Decluttering is having a major moment.
Fans of Netflix’s Tidying Up with Marie Kondo have been inspired by guru Kondo’s Japanese-based method of clearing out the clutter in their homes. The benefits are huge. Devotees report living more peacefully and co-existing better with their partners. The key element? Actively working to identify and eliminate anything that doesn’t “spark joy.”
We can apply this same thought process to our data visualizations.
When it comes to clutter in our visuals, we challenge you to regularly examine what specific elements aren’t adding information. What’s making it harder for our audience to get at the data? When we identify and remove clutter from our visuals, the data stands out more.
We’ve discussed this topic frequently. In this video, Cole provides five tips for how to avoid clutter in visuals; SWD book and workshops each have an entire section focused on decluttering. We don’t intend to create cluttered visuals—rather they often materialize when we don’t take a step back and question our tools’ default settings. Today’s post illustrates one such example and the benefit we can reap from decluttering.
I recently encountered a visualization similar to the following graph. This shows the percentage of babies born within a 24-hour period, broken down by day of the week (having welcomed a baby several months ago, all things maternity still linger in my various news feeds). I recognize this graph: it’s what happens when I put data into Excel and create a stacked bar chart with default settings.
This caught my eye not because of the topic but because of how much time it took me to figure out what information it was trying to convey. What should I do with this? There’s a lot competing for my attention in this chart and distracting me from the data.
Spend a moment examining this graph and take note of which specific elements are challenging. Make a list: what might we eliminate or change to reduce cognitive burden?
I came up with eight specific design changes I would make. How does my list compare with yours?
Remove the chart border as it isn’t adding informative value. Often, we use a border to differentiate parts of our slide/visual. In most cases, we can better set them apart with white space.
Delete the gridlines. Will the audience be physically dragging their fingers across the y-axis to identify an exact value? If that level of specificity is important, label the data point(s) directly.
Be sparing in use of data labels. Use them in cases where the exact values are important to the audience. Otherwise, remove and use the axis instead.
Thicken the bars. While there are no hard and fast rules, the bars should be wider than the white space between them so we can more easily compare. In this case, the superfluous white space can be reduced.
Title the axes appropriately. Exceptions are rare for omitting an axis or chart title. Don’t make the audience do work to figure out what they’re looking at, and instead make a habit of titling appropriately to enable the audience’s understanding before they get to the data. Let’s take two related steps here:
Use a more descriptive y-axis title: Instead of the vague %, we can eliminate the guesswork and be more specific: % of total births. While we’re at it, let’s drop the unnecessary trailing zeroes from our y-axis labels.
Clean up x-axis: Diagonally rotated text is slower to read. We can abbreviate the days of the week so they render horizontally. A super-category (such as Weekday or Weekend) could also simplify the process of taking in the information.
Move the legend directly next to the data it describes. This alleviates the work of referring back and forth between the legend and the data.
Use color sparingly. There are so many colors in this graph that our attention is scattered and it’s hard to focus on any one thing. Depending on what we want our audience to take from the graph, we can use color more effectively to focus attention on those pieces only.
Add a takeaway title. Don’t assume that two different people looking at this same graph will walk away with the same conclusion. If there is a conclusion the audience should reach, we should state it in words with an effective takeaway title.
Each step seems relatively minor on its own, but check out the impact when I apply all eight steps simultaneously:
Now we can more easily see that babies delivered on a weekend are more likely to arrive during the early hours of the day (midnight - 6am), compared to weekday deliveries. Related note: this dataset didn’t include the absolute number of babies born each day. Ideally, we’d want that information for context, but for the purposes of this illustrative example, we’ll assume the numbers are large enough to accurately compare across days of the week.
By reducing clutter, the audience can use their precious brainpower to decide what potential actions might be warranted, rather than trying to figure out how to read the graph. Taking time to modify the default settings means we can focus on the data and the message.
In my case, I might have wanted to get some extra rest on the weekends as my due date approached! As it turned out, baby Henry arrived safe and sound among the 17% of Thursday babies born in the 12am-5:59am window.
UPDATE: You can download the file for a further look at how I tackled this in Excel.
For more on the power of decluttering, check out these prior posts:
Declutter this graph: an example of eliminating unnecessary elements
Minor changes, major impact
How to declutter in Excel (with tactical step-by-steps)