The second OECD Blue Sky Forum took place in Ottawa in September 2006 to examine science, technology and innovation (STI) indicators for use in policy in the 21st century. The context for indicator development, and use, had changed considerably from the first Blue Sky Forum in Paris in June 1996 and this book looks at those changes and how they are influencing indicator development.

Policy making, while not simple, can be pursued in a straightforward way, with traditional phases of data gathering and analysis. Policy implementation is exceptionally difficult and not at all straightforward. Success in either depends critically on access to reliable and well-defined data. But high quality and clear definition, while necessary, are by no means sufficient to render data useful. The data also have to be relevant to the issues policy seeks to address and they have to be accompanied by a credible interpretive framework. These requirements, obvious though they may seem, are very difficult to satisfy. My remarks about these needs and difficulties are based on my own career as a scientist, administrator, and policy advisor, during which I have struggled with policy in nearly every stage of its complex life cycle from conception to death.

The first Blue Sky conference in Paris in 1996 introduced a wide audience to some of the results of the first Community Innovation Survey (CIS) from 1993, which was arguably one of the most comprehensive major sources of new innovation data at the time. The purpose of the CIS and other innovation surveys based on the first edition of the Oslo Manual was to overcome some of the limitations of the research and development (R&D) surveys. Two main goals were to provide data on innovative activities that were not based on R&D and to provide output measures of innovation.

When researchers say that innovation is being democratized, they mean that users of products and services – both firms and individual consumers – are increasingly able to innovate for themselves. User-centred innovation processes offer great advantages over the manufacturer-centred innovation development systems that have been the mainstay of commerce for hundreds of years. Users that innovate can develop exactly what they want, rather than relying on manufacturers to act as their (often imperfect) agents. Moreover, individual users do not have to develop everything they need on their own: they can benefit from innovations developed and freely shared by others.

The existing rhetoric on the future benefits of biotechnology could be summarized as: biotechnology is the next technology wave that will deeply transform the economy and society by providing products and processes that will solve health problems, feed the world with new agricultural products, heal the environment and provide sustainability. Beyond the rhetoric, the indicators of biotechnology activities reveal an emerging phenomenon that is still relatively small. Many actors from the private sector, governments and universities are convinced of the future success of biotechnology. A recurring policy question is: What evidence provides a solid basis or argument for government to invest in biotechnology?

As the various chapters in this volume illustrate, national statistical offices, in close interaction with the Organisation for Economic Cooperation and Development (OECD), have been particularly influential and constructive over the last 40 years in developing international standards for the measurement of research and development (R&D) and in stimulating and improving the input and output measurement of science, technology and innovation (STI) activities. Behind the early international attempts at coordinating and ultimately harmonizing the measurement of R&D through the establishment of a common statistical manual were two fundamental considerations.