what clutter can we eliminate?

Final days to register for the May 5th in-person *storytelling & presenting data* masterclass in Chicago! Join Cole and the SWD team for our last in-person public workshop of the year for a magical & energizing day of learning & practicing telling stories with data.

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Clutter is exhausting. We typically think of clutter as the accumulation of “stuff” in our homes—our closets, our attics, our garages, under sinks, the storage area that was once a dining room table, the alleged “to donate” pile, etc.  While most of it served a useful purpose at one time, those days are now a distant memory.

This has been top of mind lately, as I’ve been preparing to move to a new house. This has prompted some insightful conversations between my husband and me about why we’re compelled to hold onto things. For example, I found a box of outdated operating systems CD-ROMS, many of which I’d had since college. While I agonized over tossing something that I’d held onto for all this time, my husband simply asked, “Under what circumstances do you think you’ll want to reinstall Windows XP on any device you currently own?”  Point made. 

Why is clutter so hard for us to let go of? Perhaps because we think something has always been there, so it must belong there and we’re afraid of what might happen if we eliminate it. Or perhaps we don’t have a good framework for evaluating whether something is useful or not. 

This same concept applies to our graphs and business communications. We tend to blindly accept the default settings of our tools and very rarely consider if the included elements actually have a purpose. The “Windows XP” question, in this case, is: does this element add enough informative value to make up for its presence?” 

For example, consider the following chart and take note of your process as you intake the data.

If you’re like me, your eyes are probably drawn first to the three lines. Then I jumped down to the data table at the bottom, looking for a legend. Once I found it, my eyes were pulled to the right into the data table. Then I started going back and forth between the data table and the graph, trying to tie specific numbers to the individual data markers. It was only then that I realized I didn’t fully understand how to interpret the metric that was being graphed, so I went back to the top and looked to the chart title and subheader for context. That’s a lot of work! 

We’ve written previously about the measurable benefits of reducing clutter, so let’s turn now to the nitty-gritty of making this graph more effective. What elements are not essential? What could be stripped away allowing the data to stand out more? What other changes might you recommend? The following progression shows how I’d declutter:

STEP 1: remove heavy borders and gridlines

STEP 2: eliminate data markers

STEP 3: remove the redundant data table. For considerations on its usefulness, check out this practice exercise. I’ll also take the opportunity here to add explicit labels on the x and y axes.

STEP 4: improve the chart title. I’ll align it to start at the top of the vertical y-axis (rather than it hanging out in space in the center) and decrease the size of the font in the subheader. I’ll also not leave the audience questioning how to interpret the data but specifying that this is a cumulative measure—context that was not originally displayed.

STEP 5: move the legend closer to the data

STEP 6: tie the labels to the data using the same color

Check out the impact of all these changes:

There’s more we can do to improve this graph, including showing our audience where to look through sparing use of color, words and other design choices. But simply reducing the nonessential elements in our visuals means they are more likely to be used—unlike my CD-ROM of Windows XP, which is currently in my recycling can.

For more on decluttering, check out strip away the nonessential and some Excel tactical steps. Get hands-on practice with the community exercise declutter!

what your audience really wants

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Today’s post is a makeover-focused one, based on a graph I recently encountered. I’ll illustrate how to improve this less-than-stellar graph using the entire holistic SWD process taught in the best-selling book:

• understanding the context

• choosing an effective visual

• eliminating clutter

• focusing attention strategically

• telling a compelling data story

By working through real-world examples, such as the one we’ll talk about today, you can practice applying this process, and become more confident when incorporating similar changes and strategies into your own work.

Consider the following visual from a national retailer, showing warehouse performance. The details have been modified (to protect client confidentiality) but the spirit of the original remains the same.

When critiquing someone else’s work—particularly when it’s been removed from its original context as it is here—it’s always helpful to start by assessing what was done well.

• Visual choice: I’m familiar with a bar chart, including the specific variation shown here, the 100% stacked bar, so I’m not faced with the hurdle of trying to decide how to decipher an uncommon visual type. With some modifications (which we’ll get into later), this visual type should work well to explain the main message of this communication: the breakdown of performance (accurate vs inaccurate rates) across the warehouses of interest.

• Words: I also appreciate the helpful context included at the top of the visual stating the data source, as of date, why this subset of data is shown, and the overall picture of 85% accuracy. 

That said, there are many ways we can improve upon this visual to tell a concise and action-inspired data story. I’ll outline the changes I’d recommend and my rationale for each. 

the quick hits: declutter, focus and words

A few simple things go a long way without a ton of effort and time. The biggest bangs for your data storytelling buck are typically eliminating clutter, using color intentionally, and clarifying the intended takeaway with words.

I can improve upon the original by making the following changes:

1. Use a different color palette. There is a positive/negative connotation to this data, so I’ll elect to use blue to signal the positive (accurate) and orange, its complement on the color wheel, to accentuate the negative (errors).

2. Declutter. The original graph has many elements that make it appear more complicated to process than it really is, like gridlines, harsh bolding, rotated x-axis labels, and an out-of-place legend. I’ll declutter, leaving only those elements that add enough value to make up for their presence.

3. Use words more effectively. If I want my audience to understand that this data is conveying a success story, I shouldn’t assume that they will come to that conclusion on their own. I’ll not only state it in words, but tie the words to the data they describe using similarity of color. 

You can see all of these changes applied in the visual below.

Check out the difference already!

While this is certainly an improvement over the original, we can continue to iterate and develop an even more effective final product. 

rotate the graph horizontally

As already mentioned, I like the choice of 100% stacked bar to show the relative percentages. I have two baselines for comparison: with one at the 100% value along the top and one at the 0% value along the bottom, I can be thoughtful about which series to place along these baselines to enable an easier visual comparison. 

One change I’d recommend is to rotate to a horizontal orientation, as shown below. I’ve intentionally removed color here to keep the emphasis on the choice of graph. In the case where we had longer category names, this orientation would allow for more space to spell out the entire name without rotating labels. You can read more about horizontal vs vertical bars in this prior post.

consider how the graph will be consumed 

When you’re with your audience—whether virtually or in-person—your graphs and presentation slides can include less context and detail physically written down or shown, because you are there to fill in any blanks. The reverse is true for written communications, where people have a higher tolerance (and perhaps demand) for more detail. This is why live presentations are better suited for sparse slides than written communications.

Check out how the same presentation might look different if we were delivering it live with a series of slides, versus sending around a single summarized view for people to read on their own:

Now let’s pull it all together.

don’t just show data, tell a story!

Watch my live presentation—and more discussion on how to build your data storytelling muscle—in the replay from a recent live chat on our YouTube channel:

An effective data story doesn’t just happen. Getting comfortable applying all the components considering the context, choosing a visual type, intentional design and creating a story takes time and practice. But it is worth the effort, because any communication is likely to be more effective when we move beyond simply showing data, and instead take intentional steps to inspire and drive action.

This is what your audience wants from you.

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If interested, you can download the data and create your own makeover version in the exercise bring the data to life. For more examples of visual transformations, check out the before-and-afters in our makeover gallery.

how do i avoid reworking my entire presentation if i have to share slides?

Through virtual and in-person workshops around the globe, we have taught tens of thousands of people how to communicate effectively with data. This series captures some of the noteworthy questions we hear during those sessions—and our answers.

Often a graph that makes sense during a live presentation loses meaning when distributed as a PowerPoint later. How can we retain context when transitioning between audiences without having to rework the entire presentation?

This is a relevant question in the time constraints we constantly are under in a real organization. We want to avoid using so-called slideuments in live presentations because it is difficult to both listen to a presenter and read text-heavy slides at the same time.  

But if you’re crunched for time, there are a few time-saving strategies that will help you avoid having to create a completely separate deliverable.

Let’s illustrate with a business example. Take a few minutes to watch Cole deliver a live data story (starting at 6:08): 

This presentation was notable because:

•  The slides were well-designed, with effective graph choices, minimal clutter, and smart use of pre-attentive attributes to focus attention.

• The visuals were paired with a strong narrative. 

If you flip through the same slides on your own—would the story still be as clear? Likely not.

Back to the original question: if we must send out the entire slide deck, then let’s look at two ways we could retain Cole’s narrative without having to rework the entire presentation.

1. Write active slide titles. When you’re not presenting live, strong takeaway titles on your slides make it easy for the reader to understand the main point. The title bar is usually the first place your audience looks when consuming a slide deck on their own. Order your slides logically so that the reader can read just consecutive slide titles to get the overarching story you want to communicate. This is called horizontal logic.

2. Add a fully annotated summary slide at the beginning of the deck. In this Craveberry video, Cole created this single summarized slide for the product that gets sent around after the meeting, as shown below. The audience gets the salient information without having to hear a live presenter or having to flip through the deck, because all of the information on the slide is self-reinforcing (an example of vertical logic). Adding it at the beginning means the audience could obtain the relevant details without having to flip through all the slides.

Here’s another example of adapting a live progression for written consumption.  

Employing either of these two approaches goes a long way in effective data storytelling because they allow us to tailor our mode of delivery to how our audiences are consuming it. 

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Build your data storytelling skills in the community with these related exercises:

• Optimize your output 

• How would you tell the Craveberry taste test story?

how many words should I put on my slides?

As the workplace shifts to more remote communication, a question we’ve been receiving frequently in our virtual workshops is “How many words should I put on my slides?”

The answer? It depends on how your audience is consuming the information. If you’re sharing your data live—whether over video conference or in person—that typically calls for minimal text. You can provide the words verbally, which enables your audience to focus on what you’re saying rather than trying to read your slides. If you’re not presenting live, however, you’ll need more words on the page because your audience is left to understand the data without you there to help them interpret it. 

In a perfect world, this would call for two distinct deliverables: a presentation with sparse slides and a written report containing more detailed content. In reality, this rarely happens. Because of time constraints, we often create a “slideument”—it’s part presentation, part written report, and not exactly meeting the needs of either scenario. This term was originally coined by authors Nancy Duarte (Slide:ology) and Garr Reynolds (Presentation Zen), who have written about this unfortunately all-too-common communication in the workplace. Below are some example slideuments. 

Source: Google image search for “slideument”

In this post, I’ll discuss some tips for avoiding slideument, with an example excerpted from storytelling with data: Let’s Practice!  

Imagine you work as an analyst for a large healthcare system with medical centers in several states. Your analysis has uncovered a trend—a recent rise in patients’ diabetes rates and a forecast showing a continued trajectory—which you believe warrants a closer look to assess whether additional resources are needed. You’ve been directed to prepare one slide on your findings that will be passed along to administrators, first in a live meeting, and then emailed around for those who weren’t able to attend. The data used in your analysis is visualized in the graph below.

Assume we want to focus our audience’s attention to the projected rise in diabetes between 2020-2023 and prompt discussion on hiring additional staff to remain accredited within national standards. How might we design a communication that meets both the needs of a live meeting and an asynchronous reader? One approach: first design a single summary slide for those who might miss the meeting. Then adapt it—using animation—so that in the live setting, the presenter can highlight just a few components at a time. The beauty of this method is that it allows the speaker to lead the audience through a data-driven story by controlling the amount of detail shown at any given time but ends in a fully annotated view that can be distributed. 

My single-slide summary might look like the following visual.

For the live meeting, I’ll transform it. While the amount of words on my summary slide might be fine for an audience to consume on their own, they could distract on the screen. When we attempt to use visuals like this in our presentations, many people will tune us out as they read. We’ll explore this topic further during our upcoming live event mode & method (open to premium subscribers).

To keep my audience’s attention in a live setting, I’ll break this slide into several components and talk to one piece at a time. I used the SWD community exercise storyboard your project to construct the following narrative flow. 

This approach does take time, but it’s time well invested in ensuring that the hard work I do behind the scenes to find the interesting things doesn’t get lost when I share my findings. Premium subscribers can watch how I narrate these slides for the live meeting in the learning video, differentiate between live and standalone stories. 

The next time you’re struggling with how many words to put on your slides, pause and reflect on how you’ll be delivering those slides and design accordingly. Be sure to check out my follow-up post: how white text boxes in PowerPoint can improve your virtual presentations.

tapestry conference

At the end of November, I had the pleasure of attending the Tapestry Conference in Miami. I don’t attend a ton of conferences and this is actually the only one that exists where I’ve (two years in row!) been present for every single session (both physically and consciously) and found something useful or inspiring in each one. If you’re reading these words with slight envy for not having been there—I can’t recreate the great break-time chit chat with attendees, but I can share the presentations (huge thanks to organizers for making these available): here are the videos.

In particular, I’d recommend the keynotes. Mona Chalabi opened the conference with an entertaining session discussing a number of her hand-drawn graphs (a quick scroll through her Instagram will give you a sense of your work if you aren’t familiar; unfortunately her talk isn’t being shared). She described wanting to feel something about the data and marrying the subject and the visualization so that if you see the visual, even without labels someone can get some sense of what it is about. She also worked in good reminders on significant digits (too many conveys false sense of precision), designing with visual impairments in mind (using alt text or sound, like in this work), and how important the simple question “do you get it?” posed to people unfamiliar with your topic can help point out issues or help you to identify improvements.

Matt Kay’s keynote on Uncertainty (“A Biased Tour of the Uncertainty Visualization Zoo”) was fantastic—he made the point that it isn’t necessarily true that people aren’t good at understanding uncertainty (a claim often made) and that there are intuitive ways to communicate uncertainty that we should be using. I like the onus this puts on the designer of the information. Matt illustrated several specific methods—icon arrays, quantile dot plots, and animating—for better communicating uncertainty. I also learned a new term: subitizing, which describes how we can see a small number of something, for example three circles, and we recognize (without counting) that there are three. This is both useful to be aware of when designing graphs and also simply a word that I will enjoy adding to my vocabulary.

Elijah Meeks delivered the closing keynote on the “Third Wave of Data Visualization.” He describes wave one as Tufte-inspired with the goal of clarity and the second wave of systems following Wilkinson’s The Grammar of Graphics, leading into the third wave of today. Rather than tell you more about it, I encourage you to listen to Elijah tell you about it directly (plus more!) in Episode 12 of the storytelling with data podcast.

In addition to the keynotes, there were eight short stories (roughly 15 minutes each, standout ones for me were Jonni Walker’s and Alex Wein’s) and a number of short talks (about 5 minutes each). You can hear Jon Schwabish and me chat about more of the sessions in our Tapestry roundup. I highly recommend watching the videos of the Tapestry presentations.

Big thanks to organizers, speakers, and attendees for combining to make this an awesome event (and extra thanks to the organizers for recording and making the content widely available!).