crushing on your data viz

Oh, what a month. Those who know me understand my posting hiatus...my mind and energy have been elsewhere. But I'm starting to refocus it back on normal life-stuff, for example getting caught up on some data viz reading. 

I was perusing the Tableau Visual Guidebook and came across a snippet I appreciate; it followed the descriptions of all of the different things you can do to format your data viz using Tableau:  

Do you like your viz? After all of this arduous, tedious and difficult tweaking, you better have a little crush on your viz. If not, it may be time to break up and start over.

I like this idea of crushing on one's data viz. I find myself saying this again and again, but plotting data in a graphing program should be the first step in data visualization, not the last. After doing that, here are the typical steps I find myself going through and questions I routinely ask to get to the final ready-for-consumption visual: 

1. Assess the chart type

• 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

• Cut anything superfluous: every bit of reduction in noise makes the signal of your data stand out more. For example, assess whether you need gridlines (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.