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.

Perfect analysis. Beautiful dashboard. Zero impact. Sound familiar? Not because the data is wrong. Because the visualization quietly misleads, overwhelms, or confuses. Here are 6 data visualization mistakes that make stakeholders ignore your insights, even when the numbers are correct 👇 𝟏. 𝐒𝐭𝐚𝐫𝐭𝐢𝐧𝐠 𝐛𝐚𝐫 𝐜𝐡𝐚𝐫𝐭𝐬 𝐚𝐰𝐚𝐲 𝐟𝐫𝐨𝐦 𝐳𝐞𝐫𝐨 Makes small changes look dramatic and distorts perception. 𝟐. 𝐔𝐬𝐢𝐧𝐠 𝐩𝐢𝐞 𝐜𝐡𝐚𝐫𝐭𝐬 𝐟𝐨𝐫 𝐭𝐨𝐨 𝐦𝐚𝐧𝐲 𝐜𝐚𝐭𝐞𝐠𝐨𝐫𝐢𝐞𝐬 Human eyes can't accurately compare angles and areas. 𝟑. 𝐎𝐯𝐞𝐫𝐮𝐬𝐢𝐧𝐠 𝐜𝐨𝐥𝐨𝐫𝐬 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐦𝐞𝐚𝐧𝐢𝐧𝐠 Every extra color adds cognitive noise instead of clarity. 𝟒. 𝐃𝐮𝐚𝐥 𝐚𝐱𝐞𝐬 𝐭𝐡𝐚𝐭 𝐢𝐦𝐩𝐥𝐲 𝐟𝐚𝐥𝐬𝐞 𝐜𝐨𝐫𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬 Scale manipulation can manufacture relationships that don't exist. 𝟓. 𝐂𝐡𝐚𝐫𝐭 𝐭𝐢𝐭𝐥𝐞𝐬 𝐭𝐡𝐚𝐭 𝐝𝐞𝐬𝐜𝐫𝐢𝐛𝐞, 𝐧𝐨𝐭 𝐜𝐨𝐧𝐜𝐥𝐮𝐝𝐞 If the title doesn't state the insight, readers must guess. 𝟔. 𝐒𝐡𝐨𝐰𝐢𝐧𝐠 𝐚𝐥𝐥 𝐝𝐚𝐭𝐚 𝐢𝐧𝐬𝐭𝐞𝐚𝐝 𝐨𝐟 𝐭𝐡𝐞 𝐢𝐧𝐬𝐢𝐠𝐡𝐭 Executives want conclusions, not raw tables. The common thread? Data doesn't speak for itself. Design decisions decide what people believe. Good visualization isn't about being fancy. It's about being honest, clear, and decisive. If your charts don't guide attention, they invite misinterpretation. Which one are you guilty of? ♻️ Repost if someone in your network builds dashboards — 📚 Get 150+ real interview questions (with solutions & frameworks) in our Data Analyst Interview Prep Book: https://lnkd.in/dyzXwfVp 𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 20,000+ readers here → https://lnkd.in/dUfe4Ac6

📌 Dashboard Design Principles 101 (What Every Company Needs to Know) Dashboards are one of the most powerful tools we have to make data useful. When they are built right, they give leaders and teams: ⤷ A clear view of performance ⤷ Highlight where action is needed ⤷ And ultimately enable better decisions. But here’s the reality: most dashboards fail to deliver on this promise. And it’s not because the data is wrong or the tool is limited. They fail because of poor design choices that make them confusing, overwhelming, or simply irrelevant to the people who are supposed to use them. If you want to build dashboards that actually drive adoption and influence decisions, there are three design principles you need to follow 1️⃣ 𝐃𝐨 𝐘𝐨𝐮𝐫 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐁𝐞𝐟𝐨𝐫𝐞 𝐘𝐨𝐮 𝐃𝐞𝐬𝐢𝐠𝐧 Every dashboard starts with a purpose. Without it, you’re just arranging charts on a canvas. Ask yourself simple but critical questions: → Who exactly will use this dashboard? → What business decisions should it support? → Which insights and KPIs are truly essential? This is where most projects go wrong. Instead of focusing on the end user, dashboards get built around the data that happens to be "available" or the KPIs that someone thought might look good. The result? A nice-looking report that nobody actually uses. A strong dashboard is user-centric and decision-driven. It exists to answer questions and reduce uncertainty. Not to display every data point you’ve collected (a very common mistake). 2️⃣ 𝐆𝐮𝐢𝐝𝐞 𝐭𝐡𝐞 𝐔𝐬𝐞𝐫 𝐰𝐢𝐭𝐡 𝐚 𝐂𝐥𝐞𝐚𝐫 𝐅𝐥𝐨𝐰 Good design is invisible. A user should glance at the dashboard and instantly know where to focus. That means creating a logical flow of information that follows natural reading patterns (top left to bottom right) and keep the number of visuals under control (5 to 7 is usually the sweet spot) The goal is not to impress people with how much data you can show. It’s to guide them toward the insight that matters most. If you want to go deeper, I highly recommend exploring Nicholas Lea-Trengrouse’s work on UX/UI principles for dashboard design. 3️⃣ 𝐂𝐡𝐨𝐨𝐬𝐞 𝐭𝐡𝐞 𝐑𝐢𝐠𝐡𝐭 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐒𝐭𝐨𝐫𝐲 Data visualization is not decoration. It’s communication. The chart type you choose can completely change how your data is interpreted. The wrong choice creates confusion. The right choice makes the insight obvious, even for someone seeing it for the first time. Always think in terms of clarity: does this chart highlight the story I want the data to tell? At the end of the day, dashboards are about clarity, usability, and decision-making. If a dashboard doesn’t tell a story, guide the user, and present insights in a way that is easy to interpret, it will fail. No matter how advanced your tool or how clean your data. 📥 Save this framework. Share it with your team. And keep it in mind before your next build. #BusinessIntelligence #DashboardDesign

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.

At Maven Analytics, we see a TON of reports, dashboards and infographics designed for our monthly data challenges. Here are 5 of the most common data viz mistakes that we see: 🙅♂️ Pies & donuts with too many segments Humans are bad at comparing angles. Use bar or column charts instead, or limit donuts to 2-3 slices max. 🙅♂️ Line charts for categorical comparisons Line charts should be used to show trends. Using them for categorical data (vs. time-series) is misleading, and can suggest relationships or patterns that don't exist. 🙅♂️ Treemaps for non-hierarchical data Treemaps are designed to show hierarchies (like subcategories within categories). For simple categorical comparisons, use a bar or column chart instead. 🙅♂️ Unsorted data Don't expect viewers to make their own visual comparisons. Use intuitive sorting rules to organize your data and tell a clear story. 🙅♂️ Too much noise, too little focus While it's tempting to add background images, complex custom visuals or crazy 3-D effects, remember that effective data visualization is all about minimizing noise and maximizing clarity. Datafam, what other common visualization mistakes have you seen? #data #datavisualization #businessintelligence

Many #datavisualization, #dashboard, and #datastorytelling mistakes can be traced back to this simple problem: taking a presenter rather than an audience perspective. 🙋🏻 When designing data charts 📊, are you designing them with the audience in mind? I’ve often found that data communicators expect their audience to see the data from their perspective without evaluating their visuals from the audience’s viewpoint. They assume that what works for them will also work for their audience. This approach can be a recipe for disaster if you don’t know your audience very well. Before rushing to present some data, you should learn as much about your audience as possible. 👉 Knowledge level: How familiar are they with the topic or data? 👉 Relevance: How relevant or meaningful is your data to them? 👉 Context: What background information or assumptions are they missing? 👉 Data literacy: Will they be able to make sense of your charts? Once you've gained this understanding, you can attempt to design the data charts in a way that makes the most sense for your audience. It's also valuable to ask for feedback from colleagues or audience members beforehand to test your approach and fix potential problems. A common excuse I hear from data professionals is that they don’t have time to tailor their content to each audience. While it’s true that you might not be able to do it all the time, it is crucial to do it as much as possible. If you don’t make time to take an audience-centric approach, you will continue to be “busy” without driving meaningful outcomes. This type of shortsighted mindset makes you vulnerable when leaders begin to question what value you’re providing. What has helped you maintain an audience-centric perspective when designing your data charts, dashboards, and data stories?

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

Most confusion doesn’t come from bad data. It comes from choosing the wrong chart. We often jump straight into visuals: Pie chart because it looks simple Bar chart because it’s familiar Line chart because it’s trending But charts are not decorations. They are answers to specific questions. Before selecting any graph, I now pause and ask: Is this data categorical or continuous? Am I showing a trend or a comparison? Is this about parts of a whole or relationships? What should the viewer understand in the first 5 seconds? This small shift in thinking changes everything: 1. Fewer follow-up questions 2. Less explanation needed 3. More confident decisions from stakeholders This visual is a great reminder: Good data visualization starts with thinking, not clicking. If you’re working with Power BI / Tableau / dashboards: Don’t memorize chart types. Learn why one chart works better than another. That’s how data starts telling stories instead of causing confusion. If you want help building dashboards that make sense to business users, I share my practical approach here https://lnkd.in/gWSkyyiv #DataVisualization #PowerBI #DashboardDesign #DataAnalytics #DataStorytelling #LearningJourney

As a data analyst, you can deliver more efficient results by applying the principle of Occam’s Razor. The principle stating that the simplest solution is often the best can be a powerful mindset for data analysts seeking clarity in their analytical process. Here’s how you can apply this old wisdom to enhance your work: 1. 𝗠𝗼𝗱𝗲𝗹 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻: When building predictive models, it’s tempting to go with the most complex and hyped ones available. However, simpler models are not only easier to understand but often more robust and generalizable. Apply Occam’s Razor to choose models that achieve the needed accuracy with the lowest complexity possible.     2. 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: A focused and digestible visualization often communicates more effectively than a complex one overloaded with information. Use Occam’s Razor to strip down your dashboards to the essential KPIs and make it easy for your stakeholders to decide based on them.     3. 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: When creating new features from your data, prioritize those that offer significant insights with minimal added complexity. This practice keeps your dataset manageable and your analyses focused.     4. 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝗼𝗹𝘃𝗶𝗻𝗴: Faced with a data problem, start with the simplest hypothesis that could explain the observations. Testing and potentially ruling out simple solutions first can save time and resources, leading to a more efficient path to the root cause.     5. 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗠𝗮𝗸𝗶𝗻𝗴: When analyzing data for decision-making, present findings straightforwardly. Simplify your conclusions to make them actionable and ensure they directly address the business question at hand. By following the principle of Occam’s Razor, data analysts can avoid unnecessary complications, enhancing the efficiency of how they generate insights. Keep it simple, and transform your data into clear, impactful stories that drive decision-making. How has simplifying your analysis improved your results? ---------------- ♻️ Share if you find this post useful ➕ Follow for more daily insights on how to grow your career in the data field #dataanalytics #businessanalytics #datascience #occamsrazor #simplicity

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.