pbi-data-cleaning

You can initiate data cleaning by asking the assistant to run a comprehensive analysis on your Power BI semantic model. This scans for missingness, outliers, duplicates, and type inconsistencies to ensure your data is ready for visualization.

A trust scorecard in data cleaning evaluates visualization readiness across six dimensions: Completeness, Consistency, Uniqueness, Validity, Relationships, and Timeliness. It guides you to ensure decisions are based on reliable, high-quality data before reporting.

Detect outliers and missingness in Power BI datasets by applying data profiling and scanning techniques. This identifies nulls, placeholder values, and statistical anomalies to help surface data quality issues before visualization.

Yes, you can check type consistency and date quality directly within your Power BI workflow. The analysis evaluates formatting issues, catches time-related anomalies, and ensures type consistency across datasets to prevent visualization errors.

You need data profiling before visualizing Power BI reports when you must ensure decisions are based on reliable data. Profiling surfaces column types, cardinality, and nulls to catch hidden quality issues early in the pipeline.