Policy-development in the age of big data: data-driven policy-making, policy-modelling and policy-implementation
As societal challenges are growing more complex and interlinked, public policy innovation and experimentation, using ICT can improve the effectiveness, efficiency and the quality decisions in the public sector. Effective and reliable policies need to consider the available data (including its structure and topology) and evidence to ensure accurate and meaningful information. Big data offers many opportunities; using data analytics to generate new insights, increasing predictive power and identifying unexpected patterns and relationships that can help inform policy making. For instance data analytics tools can also help public authorities to better detect and evidence patterns of non-compliance in many policy areas affecting the health, the safety and the welfare of citizens in the internal market of goods, services and persons. Effective processing power and expertise are widely used in the retail and commercial sector, the challenge is to create effective resources to make this available to governments, allowing policy choices to become more evidence-based and analytical.
In addition, open policy-making and the integration of the citizens’ perspective through the effective engagement of relevant social actors - for example over online platforms or by crowd-sourcing - can potentially generate vast amounts of data, which can allow policy options to become more informed. Furthermore, open policy-making can support a participatory, open and collaborative government vision. Besides simulations, perceptions data pose a further promising source of information. Conducted on a regular basis, e.g. by the Eurobarometer, identifying perceived bottlenecks in relation to policy reforms as well as assessing the perceived performance of past reforms becomes feasible; in some cases these official statistics may be complemented by new sources of data. Taken together, this may lead to developing second generation data tools and assessment for more targeted policy design. It also offers opportunities for different communities to take ownership of the use and analysis of data in an age where they are at risk of being alienated by too much information. In addition, policy implementation can significantly benefit from efficient enforcement and monitoring tools that are informed by data from various sources.
In order to enable governments - at all levels - to benefit from the availability of relevant data and thereby introduce and implement effective policies, new or improved methods and tools are needed to support and establish new types of evidence-informed policy design and implementation and to facilitate the interpretation of big data for public communication, including new outcome-based. For public administrations to experiment with the possibilities offered by big data – for example through policy modelling, monitoring, enforcing, simulation, testing, analysis and policy compliance – there is a need to thoroughly understand the legal frameworks and to take into account sociological, cultural, political, legal and economic as well as behavioural aspects. Proposals should also elaborate on the relationship between evidence-based policy-making and citizens’ participation, integrating the analysis of participatory elements.
a) Research and Innovation Actions
Proposals need to address several of the following aspects:
- Methodological development for using big data in policy development, examining the extent to which policy-making structures and systems are ready to absorb and analyse big data;
- Critical interdisciplinary assessment of the economic, political, epistemological, ethical and legal premises and implications of big data practices (including algorithmic governance, smart cities, etc.), allowing for the reflection on the potential benefits and risks;
- Develop scalable and transferable methods and re-usable tools for compilation, analysis and visualisation of data, including relevant open, official or certified data;
- Develop scalable and transferable methods and re-usable tools for mining, compilation, analysis and visualisation of data from any source, including data related to social dynamics and behaviour;
- Develop scalable and transferable methods and re-usable tools for data curation, meta-data schemes, data linking or for reconciliation of multiple data sets to render coherent narratives;
- Understanding the implications of the increasing materiality of data with the development of the Internet of Things and its implications for the sustainability of government’s effective use of big data for improved policy making in the longer term;
- Develop scalable and transferable methods and re-usable tools for opinion-mining of large data sets in order to avoid the situation that the bigger the data, the less clear how they have been produced;
- Develop scalable and transferable methods and re-usable tools for policy modelling and simulation to improve the predicative analysis capacity of governments;
- Develop scalable and transferable methods and re-usable tools for iterative policy design and implementation (e.g. through the greater use of randomised controlled trials based on behavioural science);
- Develop scalable and transferable methods and re-usable tools for policy enforcement and compliance monitoring tools.
Proposals should apply their methodology to policy areas addressing societal challenges (e.g. environment, migration, radicalisation, inequalities, unemployment, internal market obstacles to the free movement of persons, goods and services). When using open and big data in order to enlarge the evidence base for effective policy-making, principles such as independence, quality, coherence and consistency, confidentiality, impartiality and objectivity as well as representativeness and extrapolation to meaningful populations need to be considered. Data protection, ethical and privacy issues will also have to be addressed as well as ethical issues around storage, use and re-use of data. Application and improvement of existing quantitative tools is preferable. Sociological as well as behavioural science approaches are encouraged, especially where they aim to develop a deeper understanding of how public policy and services interact with citizens. If relevant, proposals also need to analyse the suitability of the proposed software.
The Commission considers that proposals requesting a contribution from the EU of between EUR 4 and 5 million would allow this specific challenge to be addressed appropriately. Nonetheless, this does not preclude submission and selection of proposals requesting other amounts.
b) Coordination and Support Action
The activities should aim at encouraging networking of relevant stakeholders and teams working in the area of data-driven policy-making and policy-modelling and to support constituency building. Following an assessment of the needs of public administrations, the multidisciplinary network will identify methods, tools, technologies and applications for their implementation in the public sector, taking into consideration activities also undertaken outside the European Union and considering specificities relevant to different policy domains of public activity. The activities will conclude with the outlining of a roadmap for future research directions.
The Commission considers that proposals requesting a contribution from the EU in the order of EUR 0.5 million would allow this specific challenge to be addressed appropriately. This does not preclude submission and selection of proposals requesting other amounts.
Proposals need to demonstrate the impact to be achieved after the project phase, inter alia, in terms of improved public policy effectiveness, efficiency gains, precision gains, improved consistency, and reliance on evidence leading to increased policy compliance as well as in terms of the democratic dimension, such as greater transparency, good governance, increased trust in and the perceived legitimacy of government. Additional impact may be increased accessibility to the non-governmental players.