Trusted smart statistics: The challenge of extracting usable aggregate information from new data sources

Article type: Research Article

Authors: Vichi, Maurizio^{a; *} | Hand, David J.^b

Affiliations: [a] Sapienza University, Rome, Italy | [b] Imperial College, London, UK

Correspondence: [*] Corresponding author: Maurizio Vichi, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy. Tel.: +39 649 910 405; Fax: +39 649 910 072; E-mail: [email protected].

Abstract: Recent years have seen dramatic changes in sources of data, amounts of data, availability of data, frequency of data, and types of data. Along with advances in data analytic technology these changes have opened up huge possibilities for improving the information content and timeliness of official statistics. in this paper we characterise such “smart statistics”, examining their potential benefits and the obstacles that must be overcome if they are to be trusted and relied upon. In particular, we list eight specific recommendations which we believe producers of smart statistics should adhere to if the full potential for economic and social benefit is to be achieved.

Keywords: Computer-mediated-communication, internet of things, trustworthiness, timeliness, data sources, data types, machine learning, indicators

DOI: 10.3233/SJI-190526

Journal: Statistical Journal of the IAOS, vol. 35, no. 4, pp. 605-613, 2019