Before starting to assemble a crosstab, it is important to consider which attributes should be "foldable". This will decide the order of datafields in the column- and rows bar. In the crosstab below a list of applicationfunctions (these are BlueDolphin objects) is displayed. Each function is covered by a number of applications. If you unfold the Application column, the application names are displayed. This report allows you to pinpoint functional overlap between applications in an instant. This means you can initiate projects to rationalise the applicationlandscape.
An empty crosstab as displayed below contains no data fields in the columns and rows bar. In the middle of the screen you can find the minimum field requirements for generating the visualisation of your choosing.
A crosstab requires at least one datafield from the "Fields" category in the column-bar, and one datafield from the "Measures" category in the column- ór rows-bar. Any available fields or measures are displayed on the left side of the screen.
Drag the "Object_type" field to the columnsbar, and the "waarde" measure in the rowsbar. A basic crosstab is now generated, because we have met the minimum requirements. Each column in the crosstab now displays an object type. This example dataset contains only one object type.
The crosstab does not contain a lot of information so far. We can enrich it by adding the field "Application function" to the rowsbar. The crosstab now has a new blue column which displays an application function in each row. Now for each application function a value is displayed.
To finish this crosstab we can drag the last unused field "Object name" in the columnsbar. We now added an extra layer to the columnsbar, which enables the fold option to switch between object type and object name.
The crosstab is now complete, but it still shows sample data. To display the full range of data choose "Full Data" in the drop-down menu on the upper right. The sample data option allows you to configure your report easily without moving around large amounts of data.