Data plays a central role in many of today's business models. With the help of advanced analytics, knowledge about real-world phenomena can be discovered from data. This may lead to unintended

knowledge spillover through a data-driven offering. To properly consider this risk in the design of data-driven business models, suitable decision support is needed. Prior research on approaches that support

such decision-making is scarce. We frame designing business models as a set of decision problems with the lens of Behavioral Decision Theory and describe a Design Science Research project conducted in the context of an automotive company. We develop an artifact that supports identifying knowledge risks, concomitant with design decisions, during the design of data-driven business models and verify knowledge risks as a relevant problem. In further research, we explore the problem in-depth and further design and evaluate the artifact within the same company as well as in other companies.