Data Visualization Tips

Most people don’t need more charts. They need the right chart. This graphic shows 50 ways to visualize data — and that’s exactly why many dashboards are confusing. Too many choices, not enough thinking. Here’s how I’d use this: Start with the question, not the chart. Comparison? Use column/bar. Trend? Line, area, or sparkline. Distribution? Histogram or box/violin (not 12 pie charts…). Choose by relationship, not aesthetics. Correlation → scatter, correlogram. Composition → stacked bar/area, not donut overload. Flow or structure → Sankey, org chart, network. One insight per visual. If your audience can’t say, “This chart shows X,” in 5 seconds, it’s decoration, not communication. Reduce cognitive load. Fewer colors. Clear labels. No 3D anything. Ever. Build your “go-to 10.” From these 50, pick 10 charts you’ll master. Use them 90% of the time. The pros look “simple” because they obsess over clarity, not complexity. Save this as a checklist for your next report or dashboard. And if you want to go deeper into data storytelling and visualization, Corporate Finance Institute® (CFI)'s resources are a great place to start.

In #datastorytelling, you often want a specific point to stand out or “POP” in each data scene in your data stories. I’ve developed a 💥POP💥 method that you can apply to these situations: 💥 P: Prioritize – Establish which data point is most important. 💥 O: Overstate – Use visual emphasis like color and size as a contrast.   💥 P: Point – Guide the audience to the focal point of your chart. The accompanying illustration shows the progressive steps I’ve taken to make Product A’s Q3 $6M sales bump stand out. Step 1️⃣: Add headline. One of the first things the audience will attempt to do is read the title. A descriptive chart title like “Products by quarterly sales” is too general and offers no focal point. I replaced it with an explanatory headline emphasizing the increase in Product A sales in Q3. The audience is now directed to find this data point in the chart. Step 2️⃣: Adjust color/thickness I want the audience to focus on Product A, not Product B or Product C. The other products are still useful for context but are not the main emphasis. I kept Product A’s original bold color but thickened its line. I lightened the colors of the two other products to reduce their prominence. Step 3️⃣: Add label/marker I added a marker highlighting the $6M and bolded the label font. You’ll notice I added a marker and label for the proceeding quarter. I wanted to make it easy for the audience to note the dramatic shift between the two quarters. Step 4️⃣: Add annotation You don’t always need to add annotations to every key data point, but it can be a great way to draw more attention to particular points. It also allows you to provide more context to help explain the ‘why’ or ‘so what’ behind different results. Step 5️⃣: Add graphical cue (arrow) I added a graphical cue (arrow) to emphasize the massive increase in sales between the two quarters. You can use other objects, such as reference lines, circles, or boxes, to draw attention to key features of the chart. In terms of the POP method, these steps align in the following way: 💥 Prioritize – Step 1 💥 Overstate – Step 2-3 💥 Point – Step 4-5 Because data stories are explanatory rather than exploratory, you need to be more directive with your visuals. If you don’t design your data scenes to guide the audience through your key points, they may not follow your conclusions and become confused. Using the POP method, you ensure that your key points stand out and resonate with your audience, making your data stories more than just informative but memorable, engaging, and persuasive. So next time you craft a data story, ensure your data scenes POP—and watch your insights take center stage! What other techniques do you use to make your key data points POP? 🔽 🔽 🔽 🔽 🔽 Craving more of my data storytelling, analytics, and data culture content? Sign up for my newsletter today: https://lnkd.in/gRNMYJQ7

🍱 Design Patterns For Effective Dashboards (https://lnkd.in/ed6Rr_sC), with practical guidelines for designing better dashboards and practical UX patterns to keep in mind. Neatly put together by Benjamin Bach. 🚫 Don’t destroy user value by oversimplification. ✅ Oftentimes life is complex and tools must match life. ✅ Dashboard value is measured by useful actions it prompts. ✅ Aim to create understanding, rather than showing raw data. ✅ Start by studying audience, tasks and decisions to make. ✅ Choose what data is important for a user in each task. ✅ Choose a structure: single page, parallel pages, drill-downs. ✅ Select charts depending on data + level of detail to show. ✅ Then set layout density: open, table, grouped or schematic. ✅ Design interactions for exploration, filters, personalization. ✅ More data → more filters/views, less data → single values. ✅ Design for interface expertise levels: low, medium, high. ✅ Low: large text size, progressive disclosure, extra spacing. ✅ Medium: regular size/spacing, more data cards, shortcuts. ✅ High: small text size, heavy data, customization, filters. ✅ Support user’s transition between levels of proficiency. Dashboards are often seen as a way to organize and display data at a glance. And as such, too often it shows a lot of data without being actionable or meaningful. Yet the main task of a dashboard isn’t that — it’s to explain trends and communicate insights. Start by studying levels of user’s expertise. Segment the audience and explore what data they need to make decisions. Think carefully what charts would be both accurate and meaningful — rather than being an oversimplification or guide to misleading interpretations. Review defaults, presets and templates. Allow users to re-arrange and customize data density and widgets. Explore where a data table might help draw better conclusions. Most importantly: test your charts and dashboards meticulously. We don’t need to reveal all raw data at once, to everyone, and at the same scale and pace. But we need to support pathways for people to face complexity when they must, and discover only a set of actionable insights when they need. ✤ Useful Resources Dashboard Design Patterns & Workflow, by Benjamin Bach https://lnkd.in/eSCasdKG Practical Guide For Dashboard UX, by Taras Bakusevych https://lnkd.in/e5gMMgXv FT Visual Vocabulary (PDF), via Stéphanie Walter https://lnkd.in/ezu2w8Vr How To Design A Dashboard (free book), by David Matthew https://lnkd.in/enU-CxwU Data Dashboards UX Benchmarking, by Creative Navy UX Agency https://lnkd.in/edUgTH3G You Might Not Need A Dashboard, by Irina Wagner, PhD https://lnkd.in/eBSEkCyb #ux #design

I used to think colorful charts helped display information. Now I know they do exactly the opposite. When it comes to data visualization, color *is* crucial. But not in the way you’ve likely been taught. The general rule of thumb is that you should use color sparingly and strategically. In other words, never use color for the sake of being color*ful*. Here’s how: First, identify your core colors (I recommend 1-2 max): Option 1 ↳ Use your company’s (or client’s) brand colors. This is often the easiest and best choice. (But remember, you don’t have to use *all* the brand colors.) Option 2 ↳ Use an online color palette (check out the resources linked in the comments to get started). I’ve also searched Pinterest for things like “blue and green color palettes.” Second, follow best practices: Use grey as your default.  ↳ Create all your charts in greyscale first. Then, incorporate color to draw your audience’s eyes to the most important takeaways or data points. Use 1-2 core colors throughout your presentation.  ↳ Use your core colors to highlight the specific trends, categories, or insights you want your audience to pay attention to. Be aware of cultural associations.  ↳ Color symbolism varies across the globe - for example, red often carries a negative connotation in Western cultures, but represents luck and prosperity in Eastern/Asian cultures. Be mindful of color blindness.  ↳ Approximately 8% of men and 0.5% of women are colorblind (red-green being the most common). In general, less is more. Imagine someone were to look at your chart and say “Why is THAT particular bar blue? Why is THAT one green?” If you can’t give a clear answer, it's time to go back to the drawing board. —-— 👋🏼 I’m Morgan. I share my favorite data viz and data storytelling tips to help other analysts (and academics) better communicate their work.

Many accountants email the balance sheet and income statement to their CEOs and think,   “Job done.”  But here’s the problem: Your CEO is not necessarily trained in reading financial statements. Even if they were, you've just given them an assignment to "figure it out" If your boss doesn’t understand the numbers, then you haven’t communicated. You’ve just forwarded a report.  🚨 A financial statement without context is just data.   📊 Your job is to turn that data into insights.  How to Present Financials the Right Way  📌 1️⃣ Give a One-Page Summary 🔹 Highlight key figures—Revenue, Profit, Cash Flow, and Key Ratios.   🔹 Include clear takeaways (e.g., “Revenue grew 10%, but margins dropped due to rising costs.”).   🔹 Avoid technical jargon—simplify complex metrics.  📌 2️⃣ Answer the Big Questions   Your CEO doesn’t want numbers—they want meaning. Help them understand:   🔹 What changed? (“Profit dropped 5% due to higher shipping costs.”)   🔹 Why did it happen? (“Fuel prices increased 20% this quarter.”)   🔹 What should we do next? (“We should renegotiate supplier contracts.”)  📌 3️⃣ Use Visuals   🔹 Graphs > Tables—a well-designed chart can explain in seconds.   🔹 Use color-coded trends (e.g., 🔴 Negative, 🟢 Positive).   🔹 Keep it clean—no clutter, no distractions. 📌 4️⃣ Speak the CEO’s Language   🔹 Skip the accounting terminology—focus on impact.   🔹 Tie financials to business goals:     - Sales grew 15% → “We’re expanding market share.”     - Cash flow dipped → “We need to tighten collections.” ✅ Financial statements don’t speak for themselves—you do.   ✅ Numbers are useless without insights.  If your CEO isn’t making better decisions because of your reports, then your job isn’t done.  💡 Don’t just report numbers—explain them. That's how you add value and impact.

Many amazing presenters fall into the trap of believing their data will speak for itself. But it never does… Our brains aren't spreadsheets, they're story processors. You may understand the importance of your data, but don't assume others do too. The truth is, data alone doesn't persuade…but the impact it has on your audience's lives does. Your job is to tell that story in your presentation. Here are a few steps to help transform your data into a story: 1. Formulate your Data Point of View. Your "DataPOV" is the big idea that all your data supports. It's not a finding; it's a clear recommendation based on what the data is telling you. Instead of "Our turnover rate increased 15% this quarter," your DataPOV might be "We need to invest $200K in management training because exit interviews show poor leadership is causing $1.2M in turnover costs." This becomes the north star for every slide, chart, and talking point. 2. Turn your DataPOV into a narrative arc. Build a complete story structure that moves from "what is" to "what could be." Open with current reality (supported by your data), build tension by showing what's at stake if nothing changes, then resolve with your recommended action. Every data point should advance this narrative, not just exist as isolated information. 3. Know your audience's decision-making role. Tailor your story based on whether your audience is a decision-maker, influencer, or implementer. Executives want clear implications and next steps. Match your storytelling pattern to their role and what you need from them. 4. Humanize your data. Behind every data point is a person with hopes, challenges, and aspirations. Instead of saying "60% of users requested this feature," share how specific individuals are struggling without it. The difference between being heard and being remembered comes down to this simple shift from stats to stories. Next time you're preparing to present data, ask yourself: "Is this just a data dump, or am I guiding my audience toward a new way of thinking?" #DataStorytelling #LeadershipCommunication #CommunicationSkills

I've been doing data & analytics for 13+ years. Want to look like a data hero at work? Start with this: Mastering a few high-impact charts that business leaders actually understand. Here are the best visualizations for real-world business analytics. 1) Not all charts are created equal. Some are flashy but useless. Others are boring but make execs say, “Oh wow. Let’s take action.” Let’s focus on the second kind. (That’s where the career gold is.) 2) Line chart. This is the single best data visualization in business analytics. Use line charts to see: Trends Variability Cycles Rate of change Exceptions These are the things executives care about! 3) Stacked area line chart. Use this to show how proportions change over time: Sales by customer segment Profit by product line Defects by factory Stacked area line charts are my go-to for data stories. 4) Bar chart. Use it to compare categories: Revenue by product Conversions by marketing channel Support tickets by issue type Bar charts are a dashboard staple. 5) Stacked bar chart. Use it to compare the composition of different categories: Revenue by product by region Conversions by marketing channel by month Support tickets by issue type by organization This is another go-to for my data stories.

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.

“Dashboards are dead”? Only the context-free ones. Most teams start with definitions. They write a KPI dictionary, argue about formulas, then stack charts. Start with relationships. Map what drives what. Use metric maps and driver trees to sketch causality. ↳ Then define formulas. ↳ Then design screens. ↳ Then pick visuals. Here’s the 4-layer model we use: 1) Maps & Drivers: – metrics maps – driver trees 2) Definitions: – cohorts – formulas – granularity – attribution model – validation checks 3) Information Architecture – filters – page flow – drill paths – segments – comparisons 4) Visuals & UX – chart patterns – color semantics – legends & labels – responsive layout – conditional formatting Why this order? Because “what moved?” is useless without “why.” Common traps this avoids: ✕ Glossary-first thinking. Clean formulas ≠ causal logic. ✕ Chart sprawl. More graphs ≠ more clarity. ✕ Mixed levels. Result, diagnostic, actionable in one pot. If your dashboard doesn’t explain change, it’s reporting, not analytics. Build the logic first. Then display it. #dashboards

Some charts are memorable, while others fade away. Why? The answer lies in how our brains process visual information. Effective data visualizations aren’t just pretty pictures. They align with human cognition instead of fight against it. Here's what you need to know: 1. Contrast Captures Focus Even before your audience consciously considers your chart, their brain is already picking up on contrast. Elements that are large, bold, or vibrant grab attention first. Use contrast purposefully — not for decoration — to direct the viewer’s eye to the main takeaway. 2. Cognitive Overload Hampers Recall Your audience's working memory has limits — governed by the complex workings of our brains' prefrontal cortexes. Charts cluttered with excessive details, confusing legends, and hard-to-read fonts make the brain exert unnecessary effort on the brain, causing disengagement or misinterpretation. The most effective charts present insights clearly and effortlessly. 3. Images Come First, But Don’t Forget Your Text According to Paivio’s Dual-Coding Theory , while striking visuals attract attention, text solidifies comprehension. Strong titles clarify "What am I looking at?" Clear headlines convey "What is this chart telling me?" Direct labels remove the need for assumptions and guesses. If your audience is left to figure out your chart’s meaning, they'll come up with their own interpretation — and it probably won’t be the one you intended. The aim isn’t just to display data — it’s to ensure that people notice it, grasp it, and retain it. Understanding the science behind human visual processing will help you better understand why some charts work, while others don’t, and prepare you to design with purpose. 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