Dipartimento di Ingegneria Gestionale

Analytics and Big Data for Decision Making in the Public Sector

About the project

The value of decision-making is professed to be essential in the field of Analytics and Big Data and, as such, is studied under several angles. In all of this, one important point has been overlooked: when “humans” retain a role in the decision-making process, the value of information is no longer an objective feature but depends on the knowledge and mindset of end users. This project developed and tested a new cycle for analytics and big data in Urban context where the decision-maker is placed at the centre of the process.

Principal Investigators: Michela Arnaboldi and Giovanni Azzone

Researcher team: Michela Arnaboldi, Giovanni Azzone, Melisa Diaz Lema, Andrea Robbiani, , Paola Riva, Yulia Sidorova.

Funders: Private and public institutions

Duration: 2014-2018

Partners: Urbanscope Laboratory, TuDelft, University of Edinburgh, Data@ter transdisciplinary group


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.


  • Arnaboldi, M. (2018) “The Missing Variable in Big Data for Social Sciences: The Decision-Maker”, Sustainability, Vol. 10 (10), 3415; https://doi.org/10.3390/su10103415
  • van der Voort, H., Klievink, B., Arnaboldi, M. and Meijer, A., (2018) “Rationality and Politics of Algorithms Will the promise of big data survive the dynamics of public decision making?” Government Information Quarterly
  • Agostino, D., & Arnaboldi, M. (2018). Performance measurement systems in public service networks. The what, who, and how of control. Financial Accountability & Management, 34(2), 103-116.Arnaboldi, M., Brambilla, M., Cassottana, B., Ciuccarelli, P., & Vantini, S. (2017). Urbanscope: A lens to observe language mix in cities. American Behavioral Scientist, 61(7), 774-793.
  • Arnaboldi, M., Azzone, G., & Sidorova, Y. (2017). Governing social media: the emergence of hybridised boundary objects. Accounting, Auditing & Accountability Journal, 30(4), 821-849.
  • Arnaboldi, M., Arnaboldi, M., Busco, C., Busco, C., Cuganesan, S., & Cuganesan, S. (2017). Accounting, accountability, social media and big data: revolution or hype? Accounting, Auditing & Accountability Journal, 30(4), 762-776.