What best practices do you follow for data validation and audit trails in Power BI solutions

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What best practices do you follow for data validation and audit trails in Power BI solutions?

In my current Power BI project, I need to ensure data accuracy and maintain an audit trail for compliance purposes. I'm looking for best practices to validate data at various stages, such as during data extraction, transformation, and visualization, and strategies for implementing robust audit trails within Power BI solutions to track changes and user activities effectively.
Nov 28 in Power BI by Evanjalin
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Audit trails and data validation play a major role in getting accurate data within Power BI solutions and then remaining compliant with relevant policy directives. Some of these best practices should be applied for each of the stages of your Power BI project:

Data Validation During Extraction:

Healthy validation must be performed at the source end, wherein specific queries or other specific tools work to cover the final completeness and accuracy before bringing it into Power BI.

Number all rows in the source and check them against the numbers imported to cover lost rows or duplicates.

Create a specific query to filter out data not required in clearing or duplicates during the extraction.

During Transformation:

Clean and transform in Power Query while checking for inconsistencies; exclude errors and null values, chiefly among the standardization of date formats.

Document the data model in Power BI so that transformation logic can be tracked and verified at each stage.

Use Power Query's "Query Diagnostics" to analyze data processing steps for unexpected behavior or data anomalies.

Presently Visualization:

Have DAX measures for checking errors. For example, on the matched level, totals and subtotals are to be calculated and cross-verified with raw data.

Create Validation Reports Along with Critical Metrics, Totals, and Comparisons to Test with the Expectation Conformance.

Insert Tooltips/Annotations in Views to Emphasize Data Assumptions or Quality Checks.

Tracking Changes in Data:

Incremental Refresh and historical data Keeping will be done for larger data sets for Comparison: Some of the older data will not be overwritten so that a change would be easy to track.

Calculated columns or measures will be used to mark the date and person who updated changes (for example, from a specified metadata source used in the source systems). User Activity Logging

The Power BI Activity Log will be used for compliance monitoring of user actions (use of accessed report, update dataset, and share activities) as it contains information related to what changes users make for Power BI Pro and Premium licenses.

In Power BI Premium Per User (PPU), the Audit Log feature may be used for more relevant tenant-level activities and events, which can be integrated with monitoring tools such as Microsoft Sentinel to provide automated visibility.

Version Control and Documentation

Keep your Power BI reports' version control in PBIX files stored using OneDrive or SharePoint. Label the versions and changes clearly in the files.

Maintain a separate file or embed an entry for all the changes made to reports in the report with a clearly defined "change log" page.

Automation, along with monitoring

Automate the auditing log extraction using the Power BI REST API or incorporate logs with external systems for long-term storage or analysis.

Use Dataflows to centralize transformations and standardize them so that maintaining the audit trail becomes easier for data lineage.

Implement Power BI Monitoring Solutions like those specified in Microsoft's documentation for activity tracking, logging dataset refreshes, and reporting performance metrics.

Combining these several paths assures solid data validation at every point while maintaining a clear audit trail of user and system actions in Power BI solutions.

answered Nov 28 by pooja
• 6,530 points

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