Have you ever looked at a graph and thought, I'm not sure what I'm meant to get out of this?

When communicating with data, we sometimes forget the importance of words. We might assume that numbers—and the charts that visualize them—speak for themselves. Quite the contrary! Words have a very important place when communicating with data because they help our graphs make sense to your audience (who doesn’t live in your head).

Here’s an example, excerpted from storytelling with data: a guide for business professionals. Check out how the text makes the data more accessible in the graph on the right compared to the original.

After: action and supporting context is clear

Let’s turn our attention to a cautionary tale. When we don’t choose our words carefully, they can have the opposite effect—resulting in our audience having to do unnecessary work to understand our graphs. This example is inspired by a recent graph makeover from one of our workshops (details have been changed to preserve confidentiality).

Consider the following visual. Before you study the data, read the headline, and make a note of what you expect to see in the graph.

If you’re like me, I expected to see a chart depicting a lack of awareness with a corresponding data point showing that 91% of surveyed customers have never used the service. Upon further examination, I figured out that these three charts holistically represent the inverse measures(s)—awareness, consideration, and usage—compared to what’s annotated as the takeaway title. With some mental math, I then reconciled the 91% non-usage rate to the 9% usage rate in June 2020 (far right data bar) but only because I had enough patience and time to undertake this task!

On a positive note, the designer of this original graph took care to put the main takeaway in words in a prominent place at the top. To further improve, we can alleviate some of the mental effort our audience might encounter with this visual by making a few alternative design choices.

One option would be to reword the takeaway title to reference usage rates and employ similarity of color to provide a visual cue to the data it describes.

Another option—particularly if the conversation is better suited towards where we can improve—is to preserve the original title but change the graph to a 100% stacked bar to visually show the magnitude of opportunity.

Both alternatives are shown below. Consider how the words chosen in these two views better enable you to see evidence of what they describe. You can download the data to see how I designed these two visuals in Excel.

This cautionary tale shows that if we don’t word our takeaways carefully, then our efforts (both in the analysis and the communication) might be for naught. In data visualization, words are our friend—but only if we choose them wisely.

For more examples of using words effectively, check out a power pairing and transforming slide titles. Take it a step further and build your data storytelling muscle with an actual dataset in the SWD community exercise words help data make sense.

Last year, I wrote a post highlighting the importance of taking baby steps: incremental, achievable improvements to existing work that we can employ when faced with real-world constraints on our journey to becoming better data storytellers.

Today, I’m going to take things a literal baby step further, considering what we can learn from a toddler teaching himself to walk—and how that applies to designing data that engages and informs.

If you’ve spent time with an infant, you can likely attest: the interval between a baby taking those first clumsy steps to when he or she is suddenly everywhere and into everything is lightning fast. As a funny aside, it’s been jokingly said that the fastest species on Earth is a toddler who’s just been asked what’s in his/her mouth! It’s pretty incredible that a skill we typically take for granted as adults—walking—is something we taught ourselves to master in a relatively short amount of time in our early years.

What can we take away from the grit and determination it took to develop this proficiency that we can apply to learning a new skill amidst the chaos of our adult lives?

Here’s an observation I made during the winter holidays when my one-year-old Henry earned his gold medal in the Olympic sprinter category—toddlers are undeterred by obstacles (a staircase!), constraints (new shoes!), roadblocks (parents: “No, don’t do that!”). Rather they embrace the inherent and messy process of trial and error.

Let’s draw the parallels to practicing data storytelling using a visual from my original baby step post (repeated below). The data displayed is the dollar volume of an organization’s funding across its various initiatives. Within each initiative, the stacked bars break down the dollar volume into three stages: distributed, pending and funded.

My baby step (already employed above) was an incremental improvement—repositioning the words that originally appeared below the graph to just below the title, so the audience sees the takeaway before they get to the data. However, if you’re like me, I’m still left wondering, “So what?” (For additional baby step improvements, check out what others shared in our recently launched SWD community.)

Let’s assume we have time to make changes beyond an incremental baby step. When we communicate with data, we’re often in a unique position to not only help people better understand the data but also to get them to do something with it, to take an action of some sort. However, it is at this point that the fear of looking incompetent—and possibly some ego—can kick in. If we attempt to incite action, what if our audience disagrees, or what if we focus on the wrong thing?

Let’s take a cue from a toddler learning to walk—and taking frequent spills. The toddler would advise us to try first and if we failed, pick ourselves up, dust ourselves off and attempt another approach next time. At work, that means recognizing that even if our audience doesn’t agree with us or wants to pursue a different course of action, it likely starts a conversation—the right type of conversation that frequently doesn’t happen at all when we stop at just showing data. Because we don’t have the full context behind this graph, I’ll use it for illustrative purposes to outline a few possible strategies—and how we can embrace trial-and-error in each.

Consider the following three scenarios:

You’ve been asked for the latest data but the context behind the request hasn’t been communicated. Perhaps you’re a level removed from your audience, or your organization’s culture—or politics—makes it difficult to get at the gist. Here the trial-and-error can help build a better understanding of the context. To execute this, you could create several views of the data, get input from a colleague or your manager and then use that feedback to pick one (or several) that would be most likely to resonate. Below are three approaches I might take to highlight different takeaways. Each has its pros and cons and the resulting questions I’d receive and ensuing conversation would allow me to better understand what is needed and anticipate what additional views or data I should provide.

The context is understood and the audience needs to take a specific action. If you have a good understanding of the context, then the trial-and-error becomes designing an effective visual that makes the intended action clear. Let’s assume the audience needs to keep an eagle-eye on the volume of dollars that have been approved at any given time. I might enable my audience to easily see the important stuff with the following slide. (For related practice, check out the from data to single-slide story exercise in the SWD community.)

You’ve been asked to prepare an update for a high-stakes audience—the board of directors. This would be a scenario where the trial-and-error process fits well in the planning stages before we even think about data or graphs. The Big Idea concept can be useful to help us get succinct on the main message—often, a broader narrative than is contained in a single graph. We use the Big Idea worksheet regularly in our workshops and attendees commonly say how surprisingly useful they find this simple exercise. Below is an example of how I could complete the Big Idea worksheet for my communication in this situation. As a next step, I’d get feedback from a colleague and refine it as needed. Then I would turn back to the data to curate what I need to show in terms of supporting materials to reinforce my Big Idea.

For additional examples of the Big Idea worksheet in action and many more exercises to learn from and apply directly to your work, check out storytelling with data: Let’s Practice!

These are just a few examples of how embracing trial-and-error can help us become better data communicators (you can download the file to see how I created these visuals in Excel). The payoff is enormous and—because you were a toddler once—the ability is innate. Imagine if we’d refused to push outside our comfort zones when learning to walk: we’d still be crawling around on all fours!

Henry Charles Ricks (14 months) embracing his constraints

Bonus toddler tip: emulate your idols. In my toddler’s case, Henry’s idol is his older brother Tommy, who he observes and copies—with varying degrees of success. However, by emulating Tommy, it allows Henry to further develop and come into his own. For a data storytelling parallel, check out a recent #SWDchallenge where participants “honorably copied“ a visual they liked, improving their own skills in the process.

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