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.

10 data visualization mistakes that confuse your audience (and what to do instead) Poor chart choices can distort meaning and reduce trust, even when your analysis is correct. (Save this!) 𝟏. 𝐔𝐬𝐢𝐧𝐠 𝐏𝐢𝐞 𝐂𝐡𝐚𝐫𝐭𝐬 𝐟𝐨𝐫 𝐓𝐨𝐨 𝐌𝐚𝐧𝐲 𝐂𝐚𝐭𝐞𝐠𝐨𝐫𝐢𝐞𝐬 ↳ More than 5 slices become hard to read ↳ Pie charts work best for showing simple parts of a whole → Use bar charts when comparing many categories 𝟐. 𝐌𝐢𝐬𝐥𝐞𝐚𝐝𝐢𝐧𝐠 𝐘-𝐀𝐱𝐢𝐬 𝐒𝐜𝐚𝐥𝐞𝐬 ↳ Non-zero baselines exaggerate differences ↳ Can unintentionally mislead viewers → Start bar charts at zero or clearly indicate axis breaks 𝟑. 𝐑𝐚𝐢𝐧𝐛𝐨𝐰 𝐂𝐨𝐥𝐨𝐫 𝐒𝐜𝐡𝐞𝐦𝐞𝐬 ↳ Too many colors create visual noise ↳ Colors lose meaning without intention → Use 3–5 purposeful colors to highlight insights 𝟒. 𝟑𝐃 𝐂𝐡𝐚𝐫𝐭𝐬 𝐓𝐡𝐚𝐭 𝐃𝐢𝐬𝐭𝐨𝐫𝐭 𝐑𝐞𝐚𝐥𝐢𝐭𝐲 ↳ Perspective makes comparisons inaccurate ↳ Especially problematic in pie charts → Stick to clean 2D visualizations 𝟓. 𝐖𝐫𝐨𝐧𝐠 𝐂𝐡𝐚𝐫𝐭 𝐓𝐲𝐩𝐞 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 ↳ Line charts for categories or bars for trends cause confusion → Line for trends over time → Bar for category comparisons 𝟔. 𝐓𝐨𝐨 𝐌𝐚𝐧𝐲 𝐌𝐞𝐭𝐫𝐢𝐜𝐬 𝐨𝐧 𝐎𝐧𝐞 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝 ↳ Information overload reduces clarity ↳ Viewers don't know where to focus → Highlight 3–5 key metrics that tell a story 𝟕. 𝐈𝐠𝐧𝐨𝐫𝐢𝐧𝐠 𝐂𝐨𝐥𝐨𝐫𝐛𝐥𝐢𝐧𝐝 𝐀𝐜𝐜𝐞𝐬𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲 ↳ Red–green combinations exclude many users → Use accessible palettes (blue–orange) plus labels or patterns 𝟖. 𝐂𝐡𝐚𝐫𝐭 𝐉𝐮𝐧𝐤 & 𝐔𝐧𝐧𝐞𝐜𝐞𝐬𝐬𝐚𝐫𝐲 𝐃𝐞𝐜𝐨𝐫𝐚𝐭𝐢𝐨𝐧𝐬 ↳ Shadows, gradients, borders, and clip art distract from insights → Remove anything that doesn't add informational value 𝟗. 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐚𝐧𝐝 𝐋𝐚𝐛𝐞𝐥𝐬 ↳ Charts without titles, units, or axes create confusion → Ensure visuals are understandable without explanation 𝟏𝟎. 𝐍𝐨𝐭 𝐓𝐞𝐥𝐥𝐢𝐧𝐠 𝐚 𝐒𝐭𝐨𝐫𝐲 ↳ Data without narrative loses impact → Use insight-driven titles and annotations that answer "So what?" 𝐐𝐮𝐢𝐜𝐤 𝐜𝐡𝐞𝐜𝐤𝐥𝐢𝐬𝐭 𝐛𝐞𝐟𝐨𝐫𝐞 𝐬𝐡𝐚𝐫𝐢𝐧𝐠: → Right chart type → Honest scale → Accessible colors → Clear labels & context → One clear takeaway ⚡𝐏𝐫𝐨 𝐭𝐢𝐩: Show your visualization to someone unfamiliar with the data. If they need an explanation, simplify the chart. Which of these mistakes have you seen (or made)? ♻️Repost to help someone level up their data viz game Get 150+ real data analyst interview questions with solutions from actual interviews at top companies: https://lnkd.in/dyzXwfVp 𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 21,000+ readers here → https://lnkd.in/dUfe4Ac6

🍱 How To Design Effective Dashboard UX (+ Figma Kits). With practical techniques to drive accurate decisions with the right data. 🤔 Business decisions need reliable insights to support them. ✅ Good dashboards deliver relevant and unbiased insights. ✅ They require clean, well-organized, well-formatted data. ✅ Often packed in a tight grid, with little whitespace (if any). 🚫 Scrolling is inefficient in dashboards: makes comparing hard. ✅ Start with the audience and decisions they need to make. ✅ Study where, when and how the dashboard will be used. ✅ Study what metrics/data would support user’s decisions. ✅ Explore how to aggregate, organize and filter this data. ✅ More data → more filters/views, less data → single values. 🚫 Simpler ≠ better: match user expertise when choosing charts. ✅ Prioritize metrics: key insights → top left, rest → bottom right. ✅ Then set layout density: open, table, grouped or schematic. ✅ Add customizable presets, layouts, views + guides, videos. ✅ Next, sketch dashboards on paper, get feedback, iterate. When designing dashboards, the most damaging thing we can do is to oversimplify a complex domain, or mislead the audience. Our data must be complete and unbiased, our insights accurate and up-to-date, and our UI must match users’ varying levels of data literacy. Dashboard value is measured by useful actions it prompts. So invest most of the design time scrutinizing metrics needed to drive relevant insights. Bring data owners and developers early in the process. You will need their support to find sources, but also clean, verify, aggregate, organize and filter data. Good questions to ask: 🧭 What decisions do you want to be more informed on? (Purpose) 😤 What’s the hardest thing about these decisions? (Frustrations) 📊 Describe how you are making these decisions? (Sources) 🗃️ What data helps you make these decisions? (Metrics) 🧠 How much detail is needed for each metric? (Data literacy) 🚀 How often will you be using this dashboard? (Value) 🎲 What constraints should we know about? (Risks) And, most importantly, test dashboards repeatedly with actual users. Choose key tasks and see how successful users are. It won’t be right at first, but once you get beyond 80% success rate, your users might never leave your dashboard again. ✤ Dashboard Patterns + Figma Kits: Data Dashboards UX: https://lnkd.in/eticxU-N 👍 dYdX: https://lnkd.in/eUBScaHp 👍 Ethr: https://lnkd.in/eSTzcN7V Orange: https://lnkd.in/ewBJZcgC 👍 Semrush: https://lnkd.in/dUgWtwnu 👍 UKO: https://lnkd.in/eNFv2p_a 👍 Wireframing Kit: https://lnkd.in/esqRdDyi 👍 [continues in comments ↓]

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.

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

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

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

Master the art of Financial Storytelling 🧑🏫 Your numbers tell a story, but are you telling it right? 👇 Numbers without context are just digits on a page. The real power comes from transforming those numbers into insights that drive action. ➡️ COMMON MISTAKES IN FINANCIAL REPORTING Let's start with what NOT to do when presenting financials: 1️⃣ Dropping raw numbers without context Raw data overwhelms your audience. When you say "Revenue grew to $100K," what does that mean for the business? 2️⃣ Reading slide content word-for-word Your presentation should add value beyond what's written. Share insights that aren't visible in the numbers. 3️⃣ Rushing through without pausing for questions Financial data needs time to digest. Create moments for discussion and clarification. ➡️ BUILDING A COMPELLING FINANCIAL STORY Here's how to transform your financial presentations: 1️⃣ Start with the fundamentals Always begin by establishing context. What's normal? What's exceptional? What benchmarks matter? 2️⃣ Connect data points to strategy Show how financial results link to business decisions. If working capital improved, explain which specific actions drove that improvement. 3️⃣ Use comparisons effectively - Period over period changes - Budget vs actuals - Year over year trends - Industry benchmarks 4️⃣ Structure your narrative - What happened? - Why did it happen? - What does it mean for the future? - What actions should we take? ➡️ COMPONENTS OF GREAT FINANCIAL STORYTELLING 1️⃣ Clear Dashboards Start with a clean, focused view of KPIs that matter most. Don't overwhelm with data. 2️⃣ Strategic Context Show how financial results connect to company goals and market conditions. 3️⃣ Forward-Looking Analysis Use current data to paint a picture of future opportunities and challenges. 4️⃣ Action Items End every presentation with clear next steps and decision points. ➡️ PRACTICAL TIPS FOR IMPLEMENTATION 1️⃣ Know your audience CFO needs different details than the marketing team. Adjust your depth accordingly. 2️⃣ Use visual aids Graphs and charts can illustrate trends better than tables of numbers. 3️⃣ Practice active listening Watch for confusion or disengagement. Adjust your presentation based on real-time feedback. 4️⃣ Create discussion points Plan specific moments to pause and engage with your audience. === Remember: Financial storytelling isn't about making numbers sound good. It's about helping stakeholders make informed decisions. What techniques do you use to make financial data more engaging? Share your thoughts in the comments below 👇

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.