Data-driven business models

In recent years, the business to consumer (B2C) and business to business (B2B) sectors have undergone a fundamental transformation, in which products, processes and organizations become increasingly connected via a growing amount of generated data and digital technologies. The car industry, for example, uses generated data to (a) generate revenue through, e.g., individual offerings and personalization of the vehicle, (b) reduce costs through, e.g., better tailoring of products/services provided and (c) increase the safety and security by reducing the time for intervention. The industrial sector, through its dependency on manufacturing machinery, is also generating a massive amount of data. Initially driven to improve internal efficiencies and processes, companies started to interconnect these data, resulting in the possibility to improve external activities such as up- (e.g., supplier) and downstream (e.g., consumers) activities. Nowadays, manufacturing companies have the possibility to use the generated data over different stakeholders and the product lifecycle with improvement potential in e.g., development, manufacturing, services and/or end-of-life activities.
With the availability of data, organizations need to reflect and analyze the current value offer to customers. Besides the possibility to offer and improve customized products and/or services, companies need an underlying concept to exploit industrial data in form of a data-driven business model. Every company needs to effectively and efficiently be able to create the willingness to pay as well as to realize and capture value added to find their specific sweet spot. For manufacturing companies, the journey towards a data-driven business model starts with their physical product and domain-specific knowledge.
However, as the way to establish data-driven business model is not always linear, and there is no general recipe for success, the journey from a physical product to a data-driven business model may be different between companies. Hence, companies may experience different hurdles on their journey.
This study aims to facilitate the journey by answering the question how companies along a value chain can monetize the insights derived from industrial data. To do so, the underlying procedure covering the identification of roles along the value chain, the value of machine sensor data and monetization opportunities will be presented alongside the respective outcomes.