What are pivot tables primarily used for in data analysis?

Pivot tables are primarily used to create multiple views of data, allowing analysts to summarize, organize, and visually represent large datasets in flexible formats. They enable users to manipulate and analyze data dynamically, facilitating insights into patterns, trends, and relationships that might not be immediately apparent in raw data. For instance, a pivot table can summarize sales data by different categories, such as product type or region, helping to see the performance of each without altering the original dataset.

While performing one-dimensional calculations is a capability of pivot tables, it does not capture the full range of their functionality, which is more about organizing and summarizing multidimensional data. Sorting data in alphabetical order is also a basic task that doesn't leverage the unique strength of pivot tables to present data from multiple perspectives. Additionally, combining unrelated data sets is not a primary use; pivot tables work best with related data that can be grouped and summarized according to defined parameters. Thus, the ability to create multiple views of data truly encapsulates the essence of what pivot tables can accomplish in data analysis.