Data Visualization Software

𝙏𝙝𝙚 5-𝙎𝙚𝙘𝙤𝙣𝙙 𝙍𝙪𝙡𝙚 𝙤𝙛 𝘿𝙖𝙨𝙝𝙗𝙤𝙖𝙧𝙙𝙨: 𝘾𝙖𝙣 𝙔𝙤𝙪𝙧 𝘿𝙖𝙩𝙖 𝙎𝙥𝙚𝙖𝙠 𝙖𝙩 𝙖 𝙂𝙡𝙖𝙣𝙘𝙚? ⏱️ Ever opened a dashboard and felt like you were drowning in charts, numbers, and filters? Here’s the truth: if a decision maker can’t answer their 𝘮𝘰𝘴𝘵 𝘪𝘮𝘱𝘰𝘳𝘵𝘢𝘯𝘵 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯 within 5 𝘀𝗲𝗰𝗼𝗻𝗱𝘀, the dashboard has failed its purpose. Dashboards aren’t meant to be pretty wallpapers of data. 𝘛𝘩𝘦𝘺’𝘳𝘦 𝘥𝘦𝘤𝘪𝘴𝘪𝘰𝘯 𝘮𝘢𝘬𝘪𝘯𝘨 𝘵𝘰𝘰𝘭𝘴. And speed matters. 🔑 That’s where smart visualization hacks in Excel come in: • Sparklines → Instantly show trends without bulky charts • Traffic-light indicators → Turn KPIs into “stop, go, caution” signals at a glance • Slicers → Make filtering intuitive, not frustrating The real power of Business Intelligence isn’t about 𝘮𝘰𝘳𝘦 𝘥𝘢𝘵𝘢. It’s about 𝘧𝘢𝘴𝘵𝘦𝘳 𝘤𝘭𝘢𝘳𝘪𝘵𝘺. Because the quicker leaders can see the story in the numbers, the quicker they can act. So next time you build a dashboard, ask yourself: 👉 Can this answer the key business question in under 5 seconds? If yes—you’ve built intelligence. If not—it’s just decoration. 𝙒𝙝𝙖𝙩’𝙨 𝙮𝙤𝙪𝙧 𝙜𝙤-𝙩𝙤 𝙩𝙧𝙞𝙘𝙠 𝙛𝙤𝙧 𝙢𝙖𝙠𝙞𝙣𝙜 𝙙𝙖𝙨𝙝𝙗𝙤𝙖𝙧𝙙𝙨 𝙞𝙣𝙨𝙩𝙖𝙣𝙩𝙡𝙮 𝙘𝙡𝙚𝙖𝙧? 👇 #DataDrivenDecisionMaking #DataAnalytics #ExcelDashboards #MISReporting

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

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

A New Power BI Dashboard! 🚀 I'm excited to share one of my recent projects where I built an interactive dashboard using dummy data. The goal was to provide a clear, comprehensive view of a company's sales and revenue, answering some key business questions. The dashboard's main purpose is to help stakeholders quickly understand: 1. Where to focus our support and resources. By analyzing the data, we can see that our top-performing regions are Asia and North America. While these regions are strong, there's a significant opportunity to grow our market share in Europe. Similarly, the data shows that Electronics and Home Appliances are our highest-revenue product categories, making them prime candidates for continued support and marketing efforts. 2. The relationship between revenue and sales. This dashboard allows us to compare revenue directly to sales figures. Interestingly, North America shows the highest revenue at $36.8K despite selling fewer units than Asia. This suggests that customers in North America have the highest purchasing power, buying more expensive items or larger bundles. This insight is crucial for tailoring pricing strategies and product promotions to specific regional markets. This project was a great exercise in visualizing complex data to deliver actionable insights. It's a powerful reminder of how business intelligence can drive strategic decisions. What's a key question you've answered using data recently? I'd love to hear about it! Feel free to comment with any feedback or questions you have. #DataVisualization #BusinessIntelligence #Analytics #Dashboard #DataAnalyst

If you want to work like a senior Power BI developer, Start with their toolbox. Because seniors don’t just know DAX. They understand how every supporting tool multiplies their output. Think of your workflow in layers, not just visuals, but the backbone that makes a report reliable, fast, and scalable. Here’s the real breakdown: --- 1️⃣ Query & Formula Layer This is where most dashboards slow down. - 𝗗𝗔𝗫 𝗦𝘁𝘂𝗱𝗶𝗼: lets you see what your formulas are doing under the hood. You can test queries, analyze performance, and fix issues before they become nightmares. - 𝗗𝗔𝗫 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗿: turns your slow measures into efficient ones. Think of it as a “performance consultant” for your DAX. It shows you what to rewrite and 𝘄𝗵𝘆. --- 2️⃣ 𝗠𝗼𝗱𝗲𝗹𝗹𝗶𝗻𝗴 𝗟𝗮𝘆𝗲𝗿 Your model structure determines 80% of your report’s performance. - 𝗧𝗮𝗯𝘂𝗹𝗮𝗿 𝗘𝗱𝗶𝘁𝗼𝗿: helps you build a clean, reusable, scalable semantic model. You get best practices, scripting, role management, and fast edits that Power BI alone can’t match. --- 3️⃣ 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗟𝗮𝘆𝗲𝗿 Professional BI teams don’t deploy blindly. - 𝗔𝗟𝗠 𝗧𝗼𝗼𝗹𝗸𝗶𝘁: lets you compare PBIX metadata, handle version control, and deploy changes without breaking production. It’s the closest thing to “safe deployment” in the Power BI ecosystem. --- 4️⃣ 𝗗𝗲𝘀𝗶𝗴𝗻 𝗟𝗮𝘆𝗲𝗿 A good dashboard isn’t built in Power BI; it’s designed before it’s built. - 𝗘𝘅𝗰𝗮𝗹𝗶𝗱𝗿𝗮𝘄 / 𝗖𝗮𝗻𝘃𝗮 / 𝗙𝗶𝗴𝗺𝗮: help you map UI/UX, layout, colors, and flow before writing a single measure. This saves hours of rebuilding and gives stakeholders clarity early. --- Once these tools become part of your workflow, you stop “managing Power BI”… and start 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 dashboards like a senior developer. --- Tools are leverage. Leverage accelerates career growth. ✅ Image Source - sqlbi<dot>com #powerbi #dataanalyst #data #day29

Root Cause Analysis: Dive as deep as you want. I'm passionate about building BI dashboards that empower users to go beyond surface-level reporting and truly understand the 'why' behind their data. This Root Cause Analysis dashboard is designed to do just that (or I hope it does). It's all about giving users the power to dive into the details, even if it sometimes requires a bit of extra training for maximum effectiveness. Here's a peek at some of the visuals I've used in this dashboard: - 𝗗𝗲𝗰𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 𝗧𝗿𝗲𝗲: This visual lets you break down a metric one level at a time to pinpoint the factors behind performance trends. I’ve customized it by disabling the responsive feature, so users can zoom in and out with their scroll wheel—providing flexibility while requiring minimal training. Users can also choose their own dimensions to tailor the view. - 𝗙𝗶𝘀𝗵𝗯𝗼𝗻𝗲 𝗥𝗼𝗼𝘁 𝗖𝗮𝘂𝘀𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀: This diagram shows why a KPI is doing well or falling short by displaying different dimensions dynamically. I even wanted to calculate an effect percentage for each item based on value and percentage changes, showing the most positive and negative impacts. - 𝗝𝗶𝘁𝘁𝗲𝗿 𝗣𝗹𝗼𝘁: This visual helps you quickly identify outliers in your sales data. Are there specific products or regions that are significantly over- or under-performing? The Jitter Plot makes these immediately visible. Selecting a data point allows you to filter the dashboard and explore potential root causes, for example, a sudden drop in sales for a particular product in a specific region. - 𝗛𝗶𝗴𝗵𝗲𝘀𝘁/𝗟𝗼𝘄𝗲𝘀𝘁 𝗗𝗶𝗮𝗴𝗿𝗮𝗺: This visual clearly highlights the top or bottom contributor within a selected dimension, showing how much each one impacts the overall metric. - 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗦𝗲𝗰𝘁𝗶𝗼𝗻: A dedicated space to answer the most common questions, especially handy for features that might need a bit of user guidance. Check out the Power BI dashboard via the link below to interact with these features and see how root cause analysis can transform your data insights: https://lnkd.in/dMHK9UVC I’d love to hear your thoughts! #PowerBI #Dashboards #BusinessIntelligence

Power BI has levels to it: - level 1 Building reports and basic DAX Create visuals and dashboards with drag-and-drop ease. Understand data modeling basics: tables, relationships, and star schemas. Learn core DAX functions like SUM, COUNT, FILTER, and CALCULATE. Grasp context (row vs. filter) to avoid rookie mistakes. Master these, and you’ll never confuse Power BI with “just Excel on steroids.” - level 2 Advanced DAX & data modeling Write reliable DAX with variables and DIVIDE for safe calculations. Use ALL and ALLEXCEPT to manipulate filter context like a pro. Schema evolution: build flexible star schemas with bridge tables for many-to-many relationships. Normalize or denormalize based on query patterns. Optimize model size with proper data types and cardinality reduction—because bloated models kill performance. - level 3 Incremental refresh & advanced visuals Set up incremental refresh to process only new data—query RANGESTART and RANGEEND for massive datasets. Use custom visuals or Deneb (Vega-Lite) for bespoke charts. Implement drillthroughs, bookmarks, and dynamic titles for interactive storytelling. Row-level security (RLS): enforce data access by user or role. These make compliance and user-specific reporting painless. - level 4 Performance tuning & query optimization Use Performance Analyzer to spot slow visuals. Rewrite DAX to minimize iterator functions like SUMX when SUM + CALCULATE will do. Aggregate tables: pre-summarize data to speed up reports. Manage VertiPaq engine: reduce column cardinality and remove unused fields. Check Query Diagnostics to ensure DirectQuery or composite models aren’t hammering your source. A sneaky FORMAT in DAX can tank performance—keep it lean! - level 5 Ecosystem mastery & automation Query metadata via DMVs (e.g., TMSCHEMA_TABLES) for instant model insights or documentation. Automate with Power Automate: trigger refreshes, send alerts, or sync data flows. Use Power BI REST APIs to programmatically manage datasets or embed reports. Integrate with Azure Synapse, Databricks, or Fabric for end-to-end analytics. Build CI/CD pipelines with Tabular Editor and ALM Toolkit for versioned deployments. Treat your Power BI environment like a governed, scalable data platform—not a collection of random PBIX files. What else did I miss for mastering Power BI?

🚀 3D Meets Data: Exploring #powerbi Beyond the Basics! 📊 In this project, I integrated 3D visualisation with Power BI to enhance spatial data insights: 🔹 Process Overview: Created the 3D model using tools like Blender, Revit, and SketchUp. Integrated it into Power BI via direct connection or through a JSON export to map 3D elements with the data model. 🔹 Real-World Business Applications: ✅ Site Mapping: Visualising project areas with live data overlays. ✅ Construction Phases: Tracking progress across milestones. ✅ Inventory Management: Monitoring stock locations and availability in warehouses. Following the 3D theme, I also built this presentation using a screen recording from my dashboard, styled in a 3D environment. ✨ The possibilities for combining 3D and BI are endless - how would you use it? #PowerBI #3DVisualisation #DataAnalytics #BusinessIntelligence #LinkedIn #Innovation #Blender #Revit

𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗩𝗶𝘀𝘂𝗮𝗹 𝗩𝗼𝗰𝗮𝗯𝘂𝗹𝗮𝗿𝘆 I built the Power BI Visual Vocabulary to help people choose the right visual for their data. Too often, the wrong chart is used for the wrong scenario. This guide is a simple way to fix that. It’s made for the Power BI community - for anyone learning analytics or wanting a quick reference while building reports. The guide uses 𝗼𝗻𝗹𝘆 𝘁𝗵𝗲 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱, 𝗼𝘂𝘁-𝗼𝗳-𝘁𝗵𝗲-𝗯𝗼𝘅 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝘃𝗶𝘀𝘂𝗮𝗹𝘀. No hacks, no workarounds, no gimmicks - just the visuals as they are intended to be used. These are the visuals that answer 𝗮𝗯𝗼𝘂𝘁 90% 𝗼𝗳 𝗰𝗼𝗺𝗺𝗼𝗻 𝗿𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀. Visuals are grouped by analytical purpose: 𝗖𝗵𝗮𝗻𝗴𝗲 𝗢𝘃𝗲𝗿 𝗧𝗶𝗺𝗲, 𝗣𝗮𝗿𝘁-𝘁𝗼-𝗪𝗵𝗼𝗹𝗲, 𝗖𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻, 𝗥𝗮𝗻𝗸𝗶𝗻𝗴, 𝗙𝗹𝗼𝘄, and 𝗦𝗽𝗮𝘁𝗶𝗮𝗹. Each section starts with a short description of what those visuals are designed to show. Every chart includes: • 𝗪𝗵𝗮𝘁 𝗶𝘁 𝗶𝘀 - a short explanation of the chart type. • 𝗨𝘀𝗲 𝗰𝗮𝘀𝗲 - when it’s most appropriate. • 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 - things to watch for or improve. • 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗩𝗶𝘀𝘂𝗮𝗹 𝗶𝗰𝗼𝗻 - so you can quickly find it in the visualization pane. It’s a straightforward learning and reference tool to support clear, consistent report design and to make visual selection easier for everyone using Power BI. You can access the 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗩𝗶𝘀𝘂𝗮𝗹 𝗩𝗼𝗰𝗮𝗯𝘂𝗹𝗮𝗿𝘆 by selecting “𝗩𝗶𝘀𝗶𝘁 𝗺𝘆 𝗪𝗲𝗯𝘀𝗶𝘁𝗲” on my LinkedIn profile page. #PowerBI #UIUX #DataViz

➤ Before Redis: - DB Read Volume - 14.2TB/month - DB CPU Usage - 78–85% avg - Dashboard Load Time - ~2.1s ➤ After Redis: - DB Read Volume - ~2.1TB/month - DB CPU Usage - ~32% avg - Dashboard Load Time - <400ms <The Problem: Technical Detail> Inspite indexing & optimization: Apps DB started choking with ~90K reads per minute Most queries were simply repetitive, example: → Top 10 products → Weekly login stats → Team activity summary <The Real Impact:> ➤ Identical per org/user per time window ➤ Updated once every 1–5 minutes ➤ But, getting fetched 1000s of times! <What Was observed?:> - CPU spikes - Query timeouts - Storage IOPS hitting 85–90% consistently - DB cost = (RDS Read IOPS surge) Let’s Breakdown → Avg payload per dashboard query: ~150KB → Avg dashboard opens per user/ day: 5 → 1.5M dashboards/day = 225GB/ day → 30 days = ~6.7TB/month of duplicate reads → Other summary queries = ~5.5TB/month So, Total: ~12.2TB of redundant reads/month 🍃 Now, After introducing Redis caching layer (via @Cacheable in Spring Boot) ➤ Added Redis with TTL-based caching [Time to Live] @Cacheable(value = "dashboardData", key = "#userId + ':' + #dashboardId", unless = "#result == null") public Dashboard getDashboardData(String userId, String dashboardId) { return dashboardService.fetchFromDB(userId, dashboardId); } 1. First call → DB hit → result cached 2. Subsequent calls → Redis hit → no DB load 3. Reduces latency, DB cost, and improves performance under load ➤ Finally: This snippet Caches the dashboard result in Redis using userId:dashboardId as key skips DB if already cached & avoids storing nulls. Connect Sabari Balaji for Tech Insights 💡 #SpringBoot #RedisCache #JavaPerformance #SystemDesign