Master data is the foundation of every company. If it is not correct, all downstream processes are inevitably compromised, are delayed or result in additional costs. The Article, Material, Customer, Plant or Supplier master data thus form the basis for efficient and sustainable management, satisfied customers and reliable analyses. In the future, data complexity will also increase, so that simple maintenance processes will become more important. Lars Klimbingat, partner at retailsolutions GmbH und Lead of the competence center master data management & processes, states, "Tools can help to build up a stable foundation of master data right from the start and to use it profitably in the long term. That is why we have developed an add-on that is completely integrated into SAP. The Superior Data Quality Cockpit(SDQC), uses machine learning to enable consistent and error-free master data. Recently, we have also developed an add-on for the Business Partner and the use of the add-on for material master data."
Error-free data from the very beginning
In order to drive innovations such as the digitisation of a company or to enable system enhancements such as the switch to SAP S/4HANA, companies must work with consistent master data. In addition, studies - including those by Bitkom Research - show that poor master data quality result in consequences such as increased internal queries, longer processing times, additional costs in operational processes and poor process quality. However, the most obvious effects are seen in materials management, sales and order processing as well as production planning and warehouse management. With the SDQC add-on from retailsolutions, the continuous improvement of master data quality can be realised in an uncomplicated way. In addition to process optimisations, users can also achieve cost and time savings, make better decisions in real time and advance digitalisation overall. “SDQC’s expert rules ensure that the master data is created correctly, rules which are defined by the users themselves. Users can configure complex rules with just a few clicks without programming. In addition, the validation of unique data constellations is also checked", Lars comments further. To maintain an overview, the rules are displayed within the rule cockpit of SDQC. With the help of machine learning, the system also identifies fuzzy data constellations. For example, it can check whether the values for measurements and weights are maintained and if they are realistic values. The AI also provides automatic corrective values which allows for easy error correction. In addition, with full SAP integration, users can access metrics via an intuitive dashboard and scorecards. Here they can perform useful reporting and analysis to keep improving data quality for the business.
High data quality in the Business Partner and for the industry
Thanks to a new extension of the SDQC tool, it can now also be used in the Business Partner. This means that Customer and Supplier data, plus additional data from credit card customers or sales representatives, can be stored together. ”For the extension to the Business Partner object, we had to make some adjustments. We adapted the frontend to support additional applications, master data tables and transactions”, explains Klimbingat, and adds: “Now customer-specific tables can be customised and quickly adapted as needed. This can be done without programming and automatically, which reduces maintenance effort and costs. At the same time, extensive and automated analyses, as well as, mass checks can quickly uncover errors in the Customer, Supplier and Company master data. Even with new Suppliers or Customers, the SDQC extension facilitates data migration and harmonisation through integrated mass checks.” The original focus for SDQC was for SAP retail customers, however a growing need for a tool like SDQC is seen in other industries. retailsolutions has therefore now extended SDQC so that the SAP add-on can also be used for customers using non-retail material master data. SDQC also works in production to ensure the quality of material master data.
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