2026-06-05 05:23:49
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 [email protected] 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 [email protected].
Together, we can help kids build confidence with data and skills that will serve them for life!
2026-05-28 05:08:38
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.
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.
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.
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.
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.
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.
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.
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.
2026-05-21 02:19:09
When we think about communicating with data, a lot of energy goes into the graphs themselves—which chart type to use, how to label it, what to highlight. But there’s another decision that shapes how well your message lands, one that doesn’t always get the thought it deserves: how do you structure the slide itself?
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.
2026-05-01 22:06:06
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.
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.
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
2026-04-23 22:00:59
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.
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.
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!
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.
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.
2026-04-20 21:00:11
Gantt charts are a popular choice for illustrating the start and duration of events, which is common practice in project management. While useful for representing timelines, these charts can quickly become busy and difficult to interpret, especially when dealing with complex workflows.
Let’s consider an example.
Inspired by NASA Schedule Management Handbook
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.
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.
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.