Key concept: the ‘data assemblage’

A central concept within critical data studies is the idea of the ‘data assemblage’ – what Iliadis & Russo (2016, p.1) describe as ‘‘the technological, political, social and economic apparatuses and elements that constitutes and frames the generation, circulation and deployment of data’’. In this sense, data assemblages are sociotechnical systems that grow up around the production of particular forms of digital data. These assemblages consist of a variety of entwined ‘elements’ and ‘apparatus’ that are structured and organised to ensure the continued production of the data (Ribes and Jackson 2013). As such, these systems are constitutive of the data that they concerned with producing. In other words, “data and their assemblage are thus mutually constituted, bound together in a set of contingent, relational and contextual discursive and material practices and relations” (Kitchin and Lauriault 2018, p.8).

The notion of the data assemblage immediately foregrounds the idea of taking a ‘sociotechnical’ approach toward data – i.e. seeing the generation and processing of data as a coming-together of technical matters, scientific innovation, economic and political forces alongside social concerns (see Law 1987). Approaching any data assemblage in these terms, therefore requires paying equal attention to technological artefacts and technical processes, alongside the ways in which social, political and economic factors influence the datafication processes and practices. Indeed, Rob Kitchin and Tracey Lauriault (2018) point to a range of different technological, political, social and economic apparatuses that frame how a data assemblage might operate and work. These include: 

  • materialities and infrastructures
  • practices 
  • organizations and institutions 
  • subjectivities and communities 
  • places 
  • the marketplace(s) where data are constituted
  • systems of thought
  • forms of knowledge
  • finance
  • political economy
  • governmentalities and legalities 

Examples of such data assemblages might include the national census, standardised international measures of school performance (such as the OECD PISA statistics), national crime statistics, health indices, measures of unemployment, and so on.

Each of these assemblages will involve the bringing-together of various people, places, processes and practices. Take, for example, the data assemblage that might underpin higher education ‘rankings’ of universities around the world. Any such enterprise will be underpinned by an implicit positivist system of thought that presumes that educational activities can be quantified and be assigned a comparative value. Ideologically, this foregrounds values of managerialism and institutionalism, alongside an assumption of market-driven efficiencies (see Welsh 2021). The categories, measures and calculations of performance will have been negotiated and challenged by various stakeholders – not least individual universities and statistical commentators – and subsequently detailed in different forms of documentation. The data will be produced and provided by individual university administrative units, and national education agencies – all purporting to some form of objectivity and unbiased reporting. The rankings will be administered and promoted by organisations (such as Quacquarelli Symonds, Shanghai Ranking Consultancy and the Times Higher) seeking to profit from the generation of the data, alongside consultants seeking to assist individual universities in ‘gaming’ the generation of more favourable data. The ‘results’ will be publicised and circulated through a range of different media and infrastructures. These metrics will feed into wider market-driven uses of the data to construct rankings and measures of quality that, in turn, align with the broader political economy of university student enrolments, allocation of funding, and general public and professional understandings of ‘prestige’. 

Critical data studies researchers will therefore often focus on detailing and documenting the constituent elements and apparatus of any data assemblage – paying close attention to the connections that are formed between these apparatus, as well as broader connections with wider data regimes. Yet, as with all forms of critical data studies this is never simply a descriptive exercise. One key question to ask is how any data assemblage works to enhance and maintain the exercise of power within a society. In this sense, Kitchin makes explicit comparisons between the idea of the data assemblage Foucault’s concept of the ‘dispositif’ – in particular, the question of how any specific data assemblage functions to produce what Foucault terms ‘power/knowledge’. In this sense, critical data studies researchers are driven to interrogate the types of knowledge that any data assemblage is set up to produce, how this knowledge functions to further the strategic goals and aspirations of dominant institutions, and what wider frameworks the produced data operates within. To illustrate this point, Kitchin quotes Foucault’s (1980, p.196) observation that …

‘… the apparatus is thus always inscribed in a play of power, but it is also always linked to certain coordinates of knowledge which issue from it but, to an equal degree, condition it. This is what the apparatus consists in: strategies of relations of forces supporting, and supported by, types of knowledge’ 



Foucault, M (1980). Power/Knowledge.  Pantheon Books. 

Iliadis, A., & Russo, F. (2016). Critical data studies: an introduction. Big Data & Society3(2), 2053951716674238.

Kitchin, R. and Lauriault, T.  (2018) Towards critical data studies: charting and unpacking data assemblages and their work. in Thatcher, J., Shears, A. and Eckert, J., (eds)  Thinking big data in geography: new regimes, new research.. University of Nebraska Press  (pp.3-20)

Law, J. (1987). The structure of sociotechnical engineering—a review of the new sociology of technology. The Sociological Review35(2), 404-425.

Ribes, D. and Jackson, J.  (2013).  Data bite man: the work of sustaining a long-term study. in L. Gitelman (ed) ‘Raw data’ is an oxymoron.  MIT Press (pp.147-166).

Welsh, J. (2021). A power-critique of academic rankings: Beyond managers, institutions, and positivism. Power and Education13(1), 28-42.