Visualize and Interpret Data

{{active_language_id_uc}} {{settings.doi}}

In 2014, the Bay Area Bike Share program had six months of user data—but no clear insight into how to improve their system. That changed when researcher and data designer Bjorn Vermeersch turned the data into a visual powerhouse.

Vermeersch created a solar system-style graphic where each bike station was a planet, its size representing trip volume and its orbit representing trip duration. The visualization revealed commuting patterns, system bottlenecks, and underused docks—all at a glance.

City planners used it to make data-backed decisions that improved service, station locations, and system efficiency.

Data doesn’t speak for itself. Vermeersch made it make sense.

In business, you’re surrounded by data. But your job isn’t just to present numbers—it’s to help people understand them.

Visuals that clarify data are increasingly powerful tools to persuade and inspire action, and clarity is more important than complexity.

Whether you’re recommending a strategic change or reporting results, your credibility hinges on your ability to interpret and communicate data clearly. You’ll be expected to translate spreadsheets into insight and guide decisions using visual storytelling.

In this chapter, you’ll build the data literacy skills that help you inform and influence.

• Get to know data
• Get to know your data
• Interpret before you visualize
• Create visuals that make your message clear, not complicate it

Why Data Literacy Matters

Every click, swipe, sensor, and sale creates data. But only people with data literacy—the ability to interpret and communicate data—can turn it into insights that matter.

According to a Tableau/Forrester study, 87% of employees say data skills are critical for their jobs. But most also say they don’t feel confident using data.

Your career advantage? Learn to turn numbers into meaning.

From Solar Systems to Skill Sets

Vermeersch’s visualization didn’t just look good—it worked because he knew what kind of data he had, how to interpret it, and how to clearly communicate it. You’ll need those same skills. Let’s start by understanding the data you’re working with.

Before you visualize or interpret data, you need to understand what kind of data you’re working with. Think of this stage like scouting a landscape before you draw a map. By taking time to understand your data’s structure, source, and purpose, you’ll make stronger, more ethical decisions about how to use it.

Data Types

• Quantitative data is numerical (e.g., sales revenue, bounce rate, age).
• Qualitative data is descriptive and not numeric (e.g., customer reviews, interview transcripts, brand sentiment).
• Structured data fits neatly in rows and columns—think spreadsheets and databases.
• Unstructured data is messier—think video content, emails, or social media posts.
• Semi-structured data falls somewhere in between (e.g., JSON or XML files that have consistent markers but flexible content).

Where to Find the Data

Depending on your goals, data might come from the following:

• Government, nonprofit, or public sources: FRED, BLS, Census, Pew Research
• Business and financial sites: Yahoo! Finance, Statista
• Research platforms: Google Dataset Search, IPUMS

Collection Methods

Understanding how data was gathered helps determine its trustworthiness and relevance. For instance, if you’re writing about how calories affect body weight, you simply cannot make the same claims using data you acquired from a self-report survey (since individuals tend to underreport their weight and intake) and those from a tightly controlled scientific study.

• Surveys collect self-reported data from large groups
• Interviews provide in-depth, qualitative insights
• Experiments test hypotheses under controlled conditions
• Observational studies track behavior without interference
• Social listening pulls patterns from social media and online interactions

You don’t need to be a data scientist—but you do need to understand what your data can (and can’t) say.

• Trace the source (first!): Who collected it, when, and why? If you can’t answer, keep digging or choose a different dataset.
• Check fit: Ask, “Does this dataset answer the specific question my article must address?” If it’s tangential, pass.
• Spot limits early: Note sampling gaps, outdated figures, or proxy variables so you can disclose them up-front rather than scramble later.

Interpret Before You Visualize — Decide on the Takeaway

• Write a one-sentence headline (talking title): Do this before opening any charting tool. If you can’t state the insight plainly, you’re not ready to visualize.
• Link to purpose: Ask, “How will this visual move my argument forward?” If the answer is “It won’t,” don’t include it.
• Flag caveats: Plan a brief footnote or caption line that clarifies limitations—ethical transparency beats glossy perfection.

Make Meaning with Interpretation

Knowing the data, however, is not enough. Raw data does not tell your audience what the data means. That’s your job. Take the data you find and make it meaningful.

When interpreting data, ask questions that will help you understand the best way to use it (or indicate you shouldn’t use it):

• Who collected it—and why?
• Is the sample truly random or was it biased by convenience, quota, or purpose?
• What does the data imply that my reader should do?
• Do hidden assumptions or missing context affect this interpretation?
• What does my reader need to know to trust and act?

Show It Well: Data Visualization Best Practices

Now that you can ask the right questions, it’s time to answer them visually. But not all charts clarify. In fact, poorly designed visuals confuse or even deceive.

Good visuals do one job: they make data easier to understand. Study the best practices for displaying data by observing what seems credible to you as you navigate information online, identifying the elements of excellent data viz, and completing short courses.

Bjorn Vermeersch’s satellite visualization of bike rentals in San Francisco wasn’t just pretty; it accurately mapped size and range onto a familiar planetary diagram, helping stakeholders make informed decisions and improving the system for everybody.

Here are some data visual design basics:

Choose the right format. Although hundreds of creative chart types exist, three basics do most of the heavy lifting. Use a bar chart for comparison, a line chart for change over time, and a donut chart for parts of a whole. (Avoid pie charts, which are not good at displaying comparative volume.)

Data Visualization Guidelines

Guideline	Description
Choose the right format	Hundreds of creative chart types exist, but these three do most of the heavy lifting: Bar chart for comparison, line chart for change over time, donut chart for parts of a whole (avoid pie charts, which are not good at displaying comparative volume).
Simplify	Don’t overload visuals with unnecessary boxes, 3D effects, gridlines, shadows, or too much text. Focus on one key insight. https://www.storytellingwithdata.com/meet-the-team-cole
Label clearly	Add "talking titles", or titles that tell the story: “Sales Doubled After Launch”—not just “Figure 2” or “Q4 Sales Figures.”
Avoid distortion	Use consistent scales. Don’t crop axes to exaggerate changes.
Cite your sources	Beneath or beside your visual. Always.

Learn the Tools

Learn the tools that create great visuals: Excel, Tableau, Canva, Datawrapper, Google Charts, Power BI, and Piktochart are standards. Ask which tools will be most helpful in your industry and proactively learn the basics with online tutorials.

"If there’s a conclusion you want your audience to reach, say it out loud. Don’t assume they’ll see what you see." —Cole Knaflic, data visualist

Introduce, Insert, and Interpret

Even the best visual won’t speak for itself. You must introduce it with text, position it well, and correctly interpret it. Don’t drop a flashy visual in your text and walk away. Follow these steps:

Introduce it. “Figure 1, shows that customer satisfaction rose 20% after the support overhaul.” Use a talking title, not a topic title. The title of the graphic should convey not only the “what” but also the “so what.”

         Insert it. Place it near the related text. Add a talking           title: “Customer Satisfaction Improved 20%”

             Interpret it.Explain what it means. Why does this                  matter? What should your reader do? Do not leave              your audience to interpret what might be a                            confusing graphic. Instead, guide the audience                                                through the graphic so it complements the main                                            point.

In their book Making Numbers Count , authors Chip Heath and Karla Starr remind us that numbers don’t make people care—meaning does. Business audiences don’t automatically grasp data just because you present it clearly. To be persuasive, you must connect the numbers to something your audience already understands or values.

Their key insight? Anchor your numbers in the familiar. Translate abstract figures into concrete comparisons, relatable images, or memorable metaphors.

In presentations or reports, that means:

• Don’t just drop in a chart—frame it with context your audience understands.
• Use simple, story-driven interpretations rather than jargon-heavy explanations.
• Make the visual a step in your narrative, not an interruption.

When you insert a chart, your job is not just to show it—it’s to walk the audience through what it means and why it matters, just like you’d walk them through a story.

You’re not just visualizing data. You’re making meaning visible.

"Good visuals clarify. Great visuals persuade. But only when you connect them to the story."

Task

Visuals That Add Value

Before you design your next data visualization, ask the following:

• What’s the one insight my audience needs?
• Am I making that insight easy to see?
• Have I cited this data correctly so my audience can check my insights?

Like Vermeersch’s solar system bike map, your job is to make data not just visible—but understandable. The best visuals don’t show everything. They show what matters most.

"The best visuals don’t show everything—they show what matters most."