Improving the impact of your insights with visualisation
Browsing online recently, I’ve come across a number of useful resources to improve your visualisations. We’ve published before on the benefit of Edward Tufte’s data visualisation rules. As well as guidelines, it’s useful to have the right tools for the job.
Once your expectations rise above the constraints of MS Office graphing options, it can be daunting to find a tool to help your analysts.
Choosing which chart to use
Ideally, you want your analysts to be free to creatively recognise the most appropriate visualisation to convey the key learning and then select from a toolbox of tools to find one that makes that easy. Helpfully, Andy Kirk on his Visualising Data blog has curated just such a collection.
This is both a treasure chest of potential tools to explore and nicely designed presentation of options to make the exploration itself a visual experience:
Declutter effectively using principles of simplicity
To complement my previous recommendation of Edward Tufte’s data visualisation principles, Ben Jones (on his Data Remixed blog) does a great job of drawing out lessons from a classic on writing. In his blog post, marking the passing of William Zinsser, he usefully summarises the main points from Zinsser’s classic “On Writing Well”.
Ben points out how these principles of simplicity, clutter, audience etc directly apply to data visualisation as well. This is well worth reading and applying to your practice (as writer & visualizer):
Learn from worked example makeovers
I’m also becoming a fan of Kaiser Fung’s Junk Charts blog, where he shares good and bad examples of data visualisation. Most useful he periodically critiques data visualisations in publications, to show how they could be improved. Here’s a nice simple example that culminates in a reminder as to the power of (those often under-appreciated) bar charts:
Some time ago, this chart showed up in a NYT Magazine (it’s about sex): In this composition, the visual element (the circles) has no utility. A self-sufficiency test makes this point clear. All the data (four numbers) are printed on…
Understand the effectiveness of the chart type you choose
Which leads us on nicely to my final item in this collection. Visual.ly often have interesting content on their blog and this post caught my eye. Drew Skau points out some of the risks of infographics and some undergraduate research on the relative effectiveness of different styles. The embedded SlideShare results are interesting and reinforce the benefits of the classic bar chart design:
The world of infographics has produced a race between designers. The Internet is flooded with hundreds of infographics every day, so those graphics with good design and novelty features have a big leg up against competition in the race for eyeballs. Often this prioritizes design above other traditional goals of data visualization, and charts get tweaked and embellished.
I hope those resources are useful for you. How are you improving the quality of your analysts’ data visualisations?