The concepts should be high level abstractions, not data attributes. Arrive at the domains and their concepts by thinking in terms of the following categories. These are endogenous to the system being tracked by the data mart, and should be viewed as the effects of certain causes. Favorite examples are Amount of Sales, Unit Costs, Quantity Sold, and Discounts. Side effects are important because they can lead to feedback effects on key goals and objectives even though they initially may seem unrelated to them.
Some examples are product type, and product department.
The distinction between Type II causal concepts and Type III descriptive concepts is not hard and fast. It depends on what you think about causality. But in every data mart you'll think of some concepts as causal, and others as primarily descriptive. And your views on these matters may change as the data mart is used and more analysis and data mining is done. You can go further and formulate more specific causal relations if you have good reasons for doing this, but the data mart DDM need only provide a framework for formulating alternative causal models and theories of system dynamics.