Towards a taxonomy of Business Models for Data Intermediaries

Background: Data-driven business models (DDBMs) highlight the increasing importance of data as a key resource, raising concerns about power imbalances and data ethics. The EU’s Data Governance Act introduces data intermediaries to foster trust and promote fair data ecosystems.

Aim: This study explores the business models of data intermediaries, which remain largely unexamined despite their role in facilitating access, exchange, and control of personal data.

Method: Using Nickerson’s taxonomy methodology and the Al-Debei business model ontological structure, a comprehensive taxonomy of data intermediary business models is developed. Hierarchical clustering is then applied to identify distinct archetypes.

Results: A taxonomy of business models for data intermediaries is developed. Eight types of data intermediary business models emerge, including interorganizational data-sharing platforms, data subject control providers, and software providers.

Conclusion: This study provides a structured understanding of data intermediary business models, contributing to literature on data intermediaries and DDBMs.

Dataset: This dataset contains:

A summary of the 107 cases analyzed in this research.

An explanation of how the researchers mapped the dimensions and characteristics from the taxonomy onto these cases.

Definitions of the dimensions and characteristics in the taxonomy.

A cross-table analysis showing how frequently the dimensions and characteristics appear in the eight identified archetypes.