Introduction

Maybe 20 years ago I could forgive laziness with data visualizations. After all, the “best” tools of the time weren’t nearly as powerful, affordable, and user-friendly as they are today. It’s a big emphasis in The Visual Organization and I feel as strongly about it as I did when I first penned the book.

In my analytics course CIS450, I devote an entire class to dataviz midway through the semester. In it, I cover the good, the bad, and the ugly. I conclude the class with an exercise. For about 20 minutes, I let the students find horrible examples of charts and graphs online. (To avoid duplication of efforts, I ban the first ten results of Google.)

Invariably, the students will find something along these lines:

Terrible, right? The figure above manifests only one thing: The U.S. spends far more on healthcare than just about any country. That’s it.

A Better Tool Yields Better Understanding

Consider how much easier it is to understand interactive dataviz tools such as the one below from The Chronicle.

https://www.youtube.com/watch?v=brXb90a0cic?rel=0

At a high level, a dataviz should convey, not confuse.

For perhaps thousands of schools, I can easily compare faculty salaries against a number of benchmarks. I can break down salaries by type of faculty and assess how schools value different positions. I could spend hours with this tool because it sates my curiosity and increases my understanding of how universities pay their faculty members.

Simon Says

With a nod to George Carlin, a dataviz should convey, not confuse. Rather than launch a static, incomprehensible tool, why not devote the time and resources to building one that accomplishes its goal?

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What say you?