declutter! (and question default settings)

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?

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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:

    1. 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.

    2. 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.

  6. 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.

  7. 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.

  8. 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)

Elizabeth Ricks is a Data Visualization Designer on the Storytelling with Data team. She has a passion for helping her audience understand the ’so-what?’ as concisely as possible. Connect with Elizabeth on LinkedIn or Twitter.

#SWDchallenge: artisanal data

If you collected the data, you cleaned the data, you made the choices, you know every reason behind every decision—you are perfectly positioned to analyze that dataset. May brings a guest challenge by Mike Cisneros: visualize data that you’ve curated yourself. Read the post for more details and an example.

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we are hiring!

I’ve had a number of career conversations lately. People reach out, wanting to pick my brain on various topics: they are interested in “getting into” data visualization or want to write a book. These chats tend to prompt some self-reflection. What advice would I have given myself ten years ago? What would I do differently today?

I did not set out with a master plan to build a business. That is what has happened, though, as I’ve followed my passion, focused on doing good work, capitalized on opportunities, and tried to share as much as I can with others. I articulated my mission when I first started blogging back in 2010: to rid the world of ineffective graphs. This was the point where a class I was teaching at Google sparked interest for a conference talk, which led to workshops, where I somehow worked my way into the dream job of helping organizations around the world achieve their goals by improving the way they communicate with graphs and presentations. (For more on the evolution of storytelling with data, check out Episode 3 of the SWD podcast, “How I’m Building This.”)

At one point, I had to make the decision: am I good with things as they are, or do I want to scale? In business school, when we talked about scaling, it was mainly conversations around diversifying or acquiring or generally doing things to make more money. But that’s never been my goal. The reason to scale—from my perspective in this space—is to reach more people. I am a strong believer that there is a huge amount of value out there to be obtained by work that is already being done that simply isn’t being communicated as well as it could be. And I think I—we—can change that.

In the past few years, I’ve scaled in a couple of ways. First, through a book. In 2015, storytelling with data: a data visualization guide for business professionals was published. In 2017, Elizabeth and I found each other; she shares my love of data driven communication and is doing an awesome job bringing related strategies to organizations large and small. Randy and Jody do a ton behind the scenes to ensure we are thinking big and the business runs smoothly and efficiently. There are also a number of other lovely people who help support us in different ways. We have officially become a team! Our overarching goal today remains the same—beyond banning bad graphs, we aim to help others be better data storytellers and drive real change through data. We are fortunate that this aligns with something that individuals and organizations recognize as important and want to develop.

Still, there is so much more to do. In many ways, we’ve only scratched the surface. As a result, we’ve been evaluating additional means to scale. In the past year, I have been working diligently on my second book (it’s nearly done!) as well as new projects for you (more on this soon!). But we’ve been running at capacity. We can reach more people if we search for others who share our passion. I am excited to announce that SWD is hiring new team members to help with this goal.

So, what would I do differently today? Looking back, there have been some difficult points (here’s one, and another, more recently this). But I am very happy with where things are at currently and extremely excited about the future. Though things have been challenging at times, those bumps have, in part, led us to where we are today and I consider myself very fortunate to be able to do what I do and call it work. Because of that, I wouldn’t change a thing. What advice would I give myself ten years ago: look forward to April 2019, when you’ll kick off the process to expand your team and spread the lessons for effective data storytelling to even more people!

Visit our careers page to learn more about our current openings.

#SWDchallenge: emulate!

In this challenge, we pull from the premise of Austin Kleon’s book “Steal Like an Artist: 10 Things No One Told You About Being Creative” and challenge the community to emulate a visual they like in an effort to further hone data visualization skills and style. You can participate through 4/10—see full post for details!

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