Analytics and Big Data for Decision Making in the Public Sector

Public sector interest in Big Data and Analytics is very high. A number of studies have made claims about its importance and, through data experimentation, they provide empirical evidence about its potential value. Most of these tests, however, do not bring in the decision makers, leaving open the question of whether the resulting information is suitable and useful or not for supporting decision makers.

This project studied how to create value from analytics and big data for public decision makers. An action research project was carried out, in which a new big data cycle was proposed and experimented involving decision makers in a Urban context. Developed in the Interdisciplinary Lab Urbanscope, the findings of the project enhances previous studies revealing the centrality of the interaction between scientists and decision-makers during the cycle. The new cycle proposed is a reciprocal process of knowledge, which, allows to avoid two opposing and risky behaviors: blind faith, where decision-makers overestimate the benefits of analytics; and reluctance, where decision-makers treat all data they do not fully understand with suspicion. In pursuing the path of further knowledge, two operations embedded in the information processing system must be made transparent: filtering and framing. A novel quali-quantitative approach to big data is defined, where the cycle phases and the dimensions of interaction, in the form of boundary, unit of analysis, timing and functionalities, confer rigour to the whole.