Defining Custom Data Visualization

“Custom Data Viz is expensive and we’re not sure if it has an impact.” I hear this in industry all the time. And while I think it’s important that we challenge that position, and push forward the use of custom data visualization for insights, it might help to actually understand what custom means.

As it stands, custom data visualization is mostly described as anything you can’t do in an off-the-shelf tool, and while that has the appeal of being technically accurate and simple, it’s a definition that isn’t particularly helpful. When I make a data visualization product like the connected beeswarm plot below, it’s different than when I make other kinds of custom data visualization. This particular piece is part of my work with Mara Averick to analyze and visualize the television show Archer, and I found that beeswarm plots were great for giving a summary of the seasons in a compact way to compare seasons across the entire course of the show.

But while it is custom, it isn’t particularly complex. Beeswarm plots aren’t exotic, and in general single axis scatterplots of some sort (of which category the beeswarm plot is in) are common in a variety of Business Intelligence tools. The customization only comes into play because I have a little bit of functionality that I want to deploy that isn’t going to be present in a tool. Specifically, I want to connect the circles that represent the same character, and I want a bit of hover functionality. Oh, and I want GIFs everywhere, and that’s probably not available in Tableau.

There’s a difference between that and other custom data visualization work I’ve done which involves synthesizing data visualization methods or composing entirely new types of charts. For instance, in that same Archer project, I have a couple different ways of representing individual shows. The character paths below on the left group characters on-screen and off-screen throughout an episode. On the right is another representation of an episode, this time as an interrupted radial bump chart, with each “spoke” representing a single scene and the thickness of the arcs in the spoke corresponding to the amount of dialogue of that character in that scene.

Calling each of these things “custom data visualization” is accurate but imprecise. I’ve found that it makes more sense to split custom data visualization into three separate categories: Modded, Combinatorial and Novel. These are big, fuzzy categories, but they enshrine the investment, benefits and challenges of different types of custom data visualization in a way that makes it easier to plan for. Though they don’t directly map to skill and time requirements, they exist along a spectrum that includes Stock Data Visualization of the kind created with tools. As tools advance, what techniques fall where on this spectrum will naturally change.

In the stock data visualization realm we can think of two main subcategories:

Ready-Made is literally “off-the-rack”. You save as PDF in Tableau, or take a screenshot of a line chart in Excel.

Skinned / Themed Doesn’t change the fundamental channels that a data visualization product uses, but customizes those channels enough to differentiate its brand or improve its information delivery. This typically happens within the options of the tool (changing default scales) but may involve some post-processing in a graphics editor.

In the custom world, we have those three categories I listed above:

Modded broadly construed is like the beeswarm plot above.