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 LOVED the class!

Wondering whether the class is right for you?

What’s inside this review:

• Class logistics
• Why I signed up
• About the instructor
• What we learned
• Who this class is best for
• My favorite part
• Learn more

Class Logistics

A dozen of us met for two half-day classes held over Zoom. This was the perfect length: a half-day of dataviz principles and quick drawing exercises, followed by a half-day focused on a bigger project.

Participants were mostly based in the U.S. and Europe, with most people using data for part/all of their job.

We used an online whiteboard tool, Miro, to introduce ourselves and share our drawings. There was a small learning curve, but then it was easy to snap photos of my hand-drawn images and upload them to the site.

Stefanie also sent us a supply list a couple weeks in advance. She said we needed a spreadsheet tool (Excel, Sheets, etc.) and some basic art supplies (e.g., plain paper, black crayon, colored pencils, etc.). There weren’t any specialty art supplies needed; these were all supplies that you’d have laying around your home somewhere.

Why I Signed Up

My own dataviz consulting and training is spreadsheety and linear—almost to a fault.

I don’t think it’s helpful for any of us to work within silos.

I wanted to think about dataviz from a different lens—drawing!

Sure, I could read one of Stefanie’s books.

Sure, she probably has public-facing talks on YouTube.

But nothing compares to quality online training where your brain is fully immersed in the topic alongside the instructor and peers.

About the Instructor: Stefanie Posavec

According to Stefanie’s website, she is “a designer, artist, and author exploring experimental approaches to communicating data and information to all ages and audiences.”

Her projects “might be wearable, danceable, or hoppable, be found in hospitals, museums, or on television, and will often use a human-scaled, hand-crafted design process.”

I first heard of Stefanie Posavec after she wrote Dear Data with Giorgia Lupi.

Then, I fell in love with I am a Book, I am a Portal to the Universe.

Day 1: Dataviz Basics + Data Drawing Challenges

Here’s what we learned and practiced each day.

Data Visualization Basics

The first hour was an intro to dataviz, including:

• seven data visualization variables,
• Gestalt principles, and
• preattentive processing.

I took detailed notes, but won’t be sharing them here for obvious reasons. You’ll have to take her class to learn more. 😊

Stefanie had examples from a variety of artists (e.g., Sol LeWitt) and historical figures (e.g., William Playfair). Every dataviz person is familiar with Playfair, but the rest were new, and I loved learning about more artists.

9 Dataviz Drawing Exercises

For the next few hours, Stefanie gave us drawing exercises.

Again, I won’t be describing the exercises in detail.

Here’s what I made:

After each exercise, we shared our drawings on our webcams so we could learn from each other.

I was pleasantly surprised that everyone kept their webcams on!

Usually, I’m the only one with my webcam on. In Stefanie’s class, I was the only one with my webcam off (because I was walking on the treadmill while sketching). Then, I’d turn my webcam back on to share my drawings.

Day 2: Musician Challenge

On the second day, Stefanie gave us a public dataset to work with.

She guided us through how to approach data as a designer, like understanding which type(s) of variables we’re working with (categories, rating scales, binary data, etc.).

Here are my glyphs (new terminology and new approach to dataviz design for me):

Then, we had ~50 minutes to arrange the glyphs with an underlying architecture (e.g., a circle, spiral, clusters, etc.).

The underlying architecture was a massive a-ha for me! The structure really differentiates linear, predictable dashboards from exploratory, artistic visualizations.

We posted in-progress drafts to the Miro board as we worked, and Stefanie provided helpful feedback.

Since I’d been working with paper and pencil for a while, I was ready to switch to computer-drawn graphics. I was probably supposed to continue drawing, but… realistically, all of my client projects have to be done in everyday software like Excel, since that’s the common denominator for all my clients. I had to make sure I could actually implement my sketched ideas in Microsoft tools.

Here are some of my drafts, which were made in good ol’ Excel. (These are just bubble charts with icon fills, outline colors, and outline patterns.)

First, I tried arranging musicians by time and gender, and I started drawing the key. Don’t look too close – I had genre typos in this one, which I fixed later.

Then, I dropped the gender variable, and focused on the timeline (a.k.a. a column chart).

The timeline effectively showed how most of the top-selling artists are from the 2000s.

But, this one felt empty; I could’ve placed another variable on the y-axis.

That’s a lot within 50 minutes!

I was pleasantly surprised how much I could figure out, both design-wise and software-wise, in less than an hour. I can’t remember the last time I nearly-finished an entire non-traditional viz like this so quickly! I was on my treadmill, walking 10 miles during each of the classes, so my brain was definitely on Beast Mode.

After class, I spent another hour arranging the glyphs by the musicians’ birth country. I wanted to show how most of the top-selling artists are from the U.S. and Canada.

I didn’t spend time to make sure the glyphs didn’t overlap.

(Remember, this is an Excel bubble chart, so I’m manually assigning each artist an x and y value behind the scenes.)

At some point, you have to call it quits on just-for-fun dataviz.

Time permitting, I would’ve:

• represented gender with shapes (circles vs. squares) instead of colors (to make it colorblind-friendly);
• continued fiddling with the artists’ placements on the map so they didn’t overlap so much;
• added a second outline/ring to the artists coming after 2000, and kept the single ring for the artists pre-2000;
• added the artists’ photos (the icons are placeholders); and
• shaded the background of each photo according to… something? (Imagine a light-dark shading as the background color behind their headshots.)

Who This Class is Best For

Stefanie described the focus as:

• People who are creating or inventing data visualizations (me!)
• People who want to move beyond the standard chart types (me!)

Well, it was for me, but not really.

I knew I’d be the only full-time dataviz instructor.

I think I was the only one who made datavizzes as the major focus of my job (?). Other students seemed to make graphs, dashboards, and infographics as a piece of their job, but not as the focus. One participant had just finished undergrad.

After listening to others’ questions—Which chart should I use? How exactly do I get started??—I think this class is best suited for people who already have prior data visualization training.

This isn’t the class to learn about data analysis, like how to merge datasets, clean data, recode variables, or calculate frequencies or descriptive statistics. The participants I chatted with in breakout rooms already had those skills from their data-focused jobs.

This isn’t the class to learn the difference between chart types, like a scatter plot vs. bubble chart, or a histogram vs. a population pyramid. Stefanie provided an overview of chart types, but chart types are so nuanced that they deserve an entire class of their own.

This isn’t the time to learn color nuances, like the difference between sequential, diverging, and categorical color palettes. Again, Stefanie provided an overview, but colors deserve an hour or two on their own.

This isn’t a class to learn about Big A Accessibility (like 508 compliance, colorblindness, etc.) or to learn about Small A Accessibility (making sure graphs are easy to understand). This was tricky for me—accessibility is woven into every aspect of my work, and I kicked myself as I drew non-colorblind-friendly graphics and made my own vizzes harder to decipher just for creativity’s sake.

This isn’t the class to learn any software how-to’s. You’ll be working with paper, crayons, colored pencils, and markers. I opted to work in Excel the last hour. Another student pulled up Illustrator.

Instead, this is a class to help you think past the common chart types that are available in our everyday software programs. (“I’m having trouble thinking outside PowerPoint’s charts!” mentioned a few fellow students.)

This is a class to help you think about individual data points (glyphs) within the underlying architecture. I don’t think I’ve ever approached a project from the lens! I’ll likely use this approach from now on.

This is a class to help you approach a brand new dataset and start graphing. These skills are essential for everyone working in data.

This is a class to practice conceptualizing your ideas on paper first, which is a critical planning step that’ll make all your visualizations stronger. There’s nothing worse than sitting down to your computer without hand-sketching first. Computer-first vizzes are never as good as sketched-first vizzes.

My Favorite Part

So much hands-on learning time!

About half the first day, and nearly the entire second day, were spent drawing and drafting visualizations.

For a busy consultant like me, it’s tough to devote time to learning and sketching for my own skill-building. I loved loved loved having dedicated time with others to practice my craft.

I like to lie to myself and pretend that I can set aside time for learning, but I can’t. That’s what dedicated classes like this are for.

Learn More

I asked Stefanie when she’s offering the class next. Possibly in June 2023! Follow Stefanie Posavec on LinkedIn to hear about the next class.

There are plenty of great software programs, like Excel and Tableau—and even PowerPoint!

There’s also a lesser-known secret to creating strong data visualizations: hand-drawn sketches!

Watch Our Episode of Sketchnote Chats

Want to learn more about how hand-drawn sketches are essential for data visualization?

I was recently interviewed by Emily Mills for an episode of the Sketchnote Chats series.

Emily is a professional illustrator and expert in sketchnoting. She is also the founder of the Sketchnote Academy and author of The Art of Visual Notetaking. 

Recognize Emily’s name? She was a guest speaker in our Report Redesign course and I was happy to return the favor.

You can watch our episode here:

How Drawing Can Improve Our Data Visualizations

I use drawing in two ways:

1. To brainstorm which chart type I need, and
2. To piece together multiple charts on a page or screen (e.g., when designing dashboards that are composed of multiple smaller charts).

Drawing Helps Us Brainstorm Which Chart Type to Use

Here’s how I use hand-sketching to develop better visualizations.

First, I open my spreadsheet with my tabulated data. I might have a table with several columns and several rows of already-analyzed data.

Second, I set a timer on my phone for 10-15 minutes.

Third, I draw as many ideas as possible on paper. I think about whether this dataset can be represented through a donut chart, stacked column chart, hex map, Sankey diagram, and so on.

When I started doing this—back in 2012—I was only able to generate a handful of chart types during each brainstorming session. Now, I can generate a dozen ideas each time.

My audience benefits because they don’t have to suffer through the same ol’ bar charts over and over and over.

My audience also benefits because I’ve thought through the dataset at a much more sophisticated level. I explore whether the dataset has interesting chronological patterns that might be visualized in a line chart; whether there are interesting geographic patterns that might be visualized in a map, etc.

Here’s an example from the Harris Theater in Chicago, where I set my timer for 10-15 minutes, and was able to generate nine ideas for visualizing their (fictional) ticket sales data over time:

Here’s another example from a juvenile justice project, where I set my timer for 10-15 minutes and brainstormed 12 ideas for updating their pie charts:

Drawing Helps Us Arrange Multiple Charts on a Page or Screen

Once I’ve selected lots of individual graphs to use, I often need to arrange them together on a page or screen.

For example, if I’m designing an infographic, the one-page summary might involve three, four, or five individual graphs.

Or, if I’m designing a dashboard, the screen might involve three, four, or five individual graphs.

I need to decide which graph should be displayed first, second, or third. Is there a natural sequence?

I also need to decide which graphs “go together.” Is there a natural grouping? A categorization? Similar graphs should be next to each other on the infographic or dashboard.

I use a pen and paper to sketch what that page could look like.

Here’s an example from the National Home Visiting Resource Center, where we used sticky notes to decide how to arrange multiple visualizations on the page:

Get Started with Sketching for Data Visualization

Want to incorporate sketching into your own dataviz design process? In this article, I share my step-by-step process.

Your Turn

Have you used hand-drawn sketches to develop better data visualizations? Comment below!

Emily was a guest speaker in our Report Redesign course. She walked us through the basics of sketchnoting, her career, why hand drawn images stop the scroll, the cool brain science behind sketchnoting, and then led us through some drawing exercises. We wanted to share Emily’s tips with you, too!

Watch Our Conversation

What is Sketchnoting?

Emily explained that sketchnoting is when you’re writing and drawing at the same time. “The whole purpose to remember information, and it’s just more engaging and interesting to look at,” she said. 

Sketchnoting is also called:

• Visual notetaking
• Graphic recording
• Visual facilitation
• Graphic facilitation

What is Sketchnote Academy?

Emily founded Sketchnote Academy in 2018. She offers online courses to help you learn how to sketchnote and then level up your skills. 

Emily’s Previous Career Experience 

Emily Mills’ background is in graphic design. She was an in-house designer in education, nonprofit, and a healthcare start-up.  “I just get bored doing the same thing year after year like an annual report… again. While the data, pictures and information might change, it was still the same project… year after year,” she said. 

How Emily Got Started with Sketchnotes

Emily fell into sketchnoting from a weekly cartoon that she would draw on a whiteboard outside her office. 

She said that a former co-worker remembered those cartoons. When he went to work at a video studio, he contacted her, saying “We just booked a video client for something called a whiteboard video. We don’t really know what it is, but we need somebody to draw for it. Will you come to Houston and draw for our video?” Emily agreed and created a whiteboard video with them. 

“I was really proud of it and I put it online. Someone found it through keywords and hashtags and they said, “This is so cool! Have you heard of sketchnoting?” I hadn’t but [realized] it was pretty much the same thing,” she said. After that, she attended a sketchnote-specific workshop, and then challenged herself to sketchnote every day for a month. 

Hand-Drawn Images Stop the Scroll

Emily once shared with Ann that hand-drawn art/graphics get more view time than stock photos or computer generated graphics. Ann asked her to tell us more as we’re all trying to create reports that people *actually* want to read. 

Emily explained how hand-drawn images can stop the scroll. 

If you think about your average Instagram feed, she explained, you’re probably seeing mostly pictures, some stock photos,, an inspirational photo with a quote on it. She said that we’re used to those images now, and our brains are automatically thinking, “I’ve seen this before; show me something new.” 

Emily said when you see something different like a hand-drawn cartoon or someone’s artwork, you really stop because of human curiosity. “We keep asking questions,” she said. “Hand-drawn images invoke a lot of our human curiosity and take longer to engage with because you’re asking those questions.”

The Cool Brain Science Behind Sketchnoting

“Drawing is what I call the human language,” Emily said. 

Drawing was around before written language. We’ve been communicating in pictures for thousands of years. 

Studies also show that pairing an image with words also allows for better brain retention than just images or words alone.

Drawing Data Can Help Simplify Data

She gave an example of being at a conference where the speaker has a PowerPoint slide with a huge pie chart with 10 different slides and lots of labels. 

If you’re trying to take visual notes, there’s no way you can capture that entire pie chart, all the data points, and what all the colors mean. 

“But there’s usually one story that you’re trying to communicate with your data,” she said. Focus on just that main point rather than try and capture every single detail. 

Drawing Can Help You Sketch Drafts Quickly

Emily said she was taught in design school that you don’t even touch the design software until you have 50 designs drawn on paper first. 

“The reason is, it’s tracking your progress. I’m forced to go slow because I’m limited by how fast I can draw,” she said. 

She suggested giving yourself 30 seconds to draw what you’re trying to communicate. Then give yourself 10 seconds and then 5 seconds. This will allow you to see what you’re naturally drawn to and what can get cut. 

“See what you’re cutting and what you’re keeping, and that’s your data story. That’s the main point,” she said. 

Sketchnoting with Emily

Materials needed: Pen and paper (preferred) but an iPad is fine too. 

Emily gave us an introduction to drawing that you can follow along with. She said that every single drawing can be condensed into three things:

1. A dot (a point)
2. A line (a dot that went on a trip)
3. A shape (a dot that went on a trip and came back home)

She then shared the seven building blocks of all drawings:

1. Dot
2. Straight line
3. Curvy line
4. Crooked line
5. Circle
6. Square/Rectangle
7. Triangle

Her final tip: Go for recognizable rather than realistic. 

Grab your pen and paper and join in! 

Connect with Emily Mills

Instagram: emily_a_mills

SketchnoteAcademy.com 

Free Course: Beginner Sketchnote Drawing

Your Turn

Comment with your favorite part of learning with Emily. 

Women in Data is an international non-profit organization started in 2015 whose mission is to bring women together for career advancement and an opportunity to uplift one another. They have chapters throughout the world and each hold quarterly symposiums that include enlightening talks, expert panels and networking opportunities. 

Co-presenter Rebecca Pop is the founder of Vizlogue, a data visualization and storytelling lab that offers training and consulting services. 

Watch the Recorded Panel 

What’s Inside 

Here are some of the topics addressed during the panel. 

• Can you share your personal journey and how you got started with data visualization? 
• How do you approach data visualization problems? When you are working on a dataset, do you have standard steps/best practices that you follow every time? Are there any key focus areas one should be mindful of? Ann said, “Something so important to know in advance, is whether your audience is technical or non-technical. Technical audiences are people who like data, who love opening a spreadsheet, and are in a data career on purpose. Non-technical audiences are the opposite. They’d rather hire a consultant or let another staff member handle it. It’s probably the last thing on their to do that they want to tackle (and they probably procrastinate!)” 
• Important aspects to keep in mind while working with data are data integrity and data ethics. What is your take on data integrity and data ethics?  
• For someone just getting started in data visualization, it can be overwhelming with the number of tools and courses available these days, what is your advice for beginners? Can you also share some resources? Ann said, “At first, learn the one-hour version of about 10 different tools, but then take a 10-hour training on just one tool and go deeper and specialize. There’s a lot of great courses out there.”  
• What is the future of data visualization? How do you anticipate data visualization to differ in the coming years? 
• Data visualization is a very competitive field, how can one stand out from the crowd and make an impression? Ann said, “Don’t worry too much about having to be the best at everything, I don’t think it’s even possible. Just pick one and play on the strength that you already have and make that public in some way… For example, if you like Tableau post a lot of visualizations on your Tableau public profile. If you like R, post to your code on Github and connect with other people.” 
• What are the key skills required to be successful in data viz? How important is the tool? Ann said, “Chart choosing [is so important]. Are you going to use a pie chart, bar chart or something else altogether? It’s very difficult to take a table, rows and columns of summary statistics and figure out what chart that is going to be. I think a lot of people go to the standards like pie charts or bar charts.” She added, “One activity that you can try for yourself is find a table of data, set a timer for 10-15 minutes and see how many ideas you can come up with in that time period. When I started doing this, I could only come up with a couple of ideas in a 15-minute brainstorming session. Now I come up with 15 ideas in that same time period.” 

Learn More 

Here are some of the resources we mentioned during the panel: 

• The 3-step process for sharing data with users through data placemats: https://onlinelibrary.wiley.com/doi/full/10.1002/ev.20181  
• Ann’s favorite dataviz podcasts, which were mentioned in the Q&A after the recording ended: Explore Explain with Andy Kirk, Data Stories with Enrico Bertini and Moritz Stefaner, Data + Love with Zach Bowders, Data Viz Today with Alli Torban and Storytelling with Data with Cole Knaflic.

In this example, I’m pretending that one of the performing arts groups is tracking how many tickets they’ve sold online, over the phone, and at their in-person box office for an upcoming show:

I write about chart-choosing and sketching a lot and wanted to share these ideas with you, too.

Sketching goes like this:

You grab your already-tallied data table, like the one shown above. You’ve already done a little number-crunching, simple stuff like sums and averages.

Then, you set your cell phone’s timer for 15 minutes.

And you step away from your computer.

Your job is to draw all the different versions of this dataset before you sit down to your computer. Draw, draw, draw. Aim for 5, 10, or 15 different types of graphs. The more you learn about data visualization, the more versions you’ll be able to draw. What would your dataset look like as a bar chart? As a stacked bar chart? A line graph? A pie chart? A tree map? I advise workshop participants to even draw the bad graphs, the really bad stuff, like 3D exploding pie charts, if it’s on their mind and taking up precious mental space. Get those thoughts out of your mind and onto the paper. Put a big X through the awful graphs if you need to.

Once your rough sketching is complete, take your drafts down the hall to your coworker. Think aloud. Talk about how this graph emphasizes this one thing, and that graph highlights that other thing. What’s the message your team is going for? Which graph matches that message the closest? Sometimes you know your message ahead of time; other times, you fine-tune your message during this sketching process.

And finally, I give you permission to return to the computer and make the most promising graph in your software program of choice. If you design graphs on your computer before sketching on paper, I guarantee that you’ll overlook a few options. You’ll be boxed-in by the software program’s limited chart gallery. Explore everything on paper first and figure out the software later.

Sketch 1: A Line Graph

Here’s what my sketches looked like. I’m starting with the most basic sketch: a regular ol’ line graph that just focuses on online ticket sales. When I draw, I often go through my data table methodically, often starting with just the first row of data — online sales — and peeking at the shape of those numbers. And what did I see? A tall, flat line.

Sketch 2: More Line Graphs

Once I’ve got a handle on the first row in the table, I might add the second row, the third row, and so on, so that my brain can compare the categories to each other one at a time. Here’s another regular ol’ line graph that shows all three ticket sales types together. More contextual data = more background information available for decision-making thought processes.

Sketch 3: A Slope Graph

Or, how about a slope graph for those audiences that don’t need to see all the peaks and valleys? Some people just want to see the big-picture, starting-and-ending points. The higher-ups, like donors and some supervisors, might fall into this category. I’m pretending that a supervisor knocked on my door and said, Hey, how are we doing this year? And what about five years ago, when we launched that new sales strategy? Slope graphs cut to the chase and make before/after comparisons easy.

Sketch 4: A Bar Chart

If we’re aiming for big-picture findings, how about a bar chart that only displays the five-year sums? We could ignore the year-by-year numbers and only display the total sales numbers.

Sketch 5: Small Multiples Line Graph

Returning to the multi-year version again… This fictional dataset is semi-spaghetti, meaning that the three lines started to intersect a little when they were all displayed in the same graph. Not so crowded that the criss-crossing gets in the way of interpreting the data, but, borderline. If your real dataset gets too zig-zaggy and criss-crossy, try breaking the single graph into three separate graphs with a small multiples layout.

Small multiples graphs let my brain interpret the graph piecemeal. I can check out the online sales and think about the implications of that pattern. Then, I shift my gaze a couple inches to the right and check out the phone sales. Finally, I shift my gaze to the right a bit more and examine the in-person box office sales. The layout guides my attention through the graph slowly, rather than overwhelming me by throwing all three lines on the page at once. I see the online, phone, and in-person patterns both individually and as a whole.

Sketch 6: Small Multiples Line Graph that Compares Actual Numbers with a Goal

At this point in the sketching process, I began daydreaming about having a more interesting dataset and wishing that I would’ve included goal sales numbers alongside those actual ticket sales numbers. A target line might be dotted and/or in a lighter color to add much-needed context.

Sketch 7: Stacked Columns

Or, maybe the viewers need to see part-to-whole patterns in a stacked column chart. I transformed my table’s counts into percentages to see what proportion of tickets were sold online, over the phone, or in-person. The five-year total would be nudged to the right a bit.

Sketch 8: A Pie Chart

Finally, a sketch that’ll make the purists cringe, a pie chart. Don’t worry, I teach my workshop participants about alternatives to pie charts. I might use a pie chart when I want my fictional viewers to see the part-to-whole comparisons. I’d use a darker color to draw their eyes towards one slice and add a sentence or two beside the chart to make sure their attention stays focused on that same slice.

One dataset, many correct options.

Did you come up with additional sketches? Comment and let me know.