Over the years, we’ve seen firsthand how stronger communication skills help organizations secure funding, influence decisions, align stakeholders, and amplify their impact. Yet many nonprofits, schools, and mission-driven organizations face budget constraints that put professional development opportunities out of reach.

That’s why we’re excited to relaunch SWD Reach. It’s a special application-based program that provides our in-person storytelling with data 4-hour workshop at a substantially reduced cost to selected organizations. We’ve reserve a limited number of Reach workshops for organizations that can put these skills to work in service of their mission. The goal is simple: help organizations doing important work communicate more effectively with data so they can increase their impact.

Whether you’re seeking grant funding, advocating for resources, communicating outcomes to donors, or helping leaders make informed decisions, the ability to clearly communicate data matters. We regularly hear from organizations that have important information to share but struggle to make their insights clear, compelling, and actionable. This program is designed to bridge that gap.

The impact of these workshops extends far beyond a single training session. Participants learn practical techniques for choosing effective visuals, focusing attention, and communicating insights clearly and persuasively. Since initially launching in 2019, SWD Reach has supported more than two dozen organizations, helping teams communicate data more clearly, advocate for resources, and demonstrate impact. One participant shared:

“Everything discussed applied directly to our work. The session was highly relevant and sparked immediate conversation about how to implement these ideas in our department. The exercises and before/after examples were especially helpful, and people had a lot of aha moments.” 

If your organization is committed to using data to drive positive change—and would benefit from developing stronger communication skills—we encourage you to apply. SWD Reach applications are open through June 30th.

Please help us spread the word by sharing this opportunity with colleagues and networks who might be a great fit.

In under two years, we've helped more than fifteen thousand elementary school students across nineteen US states and eight countries build confidence collecting, visualizing, and interpreting data. 

Critical thinking and clear communication are skills that don’t need to wait until adulthood. Helping people make sense of information and present it effectively is at the heart of what we do at storytelling with data, and we love sharing our mission with kids through Daphne Draws Data. 

As SWD’s Education and Outreach Specialist, I’ve had the privilege of helping bring this work to light. In this post, I'll share a quick update on what we've accomplished since publication, and how you can help us reach the next 15,000 kids.

By partnering with nonprofits, after-school organizations, libraries, museums, and STEM-focused education groups, we’ve created hands-on graphing experiences that help kids collect data, draw graphs, and interpret what they discover. To support continued learning beyond these programs, we’ve donated nearly 9,000 books to underserved communities and Title I schools. Reach out to hellodaphne@storytellingwithdata.com to learn more about our programs and book donations.

And the impact goes beyond kids.

One of the most rewarding outcomes has been helping educators, nonprofit leaders, program coordinators, and parents learn the same core data storytelling lessons we teach business professionals: choosing an appropriate graph, removing clutter, focusing attention, and communicating through story. These skills help them advocate for funding, secure resources, and demonstrate the impact of their work.

This year, we expanded beyond books and graphing programs with the launch of the interactive Daphne Draws Data exhibit. Kids contribute their own data in real time, compare their responses with those of their peers, and see how their input becomes part of a larger story. The exhibit is currently in the Kansas Children’s Discovery Center, with hopes to expand to additional museums and science centers.

Another major driver of our reach has been our enthusiastic Data Detective leaders, who bring graphing with Daphne programs to their local communities. Using our pre-developed program resources, these volunteers teach kids how to answer their curious questions by collecting and drawing data.

If this mission resonates, you can get involved: 

• Become a Data Detective leader and share Daphne with kids near you. Join the dozens of people who’ve conducted programs in their community by reading the book and guiding kids through a graphing activity. We provide the resources, and you teach kids a powerful skill while enhancing your own data communication skills. 

• Share Daphne’s Educator Hub with your local school district, museum, or after-school organization, and introduce us to contacts who are interested in our programs.

• Share ideas for new resources and content that can support elementary educators and students, simply email hellodaphne@storytellingwithdata.com.

Together, we can help kids build confidence with data and skills that will serve them for life!

This post is part of the SWD + AI series—practical guidance for using AI as a thought partner across the various stages of your data storytelling work. Explore all of our AI resources.

Nearly everything we teach at SWD—every book, workshop, makeover—starts from the same place: context. Before you choose a chart type, open a slide deck, or think about color or layout, you need to understand who your audience is and what you need them to know or do. This is the foundation of effective data communication. It always has been.

Of course, by the time you’re thinking about communication, there’s a good chance you’ve already done significant work to get there: exploring your data, identifying patterns, and surfacing key insights. You may have even used AI to help with that. But exploratory work and explanatory work are different things. Knowing what the data says is not the same as knowing how to communicate it. That transition—from analyst to storyteller—is where context becomes essential.

This is an area where AI can add immense value as a thought partner: right at the beginning, before you’ve invested time creating anything. AI can push back on vague thinking, ask questions, introduce angles you haven’t considered, and help you stress-test your framing while it’s still easy to change. Unlike asking a colleague to carve out time in their day, AI is available on your schedule; it’s a willing thought partner whenever you’re ready to think things through.

But it can only do that if you come prepared. The quality of the conversation depends entirely on the context you bring to it. That starts with the work SWD has always taught: understanding your audience, clarifying what’s at stake, and forming a clear point of view before you communicate anything. In this post, we’ll work through all of that, using the Big Idea worksheet as our guide and AI as our thought partner.

Working with AI: start with context 

The Big Idea is a single sentence that captures your point of view and conveys what’s at stake for your audience. The Big Idea worksheet (a free single-page download) is a structured way to get there. You have a couple of options when it comes to how to use AI here. One is to complete the worksheet and provide it as a starting point. Alternatively, you can work through it section by section, using AI to pressure-test your thinking as you go. Start with your audience, then the stakes, and finally the Big Idea. The worksheet provides the structure; AI can help you sharpen each piece as you go.

Before getting to more detailed guidance and an example, let’s review some potential pitfalls of working with AI to understand your context and form your Big Idea.

Things to watch out for:

• AI doesn’t know your audience, your data, or your organization like you do—if you give it generic descriptions, you’ll get generic responses. If you are unsure about the specifics, talk to a human first; AI can’t replace the firsthand knowledge that comes from actually knowing the situation and people involved.

• AI may generate a confident-sounding Big Idea that isn’t actually yours—watch for moments where you’re adopting AI’s framing rather than refining your own.

• AI tends to broaden rather than narrow—it may suggest covering more ground than you need. SWD teaches ruthless focus; use AI to sharpen, not expand.

• Be mindful of what you share—avoid including sensitive data, personally identifying information, or confidential business details in your prompts.

The following example prompts are designed to get you started. Be sure to adapt the language and instructions to fit your specific situation and needs.

Before you begin: set the stage

I am going to work through the Big Idea worksheet from storytelling with data (SWD)—it’s a framework for clarifying who your audience is, what’s at stake, and your core message before creating any communication. I’ll share my thinking one section at a time and ask for your input as I go.

As you help me, please keep these SWD principles in mind:

• Audience analysis should go beyond who they are to consider what they care about, what biases they bring, and what would motivate them to act

• Effective data communication puts the audience’s needs ahead of the analyst’s findings

• The Big Idea should be a single sentence that articulates a clear point of view and conveys what’s at stake for the audience

Your role throughout our conversation is to be a thought partner and help me pressure-test my thinking. Help me think more clearly about who I’m communicating to and what they need. In particular, I’d like you to:

• Ask clarifying questions before giving feedback

• Surface blind spots and what may be missing or unconsidered

• Suggest additional angles I may not have considered

• Identify potential risks or weaknesses in how I’m framing the communication

• Avoid jumping straight to rewriting or completing things for me—help me think more clearly so I can make better decisions myself

To give you helpful context, here’s a brief description of my project: [2–3 sentences describing what you’re working on, who you need to communicate to, and what you hope to achieve]

Does that make sense? Confirm you understand, and then I’ll share my first section.

Potential prompt 1: who is my audience?

I’m working through the audience section of the Big Idea worksheet. Here’s what I have so far: [fill in the following bullets, or share an image of the completed audience section of the Big Idea worksheet]

• The primary groups or individuals I need to communicate to: [list names and/or roles or groups]

• If I had to narrow to a single person, it would be: [name/role and why]

• What my audience cares about: [list]

• The action I need my audience to take: [describe]

Review what I’ve shared and help me think through:

• Does it seem like I’ve identified the right primary audience, or is there a case for thinking about this differently?

• For each person or group, what might they care about that I haven’t listed?

• Is the action I’m asking for clear, realistic, and sufficiently specific?

Before offering feedback, ask me any questions that would help you give better input.

Potential prompt 2: what is at stake?

I’m now working through what is at stake. Here’s what I have: [fill in the bullets below, or share an image of the completed stakes section of the Big Idea worksheet]

• The benefits if my audience acts in the way I want them to: [list]

• The risks if they do not: [list]

Based on what you know about my audience and what they care about, help me think through:

• Are these benefits and risks genuinely meaningful to my specific audience, or do any feel too generic?

• Which benefits or risks are likely to resonate most strongly given their priorities and concerns?

• What stakes might matter more to them that I haven’t listed?

• What might I be overlooking, assuming, or oversimplifying?

• Which of these are most essential to work into my Big Idea?

Before offering feedback, ask me any questions that would help you give better input.

Potential prompt 3: form my Big Idea

I’m now ready to craft my Big Idea—a single sentence that articulates my point of view and conveys what’s at stake for my audience.

My Big Idea: [type draft sentence, or share an image of the completed Big Idea section of the Big Idea worksheet]

Help me think through:

• Does it clearly articulate a specific point of view, or does it read as neutral, vague, or descriptive?

• Does it convey what’s genuinely at stake for my audience?

• Is it a complete, single sentence? If not, can you suggest ways to wordsmith?

• Suggest 2–3 possible refinements that keep my original intent but improve clarity, relevance, or resonance.

Before offering feedback, ask me any questions that would help you give better input.

To see how this all comes together, let’s walk through an example.

In practice: start with context

To bring these ideas to life, I’ll introduce a scenario that we’ll revisit in each post in this series. It’s inspired by a real-world situation, however the details have been anonymized.

Imagine that I am a People Analytics Manager at a mid-sized consulting firm. I’ve been asked to form and share my data-informed perspective on whether the company’s hybrid work policy is effective. My team has undertaken a thorough analysis, from correlating performance ratings with in-office attendance patterns to examining collaboration network data and attrition trends. What the data reveals is more nuanced than a simple yes or no—and our recommendation, moving from the current one-size-fits-all policy to a differentiated approach, needs to land with a leadership team that has differing opinions and goals.

This is precisely the type of situation where AI as a thought partner can add value: the audience is complex and the consequences of getting it wrong are significant. Getting the framing right before I start building any content should both make the rest of the process more efficient and help lead to better outcomes. So rather than jumping in and building graphs and slides, I began working through the Big Idea worksheet with my AI partner, Claude. (The prompts in this post are designed to work across tools—feel free to use whichever you prefer.)

First, I set the stage by copying and pasting the before you begin context shared previously, plus the following short description of my project:

I am a People Analytics Manager at a mid-sized consulting firm. My team has completed an analysis of our hybrid work policy—examining performance ratings, collaboration patterns, and attrition trends. We have a recommendation to move from our current approach (three days in office, two days remote for all employees) to a differentiated approach based on role and team type. I need to communicate this to a leadership team with divided opinions and real stakes in the outcome.

After Claude confirmed it understood the setup, I went analog. I printed out the Big Idea worksheet and spent a few minutes completing the top section, Who is your audience? I could have turned straight back to AI and started typing, but didn’t let myself. Going analog first is something I recommend generally: it helps ensure your initial thinking is genuinely your own, and physically putting pen to paper slows you down in useful ways, preventing you from anchoring too early to AI’s perspective.

Here’s what I wrote:

Using potential prompt 1, I shared this image with AI and asked whether I’d identified the right primary audience, what each person might care about that I hadn’t listed, and whether my requested action was specific enough. 

Before addressing those, Claude had some initial questions for me:

Simply having to articulate answers to these questions was clarifying. I responded that I would meet with Diana first to get her comfortable with the recommendation and supporting analysis, after which point she will present it to the entire leadership team, including the CEO.

It was helpful to recognize that I framed things mainly in terms of what Diana cares about, when really I will need to be addressing others’ potential concerns as well. Priya, for example, likes to refer back to outdated data on how much employees value a hybrid work environment—she’s convinced that we won’t be able to hire the talent we need if hybrid isn’t an option. The question about Robert was also useful, because he cares especially about real estate costs, which the differentiated policy we’re recommending will actually help reduce.

Before Claude pointed it out, I hadn’t realized there were three distinct actions embedded in what I wrote for that section. I’ll need to get Diana’s buy-in ahead of the meeting that we’re ultimately preparing for. So the single key action is leadership team buy-in (which should lead to CEO approval).

These initial questions were valuable for helping me think about the specifics of the situation in greater depth. After responding to the individual questions, here was the feedback on my audience section:

Reframing as a pilot is an excellent idea—and not something I had considered previously. Not only will that likely feel less threatening to those who might be against a change, but I anticipate it will also feel more comfortable for Diana to put forward to the leadership team.

With a clearer understanding of my audience and the action I want them to take, I moved on to the second section of the Big Idea worksheet, What is at stake? I thought about what our analysis had revealed in light of what each audience members cares about to create the following lists.

Next, using potential prompt 2, I shared these benefits and risks with Claude and asked whether they were genuinely meaningful to my specific audience, which ones were the most likely to resonate given what I’d learned in the previous step, and which were most essential to carry into my Big Idea.

One of the things I’m finding super useful as I work with my AI partner is how clear my thoughts become when I have to form logical answers to questions like this. I clarified that the stakes are going to need to resonate beyond Diana, to the entire leadership team. Marcus, Priya, Robert, and Diana all need a reason to say yes (or at least not block it).

I shared that the real estate savings is a real expectation based on analysis we’ve done, which Robert will respond favorably to. That should make him a strong ally in the room when the conversation takes place, so we’ll need to make sure the finding is clear and defensible.

I also pushed back on the people analytics credibility question—its erosion would be much bigger than just me. If leadership commissions rigorous analysis and then ignores it, it sends a signal about whether the data is truly valued, which has implications far beyond this single policy decision. Claude pushed further, suggesting reframing this risk to something like “undermines data-driven decision making as an organizational practice.”

Then it suggested the following in response to my specific questions:

I recognized that greater productivity and better outcomes were vague at best when I wrote them. There is productivity data we could link to in order to make it more real and less jargony, however in seeing some of the other benefits and risks I outlined clearly tied to what individuals in my audience care about, I can see there is likely more compelling data to focus on.

With this in mind, I took a first crack articulating my Big Idea. 

After reflecting on the pilot suggestion, I decided a full change was the stronger recommendation. A pilot would undermine the cost savings argument—you can’t meaningfully reduce real estate footprint on a temporary basis. We’ll keep the pilot framing in our back pocket if there’s significant pushback, but we shouldn’t lead with it. This is a good reminder that AI’s suggestions are starting points, not directives—you should always be the one making the final call.

I provided this detail to AI, along with the questions from potential prompt 3. Here were the initial questions posed to me in response:

I explained that I intentionally don’t want to anchor people on a specific number. We have analysis on cost savings in terms of real estate and reduced attrition, but there are so many assumptions that go into the latter in particular, that I don’t want to invite people to pick apart the specifics, when the directionality and general magnitude are more important.

I also clarified that the Big Idea will frame my presentation both for Diana and the leadership team. I’m aware that “stronger, more resilient workforce” is a little vague, but I like that it can refer to smoother onboarding, less frustrated managers, and productivity gains—all benefits we expect to reap if this change is made. We’ll go into more details on each of these in the presentation itself.

Here is Claude’s response: 

Taking these options together with my initial version, I iterated to:

It’s time to shift from our current three-days-in-office policy to a differentiated approach based on role and team type—one that meaningfully reduces costs and enables people to perform better and stay longer.

Looking back at where I started versus where I landed, the difference is meaningful. I came in focused primarily on Diana and with a muddled action. Working through it with AI pushed me to think about every person in the room, sharpened my understanding of what’s genuinely at stake for each of them, and helped me arrive at a Big Idea I feel confident standing behind. That’s more than I would have worked through on my own in the same amount of time.

In our workshops, we often have participants work through the Big Idea worksheet with a human partner who asks questions, pushes back, and helps you see what you’re too close to notice. What struck me working through this with Claude is how effectively it can play that role. It’s patient, it asks good questions, and—when you direct it well—it helps you think more clearly rather than doing the thinking for you. That’s exactly what AI as a thought partner should be.

Context is the foundation that everything else builds on. Get it right here, and the rest of the process becomes easier. 

In the next post in this series, we’ll move to another core SWD skill: crafting a story. In the meantime, register for our free live event on July 13th where Simon and I will be exploring how to use AI for better data storytelling—including diving deeper into ideas from this series.

The layout of a slide—where the graph lives, how much space text gets, how the eye moves through it—is not only an aesthetic choice. It’s a communication choice. Get it right and your audience follows your logic effortlessly. Get it wrong and even great graphs can leave people confused.

One useful concept here is vertical logic. This is the idea that all information on a given slide should be self-reinforcing. The content reinforces the title and vice versa. The words reinforce the visual and vice versa. Nothing extraneous, nothing unrelated—just a clear flow where a reader’s understanding deepens with every element they encounter. (You can read more about vertical logic, alongside its companion concept horizontal logic in storytelling with data.)

My colleague Alex builds on this in storytelling with data: before & after, introducing two go-to layouts for putting vertical logic into practice. All of the examples in this post are drawn from that book.

The one-sided layout gives a single graph a large, prominent position, with space for text across the top and to the right. This leverages the picture superiority effect: people notice images faster, understand them more easily, and remember them longer than words alone. Lead with the visual, let it do the heavy lifting, and use the surrounding text to frame and reinforce the message. In before & after, we use this structure to communicate a succinct marketing recommendation.

The two-sided layout divides the slide into vertical sections, with text and graphs on both left and right. This is a good choice when you’re working with a direct comparison between two scenarios—for example, a current state versus a proposed one—where you need the audience to hold both in view simultaneously. It also works well when your story has two natural paths that belong together on the same slide, for example trend over time and composition breakdown, as illustrated below.

Knowing which to use comes down to one key question: will you be there?

If you’re presenting live, you have much more flexibility. You’re present to guide your audience through the content. You can use animation and progressive reveal to build the story beat by beat. In that context, a single graph per slide often works beautifully. The slide supports you; it doesn’t need to stand on its own (or have many words).

If the slide needs to stand alone—as a leave-behind after a meeting, a one-pager for people who weren’t in the room, or a single slide within a larger update like a quarterly business review—the calculus shifts. Now the slide carries the full weight of the communication without you there to narrate it. That’s when structure and narrative text become essential, and when the one-sided and two-sided layouts we’ve discussed really earn their keep. You may also simply face the practical constraint that everything has to fit on one slide. These layouts give you a principled way to work within it.

It’s worth asking this question explicitly before you start building: Will I be there to walk people through this, or does the slide need to do the work itself? The answer shapes almost every layout decision that follows.

And of course, two layouts aren’t the only options. Sometimes the story calls for more. For example, below is a three-graph slide I created—more visual evidence to support, still laid out in a way that preserves clarity and flow.

Whatever the configuration, the goal is the same: a slide where every element earns its place and the whole is more than the sum of its parts.

AI is everywhere in the conversations I’m having right now. Not an engagement goes by without someone asking about it. Case in point: even 8-year-old Teddy posed an AI question at a Daphne Draws Data reading! Events with “AI” in the title draw big crowds—the “Data Storytelling in the Age of AI” panel I joined at Canva Create, for example, was the highest-attended breakout of the day.

What’s striking isn’t just the interest, but how differently people are approaching it.

Whether you’re not using AI at all, just beginning to experiment, or already integrating it into your work, this month’s challenge is an opportunity to form—or expand—your perspective through hands-on use. There’s also a ton we can learn from one another by thinking critically and sharing insights:

• Where do you, as a human, add the most value?

• Where can AI be used most effectively?

• How can you combine the two to increase efficiency and impact?

Your goal this month: explore how human + AI can work together to create a data communication that’s better than either could produce on its own.

The challenge

Create a data communication using human + AI.

Use data of your choice—something you can share from your work, a personal dataset, or anything you’re curious about. If you don’t have data handy, feel free to use one of the exercises from storytelling with data: let’s practice!.

Work with an AI tool of your choice to explore, analyze, and/or create your visual. This could include brainstorming, refining your message, drafting a chart, or iterating on your design.

If you’re new to AI, start simple—experiment and see what’s possible—try ChatGPT, Claude, Gemini, Copilot, or any AI tool you’re curious about. If you already use AI regularly, challenge yourself to try something new: a different tool, a novel workflow, or a new way of collaborating.

As you work, keep in mind: where are you adding the most value, where is AI particularly helpful, how can the two best work together? Please include observations and learnings in your commentary.

Share your creation in the SWD community by May 31st at 5PM ET. If there is any specific feedback or input that you would find helpful, include that detail in your commentary. Take some time also to browse others’ submissions, and contribute your perspective through comments and datapoints over the course of the month.

Related resources

Here are a few related resources. If you are aware of other good ones, please share in your submission commentary.

• Data Storytelling in the Age of AI panel video from Canva Create

• Data Storytelling in the Age of AI blog post

• AI is shifting how analysts spend time by Elizabeth Ricks

• May 2025 SWD challenge: compare human and machine

For more on using AI thoughtfully in data storytelling, explore our growing collection of resources.

Recently, I had the chance to be part of a panel at Canva Create on Data Storytelling in the Age of AI, alongside Duncan Clark and Tey Bannerman. We discussed how quickly AI is changing what’s possible and what that means for people who work with data. The conversation kept coming back to a provocative question, If AI can generate the charts, what’s left for the human?

Here’s where I’ve landed: the hard part hasn’t changed.

AI can make graphs in seconds. Entire dashboards. Draft presentations. Even suggested narratives. And while the design quality is currently inconsistent, that gap will likely narrow quickly. This is redefining what it means to be “good with data.” Historically, it meant skill with tools: knowing how to write the code, wrangle the data, structure the dashboard, or build the graph.

AI is shifting that. But maybe not in the way people think.

The hard part isn’t making the graph

Tools have been making graphs for decades. Excel. Tableau. Flourish. The mechanics were never the most difficult piece. The hard part has always been deciding what matters: understanding your audience, interpreting what the data means, and figuring out what someone should do differently because of it.

Let’s look at an example.

The following side-by-side shows manager performance scores before and after a pilot manager training program. On the left is the AI-generated chart. On the right is the one that I designed.

The AI version isn’t wrong. But it also isn’t right for my specific scenario. That’s where I, as the data storyteller, come in. Knowing who my audience is, I can make smart decisions about how to visualize the data, what clutter to eliminate, where to focus attention, and what action to drive. That is data storytelling.

While AI might make it easier to generate output, it doesn’t make these important decisions for you—and it shouldn’t. If anything, it makes the humans making them more important.

Start with thinking—and make use of productive friction

A practical strategy I shared on the panel (one I was teaching well before AI) is to start low tech.

The temptation with any tool—AI or otherwise—is to jump straight into generating graphs and slides. The risk is that you skip the most important step: determining what you actually need to say.

Start analog. Gather your team around a whiteboard or reach for a pen, paper, or my personal favorite low-tech tool: sticky notes. Figure out the plan and the story. Who is it for? What decision are you trying to support? What action do you need to drive?

That gets at something I’ve been thinking more about lately: the role of friction.

Friction, in the classic sense, slows us down. In some instances that’s undesirable. This unhelpful variety comes in the form of confusing graphs, cluttered slides, and unclear structure. This is the friction I’ve spent my career helping others remove. If AI can help eliminate this type of friction, I think that’s great.

But there’s also productive friction. This is the type that forces us to think more clearly before we communicate. Go back to that rule I shared about starting low tech: there is literal friction as you move a pen across paper. That slows us down in really useful ways—to ask what matters, question the data, decide what action makes sense.

This reminds me of a conversation I had with Ken Field for the SWD podcast. Ken is a cartographer, and he talked about how much mapping has changed in the past couple decades. Because the materials to physically make a map were expensive, there was an incredible amount of planning before ever putting pen to paper. All of this changed with GIS tools—suddenly, basically anyone could make a map. And that planning step was no longer required to create one. But it remains just as important for creating an effective one.

That stuck with me, because it’s not really about maps. It’s about thinking.

AI is doing something similar—at a scale far beyond maps. It can remove a lot of the production friction, the mechanics of building graphs and slides. That is incredibly powerful.

But it also makes it easier to skip the most important step: thinking. That’s a major problem. This means the goal shouldn’t be to eliminate friction entirely. It should be to remove the parts that get in the way, while preserving the parts that make us better. Don’t let AI replace your critical thinking!

While AI accelerates production, humans still own judgment—and responsibility

That tension—between speed and judgment—is key. AI is incredibly powerful at generating options. It removes the blank page and speeds up exploration. These are all good things. But someone still has to decide what’s worth focusing on.

It’s important to note that this isn’t a new phenomenon. We’ve seen it before. There was a time—not that long ago—when organizations believed hiring data scientists would solve their data problems. Before that, it was dashboards. While technical expertise is critical, it didn’t crack the real issue: you can generate all the insight in the world, but if people don’t understand it or know how to use it, nothing changes. In this world, an effective data storyteller bridges the gap between insight and action. It is the data storyteller who will bridge the gap with AI as well.

AI can generate answers exceptionally fast. But someone still needs to decide what questions are meaningful, which data is trustworthy, and which action should follow. AI might be able to generate the chart, however humans still own the interpretation. They must decide what it means and what to do next.

It’s also worth highlighting that AI can generate polished-looking outputs very quickly. This can make it easy to overlook flaws. When you do use it for analysis or content creation, it’s critical to remember: you are responsible for what you put in front of an audience. A simple rule still applies: never present a graph you can’t explain.

If you can’t clearly articulate what the data shows, why it matters, and what people should do with it, then it’s not ready. AI doesn’t change that—it just makes it easier to skip that step. But the accountability hasn’t shifted. If your name is on the slide or you are the one presenting it, your judgment should be behind it.

I was talking with a lawyer friend recently who shared something that stuck with me. She had been using AI to help draft briefs and emphasized how important it is to provide strong context up front to get useful output (the corollary to a common warning from my past life building statistical models: garbage in, garbage out). But she also said something else: once the draft is done, it still needs a critical review.

That applies here, too.

AI is designed to satisfy the user. Which means if you’re not careful, it will start telling you what you want to hear. That’s where a second set of eyes becomes incredibly valuable—someone outside the back-and-forth who can question what’s there without bias.

I’ve long been an advocate of getting another perspective on your work. When AI is involved, that step becomes even more important.

AI cannot replace human connection

One of the things that struck me most at Canva Create wasn’t the technology—it was the people. I had the chance to connect with so many thoughtful, curious, creative humans, who care deeply about how their work lands and the impact it has.

There’s a lot of focus right now on what AI might replace, and a legitimate concern that it could stunt thinking or dull creativity. But I left with the opposite view. If anything, this is a moment to double down on what makes us human. Storytelling has never been about charts or slides alone. It’s about connection. It’s about understanding your audience, meeting them where they are, and helping them see something in a new way.

While AI can generate content, suggest structure, and speed up production, it doesn’t care. It can’t read a room or build trust. It can’t decide what matters in a way that reflects human context, nuance, and judgment. That part is ours. So rather than asking what AI will take away, a better question is: what does this make more important?

For me, the answer is simple: human connection. Data storytelling has always been about that. In the age of AI, it matters more than ever.

Want to go deeper? Explore more resources for using AI thoughtfully in data storytelling.

 Let’s consider an example.

This is a project schedule with risk assessments showing the probability distributions for each activity of interest. The colors of the bar represent the likelihood of completion. For instance, the green portions towards the left show optimistic timing (10–50%), while the red portions indicate later, more likely completion dates (70–90%).

The black triangles represent the planned date, or the scheduled target, of each activity listed on the left, while green triangles show the need date, the required date to meet an on-time delivery. A table at the right lists the probability of meeting each date, adding a lot of information, but also visual complexity.

Simplifying involved views like this allows the audience to spend less effort decoding the structure and more time engaging with the insight it conveys. Imagine we want to use this chart to show where the project team should focus its efforts to meet the desired delivery date. Specifically, the Functional Test, Vibe, and Therm-Vac workstreams are showing no chance of on-time completion and will require corrective action to meet current deadlines. Some thoughtful design changes could help improve the effectiveness of this message.

Reduce clutter

The original view has many lines contributing to the cluttered look. Removing the gridlines is a quick and easy way to begin to bring the pertinent information to the foreground.

With fewer borders, greater contrast is created between the background and the data, allowing the horizontal bars and milestone dates to stand out more. 

Another way to make it easier for our audience to consume this information is to be thoughtful about the order and alignment of the data and labels. Currently, the dates are at the bottom, which means they will likely be the last thing that is seen. Since dates are such an integral part of a project timeline, we should move them to a more prominent place at the top of the chart.

By moving the dates higher up, we can make sure they are seen first. Additionally, by simplifying the date formats on the axis, it becomes possible to rotate the text horizontally, making it quicker and easier to read.

Our next step is to rethink the use of color in this chart. The current green, yellow, red gradient is a challenging combination to discern for people with color vision deficiency.

A color palette that uses shades of gray for bars eliminates the visual noise from multiple bright colors, allowing the viewer to focus on the timeline and task durations. For the milestones, more colorblind-friendly colors—blue and orange—make them stand out. Opting for circle markers is visually smoother and feels less alarming than the pointy triangles. Finally, applying the same color used for the circle icons to the probabilities listed in the table on the right helps to visually tie them together. 

Now that the chart is less busy, we can turn our attention to guiding viewers toward the information that matters most. Ideally, we hope to draw our audience’s eyes to key elements or insights in the graph. We can apply some intentional focus techniques to emphasize these key takeaways for our audience.

Focus attention

As things stand, the Functional Test, Vibe, and Therm-Vac tasks have zero probability of being completed on time. A simple way to quickly get this point across is to add a takeaway in words at the top of the chart to tell our audience what we want them to know. 

The added text indicates what we want our audience to see in the chart. We can also make additional adjustments to the chart itself to ensure that the words our audience reads are reinforced visually.

A light orange band spanning the three tasks named in the title draws attention to the workstreams at risk. Sparing color highlights just the plan dates (since these areas do not have need dates), while deeper intensity of color spotlights those projected to miss the scheduled target. Finally, placing the plan and need date probability columns on the left makes them quicker to find, and bolding task names and zero-percent probability values adds prominence.

By applying decluttering and focusing techniques, we can bring clarity to a complex Gantt chart, leading to quicker decisions and a clearer understanding of schedule risks. 

To see more examples of timeline visuals, check out our April 2026 Challenge.

I remember when I was building my team at Google and spending an inordinate amount of time designing the interview case study. I wanted something that would help us assess candidate’s skills—but also give applicants a realistic preview of the type of work they’d be doing if they joined the team. (That is, until the case study was leaked and I had to redesign it!)

What I loved most wasn’t only designing the process. It was seeing the organization through fresh eyes. Through candidates’ questions and perspectives, I was reminded how special the work—and the impact we were having—really was.

I’ve felt that same sense of perspective over the past couple of months as we’ve focused on hiring for the storytelling with data team.

A humbling process

When we opened applications for the Data Storyteller role earlier this year, we weren’t entirely sure what to expect. What followed was both energizing and humbling: more than 150 people applied!

As a small team, every addition matters enormously. Each new person changes how we show up for our clients, our community, and each other. Because of that, we take an incredibly intentional approach to hiring. The process is a significant investment for our team. From reviewing applications and candidate exercises to conducting interviews and thoughtful debriefs—I’m incredibly grateful for the time and care the team brought to every step.

An aspect that made things difficult wasn’t narrowing down the field because of a lack of qualified candidates—it was quite the opposite. We met many talented people who are doing meaningful work to help others communicate more effectively with data. It was hard to say no to so many great people.

One of our goals throughout the process was to make sure that even candidates we didn’t ultimately select had a positive experience. Many of the people who applied are part of our broader community, and we hope they continue learning with us, participating in challenges, reading the blog, and pushing this field forward alongside us.

From this incredible group, we’re excited to welcome two new teammates.

Meet our newest data storytellers

Today, I’m excited to introduce two people joining our team: Alli Torban and Ryan Biesecker.

Alli brings deep expertise in data visualization and data literacy, along with a passion for helping people truly understand data. She is the host of the Data Viz Today podcast, the author of Chart Spark, and has been a thoughtful and creative voice in the data visualization community for years.

At SWD, Alli will be contributing across our workshops, content, and community initiatives, including taking ownership of the SWD podcast, helping shape its next chapter while continuing to bring thoughtful conversations to our audience.

Ryan joins us with a background in consulting and analytics, where he has helped organizations use data to answer complex business questions and drive better decisions. He brings strong technical skills, client experience, and a natural focus on translating analysis into clear, actionable insights.

At SWD, Ryan will contribute across our workshop delivery and content while also helping us better understand patterns in our client work—bringing a data-driven lens to how we think about relationships and opportunities to expand impact.

You’ll start to see more from Alli and Ryan soon, as they begin contributing across our workshops, blog, podcast, and other SWD channels.

What this means for you: our clients and community

Growing our team allows us to increase our capacity to work with organizations through team trainings. More importantly, it allows us to do this while maintaining the thoughtful, high-quality experience we strive to deliver in every engagement. This means we can continue to serve our clients and community, while expanding the reach of the work we care so deeply about.

Looking ahead

We feel very fortunate that Alli and Ryan chose to join us. Each new teammate helps us further our mission: to help people communicate more effectively with data and drive positive change through better understanding. Please join me in welcoming Alli and Ryan to the storytelling with data team!

Consider the following example. Take a moment to scan the slide below and see if you can quickly identify the main takeaway.

This slide summarizes performance for a SaaS (software as a service) company, showing how revenue from newly acquired customers grows over time based on how they were acquired and how many products they adopt.

Before we can start interpreting the data, we have some serious decoding work to do! There are numerous acronyms and abbreviations that are used repeatedly across the graphs and commentary.

By my quick count, there are more than 50 acronyms and abbreviations on this single slide!

Let’s start by defining the main ones:

• ARR: annual recurring revenue

• MOB: months on book (time since account opened)

• AE: account executive, or sales-assisted channel

• PLG: product-led grown, or self-serve channel

• MP: multi-product customers

• SP: single-product customers

• LTV: lifetime value

• Accts: accounts

For the people who produced this slide, these terms likely feel perfectly normal. For those outside that team—even someone from another part of the business—this slide requires a ton of translation before we have any chance of understanding the message.

Speaking of the message, if we refer back to the subtitle of the slide, we can learn the core insight: High Value Disparity in MP vs. SP Accts.

In plain language, this means: customers who adopt multiple products generate far more value than customers who use only one.

That key insight is buried under layers of shorthand and reporting language.

I reworked the slide with this insight in mind, articulating it succinctly at the top. I also defined acronyms, spelled out full words, and stated things simply. I condensed the data that was previously in four graphs to two, making it easier to compare each metric across all customer segments. I organized titles and commentary together with each graph to make it easier to connect the words to the data that support them. I added a discussion topic to help direct next steps.

The original slide might have worked perfectly well in a regular report. Reports often prioritize completeness and precision.

But when you need people to truly understand something and pay attention, it’s worth taking the time to translate. This means translating:

• acronyms into language your audience understands

• jargon into meaning

• analysis into a clear takeaway

The challenge isn’t the data—it’s the translation.

When we’re deep in our own analysis, it’s easy to assume that others speak the same language we do. But our audience doesn’t live inside the metrics, acronyms, and reporting structures. If we want them to engage—and act—we have to translate first.

This is exactly the kind of “aha” moment we see in our storytelling with data team trainings, which are customized around examples from your team’s own work. Often the biggest breakthroughs come when people see how small changes—like clarifying language, focusing the message, and simplifying the visuals—can completely transform how their data is understood.

Learn more about our team trainings.

With the title “CONSUMER ENGAGEMENT ANNUAL REVIEW,” an opportunity is missed to indicate what truly matters with the slide, which shows customer engagement for a department store across several retail areas. The audience is left to determine independently what deserves attention. This can be improved by making a key observation about the data. Doing so primes the viewer to look for evidence of that statement in the chart.

This is better. The most important insight from the visual, a concerning decrease in engagement for the Home & Garden area in November, is now clearly communicated. Yet, committing to a single interpretation of the data can feel uncomfortable. Perhaps this is part of the reason we sometimes see slide titles posed as questions.

At first glance, this feels engaging. A question invites curiosity and perhaps debate. But good intentions can introduce complications. When faced with a question, the audience may:

• Mentally attempt to answer it: Now those present have been asked to think. That can be engaging—but it’s also demanding. They’re trying to answer the question, interpret the chart, and listen to you at the same time. Not all of those tasks can happen simultaneously. Most likely, what suffers is attention to the presenter.

• Interrupt the presentation: Questions create a knowledge gap that the brain wants to fill. This can be powerful, but only if the answer comes quickly and clearly. More outspoken participants may try to answer immediately, steering the discussion in a direction not planned or practiced.

• Come to the wrong conclusion: In trying to answer the question, the audience might need to make assumptions or draw their own conclusions. This risks putting them on a different path from the intended narrative.

• Undermine presenter credibility: Posing a question can unintentionally signal uncertainty, implying that a firm conclusion has not yet been reached or the presenter isn’t confident in the insight. A heading like “Are our campaigns effective?” may leave those in attendance wondering whether there’s a clear answer coming at all. 

Instead, consider using an active takeaway title that explicitly states the key message—and signals how the information should be interpreted or acted upon. In the case of our example, the promotional content for the critical Black Friday period wasn’t as extensive as it could have been, resulting in consumers exploring competitors' stores for deals. A further revision of our slide demonstrates the effect this clarity of message can have.

With a clear purpose established for the slide, the added benefit is that the graph can now be revised to tie to the statement visually. Let’s iterate one final time.

In this version, color is used sparingly to emphasize the Home & Garden line, annotations (plus sparing data markers) have been added to highlight the key moments, and a clear recommendation clarifies what to do next. The meeting can now focus on discussing the proposed action rather than debating what the data means.

Slide titles framed as questions are more engaging than descriptive ones. Used deliberately, they can spark curiosity. But they can also divide attention, introduce confusion, or imply uncertainty. Instead, identify the main purpose of the slide and craft a takeaway title that states it clearly, ideally signalling the appropriate course of action. And in doing so, you can be confident the audience immediately understands the point of the communication and how to move forward.

If you are interested in learning more about the importance of words when creating slides, explore past articles for additional examples: transforming slide titles and vertical logic. Or if you’d like to practice crafting a compelling takeaway title, check out this community exercise.

Such a simple—and useful—question.

One challenge was that the graph was attempting to show multiple comparisons at once, so it wasn’t clear what mattered most. To further complicate things, the data in question spanned very different magnitudes, with one category dwarfing the rest.

Here’s an anonymized version of the original slide, showing new subscribers to a streaming service by plan type across two years:

Let’s revisit the simple question that was raised: what are we supposed to take away from this?

The story could be about one plan dominating the rest. Alternatively, the long tail of niche plans might be worthy of review and discussion. Or perhaps there’s something interesting about the year-over-year shifts across plans.

Each of those takeaways can be found in the original slide, but it takes work to get there. If this is part of a routine report or update and no action is needed, it might be fine as is. However, if we want someone to understand one or more of the nuances at play and drive a discussion or decision, we can approach things more strategically. Part of doing this means choosing different visuals for different takeaways. Another aspect to consider is whether to aggregate and how to treat categories so that we can see important points—without overwhelming with detail.

Let’s tackle one specific takeaway at a time, intentionally choosing a visual that supports it.

Composition story: simplify and stack

If our goal is to clearly communicate that one plan accounts for the majority of new subscribers, instead of burying that insight in a crowded bar chart, we can make it explicit.

This view is useful to show concentration (alternatively, stacked vertical bars or even two pie charts could have worked here). By collapsing many categories into just two—Premium vs. everything else—we remove noise and make the dominant pattern unmistakeable. This kind of aggregation is most appropriate when the goal is orientation or framing (not detailed plan-level decisions). The audience no longer needs to scan bars, compare labels, or reconcile magnitudes. The message is immediate.

I’ve kept the words descriptive here, but you can imagine how we might use a view like this to prompt a specific discussion, for example, whether the business is overly dependent on a single plan, how resilient new subscriber growth would be if Premium performance changed, or to consider whether it makes sense to invest in Premium or elsewhere.

Speaking of elsewhere—if those non-Premium plans are key, we’ll want to approach things differently.

Focus on the tail: take out the big category

It can be challenging to effectively visualize numbers of very different magnitudes in a single graph. In the instance where one or a few categories are large, but the story is in the smaller ones, removing the large categories from the visual can sometimes make sense. Key is to retain the context of the omitted categories so things aren’t accidentally misinterpreted.

In this instance, we could use words to comment on the size of Premium, then focus the graph on the non-Premium plans. See below. (If you were still afraid it might be missed, you could even play with positioning a bar under the premium note that extends off the right side of the page.)

Treating Premium separately and removing it from the graph allows us to see what’s happening in the tail. Low volume doesn’t mean low value—but it does suggest a different set of strategic questions.

A view like this could be used to drive discussions about portfolio rationalization (are there too many low-impact offerings?), whether small plans are strategically important or operationally distracting, or where simplification might free up resources without materially impacting growth.

Over time comparison: slopegraph

A third—and entirely different—focus is change over time. Which plans are gaining momentum, and which are losing it? A slopegraph can make those changes easy and fast to see.

A view like this could help frame discussions about where recent investments are paying off (or not), which plans may need renewed attention or repositioning, or simply to better understand how subscriber preferences are shifting year over year.

Start with the takeaway, not the graph

None of these views is the correct one. Each highlights a distinct takeaway—and is useful to drive a different type of conversation. The mistake isn’t choosing the “wrong” chart; it’s asking one chart to answer every question at once.

By separating stories—dominance, the long tail, and change over time—we reduce cognitive load and increase clarity. We also give ourselves flexibility: the same data can support different discussions, depending on what is important and which decisions need to be made.

You could even imagine a progression through these three views, with an overarching narrative to tie it all together. One chart doesn’t have to do it all. A thoughtful set of charts can tell a more effective story.

Meta lesson: start with the question or takeaway. Then choose a visual (or combination of visuals) that will help you make it unmistakeable.

To learn more about various graph types, check out the SWD chart guide.

This article is part of our back-to-basics blog series called what is…?, where we’ll break down some common topics and questions posed to us. We’ve covered much of the content in previous posts, so this series allows us to bring together many disparate resources, creating a single source for your learning. We believe it’s important to take an occasional pulse on foundational knowledge, regardless of where you are in your learning journey. The success of many visualizations is dependent on a solid understanding of basic concepts. So whether you’re learning this for the first time, reading to reinforce core principles, or looking for resources to share with others—like our new comprehensive chart guide—please join us as we revisit and embrace the basics.

Most people are familiar with maps—from school, the news, and navigation apps—making them a natural option to explore when visualizing geographical data. In this article, we’ll explore one common map visual: the choropleth map. We will cover what it is, how to interpret it, when to use it, and common pitfalls to avoid when using one.

What is a choropleth map?

A choropleth map, sometimes called a filled map, is one way to display a numeric variable across a geographic area. They are often used to show population statistics, such as the Pew Research map below, which shows the importance of religion around the world.

SOURCE: pewresearch.org/religion/2022/12/21/key-findings-from-the-global-religious-futures-project

The level of the geographic grouping sets the boundaries for the regions, and each defined area is shaded to signify the value for that location. The map above is grouped by country, and their shading is determined by the color scale in the legend at the bottom: blues represent lower values, while oranges indicate higher values. 

How do you interpret a choropleth map?

In a choropleth map, color fill is applied to help compare values across geographic areas. Often, darker or more intense colors represent the extreme values, making it quicker to see patterns and compare data across locations. When interpreting a choropleth map, it helps to pay close attention to both how the data is defined and how the colors are applied. Choropleths work best for normalized values, such as rates, percentages, or per‑capita figures, rather than raw totals, which can unfairly favor large or populous areas.

The ability to glean insights from a choropleth map is heavily influenced by the color scale used. While more color gradients could make it more complex to interpret, too few can make it hard to see small differences. For example, in the Pew Research example above, India and Pakistan are both dark orange, yet their values are quite different: India at 80% and Pakistan at 94%. Understanding how color ranges are defined is essential, since small changes in these cutoffs can shift which category a region falls into and, in turn, what patterns your audience perceives first.

When are choropleth maps useful? 

This type of map is most useful when the values being visualized are meaningfully tied to the geographic areas being displayed. For example, weather is specific to certain locations, so showing yearly rainfall by county in a given state could make sense if you want to display the differences across various locations.

SOURCE: ncei.noaa.gov/access/monitoring/climate-at-a-glance/county/mapping

The choropleth map above shows the total county-level precipitation across Texas for January to December 2025, with darker green representing wetter areas and lighter green representing drier areas relative to other parts of the state. East Texas stands out as much wetter, with many counties in the darkest green shades, indicating 40-plus inches of rain during the year.​ The west and southwest counties appear drier, with very light green counties showing lower annual precipitation totals.​ County-level variation is substantial, so state averages could mask important local differences in water availability, drought risk, and flood potential.​

What are some challenges of choropleth maps?

Although maps help visualize patterns and comparisons, they can be challenging to analyze and even misleading when large areas visually overpower smaller ones. 

SOURCE: https://www.iihs.org/research-areas/fatality-statistics/detail/state-by-state

For instance, the graph above shows motor vehicle crash fatalities by state. At first glance, California and Texas jump out with the darkest shade of blue. However, these states are also among the largest in both population and land area, meaning there are more people and more roads where crashes can occur.

If you factor in population, the view normalized per 100,000 people would look like this.

SOURCE: https://www.iihs.org/research-areas/fatality-statistics/detail/state-by-state

Normalizing the metric creates a very different view of the data. Here, Wyoming and Mississippi have the highest rates. Wyoming stands out a bit more, given that it is larger on the map than Mississippi.

Even when the metric is appropriate, differences in the physical size of geographic areas can still influence what appears most important. Large states can dominate visually, while smaller states risk getting lost. To account for variation in each state's geographical footprint, you might opt for an alternative display that uses a consistent hexagonal shape to represent each state.

SOURCE: https://www.iihs.org/research-areas/fatality-statistics/detail/state-by-state

With a hex-tile map, you eliminate discrepancies in US state sizes, giving equal visual space to smaller states like Hawaii and larger states like Texas.

In addition to size, the way values are grouped into ranges (for example, equal intervals versus quantiles) and the choice of color scale also shape what patterns pop out first. Small changes in how you define the breaks between ranges can shift which group a place falls into, changing the visual story. Using a clear, ordered legend and an intuitive color progression from light (less) to dark (more) is important. And when missing data is present, make it visually distinct from true zeros so your audience does not confuse “no data” with “no phenomenon.”

Just because you have geographic data does not mean you must use a map. In some cases, not using a map may be a better option. For example, if you want your audience to more precisely compare the differences between each state and see how they rank, then an ordered horizontal bar chart could be worth exploring. Having a clear understanding of your message and how you want your audience to process the information will help you choose an appropriate view for the data.

Where can you practice and learn more about maps?

Cartography, the field of map design, is a deep topic, and we’ve only covered a small bit here. If you want to explore maps further, check out these resources:

• Listen to our podcast with cartographer Kenneth Field and visit his site. 

• Tackle a map-related exercise in the SWD Community.

• Watch our live event about maps to explore other types, such as symbol maps and cartograms (available to premium members only).

• Learn how to create a map in Excel and Tableau, including hex maps.

To explore other charts, browse the full collection in the SWD Chart Guide, or for resources to teach younger audiences, check out DaphneDrawsData.com.

If you read the slide above carefully, you’ll notice it’s not wrong. The bullets are specific and describe real barriers. But imagine encountering this in a meeting or while checking your inbox. It’s hard to scan, and you’d likely give up reading after a couple of bullets. I’ll walk you through a simple transformation that reframes and reorganizes these same ideas so an audience can quickly grasp the information.

Employ whitespace

One challenge with the slide is that there is no breathing room. Allowing more space on the margins and between lines improves readability by reducing the overwhelming, cluttered feeling of the original view. One option is to reduce the font size to make more room, but that could make the resulting text hard to read. Another idea is to break the content into two columns and bump up the font size a bit.

The added whitespace around the margins and between the columns makes it a little less intimidating, but still a lot to process. Let’s take things a step further and add more white space between the bullets.

That’s much easier to scan, yet it still feels crowded because it has the same number of words. Next, let’s consider how to group these barriers to create greater visual hierarchy, making it less challenging for our audience to understand.

Create visual order

One helpful way to tame an overstuffed slide is to look for ways to group information. Our brains are better at processing and remembering a smaller amount. By chunking the bullets into a smaller set of categories, the content becomes easier to comprehend. 

Looking at the original list, each item falls into one of three broad themes:

1. Things that make it hard to get started with money

2. A lack of options that work for someone’s specific situation

3. Barriers that tend to show up more in underserved communities

Instead of a single long list, we now have three categories we can use to organize the bulleted text. With defined groups, we can structure the slide around these themes to make it quick to scan. 

I opted for a three-column layout to provide visual hierarchy, with pithy, enumerated headings at the top and more detail below as people traverse the information. Within each block of text, keywords and phrases are bolded to make it easier to scan the information. Also note how, with this layout, even if someone only reads the headings, they still get the main points.

Use animation when presenting live

The slide above is an improvement from the original, but it still contains quite a bit of text, making it challenging to consume in a live setting when your audience is also trying to pay attention to what you are saying. When presenting in person, apply animation to slowly introduce the information bit by bit, helping the audience follow along and avoid being overwhelmed by too much at once. 

Also, look for ways to reduce the amount of text. When speaking to the content, you can elaborate on each point verbally rather than have all the words on the slide, as the added details would be spoken in the delivery.

If we consider all of the above steps together, we have turned the eleven disconnected bullets into a more effective slide. The content didn’t change much—the organization did.

For more examples of visual transformations, check out the before-and-afters in our makeover gallery, including how to apply similar techniques to emails and reports. If you want to practice improving a text-heavy slide, tackle this related exercise.

“What do you do?”

It’s one of the most common questions we ask in casual conversation. It’s also one of the trickiest to answer—especially if your work involves data.

I learned this the hard way early in my career.

At that time, I worked in banking in Credit Risk Management. When someone asked what I did in a social setting, I’d respond with, “I’m a credit risk analyst.”

This was a guaranteed conversation-killer. I don’t think people were trying to be rude. It’s just that most people don’t have a mental model for what that title means. In the absence of understanding, the brain does the most efficient thing it can do: it moves on.

Looking back now, what I was doing was the professional equivalent of showing up to dinner and saying, “I’m a Level 3 wizard in the school of quantitative uncertainty.” It’s accurate—but not super helpful. It wasn’t because the role wasn’t interesting. It was meaningful work. But I didn’t know how to explain it.

Later, at Google, I figured out why.

Your job title isn’t your job

Many job titles are designed for internal systems—org charts, hiring ladders, compensation bands. They aren’t necessarily intended to help another human being understand what you do. Titles are shorthand for insiders. To everyone else, they can feel like jargon, ambiguity, or worse: an insurmountable wall.

Even today, in the analytical world common job titles can leave people guessing:

• Data Scientist

• Decision Scientist

• Insights Strategist

• Analytics Partner

• Business Intelligence Lead

• Quantitative Researcher

• Data Storyteller

To the people inside these fields, those titles signal important distinctions. Outside those circles, they may all sound like “does something with computers.”

Which brings me to my simple claim: explaining your job is a data storytelling opportunity.

Because it has the same root challenge:

• the work is complex

• the context is missing

• the terms are unfamiliar

• the audience doesn’t know what to listen for

So the solution looks familiar, too:

• start where the audience is

• provide context

• make it concrete

• explain why it matters

What I learned at Google: lead with context (not credentials)

At Google, I worked in People Analytics. I quickly realized that leading with my job title (“People Analyst”) led to the same glazed-over look I’d gotten in banking.

I tried starting with context. Instead of saying “I’m a People Analyst,” I’d say something like: “Google is a very data-driven company. So much so that they use data not just for products and marketing—but also for one of their biggest assets: people. I work with data in HR. I help leaders understand things like what makes a great manager, or what influences whether someone stays at or leaves the organization.”

Same job. Totally different entry point. This mattered: people could track with the story because I wasn’t expecting them to already know what “People Analyst” meant. I was building their understanding from familiar ground: Google, data, decisions. That’s the heart of storytelling—making something relatable and understandable.

A LinkedIn post that made me smile (and proves my point)

Recently, I was tagged in a LinkedIn post by a dad in Belgium. He shared a picture of his daughter reading Daphne Draws Data (that I used at the onset of this post) and described a moment that made me smile.

He wrote about how his kids described data after reading the book:

“Data is a beautiful princess!”—his two-year old.

“Data are numbers. And you can use data to help other people.”—his four-year-old.

Then he shared something even more meaningful: the book helped his kids understand what he does for work. Not “computers.” Not “math stuff.” Something closer to the truth: “He makes drawings of data to help people.”

I love this for so many reasons. First, kids are brilliant. Second: that framing is objectively better than most job descriptions I’ve heard from adults. And finally: it’s a reminder that what makes something understandable isn’t the accuracy of the label—it’s the clarity of the explanation. Titles rarely do that, but stories do.

Three ways to explain what you do (without losing people)

If you’ve ever struggled to explain your job to someone outside your world, you’re not alone. You’re not failing. You’re just bumping up against a very normal communication gap.

Here are three tactics that can help.

1) Start with who you help or what you enable. Instead of a title, lead with a “helping” statement.

• “I help leaders make better decisions using data.”

• “I help teams understand what’s happening and what to do next.”

• “I help organizations communicate complex information clearly.”

This gives people an immediate anchor.

2) Explain through an example. People understand examples far more quickly than definitions. Try a single sentence that starts with: “For example…”

• “For example, I analyzed what predicts employee turnover so we could test ways to reduce it.”

• “For example, I help teams figure out which steps in a process cause the most drop-off.”

• “For example, I redesign charts to executives can quickly see what matters.”

3) Use a “because” sentence. This is one of my favorite patterns because it forces meaning.

• “I work with data because it helps people make smarter decisions.”

• “I analyze customer behavior because it helps teams build better products.”

• “I teach data storytelling because insight isn’t useful if nobody understands it.”

Why this matters (beyond small talk)

On the surface, you might think this is about making conversation smoother at parties. That’s true, but it’s bigger than that. When we can’t explain our work, we make it harder for others to value it. This can have the undesirable result of making our impact invisible. We hand people a label and hope it does the heavy lifting. When people don’t understand, they disengage. This isn’t solely a social problem—it’s a professional one, too.

So yes: explaining your job is a data storytelling opportunity. And, like most data storytelling scenarios, it gets easier once you stop trying to be comprehensive and start trying to be clear.

Because the goal isn’t to sound impressive; the goal is to be understood.

Related: On the topic of job titles, if “Data Storyteller” makes your ears perk up—we recently opened a role here at SWD. We’ve had an incredible response and will be closing the application window soon. If you’re interested, please apply by January 23rd.

At storytelling with data, our work is centered on helping people communicate more clearly with data—so they can build understanding, influence decisions, and drive positive change.

As our workshops and teaching continue to reach organizations around the world, we’re looking to add data storytellers to the SWD team.

This role is a great fit for someone who enjoys facilitating and teaching, cares deeply about craft and clarity, and is excited to help others build confidence communicating with data—using an established framework and real-world examples. In addition to workshop delivery, data storytellers contribute to shared teaching content (such as blog posts and videos) that extends our work beyond the classroom.

If this sounds like you—or someone you know—you can learn more and apply, or visit our careers page to find out what it’s like to work at SWD.

As always, thank you for inspiring the work we do and for helping us spread the word!

Let’s walk through a case where this choice can mislead, even if unintentionally.

At first glance, a strong relationship appears

Below is a scatterplot showing student participation in after-school sports programs across a school district. Each dot represents one school.

The horizontal axis shows the percentage of students participating in competitive (interscholastic) athletics. The vertical axis shows participation in casual (intramural) programs. At first glance, the data set seems to show a clear and linear upward trend: schools with more competitive participation tend to have more casual participation as well.

Let’s highlight a few individual schools for comparison.

Imagine we’re trying to compare schools’ rates of casual participation, which is on the vertical axis. Coos Bay and Jeffersonville (marked and labeled in orange) appear to be quite different in their casual participation rates, while Jeffersonville and Cortez (which is in blue) seem more similar. This impression is shaped by the vertical axis being on a logarithmic scale.

How a log scale changes perception

On a standard axis, values increase by equal increments (for example, 10%, 20%, 30%). You can see that on the horizontal axis in the above graph, and in the vast majority of data visualizations we commonly see and use in business.

A logarithmic scale, on the other hand, increases geometrically (such as 1%, 10%, 100%). From a visual perception standpoint, this stretches out gaps between smaller values and compresses larger ones.

That difference matters. It alters how we perceive the distances between points.

Here is the exact same data, but with a standard, arithmetic scale on the vertical axis.

Now we can see that Cortez and Jeffersonville are not as close, vertically as they initially appeared. Elk City stands out as significantly higher than all others. This version more accurately reflects the data's true distribution.

When is a log scale appropriate?

To be clear, logarithmic scales are not inherently wrong. In fact, they are essential in some situations.

A log scale is appropriate when:

• The data spans several orders of magnitude (e.g., from tens to millions)

• The values grow exponentially, as with compound interest or viral spread

• The focus is on relative rather than absolute change

For example, a chart showing the growth of confirmed cases in the early stages of a pandemic might use a log scale to better compare countries with vastly different totals. In financial data, log scales can help normalize trends across portfolios with different starting values.

However, when working with percentages that fall within a consistent range—such as 0% to 100%—a logarithmic scale can be misleading unless the data has a clear exponential pattern.

A better way to show small differences

So why use a log scale in the original example? Most often, it’s an attempt to make small values near the origin easier to distinguish. But manipulating the axis scale to make those differences more visible can distort the bigger picture.

If small values are important, a better approach is to zoom in intentionally.

In this version, we highlight the lower-left corner of the plot and then show that region in its own focused view.

Now, the differences between Coos Bay, Jeffersonville, and Cortez are clearly visible, without changing the underlying scale. More importantly, we maintain an accurate sense of proportion across the full dataset.

Let the scale match the story

If your data does not have a logarithmic structure, avoid using a log scale as a visual shortcut. It can introduce confusion or mislead the viewer, even if the chart is technically accurate.

When small values matter, consider breaking them out into a secondary chart or zoomed-in view. Use your axes to reflect the nature of the data, not to force a certain look.

In short: let the data guide the scale, not the other way around.

The new year is a natural time to look ahead. Even if nothing magical happened the moment the clock struck midnight on December 31st, January brings with it a sense of possibility. It’s a perfect point to pause, take stock, and decide what you want to be intentional about in the months ahead.

One habit I’ve had for years—personally and professionally—is setting OKRs (Objectives & Key Results) at the beginning of each quarter. It’s a practice I learned at Google and is one of the rituals I credit most for helping us make steady progress on ambitious, meaningful goals at SWD.

We use OKRs not as a rigid checklist, but as a way to clarify what really matters right now, what success looks like, and where to focus limited time and energy. If you’re curious to learn more, check out the Goals like Google episode of the SWD podcast.

The start of a new year is also an especially good time to think about skill development. I’m a fan of framing it in a focused, realistic way that fits into the rest of your life and work. This can even be one of your OKRs: invest in how I communicate with data.

If that’s an objective you share, here are a few ways to learn with us:

• Participate in the monthly SWD challenge: it’s a low-risk way to experiment and practice. The current challenge focuses on communicating with partial data.

• Attend a virtual workshop (the next takes place February 24th) or check out our on-demand courses to move at your own pace.

• Want to dive deep? Join our 8-week online course to learn and practice the art of planning, creating, and delivering a data story; the next cohort begins January 26th.

• Organize a training for your team: we offer mini, core, and immersive sessions tailored for your needs and customized with your examples.

However you choose to approach the year ahead, my hope is that you give yourself both permission to start fresh and a plan to make and measure progress.

Here’s to a fantastic year ahead!

In music, a fermata signals an extension—a moment to hold a note, or a rest, longer than expected. It doesn’t mean the piece is over, but rather that we sit in anticipation, or we take a breath...letting something resonate a little longer before moving on.

This time of year feels like that: a natural but longer-than-normal pause in the rhythm of work and life. At SWD, we formally put work on hold (for two weeks, from close of business on Friday the 19th until Monday, January 5th), and use this time to reflect on what’s happened, what’s changed, and what lies ahead.

This year brought the publication of storytelling with data: before & after. Working on the book gave us a reason to look back through years of client work, and remember those folks who had invited us into their world, trusted us with their data, and asked thoughtful questions about how to make it better. As we selected which examples to include and revisited the visual makeovers themselves, we found ourselves remembering the unique challenges they were trying to solve, as well as the conversations and energy in the room when we revealed an "after" version. From Toronto to Trinidad, from the Bay Area to Boston to Belgium, and dozens of places in between, each session brought something different. And revisiting those moments reminded us just how lucky we’ve been to work with so many smart, curious, and generous people, all striving to make their data clearer and their stories stronger.

We also marked a milestone this year with the release of the 10th anniversary edition of storytelling with data. Revisiting the original book a decade after it first came out reminded us how much has changed (tools, terminology, ways of working), but how much more has held steady. The need for clear, thoughtful communication with data is just as relevant now as it was then, and in many ways, even more critical. What has evolved is how we teach and apply those ideas, shaped by years of continued learning and the experiences we've had working with so many of you.

And of course, this time of year inevitably inspires us to reflect on the changes we’ve seen over the past year. Change, after all, is both inevitable and unrelenting. It can be exciting, or difficult, or both. Maybe most often, it’s both. This year, like every year, brought transitions to SWD, both big and small. What we’ve come to believe is that even when change feels uncertain or uncomfortable, it creates the conditions for growth. Letting go of what was can make room for what could be.

The core of what we do is incredibly rewarding. We get to help people see their data more clearly, build confidence in their skills, and communicate in ways that make a difference. That’s work we’re proud of, and deeply grateful to do. At the same time, we recognize that doing the work we love often means putting other things on hold. Time with family. Space for ourselves. The personal projects or quiet moments that don’t always make the calendar. A pause like this gives us the chance to return to those parts of life that matter just as much.

We feel incredibly fortunate that our work is meaningful and fulfilling, but it’s not the only meaningful part of our lives. Taking time off creates space for everything else that matters, too. We hope you get that chance during this season, as we make a point of doing each year—and we’re also trying to build in more of these pauses throughout the year, not just when the calendar gives us permission.

So as we hold here at year’s end in this moment of reflection, we’re feeling grateful for the challenges, the connections, the people, and the progress we’ve made. Thanks for being part of this journey with us. Whether you’ve read our books, joined a workshop, participated in our community, or followed along with the videos, podcasts, or this very blog: thank you.

We don’t take your engagement or your trust for granted. When the fermata ends and the next movement begins, we look forward to making beautiful music along with you in 2026.

Tony Twyman made his name as a pioneer in the field of audience research for television and radio in the UK. For our discussion today, though, he’s best remembered for a single, enduring quotation, which is now known as Twyman’s Law:

“Any figure that looks interesting or different is usually wrong.”

It’s not meant to discourage curiosity or keep you from finding insights. It’s meant to remind you that the most exciting observations are often the ones most likely to be errors—either in the data, or in the way you’re interpreting it.

When I first heard of Twyman’s Law, it immediately reminded me of a personal, but very public, project I undertook back in my pre-SWD days. My experience of working on it stuck with me, as public mistakes often do, and I’m sharing it here simply to show how easy it is to get drawn in by the appeal of a dramatic discovery.

The strange time gap in a public figure’s Tweets

In 2017, I was exploring the Twitter behavior of a well-known public figure, a man in his late 60s. (You could probably guess who it was, but that’s not important to this story.) He was frequently in the news, and over the last couple of years had grown more and more active online. I wasn’t looking at the content of the tweets. I was more interested in the timing. What hours of the day did he tend to post? Were there obvious rhythms or gaps in his online behavior?

I pulled the dataset into a radial graph and almost immediately noticed a pattern that seemed too clean to ignore. 

There were no tweets—none at all—between noon and 1 PM. Eastern Time. This gap showed up across multiple months of data, and because it was so consistent, it looked meaningful. I started trying to make sense of it. Was that when he typically stepped away from his phone? Did he have a daily briefing or meeting? Had I found some hidden quirk in his routine?

I almost shared it. But something about it felt a little too perfect.

So I went back to the raw data and took a closer look at the timestamps. That’s when I found the first issue. The tweets that were supposed to be time-stamped between 12 and 1 PM had been incorrectly encoded as happening between 12 and 1 AM. Somewhere along the way, either the AM/PM flag had been dropped, or the data had been pulled in a 12-hour format and misread. The result was a tidy, hour-long void in the middle of the day that never actually existed.

After correcting the AM/PM problem, I re-engaged with the data and iterated a bit on the specific design of my radial chart, using what I now believed to be accurate timestamps:

However, I wasn’t quite finished being carelessly wrong about things yet. 

At the time, I was living in the Washington D.C. area. While the U.S. spans multiple time zones, nearly half the population runs on Eastern Time. My work, my family, and pretty much everything around me operated on that same clock, so I tended to treat Eastern as the default. With that mindset, I assumed the timestamps in the dataset were either recorded in the public figure’s local time zone (Eastern), or in what I considered the “default” time zone—also Eastern.

Unfortunately for me, this was not the case.

The timestamps in the dataset were in Coordinated Universal Time (UTC), not local time. Which meant that everything I was now confidently interpreting—early morning activity, late-night bursts, midday gaps—was five hours off. (Which explained why my analysis suggested a nearly 70-year-old man was posting on The Socials until 4 AM daily.)

Once the tweets were correctly placed on the radial diagram, the pattern looked much more reasonable:

What initially felt like a meaningful visual insight was actually the result of two separate issues: a formatting error and a faulty assumption. Neither was especially complicated, and both could have been avoided if I had slowed down and asked a few more questions early on. By the time I corrected everything and was finally working with an accurate version of the data, the story I had imagined was no longer there. What remained was the real story…unfortunately, one far less exciting than what I thought I had discovered initially.

Lessons (re)learned

If this had been a personal experiment that I never shared, it might have been nothing more than an annoying detour. But if this had been a client project, or a published post, or part of a public dashboard, I would have been in trouble. Not because the chart was unattractive or unclear, but because it would have told a false story.

There are a few basic principles I try to follow in situations like this, though clearly I don’t always follow them perfectly.

First, don’t rush to interpret the surprising thing.
When something jumps out of the data and feels unusually clean, it’s more likely to be a sign of a problem than a breakthrough. The more exciting it is, the more carefully you need to verify it. That doesn’t mean it can’t be true. But it does mean you should treat it as suspicious until you’ve ruled out the obvious errors. If the choice is between “major behavioral insight no one has ever noticed before” and “simple encoding bug,” assume the bug until proven otherwise.

Second, double-check the details you think you understand.
In this case, it wasn’t that the data was messy. It was that I thought I knew what the timestamps meant. I thought I understood the format. I thought I knew the time zone. And because of those unchecked assumptions, I built a clean-looking, wrong interpretation on top of a misaligned foundation.

Third, acknowledge the role your ego plays in all of this.
I liked what I had found. I was proud of the chart. I wanted it to be real. That made me slower to question it, and quicker to explain it. Fortunately the stakes weren’t very high for me, in that instance…but if they had been, things could have played out much worse.

So slow down. Stay calm. Take the time to validate your assumptions, and be honest with yourself about the limits of what your data can actually tell you. Because once you publish or share something, it doesn’t belong to you anymore—it becomes part of how people understand the world. That’s a real responsibility; and your own credibility, once damaged through carelessness, is difficult to rebuild.

Twyman’s Law isn’t about distrusting your data or being a kneejerk naysayer. It’s about remembering that excitement and accuracy don’t always arrive together. When something looks especially compelling, it’s worth taking a step back to make sure it’s real. 

In the end, the most valuable stories we find in data aren’t the ones that surprise us the most. They’re the ones we’ve taken the time to get right.

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. 

Option 1: Differentiate between complete & incomplete 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.

Option 2: Provide a full year estimate for reference

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.

Option 3: Explore using comparable time periods

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.

Option 4: Show more granular details 

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.

Take precautions when sharing partial information

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.