Digital tools and platforms and the data that flow through them have inspired an exciting era of innovation. Digital data gives rise to new forms of data visualization, new methods of analysis, and collaboration on a global networked scale. Digital data also present a host of new challenges. A key obstacle to unleashing the social benefits of digital data involves the development of trusted intermediaries for sharing and aggregating data. Building in some ways on the old idea of a lending library – an aggregating intermediary between bookseller and reader – many variations on “trusted data intermediaries” have emerged in the last decade.
- What range of organizational forms do TDIs take, how do they work and who do they serve?
- Are they distinct from commercial data aggregators and intermediaries? How?
- What are the common traits and structural manifestations of these organizations? How might we “reverse engineer” a generalizable enterprise form that could serve these purposes in other areas of society?
The Lab hosted an invited group of civil society leaders at Stanford on December 1, 2016 for a day-long workshop to consider how best to create and maintain trusted data intermediaries. Participants examined the nature of their data governance practices, their potential to contribute to the social sector, and the policies and practices necessary for intermediaries to establish and demonstrate trust. The group considered what responsibilities they have to data contributors, users, and rights holders, or to the people represented in the data sets.
The day was organized around panels covering several shared characteristics – including the role of rights management, intellectual property, trust, and revenue models. Prior to the workshop we shared brief profiles of several organizational models for trusted data intermediaries, including ArtStor, LearnSphere, the Mastercard Center for Inclusive Growth, and New Philanthropy Capital’s Data Labs model. Participants circulated readings on related models to prime our thinking.