This paper aims to demystify the concept of data-driven public administration and lay bare the complexity involved in its implementation. It asks the overall research question of what challenges are encountered and problematised in a nascent phase of data-driven public administration implementation. The analysis is based on a multi-method research design, including a survey, follow-up interviews with practitioners and an analysis of key policy documents in the context of the Norwegian public sector. It highlights areas of both discrepancy and harmony between what has been prioritised at the policy level and the reality of implementation on the ground. In addition, unseen issues are discussed in order to broaden this perspective. Data-driven administrative reform touches upon everything from organisational culture to technical infrastructure and legal and regulatory frameworks. The complexity laid out in the analysis thus has implications for theory and practice. Nordic countries provide an interesting object of investigation, as they hold vast amounts of data and are highly digitalised, yet, in common with many other governments, they are still in a nascent phase of implementation. This paper should therefore be relevant to other jurisdictions and it provides a call to arms for civil servants and public administration scholars to engage more deeply in this phenomenon.

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.

The administrative reform of the datafied public administration places great emphasis on the classification, control, and prediction of citizen behavior and therefore has the potential to significantly impact citizen–state relations. There is a growing body of literature on data-oriented activism which aims to resist and counteract existing harmful data practices. However, little is known about the processes, policies, and political-economic structures that make datafication possible. There is a distinct research gap on situated and context-specific empirical research, which critically interrogates the premises, interests, and agendas of data-driven public administration and how stakeholders can impact them. This paper therefore studies the conditions of participation in public administration datafication. It asks the overall research question of how citizens are problematized and included in policy and practitioner discourse in the datafication of public administration. The paper takes Norway as its case and applies Cardullo and Kitchin’s scaffold of smart citizen participation at the system level. It makes use of a unique empirical insight into the field, consisting of a survey, interviews, and an extensive document analysis. Unexpectedly, we find that citizens and civil society are rarely engaged in this administrative reform. Instead, we identify a paternalistic, top-down, technocratic approach where the context, values, and agendas of datafication are obscured from the citizen.

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This chapter analyzes one of the early efforts within the Norwegian Government to improve public services with data from public sector archives. It explores an initiative to develop AI-based services within the Labor and Welfare Administration (NAV). The Norwegian public sector is in a pioneering mood. A new wave of digitalization is drawing attention to platforms, clouds and algorithms. Artificial intelligence holds the potential and promise to revolutionize the public sector. Supervised machine learning, especially, has become the method of choice to achieve the ultimate and somehow diffuse goal of becoming data-driven. 1 There is a lot of excitement about how machine learning algorithms might be used to provide better and more personalized services, changing the way we do bureaucracy and empower citizens. Recording, storing and processing information on citizens has long been a key element of the modern state; however, the calculative systems and techniques to do so have become ever faster, more comprehensive and more autonomous (Beer 2017). In comparison to private tech-enterprises, public sector organizations possess one obvious advantage-at least "on paper". They possess massive datasets about citizens, of a personal character, often recorded through a long historical span, and continually updated. As Redden notes, "this makes them incredibly valuable from a data analytics perspective" (2018:1). Our informants are very well aware of this potential advantage-some refer to big government data as "our gold". The gold is described as rich, comprehensive, exciting and unique by its miners. Machine learning presents itself as an opportunity to mine the gold lying within the archives, providing the administrators with new and surprising insights into their own work and the citizens they govern. However, as with real-world mining, extracting gold from its ores is not necessarily a straightforward affair. Someone must dig it out, distinguish it from other