Dimensional data modeling for data warehousing is a well-known technique and has been explained in great depth. These dimensional modeling techniques focus mainly on the data of the organization and how to organize it across multiple dimensions and facts. It is important to note, however, that the very existence of dimensional data modeling techniques is for better business alignment of the organization’s data with business requirements. This concept is currently working well and has been of use to the business for their analytical needs. The next step in the logical evolution of dimensional data modeling is to make it more intelligent by including relevant semantics into the data model. This article discusses a method of introducing semantics into the dimensional data model for a more realistic business alignment with the data present in the data warehouses.  

Embedding Semantics into the Data Model

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