Crosstabs and tables show data points only as values, rather than providing a visual representation of those values. As a result, they are useful when your analysis depends on viewing exact values, or examining data from multiple measures with different scales or units of measurement.
In addition to regular sorting and ranking functionality, you can also use conditional formatting in tables and crosstabs to help identify noteworthy data points.
With a table, you can add multiple measures, which are displayed on the columns, and multiple dimensions, which appear on the rows. For example, a table could be an effective way of examining several measures related to the sales performance for a list of products. You might add a Product Category dimension so that you can display the totals for each category on the rows.
For more flexible data analysis, you can use a crosstab. You add multiple measures to
the Measures shelf, and switch the display of the measures
between the columns and rows by moving the
Measures
token.
Dimensions can be added to the rows, columns, or both, allowing complex
multidimensional analysis.
For example, adding a Year dimension to the rows of your sales analysis in a table might make it difficult to compare data across both time and product type. Instead, you could create a crosstab with the measures and Year dimension on the columns and the Category and Product dimensions on the rows, making it easier to spot relationships between the dimensions.