In this week’s In-Ear Insights, Katie and Chris discuss how marketers and business folks should approach data visualization, reports, and dashboards. What are some of the best – and worse – practices in data viz? You’ll also learn the secret of great data visualization: user stories. Tune in to find out how!

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In this week’s in your insights, we’re talking data visualization, sort of the basics of what you need to know and how to think about data visualizations.

Okay? When you think about data visualization, particularly how to make choices about how you’re visualizing data, how do you think about it?

I usually think about the lowest common denominator.

And what I mean by that is the person who has the least amount of context with the report that I’m about to give them or with the data that I’m about to give them.

And can they understand the story within, you know, five or 10 seconds? Or does it need more explanation? And so when I’m starting to put together any kind of visualization, that’s the thing that I have, you know, running through my head of like, is this understandable? You know, if I put together a pie chart, is some going to understand the data point that I’m trying to convey? Or would it be more understandable as a bar chart, and obviously, there’s different applications for those different visuals.

But really, the goal is to make sure that you can communicate the point very quickly with the visualization.

And so that’s how I approach it.

I always think, can I understand this? Or can someone who isn’t me understand this within 10 seconds? If not, I keep going back and keep going back?

Gotcha.

I really like so Dr. Andrew Abella came up with this almost a flowchart a number of years ago, I think, like 2009, was when he made this chart.

And it’s a really, really handy flow chart.

Let’s go ahead and bring it up here.

Essentially, you start in the middle, and you asked, What would you like to show this four types of visualizations, right, this comparison, where you’re comparing stuff like one thing to another thing? There’s distribution, which is looking at sort of the statistical layout of your data, there’s composition, which is a fancy way of saying what’s in the box, right? And then there’s a relationship? How do variables relate to each other, which is different than comparison? And based on that you then kind of work through and say,