You can create an analysis in Analytics that combines data from more than one subject area. This type of analysis is referred to as a cross-subject area query.
If your analysis requirements can't be met by any single subject area because you need metrics from more than one subject area, you can build a cross-subject area query using common dimensions. There's a clear advantage to building a cross-subject area query using only common dimensions, which is recommended.
A Common dimension is a dimension that exists in all subject areas that are being joined in an analysis. This provides cleaner joins generated by the OBI server.
A Local dimension is a dimension that exists only in one subject area. This can sometimes cause issues where results are not returned.
When joining two subject areas in an analysis, make sure at least one attribute from a common dimension is used in the analysis.
Always include a measure from each subject area that's being used in your analysis. These measures don't always have to be displayed measures or even use them, but you should include them. You can hide a measure if not needed in the analysis. Including it in the analysis (but leaving hidden) can sometimes allow you to combine subject areas that might not exactly match in regards to associated dimensions or facts.
When using common and local dimensions you select the option 'Show Total value for all measures on unrelated dimensions' on the Advanced tab. This can be helpful in getting results returned for subject areas that might not otherwise be available for use in a cross subject area analysis.
In certain scenarios, such as combining historical and non historical subject areas, even with different join techniques those subject areas can not be used in a cross subject area analysis. The reason here is the underlying WID's for those facts or associated rows in the dimensions do not match up between the subject areas.