In this new report, The Data Tank and the Impact Licensing Initiative develop an overview of mechanisms and components of business and data governance models for health data collaboratives. In this piece, we use the terms ‘data collaborative’ or ‘ecosystem for data reuse’ indistinguishably to refer to collaborations between different stakeholders across multiple sectors to exchange data in a way that overcomes silos to create public value (Susha et al., 2017).
It synthesises a rapid literature review of academic, policy, and industry documents, including case studies, to examine governance and business models for health data reuse. We examine in the literature different dimensions involved in sustaining an ecosystem for data reuse. The report sets these out in the current regulatory context. It also considers the role that mechanisms like a social license and impact licensing play in the sustainable governance of the different business models as essential complements to the regulatory context. It analyses case studies that can be mapped onto these models and offers pathways for a process to decide on a business model.
We conclude with the following recommendations:
-->Business and governance models for data collaboratives depend on the added value that all stakeholders -both on the supply and the demand-side of data- assign to these data.
Recommendation 1: We recommend that all stakeholders in a data collaborative explicitly articulate the value that data reuse offers to them and therefore what they seek out of the exchange and how they can contribute to the public interest.
-->While a data collaborative can serve different purposes, it is important for the governance and business model that there is agreement between stakeholders and clarity on the main purpose of the entity.
Recommendation 2: We recommend that all stakeholders in a data collaborative also agree on the main value proposition that their partnership is designed and built to offer.
-->A business model goes hand-in-hand with the values and principles that underpin the governance of the collaborative and its approach to decision-making.
Recommendation 3: We recommend that all founding stakeholders of a data collaborative are in agreement of a charter or Code of Practice that sets out the values and principles underpinning its aims and governance.
-->The regulatory context sets some guidelines but, ultimately, the reuse of data for secondary uses (that is uses other than those for which the data was created in the first place) need additional layers of individual and collective consent that address the barriers of individual-only, binary and static consent processes.
Recommendation 4: We recommend that any data collaborative puts in place mechanisms for participation of the relevant publics and stakeholders to acquire legitimacy and collective consent for the reuse of data for different purposes other than the ones for which the essential individual informed consent is obtained.
Recommendation 5: We recommend that a data collaborative considers the role of contracts and agreements with well-defined objectives for the use of data (ie. impact licenses) for potential different users of the data.
-->The financial model needs to be aligned with the value proposition of the data collaborative as well as the ecosystem.
Recommendation 6: We recommend that data collaboratives articulate the type of model (transactional, relational, systematic) that is most aligned with its value proposition, and maps out the different sources of income (public funding, private funding, membership revenue, commercial revenue, etc.) and its likelihood in the context of the ecosystem of the data collaborative.
