- Is it a pie chart? If yes, read this post. If no, move to the next bullet.
- Is there a more straightforward way to present the data? I often find myself doing an "is-this-better?" comparison, where I'll have my working version of the graph, then try graphing it differently and do a side by side comparison to see which is the easiest to interpret. Going through a few rounds of this can help ensure you've got a chart that someone else will be able to read.
- Don't leave the details in question: make your chart legible by giving it a title and labeling all axes.
2. Highlight the important stuff
- Use preattentive attributes (e.g. color, size) to create a visual hierarchy of information and draw your audience's eye to where they should focus their attention. Use color sparingly and strategically.
- Here's a fun test: look away from your visual and then back to it. Where is your eye drawn? This is likely where your audience's eye will be drawn as well, so if it isn't in the right place, revisit how you're using your preattentive attributes (especially color).
3. Get rid of the clutter
- here is a post on this).
- Push things like footnotes, data sources, as of dates to the background by making them grey, small, and positioned in lower attention areas, like the bottom of the page; this way they are there for reference but don't detract from the key parts of your visual.
4. Assess the overall visual
- Does the data viz facilitate the story I want to tell, or the data discovery I want my audience to make? Here is an example where this is done well.
- A good test of this is to hand your visual to a friend or colleague who is unfamiliar with it. Give them 10-20 seconds (and no context) and have them tell you what they see. If it isn't what you're hoping, it's time to revisit your design.
The folks at Tableau are spot on. This takes time and patience. After doing all of this work (irrespective of the specific graphing application), you should think your data visualization is just about the best thing on the planet. Or be so sick of it you never want to see it again. :-)
Here are some links to previous posts with before-and-afters that walk through different parts of the above process. I totally found myself crushing on each of these after spending so much time with each:
- CEP chart redesign
- GMN conference makeover ("generational differences are evident")
- Target makeover ("from points to poignant")
- Target makeover ("declutter your graphics")
I think the crush is good evidence that sufficient time has been spent on a very important step of the analytical process: communicating your findings visually to others.