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

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 heavi-ly 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, storage, manage and use of big data; the different roles that data may play in the different stages of a policy making process, and the many actors in the very production, storage, management and use of big da-ta. Based on this, the paper presents a sound policy cycle focusing on the exper-imental 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 re-search project, provides a policy cycle tested in relation to three pilot cases us-ing big (open) data visualizations in a clear mobility policy context: Ghent (Bel-gium), Issy-les-Moulinaux (France), and Pilsen (Czechia). By considering the cycle of the policy process, the policy making activities the pilots are experienc-ing, and the data they are processing, the paper shows how the pilot cases are internalizing the policy experimentation opportunity, addressing the further pi-lots’ activities, into a continuous policy adaptation cycle.