On Onions and Dataviz Training

This opinion is probably going to be unpopular, but here we go:

Data visualization, or “dataviz,” receives too much emphasis in research communication training and professional development.

“Hypocrite!,” you yell. “Wasn’t the first research communication training you ever attended about dataviz?!” Yes.

“Don’t you yourself offer dataviz trainings?!” Also, yes.

“And don’t you blog about dataviz all the time?!” Guilty, guilty, guilty, etc.

“So, what’s your problem?!”

Problems (plural), you mean. I can think of at least three.

1. A stand-alone data visual is rarely the best way to communicate research to an audience.*

Typically, audiences need verbiage (written or spoken) to help them string visuals together into a meaningful takeaway.

I’ve now been in business for more than two years. I’ve developed and reviewed reports, infographics, graphical abstracts, journal manuscripts, posters, 1-pagers, and logos. I’ve developed data visuals as part of some of those products, but how many clients asked me to do only data visualization? Zero.

To use a culinary analogy, dataviz is usually an ingredient and communication products are the dish. Not many people go to restaurants and just order onions. And, if they order the onions, that’s probably not the main reason they came to your restaurant.**

2. Poor data visuals is NEVER the main issue with a research communication product.

My most common criticisms of reports are that they’re unfocused, too long, overly technical, and text-heavy.

My most common criticisms of presentations are that they’re unfocused, overly technical, and accompanying slides have too much content.

If your products have these issues, no data visual, no matter how wonderful, is going to save it.

There are training topics that would have far greater impact on the overall quality of research communication products. These include audience analysis, plain-language writing, public speaking, storytelling, and graphic design.

In other words…it doesn’t matter how well you cooked your onions. If the soup, casserole, or sandwich they go into is terrible, you’re getting called out on Yelp.

3. It’s not that hard to create the most common and useful data visuals.

In general, when we’re aiming for understanding (as opposed to art), we should use simple data visuals…think line graphs, bar charts, tables, exemplar quotations. It’s pretty easy to create each of these in word processing, spreadsheet, or slideshow software, and the default settings usually aren’t terrible.

If the only thing you knew about data visualization was which buttons to push to generate a graph in Excel, you’d be something like 75% of the way to a great graph. If you had training in storytelling (i.e., how to hone your message) and graphic design, without any training specific to data visualization, you could probably get to at least 90%. That’s an A-minus on your data visuals AND you could apply your storytelling and graphic design training in all sorts of other ways, like report layout, presentation planning, and slide design.

Going back to the kitchen one last time…you probably don’t need to take a dedicated onion class, especially at the beginning of your culinary training; it’d be more useful to learn about knife skills and sautéing.

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*Data art is one exception.

**Critical exception.