Can Data be Sexy?

Ever since Charlie Goldsmith told the class last week that pie charts are a terrible way to convey information, my curiosity regarding data visualization approaches was piqued. The field of visualization is interesting to me. I believe this is also true for most people living in the information economy. Throughout our lives, info-graphics, charts, maps and diagrams depicting everything from bus routes to how our grades compare with others have bombarded us. We instinctively understand and trust visualized information, and we know when it’s done poorly.

Friedman (2008) suggests that the purpose of data analysis is to communicate data in a way that is clear and effective. It is far from new. The earliest forms of data visualization where maps, the oldest of which is thought to define the town of Konya, in modern day Turkey, dating back more than 8,000 years. The emergence of various forms of data visualization is well documented over the centuries (Friendly, 2009).

Data visualization isn’t without pitfalls, however. For example, Friedman argues that there is a tendency toward creating visually stimulating material that doesn’t carry sufficient usable information. On the other hand, Viegas and Wattenburg (2011) advocate making data “sexy” and engaging, as it increasingly becomes a public medium for disseminating knowledge, rather than a professional one. While the suggestion is certainly that visualized data should be both beautiful and informative, these visualization professionals seem to, at least partially, conceive of form and function as naturally inverse to each other.

Ebert, Favre and Peikert (2002) suggest another problem: the certainty of visualized data. When compared to statistical expressions, for example, data visualization has difficulty depicting the rate of uncertainty. To me, this speaks to the instinctive trust we have in graphics. Once something is displayed, and particularly on paper, it has a sense of finality that may not be representative of the fluidity of knowledge.

Ebert, Favre and Peikert (2002) also conceive of the notion of “data reduction;” the idea that in order to properly map, diagram, or otherwise display data, we must reduce it to it’s critical elements. The natural extension of this line of thought is that the displayed data is then missing concepts, which may create bias. Consider, for example, David McAndless’ (2008) example of military budgets. In the image labeled “War Chests,” the top 10 national defense budgets are listed. By this image, the United States is clearly seen as the largest spender. In fact, the box depicting US military spending is large enough to fit all the other budgets within it. If, however, we look at the graphic labeled “Big Spenders II,” we see defense spending as a percentage of GDP.  Here, the US falls to 8th place. These sorts of biases could be exploited for political gain.




Given the limitations of data visualization, I still can’t help but be drawn to it as a form of knowledge translation. It’s simplicity and effectiveness as a tool cannot be understated. I also believe that the limitations it faces are common to other fields of knowledge translation. Many forms of expression have difficulty expressing certainty, and even statistics is somewhat inaccessible to those without training. Data reduction occurs with any kind of knowledge summarization. Indeed, the struggle between making a piece of knowledge engaging while keeping it informative is nearly universal. I believe that as we become more and more inundated with data, we will need more and more creative, effective ways to understand it, and data visualization will play a key role.

Ebert, D.; Favre, J.; Peikert, R. (2002). Data Visualization. Computers & Graphics 26(2). P 207-208. Retrieved from:

Friedman, V. (2008) Data Visualization and Infographics. Smashing Magazine 1(14) , retrieved from:

Friendly, M. (2009) Milestones in the history of thematic cartography, statistical graphics, and data visualization. York University. [electronic document]. Retrieved from:

McAndless, D. (2008). Information is Beautiful: War Games. The Guardian. Retreived from

Viegas, F. & Wattenberg, M. (2011) How to Make Data Look Sexy. CNN. Retrieved from


One response to “Can Data be Sexy?”

  1. lili891 says :

    In practice, data visualization provides useful ways in which research can connect with stakeholders and more broadly, the general public. Importantly, the audience to which such aesthetics are targeted should not be overlooked. The purpose of generating visual material is to relay information to audiences in order to make the research more accessible, though such benefits may fall short of their goals if audiences are not considered. For example, visual material produced as a component of any campaign or initiative should differentiate between those that will be accessed by policymakers and those that will be accessed by the general public. Furthermore, the community must also be factored in to the equation. Areas of public health that involve vulnerable (affected) communities must also account for related issues such as sensitivity of the way in which the information is provided as well as the literacy levels of those targeted. Naturally, visualization of data can extensively benefit campaigns and initiatives in public health, but the way in which these materials are employed must also be based in methods to effectively communicate the research.

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