CDQ Good Practice Award

CDQ Good Practice AwardPlease click here for the German version German

The CDQ Good Practice Award was launched in 2013 as a joint initiative of the Competence Center Corporate Data Quality (CC CDQ) and the European Foundation for Quality Management (EFQM) for acknowledging world-class corporate data management initiatives. The good practices submitted by the participating organizations are evaluated by an international jury of data management experts. The three companies that make it into the final round of the competition present their good practices in web sessions for the jury to decide about the winner.

Good Practices of finalists of 2016


Good Practice presented by Emmi (Finalist)

CDQ Good Practice Award 2016 Finalist- Emmi

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.

Good Practice presented by Merck (Finalist)

CDQ Good Practice Award 2016 Finalist Merck

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.

Good Practice presented by Schaeffler (Winner)

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.


Björn Schweiger
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Jury 2016

Prof. Dr. Andy Koronios

University of South Australia

Jodi Maciejewski

ASUG Data Goverance SIG

Lwanga Yonke

International Association for Information and Data Quality

Geert Opdenbosch


Dr. Wolf Engelbach

Fraunhofer Institute for Industrial Engineering

Dr. Philip Woodall

University of Cambridge

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