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Dimensions of data quality in smart cities datafication.

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Bibliographic Details
Title: Dimensions of data quality in smart cities datafication.
Authors: Okafor, Carl Chineme1 (AUTHOR) carl.c.okafor@uis.no, Ferrer-Conill, Raul1 (AUTHOR), Sjøvaag, Helle1 (AUTHOR)
Source: Information Society. Oct-Dec2025, Vol. 41 Issue 5, p315-326. 12p.
Subject Terms: *DATA science, *DECISION making, *MUNICIPAL officials & employees, DATA quality, SMART cities, CITIES & towns, DATA integration
Abstract: Data quality facilitates data interoperability for optimal decision-making in smart cities. However, there are few studies on how technologists (e.g., data scientists), governance personnel (e.g., municipal workers), and third-party collaborators (e.g., smart city services vendors) assess data quality. In this article, we address this knowledge gap, using interviews (n = 10) with municipal workers, data scientists, and smart city service vendors, and data structure documents (n = 8) in a situated case, the Stavanger smart city in Norway. Our findings show that data quality has a vacuous meaning, enveloping the different articulations of data scientists, municipal workers, and services vendors in the assessment process. This generates friction that has a bearing on data interoperability. We, therefore, posit that assessment of data quality is ambiguous but not without value. [ABSTRACT FROM AUTHOR]
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Database: Business Source Index
Description
Abstract:Data quality facilitates data interoperability for optimal decision-making in smart cities. However, there are few studies on how technologists (e.g., data scientists), governance personnel (e.g., municipal workers), and third-party collaborators (e.g., smart city services vendors) assess data quality. In this article, we address this knowledge gap, using interviews (n = 10) with municipal workers, data scientists, and smart city service vendors, and data structure documents (n = 8) in a situated case, the Stavanger smart city in Norway. Our findings show that data quality has a vacuous meaning, enveloping the different articulations of data scientists, municipal workers, and services vendors in the assessment process. This generates friction that has a bearing on data interoperability. We, therefore, posit that assessment of data quality is ambiguous but not without value. [ABSTRACT FROM AUTHOR]
ISSN:01972243
DOI:10.1080/01972243.2025.2523249