quality includes measures to solve existing quality problems and prevent future difficulties. This can be achieved by using troubleshooting tools and expanding the set of rules for data entry in the event of recurring problems. The aim is to optimize data entry processes and continuously develop the underlying definition of quality.

Measures to optimize data quality

Validation rules can be defined with the help of SAP Data Quality Management. These can be precisely described and assigned to the relevant contact persons. The rules can also be simulated in advance to identify potential undesirable side effects and prevent them from being introduced into the productive system.

Derivation scenarios can also be created. The evaluation results can be sent directly by e-mail. If errors are detected, they must be corrected. Validation also takes place in the course of this data correction. Only then are the changed materials activated.

Advantages of SAP DQM

By using SAP DQM, companies create a single source of truth with regard to their material master data. The defined rules can be reused for different business processes. The rule history is comprehensively documented so that it is always possible to trace who made which changes and when. The use of user-friendly SAP Fiori apps enables intuitive operation of SAP DQM. The simulation of rule effects before going live avoids unexpected and unwanted situations and thus offers additional security. SAP DQM also supports user-defined fields, allowing companies to customize the application to their individual requirements.

The complex rules and requirements may call for know-how in ABAP development and experience in working with BRFplus. Accordingly, companies must ensure that the relevant expertise is available and that employees are trained to use SAP DQM. If these requirements are met, a high level of data quality can be achieved and permanently ensured with the use of SAP DQM.