Data for Policy: Data Science and Big Data in the Public Sector

Innar Liiv at OXPOL: “How can big data and data science help policy-making? This question has recently gained increasing attention. Both the European Commission and the White House have endorsed the use of data for evidence-based policy making.

Still, a gap remains between theory and practice. In this blog post, I make a number of recommendations for systematic development paths.


‘Data for policy’ as an academic field is still in its infancy. A typology of the field’s foci and research areas are summarised in the figure below.




Besides the ‘data for policy’ community, there are two important research trends shaping the field: 1) computational social science; and 2) the emergence of politicised social bots.

Computational social science (CSS) is an new interdisciplinary research trend in social science, which tries to transform advances in big data and data science into research methodologies for understanding, explaining and predicting underlying social phenomena.

Social science has a long tradition of using computational and agent-based modelling approaches (e.g.Schelling’s Model of Segregation), but the new challenge is to feed real-life, and sometimes even real-time information into those systems to get gain rapid insights into the validity of research hypotheses.

For example, one could use mobile phone call records to assess the acculturation processes of different communities. Such a project would involve translating different acculturation theories into computational models, researching the ethical and legal issues inherent in using mobile phone data and developing a vision for generating policy recommendations and new research hypothesis from the analysis.

Politicised social bots are also beginning to make their mark. In 2011, DARPA solicited research proposals dealing with social media in strategic communication. The term ‘political bot’ was not used, but the expected results left no doubt about the goals…

The next wave of e-government innovation will be about analytics and predictive models.  Taking advantage of their potential for social impact will require a solid foundation of e-government infrastructure.

The most important questions going forward are as follows:

  • What are the relevant new data sources?
  • How can we use them?
  • What should we do with the information? Who cares? Which political decisions need faster information from novel sources? Do we need faster information? Does it come with unanticipated risks?

These questions barely scratch the surface, because the complex interplay between general advancements of computational social science and hovering satellite topics like political bots will have an enormous impact on research and using data for policy. But, it’s an important start….(More)”