Learning Analytics Dashboards

Good dashboards show numbers. Great dashboards show behavior. This one does both. Let’s break it down. 1. Clear highlight of the student. When a name is attached, interpretation becomes more thoughtful. 2. Stacked bars show contribution, not chaos. Each day combines Maths, Chemistry, and Physics into one cohesive stack. Instead of separating subjects into different visuals, this layout shows: -Total daily effort -Subject balance -Variations across the week 3. Color mapping stays disciplined. Repeated in the bars and the table below. No relearning required. That’s cognitive efficiency. 4. Clearly labelled rate shown at the top. 5. Rank supports the story- it doesn’t dominate it. Rank #1 is clearly visible, but the Correct Rate still anchors the page. The message stays focused on learning, not just competition. 6. Totals are placed where decisions happen. Daily totals (930, 825, 855…) sit right above each bar. You don’t calculate. You don’t estimate. The insight is already surfaced. 7. The empty day bar is intentional. Some days have no bar. Instead of forcing a zero or fabricating a minimal value, the design leaves it blank- clearly signaling no activity recorded. No confusion. No misleading dip. 8. The table transitions from pattern to precision. The chart answers: “How did they perform daily?” The table answers: “Where are they strongest and where can they grow?” Bonus: The layout follows a narrative arc. Top → Overall standing Middle → Weekly rhythm Bottom → Subject-level precision It reads like a structured conversation, not a random arrangement of visuals. Love this? Follow hashtag #TheVisualBreakdown and hit the bell so you don’t miss the next one.

Analytics dashboards/reports should tell a story. Instead, most of them are just glorified Excel spreadsheets. You should be able to look at a dashboard and understand the key information within seconds. Charts and graphs should make the insights obvious without doing math. Everything on the dashboard should be actionable. Not just "interesting to know." You should be able to identify issues and know what to fix. Most dashboards show too many metrics, just for the sake of "seeing the numbers". You had 13,572 visits? Cool. Is that good? Bad? More than last year? WHY are they up? More doctor days? New marketing campaign? Added a chair? Great, put another card next to visits that shows doctor days (YoY - same period last year) with conditional formatted colored arrows and a delta. Without context, that number is just taking up space. If your goal is to grow new patients?  → Track new patients who have vs. haven't scheduled their next appointment.  → Track phone answer rates to make sure calls aren't being missed.  → Use a voice AI-analytics tool to tell you if the calls that ARE being answered are even being handled well. → Track lag time for scheduling—are new patients getting in quickly or waiting weeks? Show metrics that tie to your actual business goals. Dashboards must be used to be helpful. If someone non-technical can't get what they need in 30 seconds, you've lost them. The problem? Data/FP&A teams say yes to everything. Someone asks for 35 visuals, uses 2, and the rest is clutter. WHY are we showing this metric and what are we trying to solve? My favorite question when looking at any dashboard metric: "Okay, what about it?" If you can't answer that—if the number doesn't lead to action—it shouldn't be there. Build dashboards that tell stories, not ones that just display numbers. #DataAnalytics #Dashboards #DSO #BusinessIntelligence #DataStorytelling

👉🏾 One underrated dashboarding skill: Planning for future business changes AND baking them into your logic. Here is an example from my work experience: Recently, I built a Tableau dashboard for district testing scores. While the data was “today,” the design had to survive tomorrow. I included the following in my logic: ✅ Future Academic Years: I created parameters that already include upcoming years and wired them into filters and calcs. When the calendar flips, the dashboard doesn’t break; no rebuild, just select the year. ✅ School Growth (K–4 → K–5): One elementary school is adding grade 5 next year, so I modeled grades dynamically. The logic anticipates the new grade and shows it the moment data lands ✅ District Boundary Redraws: I built alternate groups to reflect future district lines. A simple toggle lets stakeholders view “current” vs “proposed” rollups without duplicating worksheets. ✅ New Reading Standards: I added a placeholder field and mapping for the incoming reading standard name now, so when the column shows up in the feed, the calculations and tooltips already know what to do The goal isn’t just a pretty viz; it’s resilience. If your dashboards crumble every time the business shifts, you’re not delivering a product you are delivering future rework! Interview tip: Be sure when explaining projects you highlight how you future-proof (this will set you apart from other candidates). Include the following points: The Change You Anticipated (e.g., new grade, new standard) The Design ChoiceYou Made (parameter, group, calc, schema placeholder) The Future Payoff (no rebuilds, consistent metrics, faster adoption) Your turn: What’s a future-proofing trick you use in dashboarding or analysis? Comment Below 👇🏾 #learningwithjelly #dataanalytics #tableau #dashboarddesign #educationdata #dataviz #analyticsengineering #stakeholdermanagement #productthinking #bi #dataops

You Have Dashboards… But Are They Helping You Make Decisions? I hear this from online retailers all the time: "We have a ton of automated dashboards. I understand most of the data they include, but I still struggle to figure out how to actually use that data to make decisions." If this sounds familiar, you don’t have a data problem—you have a decision problem. Why This Happens 🚫 Your dashboards are descriptive, not prescriptive – They tell you what happened but not what to do next. 🚫 Too many metrics, not enough direction – You have tons of KPIs, but no clear prioritization. 🚫 Data isn't tied to business goals – You see sales, traffic, conversion rates—but what does that mean for your next move? How to Fix It ✅ 1. Start with the Decision, Not the Data Instead of asking, “What does my dashboard say?” ask: 👉 “What decision am I trying to make?” For example: Should I increase my ad budget next month? Should I order more inventory before the holidays? Which marketing channel should I scale? ✅ 2. Align Dashboards to Key Business Outcomes If you’re looking at 30+ metrics without knowing why, you’re drowning in data. Instead, structure your dashboard around actionable questions like: ➡️ “What’s my expected revenue next month based on current trends?” ➡️ “Which customer segment is most profitable over time?” ➡️ “Where am I losing the most customers in the buying process?” ✅ 3. Use Predictive & Prescriptive Analytics Descriptive dashboards show what happened. But real decision-making power comes from: 📈 Predictive analytics – Forecasting future demand based on past trends. 💡 Prescriptive analytics – Suggesting the best course of action based on the data. For example, instead of just showing last month’s conversion rate, your dashboard could: 📊 Predict next month’s rate based on seasonality & trends. 🔧 Recommend actions—like adjusting ad spend or optimizing product pages—to improve it. The Bottom Line If your dashboards aren’t guiding decisions, they’re just fancy reports. So next time you’re looking at your dashboards, ask yourself: 👉 “What decision am I trying to make? And is my data helping me make it?” Are you struggling to turn data into decisions? Let’s discuss in the comments! 👇 #Ecommerce #DataDriven #RetailAnalytics #BusinessGrowth #DecisionMaking

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

📌 The Foundation of BI Success (3 Dashboards Every Company Needs) Today’s a good day for a quick reminder: Different roles = Different priorities = Different dashboards. One of the biggest reasons BI dashboards fail is lack of focus. We try to make one dashboard do everything. We blend strategic KPIs with real-time operations. We throw in filters for analysts next to metrics for executives. We hope it will work for every team at once. But the result is confusion, not clarity. And confusion kills adoption. The truth is, every business needs three distinct dashboard types, each serving its own purpose: 1) Operational Dashboards They're built for day-to-day monitoring. Example: A logistics dashboard tracking daily deliveries, delays, and fulfillment rates in real time. 2) Analytical Dashboards They're designed for deep exploration and insights. Example: A cohort retention dashboard showing how customer behavior evolves over weeks or months. 3) Executive Dashboards They're focused on strategy and oversight. Example: A financial performance dashboard highlighting revenue, margins, and growth vs. targets. This framework breaks down the 3 types you should focus on and why building the wrong one is a guaranteed path to low adoption. 📥 Save this and share with your team. It might be the clarity you need before your next dashboard project.

🚀 Power BI Ultimate Cheat Sheet Great dashboards are not about visuals alone. They come from clean data, strong models, and the right calculations. This visual cheat sheet breaks down how real world Power BI solutions are built, from raw data to interactive dashboards. 👉 What this cheat sheet covers - End to end Power BI workflow from Desktop to Service - Power Query for ETL and data shaping - Query folding and why it matters for performance - Star schema design using fact and dimension tables - Relationships, cardinality, and filter flow - Difference between calculated columns and measures - Core DAX concepts with practical examples - Context and how CALCULATE actually works - Time intelligence like YTD and period comparisons - Choosing the right visuals for comparison and trends - Slicers, drill down, and report interactivity - Power BI Service concepts like datasets, reports, and dashboards - Row level security and data governance - Best practices for sharing and performance optimization This is a practical reference for Data Analysts, Data Scientists, and anyone building dashboards for business decision making. ➕ Follow Shyam Sundar D. for practical learning on Data Science, AI, ML, and Agentic AI 📩 Save this post for future reference ♻ Repost to help others learn and grow in AI #PowerBI #DataAnalytics #DataScientist #MachineLearning #ML #DeepLearning #AI #ArtificialIntelligence #MLOps #AgenticAI #AIAgents #BusinessIntelligence #TechLearning