The continuously increasing amount of production data and the advancing development of digitization solutions promote advanced data analytics as a promising approach for failure management. Beyond the consideration of single units, examining the end-to-end value chain, including development, production, and usage, offers potential for failure in management-related investigations. Nonetheless, challenges regarding data integration from different entities along the value creation process, data volume and formats handling, effective analytics, and decision support arise. The CRISP-DM approach has become a widely established reference as a conceptual framework for data-driven solutions. However, the linkage between existing failure management procedures and the subsequent development of data-driven solutions needs to be specified. Accordingly, this paper presents a cross-value chain Data Enabled Failure Management Process (DEFMP). The central element is a process model to implement a cross-value chain data-enabled failure management, considering established quality management and data analytics approaches. Based on available failure, product, and process knowledge along the value chain, a path towards developing a comprehensive decision support system is shown. DEFMP combines a reactive failure process with a data-driven approach to incorporate data analytics for proactive improvements. Using DEFMP, the failure management process of a commercial vehicle manufacturer is adapted. With this, partial automation of failure management is made possible. In addition, the potential for improvements is identified and prioritized.