Pivot Table

A Pivot Table is a data summarization tool that allows you to group, organize, and aggregate data dynamically. It helps in analyzing large datasets by arranging fields into rows, columns, and measures

This series becomes active when a Pivot Table is selected as the visualization type.

Key Properties

The following properties can be configured for a Pivot Table:

Dimensions

Dimensions define how data is grouped and arranged in the pivot table.

• Rows – Drag and drop fields to display them as row headers. Example: Adding Category will display each category as a separate row.

• Columns – Drag and drop fields to display them as column headers. Example: Adding Region will create separate columns for each region.

Dimensions help in structuring the pivot table by categorizing data into rows and columns.

Measures

Measures represent the numeric values that are summarized in the pivot table.

• You can select a field (e.g., Profit) and apply an aggregation such as Count, Sum, Average, etc.

• Multiple measures can be added, and each will be displayed in the table.

Examples using Profit:

• Profit (Count): Shows how many transactions contributed to profit in each category.

• Profit (Sum): Displays the total profit per category, helping identify the most profitable areas.

• Profit (Average): Shows the average profit per transaction, useful for comparing efficiency across categories.

Add Dimensions and Measures to structure the Pivot Table

Best Practices

• Keep it simple – Start with fewer dimensions and measures to avoid clutter.

• Choose meaningful aggregations – Use Sum for totals, Average for trends, and Count for frequency.

• Use rows and columns wisely – Place the most important category in rows, and comparisons (like time or region) in columns.

• Check for empty values – Ensure dimensions do not have too many blanks, as they can make the pivot harder to read.