Effective Use of Graphs

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.

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.

One of the biggest challenges in data visualization is deciding 𝘸𝘩𝘪𝘤𝘩 chart to use for your data. Here’s a breakdown to guide you through choosing the perfect chart to fit your data’s story: 🟦 𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻 𝗖𝗵𝗮𝗿𝘁𝘀 If you’re comparing different categories, consider these options: - Embedded Charts – Ideal for comparing across 𝘮𝘢𝘯𝘺 𝘤𝘢𝘵𝘦𝘨𝘰𝘳𝘪𝘦𝘴, giving you a comprehensive view of your data. - Bar Charts – Best for fewer categories where you want a clear, side-by-side comparison. - Spider Charts – Great for showing multivariate data across a few categories; perfect for visualizing strengths and weaknesses in radar-style. 📈 𝗖𝗵𝗮𝗿𝘁𝘀 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗢𝘃𝗲𝗿 𝗧𝗶𝗺𝗲 When tracking changes or trends over time, pick these charts based on your data structure: - Line Charts – Effective for showing trends across 𝘮𝘢𝘯𝘺 𝘤𝘢𝘵𝘦𝘨𝘰𝘳𝘪𝘦𝘴 over time. Line charts give a sense of continuity. - Vertical Bar Charts – Useful for tracking data over fewer categories, especially when visualizing individual data points within a time frame.    🟩 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽 𝗖𝗵𝗮𝗿𝘁𝘀 To reveal correlations or relationships between variables: - Scatterplot – Best for displaying the relationship between 𝘵𝘸𝘰 𝘷𝘢𝘳𝘪𝘢𝘣𝘭𝘦𝘴. Perfect for exploring potential patterns and correlations. - Bubble Chart – A go-to choice for three or more variables, giving you an extra dimension for analysis. 🟨 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗖𝗵𝗮𝗿𝘁𝘀 Understanding data distribution is essential for statistical analysis. Use these to visualize distribution effectively: - Histogram – Best for a 𝘴𝘪𝘯𝘨𝘭𝘦 𝘷𝘢𝘳𝘪𝘢𝘣𝘭𝘦 with a few data points, ideal for showing the frequency distribution within a dataset. - Line Histogram – Works well when there are many data points to assess distribution over a range. - Scatterplot – Can also illustrate distribution across two variables, especially for seeing clusters or outliers. 🟪 𝗖𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 𝗖𝗵𝗮𝗿𝘁𝘀 Show parts of a whole and breakdowns with these: - Tree Map – Ideal for illustrating hierarchical structures or showing the composition of categories as part of a total. - Waterfall Chart – Perfect for showing how individual elements contribute to a cumulative total, with additions and subtractions clearly represented. - Pie Chart – Suitable when you need to show a single share of the total; use sparingly for clarity. - Stacked Bar Chart & Area Chart – Both work well for visualizing composition over time, whether you’re tracking a few or many periods. 💡 Key Takeaways - Comparing across categories? Go for bar charts, embedded charts, or spider charts. - Tracking trends over time? Line or bar charts help capture time-based patterns. - Revealing relationships? Scatter and bubble charts make variable correlations clear. - Exploring distribution? Histograms or scatter plots can showcase data spread. - Showing composition? Use tree maps, waterfall charts, or pie charts for parts of a whole.

Hi, Data Analysts! Choosing the right chart is critical. The right chart makes you incredibly effective and builds trust with your stakeholders. Choosing the right chart provides: 1. Clarity: Different charts are designed to highlight different types of relationships and patterns in data. Select the appropriate chart to ensure the intended message is transparent. For example, line charts are ideal for showing trends over time, while pie charts are better for displaying part-to-whole relationships. 2. Clear Decision-Making: The right chart helps decision-makers grasp complex information quickly and accurately. This leads to better, more informed decisions. A properly designed dashboard with the right mix of charts enables your leaders to monitor key performance indicators effectively. 3. Audience Engagement: Visual storytelling with data engages and persuades. An audience is more likely to understand and remember information presented in an interesting and accessible way. 4. Accuracy: The wrong chart type leads to a false understanding of the data. Matching the chart type to the data's characteristics is essential to prevent misinterpretation. Using a bar chart instead of a scatter plot for correlation analysis will obscure the strength and direction of the relationship between variables. 5. Cognitive Efficiency: The right chart conveys more information in less space. This is important in environments with limited time and space, such as executive briefings or quick reviews of performance data. 6. Credibility: Professionalism enhances your credibility. Accurate and appropriate visualizations demonstrate understanding of the data and its implications, building trust with your audience. 7. Exploration: During the analysis phase, the right charts can help the analyst uncover insights, detect outliers, patterns, or trends, and understand the data's story. This exploratory process is a fundamental step in data analysis. Want to learn more? Follow: ➡️ Aurélien Vautier ➡️ Andy Kriebel ➡️ Nick Desbarats ➡️ Dawn Harrington ➡️ Brent Dykes Happy Learning! #data #dataanalytics #datavisualization

Rule II of Effective Dataviz: Clear Meaning A strong data visualization doesn’t just present numbers — it tells a story. The key is clarity. Even if your analysis is rock solid, a poorly designed chart can leave your audience confused, forcing them to guess the insight instead of getting it. Here’s how to use common chart elements to ensure your dataviz communicate exactly what you intend: - Use clear titles & headlines: Your title should answer “What am I looking at?” and your headline should answer “What does this chart say?” Don’t make your audience work to figure it out. - Ditch legends, use direct labeling: Labels should be placed on the chart, not in a separate key. Make it easy for viewers to process information without extra effort. - Add annotations for context: A well-placed note can highlight key takeaways and provide essential background info. - Leverage visual cues: Use arrows, boxes, or subtle shading to direct attention — just don’t overdo it. Too many cues, and nothing stands out. The best data visualizations guide the audience effortlessly to the insight, freeing their minds up actually hear the story you're telling them. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

They’re not disinterested. They’re just overwhelmed by your graph. Let’s fix it. Most researchers present graphs like this: A vague title. A sea of bars or dots. No clear takeaway. The result? Your audience's working memory is overloaded. But here’s the good news: 3 simple steps can change everything: 1. Turn your title into a headline. Tell them why the graph matters. 2. Highlight key data. Use color or callouts to direct attention where it counts. 3. Align your text with the data. Don’t make people hunt for meaning, bring it to them. It’s not about dumbing down your research. It’s about clearing the path to insight. And when you do that? Your ideas land. Your message sticks. Your impact grows. That's the secret to success.