2026-10-19 22:54:29
How many words should you put on a slide?
As is the case with so many things when communicating with data, the answer is: it depends.
Too many words compete for attention and make your audience work to find what matters. Too few, and you leave them to figure out for themselves what the data means and why they should care. The goal isn’t to use more words or fewer words. It’s to use the right words.
This isn’t a new idea. Years ago, my colleague Elizabeth wrote a post called words are your friend—when you choose them wisely. Her point still holds: numbers and graphs don’t speak for themselves. The words we pair with them help our audience make sense of what they’re seeing.
What is new is how often I’m thinking about this as I work with AI.
I’ve been spending a lot of time lately asking AI to create and improve graphs and slides. It can get surprisingly far, particularly when I give it context and ground it in the principles from our SWD + AI Primer. But words are one place where I find myself intervening frequently.
AI-generated slides can struggle with the balance. Sometimes they’re overloaded with text: lengthy titles, explanatory paragraphs, labels, callouts, and summaries that all compete for attention. Other times, AI strips things back so far that we’re left with a polished-looking graph and a purely descriptive title.
Neither is ideal. More importantly, AI often defaults to describing what happened rather than interpreting why it matters. That’s an important distinction—and one that becomes clearer when we look at an example.
Consider the following slide, intended for staffing managers at a call center.
The graph shows customer service call volume throughout the day. On its own, we can see the pattern: calls climb in the morning, remain high through much of the workday, and peak around lunch and again in the afternoon.
But the audience isn’t looking at this graph simply because they’re curious about call volume. They are staffing managers. Presumably, we’re showing them this information because they can do something with it.
That context changes the words I choose.
Plan breaks & meetings around peak call times. This takeaway title tells staffing managers what matters. The descriptive graph title—Customer service calls per minute—tells them what they’re looking at. Annotations identify precisely when call volume crosses important thresholds and add useful context about the two peak periods. Finally, the recommendation translates all of this into a specific action: schedule lunch breaks or small team meetings between the peaks.
Each bit of text has a job. This is an important point. We sometimes talk about reducing words as though fewer is inherently better. It isn’t. Removing all of the text surrounding this graph would make the slide more minimalist, but it wouldn’t make it more useful. Add the right words for an effective communication.
When you’re deciding which words belong on a graph or slide, think first about what your audience needs from them.
A takeaway title can communicate the main point or action. A descriptive graph title can orient the audience to what they’re looking at. Direct labels can eliminate the work of referring back and forth to a legend. Annotations can explain what happened, call attention to important points, or add context that isn’t evident from the data alone. Axis titles, footnotes, and source information can provide additional detail when it’s needed.
Not every graph needs all of these things. The goal is to be deliberate. Ask: What does my audience need to understand this? What should they know or do as a result? Where could a few well-chosen words eliminate confusion or make the point unmistakable?
Then get rid of the words that aren’t helping.
This is where the human role becomes especially important when partnering with AI.
Imagine giving AI the call-volume data above and asking it to create a slide. It could likely identify the pattern. It might tell us that call volume is highest around lunchtime and again in the mid-afternoon. It could probably generate a polished sentence describing that observation.
But that’s not yet the insight.
The useful recommendation comes from combining the data with additional context: Who is the audience? Why are they looking at this? What do they care about? What decisions are within their control? Once we know the audience is staffing managers, the pattern can become actionable.
That idea is central to our SWD + AI Primer: give AI the context it needs before asking it to help you communicate. AI can help us find concise wording. It can suggest titles, labels, and annotations. It can help us test different ways to articulate an idea. But someone still needs to understand the situation well enough to recognize what matters.
A polished sentence isn’t the same thing as insight.
If you’ve spent time with the data, you know things AI may not. You know the history. You know what changed. You understand the organizational context and what your audience cares about. You may recognize that one movement in the graph is consequential while another that looks equally prominent is not.
Bring that knowledge to the slide. Then use words to make your thinking visible. The goal isn’t more words or fewer words. It’s the right words, doing the right jobs.
2026-10-08 21:00:05
An expected staffing shortfall is worth bringing to leadership’s attention. But a forecast alone doesn’t tell leaders where the gap is coming from. When there’s more to explain, it’s tempting to squeeze every detail into one chart.
That was the challenge in an example from a recent custom client workshop. The team needed to show that full-time equivalent (FTE) staffing was expected to fall below plan, that a decline in operations support roles was an important part of the picture, and that leadership needed to discuss an approach to resolving the gap to plan.
The important information was there, but the story was buried in the complexity of the communication.
The original slide asks readers to follow several things at once. The tops of the stacked bars, the lines, and the data table all show overall FTE in different ways. The segments within the bars show the breakdown by role. Plan, actual, YTD average and projections add another layer to interpret.
Each element answers a reasonable question, but crammed together, they make it hard to answer any one question well. Taking a step back and considering what leadership would need for the conversation ahead helps guide how to show the information. Leaders need answers to two questions: How big a shortfall should we expect? And what do the latest staffing data tell us about where the change has occurred?
Rather than pack everything into one graph, we split it in two: one visual per question.
Let’s first tackle the anticipated shortfall. A standalone line chart makes it easier to see how staffing has tracked against the plan set at the start of the year and how the forecast compares with that plan going forward. This view also gives room to add annotations at the important points. Readers can focus on the expected gap to plan without first decoding the role mix or scanning the original table.
The overall view establishes the expected difference to the target set at the beginning of the year. Next, we can look at the role-level changes in the actual data to help understand where the largest shifts have occurred.
Here, month-to-month variation matters less. Plotting only the start of the year and latest actual month with a slopegraph provides a quick sense of which roles changed most. Using color and words sparingly draws attention to the decline in operations support roles, helping the audience quickly see the largest difference.
The two visuals work as a pair. The line chart answers, “What’s happening overall?” The slopegraph answers, “Which roles have changed most so far?”
For a summary that might be read or forwarded without a presenter, the two views can sit together on one page. A takeaway slide title states the expected shortfall, while supporting text points to the decline in the operations support roles and signals that leadership needs to discuss next steps. The charts provide the evidence without competing for the same space.
For a live presentation, we can use the same data differently. Rather than ask the audience to take in the whole summary at once while we speak to it, we can walk through the overall forecast first, then reveal the actual role-level changes piecewise, and finally turn the discussion to what needs to happen next. This sequence lets the audience absorb one point before moving to the next.
The data and message stay the same. What changes is how much the audience takes in at once.
When the full picture needs more detail, it's tempting to put everything into a single view. It can feel efficient. But more information doesn’t automatically create more understanding.
In this makeover, separating the overall FTE trend from the role-level changes made both insights easier to see. Together, these two charts tell a clear and focused story that leadership can act on.
2026-09-24 06:06:57
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. If you’re just joining us, start with the first three installments: start with context, craft a story, and choose an appropriate visual. Explore all of our AI resources.
You’ve chosen your visuals. Now comes the work of actually designing them—and making sure they communicate clearly, direct your audience’s attention to what matters, and hold together as part of a cohesive presentation.
This is where many people spend the bulk of their time, and where the gap between “good enough” and genuinely effective communication is most visible. Whether it comes from a charting tool or AI, the first version of a graph is rarely ready to present. It needs to be decluttered, focused, and given the words that make it meaningful. A graph without a takeaway title makes your audience guess at the point. A slide crowded with data makes them work too hard to understand it. Getting this step right is what separates a presentation that informs to one that moves people to act.
It’s also a step where AI—given the right context and direction—can do a lot of the heavy lifting. Rather than building each slide from scratch, you can describe what you need, share your data, and let AI produce a first version to react to. It won’t be perfect, but it will be a starting point to work with—and getting there in minutes rather than hours changes what’s possible.
In this post, we’ll use AI integrated directly into PowerPoint—so the output is editable slides, not images. That’s an important distinction from the previous post: you’re not evaluating prototypes anymore, you’re building the real thing, and AI is helping you refine it in place.
Effective graph and slide design comes down to a few essential principles: removing what doesn’t belong, creating visual order through alignment, directing attention to what matters, and using words to make the message clear.
Declutter. Every element on a graph or slide contributes to cognitive load—the mental effort your audience spends processing what they see. Anything that doesn’t add informative value is clutter, and clutter makes your visuals feel more complicated than they are. The goal isn’t minimalism for its own sake; it’s reducing the extraneous so the essential stands out. In practice, this often means removing chart borders, gridlines, unnecessary tick marks, redundant labels, and default formatting that the tool added because it always does (not because it’s what serves your audience). When you take away what doesn’t belong, what remains gets stronger.
Align. When elements on a slide are misaligned—text starting at different points, graphs floating at inconsistent positions, titles not anchoring the content below them—the slide feels unintentional even if the viewer can’t articulate why. Consistent alignment creates a visual structure that your audience navigates without thinking about it. When design is thoughtful, it fades to the background; when it isn’t, your audience feels the burden.
Focus attention. A clean, well-aligned visual still leaves your audience to decide for themselves what matters—and they may not choose what you intended. The next step is to actively guide where they look. The most powerful tool for this is color used sparingly. When everything is the same color (or everything is a different color), nothing is emphasized. When one element is highlighted against a neutral field, the people look there immediately. Size, position, and contrast work similarly: make important things larger, place them where the eye lands first, and use visual weight to create hierarchy. The goal is to make the right thing obvious without having to say “look here.”
Use words wisely. Even the best designed graph needs words to make its point. Every graph needs a descriptive title that tells the audience what they’re looking at. Every slide needs a takeaway title—a sentence that answers the question “so what?” before the audience has to ask it. Axes should be titled. Key data points should be annotated where they add meaning. But more isn’t better; every word that isn’t earning its place is adding noise. The discipline here is the same as everywhere else in SWD: include what serves the audience, cut what doesn’t.
One more practical consideration: if your organization has a standard template or brand guidelines, start there. Colors, fonts, and layouts that align with your brand aren’t just aesthetic choices—they signal credibility and consistency to your audience. Before applying any of the principles above, make sure you’re working within your company’s visual identity. If you’re not sure what that looks like, check with your communications or design team.
These principles work together. Apply them with your audience in mind—what do they need to see, and what might get in their way? When design is working, it’s invisible: your audience isn’t thinking about the slide, they’re thinking about the message.
This is where having a shared foundation in SWD principles becomes especially useful. In earlier posts, we gave AI a few sentences of context before diving in. For this step, we recommend going further: share the SWD + AI primer with your tool before you begin. This free PDF download gives AI a grounding in SWD principles—including specific guidance on how to approach graph and slide design—and produces noticeably better results than prompting without it.
The workflow here is different from the previous posts in an important way that I mentioned earlier: rather than using AI as a standalone thought partner, we recommend using AI integrated directly into PowerPoint. This means the output is editable slides, not images. Tools like Copilot in PowerPoint and Claude or ChatGPT for PowerPoint all support this kind of integrated workflow.
You can come to this step with a draft slide you’ve already built and ask AI to help you refine it. Or you can describe what you want and let AI generate a first version to react to. Either way, AI can often get you 90% of the way there quickly—producing something clean, structured, and on-message that would have taken much longer to build from scratch. The remaining 10% is where your judgment comes in: the final tweaks to color, emphasis, wording, and alignment that make the difference between a good slide and a great one. Some of those refinements you’ll direct AI to make; others you’ll make directly yourself.
Before getting to the prompt and example, let’s review some potential pitfalls.
AI may get decluttering wrong—it can add to many labels, callouts, and explanatory text, or strip away context your audience needs to understand the visual. Don’t equate clean with clear. Make sure every element earns its place, and retain the context necessary to understand what’s being shown.
AI may misuse color—it often suggests using multiple colors to distinguish categories, not understanding that strategic, sparing use of a single color is far more powerful for focusing attention. Watch for suggestions that add color complexity rather than reduce it.
AI will describe, not recommend—left unprompted, AI tends to produce neutral, balanced responses. Push it to tell you whether your takeaway title is doing its job and whether the message is clear.
AI-generated slides need your eye—especially when AI produces something that looks polished, apply your own SWD judgment before accepting it. Is it free from clutter? Is where to look clear? Are the words right? Does it tell the audience what to see and why that matters?
Be mindful of what you share—avoid including sensitive data or personally identifying information in your prompt.
Before you begin, share the SWD + AI primer with your AI tool and ask it to use those principles when helping you design graphs and slides. Then proceed with the following.
Here is what I’m working on: [share your draft slide or graph, or describe what you want to create and provide the data]
My audience is: [briefly describe]
What this slide needs to communicate: [state your takeaway in a single sentence]
How it will be used: [for example, one slide in a live presentation, a standalone graph in a report]
Please help me create or refine this through the lens of clear, simple data communication.
With the primer shared and the prompt ready, let’s walk through an example.
If you’ve been following this series, you’ll recognize this scenario. I’m a People Analytics Manager at a mid-sized consulting firm, working on a presentation to recommend a change to our hybrid work policy. In previous posts, I identified my audience and formed a Big Idea (post 1), planned the story and developed a narrative arc (post 2), and chose the visuals I wanted to build (post 3). Now it’s time to design the slides.
For this step, I used Copilot in PowerPoint—which means the output is editable slides rather than images I’d need to rebuild from scratch. Before diving in, I shared the SWD + AI primer with Copilot using the suggested opening on the primer’s first page, giving it a grounding in SWD principles to work from.
After Copilot confirmed it would use the foundation learned from the primer, I followed with this prompt and a table of my summarized data:
After doing a bit of “thinking,” Copilot asked me which visual direction should frame this high-stakes policy finding, outlining the options: Editorial Ivory and Red, Crisp White and Coral, Warm Paper and Ink, along with an option to enter my own specifics via text. I responded with my company’s standard font (Monserrat) and image of our color palette.
Here is the slide it created:
Copilot’s initial slide was a strong starting point: structured, on-brand, and closer to presentation-ready than anything I could have produced from scratch in the same time. But it wasn’t finished. The next step was to iterate, and I found it useful to divide that work into two categories: changes that require thinking and reconsideration, and changes that require craftsmanship and fine-grained execution. The first category is where I kept working with Copilot; the second is where I took over myself.
For changes where I wanted AI to reconsider the communication (not just move objects around), I kept the conversation going in the prompt window.
The most important issue was message hierarchy. The slide had three competing statements: a headline, a subtitle, and a takeaway at the bottom all making similar points. That’s too much repetition, and it dilutes the impact of each. I asked Copilot:
Review the title, subtitle, and takeaway at the bottom. They feel repetitive. Recommend a clearer hierarchy that communicates one primary takeaway, with supporting text only where it adds useful context.
This is a perfect AI task: there are multiple legitimate ways to solve it and I wanted its thinking, not just execution.
I also pushed on visual emphasis. The story is primarily about early-tenure employees, but the navy lines for leadership were fairly prominent, competing for attention. I asked:
The main story is the decline among early-tenure employees across all three role types. How would you strengthen focus on that pattern while keeping the other tenure groups available as context?
Finally, I asked it to pressure-test the headline itself. The graph shows performance ratings, not support directly—and “hybrid work is widening the support gap” is a stronger claim than the data strictly supports. I asked:
Review the headline against what the data actually supports. Is “support gap” too strong given that the data measures performance ratings? Suggest alternatives that preserve the intended message without overstating the evidence.
Here are Copilot’s responses:
I confirmed the outlined changes and indicated I wanted the primary slide title to focus on the decline in early tenure performance after the hybrid work policy was introduced. After Copilot made the changes, the slide looked like this:
Once the conceptual decisions were made, I stopped prompting and took over myself. Detailed design work—exact font sizes, spacing, alignment, line weights, label positions, panel widths and margins—is faster to do directly than to describe to AI. The awkward label positioning on the red data points, for example, is exactly the kind of thing where I could spend five prompts trying to get Copilot to do something I can fix in a matter of seconds.
That said, I did turn back to Copilot for a few specific tasks where it genuinely saved time. Changing the y-axis scale across all three panels to run from 2.5 to 5.0—with consistent trailing zeros on whole numbers—was fast for Copilot to execute and would have meant a lot of clicking through graphs and menus for me. I also asked it to repeat the y-axis labels on the right of the panel group to facilitate comparisons, and to remove the x-axis tick marks. One thing to watch: when Copilot made this last change, it undid some of the changes I’d already made manually, so I had to ask it to restore those—and explicitly tell it not to touch what I’d done myself.
The work I kept for myself was largely about language. AI writes polished copy, but just because it’s polished doesn’t mean it’s right. The words it chose were reasonable starting points, but they weren’t exactly what I wanted to say. Reading every word critically and rewriting where needed is an important step that only you can do. Beyond the language, I removed the extraneous elements, adjusted label positioning and spacing, modified the size and weight of some text, colored the headline to tie visually to the relevant data, added light gray background shading to the Collaborative/client-facing panel, and changing the gridlines on that panel to white. (Yes, I usually remove gridlines, but in this case I wanted to keep them to make it clear that the same y-axis range applies across all three graphs, and so people can easily estimate the non-labeled data points.)
After about ten minutes of this work, here is my resulting slide:
This same approach can work for text-based and concept slides—those that carry your message through words, diagrams, or a simple visual rather than a graph. Share the primer, describe what the slide needs to communicate, and ask AI to create a first version. As with the data slide, expect to do the conceptual refinements in the prompt window and the fine-grained language and design work yourself. To reiterate: the language AI chooses will often be close but not quite right—read it critically and rewrite where needed.
If you find an example slide you want to emulate—a layout, a structure, a visual approach that works well—you can share this image with AI alongside your content brief and ask it to build something similar. This is particularly useful for data slides where the design logic is already solved and you just need to apply it to your own data (Alex explored this approach for a recent makeover). This is a powerful shortcut that can save significant time while keeping you in control of the final result.
Before wrapping up, I wanted to try one more thing: asking Copilot to build out the rest of the deck from the takeaway titles we developed in the story planning post. Rather than designing each slide one at a time, I shared all eleven titles and asked Copilot to create a full draft deck. It wasn’t perfect, but it got me further faster—similar to what we saw with its initial drafts of the slides—and gave me something concrete to continue to refine.
Below is the slide sorter view: a title slide, followed by the eleven takeaway titles we crafted previously, each on its own slide and ready to be built out—including slide 7, which we designed in this post. In a matter of minutes, Copilot turned a list of titles into a structured deck. The story arc is visible, the narrative is in place, and the hard thinking is already done. From here, I can work through the remaining slides in the same way I did with slide 7: prompting Copilot with specific guidance to build a first version for each, iterating on the conceptual decisions in the prompt window, then taking over myself for the language and detailed refinements. What remains is the craft—which is the part I truly love—and I have a clear path to get from here to the completed deck.
Don’t forget: if you haven’t already, download our free SWD + AI Primer to help apply SWD principles when working with AI.
2026-09-19 04:34:02
Yesterday, a participant in one of our workshops asked a question that I’ve been thinking about ever since. She works with researchers and statisticians, and the idea of telling a story with data can make some of them uncomfortable. Doesn’t telling a story mean choosing a perspective? And doesn’t choosing a perspective introduce bias? Wouldn’t it be more objective to simply show the data and let people draw their own conclusions?
I understand the instinct. I suspect the word story is actually part of the problem. It can sound like we’re asking people to embellish the facts, manufacture drama, or cherry-pick evidence to support a predetermined conclusion. That isn’t what I mean when I encourage people to tell stories with data.
But I also think “story doesn’t work for me” can become an easy excuse. It lets us focus on all the reasons storytelling can’t work in our particular situation instead of thinking openly about how it could. Storytelling with data isn’t a rigid formula. It needs to flex—to the audience, the setting, the type of work, and what the evidence can reasonably support. The story a researcher tells may look quite different from the story a salesperson tells, and it should. The challenge is figuring out what storytelling means in your situation, not dismissing it because one version doesn’t fit.
That brings me back to bias. Let’s distinguish between bias and perspective. We should absolutely work to minimize biases that distort our research or analysis. But that’s different from pretending we can eliminate perspective from the process altogether.
Long before you decide how to communicate your findings, you’ve made choices. What did you decide to study? What did you measure? How did you measure it? Which questions did you ask? What data did you collect? What did you analyze? Which comparisons did you make? Even the decision to put a particular graph in front of someone—and not the dozens of other graphs you could have made—is a choice.
There is no magical moment when we can simply “show the data” without having influenced what someone sees. The alternative to story isn’t objectivity.
I don’t think having a perspective is a bad thing—quite the contrary.
If you’ve done the research or analysis, you’ve likely spent considerably more time with the data than the people you’re communicating it to. You’ve explored it. You’ve asked questions of it. You’ve seen what surprised you and what didn’t. You’ve investigated anomalies. You know its limitations. You understand the context surrounding it.
Why would you discard all of that knowledge at the moment you communicate?
I’ve argued for years that our audience shouldn’t have to guess what we’re trying to tell them. Back in 2014, I wrote about the importance of leading with story: if you’ve done the analysis, you’re in a unique position to interpret the data and help others understand what it means. A few years later, in a post titled so what?, I made a similar argument about our responsibility as communicators to make the point clear rather than leaving our audience to figure it out themselves.
I believe that even more strongly today. When you don’t offer your perspective, you don’t eliminate interpretation—you outsource it. Your audience will make sense of the data somehow. They may arrive at the same conclusion you did, but they may also miss something important. They may misunderstand what they’re seeing. Someone could even selectively use your data to advance a conclusion the evidence doesn’t support.
“Just showing the data” doesn’t protect it from bias, misinterpretation, or nefarious use. In some cases, your silence may make those things more likely.
Perhaps the idea of telling a story still makes you uneasy. Fine. Start with something smaller: What perspective can you bring?
Given everything you know about the data, what do you see? What deserves attention? What might someone reasonably conclude? What decisions or conversations could this information inform?
There doesn’t have to be a single definitive answer. This is one of the places where we can flex what we mean by story. In exploratory or research settings, perhaps you surface several perspectives: Here’s one way to interpret what we found. Here’s another possibility. Here are the questions I think these findings raise. You can give people a useful starting point without pretending the data supports a certainty that it doesn’t.
Bringing perspective also doesn’t mean overstating what the data can tell us. Quite the opposite. Part of your responsibility is to communicate uncertainty, limitations, caveats, and plausible alternative explanations. You don’t want your work to have more impact than the evidence warrants.
But fear of having too much impact isn’t a good reason to aim for none.
I think this is ultimately where the discomfort with story can lead us astray. “My role is simply to inform” sounds admirably objective. Sometimes, though, it can also be a way of avoiding the harder work of deciding what the information means and helping someone else make sense of it.
You can be rigorous and have a point of view. You can tell a story and acknowledge uncertainty. You can direct attention and show people the evidence they need to challenge your interpretation. These aren’t contradictions. They’re part of communicating responsibly.
So if your immediate reaction is storytelling doesn’t work in my situation, resist it. Ask a different question: What could storytelling look like in my situation? Maybe it’s a strong recommendation. Maybe it’s a clearly articulated finding. Maybe it’s several possible interpretations and the evidence supporting each. Maybe it’s simply directing your audience toward what you believe deserves their attention and explaining why.
Flex the concept to fit the situation. Bring your knowledge, context, and perspective to the data. Then give your audience what they need to question it, challenge it, build on it, or act on it.
You won’t eliminate perspective by refusing to tell a story. You’ll simply leave the story to someone else.
2026-09-03 19:15:31
There’s a lot to learn when it comes to communicating effectively with data. You can improve your charts, strengthen your narrative, get better at understanding your audience, or sharpen your presentation skills. But you probably don’t need to work on all of those things right now. A useful question to ask yourself is:
What skill would make the biggest difference in my work today?
We found ourselves thinking about this recently as we looked back across 100 episodes of the storytelling with data podcast. Rather than simply asking our team to pick their favorite episodes, we asked them, “When would you recommend this episode to someone?”
The answers revealed five different areas to focus on, depending on what you’re trying to improve right now.
Maybe you’re relatively new to data storytelling. You know you want to communicate more effectively, but you’re still figuring out what “good” looks like. At this stage, focus on the fundamentals: choosing appropriate charts, reducing clutter, using color intentionally, directing attention, and making your message clear.
Don’t wait until you feel like you know enough to start creating. In one of our early podcast episodes, Andy Cotgreave offers advice we still come back to: create, create, create. Practice gives you a way to discover what you don’t know yet. Your early work might make you cringe later—and that’s evidence that you’re improving.
Here are the podcasts episodes that we recommend at this stage:
Once you can make a decent graph, you might be wondering, “How do I turn a collection of graphs into something people want to pay attention to?” This stage is where you start thinking more about narrative, structure, tension, flow, and the human element of your communication.
It also means getting comfortable with iteration. Your first attempt doesn’t have to be your final one. One of our favorite metaphors from the podcast comes from Cole’s son Avery, who described an early draft as a “sloppy copy.” Data stories need sloppy copies, too. Get the ideas out, move them around, test them, get feedback, and refine.
Here are the podcasts episodes that we recommend at this stage:
Knowing how to create an effective data story doesn’t automatically tell you what story you should tell. At this stage, the questions get bigger: What does my audience need? What should I show? What can I leave out? What decision am I trying to enable? How should I adapt when I have limited time, space, or resources?
This is also where slowing down can help you move faster. Before jumping into an analysis, ask questions. Before presenting everything you found, consider what your audience needs to know.
Here are the podcasts episodes that we recommend at this stage:
Now that you’ve made your data understandable, the next stage you might be working on is to increase your influence on your audience. Your audience gets the data perfectly well; they just aren’t doing anything about it.
Influence requires us to think beyond the graph itself. We have to consider how people interpret information, what they care about, what might make them resistant, and what will help them feel confident taking the next step. It’s especially valuable to get feedback at this stage. The work makes perfect sense to you because you created it. Seeing it through someone else’s eyes can reveal assumptions, confusion, or unintended interpretations you would never catch on your own.
Here are the podcasts episodes that we recommend at this stage:
Finally, your slides are solid, your story is clear, your recommendation makes sense, but you need to get better at delivering it. This stage is about enhancing your presentation skills. Cole makes a strong case for practicing saying your story out loud. Stand up. Think about your posture. These small changes in how you deliver your message can shift how both you and your audience experience it.
Here are the podcasts episodes that we recommend at this stage:
Circling back to where we started, don’t overwhelm yourself by trying to improve everything at once. Focus on the skill that will make the biggest difference in your work right now. These five areas aren’t necessarily a linear progression. You might be highly skilled at creating graphs but uncomfortable presenting them. Or perhaps you’re a confident presenter who needs to become more strategic about what you show.
So instead of asking, How do I get better at data storytelling? try making the question smaller:
What skill would make the biggest difference in my work today?
Start there by diving into the playlist that’s most useful to you right now. Also, we often publish new podcast episodes, and you can follow the show on your favorite podcast player so you never miss an episode: Spotify, Apple Podcasts, Overcast, or on the web.
2026-08-26 21:07:52
During a recent virtual public workshop, a participant raised an interesting question. Paraphrased, it went something like this:
"I can completely see the value of what you're teaching, but how do I tell a story that my audience doesn't want to hear, or doesn't want to focus on right now?"
My initial reaction was surprise. Why would we share something our audience isn't interested in? After a few follow-up questions, it became clear that the person they were communicating to was expecting an update framed in a particular way. However, the exploratory analysis and resulting findings pointed in a different direction than the agreed-upon strategy.
I realized the challenge wasn't necessarily that the audience didn't want to hear the story. More likely, they were expecting a different one. When that's the case, acknowledging those expectations before introducing new findings helps your audience make the transition from what they thought they would hear to what the data is actually saying.
This is a difficult situation. You need to understand why the audience holds those expectations, while still surfacing what the analysis shows. With that in mind, those were the thoughts I began with in my reply:
Understand audience constraints. Resistance isn't always a sign someone is ignoring the data; they may have priorities or constraints that weren't visible during the analysis. If you can, talk with your stakeholders directly to understand their thinking. If you can't, ask for input from others in similar roles. They may be able to help you understand why the audience is so invested in the story they expected. Understanding that context helps you proceed thoughtfully, with your audience's perspective in mind, and may even turn a skeptic into a supporter before you're in the room.
Get clear on your message. This is where taking the time to form a Big Idea—a single sentence that captures your point of view and, in particular, what's at stake for your audience—helps. Thinking through the benefits or risks depending on how they respond to your recommendation is what makes it feel relevant and clear. (For more on the Big Idea, including how AI can act as a thought partner to help compose your idea, check out this post.)
Consider your framing. If the topic is controversial, think about how to position the message constructively, and whether to soften any recommendations. Rather than "we've seen this in the data, so you must take this action," try something closer to: "we'd like to share some insights, outline the options, and get your thoughts."
Separate findings from recommendation. An audience can accept what the data shows while still disagreeing with the proposed action. Keeping the two distinct creates space for a more productive conversation.
So how do you tell a story that your audience doesn't want to hear?
By first making sure it's a story they understand, care about, and see as relevant to the decisions in front of them. If the content has been tailored to the specific audience and gives them a clear sense of why the message matters—whether in terms of opportunity, risk, or impact—even a difficult message is more likely to be received in the right spirit.
If this scenario sounds familiar, fellow data storytellers Alli and Ryan dig into a related challenge on a recent podcast episode:what to do when a leader asks you to find data to prove them right. It's a useful listen if you're facing something similar.
The goal isn't to avoid an uncomfortable truth, but to make it clear, relevant, and useful in service of the organization's broader success.