2026-06-24 22:00:08
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.
In the previous post, we used AI as a thought partner to develop your Big Idea. You know your audience, you understand what’s at stake, and you have a clear point of view. Now comes the time to turn all of that into a story.
A common instinct at this point is to open your laptop and start building slides. Resist it! Before touching any tool, the most important thing you can do is go low-tech. Get out some sticky notes. Brainstorm content that might belong in your communication—one idea per sticky note—without filtering or judging. Then edit ruthlessly: what stays, what goes, what order serves your message? The low-fidelity format matters. It keeps you from getting attached to ideas before they’ve earned their place, and rearranging is effortless when nothing is committed to a slide.
This is storyboarding—and it’s one of the most powerful planning tools we teach at SWD. The process I’ll walk through took about 30 minutes, from initial sticky notes to a tested narrative and draft takeaway titles. That’s time invested upfront so I’m not spending hours later reworking slides to fix structural problems. But brainstorming and arranging content is only half of it. The other half is finding the right structure to hold it together.
The default flow for most business presentations is a linear path: you start at the beginning of the project, walk through what you did, and end with findings and recommendations. It’s logical. It’s familiar. And it tends to be forgettable, because it mirrors the analyst’s journey rather than the audience’s needs.
An alternative—the structure we find incredibly effective for business storytelling—is the narrative arc. Rather than following the chronology of the work, the narrative arc organizes content around tension and resolution. It has a deliberate shape: plot that establishes context, rising action that builds towards the central conflict, a climax where the tension peaks, falling action that moves toward resolution, and a clear resolution—the call to action.
Making the shift from the linear path to the narrative arc isn’t always intuitive. Many people find it straightforward to brainstorm content and arrange it chronologically. But restructuring around tension and resolution requires a different kind of thinking. This is one place where AI can be particularly helpful: not to generate your story, but to help you find the arc that’s latent in your material.
The key ingredient that makes it work is tension. This isn’t the tension that matters to you as the analyst or the communicator of information, rather it’s the tension that matters to your audience. Recall what we assessed in the first installment of this series: What is at stake for them? What is the gap between where things are and where they need to be? When you identify that tension and build your story around it, you stop reporting findings and start telling a story that moves people to act.
The storyboarding process is deliberately human. Brainstorming, editing, and arranging your content is work that should happen away from the screen and away from AI. As we’ve discussed, the analog nature of it is a feature, not a limitation.
That said, AI can be a useful thought partner at two different moments in this process. The first is after brainstorming, when you have an array of sticky notes but haven’t yet found a structure. At this point, AI can help you identify a narrative arc from your raw material: finding the tension, suggesting an order, helping you see the story that’s embedded in what you’ve generated. The second is after you’ve arranged your content (with or without AI’s assistance)—once you have a draft structure. Here, AI can pressure-test whether the arc holds: evaluating the tension, identifying gaps, and flagging places where the story loses momentum.
Either way, the goal is the same: to surface blind spots and help you assess and refine before you invest time building anything.
Once your low-tech plan is solid, AI can also help you turn your sticky note topics into draft takeaway titles for your eventual slides. This is a useful bridge between planning and building. We’ll see how it works soon through an example.
Before getting to the prompt and example, let’s review some potential pitfalls of working with AI at this stage of the data storytelling process.
AI may flatten the tension—effective stories need a moment of discomfort before resolution. AI often smooths this out in favor of a more neutral, balanced narrative. Watch for responses that sand down the edges of your story.
AI will want to add, not subtract—it tends to suggest more content, more context, more caveats. A tighter story is almost always a stronger story. Use AI to find out what to cut, not what to add.
AI doesn’t know your audience—it can evaluate logical flow, but only you know what your specific audience needs to hear, in what order, to be moved to act.
Be mindful of what you share—avoid including sensitive data, personally identifying information, or confidential business details in your prompt.
If you’re continuing in the same AI conversation from the previous step, your context is already established and you can jump straight to the prompt below. If you’re starting a new conversation, take a moment to briefly reorient AI: share your Big Idea, describe your audience, and note what’s at stake and the key tension you aim to build the story around. A few sentences should suffice. Then use the prompt below, modifying as needed to meet your needs.
I’m working through the storytelling process using storyboarding and the narrative arc framework from storytelling with you by Cole Nussbaumer Knaflic (Chapters 3 and 4). I’ll share my work and ask you to act as a thought partner to help me find or pressure-test the narrative arc. Your role is to identify gaps, inconsistencies, or places where the story loses momentum. Don’t rewrite it for me and don’t suggest adding more content. Focus on whether what I have can be shaped into—or already follows—a clear narrative arc that builds tension and moves toward resolution.
Start with the option that fits where you are in the process:
Option A: after brainstorming
I’ve generated ideas for potential content but haven’t structured it yet. Help me identify a narrative arc from this raw material. [share a photo of your sticky notes or a list of your ideas]
Option B: after arranging
I’ve arranged my content into a sequence. Help me evaluate whether it follows a narrative arc or whether it could be restructured more effectively. [share a photo of your storyboard or a list of planned content]
Review and tell me:
Does the content map to a clear narrative arc—is there identifiable tension, clear climax, and fitting resolution?
What isn’t earning its place—is there anything that feels redundant, out of order, or likely to lose the audience?
Are there any logical gaps—places where I’m assuming the audience will connect dots they may not connect?
Before providing feedback, ask me questions that would help you give better input.
To see how this comes together in practice, let’s walk through an example.
If you read the first installment in this series, you’ll recognize the scenario. I’m a People Analytics Manager at a mid-sized consulting firm. My team has completed a thorough analysis of the company’s hybrid work policy—examining performance ratings, in-office attendance patterns, collaboration network data, and attrition trends. We have a recommendation: move from the current three-days-in-office, two-days-remote policy for all employees to a differentiated approach based on role and team type.
Our Big Idea is: 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.
Now the work shifts. Context is set. The Big Idea is clear. It’s time to figure out how to tell the story.
I started—as I always do—with sticky notes. I wrote one idea per note, without filtering. I thought about the project from the perspective of each person who will be in the eventual meeting room: what will Diana need to feel confident championing this recommendation? What would make Robert a supporter? What might Priya push back on? I also thought about the data: what we learned and which findings were essential, versus interesting but not necessary.
After about 10 minutes, I had twenty-five ideas in front of me. I’ll list them here so you can get a sense of the breadth:
Why we undertook the analysis
Current hybrid policy
History/timeline of policy
Methods: data we collected
Benchmark data: comparison to peers
Employee survey data: work preferences
Performance ratings by role type & tenure before/after
Collaboration network data before/after
Office utilization: actual vs. assumption
Office utilization: breakdown by office
Attrition rates before/after
Attrition rate: breakdown by office
Travel/OOO breakdown by role
Manager feedback on hybrid policy
Cost of attrition by role type & tenure
Real estate cost analysis & projections
Finding: impact varies by role type & tenure
Finding: early-tenure employees struggle most
Finding: independent/analytical employees thriving
Finding: office utilization lower than expected
Cost of status quo
Options considered
Recommendation: differentiated approach by role and team type
What differentiated policy would look like in practice
Next steps and implementation timeline
Then I edited. I put aside anything that served my process rather than my audience’s needs—the methodology, policy history and timeline, the benchmarking data, options considered. I also set aside anything unnecessarily granular for my audience or that didn’t lead to actionable output: attrition by location, manager feedback, geographic utilization patterns.
As I edited and arranged, I also found myself rewriting my sticky notes. What had started as descriptive—“attrition rates before/after”—became more pointed: “early-tenure attrition has spiked, and it’s costing us.” The process of deciding what to keep and how to sequence it was already pushing me to think like a storyteller rather than an analyst.
That left me with twelve items. I arranged them in a sequence—not yet a narrative arc, just the order that felt logical to me at the time, which turned out to be fairly chronological: policy overview, data findings, recommendation, call to action.
I had a solid foundation but wasn’t yet confident in the structure. The data findings in particular felt like a list of things I’d learned rather than a story with momentum—and I wasn’t sure the tension was apparent for my audience. This felt like a good point to bring in AI to help. I turned to Gemini.
Quick note on tools: I used Claude in the first post in this series and Gemini here. In practice, it will usually make sense to stick with a single tool throughout a project so context can accumulate over time. I’m intentionally varying the tools I use throughout this series so I can see how well this workflow transfer across models.
Given that I haven’t used Gemini yet for this communication, I shared initial context along with my storyboard and prompt:
The following is Gemini’s initial response.
Gemini’s response was more useful than I initially realized. At first glance, it seemed to confirm the order I already had. But three observations stood out on closer reading.
The first was the question about sticky 7, “one policy can’t serve everyone equally.” Gemini flagged that stating this finding too early might give away the punchline before the data has had a chance to build the case. That’s a fair point, and this caused me to consider another reason to hold it back. For an audience member like Priya, who championed the current policy and is likely to be defensive about any change, naming the conclusion too early invites resistance before the evidence has had a chance to land. A stronger approach in this instance could be to let the individual findings accumulate—the attrition spike, the performance decline, the role-type differences—so that by the time we state “one policy can’t serve everyone equally,” the audience has likely already arrived there themselves. Given this, I decided to move this note later in the sequence.
The second was the suggestion to bring the financial figures closer together. Currently, I had the early-tenure attrition costs ($2.4M) in the rising action and the real estate savings ($3.2M) much later. Gemini pointed out that combining them creates a single, harder-to-ignore financial argument—roughly $5.6M in total impact. For Robert (the CFO) in particular, that framing is more compelling than two separate numbers spread across the presentation.
The third observation—about the climax—is where I disagreed. Gemini placed the real estate opportunity at the peak of tension. But for this audience, the emotional peak isn’t the financial case. It’s the human one: early-tenure employees are leaving at rates far above our historical baseline, and the current policy is actively making things worse. That’s the moment I want the room to feel before I offer the resolution. The financial argument is powerful supporting evidence, not the climax.
This is a good example of why AI is a thought partner, not a decision-maker. Its structural suggestions were useful. But I’m the one who knows what will resonate with this specific group. AI can help you think through audience needs, but it can’t replace real audience knowledge. When that knowledge is incomplete, talking with stakeholders or people closer to the audience is often more valuable than asking AI to fill in the gaps.
I arranged my stickies along the general arc Gemini recommended, incorporating the ideas I raised above, and continuing to rearrange some things to come up with a narrative flow that made sense to me (for example, leading from traveling employees to the lower office utilization to the real estate savings, since these ideas build on each other). I explained this change to Gemini, which responded with the following.
While I’m certainly feeling good about things now, I’ve learned to be cautious about AI enthusiasm. “You have a rock-solid narrative arc” is encouraging—but encouragement isn’t the same as rigorous pressure-testing. AI tends toward affirmation, and a confident-sounding endorsement can give a false sense of security. This is exactly the pitfall I flagged earlier: AI may flatten the tension and smooth over the edges of your story precisely when you most need rigorous push back.
Before moving on, I wanted to put the arc under more scrutiny. So I shared the revised sequence (below) with Gemini and asked it to pressure-test the narrative more specifically—this time focusing on whether the tension was genuinely meaningful to my specific audience, whether anything was losing momentum, and whether there were logical gaps my audience might not bridge on their own (Option B of the potential prompt shared earlier in this post).
Here’s Gemini’s response:
Before diving into feedback, Gemini asked three clarifying questions—a direct result of the “ask me questions first” instruction in the prompt. I hadn’t explicitly shared my audience context in this exchange, and rather than plowing ahead with generic feedback, Gemini flagged that gap. That’s the prompt doing its job—and a good reminder that the quality of AI’s feedback is directly tied to the specificity of what you give it.
The questions caused me to reflect. The first pushed me to be explicit about something I’d been holding implicitly: Diana is my primary audience, and her bias is cultural—doing right by employees while presenting a position she can defend with data. But Robert’s financial bias matters too, and Marcus’s operational one. A story that lands for Diana needs to at least speak to the others.
The second question had an obvious answer for me: Priya. She championed the current policy and is likely to see any change as a threat. The most important objection to anticipate is hers.
The third identified a potential gap I hadn’t explicitly thought through: the verbal bridge between the real estate finding and the early-tenure performance findings. These two sit on opposite sides of the arc, and how you move an audience from one to the other matters. I hadn’t worked that out yet, and it’s exactly the kind of thing that’s much easier to address now than after I’ve built the slides
I’ll also note the opener. This kind of enthusiasm is AI’s default mode: charming, but meaningless. Watch for it, and don’t let it substitute for the substantive feedback that follows.
I answered Gemini’s questions and asked it to proceed with the pressure-test. Here was the response:
A few things stood out. The observation about stickies 8 and 9 was practical and correct: office utilization and real estate savings are one idea, not two, and presenting them separately risks stalling the momentum right before the climax. I’ll combine them.
The observation about sticky 7—“one policy can’t serve everyone equally”—was the most useful structural insight. Gemini suggested treating it as falling action rather than the climax itself. This doesn’t change where it sits in the sequence, but does impact how I frame it. Stated as the peak of tension it sounds like an accusation directed at the current policy—and at Priya. Let the data accumulate first: the attrition spike, the performance decline, the role-type differences. By the time we state the conclusion, the audience will already have arrived there themselves.
The two gap identifications were also valuable. The first—bridging the early-tenure performance decline to the attrition spike—isn’t only about narrative flow. It’s anticipating Marcus’s objection before he can make it. The second—a proactive nod to Priya’s perspective on hybrid flexibility—is the kind of thing I need to do but might have left implicit. Having it named as a structural gap rather than just a presentation nicety is useful.
The quality of this feedback was directly tied to the audience context I provided. Gemini’s earlier response was useful but generic; once it knew that Priya had championed the current policy, it could surface the “Priya defense” gap. In hindsight, I could have accelerated this process by providing that audience context from the beginning. Had I uploaded my completed Big Idea worksheet, Gemini would have had access to those details from the start.
Rather than rearranging my stickies again, I noted these refinements to carry into the building phase. With the arc solid and the structural gaps identified, I had one more thing to do before closing out the planning stage: turn my sticky note topics into draft takeaway titles.
I gave the following simple prompt: I’m happy with the overall structure. Can you now help me turn each point in my storyboard into a draft takeaway title for the eventual slide? Each title should be a single, pithy statement that tells the audience what to notice or understand from that slide—not a label for the content, but a clear point of view.
Gemini initially gave me much more than I asked for. Rather than parse it myself, I followed up with: Thank you—can you give me just the draft titles as a simple numbered list, without the sticky note references, arc labels, or explanatory notes in parentheses?
Here is the response:
This is a good starting point. Read top to bottom, they tell a cohesive story. Each title pushes the narrative forward and the financial figures feel integrated naturally. “The true cost of this mismatch is human” is strong as a climax title and will appeal to my audience.
That said, several are too long for slide titles. A takeaway title needs to make the point succinctly—the supporting detail lives in the content of the slide itself, not the title. With that in mind, I refined to the following:
Our hybrid policy treats all roles the same—but they aren’t
We looked beyond office attendance: perf, collaboration, & more
Independent and analytical teams thrive remotely
Office mandates work against client-facing teams
Current policy doesn’t match how people actually work (and we’re paying for it!)
The real cost is human: new hires are set up to struggle
Early-tenure attrition has spiked—and it’s costing us
One policy can’t serve everyone equally
A differentiated approach recaptures $5M+
Not removing flexibility—making it work better
Approve the change: stop the drain
These eleven titles give me a clear plan for building the deck. They aren’t final—as I build out the actual slides with my team, some will sharpen, some will shift, and a few may get trimmed or expanded as the content takes shape. But having them now means I won’t ever have to start from a blank slide. The story is mapped. The tension is clear. The call to action is explicit. Everything that follows is building on a foundation rather than figuring it out as I go.
One reason I remain such a strong advocate for storyboarding is that it saves time. The work described here may look involved when written out step-by-step, but the actual process was fast—roughly 30 minutes from initial sticky notes to a tested narrative and draft takeaway titles. That’s time invested upfront so you’re not solving structural problems after you’ve already build slides.
For this high-stakes communication, working through the storyboarding and narrative arc process with AI as a thought partner got me to a place I wouldn’t have reached as quickly on my own. What I have is stronger for it: more robust, more audience-aware, and more intentional than if I’d worked through it alone. That’s pretty incredible.
In the next post in this series, we’ll move to the third core SWD skill: choosing an effective visual. In the meantime, register for our free live event on July 13th where Simon and I will explore how to use AI for better data storytelling—including diving deeper into ideas from this series.
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.