Strategies for Clear and Concise Charts

Chart Crime Scene: When Messy Design Kills the Message This line chart tried to show the monthly distribution of VC deal activity across six years. But instead of insight, it delivered confusion. Here’s what went wrong — and how to fix it: 1. Too many lines, no hierarchy. All six years are styled the same, leaving no focal point. One year (2016) tells the story. The rest? Context. Highlight what matters, quiet the rest. 2. Circular data markers everywhere. The chart looks like a bowl of SpaghettiOs. Markers add clutter and pull attention away from the line shape itself. Remove decorative shapes unless they add clarity. 3. Rotated labels and crowded layout. Tilted text slows reading and draws attention where it doesn’t belong. Cramped axes and disconnected legends confuse further. Use horizontal labels and clean spacing. Let the chart breathe. 4. Color without purpose. Every line gets its own color — but none stand out. The result is a flat visual with no narrative. Use color to guide the eye. The best way to do this would be to design the 2016 line in a highly contrasting color (a red hue, for instance) while using a muted grey color for all the others. By doing so, 2016 (and the chart's real story) pops. The Final Verdict: Refined execution matters. Clarity isn’t just about data — it’s about craftsmanship. Design your charts with purpose to minimize distraction and ensure your audience hears the story you intend. 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

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.

Don’t let your visuals kill your insights. These 4 graph elements do exactly that. If it looks good but communicates nothing, It’s decoration - not data. Clarity > aesthetics. Here are 4 things to avoid - and what to do instead: 1. Pie Charts Hard to compare angles. Can’t judge how much bigger one slice is than another. Instead: - Use a horizontal bar chart (clear baseline) - Sort values to highlight what matters 2. Donut Charts Arc lengths are even harder to read than pie slices. Instead: - Use a horizontal bar chart (clear baseline) - Make comparisons easy and instant 3. Dual Y-Axis Charts Confusing. Readers don’t know which data belongs to which axis. Instead: - Label the second dataset directly - Or split the chart and share a common x-axis 4. Axis + Data Labels Repeating values adds clutter without insight. Instead: - Show the axis or label the data - not both - Remove gridlines to reduce noise Most charts are forgettable. Clear ones get people to act. 💬 Drop a comment - What’s one design habit you’ve had to unlearn? 👇 ♻️ Follow Mike Reynoso for more tips on clear, actionable BI communication. 🔁 Reshare to help others turn cluttered charts into meaningful insight. 📌 Save this post — better data storytelling starts with better visuals.

Your job as a data analyst is to make the data and insights accessible to stakeholders. If you can't do that, what value are you adding? Good data visualization isn't... - using the most complex charts - prioritizing aesthetics over clarity - including every single detail possible Good data visualization IS... - conveying information with simple charts - considering your audience's understanding - focusing on only the most important information 80+% of your charts and graphs should be: - line charts (for trends over a period of time) - scatter plots (for relationships between variables) - area charts (for changes in composition over time) - pie/donut charts (for composition as parts of a whole) - bar/column charts (for comparison between categories) - stacked bar/column charts (for composition across categories) Prioritize clarity, simplicity, and relevance to your audience.

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

Clarity is the first pillar of data visualization....and it's not as easy as it seems. There's something about data people when we want to present data, we struggle to make choices and/or want to add "fluff" to make it stand out. (And by fluff, I'm not talking about creativity in dataviz. That's useful and a conversation for another day.) Here are five key ideas for improved clarity: - Learn about the Gestalt Principles of design - Identify the *one* key message for your chart - Based on the above, choose the chart type that conveys this message best - Keep a high level of hierarchy of information. What needs to be highlighted? What needs to be subdued? *Avoid using 3 font families, 12 different font sizes and 4 type colors, it's confusing AF* - Use the right color palette: is your data categorical? sequential? divergent? or do you need semantic color? - Accessibility isn't a bonus. It's a necessity for clarity. Check your colors with the ACPA algorithm. - Drop the jargon. Not everyone is a PhD in your field. 👀 Bonus: Before/After of a data visualization we made for Effect & Affect. ✨ Learn more about the 3 other pillars of data visualization in the article featured on my profile.

Exhausted from grappling with inefficient dashboards that hinder rather than facilitate your decision-making process as a CFO? Picture this 👇🏼 You join a team, expecting well-polished dashboards only to find yourself drowning in cluttered data tables and perplexing pie charts. Frustrating, right? But here's the kicker. Neglecting dashboard design isn't just a minor inconvenience; it's a critical misstep with far-reaching consequences. Without intentional design, you risk being inundated with convoluted data presentations that obscure insights and hinder swift decision-making. Think about it: buried in tables and pie charts. How can you make timely, informed decisions that drive your organization forward? The good news? There's a clear path forward. Here are some actionable steps to revamp your dashboard game: 1️⃣ Simplicity Rules Strip away the clutter and opt for clear, user-friendly designs. Replace complex tables with straightforward charts like bars and lines to streamline data comprehension. 2️⃣ Actionable Insights Demand dashboards that don't just present data but also guide your decision-making process. Ensure your charts prompt action and provide meaningful insights at a glance. 3️⃣ Annotations Matter Don't overlook the importance of annotations. Clear labeling and contextual information can transform a confusing chart into a powerful decision-making tool. 4️⃣ Find Your Complexity Sweet Spot Balance complexity with comprehension. Opt for visualizations that strike the right balance – neither too simple to be insightful nor too complex to decipher. 5️⃣ Visual Consistency Maintain a cohesive visual language across your dashboards. Consistency in design elements fosters clarity and enhances user experience. By embracing these principles, you can transform your dashboards into strategic assets that empower you to make informed decisions confidently and swiftly. Ready to supercharge your dashboard game? Let's connect to explore how you can revolutionize your data visualization strategies and drive your organization's success. Drop me a message today! 🔽 🔽 🔽 👋 Hi, I'm Lisa. Thanks for checking out my Post!   Here is what you can do next ⬇️   ➕ Follow me for more FP&A insights    🔔 Hit the bell on my profile to be notified when I post   💬 Share your ideas or insights in the comments ♻ Inform others in your network via a Share or Repost #digitaltransformation #finance #cfo #data #businessanalytics

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

Visualizing data helps humans digest complex information 10X faster than text, yet most dashboards actually slow down decision-making. Edward Tufte's pioneering work reveals why: effective data visualization requires ruthlessly eliminating noise to amplify signal—what he calls "above all else, show the data." 1. Maximize the Data-Ink Ratio 🔍 Remove decorative elements that don't convey information. Every pixel should serve a purpose. Those 3D effects and heavy gridlines? They're actively hiding your insights. 2. Answer "Compared to What?" 📊 Tufte's favorite question drives his "small multiples" concept—mini-charts arranged side-by-side with consistent scales. When executives see monthly revenue across six product categories simultaneously, patterns emerge instantly. 3. Context Belongs On the Visualization 📝 Annotate directly on charts rather than in legends or footnotes. A small note "Promo campaign launch" on a sales spike explains more than a meeting ever could. 4. Embrace Sparklines for Trends 📈 These "word-sized graphics" pack tremendous insight alongside metrics. A tiny 30-day trendline next to "Conversion Rate" immediately conveys direction without requiring separate charts. 5. Design for Decisions, Not Aesthetics 🎯 The true test: does this visualization help someone make a better decision? If not, it needs rethinking. At SourceMedium.com, these principles guide our data visualization design, which has powered up to 30x growth for some of our customers over the years. We're now designing these principles into our AI data analyst agent to make it a seamless part of your daily workflow – no more thinking about the best way to make charts, you simply get the most effective visualizations based on your questions and preferences. This represents a fundamental paradigm shift from conventional dashboards and web apps. SourceMedium.ai doesn't just present data; it delivers insights with Tufte-inspired clarity and purpose, integrating directly into your team's communication channels. The best data visuals aren't the flashiest—they're the ones that disappear, leaving only understanding behind.

Ever looked at a chart and thought, “This could be so much clearer”? We’ve all been there—whether it’s trying to decode a graph or create one that tells a story effectively. Recently, I came across a guide on Matplotlib, and it really changed how I think about creating visualizations. It wasn’t just about the technical “how-to” but also the art of making data click with people. Here are a few insights that stuck with me: 1️⃣ Simplicity wins: If your audience needs extra time to figure out what your chart is saying, it’s probably too complicated. Keep it clear and to the point. 2️⃣ The little things matter: Colors, labels, and consistent formatting aren’t just aesthetics—they’re tools to guide attention and understanding. 3️⃣ Reusable design is a game-changer: Building modular and reusable code for charts not only saves time but also keeps your work consistent and polished. Visuals are more than just charts—they’re bridges that connect data to insights. And when done well, they can spark those “aha!” moments that drive decisions. What’s your go-to strategy for making visualizations pop? I’d love to hear how you approach it! #data #ai #matplotlib #theravitshow