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.

The post The Data Visualization Design Process: A Step-by-Step Guide for Beginners appeared first on Depict Data Studio.

Maybe you’re making a brand-new dashboard?

Maybe you’re revamping an existing dashboard to bring it up to speed?

Maybe you don’t have a dashboard yet, and you’re wondering if you need one?

In this article, you’ll see my 10 worst mistakes from past dashboards.

I’ve made all these mistakes (and more…) over the past 15 years. Let’s fast-forward your project and make sure you’re not doomed to run into the same issues I did.

Learn from my mistakes. Don’t let your dashboards suffer.

10 Worst Dashboard Mistakes

You can watch the video or read about the top 10 dashboard mistakes.

Mistake #1: Not Having a Dashboard  

Fifteen years ago, I assumed that a single technical report was enough.   

I started my career by working on university-based research centers. Peer-reviewed articles were the norm.  

When I transitioned into contracting for Federal government agencies, 100-page technical reports were the norm. 

Nowadays, I know better.  

Dashboards used to seem like extras.  

But they’re not extras. They’re essential.  

Mistake #2: Not Having Multiple Dashboards  

After I realized that every project could benefit from a dashboard, I realized that every project needs multiple dashboards.  

In the past, I’d make a single dashboard. All the information was crammed in. I was trying to meet every audience’s needs. 

It was destined to fail.  

Now, I have multiple dashboards so that each audience gets their own individualized dashboard.  

Audience A gets the information they need. 

Audience B gets the information they need. 

Audience C gets the information they need. And so on. 

By providing each audience the exact information they need, the data is much more likely to be understood and used.

Mistake #3: Copying and Pasting from Excel into Word or PowerPoint 

Here’s what I used to do.

I would make my graphs in Excel and then I would paste them into Word or PowerPoint.

I found workarounds to make sure the formatting was identical in both Excel and Word/PowerPoint (e.g., by setting up Themes and a standard text hierarchy in advance).

I found workarounds to avoid copying and pasting (e.g., by writing VBA code).

But the transfer process was still time-consuming and prone to errors.

Nowadays, I just keep everything in the same software program.

I’m a big fan of everyday software so I usually just use Excel for everything. You could use PowerBI or Tableau. Whatever software program you like.

Less copying and pasting between programs is a major time saver.

Mistake #4: Getting Distracted by Sparkly Interactivity 

It’s easy to get caught up in all the cool things interactive dashboards can do, like adding drop-down menus or checkboxes. Interactivity lets the user explore the data further.

In hindsight, I wish that some of my interactive dashboards were actually static dashboards.

Yes, I admit it–I wish some of my interactive dashboards were static instead.

I wish I would have used static dashboards for the high-level executives who don’t have extra time to explore data.

Or, for the non-technical audiences who just want the key takeaways.

Sometimes your audience would really just prefer a PDF’d one-pager.

Mistake #5: Placing Drop-Drops or Checkboxes Wherever There Was Space 

I used to just place drop-downs and checkboxes wherever there was space.

At the bottom of a page? Sure.

On the right-hand side of a page? Sure.

In this teeny tiny white space that’s leftover? Sure!

Now, I aim for consistency.

That means usually placing them in the upper left corner.

It’s the most valuable real estate on our screen because our eyes are naturally drawn there.

Intentional placement helps your audience know where to start.   

Mistake #6: Shoving Everything onto a Single Screen 

I used to think that a dashboard had to fit onto a single screen or page.

That’s false.

People can easily click to the next window or flip to the next page.

There’s no reason to cram all the information into one page. Especially if you have to resort to using size 6 font with no white space left.

A good rule of thumb is literally a thumb: Make sure you have about a half inch to an inch of white space between your graphs and text boxes.

Give the dashboard some room to breathe so your audience doesn’t feel overwhelmed by the sheer density of the data.

Mistake #7: Only Showing the Latest Data 

Here’s the most common mistake I see: Only showing the latest data.

Let’s pretend there’s a big meeting in March. I used to just show March’s numbers.

But what about February? Or January? Or earlier dates? My poor audience would have to hunt down the previous month’s dashboard, flip back and forth between screens, and try to make comparisons. Our audience should never have to flip between screens or flip between pages to make comparisons!

By only showing the most current data, we’re not allowing our viewers to see the bigger picture.

Nowadays, I use lots more line graphs so that my viewers can see whether there are fluctuations over time.

Mistake #8: Losing Time on Colors and Fonts 

Wasting time looking for color palettes or fancy fonts is so common.

I used to just use whatever colors and fonts I felt like. Or, I used the software defaults…

I would think, “Ooh, I heard this font is really popular right now!” or “I love this shade of blue!”

Being guided by personal preferences means your work isn’t as professional as it could be. Especially if you’re working in a group.

If everyone picks their favorite colors or fonts, nothing is cohesive or branded.

Instead, save yourself some time and boost the professionalism by using the recipient’s brand fonts and colors.

You don’t even have to think about it. It’ll just be done. Save your precious mental space for something else.

Mistake #9: Wasting Money by Outsourcing Too Much 

There’s a time and place for outsourcing work.

But dashboard design is not one of them.

It shouldn’t cost tens of thousands of dollars to develop a dashboard.

Nor should it take weeks or months.

Don’t hire somebody else to make your dashboard.

Keep it in-house.

You’ll be able to maintain it more easily and will have full control whenever changes or updates need to be made.

Mistake #10: Assuming It Was Finished 

Dashboards are never finished.

Ever.

They’re not supposed to be.

Dashboards should be updated over time.

Dashboards should evolve as the information needs evolve.

And not just once a year.

You should be making ongoing edits, adjustments, and improvements all the time.

As needs change, the dashboard changes.

Which is another reason why keeping it in-house is so much better.

Your Turn 

Those were my top 10 worst mistakes from my past projects.

Now, it’s your turn.

What are your worst mistakes from your past dashboard projects? Share so we can learn from one another and avoid some of the headaches before they start.

The post Dashboard Don’ts: My 10 Worst Mistakes from Past Projects appeared first on Depict Data Studio.

Nobody wants to pour blood, sweat, and tears into a document that sits on a dusty shelf.

In May 2021, I co-presented with Elizabeth Grim at the Eastern Evaluation Research Society’s annual conference.

Our session was titled, “How to Design Reports that Actually Inform Decisions with Software We Already Have.”

Attendees learned about:

• The 30-3-1 approach to reporting with unlimited visual appendices
• Adding a 20-minute report cover
• Adding color-coded divider pages
• Aiming for 1+ visual on every single page
• Including a variety of visuals
• Using inclusive, non-violent language

Today, let’s focus on one of those techniques: following the 30-3-1 approach to reporting, which is the bare minimum for designing useful reports.

Watch the 10-Minute Tutorial

I recorded a portion of our presentation for you. Here’s a 10-minute lesson about the 30-3-1 approach.

Hopefully you recognize this technique already; I’ve been speaking about it on panels for a decade.

I’ve raised the bar over the past decade though. I used to simply encourage us to follow 30-3-1. I’ve clarified my advice to add visual appendices, too.

The Old Way: The 100-Page Technical Report

In the video, I show you the old way of writing reports in research and evaluation settings.

The single, 100-page technical report used to be the norm. And in some settings, it still is. (I’m thinking of the government agencies and government contractors that I work with.)

Nobody’s got time to read all 100 pages. I think we’ve known that for a while. 30-3-1 to the rescue!

The 30-3-1 Approach

Don’t want your reports to sit on a dusty shelf?

At a bare minimum, I suggest following the 30-3-1 approach, which includes:

• A 30-page report (30 pages maximum) with unlimited visual appendices
• A separate 3-page PDF
• A separate 1-page PDF

In a perfect world, with more time and a bigger budget, I’d love to see slideshows, dashboards, and infographics for every single project, too.

Reports are a good starting point.

The 30-3-1 approach with unlimited visual appendices is even better.

And adding slideshows, dashboards, and infographics is even better.

This approach is all about tailoring for our audiences. Rather than pouring everything into a single document, we design a 30-pager with one audience in mind, a 3-pager with another audience in mind, and so on.

Paring Down 100 Pages into 30 Pages or Less

This is the hardest step. You’ll need to get input from your boss, your report’s audience, a stakeholder group, etc.

I suggest following Marie Kondo’s advice: Focus on what you’re keeping, not on what you’re letting go of. In the video, I discuss her technique for decluttering our closets: Make a big pile of all your clothes, and then look at each item to decide what sparks joy.

We focus our mental energy on the best pieces that are worth keeping, rather than getting stressed by what we’re discarding.

We can apply the same strategy to paring down reports. Focus on the charts, pages, or chapters that are so valuable that they need to stay within the 30-page report body.

We don’t have to delete the rest of the pages. We simply move them to the back of the report, and they form the appendices.

The 3-Pager and 1-Pager

When we’re done with the 30-page report, we also need a separate 3-page report and a separate 1-page report.

Sometimes we’re tempted to count the executive summary as the 3-pager or 1-pager. That’s not enough!!! I recommend creating separate PDFs altogether.

And of course, we don’t have to follow the 30-3-1 approach exactly. We’re aiming for a full version, a mid-length version, and a short-n-sweet version. Your 3-pager might actually be 5 pages, for example. The spirit of the 30-3-1 approach is still there. I want us to avoid only writing a 100-page technical report. 20-10-2 or 30-5-2 will still help our readers tremendously.

A Case Study from a Public Health Agency

In the video, I mentioned a guest blog post from Sara DeLong about how the Wisconsin Division of Public Health applied this technique.

Her agency developed a 130-page report, a 10-page summary, and a 1-page summary. Then, they also created videos, images, and billboards (!!!) to ensure that the messages got off the dusty shelf and into the community.

Add Unlimited Visual Appendices

In the video, I also discussed adding unlimited visual appendices to the end of our reports.

Here’s the blog post where you can learn about visual appendices and get tips for creating them yourself.

Spoiler alert: I create these tables in Excel; add Data Bars, Color Scales, and Spark Lines; and then save them as PDFs.

Learn More

My co-presenter, Elizabeth Grim, also wrote a conference session recap.

Your Turn

Have you followed 30-3-1? Have you created unlimited visual appendices? Please comment below and link to your public-facing examples or describe your project. I’d love to learn from you.

The post The 30-3-1 Approach with Unlimited Visual Appendices: The Bare Minimum for Designing Reports that Actually Inform Decisions appeared first on Depict Data Studio.

One thing I’ve learned is that organization has a little bit different communication style.

Some groups are report-heavy. Think lots of paragraphs, portrait, Calibri size 11 turned into a PDF.  

Other groups are dashboard-heavy. They either have lots of static dashboards (short PDF reports) or lots of interactive dashboards and databases.

While other groups are presentation-heavy, kind of. They use PowerPoint, but they’re making standalone documents, not slides for presentations.

One thing these groups all have in common: Everyone has reports of one kind or another. And I often see three common report hurdles over and over again.

The Data Viz Today Podcast with Alli Torban

I recently spoke with Alli Torban on her podcast, Data Viz Today. Alli is an information design consultant who discovered data visualization while on maternity leave. She started the podcast as a way to grow and learn more about the data viz field.

She and I discussed those three common report hurdles we all run into and I gave solutions of how to overcome them.

Watch Our Conversation Here 

Challenge 1: Soaring Beyond the Dusty Shelf Report

The first common challenge I see is going beyond the report.

Most organizations have a report of some kind, but it’s hard to get into the mindset of a report AND a dashboard, slideshow, handout, etc.

In many workplaces, we definitely still need a report as one of our formats (e.g., grant reports that are required to be submitted at the end of a multi-year, multi-million-dollar project).

But our data can inform more decisions if we can offer a report plus other formats.

There’s often a misperception that it’s going to be time-consuming or costly to design a report and some other formats.

But, there’s a cost-effective and time-efficient solution: 30-3-1 with unlimited visual appendices.

The 30-3-1 approach is this: you take your dusty shelf report and you limit the body of the report to just 30 pages, the most important data. The rest of it doesn’t get deleted, you simply push it to back of the report in a visual appendix (which unlimited in terms of page numbers).

Then when you’re done with the 30-page report, you also make a 3-pager and a 1-pager. Each format will reach a different audience and will help your data reach more people and actually inform decisions.

Not sure how to incorporate a visual appendix? You can learn more about how to start using this technique here: https://depictdatastudio.com/designing-visual-appendices-for-your-next-report-in-under-an-hour/

Challenge 2: Packing a Lot of Dense, Technical Information into a Report – Without Overwhelming Our Readers 

Another common challenge we all run into is needing to pack a lot of dense, technical information into a report—without overwhelming our readers.

I worked with a county to help create their 100+ page public health report card.

There was one page for each variable: traffic accidents, cancer deaths, pregnancy rates, environmental/ozone data, etc. Our challenge was to find a way to chunk all that data into a few categories that would make easier to read and navigate.

Our solution: Color-coding by chapter.

After categorizing the data, each chapter was designated its own different brand color.

The headings and visuals all used that brand color throughout the chapter (heading 1s, heading 2s, call-out boxes, sidebars, graphs, maps, bullet points, etc.).

The end result allowed readers to easily tell when a new topic began.

Your brain recognizes new color; the content must have changed as well.  

Here’s another example of when I worked with a university library to use color-coding to simply their report:  https://depictdatastudio.com/how-to-transform-a-text-heavy-report/ 

This technique can also be applied to slideshows, dashboards and infographics.

I use this technique when I give presentations as well:  https://depictdatastudio.com/visually-structure-your-presentation-around-key-points/

Challenge 3: Incorporating a Variety of Visuals—Not Just Graphs 

The final challenge I see are reports that are ALL narrative, or ALL graphs, or ALL photos.

We all tend to gravitate towards what we’re comfortable with but there are so many great types of visuals you can include.

In the video, you’ll hear Alli and I talk through this list of 15 visuals to include in our reports:

1. Graphs 

2. Photographs 

3. Icons 

4. Lists 

5. Maps 

6. Diagrams 

7. Timelines 

8. Logos 

9. Screenshots 

10. Text overlaid on images 

11. Columns 

12. Shapes 

13. Handwriting 

14. Cartoons 

15. GIFs 

Your Turn 

I’d love to hear from you. What challenge have you dealt with (or are currently dealing with)? Which of these techniques have you use in your work? Comment and let me know.  

Connect with Alli 

Website: www.allitorban.com 

LinkedIn: @allisontorban

Data Viz Today Podcast: https://dataviztoday.com/ 

The post 3 Common Reporting Hurdles—and How to Overcome Them appeared first on Depict Data Studio.

Get your stakeholders involved in the sense-making process!

Are you familiar with data placemats?

Here’s how they work:

1. Design Data Placemats
2. Facilitate an Interpretation Meeting
3. Include those Insights in the Final Report or Slideshow

Step 1: Design Data Placemats

First, we analyze the data and display key findings in data placemats. You might display survey responses, for example.

These placemats should include traditional graphs. That means the graph titles contain the topic area, not the takeaway finding, and there’s no color contrast. Imagine a bar chart where every bar is the same color, so that nothing stands out. It’s up to the viewer to figure out the “so what?” for themselves.

We can make data placemats in any software program. You know I’m a champion for everyday software. PowerPoint’s my top choice for data placemats because it’s easy to arrange multiple graphs on the slide.

Your placemats might look like this, with one page per survey topic.

Step 2: Facilitate an Interpretation Meeting

Next, we gather people together, either virtually or in-person.

During the meeting, we’re facilitators, not lecturers.

It’s a conversation, not a presentation.

We ask questions like:

• “What surprises you about the data?”
• “What factors might explain the patterns we’re seeing?”
• “What additional questions do you have?”

The goal is to get attendees to verbalize the takeaway findings in their own words. We listen, listen, listen.

Step 3: Include those Insights in the Final Report or Slideshow

Finally, we go back to our offices and create a final deliverable for the project, like a report or slideshow (if needed).

The final version should include storytelling graphs, which means the graphs have the takeaway findings in the graph titles, and include color contrast. Imagine a bar chart where everything’s grayed-out except for the one important bar in a darker color.

The interpretation meeting becomes a data collection event; we quote the meeting attendees and include that qualitative data in the final report.

This process not only gets stakeholders involved in the process–making it more likely that they’ll use the findings–but makes the final report or slideshow even richer.

Learn More

I learned this technique years ago from Veena Pankaj. Veena and I published an article about placemats together that you read can here: https://onlinelibrary.wiley.com/doi/pdf/10.1002/ev.20181

Your Turn

Have you used data placemats in your project? Comment and share tips of your own!

The post Data Placemats: A 3-Step Process for Increasing Data Use appeared first on Depict Data Studio.

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.

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