2025-11-13 22:30:09
When we’re reporting the latest information, it can be challenging to know how to handle data that is still in progress. For example, if we're reporting annual performance trends with only three quarters completed in the latest year, the numbers can appear misleadingly low. If you exclude the latest data points, it could hide crucial details from stakeholders. Audiences often want timely updates, but partial data can cause confusion if not clearly communicated.
This was a challenge for a client I was recently working with. Take a look at the slide below, which displays new subscription revenue by year.
Upon initial inspection, the 2025 drop-off appears concerning, as it's markedly below that of 2024. However, the 2018 to 2024 data covers the entire year, while the 2025 figure only includes results through September.
In this particular case, the slide was presented live, and the creator explained that the 2025 data was only through the third quarter. After the meeting, though, the slides were shared. This means that anyone who didn’t attend the presentation wouldn’t have that crucial context to avoid panic. Let’s explore four alternative solutions to clearly and effectively share partial data.
One of the easiest ways to ensure the 2025 data isn’t mistaken for the full year’s information is to distinguish between full-year and partial-year data visually. For this, we could add a label on the horizontal axis and adjust the bar to a pattern fill. (Similarly, if we had a line chart, we could use a dashed line to indicate a difference.) These visual cues make it clear what’s finalized and what’s ongoing, reducing the chance of misinterpretation.
Additionally, we could go further to provide a sense of where the full year might end up, for comparison.
Estimating where the final numbers might land for 2025 is another possible approach. Using a stacked bar that fills in the actual data through September in blue and a pattern-filled segment projecting the fourth quarter could convey this message. This visual sets realistic expectations and helps viewers compare years more accurately, particularly when the visual is accompanied by clear and explicit data labels.
Since the data for 2025 only covers a subset of the year, we could consider providing a sense of how the current year compares to the prior years for a similar time period.
Our final two options assume that we have access to the data at a more granular level than just the year summary. To compare apples to apples, try showing year-to-date (YTD) results for each time period. For instance, use a solid blue color to represent the data from January to September in each year, and use a lighter fill to indicate the remaining fourth-quarter data. Take care to differentiate 2025 data with an open fill to signal that the Q4 2025 data is not yet available. This approach helps the audience make fair comparisons across equivalent periods.
A final option is to show even more detail. For example, we might plot monthly subscription revenue for each year, excluding the outstanding months for 2025, to highlight the incompleteness of the current year. An added footnote also clarifies when the numbers were extracted, leaving no room for ambiguity.
This more granular view looks quite busy. It is worth considering how much data is required. Depending on the message we want to convey, we might consider keeping only the most recent years. Displaying just the relevant information reduces the cognitive effort needed to understand the trends.
When sharing data for an incomplete reporting period, be deliberate about context to avoid confusion. Take care to differentiate between complete and incomplete information with helpful labels and annotations, and adjust the marks to ensure they stand out clearly. Consider what your audience needs to know: do they need the latest snapshot, a full-year forecast, or a fair period-over-period comparison? Choose a strategy that provides the most applicable comparison and always makes the completeness of the data unmistakably clear.
To practice implementing these strategies, explore this related community exercise.
2025-11-11 04:12:03
This month marks the 10th anniversary of the publication of my first book, storytelling with data: a data visualization guide for business professionals.
When the book first came out, I had no idea what to expect. I was simply thrilled to see my name in print! I had been facilitating workshops for several years by then and had reached the limit of how many people I could personally teach. The book became my way to make those lessons—and everything I was learning about communicating with data—available to a wider audience. My goal was simple: to help others share their data more effectively.
It’s done that. And so much more! storytelling with data has been translated into dozens of languages (my favorite foreign cover—ironically—is the Italian edition featuring a man leaping out of a pie chart!) and is used to teach at universities around the world. Just last week, I visited Arizona State University to share lessons with employees, instructors, and students. I never could have imagined how many people I’d meet, or the places I’d travel, because of this book. It has connected me with people around the globe—and even introduced me to a number of the individuals who now make up the storytelling with data team. This book truly changed my life.
Earlier this year, when my publisher suggested creating a special anniversary edition, I jumped at the opportunity. I reread storytelling with data for the first time in years and was happy to find that its foundational lessons still hold true today (perhaps even more so in this age of AI). While the core content remains unchanged, I added a new foreword and three appendices capturing key insights from the past decade. The example graphs have been refreshed with current data. The design has been elevated: a crisp white linen hardcover stamped with magnesium ink, coordinating endpapers, and some fun surprises that I’m extremely excited to share with you.
Perfecting these details took a little longer than expected, so the official publication date (originally planned for this week) has shifted to December 2nd. Books are already on their way to retailers and will be in circulation very soon. You can order your copy today.
To coincide with publication, I’ll host a live virtual celebration on December 2nd, reflecting on key lessons that have stood the test of time. I’ll revisit classic examples and share new ones, highlighting both simple steps for better graphs and deeper insights to strengthen your data storytelling. I hope you’ll join me for this free virtual session (and I welcome you to extend the invitation to others!).
As I’ve reflected on this milestone, I’ve found myself revisiting old photos. The one at the top of this post reminded me of another that made me smile—and filled me with awe at the passage of time. Ten years ago, my oldest son, Avery, played in the box that delivered my first copies of storytelling with data. This weekend, almost-13-year-old Avery helped me move boxes of the 10th anniversary edition.
Thank you for being part of this journey. I hope you enjoy the new edition of storytelling with data as much as I’ve loved bringing it to life.
2025-10-28 08:19:41
Bringing clarity to your data storytelling doesn’t usually mean you need to learn and use more chart types. It does mean choosing visuals that are appropriate for your data and what you’re trying to communicate. Two of the most useful tools in our graphing toolkit—bar charts and line graphs—often do the heavy lifting. Knowing when to use which (and when to switch) can make all the difference.
This lesson came up recently as I revisited our new book, storytelling with data: before & after. I’ll share two scenarios where the choice between bars and lines matters. They are both from Chapter 2, which is titled “embrace basic graphs.”
Let’s start with the client-inspired example. Spend a moment reviewing the following. This slide (created by an external consulting company, Alpha Omega Group), was part of an annual business review for our client, a luxury cosmetic brand called Rapture. (Names and details have been modified to preserve confidentiality.)
Though the slide and graph appear reasonably professional at first glance, a closer look reveals opportunities for improvement. Let’s first recount some of the positives. The slide features a clear takeaway articulated at the top. Specific data points that relate to the takeaway title are encircled. The graph is mostly free of clutter. The data is plotted in a simple line graph—one of our preferred types.
But does a line graph make sense here?
When opting for a line graph, you want the lines themselves to mean something. In this instance, the lines reflect the relative difference between categories, which isn’t particularly useful. That’s not to say a line graph won’t work here, but this isn’t the one I’d recommend for categorical data. We’ll look at a variation soon. First, let’s see what this looks like in a graph made for this type of data: a bar chart.
Bar charts are almost always a reasonable option. I’ve been known to joke that if I were stranded on a desert island with only one type of graph to use forevermore, it would be a bar chart. In our collaboration with clients, bar charts often become the hero when faced with unclear or overly complicated visuals. In this instance, I started with a standard vertical bar chart, also known as a column chart. We could also plot this data in a horizontal bar chart.
I played a little here, overlapping the bars so that the latest point in time is in the forefront, with enough of the historical data showing to still make the comparison. (If this bothers you, an unoverlapping version would also work.)
While I do love a good bar chart, bars aren’t our only option. I’m going to continue to iterate. Each version reveals something new about the data, highlighting how useful it can be to explore multiple perspectives before settling on the final view. Let’s look at the data in a slopegraph.
Whereas bar charts place emphasis on absolute values (the height or length of the bars), slopegraphs and dot plots draw attention to the difference or change (via the lines or distance between the points). With the slopegraph, I more quickly see the declines in daily use across all six products.
Let’s try looking at this data in a dot plot.
Something else stands out to me in the slopegraph and dot plot that didn’t in the bar chart views: how much more mascara and lipstick are used compared to the other products. Going back to the original takeaway, it’s true that eyeshadow and eyeliner decreased the most during COVID. But is that where we want our audience to focus?
As it turns out, no. Several slides later in the client’s original deck, it became clear that the key recommendation was to focus on restoring mascara and lipstick to pre-COVID daily use, with the expectation that the other products would follow.
Now knowing what I want my audience to see when they look at my graph, I can revisit the options I’ve considered and choose one that will facilitate this.
Any of the above options could work, with the right focus and words. I find myself drawn to the horizontal bar chart. You might choose a different option, and that’s fine. The following is my redesign, assuming that I’m constrained to a single slide.
If I were going to present this information live, I’d weave together a narrative by showing multiple iterations of the same graph. I could start by making the overall point that post-COVID makeup use is lower across all products. From there, I could highlight the largest decreases in eyeshadow and eyeliner use. Then, I could shift attention to mascara and lipstick as the consistently most-used products. Finally, I can conclude with the recommendation to prioritize strengthening marketing efforts on those products, with the expectation that others will follow. See the following slides.
In the appendix of the same original client deck, there was a prime candidate for a line graph that wasn’t initially presented as one. See below.
Perhaps because it was an appendix slide, no supporting context was provided. Even in absence of that, I believe a line graph works better here. In the following iteration, in addition to transitioning to a line graph, I redesigned the visual to incorporate the client’s font and colors.
Recall the line graph at the beginning of this post, where the lines connecting categorical data didn’t make sense. Here, the lines effectively plot data over time. Each line segment conveys the relative change from one year to the next through its rise or fall.
For example, if you focus on the lower gray line in the graph above, you can see that it is relatively flat from 2020 to 2023. Then, there is a marked increase in slope between 2023 and 2024—and an even bigger increase from 2024 to 2025. Something clearly happened here that drove up minimum production (interestingly, the increase in minimum production seems to have lagged behind the increase in average production).
Now that you’re aware of this, you can go back to the bar chart and find the information. However, it would have been much harder to see without this new perspective. One benefit of using a line graph to plot data over time is that it mimics the way we intuitively follow a timeline, drawing our attention to fluctuations and trends.
As a slight twist, we could show the range from minimum to peak as a shaded area. You can see this below. I’ve also added some example text annotations. Given the lack of context, I used these to demonstrate where they might be placed and how to format them, rather than providing meaningful content.
I’m a long-time advocate of embracing basic graphs. Bars and lines have stood the test of time because they do their job exceptionally well. (The cousins we explored here, slopegraphs and dot plots, are variations of these.)
One thing that has evolved for me—and that you can see play out through the examples shared here—is understanding the nuance behind these simple forms. It’s not just what you show, but why and how you show it that sets effective data storytelling apart. The magic happens when you know which to use, when to switch, and why that choice helps your audience see what matters most.
If you enjoyed the before-to-after progression of this example, you’ll love our new book, storytelling with data: before & after. It features 20 inspiring makeovers, sharing a multitude of practical lessons along the way.
2025-10-10 16:00:03
Reducing redundant elements in our visuals and moving beyond our tools' default settings almost always makes insights faster to retrieve. It’s such a critical part of clear communication that we dedicate an entire section of our workshops to it.
However, we close that very same lesson with an important truth: decluttering alone isn’t enough. If a sub-optimal chart type is used or the critical message remains hidden, the audience may still walk away confused.
Today, let’s take a look at how much better a graph can look when it’s been decluttered—and then, how much stronger it can be when we prioritize our main message.
This visual shows the status of work orders across four critical asset types within an engineering company.
Along the horizontal axis, we see each of the four asset types: boiler feedwater pumps, high voltage substation switchgear, distributed control system, and pipe rack.
The primary (left) vertical axis of this dual-axis chart plots the absolute number of orders within an asset type, broken down by closed (blue bars) and open (orange).
The secondary vertical axis tracks the percentage of orders closed per asset type. The goal of this view is to help leadership see which asset areas are meeting order closure expectations and which require additional focus.
Everything our audience might want to know is present. But that’s part of the problem: there’s too much happening at once, and the dual axis makes it difficult to know what to focus on. Is the takeaway supposed to be the number of orders? Or the closure rate? Or both?
As with most graphs that use primarily the default settings of a graphing tool, this one can be improved with some common decluttering techniques. Let’s do this now by taking the following steps (with some useful links to previous articles on these topics included):
Removing the chart border;
Adjusting the graph proportions;
Decreasing the white space between the bars;
Removing the data labels;
Formatting the axes: providing titles, using lighter formatting and adding axis lines and tick marks;
Moving the legend from the bottom of the view to the top, acting as a subtitle;
Rewriting and aligning the chart title
The combination of these changes helps to lighten the visual load and make it easier to focus on the data. Applying a similar combination of the steps referenced above works well in most situations and will almost always lead to an improvement. And if you’re short on time, even a quick round of decluttering can make a meaningful difference.
Even with these changes, it’s still challenging to make immediate observations about the information. At this point, we should think critically about the message we want to deliver and whether our graph types and design choices support that communication effectively.
We’ve done our best to tidy the view, but any graph that incorporates dual axes requires an audience to expend a lot of mental energy, keeping track of the multiple metrics in the plot.
Instead, consider exploring different ways to present this information. One way we can transition away from the combined axes is to pull the graphs apart vertically, leveraging the same horizontal axis across both views, but with each getting its own independent vertical y-axis.
Having made this change, I’ve noticed something off. Lines work well for continuous data over time, but here we’re dealing with categorical data—the asset types. In this instance, joining the closure rates for each asset creates a relationship that doesn’t exist. To correct this, I’ll convert the line to individual bars, which will better represent this distinct, discrete data.
Technically, that’s better, but something still feels off. Maybe we’re still trying to show too much all at once. Do we need all this information? In those cases where we feel obliged to show all of the data we collected or analyzed, we sometimes wind up talking ourselves into creating needlessly busy visuals. Often, a subset or aggregation of that data would lead to a much clearer final product.
Continuing to iterate, I’m going to drop the closure rate information. Then, I’ll switch from positioning the closed and open bars next to each other to stacking them on top of one another.
The stacked bar chart allows us to see total orders per asset while allowing for straightforward comparison of the closed orders for each asset type. Now that the order closed rate has been removed, we can be clear about the intention of this visual, and displaying only the relevant data reduces the cognitive effort required to understand the information.
In conversation with colleagues, we determined that the one thing our audience cares about the most is the closure rate within each individual asset type. Is this visual helping articulate the primary takeaway of that metric, which is that most maintenance orders for pumps have been closed (82%), while fewer than half of those for pipe racks have (42%)? We can see this at an absolute level, yes, but with teams monitoring these assets of markedly different sizes, showing the number of orders is likely not the right approach.
With the original visual and closure rate line in mind, I’m inclined to explore a different view.
Switching to a 100% stacked bar chart now clearly shows the proportion of orders that have closed. At this stage, I also took the opportunity to switch to a horizontal orientation to ensure the category labels—which in the previous iterations were distracting and fell across a combination of one, two or three lines—sit neatly on a single line. Finally, I chose to order the data series in decreasing order of performance.
The final view allows for immediate and accurate comparisons to be made. Now that I’ve landed on an appropriate chart type, the final step is to add elements that help frame and explain the data: a goal line of 80%, an observational takeaway to prime our audience with the key message behind the visual, and an open question to prompt discussion.
Unlike the original, the story is obvious: only the boiler feedwater pumps order closures are at an acceptable level, while the other asset types fall below appetite. Conversation can now turn to remedial efforts to expedite order closures in these areas.
Decluttering our visuals is always a great first step, and one in which a similar approach can safely be applied to most visuals. For the strongest communication, however, decluttering alone isn’t enough. Removing redundant elements might help us see the data, but if the chart type itself isn’t aligned with the story; the ability to retrieve the insights quickly is compromised. Real success comes not just from sharpening up the original chart, but from rethinking how best to present the data, ensuring the main message you are looking to share is clearly accessible.
If you enjoy seeing how we help real teams improve their business communications via example, order our latest book: storytelling with data: before & after. Drawing on more than a decade of helping organizations and professionals transform their communications, the book features twenty powerful makeovers that turn ineffective charts into engaging visuals that captivate, inform, and drive smarter decisions.
2025-10-03 09:59:01
Over the past few weeks, in response to my posts about our newest book, it’s been amazing to hear how various titles from the SWD library have impacted your work and careers. My first book—storytelling with data—has inspired a passionate group of superfans who not only apply the lessons, but also share their passion for SWD with others.
If this sounds like you, I’d love to invite you to be part of something special.
Earlier this year, I revisited storytelling with data and have refreshed and augmented it, working with my publisher to craft a beautiful, fit-for-display 10th anniversary hardcover edition. It will be published on November 11th, ten years after the original bestseller was released, and features updated visuals and bonus content. I’m proud to celebrate a decade of data storytelling with this milestone publication.
I invite you to join our official team of superfans, helping to launch the 10-year anniversary edition (SWD10) into the world and join in the celebration. The role of a superfan is simple: share your genuine enthusiasm for SWD10 with your colleagues and network. I’ll make it easy by equipping you with content and ideas.
To become an official SWD10 superfan, preorder two copies of the 10th anniversary edition (one for you and one to share!) and fill out this form by October 10th.
As an SWD10 superfan, you’ll get some super special perks, including:
Weekly insider emails from me in October and November with never-before-seen content and easy-to-share prompts
Exclusive SWD10 swag (designed just for superfans!)
Automatic entry into weekly giveaways for storytelling with data gear
A front-row ticket to our virtual publication celebration
Thank you for helping me celebrate this anniversary and spread the power of effective data storytelling. I couldn’t do it without you!
*International readers: This edition is printing in the US, with availability in other countries to follow. If you preorder from outside the US, you can still participate fully in the SWD10 superfan program.
2025-09-25 21:11:12
What do dog training and data storytelling have in common? A lot, as it turns out.
For those who are hardcore SWD fans, you’ve probably seen me reference dogs before. I shared my dog Nemo’s progress in this makeover article, and I also share a dog-themed makeover in our new book, storytelling with data: before and after, which includes 20 examples of how we’ve helped our clients overcome common data communication challenges.
What you may not know is that dogs occupy most of my time when my brain is not focused on data. I’ve recently started an apprentice program on the weekends to become a dog trainer. This past week, while working on a particular skill, I asked my teacher if I was doing it right.
His response? “The dog will tell you!”
At first, I didn’t quite understand. But then I realized how powerful that answer was! Dog training, like data storytelling, is all about clear communication. If I want to know if I’m communicating clearly, I have to pay attention to the dog to see if they understood.
If you want to know if your graph, slides, or story is clear, the audience will let you know. They decide what works, not you.
This is a hard truth to swallow. In the exploration stage of analyzing data, it’s all about you and what you understand. However, as you shift to communicating data, your preferences and beliefs should take a back seat to those of your audience.
Even as someone who shares this lesson with others regularly, I fall victim from time to time—most recently while working on Chapter 14 in before & after.
Here’s the background information and original slide, taken directly from the book:
Electrixion Group, a local electricity company, recently expanded and is now facing a shortage of certified electricians. Employees believe the expansion was a mistake, arguing that they can’t support such a large regional area because there isn’t enough local talent to hire. The head of talent acquisition wants to challenge these ideas by showing that there are plenty of certified electricians eager to work in their main off and warehouse—the real issue is the need for better advertising and recruitment. To make their case, the director planned to present the slide below.
Knaflic, Cisneros, Velez. storytelling with data: before & after, Wiley, © 2025
At first glance, I was impressed. There’s something captivating about the funnel diagram, unique map, and bold crimson color. However, it took me a while to fully understand it. Without the back story, I’m not sure I’d walk away with the right takeaway.
Further reflection made me realize that there’s a lot going on among the funnel diagram, callout boxes, and map. A simple improvement would be to select either the funnel diagram or the map as the primary visual on the slide. This communication would further benefit from a strong takeaway title and better layout.
My immediate preference was to stick with the funnel diagram. I couldn’t pass up the opportunity to turn this diagram into a square area chart!
Below is my first remake.
Is it easier to grasp the point? I thought so.
But it’s not about me. When I considered the scenario and the audience (busy electricians eager to head to their appointments for the day), the slide felt overly complex for the message. The employees don’t really care about this level of detail.
After soliciting feedback and paying close attention to facial expressions and follow-up questions, I realized that I was designing for me and not the audience. Remember, if you want to know if your communication is clear, the audience will let you know.
Instead of leaning into the funnel diagram, I settled on the map. It provided a nice understanding of where the talent resided in proximity to the warehouse, supporting the key point that with thoughtful marketing, recruiting local talent is possible. I also decided to prominently display a key takeaway and do the math for my audience. Approximately 70% of the electricians within 50 miles of the headquarters haven’t previously worked at the company, and therefore should be who we target (5.5K divided by 7.8K). This is the final makeover that made it into the book.
Knaflic, Cisneros, Velez. storytelling with data: before & after, Wiley, © 2025
Simple, to the point, and exactly what this particular audience wanted.
If you want to learn more about how I transformed this makeover step-by-step or if you love learning via example, order our new book, storytelling with data: before & after. It’s available this week at all places where books are sold (Amazon, Barnes & Noble, BAM, and Wiley). For those outside the U.S. who want to use Wiley, pick your location here. If you’ve already snagged a copy and have a free moment, we’d greatly appreaciate if you’d share your review.