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Motivation: Maximizing the value from data has become a key challenge for companies as it helps improve operations and decision making, enhances products and services, and, ultimately, leads to new business models. The latter two have been investigated by scholars as part of an emerging research field on data-driven business model innovation. While enterprise architecture (EA) management and modeling have proven their value for IT-related projects, the support of enterprise architecture for data-driven business models (DDBMs) is a rather new and unexplored field. We argue that the current understanding of the intersection of data-driven business model innovation and enterprise architecture is incomplete because of five challenges that have not been addressed in existing research: (1) lack of knowledge of how companies design and realize data-driven business models from a process perspective, (2) lack of knowledge on the implementation phase of data-driven business models, (3) lack of knowledge on the potential support enterprise architecture modeling and management can provide to data-driven business model endeavors, (4) lack of knowledge on how enterprise architecture modeling and management support data-driven business model design and realization in practice, (5) lack of knowledge on how to deploy data-driven business models. We address these challenges by examining how enterprise architecture modeling and management can benefit data-driven business model innovation.
Research Approach: Addressing the challenges mentioned above, the mixed-method approach of this thesis draws on a systematic literature review, qualitative empirical research as well as the design science research paradigm. We conducted a systematic literature search on data-driven business models and enterprise architecture. Considering the novelty of data-driven business models for academia and practice, we conducted explorative qualitative research to explain “why” and “how” companies embark on realizing data-driven business models. Throughout these studies, the primary data source was semi-structured interviews. In order to provide an artifact for DDBM innovation, we developed a theory for design and action. The data-driven business model innovation artifact was inductively developed in two design iterations based on the design science paradigm and the design science research framework.
Contribution: This thesis provides several contributions to theory and practice. We identified a clear gap in previous research efforts and derived 42 data-driven business model-related EA concerns. In order to address the identified literature gap, we provide empirical evidence for data-driven business model innovation. Four pathways of data-driven business model design and realization were identified. Along these pathways, an overview of EA application areas was derived from the empirical and theoretical findings. With the aim of supporting practitioners in data-driven business model innovation, this thesis was concerned with the development of a reference model. The reference model for data-driven business model innovation provides a broad view and applies enterprise architecture, where appropriate. This thesis provides five recommendations for practitioners realizing data-driven business models that address the demand for support in data-driven business model innovation.
Limitations: Several limitations must be considered. We acknowledge the threat to validity based on the fact that the thesis was written over the span of two years. As DDBMs are an emerging phenomenon in the literature, our thoughts on the underlying concepts have also evolved. Our ideas evolved to include a wider range of literature, different terminology, and a broader empirical foundation. We have gathered and analyzed the extended literature on EA and DDBM interconnectivity. However, the selection of keywords restricts the set of results. The data stem from a limited number of organizations and industries; thus, our conceptual developments need further testing to ensure generalizability.
Future Research: This thesis suggests several fruitful research avenues. Complementing the current concepts with additional data and quantitative research methods could address the existing threats to validity. A deeper understanding of data-driven business model innovation pathways, in the light of the detailed methods per pathway, would enhance the knowledge on this topic. Future research could focus on conducting additional design cycles for the data-driven business model innovation reference model. It would be interesting to enrich the findings of this thesis with quantitative data on correlations in data-driven business model innovation and enterprise architecture support. Furthermore, investigating a single case study and exploring new application fields of enterprise architecture in the data-driven business model innovation context would benefit research and practice would benefit.
