Use-Case: Bayers novel approach to automated decision-making

Bayer's case represents an exemplary data management initiative that implements automated decision-making in their master data workflows based on executable business rules. Their case demonstrates the scale and cost savings that are possible by leveraging data sharing and combining community-defined data quality rules with internally defined rules.

Bayer's CDL+ framework builds on a semantic knowledge graph and more than 1,500 Data Quality Rules, defined by 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 system enabled Data Governance covering many aspects of the CDQ Data Excellence Model.


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In their joint webinar, Jens Peter Henriksen and Jens Greiner (Bayer) share their insights into how an innovative framework drives process automation. Request the webinar on demand


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