Patterns of Data-Infused Business Model Innovation

Business models have been a concept widely discussed over the last 20 years. The increasing availability of data and the growing capability to exploit them with analytics has sparked a new set of discussions, though: it is claimed that data and analytics bring to bear entirely new "data-based" or "data-driven" business models. However, there is neither a common understanding of these business models nor of the ways existing business models are transformed into those. This paper aims to create a coherent framework and common understanding of the infusion of business models by data and analytics. Contrasting popular views, our conceptual analysis reveals that there are no data-driven business models per se, instead, the utilization of data and analytics opens a "continuum" of transformation options for business models. We identify five distinct patterns in which the use of data may alter the business model, illustrate them with representative case studies and evaluate the patterns analyzing a sample of 115 industry business models. This paper will contribute to the fundamental understanding of how the use of data and the application of analytics may trigger business model innovation. The identified patterns will provide guidance to practitioners on how to utilize (big) data and analytics, in particular to draw attention to those that still seem underutilized for innovation. The developed pattern concept will also open up a broad agenda for future research.