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.

 

Request the webinar on demand

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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