Bayer's novel approach to automated decision making in Vendor Master Data Management
This year Bayer, Beiersdorf and Deutsche Bank made it to the final round of the CDQ Good Practice Award 2020. The good practices of these three 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 Bayer‘s good practice top and declared the pharmaceutical company the winner of the CDQ Good Practice Award 2020.
"This year's CDQ Good Practice Award finalists demonstrate significant advances in data management: the agile development of master data apps, machine learning for generating data quality rules as well as knowledge graphs and data sharing in the business partner domain," said Prof. Dr. Christine Legner, academic director of the CC CDQ.
The international jury states: "Bayer - as CDQ Award Winner – represents an exemplary data management initiative that implements automated decision-making in their master data workflows based on executable business rules. Their good practices demonstrate the scale and cost savings that are possible by leveraging data sharing and combining community-defined data quality rules with the internally defined rules."
We want to thank all participants for their excellent contributions and the time and effort spent for preparing their submissions! Again, like in previous years, it was a neck-and-neck race.
To all companies and participants who got inspired by the finalist presentations: Please consider sharing your success stories and submitting them to the next edition of the CDQ Good Practice Award (Deadline in September)!
Our Winner & Finalists 2020 at a Glance
The Good Practices in Detail
Process Automation CDL+ // "Cinderella"
Bayer's good practice presents a forward-looking approach to automate master data workflows and manage the trade-offs between data quality, risks and manual efforts in vendor master data management.
Bayer's CDL+ framework builds on a semantic knowledge graph and more than 1,500 Data Quality Rules, defined in the Data Sharing Community, and combines them with Bayer-specific rules. These executable business rules and the validation with external data allow an instant-risk-based approval of master data requests instead of 24h service levels.
The initial pilot scope has achieved a considerable automation rate as well as several secondary benefits in the area of data quality and documentation of data-related knowledge. The utilization of the framework translates into a monetary business case. Moreover, it provides a future blueprint for other data objects (….) and presents a for system enabled Data Governance covering many aspects of the CDQ Data Excellence Model.
About Bayer AG
Bayer is a global enterprise with more than 150-year history and core competencies in the areas of health care and agriculture. Its products and services are designed to benefit people by supporting efforts to overcome the major challenges of our times presented by a growing and aging global population. At the same time, the Group aims to increase its earning power and create value through innovation and growth. Bayer is committed to the principles of sustainable development, and the Bayer brand stands for trust, reliability and quality throughout the world.
The Bayer group is organized in three Divisions with business operations responsibility (Pharmaceuticals, Consumer Health and Crop Science) and the Enabling Functions, which operate as Group-wide competence centers in which business support services are bundled. Group Finance is Bayer's backbone when it comes to ensuring compliance with financial regulatory requirements.
Linking Cause and Effect in Data Quality with Machine Learning
In 2017 Deutsche Bank's Chief Data Office (CDO) built and implemented a Data Quality Management process called DQ Direct. This is now an enterprise wide application that captures data quality issues and manages them through to remediation, providing insightful analytics to assist the bank's divisions improve data. This has won a number of international data awards and was a finalist in the 2017 CDQ Good Practice Awards. DQ Direct was initially used to capture data quality issues that were identified during the control process, usually at the end of the production cycle.
The next challenge was how to identify DQ issues as early as possible in the process and avoid the cost and latency from retrospective identification.
In response to this challenge, the Chief Data Office has developed 'Auto-DQ'.
Auto-DQ uses machine learning to predict relevant DQ rules by looking at patterns in the data over multiple time periods.
Auto-DQ has allowed Deutsche Bank to connect data quality rules to data quality issues. This means we have the ability to evidence that a data remediation has been effective through improved rule scores.
This gives complete end to end connectivity between an objective business rule and a business process consequence.
About Deutsche Bank AG
Deutsche Bank provides retail and private banking, corporate and transaction banking, lending, asset and wealth management products and services as well as focused investment banking to private individuals, small and medium-sized companies, corporations, governments and institutional investors. Deutsche Bank is the leading bank in Germany with strong European roots and a global network. It was founded in 1870 to accompany German businesses into the world.
Data excellence in the product innovation process
Beiersdorf, a globally leading provider of innovative, high-quality skin care products, presents its first Master Data app and the first self-developed Fiori App together with its IT service provider Beiersdorf Shared Services (BSS). The product innovation process is one of the most important success factors for Beiersdorf. The Fiori app helps Supply Chain Project Leaders to coordinate all functions in the execution phase of this process quickly and efficiently in sourcing, production and logistics. It provides a high level of transparency throughout the entire execution phase and significantly improves the user experience.
The Supply Chain Project Leaders can easily track the status of the respective Master Data in their projects in real time. For the development of the app, the team used Kanban and Design Thinking as agile project management methods, supported by Build.Me as screen development technique to ensure a very high user friendliness. The app is part of the Master Data Management program at Beiersdorf, a company-wide initiative to bring more simplicity to Beiersdorf's system setup. It strives to establish a uniform and integrative Master Data system for all processes. In order to make life easier for all Master Data users and consumers, a high level of user acceptance and thus a significantly better user experience is the clear goal.
About Beiersdorf AG
Beiersdorf AG is a leading provider of innovative, high-quality skin care products and has more than 135 years of experience in this market segment. The Hamburg-based company has about 20,000 employees worldwide and is listed on the DAX, the German benchmark equities index. Beiersdorf generated sales of €7.6 billion in financial year 2019. Its product portfolio comprises strong, international leading skin and body care brands, including NIVEA – the world’s no. 1 skin care brand* – Eucerin, Hansaplast/Elastoplast, and La Prairie. Millions of people around the world choose Beiersdorf brands every day because of their innovative, high-quality products. Further renowned brands such as Labello, Aquaphor, Florena, 8X4, Hidrofugal, atrix, Maestro, and Coppertone round off the extensive portfolio. Beiersdorf's wholly owned affiliate tesa SE, another globally leading manufacturer in its field, supplies self-adhesive products and system solutions to industry, craft businesses and consumers.
*Source: Euromonitor International Limited; NIVEA by umbrella brand name in the categories Body Care, Face Care, and Hand Care; in retail value terms, 2019.