Mahesh Kumar

A great resource for building analytical prowess." — Stephen Few When we wrote The Accidental Analyst, the goal was to bridge the gap between "Data" and "Decision." Tableau Software recognized it as "One of the Great Books on Data Visualization," but its real legacy is the framework it established for forensic data investigation. The logic detailed here—audit, clean, analyze, communicate—is the precursor to the "Behavioral Revenue Forensics" we now deploy in private equity diligence. A look back at the publication that set the standard: https://lnkd.in/ee8q5v7K #DataViz #BusinessIntelligence #Analytics #Strategy

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Course: Advanced Power Query Instructor: Chandeep Chhabra Learning objectives Leverage M language essentials to automate and customize your data transformations. Employ advanced techniques for handling nested and cross-tabulated data common in business reports. Employ methods for multicolumn transformations to quickly reshape your data. Leverage iteration techniques to apply complex operations across large datasets. Create and apply custom functions to tailor Power Query to your specific business needs. Apply proven patterns and recipes for solving real-world business data challenges.

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Unlocking the full potential of Power BI can transform your organizational data into actionable insights. A tiny shift that changes everything in Advanced Power BI is mastering a few key techniques. 1. **Advanced DAX Techniques**: Elevate your data analytics by diving deeper into DAX formulas. Understanding context transition and row context can lead to more dynamic calculations. 2. **Row-Level Security (RLS)**: Secure your data by implementing RLS correctly. This ensures that users see only what they're permitted to view, making your reports safe and compliant. 3. **Time Intelligence Functions**: Utilize DAX time intelligence functions to streamline your reporting on trends over time. Understanding periods such as YTD, QTD, and MTD can provide valuable insights with minimal effort. 4. **Performance Tuning**: Improve report performance by optimizing your data model. This can involve reducing column cardinality and evaluating if calculated columns or measures are more appropriate for your needs. 5. **Visualization Best Practices**: Choose the right types of visualizations for your data insights. The proper selection can significantly enhance the user experience and comprehension of your reports. Ready to go deeper? Join us: https://lnkd.in/gwFMStk3 #PowerBI #DAX #DataModeling #Analytics #BusinessIntelligence

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A data catalog is like a organised library which helps you find and understand your data. But data governance is the rulebook that ensures your data is secure, trusted, and used the right way. Here’s how they differ: 🔎 Data Catalog  - Indexes metadata across Snowflake, Databricks & more  - Enables data discovery & classification  - Links business terms to technical assets  - Improves visibility and reuse 🛡️ Data Governance  - Enforces access controls and usage policies  - Tracks end-to-end lineage and data quality  - Enables role-based stewardship & accountability  - Supports compliance, auditability, and trust 👉 The takeaway?  A catalog is just the start. True enterprise governance needs actionability not just metadata, but workflows, policies, lineage, and quality monitoring. 💡 With Inferyx, you get a unified platform that brings governance + catalog to life, natively across your enterprise. 📅 Ready to govern with confidence?  Schedule a demo : http://ow.ly/hOlt50EnlLI

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The simplest way to understand LODs vs. Table Calcs in Tableau? Create the same viz twice (plus 2 tutorials). One viz using LODs only One viz using Table Calcs only Watch these two videos and you will master the fundamentals: 1. The Ultimate Guide to Level of Detail Expressions → https://lnkd.in/eYSc4RKN 2. Table Calculations in Tableau: Everything You Need to Know → https://lnkd.in/e47NQpmF And here's the workbook we created in our Next-Level Tableau class today → https://lnkd.in/etmtfs-V How have you learned the difference between LODs and Table Calcs? I'd love to learn your tips.

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Most data professionals know how to analyze data. Very few know how to turn insights into influence. That’s the gap Mr. Vijay Kakwani has spent 27+ years helping professionals bridge. As a renowned Data Storytelling Coach, Mr. Vijay has seen how the same data can either stay on slides or spark meaningful action, all depending on how the story is told. In our upcoming expert talk, The Art of Data Storytelling, he’ll share how to: ✅Find the one insight that truly matters ✅Structure data into stories leaders understand instantly ✅Communicate with clarity, confidence, and real business impact Because the goal isn’t more analysis. It’s making sure your insights move decisions forward. This exclusive Expert Talk is designed for Coding Ninjas 10X Club members ready to elevate how their insights are seen, heard, and acted upon. #DataStorytelling #DataAnalytics #CareerGrowth #Leadership #CodingNinjas #CodingNinjas10XClub

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💻 The Access Issue — A Small Story from Yesterday One of my friends reached out and asked, “Hey Vijay, how do I connect Databricks to Power BI?” I guided him step-by-step. Everything looked perfect — configuration ✅, credentials ✅, driver setup ✅ But still… Connection Error! ❌ After a few rounds of troubleshooting, the real culprit appeared — Netskope Security 😅 In our company, for security reasons, certain connections between applications and databases are restricted at the device level by the security team. 🔒 The we have solve the issue with help of our security team A small reminder that 👉 Sometimes it’s not about technical errors, 👉 It’s about security and access controls behind the scenes! --- Hey, I’m Vijayasankar G, sharing real-life stories and learnings from the world of Data & Analytics 🚀 #DataAnalytics #PowerBI #Databricks #LearningEveryday #DataEngineer

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📊 Master Excel, Master Analytics! 💡 Whether you’re crunching numbers, cleaning data, or building dashboards — Excel remains every analyst’s best friend. To make your data journey smoother, Anasys – The Analytics Club of IIM Raipur presents an Excel Cheat Sheet 🧾 — your go-to guide for: ✅ Essential formulas & logical functions ✅ Lookup & reference operations ✅ Text, Date & Time, and Statistical functions ✅ Data cleaning & analysis tools ✅ Power Pivot, DAX, and Excel shortcuts This concise guide is perfect for anyone looking to enhance their analytical efficiency and excel at data-driven decision-making. 📥 Download now and start leveling up your Excel skills! #Excel #Analytics #DataAnalysis #IIMRaipur #Anasys #BusinessAnalytics #ExcelTips #DataDriven #PowerQuery #DAX #DataScience #ProductivityTools #ExcelCheatSheet #ExcelShortcuts

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🚀 Day 8 of Power BI + DAX Journey – What-If Analysis for Employee Attrition Every HR leader asks – “What if our attrition increases by 2%?” Or “How much cost do we save if we improve retention by 5%?” Instead of Excel simulations, we can model this directly in Power BI with What-If Parameters + DAX. 🛠 Step-by-Step 1️⃣ Create a What-If Parameter Go to Modeling → New Parameter Example: Attrition Change % → Range: -5% to +10%, Increment: 1% This creates a slicer for HR to choose attrition change scenario. 2️⃣ Write DAX Measures Current Attrition % = DIVIDE(COUNTROWS(FILTER(Employee, Employee[Status] = "Resigned")),COUNTROWS(Employee),0) Adjusted Attrition % = [Current Attrition %] + 'Attrition Change%'[Attrition Change% Value] ⚠️ Note: The parameter should be in decimal (e.g., 0.02 = 2%), not percentage formatted string. Employees Lost =ROUND( [Adjusted Attrition %] * COUNTROWS(Employee), 0) Replacement Cost =[Employees Lost] * AVERAGE(Employee[Hiring Cost Per Employee]) 3️⃣ Visuals to Use for Maximum Insight Add a slicer for “Attrition Change %” 📊 Line & Clustered Column Chart X-axis → Month Column → Employees Lost (current vs adjusted) Line → Attrition % (trend line with What-If adjustment) 📉 KPI Card Visuals Current Attrition % vs Adjusted Attrition % Employees Lost Replacement Cost 🌀 Donut Chart Attrition by Department (HR can instantly see which dept. drives cost) 📈 Scenario Impact Chart Use a Waterfall Chart to show how adjusted attrition impacts overall workforce cost. ✅ With this, Power BI shifts from just reporting attrition → to predictive workforce analytics. This is Day 8 of 100 Days of DAX +Power Automate Challenge. Stay tuned for tomorrow's real life scenario!💡 #PowerBI #PredictiveAnalytics #DAX #WorkforceAnalytics #100DaysChallenge

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Our video uncovers the data science career growth, evolving roles, and key skills shaping the future. Don’t miss your chance to lead in a data-driven world. Find out how roles and skills are evolving, and why now’s the time to dive in. Watch the future unfold https://lnkd.in/gNpQgRj5 #DataScienceCareers #AIandDataScience #BigData #DataAnalytics #FutureofDataScience #PopulardataScienceJobs #LatestDataScienceSkills #AIDataScientists #LearnDataScience #DataScienceCertifications #USDSI #USDSICertificationPrograms #BestDataScienceSalaries

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Hi Friends 🔍 Power BI Tip: Drillthrough from Card Visuals with DAX Measures Recently, I faced an issue while using a Card visual in Power BI with a DAX measure like this: DAX CopyEdit Total Sales India = CALCULATE(COUNT(Sales[OrderID]), Sales[Country] = "India") When I used drillthrough from this card to navigate to a detailed report page, I expected it to show only India’s sales data. However, the detailed report was showing data from all countries. ❌ ✅ Why this happens: The Card visual shows the value after filtering, but it doesn't pass that filter context (like Country = "India") to the drillthrough page unless it's applied explicitly as a visual-level filter. 💡 Solution: To make drillthrough work correctly: Apply a visual-level filter on the Card: Set Country = "India" directly on the visual. OR use a field (like Country) in the Drillthrough field well of the detailed report, ensuring it receives the correct filter context. With this tweak, drillthrough will only show India’s data as expected! 🎯 📊 A small trick, but a big difference in user experience and data accuracy. #PowerBI #Drillthrough #DataVisualization #DAX #BusinessIntelligence #PowerBITips #DataAnalytics #BI

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🚀 Partitioning vs Clustering in Data Engineering — Made Simple In large datasets, smart data organization can turn slow queries into lightning-fast results. Two common techniques: Partitioning and Clustering. 1️⃣ Partitioning – “Separate folders” We split a table into chunks based on a column value. Example: 📂 /sales/country=USA/... 📂 /sales/country=UK/... Query: SELECT * FROM sales WHERE country = 'USA'; ✅ Only scans the USA folder — skips all UK data. Best for: Columns with few unique values (e.g., Country, Year). 2️⃣ Clustering – “Organized shelves” We keep the data in the same file but group similar rows together. ✅ Without Clustering The data might be stored unsorted, like: file1.parquet → (2024-01-03, USA), (2024-01-01, USA), (2024-01-02, UK)... file2.parquet → (2024-01-02, USA), (2024-01-01, UK), (2024-01-03, UK)... ✅ With Clustering by order_date The data inside each file is grouped and sorted by order_date: file1.parquet → 2024-01-01, USA, 100 2024-01-01, USA, 150 2024-01-02, UK, 200 2024-01-02, USA, 180 file2.parquet → 2024-01-03, UK, 220 2024-01-03, USA, 300 ✅ Queries can skip irrelevant ranges inside the file. Best for: Columns with many unique values (e.g., Order Date, User ID). 3️⃣ Together = Maximum Efficiency In BigQuery or Snowflake, you can do: PARTITION BY DATE(order_date) CLUSTER BY country This way: Partitioning skips entire days. Clustering skips irrelevant countries inside each day. 💡 Rule of Thumb: Partition for broad pruning (skip whole files) Cluster for fine-grained pruning (skip parts of files) 🔍 How do you use partitioning and clustering in your data projects? #DataEngineering #BigQuery #Snowflake #Partitioning #Clustering #SQLPerformance #DataModeling

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At Michelin, Data Analyst Kundana Sai Muthyala uses Dataiku to run end-to-end pipelines for data cleaning, exploration, and modeling in one place. One standout project: applying regression models and visual ML to see how temperature, pressure, and speed influence outcomes, then predicting and adjusting parameters in real time to improve consistency. What makes the difference is clarity and scale: visual flows that everyone can follow, paired with automation that keeps models performing day after day.

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Dashboards don’t solve business problems. Data does. Power BI visuals are the surface. But beneath lies the real engine: ✔️ Clean & reliable data ✔️ Robust data models ✔️ Optimized SQL & DAX ✔️ Governance & automation ✔️ Scalable ETL pipelines ✔️ Business logic implementation ✔️ Handling large datasets ✔️ Row-level security for access control ✔️ Scheduling & performance tuning ✔️ API & data source integrations #AnalyticsMindset #PowerBI #DataDriven #DataEngineering #BusinessIntelligence

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Exciting news I've just launched the Data With Pankaj WhatsApp Community! A dedicated space where we can: ✅ Share Power BI tips & tricks ✅ Get notified about upcoming free webinars ✅ Discuss DAX, SQL, Python & Excel ✅ Network with fellow data professionals This is completely FREE just like everything else I do at Data With Pankaj. 👇 Join the community here: https://lnkd.in/dPMdmSqH See you inside! 💪 #DataAnalytics #PowerBI #DataWithPankaj #Community #MicrosoftFabric

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