Data-driven business models for service innovation in small and medium-sized businesses

Abstract

Today’s world is one of growing data, yet few companies have succeeded in leveraging data as a key resource for novel business models. Meanwhile, a profound shift is likewise taking place in the dominant logic of how companies create value for customers. Established processes for business model innovation are helpful for validating and implementing an initial vision. However, little guidance is available on how to systematically envision using data in service innovation.

This thesis aims to explore how value can be drawn from Big Data in the Service Design Thinking process with a specific focus on business models. For this purpose, the thesis introduces, on the one hand, the Data Canvas as a new method for considering data resources systematically in the development of business models and, on the other hand, the Data-Need Fit as a conceptual basis for the established business model innovation process.

According to Design Science Research as an overall academic framework, Data Canvas and Data-Need Fit were built and evaluated applying Lean Design Thinking in a mindset of the service logic. Within Design Science Research, processes and methods from Service Design Thinking are a good fit with their iterative nature and their focus on early prototypes as artifacts.

Combining approaches from different theoretical and practical frameworks, a structured yet flexible approach to data-driven business model innovation is proposed. In the case study evaluation, this approach has proven simple to use. An understanding of available data sources through the use of a Data Canvas facilitates targeted user research and helps to identify relevant customer jobs that could benefit from that data. Such a Data-Need Fit is a vital basis for a compelling value proposition. Integrated into a Service Design Thinking process, Data Canvas and Data-Need Fit may help companies to leverage data as a resource in business model innovation.