Modern organisations are increasingly dependent on data to develop new products, services and to improve their operations. However, they are increasingly dependent on external data resources which has led to the rise of data marketplaces where data are traded. This rapid rise of data marketplaces has led to a fragmented data sharing landscape resulting in data discovery issues for data consumers and a lack of trust by data providers. Recently, the concept of platforms to integrate different data marketplaces to reduce fragmentation has arised: data marketplace meta-platforms. However, these platforms still need to make sure that data providers stay in control over their data, as data providers will otherwise not be willing to share data at all. Staying in control over data for data providers is about data sovereignty. As data marketplace meta-platforms can reduce fragmentation in the data marketplace landscape, but relatively little is known regarding how data sovereignty can be achieved in that context, this research project explores governance mechanisms to enhance data sovereignty for data providers in the context of data marketplace meta-platforms.
Using existing literature, four antecedents of data sovereignty were identified: data ownership, data access, data usage and data storage. Next, the current state of existing data marketplaces and data sharing initiatives was analysed using industry literature and insights from expert interviews. This analysis indicated that the use of a common legal basis among participants of data sharing ecosystems is often the first step. Additionally, processes were data providers arrange a data sharing agreement with data consumers and certification of entrants and participants are adopted in practice.
Analysis of the data marketplace meta-platform context identified three areas of challenges for data sovereignty in the meta-context, namely DMMP-ecosystem where data providers fear DMMP-dominance. Secondly, at the level of the data provider, there is a lack of capabilities to estimate required control and to estimate the value of data assets. Thirdly, data access and usage is difficult to arrange at the level of a data transaction between data providers and consumers. The use of DMMPs to exchange very different types of data, across different industry sectors further complicates the process of granting access and arranging data usage.
Using findings from the expert interviews, three categories of solutions were identified: architectural, trust-related and technology. Architectural solutions are about the structure of the DMMP and how decisions rights and ownership are distributed across the platform and its users. Trust-related solutions are both about building and sustaining trust, for example by use of certifications, and by increasing autonomy for data providers. Lastly, technology-related solutions such as data tagging and labelling can help to enhance visibility over the data chain and to further help data providers to stay in control over their data.

With the amount of available data growing and data posing as a strategic asset to firms, the data economy has started to evolve. Data marketplaces can fulfil a key role in realizing the data economy. The way a data marketplace operates and conducts business can be mapped and managed using a business model. As data marketplaces are a new area of research, not much research has been conducted on this type of digital platforms yet, nor on the business models of data marketplaces. Existing taxonomies of data marketplace business models mainly focus on the classification of multilateral data marketplaces and are developed from a single firm perspective on business models. This study aims to go beyond the state of the art by developing a taxonomy of data marketplaces business models from a multi-stakeholder perspective on business models. The term data marketplace is broadly interpreted in this research to also allow the inclusion of atypical forms of data marketplaces. A design science approach is employed and a standard taxonomy development method by Nickerson et al. (2013) is followed to develop the taxonomy. The final taxonomy comprises of 4 meta-dimensions, 17 business model dimensions and 59 business model characteristics. The results of this study contribute to the literature by improving the understanding of the notion of data marketplace business models and by providing a framework that can be utilized for the classification of data marketplace business models and for the analysis of business model patterns and business model archetypes.