Data are an infrastructural resource – a form of capital that cannot be depleted and that can be used for a theoretically unlimited range of purposes. Physical infrastructure such as roads and bridges enables benefits to ‘spill over’, for instance, by fostering trade and social exchanges. In the same way, greater access to data also has beneficial spillovers, whereby data can be used and re-used to open up significant growth opportunities, or to generate benefits across society in ways that could not be foreseen when the data were created. This includes benefits from research and the development of new products, processes, organisational methods and markets – a phenomenon known as data-driven innovation (DDI). In manufacturing, for example, data obtained through the Internet of Things (IoT) are re-used to optimise the efficiency of machines and sometimes also re-used and even commercialised in the form of new services (e.g. aftersale services). This development has been key for enabling the next production revolution (NPR).
But some of the spill-overs of data cannot be easily observed or quantified (e.g. socialisation and behavioural change, cultural and scientific exchange, or greater levels of trust induced by transparency). As a result, governments and businesses risk under-investing in data and data analytics and may end up giving access to data for a narrower range of uses than socially optimal. This risks is undermining countries’ capacity to innovate, as data and its analysis have become a fundamental input to innovation, akin to research and development (R&D).
The OECD is undertaking extensive analysis to assess to what extent enhanced access to data can maximise the social and economic value of data. Work on DDI shows that closed access comes with social and economic opportunity costs. By leveraging the nature of data as non-rivalrous, general-purpose productive capital, enhanced access to data can foster the free flow of data across nations, sectors, and organisations. However, there are also legitimate reasons for keeping data “closed” including in particular to protect confidential information (i.e. personal data and trade secrets). This calls for a more differentiated approaches to data sharing and reuse, which acknowledges that data openness is not a binary concept, but that there are many different degrees of openness on a continuum ranging from closed or limited access (only by the data controller) to open access to the public.
Degrees of data openness
The OECD project on enhanced access will be taken forward in two phases via two workstreams: The first phase will aim to address knowledge gaps on how enhanced access to data can maximise social and economic benefits, and at the same time address legitimate concerns of individuals and organisations. Depending on the outcomes of the analytical work, workstream 2 would aim at the development of general principles on enhanced access to data that could be delivered as an OECD Council Recommendation, building on existing OECD data governance frameworks such as:
As a contribution to the first phase of the OECD project on enhanced access to data, the OECD is organising an expert workshop on 2-3 October 2017 in Copenhagen, Denmark. The workshop will in particular address how enhanced access to data can maximise social and economic benefits, and at the same time address legitimate concerns of individuals and organisations (including governments). In the workshop, four approaches for enhancing access to data will be discussed in dedicated sessions on open data, community-based data sharing agreements, data markets, and data portability.
The general questions to be addressed by experts include, but are not limited to: