Selecting the best visual fit for your data

Finding the best fit between the type of chart or graph you use and the data you have is the biggest challenge for data visualizers. Once you have that, refining the aesthetics around the graph is the easy part. Even the big names in periodicals who prominently feature visualizations get it wrong sometimes, making choices that analysts look at and say, “Wait, that just doesn’t quite make sense…”

A fellow M&E/comms person, currently working in Namibia, tipped me off to Junk Charts, a really interesting & thought provoking blog that takes visualizations from The Guardian, The Atlantic, and other visual-heavy sources and reimagines them to better communicate information.


The emphasis is not on aesthetics (most of the re-visualized examples are pretty basic and could use some sprucing up before final publication), but does provide some great logic around how to better communicate the information. The author’s “trifecta checklist” for data viz is a great guide to anyone thinking through a visual:
1) What is the practical question?
2) What does the data say?
3) What does the chart say?

The answers to questions 2 and 3 should be aligned and answer the first question, if the graph or chart is appropriately designed. I think it’s an interesting, straightforward approach thinking through how to create a chart or graph of various pieces of information.

There are also some great resources and software programs that can help guide visualizers to the best fit for their data. Tableau has a built in “show me” feature that will suggest best fit options for your data, and more options than ever before in their Tableau 8.0 release. The team at Freakalytics (Tableau enthusiasts with some great training materials on using the software) also has a great set of rapid dashboard reference cards that can be useful when you’re trying to tease out the best visual for a set of indicators/data.  You can buy the cards over on Amazon, and they’re well worth the $14 price tag, particularly if you’re working on visuals in Tableau.

Ultimately, I think that the “Junk Charts trifecta” highlights the importance of having a skilled analyst visualizing data, rather than relying on a computer program or other autogenerated function though. “Show me”-like features are great for pointing analysts in the right direction, but nothing can replace a thinking brain working behind the machine to make sure the visual is the right fit for the data.

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