Communication Feminist Data Studies
Kate O'Riordan
  • LAST MODIFIED: 27 June 2022
  • DOI: 10.1093/obo/9780199756841-0275


Feminist data studies are a set of approaches to the rise of data and computational culture, or what has been referred to as datafication and the datalogical. Feminist data studies encompasses feminist data visualization, digital humanities, big data, algorithms, AI, machine learning, quantification, and datalogical and computational turns. It examines these areas in relation to meaning making, epistemology, consumption, practices, production, ontology, and labor. In tandem with the way that Foucault argued that knowledge is power, data as a dominant form of knowledge production can be thought of as power. Data science, data collection, data gathering, and visualization technologies have reached an ascendance in science, politics, education, and culture, as computation has become cultural and social. Computational culture is structured through systems that require and construct phenomena in the world to be countable and to feature as data points. Data collection and search are fundamental to knowledge production in this economy, and centers of power are clustered and intensified through data processing capacity. For example, government, border agencies, stock markets, banks, search engines, and institutions of sciences and technology of all kinds center around collecting, producing, and interpreting data. Further, almost all aspects of knowledge production are understood as authoritative through this lens and the arts and humanities have also been reformulated as digital. Feminist data studies examine the power relations of data, with particular attention to the ways in which the politics of those power relations structure societies and reproduce and exacerbate inequality. It is important to understand it as both a productive intervention, making things in the world, and a deconstructive intervention, offering critical analysis.

General Overviews

Feminist data studies deploy data-intensive approaches embedded in a tradition of situated research ethics, informed by a historical critique of technological and methodological positivism. This area of study addresses what has been referred to in Thornham 2018; Clough, et al. 2015; and other works as the datalogical turn, or datafication. It uses digital tools to produce data for social justice as well as critiquing the reification of data as an object acting in the world. The field examines the politics of the turn to data itself, taking up data as epistemology and ontology as well as culture, politics, and aesthetics. Patricia Clough provides this overview: “Human lives continually pass through datafied terrains. Even though data collection processes are unevenly distributed throughout the world, many quotidian behaviors such as making a call from a cell phone, using a mobile device to access the internet, clicking through web links, swiping a credit card to make a purchase or even visiting a hospital or accruing a speeding ticket have now become dynamic sites of data collection” (Clough, et al. 2015, p. 153). Feminist approaches, such as Kennedy and Hill 2017 (cited under Data Visualization: Production, Meaning Making and Everyday Life) situate data as cultural and ask how people make data meaningful and live with and feel about data. Feminist data studies engage with data as a source of authority and regime of knowledge production and challenge the claims made as to its objective indexicality and power. Lisa Gitelman uses the formulation “Raw data is an oxymoron” (Gitelman 2013), as a strong articulation of this position. “Raw” has a technical definition in data science practices, such as in the collection of data in the Hadron Collider. However, the more general meaning, to mean uncooked, or inexperienced, indicates some of the powerful language that frames data as naturally arising and inevitable. When the discourse of data actually often serves to provide empirical evidence or to exacerbate very hard-baked and historical social norms, this is problematic. Data are industrial and data studies look at the production of data and their industrial practices, including labor, economics, and storage. This includes work such as Gregg 2018 and Moore 2017, which focus on the practices of data visualization and technology industries and platforms. The field attends to the precarious labor of the data economy, which creates new underclasses, discussed in Gray and Suri 2019. Gregg 2018 and Jarrett 2017 demonstrate the ways in which data incorporates consumers as well as producers in their logics. D’Ignazio and Klein 2016 suggest: “Data feminism can also show us how the categories of data collection matter deeply, especially when dividing people into groups” (p. 10). The field extends traditions in intersectional feminisms that examine the ways in which whole patterns of normative assumptions are built into technologies as they emerge.

  • Clough, Patricia Ticineto, Karen Gregory, Benjamin Haber, and Joshua R. Scannell. 2015. The datalogical turn. In Non-representational methodologies: Re-envisioning research. Edited by Phillip Vannini, 146–164. New York: Routledge.

    This is a highly ambitious conceptual essay that argues for the datalogical, as a different form of sociality that challenges the basis of sociology as a discipline. The essay argues that the datalogical is the unconscious drive of sociology, which also outstrips the discipline’s capacity. It links adaptive algorithms, cybernetics, and sociology to offer a different model of sociology, sociality, and the subject, following the datalogical. The essay embeds pollical economy and subjectivity to articulate the concept of a self-appreciating, non-representational subject as the locus for a new politics.

  • D’Ignazio, Catherine, and L. Klein. 2016. Feminist data visualization. Presented at and published in the workshop proceedings from the Workshop on Visualization for the Digital Humanities at IEEE VIS Conference, Baltimore, MD, 23 October 2016.

    This paper outlines six principles to inform feminist approaches to data visualization. The analysis is conducted through a theoretical lens informed by feminist science studies and intersectional feminist approaches. The paper makes the case that data visualization is an increasingly powerful medium for understanding the world, and therefore the ethos of design in this area is an important site of intervention.

  • Gitelman, Lisa, ed. 2013. Raw data is an oxymoron. Cambridge, MA: MIT Press.

    Drawing on Geoffrey Bowker’s formulation, this book has become a landmark in the field. The collection explores historical and contemporary big data across a range of examples and fields. A unifying theme for the collection is the work of making visible the processes of data preparation. The premise underlying this is that data are often presented and understood as raw, emergent, or naturally occurring. However, they are only made intelligible by highly invested, labor-intensive processes of sense-making, media production, and visualization.

  • Gray, Mary L., and Siddarth Suri. 2019. Ghost work: How to stop Silicon Valley from building a new global underclass. New York: Houghton Mifflin Harcourt.

    Ghost Work addresses the human labor dimensions of big data and other datalogical functions, such as AI, machine learning, and disintermediation. The book offers a labor studies entry point into a field, which often follows the technological and, as such, is unusual. This intersects with the field’s interest in data visualization or data preparation, by opening up the precarious labor of making data work much more extensively.

  • Gregg, Melissa. 2018. Counterproductive: Time management in the knowledge economy. Durham, NC: Duke Univ. Press.

    DOI: 10.2307/j.ctv11smmx6

    Drawing on genres of self-help, optimization, productivity, and time management, Counterproductive provides a contemporary history of the working conditions of the data economy. It examines the rise of the algorithmic mediation of work and home life and the orientation toward software engineering, locating this at the center of thinking about datafication. The counting of units of labor time and the datafication of work, as well as the work of datafication, underpin working conditions, ideological and technical structures, and life itself.

  • Jarrett, Kylie. 2017. Feminism, labour and digital media: The digital housewife. New York: Routledge.

    Jarrett’s work examines Marxist and neo-Marxist theories of labor in relation to digital culture. The book draws together a broad and overarching interrogation of labor debates and critically applies these to highly developed case studies in digital media culture. It uses the lens of domestic work to examine feminism and labor in the data economy.

  • Moore, Phoebe. 2017. The quantified self in precarity: Work, technology and what counts. New York: Routledge.

    DOI: 10.4324/9781315561523

    Moore’s book contributes to the growing field of labor studies approaches to the quantification of work. It provides an account of quantification in the workplace with an emphasis on forms of automated quantification. It argues that stressful and precarious conditions are exacerbated by delegated quantification. As with other studies in this area, it provides a critical account of labor in the data economy.

  • Thornham, Helen. 2018. Gender and digital culture: Between irreconcilability and the datalogical. London: Routledge.

    DOI: 10.4324/9780203703915

    This volume is organized through themes of algorithmic vulnerability and irreconcilability. It examines why gender relations remain so unchanged through successive waves of technological change. This draws on a history of feminist engagements with technology through Thornham’s research into contemporary digital culture. Irreconcilability is used as a critical tool to look at generative disconnections and hopeful opportunities. This draws on empirical research into young women and technology and emphasizes vulnerability, exacerbated inequality, dismembered bodies, and the tensions of hypervisibility and invisibility.

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