Building a Project Management Dashboard

My client didn’t trust their Power BI dashboard Here's how I fixed it in 5 steps: ⤵️ Problem: → They had one dashboard for everyone. → Managers in one region could see data from another, which created noise, confusion, and security concerns. → Basically, there were no limits on what employees could see, and that's problematic What I did: 1- Identified user roles and grouped them by country and team. 2- Added 𝗥𝗼𝘄-𝗟𝗲𝘃𝗲𝗹 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 (𝗥𝗟𝗦) in Power BI based on UserPrincipalName() mapping to a security table. 3- Created a central Access Matrix in the database: user email → allowed country/team codes. 4- Tested RLS with sample accounts to ensure data visibility matched permissions. 5- Documented the process so new hires can be onboarded in minutes. Results: → 1 dashboard → used by 5+ countries and multiple teams. → Zero cross-country data leaks after launch. → Load times improved by 70% because filters were applied at the model level. Sometimes the biggest value in a dashboard isn’t a new chart, it’s who sees what. RLS can turn one messy report into a scalable, secure asset. ♻️ Repost if you enjoy tips in data analytics

🚨 Still showing Customer A's data to Customer B and hiding it with dashboard filters? Stop doing that. Databricks supports Row-Level Security (RLS) through Unity Catalog. Think of it like this: Everyone can open the fridge, but you can only take your lunchbox—not your friend's, not your teacher's. RLS works the same way. Multiple users can access the same table, but each user only sees the rows they're authorized to view. 🔹 Your data stays yours. Everyone else's stays theirs. Unlike other Unity Catalog objects that rely on simple GRANT/REVOKE permissions, Row-Level Security is implemented using functions and mapped to your security model. In production, a metadata-driven approach is the most scalable and maintainable way to implement it. I've demonstrated the complete implementation in the article, along with the code used for the demo - https://lnkd.in/gAvcWFpX #rls #databricks #dataengineering #kaninipro

Power BI, Excel, SQL & Python — Where Do They Each Shine? Choosing the right tool for data work depends on what you’re trying to achieve. Here’s how these four powerful tools complement one another 👇 🟢 Power BI If you want to tell a story with data, Power BI is your best friend. It’s built for interactive dashboards, real-time reports, and sharing insights across teams. Its strong data modeling and visualization capabilities make it ideal for monitoring business performance and KPIs at a glance. 💡Best for: Building insightful dashboards, creating automated reports, and turning raw data into strategic decisions. 🔵 Excel The classic tool that almost everyone knows. Excel shines when it comes to quick analysis, ad-hoc reporting, and small-scale data management. Its formulas, pivot tables, and charts make it perfect for exploring data on the go. 💡Best for: Simple reporting, personal analytics, and performing quick calculations without setting up complex systems. 🟤 SQL Think of SQL as the language that communicates directly with your data. It’s designed for managing and querying large datasets stored in relational databases. SQL helps you extract, filter, join, and transform data efficiently — forming the foundation of many modern analytics workflows. 💡Best for: Handling structured data, database management, and preparing data before visualization. 🟡 Python Python brings the power of programming into analytics. With libraries like Pandas, NumPy, Matplotlib, and Scikit-learn, it can handle everything from complex transformations to automation and machine learning. It’s a must-have for anyone diving deep into data science or predictive modeling. 💡Best for: Advanced analytics, automation, machine learning, and building scalable data solutions. 📌 Final Thought: Each tool serves a unique purpose — and the real magic happens when they’re combined. A modern data professional often uses SQL for extraction, Python for transformation, Power BI for visualization, and Excel for quick checks and communication. #DataAnalytics #PowerBI #Excel #SQL #Python #BusinessIntelligence #MachineLearning #DataScience #AnalyticsTools

🔒 You Just Accidentally Showed Salary Data to the Entire Company – Time to Learn RLS! Building great reports is step one. Protecting sensitive data while delivering personalized insights? That's where Row-Level Security (RLS) separates amateur dashboards from enterprise-grade solutions. What Is RLS? Imagine a magic report that shows executives everything, managers their departments, and employees only their own data – all from one single dashboard. That's Row-Level Security: intelligent data restrictions at the row level based on user identity. 🎯 Why This Is Non-Negotiable: - Data Protection: Sensitive information stays with authorized eyes only - Personalization: Each user sees relevant data automatically - Efficiency: One report replaces dozens of filtered versions 💡 Real-World Transformation: 1. Sales Report Scenario: - CEO: Sees all regions, all accounts, complete picture - Regional Head: Only their region's performance data - Sales Rep: Exclusively their own accounts and metrics One report. Three completely different views. Zero manual filtering. 🛠️ Two RLS Flavors: - Static RLS: Fixed rules like "Region = East" – simple but requires manual role creation for each group - Dynamic RLS: Smart rules using DAX functions (USERNAME(), USERPRINCIPALNAME()) that automatically adapt to logged-in users – scales beautifully ⚡ The Pro Implementation: 1. Define roles and rules in Power BI Desktop 2. Assign roles to users in Power BI Service 3. Reports automatically filter based on identity 4. Test thoroughly before deployment (critical step!) 🔥 Enterprise Use Cases That Demand RLS: - Finance: Departments see only their budgets, not competitors' - HR: Employees access only their performance data - Sales: Representatives view their accounts, managers see team totals 🚀 The Power Combination: RLS + Workspaces + Apps = Secure, scalable, enterprise-ready reporting environment that makes IT and stakeholders both happy. Without RLS, you're either building 50 versions of the same report or risking data exposure. With RLS, you build once and secure intelligently. The Trust Factor: Master RLS, and you become the person trusted with sensitive business intelligence projects. Tags : Shashank Singh 🇮🇳 | Pradeep M | Dhaval Patel | Hemanand Vadivel | Saddam Ansari | Codebasics | Indian Data Club | Munna Das | Tejas Rane | Tajamul Khan #PowerBI #DataSecurity #RLS #BusinessIntelligence #DataGovernance #Enterprise #Security #DataProtection #Analytics

🚀 Tableau vs. Power BI vs. Excel Dashboards – Which One Should You Use? Choosing the right visualization tool can make or break your insights. Let’s break down the pros and cons of Tableau, Power BI, and Excel dashboards: 📊 Tableau ✅ Pros: ✔ Best for advanced visualizations & storytelling ✔ Handles large datasets efficiently ✔ Drag-and-drop interface for easy dashboard creation ✔ Strong cross-platform integration ❌ Cons: ✘ Expensive (especially for teams) ✘ Requires learning curve ✘ Not as deeply integrated with Microsoft products ⚡ Power BI ✅ Pros: ✔ Seamless integration with Microsoft tools (Excel, SQL, Azure) ✔ More affordable than Tableau ✔ Easy-to-use with strong AI-driven insights ✔ Powerful DAX & Power Query for deep analysis ❌ Cons: ✘ Not as strong in cross-platform sharing as Tableau ✘ Slightly limited visual customization ✘ Performance slows with very large datasets 📝 Excel ✅ Pros: ✔ Most widely used tool—every analyst knows it! ✔ Great for quick, simple dashboards ✔ No extra licensing costs ✔ Strong in data manipulation with formulas & Pivot tables ❌ Cons: ✘ Limited interactivity & automation compared to Tableau/Power BI ✘ Manual updates required for dynamic dashboards ✘ Slows down with large datasets 💡 So, which one should you use? 🔹 Use Tableau if you need highly interactive & visually rich dashboards for storytelling. 🔹 Use Power BI if you're in the Microsoft ecosystem and need cost-effective, automated reports. 🔹 Use Excelfor quick insights, ad-hoc analysis, or financial data crunching. Which one is your favorite? #DataVisualization #PowerBI #Tableau #Excel #DashboardDesign #DataAnalytics #codebasics #data #visualization #businessinsights #insights #dataanalyst #powerBIdeveloper #DAX #PivotTable

The wrong BI tool can make a simple analysis unnecessarily complicated. Power BI, Tableau, Looker, and Excel can all turn data into insights, but each is designed for a different type of user, workflow, and scale. 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗶𝘀 𝗶𝗱𝗲𝗮𝗹 𝗳𝗼𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘁𝗲𝗮𝗺𝘀 𝗮𝗻𝗱 𝗮𝗻𝗮𝗹𝘆𝘀𝘁𝘀. It works especially well for organizations already using Microsoft products. Its strengths include interactive dashboards, DAX, semantic models, scheduled reporting, real-time analytics, and self-service BI. 𝗧𝗮𝗯𝗹𝗲𝗮𝘂 𝗶𝘀 𝗯𝘂𝗶𝗹𝘁 𝗳𝗼𝗿 𝘃𝗶𝘀𝘂𝗮𝗹 𝗲𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗶𝗼𝗻. It is a strong choice for analysts who need advanced visualizations, flexible drag-and-drop analysis, geospatial insights, and compelling data storytelling across multiple data sources. 𝗟𝗼𝗼𝗸𝗲𝗿 𝗶𝘀 𝗱𝗲𝘀𝗶𝗴𝗻𝗲𝗱 𝗳𝗼𝗿 𝗴𝗼𝘃𝗲𝗿𝗻𝗲𝗱 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀. It helps data teams define reusable metrics and consistent business logic through LookML. It fits organizations building warehouse-connected, embedded, and scalable analytics products. 𝗘𝘅𝗰𝗲𝗹 𝗿𝗲𝗺𝗮𝗶𝗻𝘀 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗳𝗼𝗿 𝗳𝗹𝗲𝘅𝗶𝗯𝗹𝗲 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀. It is often the fastest option for smaller datasets, financial modeling, forecasting, ad-hoc calculations, pivot tables, Power Query, and what-if analysis. The practical selection guide is simple: ↳ Choose Power BI for Microsoft integration and self-service reporting ↳ Choose Tableau for visual discovery and storytelling ↳ Choose Looker for governed metrics and embedded analytics ↳ Choose Excel for quick analysis and financial modeling There is no universally best BI platform. The right choice depends on your users, data architecture, governance requirements, analytical complexity, and existing technology ecosystem. The real goal is not adopting the most advanced tool. It is choosing the tool that helps people make better decisions with less friction. Which BI tool does your organization rely on most today?

Claude just built me a live pipeline dashboard. It refreshes itself every time I open it. This is a brand new feature in Cowork (Claude's desktop agent) called live artifacts — it's literally a few days old. So I described what I wanted to the agent, and it: ↳ 🔗 Connected directly to our Salesforce data ↳ 📊 Computed pipeline attainment vs. target by segment × theater ↳ 🏆 Surfaced the top performing pods and campaign types ↳ 🖥️ Built an interactive dashboard that lives in my sidebar ↳ 🔄 Set it to refresh on a determined schedule Not a static export. Every time I open it, Claude reprocesses the latest data and recomputes everything. No manual refresh. No stale numbers. No dashboard I have to rebuild next quarter. The whole thing took one conversation. But here's what actually excites me: This dashboard is completely custom to me — my segments, my theaters, my metrics, the exact cuts I care about. Historically, something like this either took a BI engineer a few days to build, or you settled for a generic shared dashboard that kind of answered your question. Now everyone can just build their own. A marketer. A sales ops person. A regional lead. Anyone who keeps coming back to the same question can turn it into a live dashboard in one conversation. Which raises a real question ❓ Do you actually need a Tableau or Sigma license if an AI agent can build you a fully custom, always-live dashboard on top of your own data in 10 minutes? I'm not saying the answer is no. Enterprise BI has governance and scale that matters. But for individual contributors who just need their numbers fast? The gap is closing faster than most people realize. We're in early days — but if this is what "a few days old" looks like, I'm paying close attention 👀

Most Workday users only scratch the surface with dashboards. They think... Dashboards = homepage shortcuts + a few charts. But here’s what great dashboards actually unlock: ✅ Embed interactive Worksheets managers can edit & share ✅ Surface Matrix Reports like headcount by gender, region, or management level ✅ Show Future Hires, Open Positions & Pending Offers...live ✅ Use Prism Analytics to blend external data (like CRM, ticketing, or Excel) with HCM ✅ Apply filters and prompts so users see only their slice of the org ✅ Drill down directly from visual tiles into actions or detailed reports ✅ Auto-adjust layouts for Workday Mobile App users And the best part? -Workday lets you build unlimited dashboards...tailored by role, region, or need. But designing them is just the first step. Here’s how to sustain dashboards through time, growth, and reorganizations: ->Use prompts, not hardcoding (like dynamic Sup Org or time periods) ->Group with Report Tags for scalable reuse (e.g. “Comp”, “Recruiting”) -> Assign owners for governance, upkeep, and documentation -> Respect security layering...users only see data they’re cleared for -> Test for mobile...dashboards live in the Workday app too -> Build for accessibility (color contrast, labels, keyboard nav) So how are smart companies using dashboards today? ->A DEI dashboard that visually breaks down ethnicity & gender by leadership level...highlighting representation gaps at a glance. ->A turnover insights dashboard combining Prism data from CRM + Workday...pinpointing which shifts or teams have the highest attrition risk. ->A Compensation Dashboard that maps planned vs. actual spend by cost center...helping HR stay audit-ready during salary reviews. ->A Staffing Dashboard tracking headcount trends, open requisitions, future-dated hires, and time-to-fill with filters by country and business unit. ->A Performance Summary dashboard pulling in individual goals, feedback loops, and review status...all in one view for HRBPs and managers. Each of these is more than a visual. They're decision-making surfaces that collapse time, silence guesswork, and boost alignment. If you're building in Workday and not using dashboards like these, you're missing half the system. Love this Workday bite? Enroll in my 20-Hour Self-Learn Workday HCM Course if you’re short on time but high on ambition. Comment "I love Workday" below and I will send you the next steps. #workdayhcm #workdaydashboards #workdayreporting #workdayconsultant #hrtechnology #peopleanalytics #workdayprism #workdaytraining #hris #workdayintegration #talentanalytics #workforceplanning #hcmtools #selflearnworkday #workdayskills

I'm excited to share my latest data analytics project: a comprehensive Retail Performance Analysis Dashboard. Problem: The retail company struggled with a lack of clear insights, making it difficult to track overall performance, understand customer behavior, and manage inventory efficiently. Solution: I developed and deployed an interactive, end-to-end Power BI dashboard. By connecting directly to SQL databases, the solution provides a real-time, holistic view of the business, analyzing key KPIs like sales, profit margins, customer segmentation, supplier performance, and stock health. 📊 Tools Used: Power BI | SQL | Excel | DAX | Data Modeling 💡 Key Insights & Highlights: • Total Sales: ₹5.34M • Profit Margin: 28.77% • YoY Sales Growth: 23.48% • Top Performers: The North Region (₹1.52M) and the supplier "Boat" (₹1.1M) were the primary drivers of sales. • Operational Health: Maintained a 65% delivery rate against a 9.17% return rate. • Actionable Inventory: Identified 3 critical products as "Low Stock" (Stock = Reorder Level), flagging them for immediate re-purchasing. Dashboard Link: https://lnkd.in/gHTPaTce #PowerBI #SQL #DataAnalytics #BusinessIntelligence #Dashboard #DataVisualization #RetailAnalytics #DataInsights

I accidentally gave access to confidential salary data… in a live report. That one moment changed the way I build Power BI reports forever. I was new to enterprise reporting. The dashboard looked clean. Stakeholders were happy. Everything was working... until someone shared the link with a new hire. And boom - confidential data, fully visible. To the wrong person. At the wrong time. → That’s when I realized:If your report isn’t secure, it’s not ready.Not even close. Since then, I’ve made data security and governance a non-negotiable part of my workflow in Power BI. Here's how: 🔹 Row-Level Security (RLS): Control which rows of data users can see. Static RLS → Hardcoded filters ➤ e.g., [Region] = "East" Dynamic RLS → Based on user login ➤ e.g., USERPRINCIPALNAME() with a Users table (More scalable for large orgs) 🔹 Object-Level Security (OLS): Hide entire tables or columns from certain roles.Yes, users won’t even know those fields exist. → Set up via Tabular Editor or XMLA Endpoints. 🔹 Yes, You Can Combine Them ● RLS = Restrict data by region ● OLS = Hide sensitive fields like salary or profit ●Together = Powerful, layered protection. 🔹 Real World Governance Tips: → Use Audit Logs to track access → Apply consistent naming conventions → Use the Admin Portal to enforce org-wide policies If you build Power BI reports, ask yourself this today: Are you securing the data as much as you’re visualizing it? What’s the biggest mistake you’ve made with data access? Comment down below 👇 #RLS #OLS #PowerBI