Tips for Clear Data Visualization

8 out of 10 analysts struggle with delivering impactful data visualizations. Here are five tips that I learned through my experience that can improve your visuals immensely: 1. Know Your Stakeholder's Requirements: Before diving into charts and graphs, understand who you're speaking to. Tailor your visuals to match their expertise and interest levels. A clear understanding of your audience ensures your message hits the right notes. For executives, I try sticking to a high-level overview by providing summary charts like a KPI dashboard. On the other hand, for front-line employees, I prefer detailed charts depicting day-to-day operational metrics. 2. Avoid Chart Junk: Embrace the beauty of simplicity. Avoid clutter and unnecessary embellishments. A clean, uncluttered visualization ensures that your message shines through without distractions. I focus on removing excessive gridlines, and unnecessary decorations while conveying the information with clarity. Instead of overwhelming your audience with unnecessary embellishments, opt for a clean, straightforward line chart displaying monthly trends. 3. Choose The Right Color Palette: Colors evoke emotions and convey messages. I prefer using a consistent color scheme across all my dashboards that align with my brand or the narrative. Using a consistent color scheme not only aligns with your brand but also aids in quick comprehension. For instance, use distinct colors for important data points, like revenue spikes or project milestones. 4. Highlight Key Elements: Guide your audience's attention by emphasizing critical data points. Whether it's through color, annotations, or positioning, make sure your audience doesn't miss the most important insights. Imagine presenting a market analysis with a scatter plot showing customer satisfaction and market share. By using bold colors to highlight a specific product or region, coupled with annotations explaining notable data points, you can guide your audience's focus. 5. Tell A Story With Your Data: Transform your numbers into narratives. Weave a compelling story that guides your audience through insights. A good data visualization isn't just a display; it's a journey that simplifies complexity. Recently I faced a scenario where I was presenting productivity metrics. Instead of just displaying a bar chart with numbers, I crafted a visual story. I started with the challenge faced, used line charts to show performance fluctuations, and concluded with a bar chart illustrating the positive impact of a recent strategy. This narrative approach helped my audience connect emotionally with the data, making it more memorable and actionable. Finally, remember that the goal of data visualization is to communicate complex information in a way that is easily understandable and memorable. It's both an art and a science, so keep experimenting and evolving. What are your go-to tips for crafting effective data visualizations? Share your insights in the comments below!

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

📊💡 Mastering Data Visualization: Tips for Clear and Compelling Presentation In today's data-driven world, effective data visualization is key to conveying insights and driving decision-making. As data analysts, we understand the power of information. But presenting that data in a way that is not only clear but also compelling is an art form in itself. Here are some tips and best practices for mastering data visualization: 1. **Know Your Audience**: Before diving into visualization, understand who you're presenting to and what they care about. Tailor your visualizations to their level of expertise and interests. 2. **Simplify Complex Data**: Complexity can overwhelm and obscure your message. Simplify your visualizations by focusing on the most important insights. 3. **Choose the Right Visualization Type**: Different types of data lend themselves to different visualization formats. Choose the visualization type that best conveys your message and makes it easy for your audience to understand. 4. **Emphasize Key Insights**: Use visual cues to draw attention to the most important insights in your data. 5. **Tell a Story with Your Data**: Structure your visualizations in a logical sequence that leads your audience from problem to insight to action. 6. **Iterate and Solicit Feedback**: Data visualization is an iterative process. Continuous refinement based on feedback will help you create more effective and impactful visualizations over time. Tools such as Tableau, Power BI, and Python libraries like Matplotlib and Seaborn can be incredibly useful in creating visually stunning and informative visualizations. The real magic happens when you combine technical expertise with a keen eye for design and storytelling. Let's continue to harness the power of data visualization to unlock insights, tell compelling stories, and drive decision-making in our organizations. 🚀💻 #datavisualization #analytics

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.

When it comes to #datavisualization 📊, there’s a lot of focus on choosing the right chart for your data. For example, you’ll hear about the four basic types of #data charts (comparison, composition, distribution, and relationship) and how you need to make sure your chart is appropriate for the data set. Choosing the right chart in #datastorytelling is only the beginning as you also need to decide what you’re going to emphasize within each data chart. Are you focusing on specific values (micro) or the overall shape (macro) of the data? Even though you might have chosen an appropriate chart, its design may not support the point you’re trying to make. From a MICRO perspective, you may want to emphasize how Campaign A performed compared to the other campaigns in April. When the results are side by side in the same chart, it’s easier to compare specific values. From a MACRO perspective, you might want to show how the results for Campaign A peaked in April. It’s easier to see the shape of the data when it is separated into separate charts (small multiples), and the different campaigns aren't overlapping. Some people might say, “I want to show both.” While it’s certainly possible, your chart will be more complex and potentially overwhelming. When you try to do too much by showing both micro and macro perspectives, you can weaken the power of your visual. You increase the likelihood that the audience may entirely miss the point you’re trying to communicate. I would recommend determining which is the main priority (micro or macro) and focusing your data scene on that perspective. Nothing stops you from showing both perspectives separately if they're both important to your narrative. What other factors shape how you design the visuals of your data stories? #storytellingwithdata #analytics #businessintelligence #dataviz #datavisualisation

Data Visualization: Don't Let Your Insights Become Eye Strain Let's face it, data can be a real snoozefest presented on its own. Numbers and spreadsheets can leave even the most analytical minds wandering off to dream about pie... charts? But what if I told you there's a way to make data sing? Data visualization is the magic trick that transforms dry statistics into captivating stories. Did you know that according to a study by Social Science Computer Review, people are 22 times more likely to remember information presented visually? Here's the cheat sheet to becoming a data visualization whiz: 1. Know your audience: Tailor your visuals to resonate with your viewers. Are you presenting to seasoned data analysts or explaining complex trends to executives? Complexity levels and chart types should adapt accordingly. 2. Keep it simple, silly: Fight the urge to cram everything onto one chart. Focus on a single, clear message and use visuals that complement it. Remember, your goal is clarity, not creating the Mona Lisa with bar graphs. 3. Color your world (strategically): Colors can be incredibly powerful tools to guide the eye and highlight key points. But beware of rainbow puke! Use color palettes that are easy on the eyes and adhere to accessibility standards (thinking of our colorblind friends here!). 4. Let the data do the talking: Avoid embellishments that distort the information. Fancy 3D charts might look cool, but if they make it difficult to interpret the data, ditch them! Data visualization is all about storytelling. Use visuals to take your audience on a journey, highlighting trends, comparisons, and insights. By following these tips, you can transform your data from a dusty textbook into an engaging presentation that gets people talking. ️ #datavisualization #datavis #datastorytelling #datadriven #businessintelligence #socialmediatips

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

The 5 Pillars of Data Visualization 🌟 In this article Prashanth H Southekal, PhD, MBA, ICD.D, Founder of DBP-Institute and CFO.University Contributor,  teaches how to make insights from our data stand out by describing the 5 pillars of data visualization. 💡Data visualization is an indispensable tool for modern CFOs, enabling better decision-making by improving strategic insights. Here is a summary of the 5 Pillars, 1️⃣ Purpose drives the visual: Define the purpose clearly, aligning with stakeholders' objectives. Whether it's distribution, composition, relationship, trend, or comparison, choose visuals that serve the purpose effectively.   2️⃣ Data type determines selection: Nominal, ordinal, or numeric - the data type dictates the appropriate visual representation. From histograms to line charts, match the visual to the data type for maximum impact.   3️⃣ Less is more: Simplify! Identify essential variables and streamline visuals to convey information clearly. Manage data-ink ratio and density to avoid clutter and confusion. 4️⃣ Apply consistent scales: Ensure consistency in scales to maintain accuracy and integrity. The lie factor is a handy tool for measuring scale consistency, vital for reliable visualization. 5️⃣ Aesthetics matter: Optimize visual aesthetics for better comprehension. From utilizing the golden ratio to choosing appropriate typography and color schemes, aesthetics play a pivotal role in effective data communication. The goal of data visualization is not just to dazzle but to facilitate understanding and informed decision-making.   Mastering these pillars empowers CFOs to harness the full potential of their data, driving informed decision-making and strategic initiatives. Check out the full article in the link below for a deeper dive into each pillar and start transforming your data into actionable insights today! 📚 I am the Founder of and Chief Learning Officer at CFO.University 🏫 CFO.University is a professional development center for CFOs and aspiring CFOs. Our Mission:   Develop world changing finance leaders 🔔   To see more content ring the bell on my profile 🎬 Visit our CFO Talk video series with global experts transforming the role of the CFO, https://lnkd.in/gg6bdZx 📚 Learn more about CFO.University and join our community here, https://lnkd.in/g72yWfSG   🚀 #CFO #CFOUniversity  #DataVisualization #CFOInsights #BusinessIntelligence

Executives don't have time for color legends. So in dashboards, I go with what they know: green is good and red is bad.* In the "Dash This" example, the KPI and trend comparisons all follow that red/green approach. For other color needs, I try to stick with dark colors for "this is important" and grays for "this is context." In the "Not That" example, the comparisons are orange is bad and blue is good, which isn't as easy to understand at a glance. Other color choices are jarring, like green for the current year, red for last year. Assume the executive will never look at a color legend. Does your dashboard still make sense? That's the key. For more storytelling with color tips, see this Playfair Data video tutorial from Tableau legend Ryan Sleeper: https://lnkd.in/dX5szkPm *This is for the typical U.S. business audience. And to accommodate color vision deficiency, do a second encoding such as an up arrow for good, a down arrow for bad. #dataforexecs #datavisualization #dashboards

Intentional color selection matters, particularly on dashboards. When we design dashboards, we think about more than how a color choice works for a single chart. Instead, we think about how to help our audience explore the data with ease. Sometimes that means using alerting colors like red or orange to focus attention on metrics that aren't performing well, but we're not always comparing results to a target. When I was working with the strategic information team at USAID, Aaron Chafetz, Tim Essam, Ph.D., and Karishma Srikanth did a refresh on the #dataviz style guide for the team, including revisiting colors used not just in one dashboard but across a whole suite of tools. Breaking down results and funding by agency was analytically valuable, but they didn't want to just pull the colors from logos. The colors needed to work for other data viz purposes too. So, in addition to having a dedicated set of colors (tested for contrast and other features), the guide included specific recommendations for categorical colors, like this set of colors by agency and color palettes for performing above or below goals. On charts with multiple agencies, the colors work well together. On a chart representing results for one agency with the option to filter, the color would change as the user filtered the dashboard to different slices of data, giving an added visual cue that the data had changed. The result? More consistency across dashboards and other visualizations, which can help stakeholders more quickly see patterns in information.