Tips for Improving Chart Clarity

When a chart raises more questions than it answers, it's bad dataviz. A well-designed chart doesn’t just present data. It guides the audience effortlessly to the insight. But when a chart lacks clear meaning, it forces viewers to work too hard to interpret the data, leading to misinterpretation and disengagement. Take this chart, “Gold in 2020.” Everything about its design make it harder — not easier — for the audience to understand what it means. 1. Vague Title, No Headline, No Clear Message - “Gold in 2020” is too broad — does it track price, supply, or investment? - Does it cover the full year as the given title implies or just a segment? - A missing headline leaves viewers guessing at what the chart means. Fix: Be precise and include the chart's story in writing. • Instead of “Gold in 2020,” use a more accurate title like “Gold Prices in Early 2020.” • Add a clear headline that states the main message your chart is trying to deliver. 2. Missing Labels Create Unnecessary Cognitive Load - The y-axis lacks a unit — are these prices in USD? - The x-axis doesn’t define if the data is daily, weekly, or monthly. Fix: Labels should eliminate guesswork: • “Gold Price per Ounce (USD)” on the y-axis • “Daily Closing Prices (Jan–Feb 2020)” on the x-axis 3. No Annotations to Explain Key Trends - A sharp price spike in February is left unexplained — was it due to COVID-19 fears? Market speculation? - Without context, the audience is forced to speculate. Fix: Strategically add annotations to provide clarity -- a few simple Google searches reveal these important contextual datapoints around the times of price surges: • Jan 4: WHO reports mysterious pneumonia cases in Wuhan. • Mid-Jan: First COVID-19 case confirmed in Thailand. • Jan 21: First U.S. COVID-19 case announced in Washington. • Late Feb: Markets crash; gold surges amid economic turmoil. 4. No Visual Cues to Guide Attention - All data points look equally important, even though the February spike is the real story. - No reference points to show how these prices compare historically. Fix: Use design intentionally: • Bold or darken the February spike to emphasize its significance. • Add a horizontal benchmark line for comparison to 2019 prices. • Shade key periods to highlight market shifts. The Takeaway A chart should remove ambiguity, not create it. Better data visualization means: • Writing precise titles and headlines that frame the insight. • Using labels that eliminate guesswork. • Adding annotations that tell the story behind the data. • Applying visual cues that direct attention to key insights. 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

I spent years creating "beautiful" dashboards that executives ignored. Then I discovered 4 strategies that turn complex charts into decision drivers. Here's how to make your data impossible to ignore: It all started with an insight from Storytelling with Data by Cole Nussbaumer Knaflic. Your tools don't know your story. You must bring it to life. 𝗕𝗲𝗳𝗼𝗿𝗲: Hours creating fancy charts with gradients and random colors. 𝗔𝗳𝘁𝗲𝗿: Simple visuals that stakeholders actually use. 4 Core Visualization Principles: 1. Strip Chart Junk ↳ Remove unnecessary gridlines ↳ Delete pointless labels 2. Focus Single Message ↳ One insight per chart ↳ Everything else creates noise 3. Strategic Color Usage ↳ Highlight only critical data ↳ Gray out supporting information 4. Clear Takeaways ↳ State conclusions upfront ↳ Make messages obvious The transformation results in improved attention, understanding, and taking action. Your Implementation Plan: 1. Delete pointless gridlines 2. Remove unnecessary labels 3. Choose one color for key highlights 4. Write titles that state your conclusion Small adjustments create a massive impact. Which visualization principle will you implement first? Share your approach below! 📚 Resource: Storytelling with Data: https://amzn.to/4fHenmA ♻️ Repost to help others create impactful data stories

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.

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 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.

Here's a data visualization tip: Start with a white slide. Not with Excel. Not with Tableau. Not with PowerPoint templates. A blank white page. Then write in the center: "When someone sees this, I want them to understand _______." This forces us to clarify the core message before diving into visualization details. Only then should we ask: - What's the minimum data needed to convey this message? - What's the simplest way to show this relationship? - What context is essential for understanding? - What can I remove without losing meaning? Great data visualization isn't about showing everything you know. It's about making one thing impossible to miss. Next time you're creating a chart or dashboard, start with that blank page. Define your message first. Visualization second. Your clarity of purpose will create clarity of design.

One of the biggest mistakes I see analysts make when it comes to data viz is this: Using color like they are throwing a par-tay I used to do it too. I thought every category needed its own color, and more color = more engaging. Turns out the opposite is true. At best, poor color choices water down your message. At worst, they mislead your audience entirely. There are many nuances when choosing colors, but the following quick tips will get you 90% of the way there: --- 1. Use grayscale + one pop of color to spotlight the key category or trend You can also use a darker shade to draw attention. For example, all bars in a bar chart could be light blue, and the bar of interest (say, this quarter’s data) could be dark blue. --- 2. Use distinct colors only if each category is truly critical to the story But really, I mean TRULY all are critical. For example, you want to show product revenue for your top three performing products over the past six months. --- 3. Use sequential color palettes for ranges (low to high values) Say customers rated a product on a scale of “like it”, “love it”, or “gotta have it.”. Show “like it” in light blue (or whatever color you choose), “love it” in a slightly darker shade of blue, and “gotta have it” in the darkest blue. --- 4. Use diverging palettes for data with a neutral midpoint Imagine you have survey responses ranging from Strongly Disagree to Strongly Agree. The disagree categories would be in orange, neutral category in gray, and agree categories in blue. --- 5. Consider color psychology & cultural connotations Colors carry meaning, and that meaning can shift depending on culture or context. For example, red can mean danger/caution OR luck/celebration. Using red to highlight a trend might trigger very different reactions depending on who’s looking. --- Want to see examples?  Click ‘View my newsletter’ at the top of this post to read this week’s issue: How to let color do the storytelling. -------- 👋🏼 I’m Morgan. I share my favorite data viz and data storytelling tips to help other analysts (and academics) better communicate their work.

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.

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

Most charts get ignored. Great ones get remembered. If your data doesn’t spark clarity, it won’t drive action. You don’t need louder visuals. You need smarter storytelling. Here are 7 shifts to help your charts inform, engage, and stick: 1️⃣ Focus on what matters ➟ Cut out clutter and extras. ➟ Use only what drives understanding. 2️⃣ Remove visual noise ➟ Ditch the 3D, shadows, and flashy backgrounds. ➟ Keep attention on the message. 3️⃣ Make complex info simple ➟ Use clear layouts. ➟ Break things down, step by step. 4️⃣ Use color with purpose ➟ Choose colors for contrast, not decoration. ➟ Be mindful of accessibility. 5️⃣ Lead with the point ➟ Use the Pyramid Principle. ➟ Start with the insight, support it underneath. 6️⃣ Annotate the story ➟ Add callouts or notes to guide attention. ➟ Connect the dots for the viewer. 7️⃣ Keep your style consistent ➟ Fonts, layout, and colors should flow. ➟ Design is clarity, not decoration. The takeaway: Every graph, chart, and slide is a chance to lead through insight. Use structure to show the story—and make it stick. What’s one data mistake you see all the time? Drop it below. Let’s help each other improve our slides. 📌 Save this before your next presentation 🔁 Share with your team to sharpen their storytelling 👤 Follow Jay Mount for high-trust tips on data, clarity, and communication that moves people.