Specific Challenge:

To exploit the potential of big data approaches for research and innovation policy making by providing more timely and in depth information on the performance of the research and innovation system and its links to productivity growth.

Scope:

Many of the advanced economies have, since the crisis, been faced with a productivity paradox: while the pace of innovation continues to accelerate, productivity growth has come to a quasi-standstill. The reasons for this have been the subject of intense debate over the past years and evidence increasingly points to the importance of the links between productivity growth and research and innovation. Seminal work by the OECD^[1] has pointed to the importance of technology diffusion in this respect, which could also be linked to the changing nature of the innovation process itself, which is going through profound changes, with notably digitalisation leading to increasing complexity, stronger networking effects and a growing importance of winner takes all characteristics.

If research and innovation policy making is to adapt to this rapidly changing environment in an evidence based manner, it needs solid and timely data to support its decisions and it is becoming increasingly clear that official statistics, if relying on traditional sources, cannot continue to provide a full picture of all the dynamics of today's research and innovation systems. However, in today's increasingly digitalised world, alternative sources of data have being emerging exponentially, generated by the use of information and communication technologies and their diffusion through the web. This includes, for instance, information contained in company websites, social media posts, but also increasingly databases being made available by e.g. governments. Such data sources, commonly known as big data, have the advantage of being widely available in a timely manner, have the potential of being able to cover a variety of aspects of research and innovation performance, allow to provide information at a more granular level and examine in a better way social interactions, all of which is not possible through the indicators currently provided by official statistics.

Proposals should aim at exploiting the potential of big data to produce information on research and innovation activity, performance, output and/or impact which has the potential to be available in real time, focusing notably on research and innovation investments in the private sector, public-private cooperation and technology diffusion between private actors. Proposals should also take into account aspects of data accuracy and data security.

Expected Impact:

The outcomes of the Research and Innovation Actions are expected to provide research and innovation policy making with more timely and diverse data on research and innovation activity, performance, output and/or impact.