A retail dataset contains 50 000 transaction rows with fields for Region, Product Category, Salesperson, Date and Revenue. Finance managers want a single interactive view that lets them swap Region and Product Category between rows and columns to compare totals from different perspectives, apply on-demand filters for Date or Salesperson, and double-click any subtotal to inspect the underlying transactions. Which visual element should the analyst choose to satisfy all of these requirements?
Generate a geographic heat map of revenue by region
Create a pivot table based on the transaction data
Use an animated scatter plot that encodes Region and Product Category
Build a clustered bar chart from a pre-aggregated summary query
A pivot table is purpose-built for interactive cross-tabulation. Users can drag fields between the Rows, Columns, Values and Filters areas to pivot Region and Product Category, apply slicers or report filters for Date or Salesperson, and double-click any value cell to drill down to the source records. Clustered bar charts, scatter plots and geographic heat maps can display summarized results, but they cannot be re-pivoted or expose underlying transaction rows without rebuilding the visualization, so they do not meet all of the stated needs.
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