2025-02-15 02:54:42
There’s a well-known phrase that pops up in certain corners of the internet: Explain it like I’m five (often abbreviated as ELI5). When people encounter a complex idea, a breaking news story, or even the latest reality TV scandal, they sometimes just want the simplest possible explanation—something that reads like a summary from the Simple English Wikipedia.
When you’re presenting complicated information, you might be tempted to take this same approach—assuming that your audience wants you to break things down as if they were five years old. Don’t do it! The ELI5 method encourages the wrong mindset. Five-year-olds need explanations that are superficial and stripped of nuance because their brains are still developing. We tailor our speech to their level, stretching their comprehension to its limits. But if you talked to an adult that way, they’d feel completely condescended to.
Your audience isn’t made up of children. In almost every business setting, you’re speaking to intelligent adults—capable thinkers who can process complexity. The only difference between you and them at the moment you start your presentation is that they’re simply under-informed. Rather than dumbing things down, imagine you’re explaining the topic to the smartest people you know—on their very first day at a new job. Your role is to orient them, equipping them with the knowledge they need to make decisions, take action, and get others on board with your recommendations.
With this mindset, you’ll frame your communication effectively. You’ll provide crucial context, guide people through key considerations, and help them understand both what needs to be done and why it matters. And as any parent can tell you, “Why?” is a favorite question of five-year-olds—but it’s not one they’re great at answering.
2025-02-07 22:30:09
Data doesn't always tell a happy story. For example, when a marketing campaign doesn't reach its desired performance or when a new product your company is launching receives negative customer feedback. It's tempting to hide or minimize less-than-ideal outcomes, but we have a responsibility to present all findings accurately. Here are five tactics for positioning results that don't meet expectations.
Embrace transparency. First and foremost, don't shy away from the truth. Transparency builds trust with your audience. When presenting unfavorable data, be direct and honest about what the numbers show. You don’t have to dwell on the negative, but be forthright and acknowledge it clearly. Then, confidently (and quickly) move on to potential solutions or recommendations.
Practice empathy. When presenting unfavorable results, consider how this information may affect your audience, both professionally and emotionally. Even in business settings, we are all human beings, and empathy matters. Recognize and acknowledge their potential disappointment using neutral, factual language. Tailor your delivery to address their concerns, and be prepared to discuss implications and next steps.
Provide context. Bad results rarely exist in isolation. Help your audience understand the broader context surrounding the data—not in search of an excuse, but rather an explanation. Were there external factors at play? How do these data compare to historical trends or industry benchmarks? When needed, consult for context and have conversations with others to get a robust understanding of the situation. Putting a perceived negative outcome in a broader context can help frame the discussion more productively (see the next point below) and potentially soften its emotional impact.
Focus on learning and action. Instead of viewing poor results as failures, position them as opportunities for growth and improvement. Things may feel bad today, but by learning from what went wrong, we can inform future strategies and decision-making. By shifting the focus to learnings and action items, you transform a potentially negative story into a constructive one.
Tell a complete story. Remember, unexpected findings are often just one part of a larger narrative. Perhaps they represent a temporary setback in an otherwise positive trend or highlight an area ripe for innovation. By zooming out and considering the complete picture, you can present a more balanced and nuanced story.
Here are a few examples of how you can present disappointing data discoveries fairly, accurately, and in a way that drives meaningful action.
By applying the tips above, you can navigate the challenge of presenting less-than-ideal results with grace and professionalism. Remember, effective data storytelling isn't always about having good news to share—it's about communicating all the information in a clear and helpful way.
2025-01-30 23:51:57
So many data visualization and design-related questions are context-dependent. For example, if someone asks if one chart type is better or worse than another, the answer is, “It depends.” Who's the audience? What's the data? How's it being shared? It’s impossible to say one way or another in every case. We all crave simple, binary solutions, but unfortunately, real life is messy like that.
During a client workshop, someone asked me if I was a fan of error bars and whether they should use them in their presentations. As I readied my standard “it depends” response, I realized that for once, it didn’t depend. I couldn't think of a single time when error bars would be the ideal solution for communicating data. (For clarity, if they had asked whether they should articulate the margin of error around their data, my answer would have certainly been it depends. I just wouldn't use error bars to do so.)
Before I discuss why I'm not a fan of error bars and an alternative solution, let's explore what they are.
The purpose of error bars is to convey the precision of the data in a graph. Most commonly employed in technical communications, they are lines that extend from a data point to represent a related statistical measurement, like a standard deviation or a confidence interval. Error bars can be added to any quantitative graph: dots, bars, lines, and scatterplots, to name a few. In all cases, they usually appear by default as black lines with a flat cap at the end.
Let's consider an example from a client that uses error bars. (I modified the details to protect confidentiality.) This client captures various health metrics for patients in their studies. The example below shows the average white blood cell count (WBC) for a particular cohort. The blue line depicts the average WBC over time, and the black, capped lines are the error bars, which represent the range of recorded WBC values for all 33 patients in the cohort.
When designing a graph for communication purposes, you should aim to make it simple and intuitive to process. This involves removing distracting elements. Error bars, no matter how you format them, are attention-grabbing.
I can't help but focus on the error bars in the WBC line graph. There are so many lines cutting across the average trend. One option could be to modify the added lines so they don't compete for attention with the main series on the graph. I can make them gray so they contrast less against the white background. I can remove the caps at the ends, too.
This is an improvement, but a limited one from a design perspective. There are still more than 40 lines dissecting a small chart, and I’m feeling the eyestrain as I try to read the trend. Rather than showing so many individual gray lines, we could combine them into a single area or shaded region around the blue data series.
Notice how much simpler this feels. We can still see the variance around the average, but the primary focus is on the WBC line. If I wanted to label a few values or add reference lines, I could now do so without overwhelming the chart.
In almost all cases, when communicating uncertainty around my data, I opt for an error region over multiple error bars. It looks cleaner, is less to process, and feels more intuitive for an unfamiliar audience. This approach works for other charts as well. Let's consider some categorical data.
An organization conducted a survey to determine employee happiness. Each question was optional. Not all employees were selected to participate in the survey, and the number of responses differed per question. To summarize the results accurately, the company used a bar chart with error bars to represent the uncertainty around each question. (Again, this is a real example that I’ve simply anonymized for confidentiality.)
While the error bars aren’t nearly as distracting as they were in the previous example, they still draw attention away from the ends of the bars. Also, the portion that overlays the colorful bars is hard to read since there is so little contrast.
Let’s modify the chart to use a shaded region approach. First, we’ll need to use a different chart that has space to the left and right of the data values. I’ll switch from a horizontal bar chart to a dot plot.
Now that there is empty space, I can add the uncertainty in a shaded region behind each dot.
Notice how much cleaner this looks. Your attention is on the large dots for each attribute, but you have the additional detail present to help you more accurately interpret the survey results. Department A’s level of happiness does not differ from the overall organization for Attributes 1 and 5. This is shown by the overlapping error regions.
This example highlights the challenge of effectively conveying uncertainty around your data, while at the same time ensuring that the key takeaways shine through. Error bars may be familiar, easy to add, or commonly used in certain scenarios, but they often add clutter and make it harder to see the underlying story. Instead, using a shaded region or an alternative visualization can make the information clearer and more intuitive.
2025-01-28 01:04:00
What if a simple pause could transform your work? In the latest episode of the storytelling with data podcast, I explore the power of intentional pauses—statio—and how they can improve the way we analyze and communicate data.
The idea behind statio is simple: take a moment to pause during transitions to reflect and ensure your next step is intentional. Whether you’re about to dive into an analysis, visualize your data, or prepare to present, a deliberate pause can lead to sharper focus, creative breakthroughs, and more impactful results. For example, you might integrate statio into your workflow by:
Asking smart questions when you're first assigned a project
Taking strategic breaks during analysis to avoid tunnel vision
Refining your visualizations and slides to align with your story
Preparing to present with clarity and confidence
This is more than a productivity hack; it’s a mindset shift that can elevate your work and make your stories more effective.
Ready to reflect and refine? Listen to the episode below and discover how small, intentional moments can lead to major improvements. Let me know how you practice statio in the comments or on social media.
You can view the show notes and transcript; also check out our newly revamped podcast page.
2025-01-17 22:00:00
After attending our workshops, participants are often eager to apply what they have learned to their own work. However, some have concerns about the lack of the practical skills and tool know-how to implement effective data storytelling strategies. Recognizing this challenge, the SWD team is excited to announce a new on-demand video course that bridges the gap between theory and practice. The behind the slides: good to great PowerPoint presentations course allows individuals to learn and develop skills at their own pace and from the comfort of their own space.
This new offering builds upon the popular good-to-great mini-workshop presentation, in which Cole takes a collection of disparate data and transforms it into a stellar story.
We’re keen to bring you an expansion of this mini-workshop, which we’ve developed into a comprehensive, step-by-step learning experience to show you how Cole created her graphs and slides. By focusing on real-world examples and practical techniques, the course empowers you to quickly elevate your data communication skills.
You’ll have access to 31 distinct video lessons across 5 core learning modules:
Get Ready to Learn introduces the course structure and key PowerPoint features.
Create Good Graphs shows how to design clear, straightforward visuals.
Design Great Graphs demonstrates how to focus attention on the most important details.
Present Stellar Slides involves story and animation techniques that engage audiences.
Keep the Momentum provides access to bonus materials and more practice exercises.
The behind the slides: good to great PowerPoint presentations course is ideal for anyone looking to enhance their data presentation skills. It offers a unique opportunity to see SWD's content development process behind the scenes and learn directly from our highly regarded data storytellers.
Sign up today to gain the tools and knowledge needed to transform PowerPoint presentations from good to great, effectively communicating complex data and driving informed decision-making.
2025-01-07 05:37:37
It’s cold in the United States right now. Really cold. The polar vortex has swept across the lower Midwest and eastern seaboard, leaving tens of millions of people shivering under uncommon and dangerously low temperatures. But while the weather itself is notable, this extreme cold snap also provides a perfect opportunity to reflect on a broader issue: how we communicate complex information, like weather data, in a way that is both accurate and meaningful.
When it comes to data visualization, the stakes can be high—especially when the data we’re sharing could save lives or change behaviors. A simple graph showing cold temperatures may not convey the urgency of the situation, and worse, it may even mislead. Let’s explore how we can avoid these pitfalls by focusing on four key principles: choosing the right level of detail, grouping data thoughtfully, selecting the appropriate chart type, and calling out critical insights.
Over the weekend, I was chatting on the phone with my parents, who live in the northeastern part of the U.S. They assumed that in Minnesota, where I lived, the polar vortex must be making it unusually cold. “No, not really,” I told them. “Highs in the teens, not bad for this time of year.”
“Oh,” said my mom. “That seems cold to me. But I guess it’s normal for you guys. I heard it was really cold in Kansas and Kentucky. They probably aren’t nearly as used to it.”
Her comment is right on the money. While single digit low temperatures in Minnesota might not raise eyebrows, the same cannot be said for Kentucky or Kansas, where infrastructure and daily life are not built to withstand these extremes. The same data—a frigid forecast—means very different things depending on where you live.
This is exactly why the way we present data matters. If weather forecasters simply shared a graph of predicted temperatures without any context, the severity of the situation in regions like Louisville could easily be overlooked.
Let’s start with a basic bar chart showing the upcoming highs and lows in Louisville, compared with the normal temperatures one could expect for that day of the year.
Bar graph of the high and low temperatures predicted for Louisville over the next two weeks or so.
At first glance, the data might look alarming. But here’s the issue: if we showed the same graph for Minneapolis, it would likely appear even more extreme, because the raw numbers are colder:
The bar graph of forecasted temperatures for Minneapolis over the same time period makes it seem like Louisville is getting the better weather…but that isn’t the whole story.
Without the proper context, someone might incorrectly conclude that Minneapolis faces the bigger problem.
Alternatively, we could combine forecasts for both cities into a single graph…
…but this creates a new challenge: the viewer now has to compare two sets of data and interpret their significance. Which city is worse off? How does each city’s current forecast deviate from what’s typical? The burden of making sense of the data shifts to the viewer—often leading to misunderstandings.
Small considerations in design, data sorting, and layout can often lead to substantial gains in graph legibility. With the chart above, it’s possible that sorting the bars by city and using consistent colors might make it easier to read.
In this iteration, Louisville is orange, Minneapolis is blue, the bars for each city’s high temps are bold and next to one another…and we’re still at a loss as to what the takeaway might be here.
However, this approach still struggles to tell a meaningful story. While the data is organized, it lacks the critical context needed to highlight why Louisville’s forecast is concerning.
This is where thoughtful assessment of what data should be included becomes essential. By shifting the focus from comparing cities to comparing each city’s forecast against its historical averages, we can begin to highlight what’s truly unusual about the situation. This approach reframes the conversation from “how cold is it?” to “how much colder than normal is it?”
Even with better grouping, the choice of chart type is crucial. A bar graph, for instance, might not be the best way to communicate weather patterns over time. Temperature is a continuous variable, making a line graph more appropriate.
The switch from bars to lines is a big visual improvement. Is this the best option, or is there more iterating to do?
The line graph certainly improves on the bar chart, but considering that we’re trying to clearly show a range of temperatures on a daily basis across a two-week period, it might be the area graph’s time to shine. Let’s imagine an overlapping area chart that shows the predicted high and low temperatures for Louisville and Minneapolis over the next two weeks.
Overlapping area charts are a good way to show how ranges rise and fall over time. (Side note: an overlapping area graph like this one can be created in Excel by stacking multiple graphs on top of one another. In this example, the bottom chart is an area graph showing just the Louisville range, and the top chart shows the Minneapolis range, plus the lines for both Louisville and Minneapolis’s high and low temperatures.)
This format makes it easy to see trends and ranges at a glance. However, even this approach has limitations. Without historical data, the chart might lead viewers to conclude that Minneapolis has the more severe forecast, simply because the numbers are lower.
To fully understand the impact of the polar vortex, we need to go a step further. By overlaying each city’s forecast with its historical averages, we can provide a clear and meaningful picture of what’s happening. For Louisville, this might reveal that predicted highs are barely reaching the city’s typical low temperatures—a clear signal of abnormal and potentially dangerous conditions.
For Minneapolis, on the other hand, the forecast might show temperatures close to historical norms, or even a warming trend by the end of the two weeks. This contrast makes it clear why Louisville deserves special attention, despite being objectively “warmer” than Minneapolis.
Even with a well-designed graph, there’s still one more step: explicitly calling out the insights we want viewers to take away. In the case of Louisville, you’ll notice we added a callout box to highlight the prolonged cold snap and its potential risks ensures that the most critical information doesn’t get overlooked. For Minneapolis, a note about the relatively normal conditions would help dispel any misconceptions about the severity of the weather there.
This step transforms a passive visualization into an active communication tool. Instead of leaving the viewer to draw their own conclusions, we guide them to the key takeaways, ensuring our message lands effectively.
The polar vortex offers a timely reminder that data alone isn’t enough. Without meaningful context, our visualizations can confuse, mislead, or fail to resonate. Whether we’re sharing weather forecasts, business metrics, or scientific findings, it’s our job to make the data not just easy to read, but easy to understand in the appropriate context.
By choosing the right level of detail, grouping data thoughtfully, selecting the best chart type, and calling out key findings, we can transform raw numbers into insights that inform, persuade, and inspire action. After all, the goal of any data visualization is not just to show information, but to tell a story—and every good story needs context.
By reframing the way we think about data visualization, we can ensure that our work not only informs but also empowers. So the next time you create a graph, ask yourself: what’s the story I want to tell, and how can I make sure my audience understands it in the way I intend?