Data Visualization: Should We Divide It?

Road to Copenhagen (detail)

For some time now there has been some discussion about finding a new terminology for the Data Visualization field. The intention is to find names that reflects the two main different directions that are seen today on Dataviz: visual data analysis and data driven aesthetics images. The concern is that Data Visualization might lose its ‘serious’ role as an analytic tool by including works that, without doubt, praise aesthetics before clarity.

This is a guest post by Pedro Monteiro. For more information about Pedro, see the bottom of this page – RK

Manuel Lima’s Manifesto and this blog’s author Robert Kosara presented some great arguments on this subject. I’ve been thinking a lot about this and since I come from the ‘design side’ of the equation, I think that there are some lessons to be learned from graphic design on this.

To make it clear from the start, I defend that the new name for Data Visualization should be… Data Visualization. In terms of semantics, there is a clear definition within this name: finding ways of presenting data in a visual way. Having stated this let me use the field of graphic design to justify my position.

Graphic design is a well-established field that has existed for a long time now and that has gone through a lot of revolutions, growth, and discussion. Inside it there are lots of subfields – some with specific names like editorial design – that coexist.
This coexistence allows for practitioners to move from one subfield to another without much regard about it. For example, it is not strange to see designers producing posters, CD covers, and spreads for a magazine. For those of you that don’t know very much about graphic design, let me tell you that these are very different fields, with very different languages and a huge history. These experiments in different subfields by designers are seen by other designers as normal, expected, and (at least by many of us) desirable.

By moving around the different subfields, graphic designers are able to learn a lot by means of investigation, experimentation and, of course, the critiques from their peers. On the other hand, these professionals bring with them new sights, new ideas and new practices to the subfields, and this is how evolution is achieved.

My concern with the idea of dividing Data Visualization is that we might, even by simply changing names, be losing the opportunity to open doors to a mobility that might improve the field. If we want to learn from each other and grow with that knowledge, we must learn to think and discuss the various experiments that are being made within Data Visualization. Of course, as with everything that grows, mistakes are going to be made, different views are going to be fought and some ‘blood is going to be ‘spilled’.

Robert Kosara states, To most people, visualization means pretty pictures first, and maybe also a fact or two. We have to fight that or risk the trivialization and marginalization of visualization as an analytic tool. Using graphic design as an example, I say that it has not been trivialized or marginalized as a communication tool by the many experiments we’ve seen in the field for the past 150 years (give or take). If anything it has risen. And please bear in mind that a lot of mistakes where made, a lot of people pushed it ‘too much’. But graphic design stands as a field where ongoing healthy discussions, ideas and criticism benefit both the veterans and the newcomers.

In his Manifesto, Manuel Lima writes, The recent outburst of interest for Information Visualization caused a huge number of people to join in, particularly from the design and art community, which in turn lead to many new projects and a sprout of fresh innovation. But with more agents in a system you also have a stronger propensity for things to go wrong. Again, I don’t see this as a reason to divide the waters for a clearer definition but, instead, as a thing to welcome. For anything else, every time something goes wrong, a strong and open community may discuss it and learn from it. This is the way we humans learn, by trial and error. With more people trying and making mistakes the learning becomes faster.

Manuel Lima, in his Observations on the Manifesto says, This is something we can certainly discuss as a community, and there are many benefits to do so. Once we all agree on what we do, it will be easier for others to recognize the goals and boundaries of our growing discipline. I agree that discussion will benefit the community, but I think that we won’t achieve it by changing names and dividing the field.

So what do I propose with the keeping of Data Visualization name? Not only keeping a name that is becoming more and more recognized, but also improving our ability to criticize and discuss within and outside the community. By keeping the name, a greater concern must be given to the communication of our intents within a given work of the field. Instead of making something for the ‘Information Visualization’ side of the field, we should worry about stating in a clear way what our intentions where for the work.

In my opinion, it is much harder – and therefore it brings much more potential to enlighten – to criticize and discuss something once you’ve learned about the thoughts, research and intentions behind it.
A graphic design example: it might be easy to discuss that David Carson’s design on Ray Gun magazine was plain wrong according to the canons of editorial design. But it becomes harder to argue (and I believe that then reflection and discussing begins) when we learn that his work on the magazine earned him more than 150 design awards and is still being talked about today.

In conclusion, I think that we should worry more about communicating in the Data Visualization field with each other and everyone outside. We should keep a name that works and has recognition, but we must change our way of discussing and criticizing each other, both by making clear what our intentions were for a specific project, but also by broadening our vision on the expectations we have for the field.
I do agree with this statement by Manuel Lima on his Manifesto: I don’t tend to be harshly censorial of many of the projects that over-glorify aesthetics over functionality, because I believe they’re part of our continuous growth and maturity as a discipline. They also represent important steps in this long progression for discovery, where we are still trying to understand how we can find new things with the rising amounts of data at our disposal. I only think that we might gain more without dividing the field – as opposed to what we might achieve by following his or Robert’s suggestions.

I would like to take this opportunity to thank Robert for allowing me to post my ideas on his blog. Robert gives us the best example that greater growth is only achieved trough discussion, even if this means allowing me to publish my contrary opinion on his blog. Robert, you rock!

About the author: Pedro Monteiro is a Portuguese graphic designer working for Portugal’s best selling newsmagazine Visão and as a consultant for INNOVATION MEDIA CONSULTING. He also runs Whatype, a blog with personal projects where he has done some experiments with Data Visualization, among other things. Pedro has been choosen to be included in Business Week’s list of 21 Heroes of Data Visualization and has shared some of his views about Data Visualization on Brain Pickings. He can be contacted at

Published by

Robert Kosara

Robert Kosara is a Research Scientist at Tableau Software, and formerly Associate Professor of Computer Science. His research focus is the communication of data using visualization. In addition to blogging, Robert also runs and tweets.

2 thoughts on “Data Visualization: Should We Divide It?”

  1. This is a great site along the lines of Edward Tufte. Will feature it in my Reference List plus list it there permanently. Keep up the good work! For all students or researchers: I have just added an ( “Economics Reference List”) to my economics blog with economic and statistical data series, history, bibliographies etc. for students & researchers, probably the most comprehensive on the Internet. Currently over 200 meta sources, it will soon grow to over a thousand. Check it out and if you miss something, feel free to leave a comment.

Leave a Reply