The good practice presented by Emmi illustrates how the company managed to successfully build up an integrated master data management function. This was mainly achieved through business process harmonization and the establishment of a central, leading system for master data management. These measures, together with the definition of new roles and responsibilities to maintain master data continuously across the entire product lifecycle, have led to significant improvements in overall data quality: beside an increase in productivity and customer satisfaction, Emmi was able to substantially reduce the risk of poor data quality plus associated cost. The company is now prepared for new business and market challenges, and it is able to keep growing without the costs of data maintenance rising disproportionately. Here you can access the presentation from Emmi.
Merck presented their good practice describing the establishment of a new team named “Data Analytics” within the company’s data management function. The team’s mission was to shift the focus from rather “old-fashioned” data quality dimensions to business related “fit-for-purpose” metrics. The goal of this new approach was twofold: 1) to optimize business outcome through enhanced information quality and 2) to identify new opportunities for improving business performance and process efficiency. Merck makes use of the SCRUM methodology to efficiently develop new, business-oriented metrics, which are continuously evaluated and developed further together with internal customers. The growing number of metrics developed and a substantial increase in the demand for development services are clear indications to Merck that this highly flexible and customer oriented approach has brought about the desired results. Here you can access presentation from Merck.
The good practice presented by Schaeffler illustrates how the company systematically evolved its master data management initiative starting in 2009. A self-assessment conducted two years ago revealed that Schaeffler had successfully built up capabilities and raised the level of maturity in all relevant areas of master data management. Aspects still demanding for substantial improvement were successfully tackled by Schaeffler during the past two years. The company has continued to develop its data management strategy further and communicate this strategy across the entire group. Among other things, Schaeffler uses performance indicators to measure and sustainably improve the quality of its data. Furthermore, the company defined clear roles and processes for data maintenance and implemented appropriate data models and metadata models. All these measures combined have led to a reduction in service processing time and to a continuous improvement of data quality (customer master data processing time has been reduced by 60 %, for example). Here you access the presentation from Schaeffler.