Business Intelligence Dashboard Design

🍱 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 ↓]

📌 Dashboard Design Principles 101 (What Every Company Needs to Know) Dashboards are one of the most powerful tools we have to make data useful. When they are built right, they give leaders and teams: ⤷ A clear view of performance ⤷ Highlight where action is needed ⤷ And ultimately enable better decisions. But here’s the reality: most dashboards fail to deliver on this promise. And it’s not because the data is wrong or the tool is limited. They fail because of poor design choices that make them confusing, overwhelming, or simply irrelevant to the people who are supposed to use them. If you want to build dashboards that actually drive adoption and influence decisions, there are three design principles you need to follow 1️⃣ 𝐃𝐨 𝐘𝐨𝐮𝐫 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐁𝐞𝐟𝐨𝐫𝐞 𝐘𝐨𝐮 𝐃𝐞𝐬𝐢𝐠𝐧 Every dashboard starts with a purpose. Without it, you’re just arranging charts on a canvas. Ask yourself simple but critical questions: → Who exactly will use this dashboard? → What business decisions should it support? → Which insights and KPIs are truly essential? This is where most projects go wrong. Instead of focusing on the end user, dashboards get built around the data that happens to be "available" or the KPIs that someone thought might look good. The result? A nice-looking report that nobody actually uses. A strong dashboard is user-centric and decision-driven. It exists to answer questions and reduce uncertainty. Not to display every data point you’ve collected (a very common mistake). 2️⃣ 𝐆𝐮𝐢𝐝𝐞 𝐭𝐡𝐞 𝐔𝐬𝐞𝐫 𝐰𝐢𝐭𝐡 𝐚 𝐂𝐥𝐞𝐚𝐫 𝐅𝐥𝐨𝐰 Good design is invisible. A user should glance at the dashboard and instantly know where to focus. That means creating a logical flow of information that follows natural reading patterns (top left to bottom right) and keep the number of visuals under control (5 to 7 is usually the sweet spot) The goal is not to impress people with how much data you can show. It’s to guide them toward the insight that matters most. If you want to go deeper, I highly recommend exploring Nicholas Lea-Trengrouse’s work on UX/UI principles for dashboard design. 3️⃣ 𝐂𝐡𝐨𝐨𝐬𝐞 𝐭𝐡𝐞 𝐑𝐢𝐠𝐡𝐭 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐒𝐭𝐨𝐫𝐲 Data visualization is not decoration. It’s communication. The chart type you choose can completely change how your data is interpreted. The wrong choice creates confusion. The right choice makes the insight obvious, even for someone seeing it for the first time. Always think in terms of clarity: does this chart highlight the story I want the data to tell? At the end of the day, dashboards are about clarity, usability, and decision-making. If a dashboard doesn’t tell a story, guide the user, and present insights in a way that is easy to interpret, it will fail. No matter how advanced your tool or how clean your data. 📥 Save this framework. Share it with your team. And keep it in mind before your next build. #BusinessIntelligence #DashboardDesign

✈️ Most dashboards are designed like airplane cockpits…when what you really need is a Control Tower. Too many BI dashboards try to show everything at once: KPIs, segments, raw data — all mashed together. It overwhelms users and kills decision speed. Instead, think about your dashboards as a Control Tower. The top of the tower offers a clear, panoramic view. You’re scanning for major movements and disruptions. When needed, you can zoom in with instrumentation or speak directly to pilots, but that's not your default. By managing your information hierarchy in layers, you can start simple and progressively reveal complexity. Here’s how it works: 📊 L1: The Tower View – high-level KPIs, trends, and alerts. What’s happening? 🔍 L2: Segment View – explore segments and categories. Where is it happening? 🧾 L3: Transaction View – detailed records and raw data. Why is it happening? Each level is built for a specific cognitive mode. Mixing them forces your brain to multitask and that’s where insight gets lost. 🧠 Rule of thumb: Dashboards should optimize for low cognitive load at entry. Users should never have to reconcile different zoom levels simultaneously. Control Tower dashboards allow users to scan, zoom, and act without overwhelming them. By designing dashboards to reflect human cognitive modes and information hierarchy, you create tools that are not just insightful but usable. #dataviz #dashboards #BI #uxdesign #analytics #productivity

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

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

📊 Why I Always Sketch My Dashboard Before Building It Before I open Power BI, I open my mind and a blank page. One habit that has completely improved the quality of my dashboards is sketching the layout first—even before touching the data model or visuals. Here’s how I do it 👇 ✏ I start with the business questions What should the dashboard answer? Revenue? Growth? Customer behavior? Performance by location? 📐 I map the structure visually – Header (title & filters) – KPI cards (what matters most) – Trends (time-based insights) – Comparisons (plans, states, campaigns) – Details (tables for drill-down) 📊 I decide the chart types early Line charts for trends Bar charts for comparison Maps for geography Donuts for distribution This saves time and avoids random visuals. 🚫 No guesswork. No clutter. No confusion. Everything has a purpose before it’s built. ✅ Faster development ✅ Cleaner dashboards ✅ Better storytelling ✅ Executive-ready reports A good dashboard doesn’t start in Power BI. It starts with clear thinking and a simple sketch. If you’re teaching data analytics or building dashboards professionally, this step is non-negotiable. How do you plan your dashboards—straight into Power BI or sketch first? #DataAnalytics #PowerBI #DashboardDesign #DataStorytelling #BusinessIntelligence #AnalyticsTips #LearningInPublic

15 Rules to Design a Perfect Dashboard (that actually tells a story!) When I first started creating dashboards in Power BI, I thought “the more visuals, the better.” But as I gained experience, I realized a perfect dashboard isn’t about showing data; it’s about communicating insight clearly. This visual by SQLBI beautifully captures the 15 golden rules every data analyst should follow 👇 💡 Some lessons that truly changed how I design: 1️⃣ Design for a target : Every dashboard must have a purpose. Who will use it? What action should it drive? 2️⃣ Keep everything at a glance : If users need to scroll or search, it’s not a dashboard, it’s a data jungle. 3️⃣ Be consistent Fonts, colors, and spacing matter more than you think. 4️⃣ Show the context : A number alone means nothing without comparison. 5️⃣ Pick the right charts : Not every story needs a pie! 😉 A good dashboard doesn’t scream “Look at my visuals!” It gently says “Here’s what matters.” 💬 I’d love to hear from you what’s one dashboard design mistake you’ve seen that taught you a big lesson? If you’re someone who wants to learn how to build meaningful dashboards or wants personal feedback on your Power BI work 👉 You can connect with me on Topmate , I’d be happy to guide you one-on-one. https://lnkd.in/gasgBQ6k #PowerBI #DashboardDesign #DataVisualization #Analytics #DataStorytelling #SQLBI #DataAnalyst #DataScientist

Designing Effective Dashboards📈📊 (links below) A well-crafted dashboard should be intuitive, efficient, and capable of delivering insightful information. Here is a checklist to ensure you incorporate the key features of effective dashboard design: ✔ Focus on key information. Rather than displaying all available data, identify and present what is most relevant to the dashboard's objectives. ✔ Choose the right visualizations. Opt for charts, graphs, and tables that accurately convey your data in an easily digestible format. ✔ Prioritize the conveyance of insights over the mere presentation of raw data. ✔ Highlight critical data points and insights prominently. ✔ Arrange the content logically for easy navigation. Apply the principle of proximity by grouping related data visualizations close to each other. ✔ Ensure each chart communicates a single, clear message. ✔ Enable detailed exploration through drill-down functionality, allowing users to delve into more specific data by clicking or tapping on visual elements. ✔Test and Iterate 📖 Guides: ✔ What you should know before designing a dashboard (by Mimi) https://lnkd.in/diyVTWbj ✔ Data visualisation principles (by Kamila Giedrojc) https://lnkd.in/dtjZWEnH ✔ You might not need a dashboard (by Irina Wagner, PhD)  https://lnkd.in/daJ-wmaE ✔ Practical rules for better dashboard design (by Taras Bakusevych) https://lnkd.in/djS5Z8ye "Storytelling with Data" by Cole Nussbaumer Knaflic: A must-read for anyone interested in presenting data effectively. Tableau Public: Explore thousands of dashboards for inspiration and learning. https://lnkd.in/g4EXXQtP Dribbble's Data Visualization: For design inspiration and the latest trends in dashboard aesthetics. 🔨 Tools: ✔ Hope UI Admin Dashboard Kit for Figma (by Iqonic Design)  https://lnkd.in/dN45ygit ✔ SaaS B2B Dashboard template for Figma https://lnkd.in/dfnqhHmm 

Day 1 of #BusinessIntelligence Ever wondered why some dashboards make an impact while others confuse users? Here are 5 essential principles that I always follow when building dashboards: • Know Your Audience: Understand the decisions they need to make. • Prioritize KPIs: Focus on the most critical metrics. • Simplicity is Key: Clutter can distract, so aim for clarity. • Consistent Design: Maintain a consistent format, color scheme, and chart types. • Iterate and Improve: Gather feedback and continually refine your dashboard. I’ve applied these principles to a recent project where simplifying a complex dashboard led to higher user engagement and clearer insights. By understanding user needs and removing non-essential data, I turned it into an actionable tool. What’s the one principle you never skip when building dashboards? #BusinessIntelligence #DashboardDesign #DataVisualization #PowerBI #Tableau #DataAnalysis

It’s not cells vs charts. It’s not aggregation vs detail. It’s not even dashboard vs report. It’s almost always: What combination will bring the most value to the user? The best dataviz products rarely rely on a single format. They combine the right levels of detail, layouts, and entry points, depending on what people actually need to do. And yet… no one really talks about choosing the right type or mix of dashboard(s). We’ve all heard of the 4 classic categories: - Strategic - Tactical - Operational - Analytical They’re useful, they help clarify the purpose behind a dashboard. But they’re hard to distinguish. That’s where the dashboard chooser comes in. It helps you pick the right dashboard type based on the amount of information you need to show. It also helps you transition: - Migrating from a report to a dashboard? Try combinations like C3, B3, B2, B1 - Lacking detail in your dashboards? Look at A1 or B2 - Or maybe you need a mix of views, like A1 + C3, in the same product My favorites: - A1 for the amount of screen space dedicated to pure data visualization - B2 for its balance of visual insight and access to detailed data - And the C1 + A1 + C3 combo, great for drilling down and presenting views to different personas If you enjoyed this, you’ll probably like the visual newsletter I send every Tuesday. It's packed with insights on dashboard design, dataviz, and analytics. 📌 Link to join in the comments below. (No archive, so if you miss it, it’s gone.) #dataanalytics #datavisualization #businessintelligence