Data validation Techniques

Hi,

Refer to the below validations to better validate the data in Power BI

• Source-Level Validation: Clean data at the source using queries (e.g., filter invalid rows in SQL).
• Data Profiling (Power Query): Use Column Quality, Column Profile, and Column Distribution to check null values, duplicates, and outliers.
• Data Type Validation: Ensure correct data types in Power Query (e.g., Date, Text, Number).
• DAX Measures: Create measures to identify anomalies (e.g., count missing or invalid values).
• Validating Relationships: Check relationships in Model view for correct cardinality (e.g., One-to-Many).
• Reference Tables: Compare data against a reference/lookup table to flag invalid entries.
• Custom Rules: Add conditional columns in Power Query (e.g., flag rows with invalid values).
• Visual-Level Validation: Use visuals like scatter plots or conditional formatting to highlight outliers.
• Alerts: Set up alerts on dashboards in Power BI Service for key metrics.
• Advanced Tools: Use DAX Studio, or Python/R scripts for deeper validation.

Hope this helps.

Thanks!