The Industry 4.0 phenomenon, internationally known as the Industrial Internet of Things, is expected to enable data-driven business models across the manufacturing sector. While data-driven business models in business-to-customer (B2C) markets are flourishing, driven by trends such as on-demand services, improved resource allocation, niche advertising and the sustainability movement at large, business-to-business (B2B) data-driven business models and the corresponding literature are less pervasive. While scholars have begun exploring firm-specific cases analyzing the introduction of new data-driven business models, e.g. on automotive shop floors, along manufacturing value chains, or in areas such as rail mobility, a comprehensive overview is missing. In response, this paper condenses extant research on data-driven business models in Industry 4.0 and develops several archetypes. These refer to (a) the types of data, which enable new B2B business models, (b), the forms of data-driven business models, e.g., building on sensor data for predictive purposes, and (c) new monetization forms for data-driven business models. The paper further distinguishes the accelerating and decelerating forces, which influence the implementation of data-driven business models in organizational ecosystems. In doing so, the paper intends to create a framework for future research and for practitioners on data-driven business model innovation in Industry 4.0.