Assessing data governance models for smart cities: Benchmarking data governance models on the basis of European urban requirements

Smart cities aim to improve residents’ quality of life by implementing effective services, infrastructure, and processes through information and communication technologies. However, without robust smart city data governance, much of the urban data potential remains underexploited, resulting in inefficiencies and missed opportunities for city administrations. This study addresses these challenges by establishing specific, actionable requirements for smart city data governance models, derived from expert interviews with representatives of 27 European cities. From these interviews, recurring themes emerged, such as the need for standardized data formats, clear data access guidelines, and stronger cross-departmental collaboration mechanisms. These requirements emphasize technology independence, flexibility to adapt across different urban contexts, and promoting a data-driven culture. By benchmarking existing data governance models against these newly established urban requirements, the study uncovers significant variations in their ability to address the complex, dynamic nature of smart city data systems. This study thus enhances the theoretical understanding of data governance in smart cities and provides municipal decision-makers with actionable insights for improving data governance strategies. In doing so, it directly supports the broader goals of sustainable urban development by helping improve the efficiency and effectiveness of smart city initiatives.

Citation

Bozkurt, Y., Rossmann, A., Pervez, Z., Ramzan, N. (2025) Assessing data governance models for smart cities: Benchmarking data governance models on the basis of European urban requirements, Sustainable Cities and Society, 130, Article no 106528 doi: https://doi.org/10.1016/j.scs.2025.106528

Type

Journal article

Description

© 2025 The Authors. Published by Elsevier. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1016/j.scs.2025.106528

ISSN

2210-6707

Collections

Faculty of Science and Engineering