How to Format Dashboards for Data Visualization

📌 Most Dashboards Fail Because of Bad UX Here’s the hard truth: You can have the cleanest data and the most advanced models… But if your dashboard is confusing, cluttered, or hard to navigate? Nobody will use it. BI isn’t just about data. It’s about experience. Dashboards are in fact UX products and should be treated that way. Great dashboards don’t just “show data.” They guide attention. Simplify decisions. Reduce friction. And just like any great product, they follow strong UX principles: → Clear layout → Logical flow → Minimal cognitive load → Built for the user, not the developer Let’s break down the 3 dashboard principles that make this possible 👇 1️⃣ 𝐃𝐞𝐬𝐢𝐠𝐧 𝐖𝐢𝐭𝐡 𝐭𝐡𝐞 𝐄𝐧𝐝 𝐔𝐬𝐞𝐫 𝐢𝐧 𝐌𝐢𝐧𝐝 This is where most dashboards go wrong. They’re built from a technical perspective and not a business one. Before touching a single chart, ask: → Who is this dashboard for? → What do they care about? → What action do they need to take from it? → What single question should this dashboard answer? If a dashboard tries to do everything for everyone, it ends up doing nothing for anyone. Treat your dashboard like a product. Build it around one user persona and one decision-making flow. 2️⃣ 𝐆𝐮𝐢𝐝𝐞 𝐭𝐡𝐞 𝐄𝐲𝐞 𝐰𝐢𝐭𝐡 𝐚 𝐂𝐥𝐞𝐚𝐫 𝐋𝐚𝐲𝐨𝐮𝐭 A great dashboard feels effortless to use. You don’t need to explain how to read it because it guides the user by design. Here’s how to do it: 1) Follow a natural reading pattern (top-left to bottom-right) 2) Use consistent spacing, alignment, and visual hierarchy 3) Group related charts and KPIs together 4) Avoid visual noise (limit to 5–7 key visuals per view) Think of your dashboard like a story It should unfold logically and lead the user to an insight without them having to look for it. 3️⃣ 𝐔𝐬𝐞 𝐭𝐡𝐞 𝐑𝐢𝐠𝐡𝐭 𝐕𝐢𝐬𝐮𝐚𝐥 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐉𝐨𝐛 Just because you can use a radar chart or sunburst doesn't mean you should. The best dashboards use simple, familiar visuals that communicate clearly. Here’s a cheat sheet I use: ⤷ To show progress or results → Use Scorecards or KPIs ⤷ To show trends over time → Line Charts or Area Charts ⤷ To compare parts of a whole → Pie Charts or Bar Charts ⤷ To analyze distributions → Histograms or Bell Curves ⤷ To show multivariate complexity → Heatmaps, Bubble Charts, or Pivot Tables Here what you need to remember is prioritizing clarity over creativity. Your dashboard isn’t a dribble a piece of art. It’s a decision tool. The bottom line is: Dashboards aren’t “data displays.” They’re interfaces for decision-making. And just like a product interface, design is everything. ☑ Good UX = Faster insights ☑ Good flow = Higher adoption ☑ Good visuals = Better decisions Build with purpose. Structure with clarity. Design for people. That’s how Business Intelligence becomes actual business impact. #DataStrategy #BusinessIntelligence #DataAnalytics

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

🍱 How To Design Effective Dashboard UX (+ Figma Kits). With practical techniques to drive accurate decisions with the right data. 🤔 Business decisions need reliable insights to support them. ✅ Good dashboards deliver relevant and unbiased insights. ✅ They require clean, well-organized, well-formatted data. ✅ Often packed in a tight grid, with little whitespace (if any). 🚫 Scrolling is inefficient in dashboards: makes comparing hard. ✅ Start with the audience and decisions they need to make. ✅ Study where, when and how the dashboard will be used. ✅ Study what metrics/data would support user’s decisions. ✅ Explore how to aggregate, organize and filter this data. ✅ More data → more filters/views, less data → single values. 🚫 Simpler ≠ better: match user expertise when choosing charts. ✅ Prioritize metrics: key insights → top left, rest → bottom right. ✅ Then set layout density: open, table, grouped or schematic. ✅ Add customizable presets, layouts, views + guides, videos. ✅ Next, sketch dashboards on paper, get feedback, iterate. When designing dashboards, the most damaging thing we can do is to oversimplify a complex domain, or mislead the audience. Our data must be complete and unbiased, our insights accurate and up-to-date, and our UI must match users’ varying levels of data literacy. Dashboard value is measured by useful actions it prompts. So invest most of the design time scrutinizing metrics needed to drive relevant insights. Bring data owners and developers early in the process. You will need their support to find sources, but also clean, verify, aggregate, organize and filter data. Good questions to ask: 🧭 What decisions do you want to be more informed on? (Purpose) 😤 What’s the hardest thing about these decisions? (Frustrations) 📊 Describe how you are making these decisions? (Sources) 🗃️ What data helps you make these decisions? (Metrics) 🧠 How much detail is needed for each metric? (Data literacy) 🚀 How often will you be using this dashboard? (Value) 🎲 What constraints should we know about? (Risks) And, most importantly, test dashboards repeatedly with actual users. Choose key tasks and see how successful users are. It won’t be right at first, but once you get beyond 80% success rate, your users might never leave your dashboard again. ✤ Dashboard Patterns + Figma Kits: Data Dashboards UX: https://lnkd.in/eticxU-N 👍 dYdX: https://lnkd.in/eUBScaHp 👍 Ethr: https://lnkd.in/eSTzcN7V Orange: https://lnkd.in/ewBJZcgC 👍 Semrush: https://lnkd.in/dUgWtwnu 👍 UKO: https://lnkd.in/eNFv2p_a 👍 Wireframing Kit: https://lnkd.in/esqRdDyi 👍 [continues in comments ↓]

Most teams polish visuals. Few design the thinking. That’s why dashboards often look fine — but explain nothing. Try this flow instead: 1) Structure metrics – map relationships, drivers, and shared definitions. 2) Define purpose – clarify what decisions it supports. 3) Build & format – choose charts that mirror logic. 4) Add context – if-then prompts, comparisons, slices, thresholds. 5) Maintain & evolve – track usage, prune, update. Pretty dashboards inform. Logical dashboards explain. Save this for your next redesign.

There are so many ways to build dashboards in Excel. Turning those dashboards into web apps gives users a live window into data without exposing the underlying workbook. Many professionals build dashboards in Excel to monitor KPIs, performance, or team activity. The spreadsheet layer is powerful: charts, formulas, formatting, and filters all work together to provide real-time insight. But sharing those dashboards often means emailing files, managing version chaos, or exposing data you’d rather keep hidden. Here’s how to build a focused dashboard in Excel — and how Sheetcast can help turn that dashboard into a secure web app without rework. In Excel: Build a responsive dashboard layout. ·    1. Design your workbook with a dedicated Dashboard sheet. Use formulas like =SUMIFS() or =AVERAGEIFS() to summarize key metrics from your data. ·    2. Add Excel charts (e.g., column, line, pie) linked to summary cells. Use slicers or dropdowns for filters. ·    3. Use named ranges (via Formulas > Name Manager) to create reusable metrics like SalesLastMonth or OpenTickets. ·    4. Apply conditional formatting to visually flag trends — for example, highlight cells with =B2>B1 to indicate improvement. ·    5. Add Pivot Tables and Charts to provide a more interactive and flexible view of the data. ·    6. Protect the dashboard sheet to prevent accidental edits while allowing dynamic updates. ·    7. Use =NOW() or =TODAY() to timestamp the dashboard. ·    8. Export and share snapshots as a view-only PDF and charts manually for reporting. This method works — but manual sharing and version control are ongoing burdens, and user-specific filters or permissions require duplicating the file. With Sheetcast: Turn your Excel dashboard into a live web app. Start with your existing workbook, then: ·    1. Upload your file to Sheetcast and create a Layout Container Page for your dashboard. Add the different pages/views to this as you create them. ·    2. Use Filter and Slicer Pages to allow real-time filtering of the dashboard by region, status, or timeframe. ·    3. Create List Pages and add Item Templates to cleanly present summaries of records. ·    4. Combine Solo Forms and Report Pages to create dynamic projection tools. ·    5. Transform your Pivot Tables and Charts with the Pivot Explorer Page to make them interactable from the web and choose the level of data security you’d like to apply. ·    6. Add a Tabs Container to group multiple dashboard views (e.g., Overview, Department, History) into a single interface. ·    7. Apply permissions when you share pages, so users only see the data and charts they’re authorized to view. No need to split files. ·    8. Embed the dashboard in your intranet or portal using the HTML code created for you when you share the app. The result: dashboards that stay live, secure, and personalized — all powered by Excel, without file sprawl.

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.

I draw my dashboards on paper before I touch Power BI. Sounds prehistoric, I know. But it changed everything. Last week, a startup founder showed me their "data strategy." Beautiful mockups in Figma. Gradient colors. Animated transitions. They'd spent 3 weeks perfecting the design. I asked to see their data. "Oh, we haven't connected that yet." That's when I pulled out my notebook. We sketched their dashboard in 15 minutes. No colors. No animations. Just boxes and arrows on paper. And that's when the problems appeared. That KPI they wanted front and center? The data didn't exist. The trend line they designed? Would need 6 months of history they didn't have. The real-time updates? Their source system updated once a day. By minute 20, we'd redesigned everything. Based on data they actually had. Here's what paper forces you to do: • Focus on the questions, not the aesthetics • Think about data flow before visual flow   • Spot the gaps before you've invested hours • Have honest conversations about what's possible When you draw on paper, you can't hide behind fancy visuals. You're left with the brutal truth: Does this dashboard answer the question or not? Now I start every dashboard project the same way. Coffee, notebook, pencil. Draw the ugliest version possible. Get the logic right. Then, and only then, I open Power BI. The prettiest dashboard in the world is worthless if it's showing the wrong data. But the ugliest sketch that answers the right question? That's gold. My rule: If you can't draw it on paper, you're not ready to build it. What's your pre-build ritual that saves you hours of rework? #PowerBI #DataVisualization #DashboardDesign #DataStrategy #DesignThinking

162 slides. Every single month. One dashboard eliminated them all. But the biggest lesson wasn’t about automation — it was about attention. I have built dashboards in Power BI for 3 years. Sharing the lessons that actually stuck. One of those lessons came from a real problem inside our Communications Team: → 27 airports → 6 slides per airport → 162 total → All built manually in PowerPoint It wasn’t just time-consuming. It was soul-crushing. 🔁 Every month, the same painful routine: → Export from 5 systems → Copy-paste into Excel → Build charts in PowerPoint → Repeat 162 times So I built them a Power BI dashboard. Clean. Dynamic. Fully automated. I thought I nailed it — until my stakeholder looked at it and said: "Yash, this looks impressive, but I'm walking past this screen in the hallway. Can I understand the story in 30 seconds?" 🧠 That changed everything. That’s when I learned the 30-Second Rule: 🚫 If you can’t get the story in 30 seconds, it’s not a dashboard — it’s noise. 🖼️ Sample image below shows the idea. Clutter vs clarity. Same data. Two very different outcomes. Now I follow this framework for dashboards that actually get used: ✅ One main message per screen ✅ Font hierarchy — make key insights pop ✅ Color grouping — connect related metrics ✅ Company color palette — builds familiarity ✅ White space — reduce visual stress ✅ Largest visual = core story ✅ Executive summary always in the top-left 📉 The result? → 162 slides became 1 dashboard → 40 hours of work dropped to 5 minutes → Leadership gets insights while walking to their next meeting 🚀 This post kicks off a Power BI series based on what I’ve learned building real dashboards in real teams. 💬 What’s the #1 reason your dashboards aren’t getting adopted? #PowerBI #DashboardDesign #DataVisualization #LearningInPublic #BusinessIntelligence #DataStorytelling #AnalyticsJourney #PersonalBranding

Most “𝘐𝘛 𝘥𝘢𝘴𝘩𝘣𝘰𝘢𝘳𝘥𝘴” are just prettier ticket queues. 𝗧𝗵𝗶𝘀 𝗼𝗻𝗲 𝗶𝘀 𝗯𝘂𝗶𝗹𝘁 𝗳𝗼𝗿 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀. Highlighting this RWFD Help Desk Tableau dashboard by Andreea Scintei. Here’s what it does really well: 1️⃣ 𝗘𝘅𝗲𝗰 𝘀𝗻𝗮𝗽𝘀𝗵𝗼𝘁 𝗮𝗰𝗿𝗼𝘀𝘀 𝘁𝗵𝗲 𝘁𝗼𝗽 Total tickets, % resolved, % open, avg days open, and backlog % — all with trend lines and prior-period comparisons. In 5 seconds you can answer:  • Are we drowning?  • Is it getting better or worse?  • Where do we need to jump in? 2️⃣ 𝗖𝗹𝗲𝗮𝗿 𝘀𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗯𝗲𝗹𝗼𝘄 𝘁𝗵𝗲 𝗳𝗼𝗹𝗱 The second row breaks tickets down by:  • Type (issue vs request)  • Satisfaction  • Severity Two things I like here: “Unknown” satisfaction and “Unassigned” severity are visually loud. They’re treated as red flags, not just extra categories. Leaders can immediately ask, “𝘞𝘩𝘺 𝘪𝘴 𝘵𝘩𝘪𝘴 𝘣𝘶𝘤𝘬𝘦𝘵 𝘴𝘰 𝘣𝘪𝘨, 𝘢𝘯𝘥 𝘸𝘩𝘰 𝘰𝘸𝘯𝘴 𝘧𝘪𝘹𝘪𝘯𝘨 𝘪𝘵?” 3️⃣ 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗹𝗲𝘃𝗲𝗿𝘀 𝗳𝗼𝗿 𝘁𝗵𝗲 𝘁𝗲𝗮𝗺 The bottom row digs into:  • Status (open/awaiting feedback/resolved/closed)  • Issue type (access, hardware, software, systems)  • Owner group (architecture, hardware, networking, security, software) This is where frontline managers can:  • Rebalance workload  • Prioritize high-impact issue types  • Spot chronic problem areas (e.g., access/login dominating tickets) 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 A dashboard like this doesn’t exist to “show data.” It exists so a help desk lead can log in, see the story, and make one concrete move:  • Reassign tickets from overloaded teams  • Attack the biggest driver of dissatisfaction  • Put a project around the most common issue type  • That’s the difference between a report and a control panel. 👏 Big shoutout to Andreea Scintei for a layout that’s clean, balanced, and actually drives action. #Tableau #DataVisualization #HelpDesk #Analytics #UXinData

Creating Dashboards Teams Actually Use Data visualization in healthcare performance management often creates pretty charts nobody looks at. Here's how to build dashboards that change behavior and improve outcomes. Focus on Actionable Metrics: Display information people can actually influence. Unit staffing effectiveness, patient satisfaction trends, safety incident patterns. Skip metrics that people can see but can't impact. Real-Time Updates: Weekly data updates, not monthly reports. People need to see the connection between their actions and results quickly enough to adjust their approach. Visual Clarity: Use simple graphs and clear colors. Green for meeting targets, yellow for approaching concerns, red for immediate attention needed. Avoid complex analytics that require interpretation. Accessibility Design: Make dashboards visible in common areas and accessible on mobile devices. If people have to search for the information, they won't look at it regularly. Team Ownership: Let teams help design their own dashboards. They know which metrics matter most for their daily work and how they prefer to see information displayed. The Implementation Test: If your dashboard doesn't change how people work within two weeks of implementation, it's not working. Adjust the metrics, the display, or the access points until it becomes a tool people actually use. What performance data would be most helpful if your team could see it in real-time? #PerformanceMetrics #DataVisualization #TeamDashboards #HealthcareAnalytics