Data

Data-driven innovation is unlocking new opportunities for Europe to grow its economy and address pressing social challenges. While Europe has achieved some early successes in data-driven innovation, including in areas such as education, energy, environmental management, health care, open data, smart cities, and smart manufacturing, it has not yet come close to reaching its full potential. The primary obstacle is that Europe’s policymakers, both in its capital cities and in Brussels, have not yet fully embraced data-driven innovation as a core driver of economic and social progress. To inject new leadership into this debate, Member States should appoint national chief data officers to not only champion data innovation domestically, but also serve on a new, independent advisory panel charged with counseling the European Commission on how to seize opportunities to innovate with data.

Data-driven innovations have the power to address some of the most pressing social challenges in Europe. While many government and non-governmental organizations (NGOs) are using data in their attempts to tackle a range of social issues from high unemployment to the refugee crisis, more can be done. To accelerate progress, public and private-sector leaders should take steps to collect data on disadvantaged populations, facilitate cross-sector collaboration on data projects for social good, and implement policies that encourage data use, reuse, and sharing in support of social goals.

Data innovation—the innovative use of data to create social and economic benefits—is making a significant mark in Europe.In economic terms, data innovation contributed about €300 billion to Europe’s economy in 2016 (or approximately 2 percent of GDP), and its value will likely more than double by 2020. Across society, data innovation is creating more responsive governments, better health care, and safer cities. But EU nations differ in the degree to which they are harnessing the benefits of data. This report uses a variety of indicators to rank EU member states and discusses why some countries are ahead and what others can do to catch up.

Cities around the world are undergoing two important transformations. First, they are growing. For the first time in history, a majority of the world’s population lives in urban areas.1 Second, they are beginning to evolve into “smart cities”—cities capable of collecting and analyzing vast quantities of data to automate processes, improve service quality, provide market signal feedback to users, and to make better decisions. While city governments can and should manage much of this transformation, national governments have an important role to play in accelerating and coordinating the development of smart cities. Indeed, the long-term success of smart cities in any particular nation will likely depend on whether the national government supports their development.

Artificial intelligence (AI) is on a winning streak. In 2005, five teams successfully completed the DARPA Grand Challenge, a competition held by the U.S. Defense Advanced Research Projects Agency to spur development of autonomous vehicles.1 In 2011, IBM’s Watson system beat out two longtime human champions to win Jeopardy! In 2016, Google DeepMind’s AlphaGo system defeated the 18-time world-champion Go player. And thanks to Apple’s Siri, Microsoft’s Cortana, Google’s Google Assistant, and Amazon’s Alexa, consumers now have easy access to a variety of AI-powered virtual assistants to help manage their daily lives. The potential uses of AI to identify patterns, learn from experience, and find novel solutions to new challenges continue to grow as the technology advances. Moreover, AI is already having a major positive impact in many different sectors of the global economy and society. For example, humanitarian organizations are using intelligent chatbots to provide psychological support to Syrian refugees, and doctors are using AI to develop personalized treatments for cancer patients. Unfortunately, the benefits of AI, as well as its likely impact in the years ahead, are vastly underappreciated by policymakers and the public. Moreover, a contrary narrative—that AI raises grave concerns and warrants a precautionary regulatory approach to limit the damages it could cause—has gained prominence, even though it is both wrong and harmful to societal progress. To showcase the overwhelmingly positive impact of AI, this report provides a description of the major uses of AI as well as details on 70 real-world examples of how AI is already generating social and economic benefits. Policymakers should consider these benefits as they evaluate the steps they can take to support the development and adoption of AI.

Is data really the new oil? Some say that access to this basic commodity is decisive for the success and failure of entire business models in the digital markets. Would an obligation to share data with competitors be an adequate means of ensuring fair competition in these markets?

Europe needs improved competitiveness to escape the current economic malaise, so it might seem surprising that there is no common European definition of competitiveness, and no consensus on how to consistently measure it.

To help address this situation, this Blueprint provides an inventory and an assessment of the data related to the measurement of competitiveness in Europe. It is intended as a handbook for researchers interested in measuring competiveness, and for policymakers interested in new and better measures of competitiveness.

MAPCOMPETE has been designed to provide an assessment of data opportunities and requirements for the comparative analysis of competitiveness in European countries at the macro and the micro level.

This paper briefly outlines possible futures scenarios of science 2.0, analyses its implications and draws policy recommendations “fit for the future”. Science 2.0 is more than open access: it refers to the emergence of open, data-intensive and citizen science across the full research cycle, from data gathering to reputation management.
Science 2.0 is here to stay and it is already growing well beyond individual projects. On the supply side, an ecosystem of services and standards is emerging. Adoption is growing and becoming mainstream already in some phases such as preprint publication, reference sharing, open access publication. Impact is already visible and will address some of the most burning issues of science, such as the slowness of the publication process and the challenge of reproducing research results.
Based on the extrapolation of existing trends and on analogies from different domains, we anticipate a set of “scenario snippets”:
- The full integration of data, publications and intermediate product will enable reproducibility by default. But adoption of such sharing culture will require time and a new system of incentives based on impact metrics and career structure.
- Evaluation metrics will become multidimensional, granular and instantaneous;
- The work of scientist will change with greater collaboration and independence from institutions.
Overall, we will see an unbundling of services, which are today integrated. Research will be separated from teaching, data collection from data analysis, publication from reputation management. Different specialised service will emerge and displace the incumbents such as publishers and universities. At the same time, the value chain will reorganise through vertical integration around new platforms. These could be built around unexpected positions in the value chain, including electronic reading devices.
In terms of implications, these scenario show opportunities and risks in three main areas.