Data science in the design of public policies: dispelling the obscurity in matching policy demand and data offer

Michela Arnaboldi,Giovanni Azzone

doi:10.1016/j.heliyon.2020.e04300

Abstract

Data Science (DS) is expected to deliver value for public governance. In a number of studies, strong claims have been made about the potential of big data and data analytics and there are now several cases showing their application in areas such as service delivery and organizational administration. The role of DS in policy-making has, on the contrary, still been explored only marginally, but it is clear that there is the need for greater investigation because of its greater complexity and its distinctive inter-organizational boundaries. In this paper, we have investigated how DS can contribute to the policy definition process, endorsing a socio-technical perspective. This exploration has addressed the technical elements of DS - data and processes - as well as the social aspects surrounding the actors’ interaction within the definition process. Three action research cases are presented in the paper, lifting the veil of obscurity from how DS can support policy-making in practice. The findings highlight the importance of a new role, here defined as that of a translator, who can provide clarity and understanding of policy needs, assess whether data-driven results fit the legislative setting to be addressed, and become the junction point between data scientists and policy-makers. The three cases and their different achievements make it possible to draw attention to the enabling and inhibiting factors in the application of DS.

Highlights

The era in which we live, this “information age”, is brazenly producing enormous volumes of data [1, 2]
Data Science - the umbrella name given to the innovative use of “analytics” to extract information and insights from these many and diverse datasets [5, 6] - was initially developed for business purposes
This paper studied three real cases, investigating how big data and Data Science (DS) can be applied to policy-making

Summary

Introduction

The era in which we live, this “information age”, is brazenly producing enormous volumes of data [1, 2]. Helped by the many innovative technologies and technical tools in play - consisting of a wide range of mathematical techniques, data analysis techniques, visualization techniques, cloud computing and fuzzy sets and systems [3] - these data can be elaborated to build exciting datasets that can be accessed by all. These progressively large datasets contain highly detailed information obtained ever more promptly from different sources, combining data of a traditional, transaction-based origin with those collected either automatically, like the signals emanating from mobile phones and web connections, or on a voluntary basis, like the material we publish on social media [4]. Availability and use of analytical information can help, in particular to achieve: greater efficiency and effectiveness in planning and implementing public policies, as it becomes increasingly easier to understand what each user's expectations are and so “target” any initiative [11, 12, 13]

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Conclusion