Visualizing data in charts, graphs, dashboards, and infographics is one of the most powerful strategies for getting your numbers out of your spreadsheets and into real-world conversations.

But it can be overwhelming to get started with data visualization. Does data visualization leave you feeling like the numbers are about to topple over on you??

If so, this step-by-step data visualization guide is for you!

I’ll walk you through the data visualization design process so you know what to do first, second, and third as you transform your spreadsheets into data stories.

Step 1: Understand Your Audience

Wait! Don’t start making graphs on your computer! First, we have to do some planning. A little bit of up-front planning will save you hours of blood, sweat, and tears in the long run.

First, we need to consider our audience and context. Who, exactly, is going to be using the data to make decisions?

Here are some discussion-starter questions to talk about with your colleagues.

Who is Your Audience?

A chart designed for a group of foundation program officers will not be appropriate for a group of high school principals, and vice versa.

List all your audience types on a piece of paper, or a whiteboard, or in a spreadsheet, or even on the back of a napkin. Share the list with your colleagues and make sure you’re on the same page.

Have you reached consensus about who you’re targeting with your data?

What’s Your Audience’s Numeracy Level?

Do they enjoy or fear data? Unless you’re designing charts for a group of economists or statisticians, you can usually leave out details like the effect size, power analysis, and margin of error. Laypeople are often more interested in practical significance (the “so what?” and implications of findings) than in statistical significance.

What’s Your Audience’s Data Visualization Familiarity Level?

If they’re brand new to dataviz, stick with the traditional charts like pie charts, bar charts, and line charts—otherwise they’ll spend more timing ooh-ing and aah-ing over the chart’s novelty than paying attention to the information contained in the chart.

How Much Time Does Your Audience Have?

Little time or interest: Simple static chart.

Lots of time and interest: Interactive charts.

What Types of Decisions Does Your Audience Make?

What information do they need? What information do they already have? What information are they expecting? How will your chart(s) add value for them?

If you can’t think of how your chart will add value for the readers, don’t make one. Every chart needs a purpose and so what?

How Much Precision is Necessary?

As the data visualization designer, you have the freedom (and responsibility) to select how much precision is necessary. Your selection should be well thought-out and intentional. Your decision plays out in two ways: the chart type you select, and how you label the data points.

When selecting chart types, remember that some charts are better than others in displaying precision. For example, charts that rely on angles and area to show differences, like pie charts, are for communicating general patterns. Charts that rely on length to show differences, like bar charts, are for communicating specific details.

How Many Decimal Places Are Necessary?

A related decision is how exact your data labels will be. Will you include decimal places? How many?

In most scenarios, you can safely round your decimal places to the nearest whole number. Your audience is rarely using the tenths, hundredths, or thousandths place to make decisions.

Are My Viewers Expecting a Story?

Think about whether your audience is expecting a traditional or storytelling graph.

You’ll learn about the distinctions in this video:

Step 2: Choose the Right Chart

It takes a while to understand all the different chart types and to pick the best one for your desired takeaway message. There are tons of great graphs to choose from!

Consult a Chart Chooser

My interactive Chart Chooser includes dozens of chart types, resources, tutorials, and templates.

New to Dataviz? Start with Classic Chart Types

If you’re not sure which chart to use, stick with classics like the bar chart to compare categories and the line chart to visualize how things change over time.

These charts will be “right” most of the time, so they’re a safe bet.

Use Pie Charts Sparingly

Contrary to popular belief, pie charts are not evil and don’t have to be avoided altogether. I have seven guidelines for using pie charts and donuts. In this pie chart makeover, I show you how to transform a 3D pie chart with way too many slices into a storytelling bar chart with icons:

Getting Comfortable with Dataviz? Branch Out and Try Other Chart Types

Once you’ve mastered the classic chart types, you can play around with less-familiar chart types like bubble charts, bullet charts, dot plots, heat maps, scatter plots, slope graphs, social network maps, tree maps, waterfall charts, and more.

Surround Yourself with Positive Inspiration

Surround yourself with great graphs so you can expand your worldview of what’s possible with data visualization. I suggest following top-notch data journalism teams like @PostGraphics, @NYTgraphics, and @WSJgraphics.

You can even create a physical or digital library of great graphs. For example, you might print full-page, full-color charts and tape them near your desk. Surrounding myself with a variety of chart types, all of which have been used in different reports and for different groups of people, helps me create brand new charts easily. All I do is glance up at my gallery, and then I quickly figure out which chart is best for my new situation.

Dive Into Your Dataset with Exploratory Data Visualization Techniques

I also use exploratory computer strategies, like Microsoft Excel’s spark lines, data bars, and conditional formatting, to help me narrow down the focus of my charts.

Spark Lines

Here’s a tutorial that shows you how to get started with spark lines:

Data Bars

And here’s a tutorial that shows you how to get started with data bars:

Conditional Formatting

You can set up rules in your spreadsheet that automatically change the color of certain cells based on their values. I regularly use heat tables to scan my dataset for patterns. You can follow my step-by-step tutorial to make heat tables for your data.

Sketch Rough Drafts on Paper

Step back from your software program. This is especially crucial if you’re using Excel or R (versus Tableau) where you usually need a solid idea of your chart’s design before implementing that design on the computer.

I sketch, draw, and doodle plenty of drafts before I create anything on the computer.

Here’s how it works: First, sketch plenty of rough drafts on paper. Give yourself permission to doodle as many drafts as you need. Share drafts with colleagues early and often. Gather as much feedback as you can. Next, create one or two of those promising drafts on the computer. Finally, edit, edit, edit! Put your easiest-to-follow chart in your final presentation or report. You might sketch five or more drafts. Only the single best chart will survive the editing process.

Step 3: Select a Software Program

Once you’ve got a rough mental idea of what your visualization might look like, sit down and build the first draft of your visualization on the computer.

There are dozens of software programs available for building data visualizations. Some are free. Others are low-cost. And others are quite costly, at least for smaller organizations.

I’m software-agnostic at my core, meaning that I don’t care which program you use. You can create great — or terrible — graphs in any software program.

That being said, 99% of my data visualization consulting is done in Microsoft products: Excel, Word, and PowerPoint. Those are the common denominator for the companies that hire me. I’d never create a dashboard in a specialty software program… if you don’t also have access to it and know how to use it. It would be useless!

Here’s an example of an interactive dashboard made in good ol’ Excel. You can learn how to make these, and many other types, inside my Dashboard Design online course.

Step 4: Declutter

After you’ve got the first draft of your data visualization created on the computer, it’s time to refine your visualization and make your message shine. No computer program is perfect. You’ll have to roll up your sleeves and make intentional edits no matter which software program you’re using. The very first edit I make is to declutter my visualization. Software programs come with way too many borders, lines, and unnecessary ink. Examine each and every speck of ink on the chart. Does it have a specific purpose? If you can’t articulate a reason for that ink, you don’t need it.

Apply the Squint Test

In these before scatter plot on the left, the cluttered appearance distracts us from the data. All these extra lines make the charts look overly scientific—and outdated. In the after version on the right, I removed the background shading and borders. I kept the x and y axes and some of the grid lines, but I intentionally changed the black ink to gray ink.

How do you know when you’re done decluttering? Apply the Squint Test. Here’s how it works: Squint your eyes so that you’re peering at the chart through your eyelashes. Everything should look a little blurry. Can you see the overall shape of the data? For example, you should be able to tell if a line chart is jutting upwards or downwards over time. If not, try removing more clutter.

Outline Shapes in White

You’ve got the gist of decluttering. Now, let’s fine-tune!

Sometimes reducing clutter means outlining shapes in white, rather than black, so that they match the chart’s background color.

Step 5: Clarify Your Message with Color

There are three goals for color:

1. Branding (Using your company’s colors, which saves time and helps you look professional)
2. Accessibility (Making sure your colors pass official guidelines so they’re legible for people with disabilities, like ADA/508 compliance in the United States)
3. accessibility (Using colors to make the graph feel intuitive)

Brand Your Visuals with Custom Colors

I’m begging you! Do not use the default colors from Excel, Tableau, or Google Charts. Nothing screams novice! or 2002! more than default color schemes. If you’re designing charts for a report, handout, or presentation for a client, use their color scheme. Consultants, this means the report will look like it came from the client. It will not have your firm’s look and feel.

In this example, Johanna Morariu and I were designing a slidedoc for the Working Families Success Network. We began by investigating the Working Families Success Network’s logo, website, and publications. Their logo has a distinctive blue, orange, and pink and their publications use dark gray text rather than black. Throughout their website they use color blocks with white text and white outlines. Next, we adapted that layout and color scheme for our slidedoc. The images on the right are separate slides (pages) of the report.

You can locate custom color codes in style guides, with a free eyedropper tool, or even with Microsoft Paint. Then, enter your custom color codes in Microsoft Excel or in Tableau.

Make Sure Your Colors Are Legible for People with Color Vision Deficiencies

Here’s how:

1. First, by proactive and avoid using red-green color combos.
2. Second, make sure you directly label your data.

Although we’re used to seeing legends, we rarely need them. Legends can lead to unnecessary zig-zagging around the screen or page, and legends can also be difficult to interpret if your graph is printed in grayscale.

Instead of using legends, directly label the data. Direct labels mean that you add labels as close as possible to the data. For example, in a line graph, you would delete the separate legend and place the category labels off to the right of each line. For bonus points, color-code the text in the labels to match the line.

This is what direct labels look like:

Then, you can upload your draft to www.color-blindness.com’s Color Vision Deficiency Simulator to get a preview of what it’ll look like for people with protanopia and deuteranopia.

Emphasize the Takeaway Message with the Action Color

When you want to tell a story with data, you can guide your viewer’s attention to your desired takeaway finding by creating a dark/light contrast. This example comes from one of my graduate school projects a decade ago, so I used the exact shade of green from my university’s logo. Then, I used dark green to draw my audience’s attention to a couple key parts of the slide. This slide comes from the fourth section or chapter of the presentation, the Limitations section, so that tab was highlighted in dark green so that it contrasted with the other tabs, which are in gray. The topic of this particular slide was Brevity of open-ended survey responses, so that text is in green so that it stands out against the rest of the text. And the box-and-whisker plot itself also uses dark green.

Step 6: Clarify Your Message with Text

It’s hard to get wording just right, so I usually save my titles, subtitles, and annotations for the end.

Brand Visuals with Custom Fonts

Rather than using Microsoft’s plain ol’ Calibri, make sure your visualization’s fonts match the project’s branding.

Write the Takeaway Finding in the Graph’s Title

Need to tell a story with data? Rather than using a generic title (“Figure 1” or “Number of youth served”), state the takeaway message in the title.

I first learned about this technique through Cole Nussbaumer’s Storytelling with Data workshop back in 2012—but geez, was it tough to apply! This is one of the hardest practices for social scientists to learn because we’re so comfortable with APA formatting and its generic figure titles.

Think Twitter-like and aim for six- to eight-word titles. Look to newspaper articles for inspiration; journalists know how to include the “so what?” in their title. You may or may not read the full newspaper story for additional details. Same thing with charts: your audience may or may not read your full chart, so your title must give them the gist of your findings.

Add Context with Annotations

Annotations are call-out boxes that provide important contextual details. In PowerPoint, Word, or Excel, you can easily create annotations by inserting a text box. No fancy software required!

Here’s a great example from Mother Jones. A generic title would’ve been “Number of children living in poverty” or “Relationship between poverty and geographic location.” This 6-word title, “In Climbing Income Ladder, Location Matters,” ensures that readers grasp the chart’s message instantly. A 2-line caption adds more details underneath the title, and a few cities are annotated. The tweet’s text also reinforces this message.

This is how likely poor kids are to grow up and move out of poverty based on where they live http://t.co/5A5VIZkLBN pic.twitter.com/7BBZQJ9bdg — Mother Jones (@MotherJones) January 31, 2014

Establish a Text Hierarchy

Size your fonts according to their importance. A text hierarchy tells your viewers which information is most important (headings) and which information is least important (the regular ol’ paragraphs). In this example, I transformed a university’s annual report simply by adding an intentional text hierarchy. I call this makeover a two-hour turnaround because these are changes that anyone can make in two hours or less. Before, all the font was the same size, so the headings didn’t stand out. The report looked like a sea of words. After, we made the headings stand out by with larger fonts and by overlaying the text on top of a photograph. We also used a different color for each section to break up the sea of words into manageable chunks.

Lower the Reading Level

The vast majority of reports, handouts, infographics, dashboards, and slideshows that I review with clients are written at a reading grade level that’s so high that reading the documents feels like homework. In this example, we assessed our draft’s reading grade level with a free tool called readable.io. Then, we re-worded the title so that it was a closer match for our intended audience.

Finally, go share your chart!

You’ll need to edit it slightly depending on the medium — a chart for a presentation should look different than a chart for a dashboard. You can learn about presentation-specific, dashboard-specific, and report-specific techniques.

Learn More

Sign up for my free online course called Soar Beyond the Dusty Shelf Report. There are several quick lessons that help you get started with data storytelling.

Or, contact me about online courses, private workshops, and conference keynotes.

I did.

I taught myself how to write in third-person language.

I called myself “The researcher…” instead of plain ol’ “I…”

I replaced my everyday words with “smart” synonyms. I literally paged through my GRE study guide. I tried to use as many GRE vocab words as possible.

Then, I started working in the real world.

My bosses rolled their eyes.

Another one from an academic background, they sighed. We’ll have to re-train her from scratch.

I panicked. But if I wasn’t supposed to sound like a textbook, what was I supposed to sound like???

A human!

It took me years to grasp that simple concept. I’m a person. It’s okay to sound like a person.

Nowadays, I make a living by teaching humans how to sound like humans again.

Before/After Makeovers for Common Methodology Sentences

Here are some before/after examples in case you’re still on the textbook-back-to-human journey like I was.

Please please please use these transformations in your technical reports.

I’m not so worried about peer-reviewed journal articles — that’s another battle for another day. Today, I’m focusing on your non-journal writing scenarios.

Who Designed the Survey?

• Before: A survey instrument was designed by the ABC Research Company working under the supervision of the DEF Foundation staff, and key department heads of GHI Agencies.
• After: The ABC Research Company, DEF Foundation, and GHI Agency teamed up to collect data.

Who Responded to the Survey?

• Before: A series of survey instruments were developed to administer among students in the ABC programs.
• After: We designed surveys to collect information from students in the ABC programs.

How Many Responses?

• Before: A total of 14 programs participated in the survey.
• After: Fourteen programs participated in the survey. (Remove redundancies like “a total of.”)

Or…

• Before: A total of 144 programs participated in the survey.
• After: We collected surveys from 144 programs. (Because writing out numbers at the beginning of a sentence is the worst.)

When Did You Collect Data?

• Before: Initial surveys were launched on March 7, 2018 with fieldwork continuing to accommodate the schedules of participating institutions. Data collection was cut off on April 25 to begin data processing. A total of 789 surveys were attempted, with a total of 654 surveys completed sufficiently to include in the final tabulated results. A total of 123 individuals entered their contact information for a drawing.
• After: We collected surveys in Spring 2018. We tried to collect data from 789 people, and 654 people participated, for a response rate of 83 percent—one of the highest response rates we’ve ever had on a survey.

Referencing the Just-in-Case Tables

• Before: We are providing detailed data tables with this report that shows the responses by institution.
• After: Want to view responses by institution? View the appendix on page 31.

Demographics on Respondents

• Before: Overall, undergraduate students comprise 65% of total responses and graduate students comprise the remaining 35%.
• After: Two out of three responses (65%) were from undergraduate students. The rest were from graduate students. (Getting rid of the word “comprise.)

Describing the Survey’s Topics

• Before: One way the important resources and individuals specifically helped at least two-thirds of students were giving them a good sense for the kinds of careers they could pursue with a degree.
• After: We asked students which resources were most useful. Two out of three students said that others had given them a sense of career options that they could pursue with their degree.

Summarizing the Survey’s Findings

• Before: The largest mean share of the total cost are paid for by the student or their family, who account for 50% of the total cost. Student loans are used to cover a mean of 20% of the total cost, and scholarships or other financial aid pay for 30%.
• After: For the typical student, 50% of their costs are covered by the student and their family, 30% are covered by scholarships or financial aid, and 20% are covered by student loans. (Getting rid of awkward language like “mean share” and “account for.”)

Objectively Scoring the Before/After Translations

In my gut, I know the translations are easier to read.

Let’s objectively test them.

Before: 12.1 Grade Level

The human-trying-to-sound-like-a-textbook wrote at a 12.1 grade level.

Okay, that’s not the worst I’ve seen.

The highest I’ve seen is a 36 (from a team of Ph.D. psychologists).

Can you beat a 36??? Let me know if you find any contenders. I’d love to (try to) read it.

(This screenshot is from Readable.com, which used to be a free reading level checker. It looks like they require payments nowadays, but there are plenty of free- and low-cost tools. Like good ol’ Microsoft Word! Comment below if you’ve got a favorite.)

A­fter: 9.3 Grade Level

I personally aim for grade level 6 to 8—throughout my blog posts, books, and even contracts.

I didn’t quite reach my goal. But a 9.3 isn’t horrible, either. The Readable site gives this an “A!”

Higher is not better. Lower is better.

You are a human who’s writing for humans. You are not a textbook. You are not a textbook. You are not a textbook. You are not a textbook.

How to Lower the Reading Grade Level

Try one (or more!) of these techniques:

• Shorten the sentences. An easy fix is to look at your longest sentences. Replace your commas with periods (i.e., break one long sentence into two shorter sentences).
• Shorten the paragraphs. Press the “enter” key lots and lots and lots.
• Use first-person language. Adjust the sentence structure. Change “A survey was administered…” to “The agency administered a survey” or “We administered a survey.”
• Find synonyms. This is the hardest one for me. What’s an accurate, understandable translation of calculations like standard deviation or confidence interval??? I used to pack those terms into the report’s body and hope for the best. What happened? Lots of Dusty Shelf Reports! Nowadays, I follow the 30-3-1 Approach to Reporting. I keep the methods section in the report’s body as short as possible, and I tell readers to check the appendix for more info. I don’t care if the appendix is packed with jargon. Only the technical readers are going to look there anyway, and they’ll understand the jargon.

Your Turn

Upload one of your own paragraphs into your favorite reading level checker.

How did you score?

And more importantly, how can you adjust the language to lower the reading level??

There’s a misconception that accessibility takes all day, that’s it’s costly, or that it’s complicated. Those are all false.

Accessibility is woven into all my trainings, but since this is a topic I get asked about a lot, I decided to make a new talk that’s focused just on accessibility for dataviz.

In Spring 2021 I gave a talk at the Good Tech Fest conference about dataviz accessibility quick wins.

The talk was “Choose Your Own Adventure” style where the audience chose what we discussed from a list of options. They chose:

• direct labels,
• lower the reading level, and
• lower the numeracy level.

You can watch the recording or read the highlights. Enjoy!

—–

Watch the Conversation

Why Bother Lowering the Reading Level?!

We’re writing for busy people. The ones who see tons of graphs coming into their inbox every day.

We need to lower the reading level.

Not because our readers are dumb, but because they’re busy.

They need to be able to understand what you wrote the first time—not the second, third, or fourth read-through.

Before: A Dense Slide Title

Here is a real-life graph from a public health agency.

I had to read this slide title at least five times to figure out what it was talking about.

After: A Skimmable Slide Title

I talked with the epidemiologist who made that slide, and here’s what we came up with:

Here’s what we did:

• We put the main takeaway point in the title.
• We used a text hierarchy so that your eye is drawn to the largest, darkest, boldest text first. 
• We kept her original title so that the technical people had all of the information they would need, but made it the subtitle instead.
• We rotated the diagonal text and used horizontal text instead (for speed-reading).

Objectively Testing the Reading Level

We tested both titles with an official readability website.

In the past, I’ve used https://readable.com/.

They used to be free, but now have a fee.

(Comment below if you have a great, free readability website you use. You can also use Word or Google for this.)

The before graph title was a 14.2, which in the U.S. would mean you’ve graduated high school.

The after version was a 6, woohoo!

The Average American Reading Level

Speaking of grade 6 being a great spot to be at.. What do you think is the average reading level?

Hint: it’s lower than you think.

The average American reading level is a 6 – 8.

While a lot of people have gone on to higher education levels than that, it’s not a one-to-one comparison.

For example, it’s not, “I finished 12th grade, therefore I read at a grade 12 level.” A lot of us read a little bit below our formal education years.

Rule of Thumb: Write 1-2 Levels Below

Sure, you might not be writing for “the general public.”

You might be writing for grant makers, policy makers, trustees, etc.—folks who likely read much higher than a grade 6-8.

A good rule of thumb is to aim for 1-2 levels below your audience’s education level.

For example, if I’m working on a board packet, and I know all the board members have an undergraduate degree, then I write two levels below that—at a middle school level.

If I’m working on a technical report for a government agency, and I know that all the recipients have Master’s degrees or higher, then I write two levels below that—at a high school level.

How to Lower the Reading Level

Here are some quick wins:

• Active voice (instead of passive voice)
• Shorter words, sentences and paragraphs
• Replace jargon with synonyms

After drafting your sentence or paragraph (they’ll probably be really long—mine usually are!), you’re going to go back and edit your writing.

Anytime you see a comma, replace it with a period.

Or, if your paragraph is six sentences long, break it into two shorter paragraphs.

Please make sure to objectively test your own writing (with Readable, Word, etc.). I don’t care what tool you use.

Let’s Practice

During the Good Tech Fest conference session, we practiced lowering the reading level for a few common data sentences.

How would you lower the reading level for these examples?

Example A: A survey instrument was designed by the ABC Research Organization.

The quickest wins would be:

• shorter words, sentences and paragraphs; and
• replacing jargon with synonyms.

Here’s the after:

What if you’re trying to explain the number of participants in a survey?

How would you lower the reading level here?

Example B: A total of 14 people participated in the survey.

Example C: A total of 144 people participated in the survey.

Here’s the after versions:

One last example with some jargon.

Example D: Undergraduate students comprise 65% of total responses.

How would you replace the jargon with synonyms to lower the reading level?

Here’s the after:

Your Turn

How’s the reading level in your writing? Comment and let me how your writing scored.

This blog post has a Flesch-Kincaid Grade Level of 6.3.

Download the eBook

Want to learn more about accessible data visualization?

In this ebook, you’ll learn 10 quick wins for designing accessible data visualizations. These small edits can have a big impact for our coworkers, board members, and funders who have color vision deficiencies, hearing loss, or learning disabilities–and for all of us who are pressed for time.

For your complimentary copy, use code: goodtechfest

Before

This attendee worked at a large university library and was responsible for writing an annual report about the library’s operations and accomplishments. The report was full of tables, graphs, and photographs.

This is a screenshot from the beginning of the report that offered background information about the library, like how many visitors came to the library each hour and each day. Library headcounts inform decisions about staffing and about future library hours. For example, if they find that library attendance peaks in the morning, then the university might decide to open the library earlier to accommodate their visitors’ preferences.

After: Reenvisioning the Individual Graphs

The first graph was about average hourly headcounts—how many students, staff, and other visitors were present in the library at any given hour in the day.

In this makeover, we:

• Decluttered the graph by removing the border, tick marks, and horizontal x-axis line;
• Decluttered the vertical y-axis by removing all of the labels except for the smallest value (0 visitors) and largest value (50 visitors);
• Decluttered the horizontal x-axis by only labeling a handful of key hours;
• Nudged the columns closer together (here’s a tutorial on shrinking the gap width) so that viewers could see the smoothed-out shape of the graph rather than the individual columns;
• Applied a mix of darker and lighter colors; and
• Wrote an annotation above the graph to highlight the key finding. This takeaway—that the library’s peak hours are between 10am and 7pm—was hiding in the paragraph above the graph. I wanted to make the report skimmable.

Here’s the second graph, which is about average daily headcounts. I decluttered the graph and then brought their key message into center stage with dark colors and an annotation.

After: Re-envisioning the Page as a Whole

Next, we had to think creatively about the page as a whole. How would we arrange the graphs on the page? How big or small should we make each graph? How would we re-write the existing paragraphs?

While editing the page as a whole, we:

• Adjusted the report’s text hierarchy by making the Heading 1 and Heading 2 text large, bold, and dark.
• Re-wrote the introductory paragraph and moved some of those sentences closer to their respective graphs. The sentences about daily headcounts belong next to the graph about daily headcounts.
• Wrote graph titles. I usually advocate for storytelling titles that explicitly state the graph’s desired takeaway message. But in this makeover, I decided that annotations—Peak Days; Monday through Thursday—would be just as powerful. I didn’t want the graph’s storytelling title to be redundant with the graph’s annotation, so I combined a generic title with a storytelling annotation.
• Added icons because icons can make our graphs more memorable; and
• Paid careful attention to alignment. The words are left-aligned. The graphs are aligned with each other, too. You could draw a single vertical line from the top graph’s y-axis down to the bottom graph’s y-axis. It took a few minutes to get the spacing just right, but alignment is always worth the extra time because it makes the finished product look more professional and purposeful.

Here’s the full before/after data visualization makeover:

Bonus

Would you like to explore the Excel file, Word document, and PowerPoint slides that I used to create this makeover? Purchase the materials and use them as inspiration for your own projects.

Purchase the Templates ($5)

Except… Mine were way worse! I loved to write 100 pages of 11-point font and bullet points instead of these four summarized pages.

If you’re a full-time graphic designer, then you have 40 hours a week to get fancy with covers and other visuals.

But if you’re a full-time researcher, evaluator, scientist, or university faculty member (like this report’s author), your time to mess around with reports is limited.

I’m going to walk you through five easy steps that you can tackle in just two hours:

1. Add a 20-Minute Cover
2. Add a Text Hierarchy
3. Color-Code by Section
4. Add Intentional Page Breaks
5. Add More Visuals

Step 1: Add a 20-Minute Cover

Covers for short reports are optional, but they sure add a lot of visual interest to a text-heavy report.

I experimented with two 20-Minute Covers. The first cover is a full-bleed photograph. The second cover is a word cloud made from the report’s key words.

Here’s how I added the 20-Minute Cover with the full-bleed photo:

I added a new, blank page to the beginning of the document. I typed The ABC Library and 2017-18 Annual Report in large font.

You’ll use your institution’s fonts, not mine.

I went to www.Pexels.com and searched for library. I like Pexels because it has enough options without having too many options. You’ve got better things to do than spend your entire afternoon searching for stock photography. Plus, the photos on Pexels are free for both educational and commercial use.

If your organization already has its own repository of stock images—great! Use those.

I downloaded this photograph of a bookshelf.

I inserted the image file into my Word document.

I moved the image behind the text. To do this, I clicked on the image, went to Picture Tools: Format, and selected Wrap Text: Behind Text.

Tiny photos are so 1995. Nowdays, the bigger, the better. I wanted a full-bleed photograph; in other words, a photograph that filled the entire page with no margins whatsoever.

I enlarged the image until it filled the page (11 inches tall).

The photograph and the words were competing for attention, so I changed the font from black to white.

It was still too hard to read the text. I covered the image in a semi-transparent purple rectangle. You’ll use your institution’s color palette, not mine.

You’ll need to experiment with different transparencies (e.g., 10%, 20%, 30%, or higher).

Here’s how I added the 20-Minute Cover with the word cloud:

I added a new, blank page to the beginning of the document. I typed The ABC Library and 2017-18 Annual Report in large font.

I highlighted the entire report’s contents with a CTRL + A keyboard shortcut.

I went to www.wordle.net to make a word cloud. This site works best with Internet Explorer.

I spent 19 of my 20 minutes updating Java and giving permissions for the site to run on my computer.

I pasted the report’s contents into Wordle.

I adjusted the word cloud’s settings. I used horizontal text (I didn’t want distracting vertical text on my cover). I selected a Sans Serif font. I chose a grayscale color scheme.

I downloaded the word cloud.

I inserted the image file into my Word document.

I enlarged the image until it filled the page (11 inches tall and a million inches wide).

I moved the image behind the text. To do this, I clicked on the image, went to Picture Tools: Format, and selected Wrap Text: Behind Text.

The word cloud and the words were competing for attention, so I changed the words from black to white and covered the word cloud in a semi-transparent purple rectangle.

Which 20-Minute Cover is your favorite?

I liked the photograph better than the word cloud.

Step 2: Add a Text Hierarchy

When you zoom out and glance at the report, nothing stands out. It’s just a sea of text because everything is size 11.

I enlarged the Heading 1s from size 11 to size 38 bold. I often use even larger font for my headings (40s, 50s, or even 60s). But, some of the headings were long, and I didn’t want them to spill onto a third line. I kept all the headings to just one or two lines of text.

I also enlarged the Heading 2s from size 11 to size 13 bold.

Finally, I changed the black text into colored text to further differentiate the Heading 1s and Heading 2s from the regular ol’ body font.

Now, you can spot the sections of the report at a glance.

Step 3: Color-Code by Section

It’s a short report but it still feels dense. We need to break it up even more. Readers love skimming.

I color-coded the headings by section:

Step 4: Add Intentional Page Breaks

Make sure every section begins on a new page.

I use CTRL + Enter to add page breaks (rather than pressing the Enter key a dozen times).

Intentional page breaks are the easiest edit, yet researchers, evaluators, and scientists shy away from page breaks.

Why are we so afraid of white space? It’s not going to bite us.

Why are we so afraid of adding another page or two to our already-way-too-long reports? A well-designed report beats a non-designed report every time, regardless of the length.

Step 5: Add More Photos

The zoomed-out, at-a-glance view was really coming together! Our readers can see that the report has a few different sections because each section begins on a new page, with large font, and in a new color.

I still needed to add visuals. I suggest aiming for one visual per page—a graph, photograph, table, diagram, logo, etc. It would be great to comb through the entire report and find ways to incorporate graphs, tables, or diagrams. But this report was largely a list of bullet points, and I didn’t have the time or the background knowledge to completely overhaul its contents. I wanted to focus on the low-hanging-fruit edits.

When you’re pressed for time, or if you’re new to data visualization, you can add icons and/or photographs.

The default Microsoft book icons looked cheesy when placed next to the nice stock photograph. Mixing icons and photos just didn’t look right.

I decided to repeat the cover photo behind each of the Heading 1s.

I inserted the photograph, placed it behind the text, expanded it to fit the full width of the page, cropped it to be just a few inches tall, and overlaid colorful semi-transparent rectangles.

These small edits can transform a text-heavy report into a visual-lite report.

This transformation took me an hour. The vast majority of that time was spent banging my head against the wall thanks to the Wordle/Internet Explorer/Java trifecta of insanity. I’m calling it a Two-Hour Turnaround because everything takes longer when you’re doing it for the first time.

Don’t lose sleep over report formatting. You’ve got better things to do. Instead, just follow my easy steps and make drastic improvements to your document’s appearance within an hour or two.

• the graph type you select (3D exploding pie charts with 999 slices are inherently hard to read);
• font size (I suggest a minimum of 11-point font in reports and 18-point font on slides);
• text direction (we read horizontal text faster than diagonal or vertical text);
• colorblindness considerations (no red/green combos);
• grayscale printing considerations (test it beforehand); and
• the reading level of your titles, subtitles, and annotations.

Last time, I showed you this makeover:

In that before/after transformation, our goal was to make the years along the horizontal x-axis legible. Before, the labels were too small (size 9) and they were diagonal (which is slower to read than plain old horizontal text). We freed up space by abbreviating the years (1985 to ’85). We also opted to label just four points along the line to emphasize key milestones.

We also re-wrote the slide’s title. For slides, I aim for short titles (a couple key words). When the presenter is physically present, the presenter’s voice can elaborate on the cool parts of the graph.

The fewer words, the better. You don’t want to lose your audience’s attention, i.e., you don’t want them to be reading full sentences on your slides while you’re speaking. For other types of materials (like reports, handouts, and infographics), I use storytelling titles. Storytelling titles might be 6 to 12 words long. Storytelling titles give you room to elaborate on the cool parts of the graph, like the takeaway finding or “so what?”

Measure Your Text’s Reading Grade Level

While redesigning this graph, it took me a while to understand the slide’s title. There were a lot of technical terms that I wasn’t familiar with. I was also concerned about the title’s length.
I wanted to test my gut instinct. There are several free and low-cost tools for objectively measuring text readability.

I’ve used https://readable.io/ for years and love it. You’ll need to enter your email address. Then, you can access the free portion of the tool. You get 15 minutes of free usage each day. Or, you can pay $4/month for the pro version. I don’t get paid to promote Readable, but I probably should! I love sharing this tool with others.

I pasted the before title into Readable’s website: “Stage 3 (AIDS) Classifications and Deaths of Persons with Diagnosed HIV Infection Ever Classified as Stage 3 (AIDS), among Adults and Adolescents, 1985-2014 US and 6 Dependent Areas.”

The before title scored a D, yikes! The before title was a 14.2 grade level, which is equivalent to a high school diploma and two years of college.

Yes, this graph was designed for people who had college degrees. Just because your viewers can read at a 14.2 grade level doesn’t mean they want to read at a 14.2 grade level. Reading at or above your level would feel like homework. Do you want your graph to feel like homework??

(P.S. Readable’s website got a makeover since I wrote this blog post, so your view will look a little different.)

Measure Your Text’s Reading and Speaking Times

Readable also measures reading and speaking times. Our title took 6 seconds to read and 11 seconds to speak! The epidemiologist could lose her audience for an entire 6 seconds.

On another recent project, I measured a company’s entire draft report with Readable’s website. The report was around 100 pages long, and Readable told us it would take someone 16 hours to read! The report was designed for state-level policymakers. Are we really expecting a busy policymaker to set aside two full working days to read a report? We’re all inundated with information.

Cut down your document’s length by focusing on essential content. Push non-essentials to an appendix. Then, make the remaining text faster and easier to read.

Edit Your Writing and Try Again

I read the title again. And again. And again. And I finally realized that it was just talking about AIDS diagnoses and deaths.

I tried that new title—AIDS Diagnoses and Deaths. It scored an average grade level of 6.5. Hooray!

The average American adult has an 8^th grade reading level. This isn’t the time to laugh at people with 8^th grade reading levels. I could talk about the systemic problems with our educational system for hours.

Your audience may not be the general public, of course. Your audience might be your boss, or your Board of Directors, or state-level policymakers. Their reading levels may be much higher. Your audience doesn’t want to read at their peak ability for hours. Don’t make your graph feel like homework!

This shortened title will only take the audience a second to read—six times faster than the original.

Still Not Convinced? Here’s When It’s Time to Care About Reading Levels…

Look for these clues from your readers:

“Well.. I can tell that really smart people worked on this project.” The first few times I heard this feedback, I mistook it for a compliment. I thought, “YES!!!! I used all the terms from my grad school stats classes correctly!” Now, if I hear that feedback, I cringe. This “compliment” is a sign that your documents are too technical. It’s time to revamp your graphs and your writing style.

“The report was really… comprehensive.” Ouch! This “praise” is a sign that your documents are too dense.

“Thanks for sending the report to us. We’ll let you know if we have any questions.” Ouch! This “engagement” is a sign that readers aren’t connecting with your documents.

Lowering your document’s reading grade level won’t solve all your reporting problems, but it’s a first step.

7 Practical Tips for Improving Your Text’s Reading Level

Sold??? Ready to improve your readability???

I love Readable because it gives you practical suggestions for improving your writing. For example, in our before title, the website highlighted the word classifications in a darker color. It also told me I was using too many long words and that the sentence was too long.

Here are practical tips for lowering your text’s reading level:

1. Find synonyms for technical terms. In this project, we changed classifications to diagnoses. In another project, we changed the counterfactual to comparison group and described the nitty gritty details of the counterfactual analyses in the appendix. In another project, we changed at baseline to when people enrolled in the program.
2. Avoid acronyms. I don’t care if you define the acronym the first time you use it. I shouldn’t have to flip back to page 1 while I’m trying to understand page 10. I shouldn’t have to memorize acronyms in addition to understanding your content and visualizations. When in doubt, spell it out.
3. Use shorter words. Look for words with lots of letters and lots of synonyms, and then find replacements.
4. Write shorter sentences. One of my personal weaknesses is writing run-on sentences. As I edit my own writing, I break long sentences into several short sentences. Search for your commas and semi-colons. Replace them with periods.
5. Write shorter paragraphs. Several short paragraphs > one long paragraph.
6. Write narrower paragraphs. I was That Nerd who took a speed-reading course during high school. I learned about how our eyes scan a page from left to right to read a line of text. I was surprised to learn that we don’t actually look alllllll the way to the left or allllll the way to the right. Instead, our eyes stay towards the center and we take advantage of our peripheral vision to scan the words on the far left and far right. Accordingly, there are studies about how long we should make each line of text (i.e., how wide or narrow our paragraphs should be). Results are somewhat mixed; readers have personal preferences about exactly how many inches wide they prefer. Nobody can speed-read a super long line of text, though. In portrait layouts, I use one or two columns of text. In landscape layouts, I use two or three columns of text.
7. Remove redundancies. Say what you need to say—once! Avoid repetition across your sentences, graphs, and tables. I’ve got an upcoming before/after makeover blog post where I’ll show you how to remove redundancies.

I also have a personal preference for active voice and first-person writing. I can’t connect with reports that sound like they were written by robots.

Lowering your graph’s reading level is not the same as dumbing-down your graph. Lowering your graph’s reading level shows that you respect your audience. You recognize that these are busy, important people. Busy, important people have lots of busy, important priorities. Your graph is one of many, many pieces of information to come across their desk each day. Give your audience the information they want in a format that doesn’t take all day to decipher. Then, your audience can make informed decisions and move on.

Have you used other readability tools? Share them here!