Data-driven innovation (DDI) has attracted a lot of attention during recent years. Data is regarded as a key resource; the process of turning data into value is an activity, and products or services as value propositions are key objectives. However, few organizations are successful in their efforts to establish process models that support DDI. Several scholars report a lack of knowledge regarding how to manage data-driven processes. Against this background, the purpose of this paper is to fulfill this research gap by identifying and generating knowledge about DDI from a process perspective. A thorough literature review has been used to identify issues related to the DDI. The knowledge is articulated and implemented in a process model supporting organizations to exploit data in order to develop value propositions. The suggested process model consists of three integrated components: data strategy, value cycle, and data management. We can conclude that the process model extends previous models by presenting a detailed process model, applying a socio-technical perspective, offering both normative and prescriptive guidance supporting a systematic approach to data-driven innovation, and using data analytics as a generic tool for several actions in the DDI process.