Stop trying to create “general purpose” charts (because they don’t exist)

I frequently encounter the misconception that, for a given set of data, it’s possible to design a chart that will be useful regardless of the audience or the reason why that audience might need to see that data. Such “general purpose” charts don’t exist, though, since any visualization of a given data set will inevitably serve some audiences and purposes well and others not. In order to create a useful chart, then, the target audience and reason(s) why that audience needs to see that data must be identified beforehand.

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Deep dive: Visualizing wide-variation data

In March 2016, I guest-wrote Stephen Few's popular quarterly VIsual Business Intelligence Newsletter. The topic was one that came up often enough in training workshops to merit a longer write-up (i.e., a "deep dive"): how to visualize data sets that include a combination of very small values (i.e., close to zero) and very large values (i.e., far from zero). Creating a standard line or bar chart based on such data sets yields bars or lines that are too small to visually estimate or accurately compare with one another, so the newsletter suggests some creative solutions to address this common challenge.

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Do tooltips reduce the need for precision in graphs?

In many modern data visualization software applications, users can hover their cursor or finger over any bar, dot, box, line, etc. to see the exact, textual value(s) of each element. Since this allows users to see exact values whenever they need to know them, does this mean that graph designers no longer need to worry about how precisely values in their graphs can be estimated visually (i.e., without seeing a tooltip)?

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Avoiding quantitative scales that make graphs hard to read

Every so often, I come across a graph with a quantitative scale that's confusing or unnecessarily difficult to use when visually estimating values in the graph. In this post, I propose simple guidelines for data visualization software developers to follow to ensure that the default quantitative scales that their products generate make it easy for audiences to "eyeball" values in the graph easily and accurately.

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