The second type of column dependency is needed when you have pre-aggregated measures in your table.
When we are setting up column dependencies in this situation, we will add the measures to the group level to which they are aggregated. For example, we will add Yearly Revenue to the Year column dependency.
Here is the data module we are working with:
When we build a report using this data module, we see that both the details for Yearly, Quarterly, and Monthly revenue are incorrect and that the subtotals are also incorrect. In the example below, we should see the same value in the Monthly Revenue column that we see in the Revenue column.
When we add column dependencies, we start by adding the level groups.
Then we will link each column dependency from coarsest grain to most detailed grain.
Then we add the measures to the appropriate grain.
When we retest the data module in the reporting tool, here is what we see now:
If we add Date to the report, we can see all the values.
We can see how vital column dependencies are for returning accurate data when using data modules.
(Return to overview of column dependencies)