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Article type: Research Article
Authors: De Cubellis, Massimoa; * | Grippo, Gerardaa; b; *
Affiliations: [a] Italian National Statistical Institute (Istat), Rome, Italy | [b] La Sapienza’ University, Rome, Italy
Correspondence: [*] Corresponding authors: Massimo De Cubellis, Istat, Italy. E-mail: [email protected]; Gerarda Grippo, Istat, Italy. E-mail: [email protected].
Abstract: The datification processes have driven National Statistical Institutes (NSIs) to explore new data sources with the goal of enhancing the quality of statistical information. The creation of innovative statistical products, known as Trusted Smart Statistics (TSS), necessitates new production processes that integrate with traditional methods, maintaining levels of relevance, quality, and trust. To facilitate this transition in statistical production, NSIs must embark on change management initiatives involving all organizational dimensions engaged in statistical processes. In the scientific debate, sustainability has assumed a broad connotation. This paper delves into the organizational aspect, viewing sustainability as a paradigm that encompasses all organizational areas and their interactions crucial to supporting the shift to new statistical production models, without compromising traditional practices. The paper explores the path taken by Istat in this direction, the organizational solutions implemented, the benefits realized, and investments in research and innovation initiated years ago on new data sources, aligning with developments in the European statistical community. Istat has been at the forefront of big data experimentation in Europe for over a decade, implementing a modernization program and establishing research infrastructures. Istat took a significant step by founding the TSS Center to guide organizational adaptation at technological, methodological, legal, communication, and human resources levels toward the new production system.
Keywords: Sustainability, trusted smart statistics, organization, Istat
DOI: 10.3233/SJI-230126
Journal: Statistical Journal of the IAOS, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
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