Data Analytics

This makes time-based analysis much easier. 🔹 12. Why Month Number Is Important If you display: January February March April Power BI may sort month names alphabetically depending on the setup. You need a: Month Number January → 1 February → 2 March → 3 Then sort Month by Month Number. 🔹 13. Understand the Grain Before creating relationships, ask: "What does one row represent?" For example: Sales table → One row = One order or: Sales table → One row = One order item These are different grains. If you don't understand the grain, you can accidentally double-count sales. 🔹 14. Example of a Grain Problem Suppose one order contains: Order 1001 Laptop → ₹60,000 Mouse → ₹2,000 The order-item table has two rows. If you join this with another table incorrectly, the ₹62,000 order value could potentially be repeated. So before creating relationships or calculations: Always understand the grain of your tables. 🔹 15. Active and Inactive Relationships Sometimes two tables can have more than one possible relationship. For example, Sales may contain: Order_Date Ship_Date Both could connect to the Date table. But Power BI generally allows only one active relationship between the same pair of tables at a time. The other relationship can be inactive and activated when needed using DAX. This becomes important when building advanced date analysis. 🔹 16. Filter Direction Relationships control how filters move between tables. In a simple star schema: Customer ↓ Sales filters usually flow from the dimension toward the fact table. Avoid using bi-directional filtering everywhere. It can create: • Ambiguous relationships • Unexpected results • Difficult-to-debug models • Performance issues 🔹 17. Don't Create Relationships Just Because Column Names Match For example: Customer_ID appearing in two tables doesn't automatically mean they should be connected. Check: ✔ Same business meaning ✔ Compatible data type ✔ Correct grain ✔ Unique values on the "one" side ✔ Correct cardinality 🎯 Interview Question What is the difference between a Fact Table and a Dimension Table? Fact Table Contains business transactions and measurable values. Example: "Sales, Quantity, Cost" Dimension Table Contains descriptive information used to analyze those transactions. Example: "Customer, Product, Date, Region" Easy way to remember: Fact = What happened Dimension = Describe what happened 💡 Key Lesson Don't build your Power BI visuals before understanding your data model. A good model makes your calculations easier, your reports more reliable, and your analysis much easier to maintain. 🚀 Double Tap ❤️ For More ----- 13.17 ₽ · /balance_help