Tips for Simplifying Data Consumption with Dashboards

From 5 Pages of a Dashboard to 1: Simplifying Insights Without Losing Depth Most dashboards tell a story in pages. Mine tells it in one. When I designed this one-page dynamic Power BI dashboard, the goal was simple: Make data interaction intuitive, fast, and insightful. So instead of switching between five different pages for Sales, Profit, Profit Margin, Discounts, and Quantity, I created a single, fully interactive dashboard. Here is how it works: - Each KPI card isn't just a number. It's a button. - When you click on a metric, the entire dashboard transforms to show detailed visuals and information related to that specific metric. No page reloads. No clutter. Just pure insights in one glance. What it took to build it: - Used the Button Slicer for the KPIs. - Used the New Card visual to add YoY Metrics. - Created a Field parameter with all the KPI metrics. - DAX measures to keep metrics accurate and flexible. - A clean, consistent color theme to enhance readability. - A focus on user experience, not just on data visualization. The result? - A dashboard that saves time, reduces complexity, and keeps decision-makers focused on what truly matters, the story behind the data. 👉🏽 Check the short clip attached to see the functionality. If you have ever designed dashboards, you know how challenging it is to make simplicity powerful. P.S. Have you tried turning a multiple-page dashboard into a single dynamic view before? I would love to hear how you approached it.

✈️ 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

The biggest lesson I have learned from years of working with data leaders - more dashboards do not create better decisions. In fact, the opposite is often true. I have seen executives paralyzed in rooms filled with a hundred metrics, each dashboard offering a different slice of the truth. The more dashboards we build, the harder it becomes to know where to look or what to do. What truly matters is not the quantity of dashboards, but the clarity of the narrative. --- One clear signal is worth more than fifty colorful charts --- The best data leaders I know act not as builders of endless dashboards, but as ruthless simplifiers. They kill most dashboards, protect a few, and translate those into a story that drives action. This is not only intuition. Research confirms it. Studies in decision science show that simplified dashboards consistently lead to faster and more accurate decision making than complex ones (for example, https://lnkd.in/dFbkSJjg).

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 reduced a Power BI dashboard load time from 45 seconds to 3. Not by buying better hardware. Not by rewriting every DAX formula. But by fixing how I built the model. Most people try to speed up dashboards at the visual layer. But the real slowdown usually hides in the data model. Here’s what worked for me 👇 ✅ 1. Removed unnecessary columns and tables If a field wasn’t used in visuals or relationships, it was gone. Smaller models run faster - every column adds weight. ✅ 2. Disabled auto date/time This tiny setting adds hidden overhead. Turn it off - especially with large date columns. ✅ 3. Aggregated data before import I summarized data in SQL and Power Query first. The row count dropped by 80%. Power BI isn’t meant to store raw transactions - it’s meant to analyze. ✅ 4. Replaced calculated columns with measures Calculated columns sit in memory. Measures calculate on demand. Same output - huge performance difference. ✅ 5. Optimized visuals Fewer slicers. Simpler visuals. Cards instead of massive tables. Cleaner design - faster queries. Result? From 45 seconds down to 3. Stakeholders noticed immediately. No more “is this dashboard broken?” messages. Speed builds trust. A slow dashboard feels like bad data - even when it’s not. Have you ever optimized a dashboard that suddenly became everyone’s favorite? What was your biggest Power BI performance win? #powerbi #dataanalytics #dax #businessintelligence #datamodeling #datavisualization

Stop asking executives what they want in their dashboards. It's the fastest way to build something they'll never use. Here's what actually works for executive Power BI adoption: 1. Start With Decisions, Not Designs Wrong question: "What reports do you want?" Right question: "Which decisions need better data?" Focus on enabling better decisions, not prettier charts. 2. Build Trust Through Small Wins Perfect dashboards mean nothing if no one believes the numbers. What we've seen work well: • Week 1: Simple table visual (verify the numbers) • Week 1-2: Basic automation (show consistency) • Week 2-3: Added insights (demonstrate value) • Week 4+: New features (expand impact) Consistency builds confidence more than complexity. 3. Design for Quick Consumption Most-used reports follow these rules: • Readable in 90 seconds • Key metrics front and center • Clear visual flow and storytelling • Works on mobile If it takes too long to understand, it won't get used. 4. Start Small, Grow Smart Our most successful dashboards usually start with: • 3-5 must-have metrics • 1-2 clear visualizations • Daily refreshes • No complex features We evolved based on actual usage, not assumed needs. Success came when we stopped thinking like technical experts and started thinking like executive assistants. What's worked in your experience with executive dashboards? — ♻️ Repost if your network needs to see this, and follow Austin Levine for more.

A good dashboard is not a collection of charts. It’s a sequence. The order matters just as much as the visuals themselves. When everything is shown at once, nothing feels important. A clear dashboard guides the viewer step by step, from context to insight to action. A simple way to structure your dashboard: - Start with the big picture metric at the top. This sets context immediately. - Follow with supporting trends or comparisons that explain the number. - Place details and breakdowns last, for anyone who wants to dig deeper. - Keep the visual weight strongest at the top and lighter as you move down. - Make sure each chart answers a natural follow-up to the one before it. When the flow makes sense, people don’t need instructions. They scroll, scan, and understand. Strong dashboards feel effortless because the thinking already happened in the design.

📌 7 Tips to Build Effective Dashboards I've built over 20 BI dashboards in the last 6 months. I can tell you — creating a dashboard that truly drives value goes far beyond just selecting metrics. 👉 If you're a data analyst, manager, or business owner looking to leverage data analytics, these 7 actionable tips will help you build dashboards that generate real business value. 1️⃣ Define One Clear Purpose Dashboards can become cluttered when trying to answer multiple questions at once. Before you start, write a single sentence that defines the main purpose of the dashboard. For example: ⤷ “Display monthly sales performance across regions to help identify growth areas.” Use this sentence as your guide to determine which metrics and visuals to include. 2️⃣ Start with Key Metrics Only Users often feel overwhelmed when faced with too many metrics at once. Identify the 3-5 metrics that are most relevant to the dashboard’s purpose. For example: ⤷ In a sales dashboard, your top KPIs might be revenue, profit margin, and customer acquisition rate. 3️⃣ Use Color to Guide, Not Decorate Using a limited color palette helps guide users’ attention naturally. Choose a primary color to highlight important information, such as high-performance metrics or alerts. Use a secondary, neutral color for less critical data. 4️⃣ Optimize Layout for Scanning People naturally scan from left to right and top to bottom. Placing high-priority information in the upper left corner and arranging other elements in a natural flow helps users process information efficiently. ⤷ Avoid breaking this flow with distracting elements or unrelated visuals. 5️⃣ Add Filters for Self-Service Analysis Filters help users to customize views based on what they want to analyze without compromising the dashboard’s clarity. Make sure to include intuitive filters, such as date ranges, regions, or product categories, depending on the data’s nature. Keep these filter options concise and avoid overloading users with too many choices. 6️⃣ Use Trend Indicators KPIs are most useful when viewed in context. Trend indicators, like arrows or icons, show performance direction, which helps users quickly gauge whether metrics are improving or declining. For example: ⤷ You can add trend indicators, like green arrows for growth and red arrows for declines, alongside key metrics. 7️⃣ Test and Gather Feedback Early Getting feedback during the design process helps identify usability issues that may not be obvious. Testing with real users ensures that the dashboard meets their needs and is intuitive. Ask specific questions like: ⤷ “Can you find the sales trend?” ⤷ “Does the layout make sense to you?” Use their feedback to adjust elements that may be unclear. 👉 What other elements do you consider crucial in dashboard design? Share your thoughts in the comments! #DataAnalytics #DataVisualization #BusinessIntelligence

I always recommend a simple playbook for building and refining dashboards: 1. What's happening 2. Why is it happening 3. How do we fix it 4. Fix the problem, a bunch of times 5. Automate the problem away 6. Delete the dashboard Why not just skip to step 5 (automating stuff)? Because you really need to get step 1 correct, otherwise steps 2-5 will be a waste. Let's drill down into each step: ___ 1. What's happening Most dashboards start with a bar chart and a table. A bar chart of a key metric over time, and a table below it showing the raw data (to double click into stuff). The goal of this step is to identify either an output metric (like revenue, sign ups, etc) or an input metric (emails sent, candidates reached out to) and watch it move. 2. Why is it happening Don't skip to step 2 too early. First make sure that looking at step 1 (what's happening, is actually worth double clicking into). For some KPIs, all you need is a bar chart and a table. But when you need to understand the why, I tend to start with a drill down into a row of the table from row 1. This could be a single customer view, an employee view, etc. Get more details (tables, charts, summaries) and make them available to users to try and figure out why things are happening. Don't try and skip to a solution. Just throw a bunch of raw data into one place. 3. How do we fix it Over time, you'll add data points in step 2 and remove them. The layout of the dashboard will change and evolve. This is because you're iterating towards a clear path to fix the problem in a repeatable way. The goal of this step is to find a model that flows naturally and works in a repeatable way to fix the problem. 4. Fix the problem, a bunch of times Now that you have a working approach, start using the dashboard to solve the problem. Use it again. And again. Make tweaks any time the solution is not perfect. Add toggles, optimize the layout etc. Make sure it flows, and works for edge cases. 5. Automate the problem away Now you know you're solving a real problem, you found the main data points to identify and address the issue, you've created a step-by-step workflow to resolve the issue, and you've battle tested the solution. At this point, start figuring out if there's a way to automate the solution. It might involve engineering effort. It might involve an automation tool or RPA solution. But just imagine. Once you automate the solution, you can finally... 6. Delete the dashboard This is always the best part :) If you find a solution to the problem, it's time to move onto the next problem. ___ Everything in the business doesn't warrant all of these steps. I've built dashboards that get to step 1, we build a bar chart and a table, use it to measure progress for a few months, and delete the whole thing when our priorities change. Priorities always change. Make sure you're only going deep on the problems that are absolutely critical to your business RIGHT NOW.

Dashboards should be designed for action, not data. Most dashboards contain plenty of data. Dozens of metrics and pretty charts. We've been taught that data drives action, but in practice, it rarely does. As you build your dashboards & and reports, consider the question: What is the user's "next best action"? Then, build solutions to prompt (or enable) that action. Some examples of "next best action": 1.) More Data Sometimes, the user will have more questions. That's ok! We build in self-service filters, segments, and drill-downs to dive in deeper. Self-service > fewer questions for the data team > faster time to action. 2.) Related Data Most businesses will have dozens of reports, often fragmented and disjointed. We can build links to bridge between the reports. Additionally, those links can be dynamic to carry through important filters (date ranges, segments applied) and help users keep their contextual flow. Less time hunting for reports > faster action. 3.) Sharing the data Once users find interesting data, they want to save it or send it to a coworker or client. Enable sharing via email, slack, raw export, etc. Sharing > More distribution > more action. 4.) Actions in another platform (Shopify, Meta, Salesforce, etc) Based on the data, users will need to make a change in another tool. Take someone in merchandising. They see product reports showing that certain products have low conversion rates, likely due to dwindling inventory levels. We can build a link in the dashboard that takes them DIRECTLY to the Shopify admin portal to the product setup and re-merchandise their collection. With one click, they've gone from data > to action. Fewer clicks > faster action. 5.) Alerts Users may see a number and wish they knew about it sooner. For this we setup alerts (email, slack, sms, webhook, etc.) Faster alerts > faster action. Our goal is to transform data-heavy dashboards into tools for action. Consider: - Can we make them more self-service? - Can users set up alerts? - Can they export and share the data easily? - Can we link tools and reports together to avoid context switching? - Can we automate the data to drive action? Are there any tricks you're using to make your dashboards more actionable? #businessintelligence #looker #ecommerceanalytics #measure