The architecture layer describes the structure of the data in the warehouse. An important component of the architecture layer is flexibility. The level of flexibility is measured in terms of how easy it is for the analyst to break out of the standard representation of information offered by the warehouse in order to do custom analysis. Custom analysis is where semantic thickness becomes important.

Semantic thickness is the degree of clear business meaning embedded in both the database structure and the content of the data itself. Field names such as “F001” for customer number and obscure numbers such as “01” to indicate “Backorder” status are considered semantically thin, or ambiguous and difficult to understand. In contrast, field naming standards such as "Customer_Name" containing the full customer name and "Order_Status" containing the complete description "Backorder&” are semantically thick, meaningful and easily understood. In other words, data structure and content must be clear to the analyst at the presentation layer of the data warehouse. The underlying data schema for the warehouse should be simple and easily understood by the end user of the data.