The Data Visualization Polarity

The power and capability of today’s visualization tools have exacerbated the timeless dataviz tension between pretty and informative

Mike Raper
Apr 8, 2020 · 5 min read

Dashboards. Seems everyone wants them, though often users don’t really know quite what they are, or how to use them. One of the issues I find in socializing business intelligence dashboards like those you can make with Tableau, Power BI or other tools of that type is that only about 20% of the employees are skilled at making them, and the other 80% consume them. So, it’s critical for the 20% to know what the 80% really need.

These tools are incredibly powerful, and with the right know-how, a skilled user can make some eye-popping visualizations that would make almost any data geek (and I’m proud to be a part of that particular tribe) go, “Whoa! That’s awesome!” as soon as they see them.

However …

Often, I find that after I get over my initial excitement and urge to recreate the visuals someone has made (often just to see if I can), the analytical side of my personality kicks in and starts looking for information. And more often than not, I don’t find it. I see something pretty, sure enough, but I have to work way, way too hard to actually get any particularly useful insights from it. And that’s the challenge I see with data dashboard tools; they make it even harder to balance attractive visuals with informative design.

I tend to lean towards making my dashboards user friendly. What I mean is, I’ll talk to my stakeholders and ask a few key questions:

1. What do you want to learn from this dashboard?

2. What information do you need to be able to find out?

3. What decisions will this help you make?

I then build the dashboard based on that discussion, and I always do so in the most accessible way I can. See, to me, there’s an over-riding priority with dashboards: they should be easily understood and provide the necessary information to the user quickly.

There’s a term called “affordance” in visual design. (If you want to learn more about this, I highly recommend the excellent book Storytelling With Data by Cole Nussbaumer-Knaflic.) It has to do with the purpose of a visual being intuitive and obvious in its makeup. What that allows a user to do is process the information more quickly … which is really what any good visual should do. The problem with this is that easy to understand is often not particularly flashy. I’m often asked why I use bar graphs in reports or visuals. My response is always the same; because they work. I can show a properly formatted bar graph that takes advantage of attributes like color, order, and an informative title and know that my audience will get it. That’s what I want in any visual I make, or any dashboard. I want my users to understand it and get to the information they need quickly, so decisions can be made. What I don’t want is for them to look at a dashboard and be puzzled by what they’re seeing. If you’re a dashboard designer, the thought of your users not understanding your visuals should scare the Hell out of you … because if they don’t, they might make incorrect decisions. And that would be very bad indeed.

So what’s to do? I focus on usability. I make my dashboards attractive through the use of color, spacing, arrangement, informative titles, and visual order. If my users think that my dashboard is pretty, that’s awesome! But what really makes my day is not when I’m told a dashboard is pretty. It’s when I’m told my dashboard is useful. That they used it to make an important decision, and that they understood the information it conveyed.

So as a super-talented Tableau whiz who knows how to make everything from viola charts to Sankey diagrams, how do you know what to do? How do you show off your skills but still provide your users with what they need?

I go by three simple rules. They work for me. I can’t swear they will for anyone else, but I’ve used these rules for a long time and they get me through.

Rule #1 — Remember who the dashboard is for. The dashboard you’re building, unless it’s purely for fun, isn’t for you. It’s not a chance to show off how smart you are with making nifty visuals. It’s a chance for you to show you can make something your users understand and can get insights from. Sure, it might mean you put in a ton of work to make something simple and no one knows it, because what you did is seamless and they have no idea how hard it was. But that’s the job. Be user-focused and leave your ego at the door.

Rule #2 — Keep it simple. Simple is better. If a bar graph or a line graph will accurately show your data in an easy to digest way, then do it. Sure, you could do something else that looks really cool … but if it takes more cognitive load for the user to understand it, why do it? Some of the most effective dashboards are also the simplest. A combination of bar graphs and tables can do wonders for many of your consumers, so why not give them that? Even if they think it looks easy, and it wasn’t, it doesn’t matter. Give them simple designs that provide insight quickly and accurately. That’s more important.

Rule #3 — When in doubt, ask. One of the big challenges in being a data geek is that what is often very obvious to us isn’t to our users. Executives may have taken an introductory stats class in college, but that doesn’t mean they understand how to interpret a box plot … even if it’s a great way to show your data. So if you’re doing something new, run it past someone you trust. Someone who isn’t a data scientist, who’s similar to the audience you’re working for. Get their input. It can be invaluable and save you a lot of time and energy if you know up front what your audience will and won’t accept.

I hope these help!

Nightingale

The Journal of the Data Visualization Society

Mike Raper

Written by

Data and visualization is a passion of mine. I’ve worked with both for over 20 years, and teach courses and workshops on data visualization and software.

Nightingale

The Journal of the Data Visualization Society

Mike Raper

Written by

Data and visualization is a passion of mine. I’ve worked with both for over 20 years, and teach courses and workshops on data visualization and software.

Nightingale

The Journal of the Data Visualization Society

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