To extract the most value from visual analytics, your solution must be easy to use. It must make use of real-time data. It must allow you to easily create and share data visualizations.
And perhaps most importantly, those data visualizations must be carefully designed to clearly emphasize the most important data points. After all, a data visualization won’t be effective if it’s unclear or confusing to your audience
Here, in her own words, Knaflic explains how to approach data visualization as a design challenge.
Designing effective data visualizations comes down to a lot of thinking about and paying attention to detail—making sure elements are aligned, that there are clear horizontal and vertical lines, and that you’re leveraging white space and not overly cluttering your visuals and making them harder than they need to be for your audience to digest.
It’s about using color sparingly and strategically, rather than trying to make a colorful rainbow with your visualizations. It’s really about trying to direct your audience’s eyes to where you want them to go.
When it comes to visualizing our data and the things that we have at our disposal to be able to make that easy for audience, all of those cues are visual. You want to think about how you’re using things like color and size to both emphasize some things on the page or on the screen and also de-emphasize others. Just make it something that feels approachable for your audience.
When asked if de-cluttering a data visualization can actually make it too simple, Knaflic had this to say:
When it comes to de-cluttering, it’s not about oversimplifying. It’s more about not making things more complicated than they need to be. Again, that’s a place where getting a fresh perspective and getting input from somebody else can be really helpful.
Sometimes we don’t realize when we’re overcomplicating something, but in talking about it out loud to somebody else or showing it to somebody else, you can see those micro-movements in somebody’s face before they guard themselves. If there’s any look of confusion or frustration or dismay, these are things that we want to avoid in our audiences, so try to pick up on those cues. They might be signaling that the graph isn’t working in the way that you need it to, at which point you can adjust.
In Knaflic’s view, everybody has what she calls a “design eye” and a “design gut.”
People just have to learn how to trust themselves,” she says. “In [Storytelling with Data], we try to get people comfortable with that design sense, and we give them specific things to try out if something isn’t feeling right.
Knaflic has this advice for dealing with the challenges of designing an effective data visualization:
Becoming adept at visualization does take practice. Like any skill, it’s about doing it over and over again and refining as you go and learning from other people out there who are doing it. You can learn from things that you see that are done well, that you can emulate, as well as things that you see where you think, “Oh, goodness, that doesn’t work.” Stay away from those things.
Practice is definitely the way to get good at data visualization. Take some data and play with it and look at it in different ways. Put it into different types of graphs and see what it looks like with this color and that color. You can develop a personal data visualization style along the way.
Read the other posts in this series:
- Data Visualization: Essential Info from an Industry Thought Leader
- How to Tell Stories with Data: An Industry Thought Leader’s Perspective
Next in the series: Cole Nussbaumer Knaflic discusses why understanding your audience is a crucial component of creating effective data visualizations.