Data validation Techniques

[Kmanikanta]

Can someone please provide the info on different data validation techniques in power BI and their implementation. 

[Anonymous]

Hi @Kmanikanta ,

As a complement, data validation is very important in Power BI to ensure the accuracy and reliability of reports and dashboards.
Validation rules can be customized: Implement custom validation rules based on business logic. For example, ensure that the order date is before the delivery date. This can be done using DAX to create calculated columns or metrics to flag invalid data.
The following demonstrates how to validate that sales data is within reasonable limits:

Sales_Validation = IF(Sales[Amount] < 0 || Sales[Amount] > 1000000, "Invalid", "Valid")

Best Regards,
Adamk Kong

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[uzuntasgokberk]

Hello @Kmanikanta ,

Power BI does not have built-in data validation tools like databases, but you can use various techniques to ensure your data is accurate, complete, and reliable. Here's an overview of key data validation techniques in Power BI:

* Data Source Validation. For example you use SQL data source, you can write a query to count records in the source and compare it with Power BI's visual or table counts.
* Schema Validation. Ensure the data structure (e.g., column names, data types, and relationships). For example validate column names and data types in Power Query.
* Null or Missing Data Checks. You can ıdentify and handle missing or null values in the dataset. you can use Power Query to filter and check for null values.

* Duplicate Data Checks you can ıdentify and remove duplicate rows or values. you can use Power Query to remove duplicates in a table.
* Date Validation you can validate that date fields are complete and sequential.
* Summary and Aggregate Validations you can validate summary metrics like totals and averages.

Kind Regards,
Gökberk Uzuntaş

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[SamInogic]

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!