2026-06-18 21:20:57
“I don’t have a design eye.”
Kaitlin says this matter-of-factly, as if it’s simply a fact about herself, like her height or hometown. Kaitlin, our education and outreach specialist at storytelling with data, is passionate, creative, and deeply committed to her work—particularly when it comes to raising awareness of our children’s book, Daphne Draws Data. She is also, by her own admission, not a designer.
I’ve been working with her on this. I’m seeing progress—more intentional use of color here, a cleaner font choice there, and a growing awareness of how the things she creates will actually be used. Which got me thinking about a question I find genuinely interesting: can design be taught?
My answer is yes. Unequivocally. With a caveat.
People come to design with different natural starting points. Some have an innate sensitivity to visual harmony—they notice when something is off before they can articulate why. Others have to work harder to develop that instinct. But here’s what I believe, and what I’ve seen borne out again and again in my work: everyone can improve. The gap between where you start and where you can get is largely a function of attention, practice, and knowing what to look for.
This mirrors something I feel strongly about when it comes to designing effective graphs. It’s not a talent you either have or you don’t. It’s a skill. Like any skill, it can be cultivated.
So for Kaitlin, and for anyone else who has ever said “I’m just not a design person”—and for those trying to help someone get there—here are ten things worth learning:
This is the first question to ask before you open any tool or touch any layout: who will see this, and what do they need from it? It sounds obvious, but it’s remarkable how often we skip it. We start with what we have—the content, the existing template, the thing we made last time—and work forward from there. The result is design that serves the creator, not the audience.
Every tool you use—PowerPoint, Canva, Google Slides, your website platform—makes design choices on your behalf. Default fonts, default colors, default layouts. These aren’t neutral. They’re someone else’s decisions, made for a generic use case that probably isn’t yours.
Accepting defaults uncritically isn’t the same as making no design choice. It’s making an unconsidered one. The first step toward better design is simply noticing that these choices exist—and asking whether they are serving you.
More options are not more freedom. In design, it’s usually more noise. When everything is available to you—every font, every color, every layout—the result is often a visual cacophony where everything competes and nothing stands out.
Constraints help. A limited color palette. A single typeface. A layout you commit to. If your organization has existing templates or brand guidelines, lean into them—they’ve already done some of this work for you, and consistency across your materials builds recognition and trust. The goal isn’t creative restriction; it’s coherence. Within constraints, the real design decisions become clearer.
Your viewer’s eye will move through your design whether you direct it or not. The question is whether it moves the way you intend. Visual hierarchy is how you answer that question deliberately.
What should someone notice first? What’s the most important thing? Make it the most visually prominent. What comes second? Third? Design the sequence. This doesn’t require elaborate technique—size, weight, position, and spacing can all establish hierarchy simply and effectively. What it does require is that you think it through before you start placing elements, rather than after.
This is one of the most important design principles I know, and one of the most commonly violated. When something in your design is different—bigger, bolder, a different color, a different font—viewers read that difference as meaningful. It signals: pay attention here, this matters more.
The corollary is that unintentional variation creates confusion. If three elements are slightly different sizes but you didn’t mean anything by it, your viewer will still try to interpret the difference. They’ll wonder what they’re missing. Inconsistency reads as noise, while intentional variation reads as signal. Before you finalize anything, ask yourself: does every difference in this design mean something? And does everything that should look the same actually look the same?
Color is one of the most powerful tools in design—and one that is often misused. The right approach depends entirely on what you’re trying to do.
If you’re creating something expressive—an illustration, a poster, a book cover—color can be rich, playful, and abundant. In Daphne Draws Data, our children’s book, color is used exuberantly, because that’s what serves young readers and the spirit of the book.
But if you’re trying to direct attention in a graph or a slide, a different logic applies. A mostly neutral palette with one intentional color used sparingly is far more effective than many competing colors.
When it comes to this, the question to ask isn’t “what colors do I like?” It’s “what is color doing in this design, and is it doing that well?”
Of all the things that make a design feel polished versus amateurish, alignment is one of the most telling. Misaligned elements—text that doesn’t quite line up, objects that are close to a grid but not on it—create a subtle but persistent visual unease. Viewers may not be able to name what’s wrong, but they feel it.
One particular pet peeve worth calling out: center-aligned text. It doesn’t align to anything, which leaves it floating unmoored on the page. When it runs to multiple lines, you end up with two jagged edges—one on the left and one on the right—that create visual tension rather than resolving it. Left-align your text and give it something to anchor.
The fix is straightforward: use your tool’s alignment functions, turn on gridlines, and be deliberate. Every element should be aligned to something—another element, a margin, a grid. “About here” is not a design decision. Alignment is one of those things that, once you train your eye to see it, you can’t unsee. Which is useful, because then you can fix it.
Whitespace—the empty space in a design—is not wasted space. It is doing work. It creates separation, establishes relationships, and gives the eye somewhere to rest. Designs that crowd elements together feel anxious and hard to read. Designs with generous spacing feel calm, clear, and considered.
The instinct when you're new to design is often to fill space, perhaps due to worry that emptiness looks unfinished. Resist it. Some of the most effective designs are defined as much by what isn’t there as by what is.
Design literacy is built by looking. When something catches your eye—a beautiful book cover, a well-designed menu, a pleasing color combination, a graph that makes data suddenly clear—pause and ask why it works. What specifically is it doing? What could you learn from it or apply in your own work?
Do the same with bad design. Why is it ineffective? What would you change? This kind of active, analytical looking is a habit, and it compounds over time. The more you do it, the faster your eyes develop. Inspiration is everywhere—you just have to start noticing it.
If you want to practice this specifically with data visualizations, the SWD community’s discover page is a great place to start. Thousands of visuals created by SWD team members and the broader community are there to browse—shuffle and see what catches your eye.
Here’s the thing about design instinct: most people have more of it than they realize. You know when something feels off, even when you can’t say why. That feeling is data. Trust it.
But instinct is a starting point, not a ceiling. It can be developed. The difference between someone who considers themselves “not a design person” and someone who does isn’t innate talent—it's mostly accumulated practice, awareness, and feedback. You train your eye the same way you train anything else: by using it, paying attention to what you notice, and being willing to iterate.
You don’t have to be a designer to think like one. You just have to start looking.
2026-06-11 04:20:17
Do you dread hearing those words from your manager? I certainly used to. Whether I was pulling together slides for a high-stakes client presentation or making a first pass at an analysis I’d never done before, those six words always sank my heart. While my leader was on their way to giving me some feedback that may have been constructive, all I heard was “this isn’t good enough.”
My name is Ryan, and for my first storytelling with data (SWD) blog post I’m collaborating with my new colleague, Alli Torban, to share insights into how to reframe your thinking when entering a feedback-rich environment like the one we foster at SWD.
After joining the team, we both had a bit of a wake-up call. On our very first day, we were asked to introduce ourselves to the team at our weekly virtual team meeting. Then we spent the rest of the week practicing our intros and incorporating feedback from the team. This is standard practice here, and it didn’t take long for this culture to start changing how we think about feedback.
As we began to internalize and embrace this environment, we noticed we were starting to hear feedback differently than we had before SWD. The shift in perspective is small, but it shows how the same words can land very differently depending on the surrounding context. Here are a few examples:
Before joining SWD: “It’s too soon, they’ll judge my incomplete work.”
After joining SWD: “Perfect timing to get direction and practice!”
In a prior role, I felt overwhelmed with work. I would hurry through a project to meet a deadline, only to have another looming around the corner. One bad habit I developed was waiting until a project was nearly complete to request feedback. Often I would ask for thoughts shortly before a deadline.
This created lose-lose situations: either my feedback provider would feel cornered into avoiding any true constructive criticism, or they’d be honest and recommend many changes at the 11th hour. The latter was tough, as I would need to stay late at the office making major edits to meet the deadline.
During our eight-week onboarding program at SWD, I shared my work-in-progress introduction to my peers. Instead of calling it incomplete, or telling me it needed work, they chimed in with praise and edits each step of the way. Their input guided me as I honed my verbal intro into its best version, long before I internalized early drafts.
Lesson learned: Now, I ask for feedback early and often, and I’ll jump at an offer from a colleague to review my early work.
If I don’t get the chance to connect with my feedback group until closer to the deadline, I make a point of that in the request. Questions like the following are better suited for feedback later in the creative process:
“Are there any glaring problems that need to be addressed?”
“I only have time for word choice, and other light edits, do you have any suggestions within that scope?”
“If you only had an hour to make changes, what would you prioritize?”
Before joining SWD: “My work is not good enough.”
After joining SWD: “Nice—I’m on the right track!”
Alli shared with me a great story about this classic feedback opener:
Early in my career, I had a more seasoned colleague who would start his feedback with, “This is a great start…” and then change everything I had done. His comment was meant to be kind, but I came to hear that phrase as shorthand for, “let me do it.”
At SWD, the culture is iterative, honest, and still kind. For a recent client chart makeover, I shared my in-progress files with Amy, and she told me it was a great start. I braced for a fully redone version. Instead, she sent a file full of comments, and she gave me agency to make those changes in my own way. It wasn’t a prescriptive list of edits. She truly meant it was a great start, and with guidance, my next iteration would move closer to the final product.
Lesson learned: In a culture where feedback is part of the process, “great start” can actually mean trust: trust that you’re capable of taking it the rest of the way. Now when you hear “great start” think of it as an invitation to iterate, not a cue to step aside.
A device that helped Alli to hear those words in a new light is a classic improv game called “bad gifts.” To hear her and Cole talk more about this, check out episode 99 of the SWD podcast where they practice the game together, and share additional insights on being a better recipient of input from others.
Before joining SWD: “Uh oh, they are too busy to give me all the notes they have.”
After joining SWD: “Great, I can guide the conversation.”
As soon as we started the lessons on feedback at SWD, I was brought back to the dozens of times I cast too wide a net when inviting critique. I’d send an in-progress project to a peer or manager and ask “any thoughts?” or simply say “please review when you have a chance.” All I ever got back in response was generic answers: “great work,” “it’s missing something,” and/or “cut it down.”
I left those requests with more questions than answers. If it's great, should I change anything? What is it missing? Do I need to cut a slide? How can it be too long, missing something, and great all at the same time?
At SWD, asking specific questions as a part of the input cycle helps me to realize what I’m asking for. Clear-cut questions also point my colleagues in a direction that gives them the confidence that their commentary will be helpful to me. When I ask Simon “Before I deliver my intro, can you let me know if you think it’s too long?”, that’s his cue to look out for sections to trim as he listens.
Lesson learned: before I request feedback, I make a short list of questions for my feedback-giver. This forces me to critically assess aspects of my in-progress work and to prioritize my needs in a way that ensures I’ll get the productive edits I’m seeking.
We adapted to this culture by embracing several key learnings and putting them into practice firsthand:
Get input from peers and managers early and often
Treat projects as iterative, collaborative work
Request and give specific, actionable critiques
Implement one or more of these lessons to help create a positive, feedback-rich environment in your workplace.
2026-06-09 00:11:04
At storytelling with data, our mission is to inspire positive change through the stories people tell with their data.
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.
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.