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

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.

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.

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

7 data visualization mistakes that scream amateur. (I learned this after messing up 100+ charts) Most people think good charts just happen. They don't. Here are the deadly sins that kill your credibility: 1. Rainbow Overload Using every colour in existence. • Use colour to highlight, not decorate • Stick to 3 colours maximum • Grey is your friend Less is always more. 2. Pie Chart Obsession Pie charts work for one thing: parts of a whole. • Save pies for percentages that sum to 100% • Use bar charts for comparisons • When in doubt, choose bars Your audience will appreciate the honesty. 3. 3D Everything 3D effects look fancy but distort data. • Focus on the message, not the medium • Skip the shadows and gradients • Flat charts are clearer Clarity beats creativity, which improves communication. 4. Axis Manipulation Starting your Y-axis at random numbers. • Always start at zero for bar charts • Label your axes clearly • Show the full picture Misleading data destroys trust instantly. 5. Font Explosion Mixing 5 different fonts and sizes. • Consistent sizing throughout • Readable from 6 feet away • One font family maximum Consistency signals professionalism. 6. Label Madness Labeling every single data point. • Remove redundant information • Highlight only what matters • Let the pattern speak Sometimes less information is more insight. 7. Chart Type Confusion Using scatter plots for categories. • Consider your audience's familiarity • When confused, choose simpler • Match chart type to data type The right chart type does half the work. The best visualization is invisible. Your audience sees insights, not charts. Which mistake do you see most often? #research #visualization #datavis  

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.

Your chart formatting will benefit from condensed fonts. You can tell a font *isn't* condensed if you look at an o. In the first pic, you'll see than an 0 looks almost like a perfect circle. It's too wide. We have a label spilling on to a second line. Compare that to the condensed font in the next pic. The o's look more like ovals and the text fits better. Keep your condensed fonts sans serif, unlike the third pic. Such a hot mess. The serifs add noise. Condensed fonts will help your labels fit into smaller spaces, like the dots in the 4th pic. Notice how the text doesn't fit when it's regular width, in the last pic. Of course, you want a sans serif condensed font that's still highly readable, so pick one that has space between the letters (not too scrunched) and where you can tell the difference between a lowercase l, a capital I, and the number 1. I made a little comparison table of some common condensed fonts so you can see how this looks: https://lnkd.in/gYUiSUFj