Big Data and Policy Making: Between Real Time Management and the Experimental Dimension of Policies

Abstract

The paper aims at exploring how big data can support decision making for and about cities at different strategic levels and temporal perspectives. Big data can improve the effectiveness of urban mobility policy, but such contribution heavily needs to consider the multiplicity of big data, as reflected by three elements: the different sources that produce data and the knowledge they provide; the many actors who produce, store, manage and use big data; the different roles that data may play in the different stages of a policy making process. Based on this, the paper presents a sound policy cycle focusing on the experimental dimension of policy making and provides a ground for the assessment of project implications for the ‘business of government’. The paper considers specifically mobility policies and, referring to the experience of the Polivisu research project, provides a policy cycle tested in relation to three pilot cases using big (open) data visualizations in a clear mobility policy context: Ghent (Belgium), Issy-les-Moulinaux (France), and Pilsen (Czechia). By considering the cycle of the policy process, the policy making activities the pilots are experiencing, and the data they are processing, the paper shows how the pilot cases are internalizing the policy experimentation opportunity, addressing the further pilots’ activities, into a continuous policy adaptation cycle.