This week, I have the pleasure of presenting to three audiences on the topic of storytelling with data. Thank you to those who attended (and those who soon will) for your interest in the topic, example visual submissions, attention, and thoughtful questions and comments.

After today's session, one participant made an insightful observation: the process I teach for making effective data visuals is very similar to the editing process for the written word. When I paused to think about it, the parallels are striking. Here is a sample:

Make your point crystal clear. When possible, state it up front.
Remove things that are ancillary or detract/distract from the core message.
If you can say the same thing with fewer words (or visuals!), do so.

The list of similarities goes on. With a day job as an editor, in addition to this observation, the participant noted a challenge he often faces. While with the written word, there are clear rules guiding and providing rationale for critique - it's difficult for someone to argue with feedback when a word is misspelled or grammar is incorrect - we don't seem to have agreed upon rules or language to use when it comes to visuals, which can make convincing someone to take editorial feedback in this area difficult.

The specific question posed was: what language can I use to justify editorial feedback on data visualizations?

I think this is one of those areas where there is no single right answer. I can talk for hours (and have a couple times in the past two days) about why the things I teach are important and show empirical evidence on the impact of well-designed visuals time and time again through examples. But is there an easy, quick, universally accepted way to convince someone of the 'right' way to visualize information? I think it's in part because there isn't a single right way - there aren't so many rules when it comes to data viz, and even in cases where there are accepted best practices, they aren't widely taught - that this is a challenging space.

Though many of the ideas are, the language itself that experts use in this space isn't pervasive or consistent (perhaps because data visualization draws from a number of different fields, each with their own language: statistics, design, computer programming, journalism). Nancy Duarte discusses maximizing the signal to noise ratio. Edward Tufte focuses on ridding visuals of chart junk. I personally don't use consistent language: in one sentence I'll talk about identifying and eliminating clutter, while in the next I'll discuss the "crud" 3D introduces or being aware of adding items to your visual that are taking up space or attention but aren't adding informative value. I found that my answer to the specific question posed focused on reducing cognitive load, which may be fine for helping to convince some audiences, but fall flat with others.

Because I feel I'm still thinking my way through a good answer to this question - how to convince someone quickly of the value of drawing attention to the important parts of your visual and stripping out the things that don't need to be there - I will pose the question here in hopes of gaining some wisdom from the crowd:

What language do you use to convince yourself and others of the value of the visual editing process when it comes to data visualization? Leave a comment with your thoughts!