Increasing digitization is generating more and more data in all areas of business. Modern analytical methods open up these large amounts of data for business value creation. Expected business value ranges from process optimization such as reduction of maintenance work and strategic decision support to business model innovation. In the development of a data-driven business model, it is useful to conceptualise elements of data-driven business models in order to differentiate and compare between examples of a data-driven business model and to think of opportunities for using data to innovate an existing or design a new business model. The goal of this paper is to identify a conceptual tool that supports data-driven business model innovation in a similar manner: We applied three existing classification schemes to differentiate between data-driven business models based on 30 examples for data-driven business model innovations. Subsequently, we present the strength and weaknesses of every scheme to identify possible blind spots for gaining business value out of data-driven activities. Following this discussion, we outline a new classification scheme. The newly developed scheme combines all positive aspects from the three analysed classification models and resolves the identified weaknesses.