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What Exactly Is Visualization?

I love a good visualization. I’ve always been fascinated by those images that manage to inform and entertain at the same time. From time to time I’ve tried to supply tips and insight to help those interested in creating better visualizations. One morning while researching data visualizations I thought to myself “what really makes up visualization”, is it definable? So I did what most of us do when we’re in search for information; I Googled it. After a few minutes of clicking various links I came across an awesome post from Column Five Media that tries to explain what makes up good visualization that I wanted to share with you.

Before I attempt to define what visualization is, let’s frame this discussion up a little bit. Last week at SXSW, I attended a data visualization panel where I had the pleasure of listening to Rosten Woo give a brief history of the subject, among other things. Turns out an Austrian born philosopher, sociologist and political economist, Otto Neurath, is regarded as the founding father of data visualizations. He is credited with creating  isotype (think airport and public bathroom signs) as well as creating a pictorial system to easily display quantitative data visually (pictograms).

His early work really introduced the general public to data visualization. Today, data visualization has come a long way since Otto Neurath’s work in the 1920s and 1930s. With all the continual tweaks and iterations data visualization has undergone in the past ninety years, it makes it difficult to corral the field and accurately define it. Luckily, for us, Nathan Yau, who runs the visualization blog Flowing Data, helps us out a little in his post from Column Five Media.

According to Yau, you need to think of data visualization as “a medium. It’s not just an analysis tool nor just a way to prove a point more clearly through data.” I think given all the various mediums and purposes that visualizations are created for today Yau’s view is right. It’s difficult to pin visualization down as a black or white definition partially because it’s such a fluid field. The boundaries of data visualization are always being pushed. Yau further illustrates his point saying “Visualization is like books. There are different writing styles and categories… they communicate ideas in different ways for varied purposes.” There are rules that you should always follow. “When you think of visualization as a medium rather than a monolithic tool, it’s something much more flexible that can be used for a lot of things. It’s also more exciting. You can tell stories with data through analysis, journalism, or art. Visualization can be fun or serious; it can be beautiful and emotional or barebones and to the point.”

The best way to define visualization is as Yau described it, as a medium. The wide-ranging uses of visualizations makes it very difficult, and quite frankly too constricting to define it as otherwise. Part of the appeal of fun visuals is open endedness, you never really know what you’re going to get.

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