Data categorization

Data categorization in Power BI involves assigning a specific type or category to a data column, thereby providing hints to Power BI about the nature of the data. This categorization ensures that Power BI understands and appropriately represents the data, especially when used in visu-als or calculations.

WHY DATA CATEGORIZATION MATTERS

Data categorization in Power BI is pivotal for extracting maximum value from your datasets, impacting everything from visualization choices to data integrity. It enables Power BI to pro-vide tailored visual suggestions, enhances the effectiveness of natural language queries, and serves as a critical tool for data validation. Here’s why categorizing your data correctly matters:

• Enhanced visualization interpretation: By understanding the context of your data, Power BI can auto-suggest relevant visuals. Geographical data, for instance, would prompt map-based visualizations, while date fields might suggest time-series charts.

• Improved search and Q&A features: Power BI’s Q&A tool, which allows natural language queries, leans on data categorization. When you ask for “sales by city,” the tool knows to reference geographical data due to the categorization of the City column.

• Data validation: Categorization can act as a form of data validation. By marking a column as a date, any nondate values become evident, highlighting potential data quality issues.

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COMMON DATA TYPES IN POWER BI

In Power BI, the clarity and accuracy of your reports hinge on understanding the core data types at your disposal. Each data type serves a specific purpose, shaping how information is stored, analyzed, and presented. The following are common data types:

• Text: Generic textual data, from product names to descriptions

• Whole number: Numeric data without decimal points, like quantities or counts

• Decimal number: Numeric data with decimal precision, suitable for price or rate data

• Date/time: Fields that have timestamps, including date, time, or both