Designing Sustainable Business Models for Data Spaces: Insights From the Industrial Sector

Abstract

Secure, sovereign data sharing is essential for the digital transformation of industrial sectors, yet designing sustainable Business Models (BMs) for multi-stakeholder Data Spaces remains a significant challenge. This study situates BM innovation for Data Spaces in the context of European manufacturing, addressing regulatory, technological, and economic constraints. The methods involve mapping stakeholder needs to platform capabilities, and systematically developing and validating revenue and value-capture models using established frameworks and real-world use cases. Results show that collaborative, modular BMs—built on principles of data sovereignty, interoperability, and trust—enable the creation and distribution of economic and strategic value for operators, data providers, and consumers. Application in predictive maintenance use cases for refineries and wind farms validates these models, demonstrating cost reductions, improved operational efficiency, and compliance with current European data governance standards. The findings suggest that viable BMs for industrial Data Spaces hinge on inclusive stakeholder participation, flexible monetization strategies, and continuous adaptation to regulatory environments. These insights provide practical guidance for stakeholders seeking to realize the full value of data-driven manufacturing ecosystems.