Data Visualization Tips

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.

At Maven Analytics, we see a TON of reports, dashboards and infographics designed for our monthly data challenges. Here are 5 of the most common data viz mistakes that we see: 🙅♂️ Pies & donuts with too many segments Humans are bad at comparing angles. Use bar or column charts instead, or limit donuts to 2-3 slices max. 🙅♂️ Line charts for categorical comparisons Line charts should be used to show trends. Using them for categorical data (vs. time-series) is misleading, and can suggest relationships or patterns that don't exist. 🙅♂️ Treemaps for non-hierarchical data Treemaps are designed to show hierarchies (like subcategories within categories). For simple categorical comparisons, use a bar or column chart instead. 🙅♂️ Unsorted data Don't expect viewers to make their own visual comparisons. Use intuitive sorting rules to organize your data and tell a clear story. 🙅♂️ Too much noise, too little focus While it's tempting to add background images, complex custom visuals or crazy 3-D effects, remember that effective data visualization is all about minimizing noise and maximizing clarity. Datafam, what other common visualization mistakes have you seen? #data #datavisualization #businessintelligence

Most confusion doesn’t come from bad data. It comes from choosing the wrong chart. We often jump straight into visuals: Pie chart because it looks simple Bar chart because it’s familiar Line chart because it’s trending But charts are not decorations. They are answers to specific questions. Before selecting any graph, I now pause and ask: Is this data categorical or continuous? Am I showing a trend or a comparison? Is this about parts of a whole or relationships? What should the viewer understand in the first 5 seconds? This small shift in thinking changes everything: 1. Fewer follow-up questions 2. Less explanation needed 3. More confident decisions from stakeholders This visual is a great reminder: Good data visualization starts with thinking, not clicking. If you’re working with Power BI / Tableau / dashboards: Don’t memorize chart types. Learn why one chart works better than another. That’s how data starts telling stories instead of causing confusion. If you want help building dashboards that make sense to business users, I share my practical approach here https://lnkd.in/gWSkyyiv #DataVisualization #PowerBI #DashboardDesign #DataAnalytics #DataStorytelling #LearningJourney

Master the art of Financial Storytelling 🧑🏫 Your numbers tell a story, but are you telling it right? 👇 Numbers without context are just digits on a page. The real power comes from transforming those numbers into insights that drive action. ➡️ COMMON MISTAKES IN FINANCIAL REPORTING Let's start with what NOT to do when presenting financials: 1️⃣ Dropping raw numbers without context Raw data overwhelms your audience. When you say "Revenue grew to $100K," what does that mean for the business? 2️⃣ Reading slide content word-for-word Your presentation should add value beyond what's written. Share insights that aren't visible in the numbers. 3️⃣ Rushing through without pausing for questions Financial data needs time to digest. Create moments for discussion and clarification. ➡️ BUILDING A COMPELLING FINANCIAL STORY Here's how to transform your financial presentations: 1️⃣ Start with the fundamentals Always begin by establishing context. What's normal? What's exceptional? What benchmarks matter? 2️⃣ Connect data points to strategy Show how financial results link to business decisions. If working capital improved, explain which specific actions drove that improvement. 3️⃣ Use comparisons effectively - Period over period changes - Budget vs actuals - Year over year trends - Industry benchmarks 4️⃣ Structure your narrative - What happened? - Why did it happen? - What does it mean for the future? - What actions should we take? ➡️ COMPONENTS OF GREAT FINANCIAL STORYTELLING 1️⃣ Clear Dashboards Start with a clean, focused view of KPIs that matter most. Don't overwhelm with data. 2️⃣ Strategic Context Show how financial results connect to company goals and market conditions. 3️⃣ Forward-Looking Analysis Use current data to paint a picture of future opportunities and challenges. 4️⃣ Action Items End every presentation with clear next steps and decision points. ➡️ PRACTICAL TIPS FOR IMPLEMENTATION 1️⃣ Know your audience CFO needs different details than the marketing team. Adjust your depth accordingly. 2️⃣ Use visual aids Graphs and charts can illustrate trends better than tables of numbers. 3️⃣ Practice active listening Watch for confusion or disengagement. Adjust your presentation based on real-time feedback. 4️⃣ Create discussion points Plan specific moments to pause and engage with your audience. === Remember: Financial storytelling isn't about making numbers sound good. It's about helping stakeholders make informed decisions. What techniques do you use to make financial data more engaging? Share your thoughts in the comments below 👇

Bad data visualization is everywhere — here’s how to fix it. Understanding the essentials of effective data visualization is one thing, but witnessing poor data visualization in practice offers the real lessons. Take a look at this chart, “How Baby Boomers Describe Themselves,” which had some fundamental errors. The major problem? It disregards the rule of relativity. The design implies the data forms a complete whole, yet the percentages total 243%. This clearly indicates the wrong visual format was selected. If respondents could choose multiple answers, the data should be shown as a grouped bar chart rather than being forced into a single human figure. Additionally, contrast is mishandled: • Size contrast is deceptive – Larger sections don’t correlate with larger values. • Color contrast is excessive – Every section demands attention, causing nothing to stand out. • Shape contrast is absent – The chart depends solely on color to distinguish categories, reducing clarity. • Annotations cause confusion – Instead of providing clarity, extra design elements divert attention from the main insights. So, how to fix it? Opt for the correct visual structure, use proportional sizes, apply color contrast wisely, introduce meaningful shape variations, and ensure annotations are purposeful. Bad data visualization doesn’t just appear cluttered. It misleads. Correcting it involves directing the audience to the right insights without making it a struggle. 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

🕵️ 𝗧𝗵𝗲 𝗦𝗶𝗹𝗲𝗻𝘁 𝗕𝗶𝗮𝘀 𝗶𝗻 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀: 𝗔𝗿𝗲 𝗬𝗼𝘂 𝗙𝗿𝗮𝗺𝗶𝗻𝗴 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗔𝗻𝘀𝘄𝗲𝗿 𝗬𝗼𝘂 𝗪𝗮𝗻𝘁? We often say, “𝘓𝘦𝘵 𝘵𝘩𝘦 𝘥𝘢𝘵𝘢 𝘴𝘱𝘦𝘢𝘬.” But what if it’s whispering… exactly what we want to hear? Data is neutral. 𝗕𝘂𝘁 𝗵𝗼𝘄 𝘄𝗲 𝘃𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲 𝗶𝘁? 𝗧𝗵𝗮𝘁’𝘀 𝘄𝗵𝗲𝗿𝗲 𝘁𝗵𝗲 𝗯𝗶𝗮𝘀 𝗰𝗿𝗲𝗲𝗽𝘀 𝗶𝗻 — 𝘀𝗶𝗹𝗲𝗻𝘁𝗹𝘆. 👉 A truncated y-axis that makes a small change look dramatic 👉 Cherry-picked KPIs that support a preset narrative 👉 Filters that hide outliers or unfavorable segments 👉 Colour cues that draw the eye only to positives These aren’t 𝘢𝘭𝘸𝘢𝘺𝘴 intentional. But the effect is the same — 𝘀𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿𝘀 𝘄𝗮𝗹𝗸 𝗮𝘄𝗮𝘆 𝘄𝗶𝘁𝗵 𝗮 𝘀𝗸𝗲𝘄𝗲𝗱 𝗽𝗶𝗰𝘁𝘂��𝗲, and decisions get shaped by perception, not truth. I’ve seen dashboards that are technically correct… but ethically questionable. Not because someone lied — but because the framing nudged the story. 🔧 𝗧𝗶𝗽𝘀 𝘁𝗼 𝗸𝗲𝗲𝗽 𝘆𝗼𝘂𝗿 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀 𝗵𝗼𝗻𝗲𝘀𝘁: • Always label filters clearly — no hidden slicers • Use full axis ranges unless deviation is explicitly the point • Show summary and detail views to give context • Include disclaimers when data is incomplete • Pick KPIs that show both sides — not just the wins 🧭 Remember, 𝗮 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗶𝘀𝗻'𝘁 𝗷𝘂𝘀𝘁 𝗱𝗮𝘁𝗮 — 𝗶𝘁'𝘀 𝗱𝗲𝘀𝗶𝗴𝗻, 𝗶𝗻𝘁𝗲𝗻𝘁, 𝗮𝗻𝗱 𝗶𝗺𝗽𝗮𝗰𝘁. And when trust is lost in how we present insights, everything else suffers. 💬 𝙃𝙖𝙫𝙚 𝙮𝙤𝙪 𝙚𝙫𝙚𝙧 𝙨𝙚𝙚𝙣 𝙖 𝙙𝙖𝙨𝙝𝙗𝙤𝙖𝙧𝙙 𝙩𝙚𝙡𝙡 𝙖 𝙨𝙩𝙤𝙧𝙮 𝙩𝙝𝙖𝙩 𝙬𝙖𝙨𝙣’𝙩 𝙦𝙪𝙞𝙩𝙚 𝙩𝙝𝙚 𝙛𝙪𝙡𝙡 𝙩𝙧𝙪𝙩𝙝? #DataDrivenDecisionMaking #DashboardDesign #DataVisualization #DataAnalytics

Communicating complex data insights to stakeholders who may not have a technical background is crucial for the success of any data science project. Here are some personal tips that I've learned over the years while working in consulting: 1. Know Your Audience: Understand who your audience is and what they care about. Tailor your presentation to address their specific concerns and interests. Use language and examples that are relevant and easily understandable to them. 2. Simplify the Message: Distill your findings into clear, concise messages. Avoid jargon and technical terms that may confuse your audience. Focus on the key insights and their implications rather than the intricate details of your analysis. 3. Use Visuals Wisely: Leverage charts, graphs, and infographics to convey your data visually. Visuals can help illustrate trends and patterns more effectively than numbers alone. Ensure your visuals are simple, clean, and directly support your key points. 4. Tell a Story: Frame your data within a narrative that guides your audience through the insights. Start with the problem, present your analysis, and conclude with actionable recommendations. Storytelling helps make the data more relatable and memorable. 5. Highlight the Impact: Explain the real-world impact of your findings. How do they affect the business or the problem at hand? Stakeholders are more likely to engage with your presentation if they understand the tangible benefits of your insights. 6. Practice Active Listening: Encourage questions and feedback from your audience. Listen actively and be prepared to explain or reframe your points as needed. This shows respect for their perspective and helps ensure they fully grasp your message. Share your tips or experiences in presenting data science projects in the comments below! Let’s learn from each other. 🌟 #DataScience #PresentationSkills #EffectiveCommunication #TechToNonTech #StakeholderEngagement #DataVisualization

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.

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