Defining Data Visualisation
Does Data Visualisation encompass Data Art?
I’m concerned about putting Data Art as a sub-category of Data Visualisation….let me try to explain why.
Andy Cotgreave recently published a great blog post in his Sweet Spot series picking up on three articles I’d not seen (one from a colleague!) and asked the question “Can dataviz be art?”.
While perhaps rhetoric in nature this question got my brain turning over as I looked at each piece. All three of them were great examples of art, but as I reviewed them I realised I took more issue with them being labelled as “data viz” than being art. For me, only the second example felt like it was a data visualisation as it was the only piece that attempted to convey some understanding of a subject to the viewer.
Andy’s response surprised me, as he questioned whether Data Visualisation should need to amplify cognition. I’d always assumed fellow professionals all shared my definition of data visualisation.
Defining Data Visualisation
Clearly, Andy is taking the literal, linguistic translation for Data Visualisation — [some] data [that has been] visualised. For me though this is a rather facile definition; akin to describing a writer as “someone who writes”. By that definition, my 7-year old is a writer.
Let me attempt to define data visualisation as I understand it:
Data Visualisation is both a scientific discipline and an art that is undertaken to increase the cognition of the audience through the visual representation of data.
For me there are several aspects to this definition that are worth working through:
a scientific discipline: we can, using scientific methods and research, determine the graphical representations of data that provide the most understanding and ability to recall information to an audience. The use of pre-attentive attributes, as well as best practices, stem from this research and, while still being understood, the scientific process is key to furthering the discipline.
an art: of course, that doesn’t mean that the best visualisation for a situation is necessarily governed by science. Beauty and emotions play a huge part in an audiences engagement with a piece, and thus cannot be ignored.
to increase the cognition of the audience: for me, as explained, this is the key to data visualisation. It is the why behind that we do. We’ll spend longer on this later.
visual representation: this speaks for itself, it’s the charts, graphs, and representations that make up the visualisation.
data: we are representing raw data and thus turning it into information by giving it meaning and context.
How others have defined Data Visualisation
My definition isn’t that important, I’m an inconvenient speck on the history of data visualisation, but how have others in the field defined it?
Data visualisation is the graphical representation of information and data. By using visual elements like charts, graphs and maps, data visualisation tools provide an accessible way to see and understand trends, outliers and patterns in data.
— Tableau.com
Tableau are firmly in Andy’s camp. Quelle Surprise.
The representation and presentation of data to facilitate understanding.
— Andy Kirk, in his book Data Visualisation.
Goal for me. One all.
The fact that an information graphic is designed to help us complete certain intellitectual tasks is what disinguishes it from high art.
— Alberto Cairo, in his book The Functional Art
Not strictly a definition, I hit the woodwork.
Data visualization is a collection of methods that use visual representations to explore, make sense of, and communicate quantitative data.
— Stephen Few
If I need to quote Few to win a data visualisation debate then I fear I may not be on the surest footing, so let’s continue.
Why is the “improving cognition” clause so important?
Personally, I think it serves our field a huge benefit if we separate #dataviz from #dataart where possible. Data Art (or Information Art) is a field where artists express themselves artistically using data as a medium (usually using computers).
There are many examples of Data Visualisation which might be considered art, of that there is no doubt. Let me lay out how I see things:
The intent of the author is not paramount here, it is the perception of the audience that separates data art from data visualisation. Can they extract the meaning?
As an example let’s consider the inclusion of Dear Data in the Museum of Modern Art. Clearly, by its inclusion, this is art.
What happens though if we remove the legend from the rear of the postcard. Does it devalue this piece as a Data Visualisation? Yes. By removing the legend we remove all semblance of the link back to understanding and information. Any attempt to perceive meaning is lost.
Does this devalue the art? Yes. Would the version without the legend have got into the MoMA? Would Stephanie and Giorgia sending each other random diagrams on postcards have captured the imagination in the same way if they hadn’t attached any way to interpret the meaning? I seriously doubt it.
The value of a piece, both artistically and operationally, is critically linked to its function.
Does that matter to us in the field? Yes. A million times. Yes.
The art behind a data visualisation is a very different art to that behind a piece of data art. Assessing the quality of a data visualisation is hard enough, the field needs clear boundaries and definitions of its aims.
Moreover, Data Visualisation “Best Practice” in a world where the term can include Data Art becomes impossible. What is “Best Practice” for poetry or painting? Data Visualisation would soon fall into the same trap.
Every conversation around Data Visualisation harder and more confusing if we’re forced to define our frame of reference as “art” or “understanding”. I always argue that critique of a data visualisation isn’t possible without knowing what the author is trying to convey, imagine how hard it becomes when they don’t need to convey anything at all.
The point is that Data Visualisation (as per my definition) is both a science and an art — and requires its own term to frame conversations and learning to ensure students can put their arms around the field. We could coin a new term for this but I propose Data Visualisation is currently generally used in this parlance and so should be constrained to my definition.
Many Data Visualisation sins are committed under the name “data art”. They do little to service either field. Data Art deserves its own pedestal and its own champions, for it has its own audience. It deserves its own, separate term, separate from the umbrella of Data Visualisation so it can grow and thrive.
