I’m back from a brief blogging break! I’ve spent the past few months speaking and designing – mainly designing. The reports and slideshows I’ve consulted on are starting to be published and it’s been so rewarding to watch organizations’ drafts transform into well-articulated masterpieces.

 

A few weeks ago I was invited to speak at Chicago’s Harris Theater – definitely one of the coolest places I’ve ever explored in Chicago!

 

The attendees specialized in all different aspects of the performing arts – writing grants, collecting data to demonstrate how their organization is reaching outcomes, monitoring their group’s performance, and so on.

During the chart-choosing segment of the workshop, we thought about different ways of displaying fictional ticket sales data. In this example, I’m pretending that one of the performing arts groups is tracking how many tickets they’ve sold online, over the phone, and at their in-person box office for an upcoming show:

 

I write about chart-choosing and sketching a lot and wanted to share these ideas with you, too.

 

Sketching goes like this.

You grab your already-tallied data table, like the one shown above. You’ve already done a little number-crunching, simple stuff like sums and averages.

Then, you set your cell phone’s timer for 15 minutes.

And you step away from your computer.

Your job is to draw all the different versions of this dataset before you sit down to your computer. Draw, draw, draw. Aim for 5, 10, or 15 different types of graphs. The more you learn about data visualization, the more versions you’ll be able to draw. What would your dataset look like as a bar chart? As a stacked bar chart? A line graph? A pie chart? A tree map? I advise workshop participants to even draw the bad graphs, the really bad stuff, like 3D exploding pie charts, if it’s on their mind and taking up precious mental space. Get those thoughts out of your mind and onto the paper. Put a big X through the awful graphs if you need to.

Once your rough sketching is complete, take your drafts down the hall to your coworker. Think aloud. Talk about how this graph emphasizes this one thing, and that graph highlights that other thing. What’s the message your team is going for? Which graph matches that message the closest? Sometimes you know your message ahead of time; other times, you fine-tune your message during this sketching process.

And finally, I give you permission to return to the computer and make the most promising graph in your software program of choice. If you design graphs on your computer before sketching on paper, I guarantee that you’ll overlook a few options. You’ll be boxed-in by the software program’s limited chart gallery. Explore everything on paper first and figure out the software later.

 

Here’s what my sketches looked like. I’m starting with the most basic sketch: a regular ol’ line graph that just focuses on online ticket sales. When I draw, I often go through my data table methodically, often starting with just the first row of data — online sales — and peeking at the shape of those numbers. And what did I see? A tall, flat line.

 

Once I’ve got a handle on the first row in the table, I might add the second row, the third row, and so on, so that my brain can compare the categories to each other one at a time. Here’s another regular ol’ line graph that shows all three ticket sales types together. More contextual data = more background information available for decision-making thought processes.

 

Or, how about a slope graph for those audiences that don’t need to see all the peaks and valleys? Some people just want to see the big-picture, starting-and-ending points. The higher-ups, like donors and some supervisors, might fall into this category. I’m pretending that a supervisor knocked on my door and said, Hey, how are we doing this year? And what about five years ago, when we launched that new sales strategy? Slope graphs cut to the chase and make before/after comparisons easy.

 

If we’re aiming for big-picture findings, how about a bar chart that only displays the five-year sums? We could ignore the year-by-year numbers and only display the total sales numbers.

 

Returning to the multi-year version again… This fictional dataset is semi-spaghetti, meaning that the three lines started to intersect a little when they were all displayed in the same graph. Not so crowded that the criss-crossing gets in the way of interpreting the data, but, borderline. If your real dataset gets too zig-zaggy and criss-crossy, try breaking the single graph into three separate graphs with a small multiples layout.

Small multiples graphs let my brain interpret the graph piecemeal. I can check out the online sales and think about the implications of that pattern. Then, I shift my gaze a couple inches to the right and check out the phone sales. Finally, I shift my gaze to the right a bit more and examine the in-person box office sales. The layout guides my attention through the graph slowly, rather than overwhelming me by throwing all three lines on the page at once. I see the online, phone, and in-person patterns both individually and as a whole.

 

 

At this point in the sketching process, I began daydreaming about having a more interesting dataset and wishing that I would’ve included goal sales numbers alongside those actual ticket sales numbers. A target line might be dotted and/or in a lighter color to add much-needed context.

 

Or, maybe the viewers need to see part-to-whole patterns in a stacked bar chart. I transformed my table’s counts into percentages to see what proportion of tickets were sold online, over the phone, or in-person. The five-year total would be nudged to the right a bit.

 

Finally, a sketch that’ll make the purists cringe, a pie chart. Don’t worry, I teach my workshop participants about alternatives to pie charts. I might use a pie chart when I want my fictional viewers to see the part-to-whole comparisons. I’d use a darker color to draw their eyes towards one slice and add a sentence or two beside the chart to make sure their attention stays focused on that same slice.

 

One dataset, many correct options.

 

Did you come up with additional sketches?