Data and technology-driven transitions in society and economy are evident but difficult to assess and predict. Similarly, Big Data has been identified as a driver for innovation and growth and the potentials of Big Data to improve policy making has been recognized, but the methods and tools for appropriate data-handling still have to be established. New methods for providing scientific data-informed evidence to decision-making (e.g. use of new integrative models and indicators) need to be developed in areas including, but not restricted to official statistics, environmental monitoring, behavioral analytics, financial markets.
We recognized this growing work area of Big Data and the related needs for scientific and technical support to EU-level policy making. Our work particularly concentrates on the innovative use of existing sources, including private companies (primarily mobile phone network operators and providers of online platforms, for example, for social media or ride sharing), and individual citizen (for example, by volunteered data collection using low-cost sensors). The graphic below highlights some of our recent examinations.
Major future tasks include:
- Investigations of indicators and measures for digital transformation in economy and society (leveraging new data sources).
- Development of new methodologies for evidence based social-economic policy (use of the indicators and measures in the EC context).
- Empirical studies that anticipate, identify and help to understand new modes of production and consumption that emerge from the digital transformations in the economy and the society.
With this we aim to support the formation of the right policy conditions for value extraction from data, digital technology and the Internet with methodological developments and empirical studies. New indicators will leverage available public sector information together with complementary (Big) data sources, such as mobile phone operators, the Internet of Things, online platforms, and citizens’ observatories. They will be especially used to investigate evolutionary effects of new technologies on digital living and social welfare.