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Data as Relation: Ontological Trouble in the Data-Driven Public Administration

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This paper examines how the intense focus on data in political digitalization strategies takes effect in practice in a Danish municipality. Building on an ethnographic study of data-driven management, the paper argues that one of the effects of making data a driver for organizational decision-making is uncertainty as to what data are and can be taken to mean. While in political discourse and strategies, data are considered as a resource for collaboration across organizational units as well as for optimization of their performance, in practice, data are not this straightforward entity. The paper presents a kind of data work that identifies data as part of different worlds (ontologies). The management task that results from this is nurturing organizational spaces that articulate data as relational. The paper argues that being attentive to the troublesome experiences public sector employees have when encountering data may help mitigate some of the risks of seeing data merely as a resource. The paper concludes that as public sector managers learn to nurture spaces where differences in data can be articulated, they also protect core values of welfare bureaucracies. Acknowledging that data work is about what we take to be real and what not (ontological work) is a first step in this direction.

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Winthereik, Brit Ross (2024): Data as Relation: Ontological Trouble in the Data-Driven Public Administration. Computer Supported Cooperative Work (CSCW): Vol. 33, No. 3. DOI: 10.1007/s10606-023-09480-9. Springer. ISSN: 1573-7551. pp. 371-388

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Civil Service/Bureaucracy, Data integration, Data-driven Science, Ontology, Public Administration, Public Management

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Number of citations to item: 4

  • Mads Solberg, Ralf Kirchhoff, Jannike Dyb Oksavik, Lauri Wessel (2024): Organizing visions for data-centric management: how Norwegian policy documents construe the use of data in health organizations, In: Journal of Health Organization and Management 4(38), doi:10.1108/jhom-12-2023-0378
  • Helene Friis Ratner, Nanna Bonde Thylstrup (2024): Citizens’ data afterlives: Practices of dataset inclusion in machine learning for public welfare, In: AI & SOCIETY 3(40), doi:10.1007/s00146-024-01920-4
  • Trine Rask Nielsen, Thomas Gammeltoft-Hansen, Naja Holten Møller (2024): Mobile Phone Data Transforming Casework in Asylum Decision-making: Insights from the Danish Case, In: ACM Journal on Responsible Computing 4(1), doi:10.1145/3696469
  • Ilaria Crivellari, Johanne Svanes Oskarsen, Charlotte Husom Grøder (2024): Unveiling the Invisible: Caseworkers’ Data Work in a Public Welfare Organization, In: Lecture Notes in Computer Science, doi:10.1007/978-3-031-70274-7_11
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