Theorizing Data Visualization: A Comparative Case-Study Approach

July 17, 2013, 13:30 | Long Paper, Embassy Regents D

Data visualization is a digital technique for organizing, representing, and interpreting information visually. Etymologically, data visualization is the process wherein “that which is given” is “made visible to the eye,” translating a particular collection of statistics, values, or words into a visual diagram, design, or architecture. Due to its efficient (computational) and scalable workflow, data visualization lends itself particularly well to working with large amounts of raw or unprocessed information, which is typically collected and organized into datasets and stored in electronic collections called databases. This project takes as its critical object the application of data visualization as a means of textual analysis in the digital humanities, examining how a technology with a long-running history in corporate business contexts and the social and STEM sciences might affect the practice of literary scholarship, particularly in terms of reading and interpretation.

Due in large part to its often powerful and aesthetically pleasing visual impact, relatively quick learning curve, and overall “cool” the practice of visualizing textual data has been widely adopted by the digital humanities. This prevalence is evidenced by, for instance, the high frequency of the term “data visualization” in the 2011-2012 Digital Humanities conference abstracts as well as the 2011-2013 Modern Language Association panels related to the digital humanities. If the first wave of large-scale database projects in the digital humanities is exemplified by the practices of digitizing texts, constructing archives, and determining best practices for digital preservation, then the practice of data visualization is emblematic of the second wave of projects devoted to mining this new data. The NEH-funded “Digging into Data” granting program, a yearly challenge that asks how the notion of scale affects humanities research, has specifically supported this practice of engaging with huge databases and archives.

This paper directly engages with the (oft-overlooked) notion that data visualization is in fact an argumentative, non-neutral process and asks: what is and is not visualized, how are visualizations produced, how do aesthetics factor into this discussion, why and how are digital humanists using this technology, and how can data visualization be contextualized historically, materially, and politically. The paper does so by offering focused case studies of two specific data visualization environments:

1.) IBM’s Many Eyes — This case study involves close-reading mission statements, web content, user-instructions, and sample and showcase datasets and visualizations. This case-study helps to construct and illuminate the project’s corporate identity. IBM’s history, after all, is deeply intertwined with the invention of the punch-time clock and other aspects of contemporary (Taylorist) business culture and embodies the corporate values of efficiency and time management. Further, due to its work with Swiss-style graphic designer Paul Rand on the company’s logo and minimalist aesthetic identity, IBM is emblematic of the intersection of early computing and mid-century graphic design. The company is unique in that it managed to synthesize cutting edge technology, profit-driven corporate culture, and “cool” graphic design, an accomplishment that I argue contributes to the prevalence of Many Eyes data visualizations in the digital humanities, especially in projects produced by individuals who are making their first forays into the field.

2.) Alan Liu’s NEH-funded Research-oriented Social Environment (RoSE) — This section contrasts Many Eyes with RoSE, a data visualization platform with a different material and cultural history. I use my insight here as a humanities developer on the project team to explain how RoSE privileges critical approaches to design, collaborative development, and data transparency. In fact, the data itself is produced collaboratively through cooperation with individual users and large-scale institutional partners, including Project Gutenberg and the SNAC team at the University of Virginia. Finally, unlike Many Eyes, RoSE hosts a supplementary website that provides details about the project’s goals, developers, and potential limitations.

The project complements other recent and historical moves to examine data visualization, particularly as it is embedded within a longer engagement with graphic design, information aesthetics, and visual rhetoric. The work of theorists of information design and large-scale data analysis, including Edward Tufte, Andrea Lau, and Lev Manovich, has been crucial in formulating an informational aesthetic. This aesthetic framework extends from the mid-twentieth century introduction of the so-called Swiss graphic design, known for its emphasis on minimalism and reliance on the “grid,” to new media art practice. Whereas much of the research to date has dealt with database and visualization design, my intervention is to engage with the importation and application of data visualization in the digital humanities as such. Previous research on data visualization in the field has been concerned primarily with the development of visualization software and the technology’s exciting new capabilities, rather than how such development might in fact transform literary scholarship. This project takes a critical step back from these discussions in order to consider how visualization techniques, tools, and technologies might in fact transform literary scholarship, what is at stake in their instrumentalized use, and how humanistic modes of critical engagement might be applied to them.

The focus here is on differentiating the cultural genealogies of each platform to shed light on their unique ideological foundations. The paper examines the corporate lineage of Many Eyes, while constructing a parallel but alternative genealogy for RoSE, one rooted more in scholarship, new media art practice, and collaborative knowledge production than profit-driven commercial activity. The goal of the paper is not to critique the corporate affiliation of Many Eyes as such, but to ask what might be at stake in the implementation of a technology that has its roots outside of the humanities, specifically a technology that has existed for half a century in other contexts and contains its own embedded goals, methodologies, and ideological underpinnings.


Borner, K. (2003). Visualizing Knowledge Domains. Annual Review of Information Science & Technology, 37, Medford, NJ: Information Today, Inc./American Society for Information Science and Technology. 5. 179-255.
Fitzpatrick, K. (2011). Planned Obsolescence: Publishing, Technology, and the Future of the Academy. New York: New York University Press.
Gold, M. (2012). Debates in the Digital Humanities. Minneapolis: University of Minnesota Press.
IBM Research (2012). Many Eyes Data Visualization Platform. 2007-present. URL: http://www-958.ibm.com/software/data/cognos/manyeyes/ (accessed 15 September 2012).
Lau, A. and A. Vande Moere (2004). Towards a Model of Information Aesthetics in Information Visualization. http://web.arch.usyd.edu.au/~andrew/publications/iv07.pdf (accessed 15 September 2012).
Lima, M. (2011). Visual Complexity: Mapping Patterns of Information. New York: Princeton Architectural Press.
Liu, A. (2004). The Laws of Cool: Knowledge Work and the Culture of Information. Chicago: University of Chicago Press.
Manovich, L., and J. Douglass (2007). Cultural Analytics. UC San Diego Software Studies Initiative. 2007-present. URL: http://lab.softwarestudies.com/2008/09/cultural-analytics.html (accessed 18 September, 2012).
Mirzoeff, N. (2011). The Right to Look: A Counter History of Visuality. Durham: Duke University Press.
Moretti, F. (2007). Graphs, Maps, Trees: Abstract Models for Literary History. London: Verso.
Nowviskie, B., and J. Unsworth (1999). Is humanities computing an academic discipline? An interdisciplinary seminar. University of Virginia.
Ramsay, S. (2011). Reading Machines: Toward an Algorithmic Criticism. Chicago: University of Illinois Press.
Terras, M. (2008). Digital Images for the Information Professional. London: Ashgate.
Tufte, E. (1983). The Visual Display of Quantitative Information. Cheshire, CT: Graphics Press.
Vesna, V. (2007). Database Aesthetics: Art in the Age of Information Overflow. Minneapolis: University of Minnesota Press.
Yau, N. (2011). Visualize This: The FlowingData Guide to Design, Visualization, and Statistics. Hoboken: Wiley Press.