Constraining context: Situating datafication in public administration

Abstract

The imaginary of data-driven public administration promises a more effective and knowing public sector. At the same time, corporate practices of datafication are often hidden behind closed doors. Critical algorithm studies, therefore, struggle to access and explore these practices, to produce situated accounts of datafication and possible entry points to reconfigure the emerging data-driven society. This article offers a unique empirical account of the inner workings of data-driven public administration, asking the overall question of how sociotechnical imaginaries of datafication are constrained in the context of public administration. Teams working on datafication in two Norwegian public sector entities have been followed and interviewed over the course of 2 years (2018–2020). While sociotechnical imaginaries thrive in organizational culture and policy discourse alike, the observed data teams struggle to realize data assemblages due to a variety of structural and institutional constraints.

(In)visible everyday work of fostering a data‐driven healthcare and social service organisation

“…Moreover, the study contributes to the current literature on breakdowns, repairs and workarounds involving new data technologies (Dupret, 2017;Sachs, 2020;Schwennesen, 2019) by showing how articulation work is inherent to the ongoing transition to data-driven organisations, which is messy and fragile rather than well planned. Finally, the study advances the current discussion (Kaun, 2022;Ratner & Schrøder, 2022;Reutter, 2022) on the perpetual piloting nature of this transition in one data-driven healthcare and social service organisation.…”

“…With these insights, this study advances our understanding of the complexity, dynamics and instability of work invested in transitioning to data‐driven organisations and its associated challenges (see also Reutter, 2022). Acknowledging the multifaceted articulation work that underlies the everyday work of fostering a data‐driven organisation is an important step towards understanding the tensions and conflicts inherent in the transition as well as the resources needed for managing it.…”

Metodología para estudiar los espacios públicos digitales producidos por las instituciones

“…Otro tema importante es el estudio del uso de las tecnologías de la información y de las comunicaciones (TIC) por parte de los gobiernos para gestionar, promover y representar la participación ciudadana (González Galván et al, 2021;Gonzalez-Galvan & Espín-Espinoza, 2020;Hansson & Page, 2022;Hao et al, 2022;Haro-de-Rosario et al, 2018;de Quadros & Bastos de Quadros Junior, 2015). Recientemente se ha abordado la incorporación de nuevas tendencias, como la datificación y los algoritmos en el manejo de la administración pública (Chaudhuri, 2022;Reutter, 2022), así como la evaluación de las relaciones públicas (Cuenca-Fontbona et al, 2022).…”

Social data governance: Towards a definition and model

“…Among these studies, the focus lays not on data governance as part of organizational management per se . Instead, it addresses (the disclosure of) corporate practices of dataism, datafication, and governance issues that entail a broader implication for managing and steering society beyond organizational borders but are “often hidden behind closed doors” (see Reutter, 2022: 904 on such discussion in critical algorithm studies). Like Micheli and her colleagues (2020) contend, major attention is currently devoted to the model of data governance established by a few corporate big tech platforms, such as social networking platforms (e.g.…”

“…Basukie, Wang, & Li, 2020). Studies on “data-driven governance”—which refers to specific societal steering equipped with “data-driven practices of categorization, classification, segmentation, selection and scoring” (Hintz et al, 2018: 146; Dencik et al, 2019)—mark organizational and institutional setting as the key framework that both enables and constrains, for instance, specific data assemblage (Kitchin, 2014; Reutter, 2022). Similarly, scholarship on “algorithmic governance” (see Danaher et al, 2017 for an overview) advocates to concentrate on code and organizational processes (Coletta & Kitchin, 2017: 4; Kitchin, 2017: 27), so as to unpack the key properties of datafied systems (Just and Latzer, 2017; Smith, 2020) in the steering of human behaviors and activities.…”