We're Happy to Announce the Winner of the CDQ Good Practice Award 2019: Schaeffler Technologies AG & Co. KG
This year Grundfos, Robert Bosch GmbH, SAP SE and Schaeffler Technologies AG & Co made it to the final round of the CDQ Good Practice Award 2019. The good practices of these four finalists have been evaluated by an international jury as well as by the member companies of the Competence Center Corporate Data Quality (CC CDQ). The community and the international jury finally ranked Schaeffler‘s good practice top and declared the global supplier to the automotive and industrial sectors the winner of the CDQ Good Practice Award 2019.
“This year's CDQ Good Practice Award finalists clearly brings data management to the next level by showcasing how data can be made "fit-for-purpose" for digital end-to-end processes and smart products. The jury had a tough job, since we received excellent submissions that clearly demonstrate the increasing importance and maturity of data management,“ said Prof. Dr. Christine Legner, academic director of the CC CDQ.
"Schaeffler - as CDQ Award Winner – represents an exemplary data management initiative in terms of strategic vision, scope and global outreach. By rolling out data management to 47 data domains and at global scale, Schaeffler has built on their strong master data management experience to develop the data foundation that supports digital transformation and future business models."
We want to thank all participants for their excellent contributions and the time and effort spent for preparing their submissions! We received several excellent applications. Again, like in previous years, it was a neck-and-neck race.
To those participants who did not make it into the final round this year: Please rest assured that the time and effort you spent for writing your application was not wasted! Everything you prepared for participating in the competition is a good documentation of a great success story, which may be perfectly suited to serve other internal documentation or public relation purposes as well.
Our Winners 2019 at a Glance
Finalist: SAP (Christopher Reimann, Julian Blasch, Bastian Finkel, Thomas Ruhl, Vika Venugopal, Martin Stocker)
The Good Practices in Detail
Professional Data Management in 47 Data Domains
The global supplier to the automotive and industrial sectors is helping shape the rapid developments taking place worldwide as part of Mobility for Tomorrow including innovative and sustainability solutions for E-mobility and Industry 4.0. Professional data management is a key success factor in these increasingly complex conditions, as data is strongly linked to business value.
Schaeffler Corporate Data Management, the CDQ Good Practice Award winner from 2016, implemented the project “Data Domain Management in all Data Areas” to support the digital transformation process. The goal was to guarantee high standards of data quality as a foundation for future business opportunities, advanced analytics, efficient processes and to ensure information security needs with a group-wide data responsibility. The scope of the project was huge and covered all types of data from all business functions and divisions.
The corporate data management experts defined and analyzed all data areas. The responsible data domain managers for each of the 47 data domains have been nominated with the help of top management. Afterwards, they were trained and consulted. As a result, Schaeffler observed several business improvements. For example, the data quality in the plants’ supply chain increased significantly.
The Data Domain Management Project also contributed to a cultural change. Today, data management at Schaeffler is considered a discipline which is executed in all business functions and divisions; not just by a master data expert team in the IT department. The awareness, transparency and knowledge regarding data was raised substantially.
Data Management in all Data Areas
Corporate Data Management
Markus Rahm, Achim Gooren, Sabrina Karwatzki
Schaeffler Technologies AG & Co. KG
Using AI to Become a Digital Factory
"The Robert Bosch plant in Blaichach/Immenstadt produces brake control systems, fuel injection components and sensors for autonomous driving. In 2017, 6.5 million have been produced in the Blaichach/Immenstadt plant. The plant has a clearly defined strategy of becoming a «digital factory». As Bosch already has a complete digital version of the end-to-end process of material and data of all products, all IT systems have interfaces to the systems of suppliers and customers. Now, Bosch aims at making better use of knowledge in the areas of plant engineering, IT and plant operation, targeting a significant annual productivity increase with the help of AI.
The operational excellence team at the Blaichach/Immenstadt plant started with the implementation of a first AI use case for functionality checks of the ABS/ESP parts at the final assembly. In the past, each part had to be checked up to 4 times until the result was determined. Now, the decision is made by a neuronal net and processed directly on the machine, which eliminated repeated checks at the test bench and resulted in thousands of euros in savings.
The Bosch team works according to the CRISP-DM standard; for them, collaboration and cross-functional teams have been a key success factor. The data scientists work on analyzing the data in very close cooperation with process specialists. Even during the modeling phase, there is an extensive exchange with automation technicians, as they will intervene in the course of production based on the decisions of the AI. To train a single AI model is easy, but to maintain and deploy thousands of models is not! The Bosch team has set the foundation for thousands of other use cases they want to implement. Currently, they are working on the next ones with the clear goal to industrialize AI.
Indistrialized machine learning in high-volume production
Corporate Data Management
Robert Prager, Marco Keith, Dr. Tobias Windisch, Andreas Huditz
Robert Bosch GmbH
Driving Efficiency in Data Management with Robotic Process Automation
The leading provider of enterprise software is applying robotic process automation (RPA) to drive operational efficiency in its shared services centers for data management. Furthermore, RPA helps improve the employee experience and ensures trusted data for SAP as a company.
The data strategy & operations team of SAP followed a systematic approach of running a pilot to drive adoption of this new practice within their organization. This resulted in identifying, assessing and prioritizing more than 20 RPA use cases in data management. They have already implemented 5 use cases with tangible business results. Some of these cases achieved up to 800% process efficiency gains, data quality improvements and significantly better user experience. The team went one step beyond the initial goal by showcasing how to orchestrate RPA and human intelligence into an intelligent workplace of the future for their data agents. By the end of the project, they have turned it into a program aimed at establishing a center of expertise surrounding RPA for data management.
Driving Efficiency in Data Management
Robotic Process Automation
Thomas Ruhl, Christopher Reimann, Julian Blasch, Bastian Finkel, Vika Venugopal, Martin Stocker
Considering Data Requirements as Product Requirements
The world's largest pump manufacturer considers data assets as key success factors for their ongoing digital transformation. The data quality of their pumps’ IoT/streaming data is essential for good customer experience, to accelerate digital offers and as a foundation for analytics. At Grundfos, data specialists now convert specific data demands into actual product requirements early in the development process to proactively improve data quality and, thereby, the product.
During product development, they focus on what data will be needed from a data science perspective. The data quality assessment method scores the data sets of a new product based on four dimensions. The first dimension is data definition which evaluates the meta data. The second category is data quality which assesses the quality of the data sets according to the previously defined requirements, e.g., completeness or consistency. The third category is data availability which scores how FAIR (findable, accessible, interoperable, reusable) the data set is. The last dimension is data documentation which evaluates how raw and processed data is documented.
The resulting report points out clear recommendations for how to improve the data quality before the product will enter the «prepare production» phase. Besides better IoT/streaming data, this initiative also helped improve the awareness of data quality within the company and to establish a common language between business experts and data scientists.
Data Quality Assessment Method (DQAM)
Prioritizing data quality as much as the physical quality of the product
Signe Horn Thomsen, Pepe Wadsholt Herrera Julio