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.

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.

10 reasons why your dashboard lacks clarity. A - Don't put everything in one dashboard. => A dashboard made for everyone, is a dashboard used by no one. B - Help users see, not read. => "Good data visualization takes the burden of effort off the brain and puts it on the eyes." Stephen Few's C - Don’t use maps if they’re not relevant. => Even if your colleague worked so hard to get these ZIP codes. Ask yourself : Does the map add value to the business? D - Zoom in when necessary. => Sometimes (for specific reason) you'll need to truncate your axis. Because Usain Bolt has no intention of running the 100m in under 7 seconds. E - Declutter your charts. => It's a constant balance between space optimization and chart comprehension. F - Use double encoding on purpose. => Displaying the same KPI twice in the same chart may raise questions you don't want to hear during the kick-off meeting. Keep it clear. G - Rotate your charts to see full labels. => "My neck has been hurting lately, but I'm not sure why." H - Clean your pie chart. => Pie charts are hard enough to understand quickly, so let's not make them even trickier. I - Use aggregation to your advantage. => If your message is clear with 36 bars, why use 156? J - Use color to your advantage => The purpose of color is not to make your dashboard funky, but to attract the eye, to alert and to assist readability... Find this High Resolution visual + 50 other in the Dataviz Clarity Gallery here : https://lnkd.in/eThSWtWv #Businessintelligence #Datavisualization #DataAnalytics

Choosing the right chart is half the battle in data storytelling. This one visual helped me go from “𝐖𝐡𝐢𝐜𝐡 𝐜𝐡𝐚𝐫𝐭 𝐝𝐨 𝐈 𝐮𝐬𝐞?” → “𝐆𝐨𝐭 𝐢𝐭 𝐢𝐧 10 𝐬𝐞𝐜𝐨𝐧𝐝𝐬.”👇 𝐇𝐞𝐫𝐞’𝐬 𝐚 𝐪𝐮𝐢𝐜𝐤 𝐛𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧 𝐨𝐟 𝐡𝐨𝐰 𝐭𝐨 𝐜𝐡𝐨𝐨𝐬𝐞 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐜𝐡𝐚𝐫𝐭 𝐛𝐚𝐬𝐞𝐝 𝐨𝐧 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚: 🔹 𝐂𝐨𝐦𝐩𝐚𝐫𝐢𝐬𝐨𝐧? • Few categories → Bar Chart • Over time → Line Chart • Multivariate → Spider Chart • Non-cyclical → Vertical Bar Chart 🔹 𝐑𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬𝐡𝐢𝐩? • 2 variables → Scatterplot • 3+ variables → Bubble Chart 🔹 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧? • Single variable → Histogram • Many points → Line Histogram • 2 variables → Violin Plot 🔹 𝐂𝐨𝐦𝐩𝐨𝐬𝐢𝐭𝐢𝐨𝐧? • Show part of a total → Pie Chart / Tree Map • Over time → Stacked Bar / Area Chart • Add/Subtract → Waterfall Chart 𝐐𝐮𝐢𝐜𝐤 𝐓𝐢𝐩𝐬: • Don’t overload charts; less is more. • Always label axes clearly. • Use color intentionally, not decoratively. • 𝐀𝐬𝐤: What insight should this chart unlock in 5 seconds or less? 𝐑𝐞𝐦𝐞𝐦𝐛𝐞𝐫: • Charts don’t just show data, they tell a story • In storytelling, clarity beats complexity • Don’t aim to impress with fancy visuals, aim to express the insight simply, that’s where the real impact is 💡 ♻️ Save it for later or share it with someone who might find it helpful! 𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 14,000+ readers here → https://lnkd.in/dUfe4Ac6

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

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.

After designing hundreds of business dashboards, I keep coming back to these four patterns: Tall + Scrolly Stack everything vertically, organized by metric family, and let people scroll to their level of depth. Best for mobile viewing and email delivery with basic chart types that doesn't require instructions. Where I've seen this work: New product/feature introductions where audiences are different levels (executive to operators) and functions. BANs + Decomp Big numbers that focus attention and breakdowns that show differences. For when you've identified the important metrics, but want to show segment granularity. Switch group-by dimension while maintaining familiar layout. Where I've seen this work: Operational monitoring for teams that have ownership of metric outcomes. Sankey + Wide Table Flow diagram establishes a map of the whole system and reference tables show details. For diagnosing conversion and retention patterns across nodes and segments to know where to optimize. Where I've seen this work: Growth teams figuring out behavior across complex funnels and overlapping segments. Potential Show what you could be delivering versus what you're actually delivering. Makes the gap between current performance and available capacity visible. Where I've seen this work: Operational teams that have a clear action to take, but limited time. What each of these have in common: - Establish big picture awareness, but direct small picture action (think global, act local) - Strengthened by KPI ownership - Act as a prioritization mechanism Organizations often start with one dashboard trying to serve everyone, then evolve into multiple dashboards with different patterns for different groups. The more established the business, the more discrete the problems being solved are. That means early on, you go from optic oriented communications to more optimization oriented direction. I've found that organizations lack a portfolio strategy for their analytics interfaces, they take templates from one context and try to apply them to another OR they try to combine use cases together into a singular dashboard because they only have budget for one but multiple stakeholders with different needs, so they get a flying-boat-car of compromises. Some data work and analytics are going to be a cost of doing business, like reporting that just keeps everyone informed. While other data work is a strategic bet. The challenge is that some analytics deliver hard value you can measure in dollars, while others provide soft value like better collaboration and shared understanding that's difficult to quantify. Most organizations don't think about this mix deliberately. #dataAnalytics

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

🍩 Practical Guide To Accessible Data Visualization. With useful pointers on how to design accessible charts and tables ↓ 🚫 Don’t rely on colors alone to communicate your data. ✅ Consider patterns or textures to distinguish bars and lines. ✅ For line charts, use different widths/dashes to set them apart. ✅ Place labels on lines, areas and pie charts directly. ✅ Make interactive visualization keyboard-accessible. 🚫 Don't rely on hover effects for explanations. ✅ Allow users to turn off animation and movements. ✅ Test in various screen sizes and zoom levels. ✅ Duplicate data from charts to the table format. ✅ Provide a text summary of the visualization. 🚫 Don’t mix red, green and brown together. 🚫 Don’t mix pink, turquoise and grey together. 🚫 Don’t mix purple and blue together. 🚫 Don’t use green and pink if you use red and blue. 🚫 Don’t mix green with orange, red or blue of the same lightness. ✅ Use any 2 colors as long as they vary by lightness. The safest bet is to never rely on colors alone to communicate data. Use labels, icons, shapes, rectangles, triangles, stars to indicate differences and show relationships. Be careful when combining hues and patterns: the pattern changes how bright or dark colors will be perceived. Use lightness to build gradients, not just hue. Make all interactive components accessible via keyboard. Add an option to explore data in a data table format. And always include people with accessibility needs not just in usability testing but in the design process. ✤ Useful resources Free Online Course On DataViz Accessibility (11 modules) https://lnkd.in/ejFYw5iA Intro To Accessible DataViz, by Sarah Fossheim https://lnkd.in/dEzvCsdP Data Viz Accessibility Resources, by Silvia Canelón, PhD Full list: https://lnkd.in/eM27dp7e Summary: https://lnkd.in/eGFKh4dk Colorblindness In DataViz, by Lisa Charlotte Muth https://lnkd.in/evn95YBp Accessibility-First Charts, by Kent Eisenhuth, Kai Salmon Chang https://lnkd.in/dnE2bfzZ Guidelines for DataViz Accessibility, by Øystein Moseng https://lnkd.in/epq5jwe6 Accessible Alternatives To Complex Charts, by Sheri Byrne-Haber (disabled) https://lnkd.in/eTJgvBWH Data Visualization Design Systems + Guidelines https://lnkd.in/dgADUDcz ✤ Tools For Accessible DataViz Highcharts: https://www.highcharts.com Datawrapper: https://www.datawrapper.de Viz-Palette: https://lnkd.in/e-JxgwHh Visa Charts: https://lnkd.in/e675Fsgr #ux #dataviz #accessibility