As part of the data evolution, data-driven business models (DDBMs) have emerged as a phenomenon in great demand for academia and practice. Latest technological advancements such as cloud, internet of things, big data, and machine learning have contributed to the rise of DDBM, along with novel opportunities to monetize data. While enterprise architecture (EA) management and modeling have proven its value for IT-related projects, the support of EA for DDBM is a rather new and unexplored field. Building upon a grounded theory research approach, we shed light on the support of EA for DDBM in practice. We derived four approaches for DDBM design and realization and relate them to the support of EA modeling and management. Our study draws on 16 semi-structured interviews with experts from consulting and industry firms. Our results contribute to a still sparsely researched area with empirical findings and new research avenues. Practitioners gain insights into reference cases and find opportunities to apply EA artifacts in DDBM projects.