Dear Data: Feminist Information Design's Resistance to Self-Quantification

Abstract

Feminist Studies 45, no. 1. © 2019 by Feminist Studies, Inc. 129 Miriam Kienle Dear Data: Feminist Information Design’s Resistance to Self-Quantification Every Sunday for one year, information designers Giorgia Lupi and Stefanie Posavec sent each other a hand-drawn postcard that featured a data visualization of their week as it pertained to a single aspect of their daily lives: doors opened, clocks checks, sounds heard, smells perceived, and so on (figs. 1–4). With this series of postcards exchanged between Brooklyn and London, Lupi and Posavec gained intimate knowledge of one another through their small/slow data, and at the same time, they produced a critical examination of the capture, interpretation, and visualization of their daily data from a uniquely feminist perspective. Although the field of information design prizes clear, efficient, and seamless presentations of quantified activity, Lupi and Posavec’s Dear Data (2015) is comprised of visualizations that underscore complexity over clarity, present questions rather simply display information, and expose the instability of data itself.1 Against the quantification of embodied experience into seemingly objective and all-encompassing datasets, Dear Data instead looks at data from a feminist perspective, as tied to concrete bodies, localities, and temporalities and visualized in a manner 1. The approach to infographics that prioritizes efficiency, accuracy, and elegance , and thus suppresses the constructed and interpretative nature of both data and its visualization, is perhaps most attributed to Edward Tufte. For Tufte’s aims and methods, see Edward R. Tufte, The Visual Display of Quantitative Information (Cheshire, CT: Graphics Press, 2001). 130 Miriam Kienle that is incomplete, contingent, and constructed.2 This paper will analyze Lupi and Posavec’s designs as providing a model for a feminist approach to data visualization—a model imperative in an age in which the quantitative information (much like a binary understanding of gender) is too often treated as a self-evident fact rather than what feminist philosopher Donna Haraway famously called “situated knowledges.”3 Recently acquired by MoMA’s architecture and design collection, the Dear Data project not only stands out in the collection as the product of two female designers working in the male-dominated fields of technology and design, but also as a project that stresses feminist knowledge production by underscoring how data voids shape datasets and debunking the myth that the world is data that can be captured and visualized with neutral, distanced, and all-encompassing technologies of vision. As Catherine D’Ignazio and Lauren Klein describe in Data Feminism, data visualization from a feminist perspective understands that “bar charts might seem neutral and objective, but are in fact the result of very human and necessarily imperfect design processes.”4 Such a perspective is evident across Dear Data. Take, for example, “Week 42: A week of laughter” (fig. 5). On the key in Posavec’s visualization, she notes: “I tried to capture my laughs which was really hard + got in the way of the enjoying life, hence the data voids. / For a card about laughter I am sad about how this card turned out.”5 This willingness to show the gaps in her data, the complexities of her process, and the imperfections of her visualizations contrasts with the seamless and straightforward displays of information 2. For a feminist approach to data visualization, see Catherine D’Ignazio and Lauren Klein, Data Feminism (Cambridge, MA: MIT Press, 2019), ebook; Catherine D’Ignazio, “What Would Feminist Data Visualization Look Like?” Center for Civic Media. Massachusetts Institute of Technology (blog), December 20, 2015, https://civic.mit.edu/feminist-data-visualization; and Johanna Drucker, Graphesis: Visual Forms of Knowledge Production (Cambridge , MA: Harvard University Press, 2014). 3. Donna Haraway, “Situated Knowledges: The Science Question in Feminism and the Privilege of Partial Perspective,” Feminist Studies 14, no. 3 (1988): 575–99. 4. D’Ignazio and Klein, introduction to their Data Feminism. 5. Giorgia Lupi and Stefanie Posavec, Dear Data (New York: Princeton Architectural Press, 2016), 231. Miriam Kienle 131 or what infographic guru Edward Tufte describes as “Designs so good that they are invisible.”6 Counter to the invisible and seemingly objective visualizations of our personal data to which we have become accustomed (on calorie counters, credit card statements, etc...