Best Practices for Data Visualization of KPIs

After designing hundreds of business dashboards, I keep coming back to these four patterns: Tall + Scrolly Stack everything vertically, organized by metric family, and let people scroll to their level of depth. Best for mobile viewing and email delivery with basic chart types that doesn't require instructions. Where I've seen this work: New product/feature introductions where audiences are different levels (executive to operators) and functions. BANs + Decomp Big numbers that focus attention and breakdowns that show differences. For when you've identified the important metrics, but want to show segment granularity. Switch group-by dimension while maintaining familiar layout. Where I've seen this work: Operational monitoring for teams that have ownership of metric outcomes. Sankey + Wide Table Flow diagram establishes a map of the whole system and reference tables show details. For diagnosing conversion and retention patterns across nodes and segments to know where to optimize. Where I've seen this work: Growth teams figuring out behavior across complex funnels and overlapping segments. Potential Show what you could be delivering versus what you're actually delivering. Makes the gap between current performance and available capacity visible. Where I've seen this work: Operational teams that have a clear action to take, but limited time. What each of these have in common: - Establish big picture awareness, but direct small picture action (think global, act local) - Strengthened by KPI ownership - Act as a prioritization mechanism Organizations often start with one dashboard trying to serve everyone, then evolve into multiple dashboards with different patterns for different groups. The more established the business, the more discrete the problems being solved are. That means early on, you go from optic oriented communications to more optimization oriented direction. I've found that organizations lack a portfolio strategy for their analytics interfaces, they take templates from one context and try to apply them to another OR they try to combine use cases together into a singular dashboard because they only have budget for one but multiple stakeholders with different needs, so they get a flying-boat-car of compromises. Some data work and analytics are going to be a cost of doing business, like reporting that just keeps everyone informed. While other data work is a strategic bet. The challenge is that some analytics deliver hard value you can measure in dollars, while others provide soft value like better collaboration and shared understanding that's difficult to quantify. Most organizations don't think about this mix deliberately. #dataAnalytics

Most “Customer Rating” KPIs show a number and stop there. This one does something smarter- it turns a simple score into a layered story about perception, movement, and comparison. There are 5 intentional design decisions here. Let me break them down. 1. The headline metric answers the main question instantly: “4 out of 5” is large, bright, and impossible to miss. Before users look at the trend line, they already know the current performance level. The takeaway is immediate- no chart reading required. 2. Stars translate numbers into emotional context: Stars make the score relatable and human-friendly. Users don’t interpret “4” mathematically first- they feel it as “pretty good" 3. Time filtering is separated from the story space: Year selection sits outside the KPI card. This keeps the visual clean while still allowing temporal comparison. Interaction exists but doesn’t interrupt interpretation. 4. Previous period comparison adds quiet context The dotted gray line doesn’t compete visually. It sits in the background as a benchmark- not a distraction. Users quickly see whether performance is improving or slipping. 5. Minimal labeling keeps cognitive load low Only essential elements are labeled- months, legend, rating scale. No unnecessary gridlines or chart clutter. This makes the KPI feel calm and easy to scan. BONUS: Summary and trend live in one cohesive story. Top: overall rating snapshot. Bottom: monthly movement. Leaders can stop at the score. Analysts can explore fluctuations. No extra pages. No drill-through required. Love this breakdown? Hit the bell if you do not want to miss the next #TheVisualBreakdown.

🚀 Power BI: Adding Custom Tooltips to KPI Cards! While exploring Power BI capabilities, I discovered a creative workaround to add detailed tooltips to KPI cards - something that's not natively supported! The Challenge 🎯 Standard KPI cards in Power BI lack the ability to show comprehensive tooltip information, limiting user interaction and data exploration. My Solution 💡 I developed a custom HTML-based approach that transforms regular KPI cards into interactive, information-rich components: ✅ HTML-Wrapped DAX Measures: Converted standard measures into HTML format for enhanced styling ✅ Dual Theme Support: Implemented both light and dark modes for better user experience ✅ Dynamic Theming: Created a separate theme table with all color variations ✅ Rich Tooltips: Added detailed breakdowns including Monthly Recurring Revenue, One-time Sales, and Refunds Key Features 🌟 Responsive Design: Adapts to different screen sizes Theme Consistency: Seamless light/dark mode switching Enhanced UX: Detailed information on hover without cluttering the main view Professional Styling: Clean, modern card design with gradients and shadows Implementation Highlights 🔧 // Theme table with comprehensive color schemes Theme = DATATABLE( "ThemeMode", STRING, "ColorType", STRING, "ColorValue", STRING, // Light & Dark theme definitions... ) Important Consideration ⚠️ While this solution works effectively, be mindful of the HTML card container's width as it can overlap with other visuals. Proper positioning and sizing are crucial for optimal user experience. Results 📊 ✨ Enhanced user engagement with interactive KPI cards ✨ Better data storytelling through rich tooltips ✨ Professional, modern dashboard appearance ✨ Improved accessibility with theme options This approach opens up new possibilities for creating more engaging and informative Power BI dashboards. Sometimes the best solutions come from thinking outside the box! Share your experiences in the comments! #PowerBI #DataVisualization #BusinessIntelligence #DAX #HTML #DataAnalytics #Innovation #Dashboard #Microsoft #DataScience

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.

𝐈 𝐮𝐬𝐞𝐝 𝐭𝐨 𝐭𝐡𝐢𝐧𝐤 𝐝𝐚𝐭𝐚 𝐯𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐰𝐚𝐬 𝐣𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐦𝐚𝐤𝐢𝐧𝐠 𝐜𝐡𝐚𝐫𝐭𝐬… 𝐮𝐧𝐭𝐢𝐥 𝐈 𝐫𝐞𝐚𝐥𝐢𝐳𝐞𝐝 𝐈 𝐰𝐚𝐬 𝐝𝐨𝐢𝐧𝐠 𝐢𝐭 𝐚𝐥𝐥 𝐰𝐫𝐨𝐧𝐠. When I first started with data visualization, I thought it was just about making pretty charts. But I quickly realized that true mastery lies in telling a story with data turning raw numbers into insights that drive real decisions. If you’re looking to level up your data visualization skills, here’s the structured path I followed (and continue refining every day): 1️⃣ Build a Strong Foundation 🔹Understand why we visualize data - clarity and decision-making over aesthetics. 🔹Learn chart selection - when to use bar charts, line graphs, heatmaps, or scatter plots. 🔹Master the basics of color theory, contrast, and accessibility to make visuals effective for all audiences. 2️⃣ Get Hands-On with the Right Tools 🔹 Beginner: Excel, Google Sheets (Great for understanding core visualization concepts) 🔹 Intermediate: Tableau, Power BI (Essential for dashboards and interactivity) 🔹 Advanced: Python (Matplotlib, Seaborn, Plotly) & R (ggplot2) for full customization and automation 3️⃣ Learn to Tell a Story 🔹A great visualization isn’t just about good design, it’s about answering the right questions. 🔹Focus on context: Who is your audience? What action should they take? 🔹Follow frameworks like “Who, What, Why, How” to structure your storytelling. 4️⃣ Practice, Share, Get Feedback 🔹Recreate visualizations from reports and dashboards you admire. Join communities like #DataVizChallenge, or share your work on LinkedIn. 🔹Get feedback and iterate your first draft is never your best! 5️⃣ Stay Inspired & Keep Learning 🔹Read books like Storytelling with Data and The Truthful Art. 🔹Explore real-world dashboards and case studies to see how pros do it. Data visualization is both an art and a science. The more you practice, the more intuitive it becomes. I’d love to hear what’s your biggest challenge in mastering data visualization? Let’s discuss in the comments! 🚀 #DataVisualization #DataStorytelling #BusinessIntelligence #Analytics #LearnWithMe #CareerGrowth #StorytellingWithData #DashboardDesign #PowerBI #Tableau #Python #DataDriven

Want to create impactful dashboards? Here’s what you need to keep in mind!  1. 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗣𝘂𝗿𝗽𝗼𝘀𝗲: Start with a clear objective. What questions should your dashboard answer? Align it with your business stakeholder's goals to ensure relevance and impact.       2. 𝗞𝗻𝗼𝘄 𝗬𝗼𝘂𝗿 𝗔𝘂𝗱𝗶𝗲𝗻𝗰𝗲: Tailor your dashboard to the needs of your end-users. Are they executives looking for high-level insights or operational managers needing detailed data?       3. 𝗞𝗲𝗲𝗽 𝗜𝘁 𝗦𝗶𝗺𝗽𝗹𝗲: Avoid overloading them. Focus on key metrics and visualizations that provide the most value. Simplicity will increase their clarity and usability.       4. 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗩𝗶𝘀𝘂𝗮𝗹𝘀: Use the appropriate chart types for your data like bar charts for comparisons, and line charts for trends. The right visuals make your data intuitive and engaging.       5. 𝗦𝗵𝗼𝘄 𝗞𝗣𝗜𝘀 𝗖𝗼𝗿𝗿𝗲𝗰𝘁𝗹𝘆: Group related KPIs next to each other. Be aware of if they need to show a development over time or just the latest status. Always include indicators for what is a good or problematic value. Be transparent about units. Colors help, but don't go too crazy on them.       6. 𝗘𝗻𝘀𝘂𝗿𝗲 𝗗𝗮𝘁𝗮 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆: Double-check your data sources and calculations. Inaccurate data undermines trust and can lead to poor decisions. Validate everything you use.       7. 𝗗𝗲𝘀𝗶𝗴𝗻 𝗳𝗼𝗿 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗶𝘁𝘆: Make your dashboard interactive. Allow users to drill down into details, filter data, and explore different views. Interactivity enhances user engagement and insight discovery.       8. 𝗧𝗲𝘀𝘁 𝗮𝗻𝗱 𝗜𝘁𝗲𝗿𝗮𝘁𝗲: Gather feedback from your users and iterate. Continuous improvement ensures your dashboard remains relevant and useful over time.       9. 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝘀𝘁𝗼𝗿𝘆𝘁𝗲𝗹𝗹𝗶𝗻𝗴: A great dashboard doesn’t just present data but it tells a compelling story that enables action.      10. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝘁𝗵𝗲 𝗡𝗲𝗲𝗱: Check if the dashboard should be created at all. Building it might not be the best course of action if it's only needed for a single time. By keeping these tips in mind, you’ll create dashboards that not only look great but also deliver real business value. How do you balance simplicity and detail in your dashboards? ---------------- ♻️ Share if you find this post useful ➕ Follow for more daily insights on how to grow your career in the data field #dataanalytics #datascience #dashboards #datavisualization #careergrowth

Rule II of Effective Dataviz: Clear Meaning A strong data visualization doesn’t just present numbers — it tells a story. The key is clarity. Even if your analysis is rock solid, a poorly designed chart can leave your audience confused, forcing them to guess the insight instead of getting it. Here’s how to use common chart elements to ensure your dataviz communicate exactly what you intend: - Use clear titles & headlines: Your title should answer “What am I looking at?” and your headline should answer “What does this chart say?” Don’t make your audience work to figure it out. - Ditch legends, use direct labeling: Labels should be placed on the chart, not in a separate key. Make it easy for viewers to process information without extra effort. - Add annotations for context: A well-placed note can highlight key takeaways and provide essential background info. - Leverage visual cues: Use arrows, boxes, or subtle shading to direct attention — just don’t overdo it. Too many cues, and nothing stands out. The best data visualizations guide the audience effortlessly to the insight, freeing their minds up actually hear the story you're telling them. 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

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.”👇 The right chart makes insights stick. The wrong one? Confusion. 𝐇𝐞𝐫𝐞'𝐬 𝐦𝐲 𝐃𝐚𝐭𝐚 𝐒𝐭𝐨𝐫𝐲𝐭𝐞𝐥𝐥𝐢𝐧𝐠 𝐂𝐡𝐞𝐚𝐭𝐬𝐡𝐞𝐞𝐭 – which chart to use, when, and why: 𝟏. 𝐁𝐚𝐫 𝐂𝐡𝐚𝐫𝐭 ��� Compare values across categories • When: Sales by region, product performance • Why: Our brains process length differences instantly 𝟐. 𝐋𝐢𝐧𝐞 𝐂𝐡𝐚𝐫𝐭 – Show trends over time • When: Revenue growth, user adoption curves • Why: Makes patterns and changes obvious 𝟑. 𝐏𝐢𝐞 𝐂𝐡𝐚𝐫𝐭 – Display parts of a whole • When: Market share, budget allocation • Why: Works when you have 5 or fewer segments 𝟒. 𝐒𝐜𝐚𝐭𝐭𝐞𝐫 𝐏𝐥𝐨𝐭 – Find relationships between variables • When: Price vs. demand, experience vs. salary • Why: Reveals correlations and outliers 𝟓. 𝐇𝐢𝐬𝐭𝐨𝐠𝐫𝐚𝐦 – Show frequency distribution • When: Customer age ranges, response times • Why: Spots normal vs. skewed distributions 𝟔. 𝐑𝐚𝐝𝐚𝐫 𝐂𝐡𝐚𝐫𝐭 – Compare multi-dimensional data • When: Employee skills assessment, product features • Why: Shows strengths and gaps at a glance 𝟕. 𝐌𝐚𝐩 – Visualize geographic data • When: Sales by state, store locations • Why: Location patterns jump out immediately 𝟖. 𝐇𝐞𝐚𝐭𝐦𝐚𝐩 – Highlight intensity patterns • When: Website clicks, correlation matrices • Why: Color gradients reveal hot spots 𝟗. 𝐁𝐮𝐛𝐛𝐥𝐞 𝐂𝐡𝐚𝐫𝐭 – Display three variables • When: Market cap vs. growth vs. profit margin • Why: Adds a third dimension through size 𝟏𝟎. 𝐃𝐨𝐧𝐮𝐭 𝐂𝐡𝐚𝐫𝐭 – Modern take on pie charts • When: KPI progress, category breakdown • Why: Center space for key metrics 𝐏𝐫𝐨 𝐭𝐢𝐩: Match your chart to your audience's decision. Executives need trends? Line chart. Team needs to compare options? Bar chart. The right visualization = clearer insights, faster decisions, stronger impact. ♻️ Save this guide for your next presentation! 𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 16,000+ readers here → https://lnkd.in/dUfe4Ac6

Before you open Power BI / Tableau… understand the data first 📊 Creating a dashboard without understanding data is like decorating a house without a foundation. Here’s how to truly understand data before building a dashboard: 1️⃣ Know the goal Ask: What decision should this dashboard help make? (No goal = useless visuals) 2️⃣ Understand the source • Where is the data coming from? • Is it manual, automated, or merged from multiple systems? 3️⃣ Identify key metrics (KPIs) Focus on what actually matters — not everything available. 4️⃣ Check data quality Look for missing values, duplicates, wrong formats, and outliers. 5️⃣ Understand relationships Know how tables connect (dates, IDs, categories). 6️⃣ Sketch before you build ✏️ Roughly plan visuals on paper — it saves hours later. (like below image) 📌 Remember: A beautiful dashboard means nothing if it answers the wrong questions