Dashboard Design Principles

The biggest dashboard mistake? Saying yes too fast. I've built 50+ dashboards and learned this the hard way. Every "yes" has a hidden cost. Junior analyst: "I'll build that for you." Senior analyst: "Let's find the best solution together." Here's what saying yes too quickly costs: → Development time: 8-40 hours per dashboard → Maintenance: 2-4 hours monthly per dashboard → Dashboard sprawl: Users can't find what they need → Opportunity cost: Time not on high impact projects Senior analysts protect company resources by being strategic partners. They guide stakeholders to the right solution by: First, assessing the request: → "Is a dashboard the best solution for this?" → "Does something already exist that solves this?" → "What decision does this enable?" Then, ensuring impact: → "How does this tie to our key business KPIs?" → "Does it align with company goals?" Finally, validating feasibility: → "Is the data available and reliable?" This diagnostic approach leads to three outcomes: Sometimes the answer is building something new. Sometimes it's pointing to what already exists. Sometimes it's delivering what they actually need. Which may not be a dashboard. Our job isn't to build everything requested. It's to enable the decisions that matter. Repost this for your data team ♻️

🍱 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 ↓]

👀 This dashboard is not flashy but quite the opposite. It's clean, sleek and helps the reader find their most valuable information FAST! It's because of some small, intentional design choices that improve clarity, and add real value. 1️⃣ Dynamic slicer label - The top right clearly says “Data shown as on Jan-19 ending” – so users know this is an 'as on' dashboard, not a summary for the selected month. 2️⃣ Red dot alert 🔴 - A simple red dot next to the customer name instantly signals that they haven’t cleared all invoices. 3️⃣ Title as legend - No need for extra legends. The chart title itself is colour-coded (Billing in red, Receipts in blue), making it easy to read at a glance. 4️⃣ Descriptive table header - Instead of a generic “Invoices Table”, it says exactly what it shows: total invoices, paid count, and balance. Clear and straight to the point. 5️⃣ Subtle checkmark ✔- A clean visual cue to mark paid invoices. It’s not loud, but does the job efficiently. 6️⃣ Thin green pipe 🟩 - Placed next to the receipts column, it’s a quick indicator of incoming cash for the month, without adding any clutter. 7️⃣ White space - is the real hero. Makes everything feel breathable and readable. Helps the important things stand out without boxing or borders. These small visual cues may seem minor, but they make a big difference. 👉🏼 They reduce cognitive load. 👉🏼 They make the dashboard feel smoother. 👉🏼 And they actually help people use it better. This is real design. Not just splashing colours and shadows, but adding meaning with every element. If you’re building dashboards, this is the kind of polish that sets your work apart. #PowerBI #Excel #Dashboards

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

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.

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.

🧠 𝗗𝗲𝗮𝘁𝗵 𝗯𝘆 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱: 𝗪𝗵𝘆 𝗠𝗼𝗿𝗲 𝗩𝗶𝘀𝘂𝗮𝗹𝘀 ≠ 𝗠𝗼𝗿𝗲 𝗖𝗹𝗮𝗿𝗶𝘁𝘆 We’ve all seen dashboards that look like digital fireworks—colorful charts, heat maps, gauges, KPIs, and slicers… all squeezed into a single view. The 𝘪𝘯𝘵𝘦𝘯𝘵𝘪𝘰𝘯? To impress. The 𝘳𝘦𝘴𝘶𝘭𝘵? Confusion. 🎯 Dashboards are not art projects. They are decision-support tools. And when we try to display 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴, the audience understands 𝗻𝗼𝘁𝗵𝗶𝗻𝗴. Visual clutter creates cognitive overload—leaders spend more time deciphering the dashboard than acting on it. 🔑 𝗛𝗲𝗿𝗲’𝘀 𝗺𝘆 𝗴𝗼-𝘁𝗼 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲: 📌 “𝘖𝘯𝘦 𝘗𝘢𝘨𝘦, 𝘖𝘯𝘦 𝘗𝘶𝘳𝘱𝘰𝘴𝘦.” If your dashboard doesn't answer a specific business question or support a single decision, it's just decoration. ✅ Instead of asking “𝘞𝘩𝘢𝘵 𝘦𝘭𝘴𝘦 𝘤𝘢𝘯 𝘐 𝘢𝘥𝘥?”, ask: “What can I remove to sharpen focus?” “Which chart drives action?” “Is this helping someone decide faster, or just showing off the data?” 🧹 Simplify. Prioritize. Align visuals to the why behind the dashboard. 💬 𝙒𝙝𝙖𝙩’𝙨 𝙮𝙤𝙪𝙧 𝙗𝙞𝙜𝙜𝙚𝙨𝙩 𝙙𝙖𝙨𝙝𝙗𝙤𝙖𝙧𝙙 𝙙𝙚𝙨𝙞𝙜𝙣 𝙥𝙚𝙩 𝙥𝙚𝙚𝙫𝙚? (𝘐’𝘭𝘭 𝘨𝘰 𝘧𝘪𝘳𝘴𝘵: 𝘴𝘱𝘦𝘦𝘥𝘰𝘮𝘦𝘵𝘦𝘳𝘴 𝘧𝘰𝘳 𝘦𝘷𝘦𝘳𝘺𝘵𝘩𝘪𝘯𝘨 🤦♂️) #DataAnalytics #DashboardDesign #DataDrivenDecisionMaking #MISReporting

Claude Design is genuinely terrfifying… A year ago I would've paid thousands for an analyst to mock these up. Instead… I built these custom market research dashboards in an afternoon. And all I did was show it a screenshot of a coding interface I liked, mono typeface, dark background, terminal feel, and asked it to reproduce that style. Then I had it conduct real market research for each dashboard separately. I ended up with: (1) A cross-channel performance report covers spend, CPM, conversions, and ROAS across CTV, Display, Mobile Apps, Native, Audio, and DOOH with line-item detail per campaign. (2) A channel mesh topology view shows how signals, identity graphs, and suppression lists flow between channels in a cross-channel setup. (3) A workflow monitor tracks deployment and release status across the stack. All three used the same terminal-style visual language, populated with real-looking data from the research it pulled. A few things stood out. • The research was ACTUALLY specific. It pulled REAL numbers and competitive detail I could use to optimize our client’s performance • The visual consistency held across every dashboard. I gave it one style brief, threw three different topics at it, and got the same design language across all of them. • The edit mode is incredible Generation gets you 80% of the way there, and being able to tweak specific elements after the fact is what turns it from a cool demo into a real deliverable. The thing I keep thinking about is what this does to the cost of custom internal tools. Dashboards and research docs used to be a budget item. You'd either pay a vendor for a generic template, or you'd pay a designer and an analyst to build something bespoke. That whole layer of work can now be collapsed into a SINGLE session with a chat interface. If it would be useful, I can record a Loom walking through exactly how I set these up: • prompting • the visual reference • my edit mode workflow All of it. That way you can build your own dashboards for your ad accounts. Comment “DASHBOARD” and I’ll do it.

Back when I was just starting out with Power BI, my dashboards were… well, busy. Every insight had a slicer, every page had ten filters, and I thought more controls meant more power for the end user. Until one day, a colleague glanced at my report and said, “Where do I even start?” That was my lightbulb moment. I realized that too many slicers don’t make a dashboard better... they make it overwhelming. That’s when I discovered the magic of the Filter Pane: keeping things tidy, guiding the user, and reducing cognitive overload. I’ve put all my learnings into my latest blog: #PowerBIThisOrThat – Slicer vs Filter Pane in Power BI. If you’ve ever struggled with slicer chaos or wondered how to make your dashboards user-friendly, this one’s for you. Hint: sometimes less really is more. Read here: https://lnkd.in/e74AqDp5

📌 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