The most difficult task you could give to a designer is to create a typical “by the numbers” infographic. While a good designer can make a fistful of numbers look pretty, it’s a serious challenge to make some random smattering of numbers meaningful.
A good designer can surely make an infographic that catches some eyes and causes people to pause their Facebook scrolling to look. Like this one, from the SPCA of Northern Nevada (where I have no connection whatsoever and firmly believe they did the best with what they had to work with).
So some Facebookers stop and engage with the top left box: 2,147 pets saved.
In approximately a millisecond, they’ll think:
Cool! Wait. Is it cool?
Is that a lot of pets saved? A little?
Folks, there’s not much meaning in the single number data communication strategy. And without the meaning, there’s nothing for our hearts and brains to hook into. So we just keep scrolling.
Construct more meaning – and more engagement – through one of these three additions.
What was your goal?
When you add goal information to your visual, you get a richer story.
Hey, we passed our goal this year!
Hey, why doncha donate to help us meet our stretch goal?
See how this works?
What did you do last year?
You can also add meaning by adding history. Seeing the trend helps to frame the current year.
Wow, we’re saving more and more – awesome!
Amazing, we’re saving about 100 more pets now than we were just 3 years ago!
(All of these numbers are completely made up.)
What did those other guys do?
Drop in some comparison points to let your audience see how you stack up. And use this to create an actual story (though you’d use real numbers and comparisons instead of the fake ones I’ve got here).
We’re way ahead of Northeast California, even though they’re better funded!
We need your donations to help us catch up to those richie riches in Northern Utah!
We’re the 2nd most life-saving center in the region!
This gives you so much more to work with than just the single big number.
So three ways to add meaning are: goal, history, comparison.
We’re adding context, which makes it possible to create meaning from the one number we’re hyping up at the moment.
The next time someone flings you a single number to visualize, ask for more data points so you can actually tell a story.
Look, I’m not one of those data vizards that soapbox-screams NO PIE CHARTS and drags any offender through the mud on Twitter. Sometimes, some pies are ok. Sometimes.
But I’m ready to call for a ban on any pie chart in a company’s annual report. I don’t know what it is about that particular environment, but annual reports are home to the worst pie chart offenders. I’ve seen companies nail their C-Suite presentations but absolutely lose it in their annual report.
It’s like when you go shopping at Target and you’re super well-behaved and only pick up the things on your list until you near the check out lanes and forget your discipline at the bargain bins.
Speaking of, this showed up in Target’s annual report from 2019:
Why do this to readers?
I get the intention – make it look like a target!
We can keep that intention but make the chart easier to read with a lollipop:
where the target is the lollipop head. This version of the chart makes it so much quicker to spot that three departments had an equal percentage of sales.
The five separated target/donuts slow us down there because it’s harder to compare those donut segments across three charts – you’ve essentially gotta rely on the percent sign within the donut… which, then, what’s the point of the viz?
Side note: This is totally bringing me back to my days restocking toilet paper in Hardlines. I haven’t worn red and khaki since then.
This annual report pie pair comes from Lilly Endowment, one of the world’s largest private philanthropy groups.
Do you see what I’m seeing? I mean, you might not. Because that light green text on the medium gray background is entirely miss-able.
At first, I thought those pies were purely decorative. It took a LOT of eye jumping, back-and-forth, so spot that they weren’t just two identical pictures – they were, in fact, actual charts with just a slight difference in their percentages.
What percentages? You’ll have to dig through the text to find that. While I can get behind a good color-coded text situation, all the juicy data is in the paragraphs. People aren’t going to bounce from the pie to to the paragraph, back to the pie, then back to the paragraph. People don’t do that. They just quit engaging.
In this case, the pies are pointless. Which is ironic – because this is a scenario when a pie chart is a perfectly suitable choice. Just three wedges, plenty of space to add data labels. You’re good, Boo!
My final exhibit in the argument to outright ban pie charts from annual reports comes from Wellcome Trust, another one of the largest philanthropic groups in the world (they co-founded the Human Genome Project, for example). All that money, and this is what’s in their annual report:
If you’ve got so many wedges you need to start using patterns, a pie is not the right choice.
If you’ve got tiny wedges such that you have to label your percentages with sticks, a pie is not the right choice.
If you’ve got a legend below the chart, 5 options deep, that requires the reader to do eyeball gymnastics, a pie is not the right choice.
At a recent workshop (not at any of these companies – though, hi, call me), an attendee bravely admitted, out loud “I thought that if it was percentages, it should be a pie chart.”
And first let me just say that I’m so proud that we’re quickly able to establish a learning space that’s open, honest, and vulnerable, so that we can bring myths to light and address them.
But Honey, no.
I don’t know where these data viz myths come from, but I think it’s the same place that says “6 bullet points per slide, 6 words per bullet point” and “I’m just popping into Target real quick – I’ll be out in 10 minutes.”
Henceforth I’m calling for a ban on pie charts in annual reports.
The ban will be lifted when a company is able to credit a data viz designer (even an internal one) as a contributor to the report.
“We are doing a capacity report, and one of the survey questions lists 11 analytic programs with the average user proficiency. Normally, I would only pull the top 3 or 5 and make an infographic for the information. This time, the partner wants all of the information.”
Super smart to edit down the data to a Top 3 or Top 5 list, especially when you know you’ll be presenting a lot of other data too.
So what do you do if your audience is asking you to show allll the data?
First, let’s interpret that as a really good sign: They’re into the data! Awesome!
Here’s the table of data this student sent with the question:
The simplest place to start is with a bar chart.
I sorted the data in this chart from greatest to least, which makes it easier to see the pattern at-a-glance. I also put the bar chart on a scale of 0-5, assuming that was the scale the respondents used to rate their proficiency.
Sure, it shows all the data.
However, this student clarified that the scale actually ran 1-5. Well that takes a bar chart out of the running because bars need to start at zero.
So we swapped in a dot plot instead.
With the adjusted scale, it’s even easier to see that proficiency is pretty low in all programs.
But the second time I reviewed the audience’s request to my student – to see all the data – I heard something different. Perhaps they don’t want to just see the full list of 11 programs and the average scores. Maybe they’re really asking to see every single data point instead of averages.
Beeswarm charts are such a good choice when you want to show the distribution of values within your whole dataset. This student’s beeswarm ended up looking like this:
I sorted this beeswarm such that the program with this highest average proficiency is on the left and the lowest average proficiency is on the right. It’s not too hard to see that there ain’t much activity at the top of this chart (aka not much high proficiency). But it’s a little harder to see how much low proficiency there is because there are so many dots clustering on the 1 line.
This beeswarm can work, but this chart type tends to work better when the possible response options are continuous rather than discrete. You can see what one of those looks like in this post.
A solid, middle ground choice here could be a ridge plot. The ridge plot shows the distribution of the data but doesn’t specify exactly how many people said what, where. You get the big picture of the shape of the data.
The point with this ridge plot is not to focus on the exact numbers (like “17 analysts rated themselves a 1 in Tableau”) but rather to see the big picture.
Like how for most software, the peak is at 1, meaning most analysts say they are aware of the software but not proficient in it.
And other peaks or plateaus, like how at least some analysts report pretty high proficiency in SAS, SPSS, and Tableau.
That might be info you’d assume from looking at the average scores in the dot plot, but the evidence is right in front of your face, no assumptions necessary, here in the ridge plot.
Matter of fact, you could show *both* the dot and the ridge to take your storytelling step-by-step.
Unless they actually don’t want to see the distribution of the values. What exactly do they mean when they ask to see alllll the data? Start by clarifying that question.
Ricky Gervais has this joke about how the daddy long legs spider is the world’s most pointless insect. Because it’s got the deadliest venom. But teeth too tiny to pierce human skin.
Research data can be like that, too: Probably amazing but very little impact.
And that’s usually because the main circulation focus is just a journal article. If the data gets beyond the journal, like, say, to a conference presentation, it’s typically just the journal article copy/pasted onto slides and shared with other researchers.
To reach a broader audience with your data, to actually impact society with your work, you’ve got to (1) make it more understandable and (2) distribute it in more spaces.
Social media has opened up brand new forums for researchers to make their work more available. But still, only one-third of research is posted on social media. It should be even more.
And it should be presented better.
Did you see this academic data visualization making the rounds on social media?
You can do so much better. Let me show you the three charts you never knew you’ve always needed.
Dot and Bar
This chart is the best way to show a measure of central tendency and a measure of variation.
This example of a dot and bar is showing average responses on a satisfaction survey of zoo visitors, and comparing whether zoo visitors who interacted with staff rated their experience better (they didn’t). The dot represents the average and the bar shows us the confidence interval.
The gray of the bar marks it as supporting information and its thickness is a better reflection of the underlying idea: The actual value could potentially be anywhere in that range. The typical black Ts don’t even come close to this accurate visual explanation. They can’t touch this.
Proportion Plot
Proportion plots are my favorite way to compare the makeup of your sample to the overall population.
Your hope is that the lines are not very curvy aka that your sample adequately reflects your population. But we all know that often isn’t the case. And I know that you know that the best way to hide the fact that your sample is a bit off is to just pop that data into a dense table. But we’ll get more traction if we’re transparent. Make that story visible.
My students have also used this chart type to compare outcomes for control and intervention groups. It works in any situation where both sides would add up to the same amount and share the same categories.
Nested Boxes
This is the chart type that will tell your story about how you ended up with the sample you’ve got.
If you’re using language like “of those” or “among these,” you should be thinking about nested boxes. These depict a subgroup of a subgroup of a subgroup of a… you get it. Use clear sentences inside each box, align your boxes well, apply smart colors and you’ve got a concise dataviz.
Of course your chart arsenal is going to include your trusty bars and scatterplots. But if you add these three to your mix, you’ll gain so much clarity, you’ll get more eyeballs on your work, you’ll be better understood by a broader audience.
And from there it’s just a short leap to a segment on Good Morning America. Ok, ok, that may be a bridge too far. But it WILL help you create a stronger case for the impact of your research and you can put that in your next promotion packet.
I hear your brain thinking “yeah but how do I make these?”
Babe, you can make this in almost any software, as long as you know how. Heck, everything I’m showing you here was made in Excel and PowerPoint. I’ve got instructions for ya in my Data Visualization Academy.
When I finished reading this research article, I thought “Can a bar chart be racist?” Yep, looks like it. Sounds impossible, doesn’t it? Together, let’s release the clutch on our pearls, take a deep breath, and look into this study. It’ll help us be more inclusive.
Pieta Blakely, Eli Holder, Cindy Xiong, and others have conducted a cluster of studies that look at how certain charts can reinforce deficit framing. It works like this:
When we compare group averages in something like a bar chart, we inherently lead viewers to compare those bars against one another and end up focusing in on the differences in the outcomes. Here’s one example they tested:
In their study, they found that over half of the respondents agreed with statements about the data like “These outcome differences are because [Group] works harder than [Other Group].”
When they replaced the generic group labels with names of racial groups, they found fewer people agreed to the same statement, though the study authors attribute that to social desirability.
In other words, whether the group names are generic or specific to certain racial groups, at least a third of folks are drawing conclusions about the data that reinforce unfair (and racist) thought patterns.
Sure, there’s always more research to be done to establish this finding as canon. But I’ve personally seen enough to make me pause.
So now what?
Look, go all out with your bar charts on datasets that aren’t about comparing racial groups (or, I’d think, gender or class or any other group that is already dealing with some unwarranted discrimination).
And when you are graphing data on those groups, try a different chart type.
Blakley and team tried out a beeswarm instead (this also goes by other names like a jitter plot but that just ain’t as fun as a beeswarm). Beeswarms show the individual data points that make up that average that woulda been shown as a bar.
Turns out, when people see all the individual data points displayed, they’re more likely to notice that data points for each racial group overlap each other – a lot. And that makes them less likely to attribute over-generalizations to any particular racial group.
Taking race out of it for just a sec, beeswarms are pretty fantastic little charts. Here’s one that Cameron Cross made. He’s one of my Academy students. His graph is showing the test scores for the 8th graders in his district.
When you see every data point like this, it’s easy to spot some outliers that woulda been pushing your bar-chart-average in a skewed direction. It’s also easy to spot those kids who are right on the margins of advancing to the next category. You’d catch none of that in a bar chart.
But bar charts are so easy to make, right?
How in the world would you even make a beeswarm?
Babe, I’ve got all the instructions you need in the Data Viz Academy, where Cam learned it.
Bottom line: A change in chart type can guide our thinking toward what matters and away from racist thought patterns.
That dashboard you’ve been developing? That one that’s cost hundreds of hours and thousands of dollars? Wanna know why it doesn’t have the leverage you thought it would?
It shoulda been a webpage.
Don’t scream. Hear me out first. Then propose this change to your boss. Who’s gonna see you as a freakin *genius*. Then, send me an I’m-sorry-I-doubted-you-thank-you note. I’m already drafting you a pre-emptive “you’re welcome, boo.” 😘
Here’s a perfect case in point to share with your team.
My clients at the National Education Association published this webpage. It’s long, so I’m going to include snippets in this post and link you to the whole thing, which you really should see, here.
They’ve smartly titled this thing with a catchy (you could say “click bait-y” if you wanna be judgy – doesn’t matter much cause this works) 6 Charts that Explain the Educator Shortage.
Think about this: if you title any data communication – dashboard or otherwise – with something like “5 charts that explain our sales drop” or “7 charts that explain our amazing year” and your higher ups are gonna click. Period.
We don’t think of this kind of phrasing for dashboard titles.
We get into this copywriting mindset when we’re thinking about webpages, journalism, and storytelling.
Shifting your communication platform shifts your thinking.
And shifting to a webpage makes you think about the rest of the words, too. Webpages don’t just have a title. They have narrative.
Here’s one snippet from the NEA’s page:
Good looking graph, right? Awesome, color-coded title. Solid chart choice. Smart design. No complaints.
But what makes this into an actual story is the accompanying narrative between 1. and the graph.
And that’s the stuff you don’t get in a dashboard.
That narrative is where you get nuance. Expansion. Explanation. Clarification.
When we think in dashboard, we often constrict ourselves to the canvas size that our dashboard software has laid out for us.
In Tableau, that’s 1,000 x 800 pixels.
In Power BI, you’ve got 1,280 x 720.
And, too often, we don’t question those constraints. We just cram within them.
A dashboard of the 6 NEA charts would look like this:
Sure, the titles and graphs are so strong you can get a story out of it. But it’s tiny. I have to pinch to zoom.
It isn’t the full, detailed picture.
And, frankly, doesn’t it feel a bit… tight? Full? Dense?
Thinking like a webpage makes you balance out the text and the chart. Never so much text that you’ll get bogged down. And never so many visuals crammed into one spot that you get overwhelmed.
Thinking like a webpage brings some balance to your storytelling.
Here’s the thing: You’re probably already publishing your dashboard and sending around a link, right?
That part doesn’t change.
This strategic approach to your data communication just brings you more utility. It makes your work more accessible. Because the dashboard version requires a ton of background info that only a select group is going to have. People need that background scoop to fully understand the charts, if all they’ve got are the charts.
You bring more people into your audience when you think like a webpage and explain your data.
Now please, PLEASE, do not write me defensive emails saying that this can’t possibly be done when your data are interactive and dynamic. I’ve heard the question so many times: How can I have narrative if my audience can filter the view?
My dear sweetheart, that’s the same restricted-by-the-platform thinking that’s made our dashboards fall short all this time.
Of course your narrative can change if your view changes. We show you how in the Data Viz Academy.
The default canvas in Tableau and Power BI aren’t setting you up for success. But the default canvas isn’t as creative and smart as you are. Let me help you think bigger.
The most common question I get in my work, by far THE top question, is What software did you use to make that?
If I’m only thinking selfishly, I’m flattered. Heck yes. My graphs are so good people want to know what magic tool created it.
Most of the time, my answer is Excel and then I have to pass out kerchiefs like I’m some old dandy to help mop up all the drool when my audiences jaws hit the floor.
When my more community-oriented angels prevail, I start thinking differently about that Which software question. I start feeling bad for the person who asked it.
Because underneath that question is a hope. That there will be some perfect solution. That if we just purchased the right software, this whole data visualization business would be faster, easier, more accessible to the entire team.
I hate crushing hopes.
Truth is, every software is going to require some learning curve.
And learning curves are always obstacles. Every software is going to require some patience and tenacity, some education and hand-holding.
Just like learning to drive a car. Even though my drivers ed instructor, Mr. Bramble (who was also my swim teacher), “taught” us how to drive by popping in a video of horrible highway accidents so he could nap. On his caffeinated days, we’d drive the tan sedan, me in the driver’s seat and Mr. Bramble with his foot hovering over the specialty brake installed on the passenger side.
Despite his semi-wakeful state, I couldn’t have gotten over that learning curve as fast and ticket-less as I did without his instruction.
Coaches are indispensable. We have data visualization coaches ready to walk you over that learning curve inside the Data Viz Certification Program. We take you step-by-step, giving you feedback, showing you shortcuts, and cheering you on.
Even then, you’ll run into frustration moments.
Like when Juliana was learning how to make overlapping bar charts in Tableau and she asked “why is this size tool so annoying and inefficient?” YUP. As great as Tableau is, parts of it still kinda suck.
The Power BI request site doesn’t tell you how many submissions they’ve had but there are 500 pages of results.
The hope that we’ll find a perfect tool that no one struggles to learn, that can make every chart with the click of a button – you’re fishing at the wrong wishing well, my friend.
The question should not be Which Software.
Come to my class to learn about the questions you shouldbe asking.
When you start with these questions, you will, eventually, figure out which software to invest in. We’ll get to the same goal: stunning, useful, informative data visualization. We’ll just take a clearer path to get there.
Oh dang, this class already took place! If you want to find out about future free classes, sign up for my newsletter.
When it comes time to share your data in a presentation, you’ll have way more success if you learn about these three methods of circulation: slidedocs, slide handouts, and one-pagers.
(Side note: I used to call this “dissemination” as I was taught to do in school but have since learned that the core of the word relates to, well, you can probably figure that out for yourself.)
Your method of circulation probably involves some kind of presentation. A slideshow. A PowerPoint. Where you’re there telling everyone all about your awesome findings. Right?
The thing is, most PowerPoints suck.
Because they’re often created to do double duty – to work as your backdrop, while you talk in front of a group of people, and to work as the read-ahead or leave-behind.
It’s just that when we aim for double duty, we end up creating something that doesn’t work well for either situation.
This slide (set up by Microsoft – it’s in their default design collection) is not appropriate for presenting to large groups. The bulleted text is way too small for that scenario. And no one wants to read that much text on a slide while you’re also jabbering in their ear.
As a leave behind, it also doesn’t work that well because the title (currently the very illuminating “Content 2”) is way too big for comfortable reading at arms length.
You need solutions for this dilemma.
Try Slidedocs.
Nancy Duarte coined the term “slidedocs.” Her position is that if the PowerPoint’s sole intent is to be a read-ahead or leave-behind, design it as such. Sure, you’re in slideshow software. But treat it more like a page layout program.
Use columns for text. Use font sizes appropriate for closer reading.
Duarte has a free e-book about slidedocs. The book itself is a slidedoc.
To work this way, you just open up your trusty PowerPoint and think of each slide more like a blank page. You’d want to shrink down any default textboxes so the font is smaller. You’d want to go into the Slide Master and set up some grids and standard font sizes and themed colors.
You wouldn’t present slidedocs live. It’s too dense and tiny for in-person presentations.
Try Slide Handouts.
So what if you need to both present the content in-person but you also have a lot to convey and you’re gonna run into the tiny font problem? Make a slide handout. A slide handout combines your presentation and a reader-friendly handout into the same PowerPoint file.
Let me show you an example.
You can see in this screenshot that I only have a handful of slides I created for this presentation. They’re designed around the best practices discussed in my book: mainly pictures, with a few supporting words. Everything important I wanted to articulate during the presentation is down in the notes section. The magic of turning this into a slide handout happens in that notes section. Let’s look at a typical notes view.
You’re probably very used to this layout. You get there by clicking the View tab inside PowerPoint and clicking the button that says Notes Page.
There’s a big placeholder at the top where the slide will appear and a big placeholder at the bottom where the notes will appear. The slide handout secret is that those placeholders can be rearranged and we can add graphic enhancements to the notes page to make it look more like an actual useful document – a slide handout. Here is how one page of notes from my presentation/proposal looks.
I shrunk that big slide placeholder into the corner and put it inside a big green rectangle, which also contains a summary sentence of my ideas.
I enlarged the text placeholder and picked a great font.
Look more closely that the text in this notes page. The text is not bits and phrases or a bulleted list of highlights I want to cover. It’s full of complete sentences, written in narrative format. It’s. My. Proposal!
All I had to do was print my notes pages to PDF and my reporting was done. My audience members then received a pretty visually engaging proposal to review, easy to swipe through on a tablet, with a one-to-one correspondence between the page and the presentation because of the little slide thumbnail in the corner.
This way I can present well-constructed slides and deliver a visual report within the same file.
To make this happen, you’ll do most of your work in the Notes Master (why do they still call it this?). Look again in the View tab. We had been in the family of buttons all the way on the left called Presentation Views. To the right of that is a family of buttons called Master Views. Click on the Notes Master.
In here you will reposition the slide placeholder, the text placeholder, and add in anything that you want to appear on every notes page, like a big green rectangle. Anything you change here in the Notes Master will be reflected over in the Notes Pages. So if you want to check how your Notes Master layout is affecting your Notes Pages, go back to the View tab and click on Notes Pages.
What I love about the Notes Pages is that they are a great place to offload extra things that would otherwise clutter the slide. Put your logo on your notes page! Put that giant table on your notes page! Put your contact info on your notes page!
This method creates a win for your audience because they get all of your content in a font size that’s comfortable to read and it creates a win for you because your slides have dignity.
However – if your slidedecks are like mine, they’re 300 slides long and that’s way too much for a readable handout.
I’ll never forget when I was visiting the World Bank, a year or so after giving a workshop there. As I walked to my meeting, I saw my own one page handout from my workshop posted in people’s cubicles. That doesn’t happen to a 300-slide PowerPoint. And that doesn’t happen to a website.
It summarizes their study. Sure, policymakers are going to want their hands on the larger report. But this one pager has readability built in. Rakesh can follow up with the PDF as an email attachment that policymakers can forward on to any constituents or committee members.
A webpage can serve this same purpose. Almost. At best, a website gets bookmarked in a long list of other bookmarked sites. It can’t, by its nature, stay front and center. Not in the way a one pager gets magneted to refrigerators and pinned to community corkboards.
One of these methods of circulation is going to save your tushy. Because it’ll provide you an outlet to get all your main ideas to your audience, while also making you look like a freakin pro.
When a bar is boring, buy everyone a round of tequila shots.
LOL ok that might work for your favorite neighborhood pub but your bar chart is gonna need something else.
But first let me just back up to say:
There’s absolutely nothing wrong with a bar chart.
Bar charts are easy to read, for most people. Being easy (in this case, but not at the neighborhood pub) has its advantages.
Bar charts are also familiar, for most people. Easy + familiar means your audience doesn’t get hung up on decoding the chart. They just move right on to the thinking you’re prompting and the discussion you’re hosting. That’s awesome, right?
However, I know you run into situations where the trusty bar just isn’t cutting it anymore.
Your boss is asking you to make the data viz more exciting (and you tried giving it a tequila shot but nothing happened).
Your data makes sense as bar charts but a whole report full of bars is making your eyes cross. You wanna shake it up a little.
You’ve just learned about a new chart type and you wanna show off your chops.
Whatever the reason, here are some decent alternatives for a single series (that’s just one set of bars, versus a cluster of bars side by side) bar chart.
Try a lollipop.
Lollipops focus on what would be the end of the bar, but it’s kinda still essentially a bar chart. A good move if you aren’t trying to freak out your audience with too many big changes.
Try a dot plot.
Dot plots ramp up a lollipop a little more and have the added advantage of a flexible x-axis. Whereas bars should start at zero, because we’re decoding by looking at length, you don’t have that issue with a dot plot.
Is it actually change over time?
I see this confusion often. If the labels are years, maybe this should be a line chart. But one series in a line chart can look kinda lonely, so try a line + area chart.
This better tells a story about trend.
Highlighting just one bar?
Go for a pie, why doncha?
Just group up all the other categories into one wedge and put your focus wedge in an action color.
Backpedal to the bar and add color.
If you’ve got some meaningful subgroups in your bar, color-code them. Now it’s no longer a bar chart, it’s a **~~colorful~~** bar chart.
Sometimes this bar chart rumspringa lands you a new mate and other times you go through all those other options just to discover your original bar chart is perfectly fine.
How will you decide which option is best? Listen to your audience and picks what engages them with the data (not the chart type – with the data itself).
If you’ve got two series in your bar chart, you have even more options. Check out Ama Nyame-Mensah’s ideas here. She’s a Tableau and R coach in our Certification Program and she’s full of good suggestions.
Any place where you’re assembling data and a message.
You need 3 elements. Your one pager needs to be:
For a specific, intentional audience
Balancing visuals and text
With a clear call-to-action
Let’s dig in to some examples:
For a Specific, Intentional Audience
Even if you aren’t aware of it, your data communications are reaching a specific audience. I’ll show you what I mean. My ask is that you become extremely intentional about your audience.
Check out this example from the Great Lakes Inter-Tribal Epidemiology Center (clients of mine):
This is for a specific audience: People who aren’t really in public health. Folks who have maybe heard a bit about monkeypox in the news but don’t know much about it. You can tell by the language they use, right? This isn’t how you’d phrase things – heck, this isn’t even the aspect of monkeypox you’d be discussing – if the audience here was other public health officials.
Beyond the word choices and topic framing, the graphic support at the top and bottom of the page contain choices that are more likely to resonate with their intended audience: the general public who belong to their tribes.
Contrast that with this webpage from Michigan’s Department of Health and Human Services. This is data on the top twenty diagnoses, by gender (binary gender, at that – and there’s a mix of terminology here with gender and sex):
Yeah, 2019 data even though I took this screenshot in November 2022. I can totally forgive – they’ve had a lot on their plate between now and then.
Can you tell which audience this is for? It might be a little hard to tell at first.
But we can likely agree that we can rule out the general public.
This website is probably a resource for statisticians in public health. They don’t need “gratuitous” graphics. Just the facts, thanks.
And this is a common mistake I see in public health communication in general (and surely in other industries too): We accidentally design for ourselves, not for a specific, intentional audience.
Balancing Text and Visuals
Rakesh Mohan and his team at the Idaho Office of Performance Evaluation (also my clients) know how to make a good handout. They know their audience is (1) policymakers, who need a quick read before diving in to the full report, (2) the media, who need complex policy issues broken down and (3) the public, who are paying the salary of Rakesh, his team, and the policymakers.
Given those audiences, the tone of this one pager is somewhat formal but they’re using clear and approachable language.
They’re giving the gist without going overboard. Providing plenty of text to explain context and nuance, but balancing that out with a strong graph and other graphic support.
Contrast that with Idaho’s COVID dashboard.
It’s all numbers, no nuance. No context. This thing needs some words. Some story. That’s what helps us make sense of the data (and we are all sense-makers).
When we present our data this way, we end up failing on my first criteria too: we limit the audience. The only people who can decode this dashboard are those with high data literacy, strong willpower, and a deep sense of the context. AKA – other public health officials.
Dashboards need words. Good one-pagers (infographics/webpages/dashboards) have a balance of words and pictures.
With a Clear Call to Action
Ok, one more example for ya. We made this infographic for the Kansas Department of Health and Environment. The topic is squicky.
It ain’t right to pull a reader through that much squicky content and just leave em hanging. We need to give them a place to go with the uncomfortable feelings this data has stirred up. We need a call to action.
And this infographic has several: What Kansas is doing to address the issue, what you can do to help, and even places to get the raw data and more info.
We don’t just dump data, we direct people to next steps.
Next steps are easy to write when you have a clear, intentional, specific audience.
In fact, knowing your audience helps you determine whether you need to produce an infographic or a webpage or a dashboard or a one-pager (or something else entirely).
I hear you asking “What if I have a bunch of different audiences?”
Babe, you need a bunch of different one pagers.
Now what would this blog post be without a call to action? Here it is:
That data communication piece your working on right now needs a solid review. Check it against these three criteria.
Then, email it to me. People are always asking for great examples and I’d love to feature yours.
