Purpose - The objective of this study is to explore how Data-Intelligence Service-Providers (DISP) can adapt existing Business Model (BM) dimensions to leverage the potential value and mitigate the emerging challenges Open Data (OD) introduces. Method – By developing a multiple case study, we intend to qualitatively explore what BM practices DISPs employ when incorporating OD. Interviews are conducted in multiple phases with a total of 25 interviews and results generated using a thematic analysis. Findings – Through empirical investigation and analysis of DISPs actions and strategies, the study uncovers how these firms navigate challenges and opportunities presented by OD. By portraying the strategies across three BM dimensions—value creation, delivery, and capture—this study identifies six key practices that help DISPs competitively differentiate themselves in the OD environment. The identified practices include Use-case understanding and Data-driven Service Innovation for value creation, Enhanced Data Delivery and Collaborative Data Optimization for value delivery, and AdjustedRevenue Model and Market Expansion for value capture. Implications – In our contribution to existing literature, we present empirical evidence spanning across all dimensions of the BM, shedding light on the competitive advantages facilitated by OD. Additionally, through identifying key practices, this thesis uncovers several areas where there is a lack of understanding on ODs impact in a commercial context. Specifically, by solely focusing on the perspective of DISPs, we offer detailed insight into how these practices are practically unfolding. Furthermore, the thesis presents a framework categorizing practices based on priority and ecosystem dependency. This framework delineates certain practices that are considered fundamental when incorporating OD while also recognizing their intricate requirement of involving external parties, offering managers a visual overview of how to systematically adapt their BMs to incorporate OD into their services. In addition, we manage to address the common distortions about OD by offering a thorough theoretical foundation and defining it clearly within a commercial context, making this complex topic more accessible and better understood. Limitations and future research – As this study is limited to data-providers and DISPs, this thesis advocates for exploring end-user perspectives in future research deemed crucial for gathering a comprehensive understanding of their needs and interactions with OD solutions to solidify findings in this study. Additionally, it is encouraged that future research should investigate misalignments between data-providers and DISPs (e.g. regulatory and technical matters) which currently, are leading to massive inefficiencies in data supply chains. Understanding these issues and implementing strategies to address them can optimize OD resource utilization, thereby facilitating greater innovative potential for service-providers leveraging it.