The Importance of Clarity in Data Visualizations

Clear communication of research findings is one of the most overlooked skills in UX and human factors work. It’s one thing to run a solid study or analyze meaningful data. It’s another to present that information in a way that your audience actually understands - and cares about. The truth is, most charts fall short. They either say too much, trying to squeeze in every detail, or they say too little and leave people wondering what they’re supposed to take away. In both cases, the message gets lost. And when you're working with stakeholders, product teams, or executives, that disconnect can mean missed opportunities or poor decisions. Drawing from some of the key ideas in Storytelling with Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic, I’ve been focusing more on what it takes to make a chart actually work. It starts with thinking less like an analyst and more like a communicator. One small but powerful shift is in how we title our visuals. A label like “Sales by Month” doesn’t help much. But a title like “Sales Dropped Sharply After Q2 Campaign” points people directly to the story. That’s the difference between describing data and communicating an insight. Another important piece is designing visuals that prioritize clarity. Not every chart needs five colors or a complex legend. In fact, color works best when it’s used sparingly, to highlight what matters. Likewise, charts packed with gridlines, borders, and extra labels often feel more technical than informative. Simplifying them not only improves readability - it also sharpens the message. It also helps to think ahead to the question your visual is answering. Is it showing change? Comparison? A trend? Knowing that upfront lets you choose the right format, the right focus, and the right amount of detail. In the examples I’ve shared here, you’ll see some common before-and-after chart revisions that demonstrate these ideas in action. They’re simple changes, but they make a real difference. These techniques apply across many research workflows - from usability tests and survey reports to concept feedback and final presentations. If your chart needs a walkthrough to make sense, it’s probably not working as well as it could. These small adjustments are about helping people see what’s important and understand what it means - without needing a data dictionary or a deep dive.

Most plots fail before they even leave the notebook. Too much clutter. Too many colors. Too little context. I have a stack of visualization books that teach theory, but none of them walk through the tools. In Effective Visualizations, I aim to fix that. I introduce the CLEAR framework—a simple checklist to rescue your charts from confusion and make them resonate: Color: Use color sparingly and intentionally. Highlight what matters. Avoid rainbow palettes that dilute your message. Limit plot type: Just because you can make a 3D exploding donut chart doesn’t mean you should. The simplest plot that answers your question is usually the best. Explain plot: Add clear labels, titles. Remove legends! If you need a decoder ring to read it, you’re not done. Audience: Know who you’re talking to. Executives care about different details than data scientists. Tailor your visuals accordingly. References: Show your sources. Data without provenance erodes trust. All done in the most popular language data folks use today, Python! When you build visuals with CLEAR in mind, your plots stop being decorations and start being arguments—concise, credible, and persuasive.

99% of marketers don't know data visualization basics. Here are 10 principles you should know. ✔️ Simplify to Amplify Strip away unnecessary elements in visuals and messaging. Focus on clarity to ensure the most important information stands out. ✔️ Respect Your Audience’s Intelligence Avoid oversimplification. Provide meaningful, data-rich content. Inform and empower your audience rather than dumbing things down. ✔️ Use Visuals to Tell Stories Every chart, graphic, or image should serve a purpose and align with the narrative. Visuals are there to enhance comprehension, not to decorate. ✔️ Maximize Data Density Consolidate relevant information into compact, visually intuitive formats. Deliver value in every square inch of your content. ✔️ Avoid Chart Junk Eliminate unnecessary visual clutter (like excessive 3D effects, gridlines, or distracting elements) to maintain focus on the data or message. ✔️ Integrate Words and Images Combine visuals and text seamlessly to create cohesive, easy-to-follow stories. Don’t let one overpower the other. ✔️ Emphasize Comparisons Help your audience understand trends and patterns by comparing data points. This can reveal insights that static numbers alone cannot. ✔️ Show Cause and Effect Ensure your data and storytelling reveal how actions or changes lead to outcomes, making your campaigns more actionable and persuasive. ✔️ Encourage Exploration Give your audience layers of information to explore. Well-designed, interactive visuals or long-form content invite deeper engagement. ✔️ Be Truthful and Transparent Never manipulate visuals or data to mislead. Credibility and trustworthiness are your most valuable marketing assets. The expert? Edward Tufte. I attended his seminar more than a decade ago, and it's the course I rely on most today as a CMO. His philosophy of clear, impactful communication, elevated my ability to connect with an audience via data-driven storytelling. ♻️ Repost to help others learn ➕ Follow me (Nataly Kelly) for more tips like these

Clarity is the first pillar of data visualization....and it's not as easy as it seems. There's something about data people when we want to present data, we struggle to make choices and/or want to add "fluff" to make it stand out. (And by fluff, I'm not talking about creativity in dataviz. That's useful and a conversation for another day.) Here are five key ideas for improved clarity: - Learn about the Gestalt Principles of design - Identify the *one* key message for your chart - Based on the above, choose the chart type that conveys this message best - Keep a high level of hierarchy of information. What needs to be highlighted? What needs to be subdued? *Avoid using 3 font families, 12 different font sizes and 4 type colors, it's confusing AF* - Use the right color palette: is your data categorical? sequential? divergent? or do you need semantic color? - Accessibility isn't a bonus. It's a necessity for clarity. Check your colors with the ACPA algorithm. - Drop the jargon. Not everyone is a PhD in your field. 👀 Bonus: Before/After of a data visualization we made for Effect & Affect. ✨ Learn more about the 3 other pillars of data visualization in the article featured on my profile.