Data Warehouse Design

Design Methodologies of Kimball and Inmon...Plus a Third Way

By Hari Mailvaganam

Data warehousing is more an art-form than cookie cutter science. The business variables and technical risks are very unique to each installation. The business users have different goals and expectations. Data warehousing is more often successful than not if there is a reservoir of data warehousing expertise in-house.

This article will focus on the data warehousing design methodologies most commonly proposed. These designs are in an evolving flux as business needs and technical cost change.

Quite often the design chosen will be a combination of the methodologies below and additional requirements  - the data warehouse design third way. I am a proponent of the third way data warehousing design. Third way takes into account the business specifics and needs of the installing company and technical resources available. It uses the best design patterns of both methodologies plus additional requirements unique to the business.

The two major design methodologies of data warehousing are from Ralph Kimball and Bill Inmon. The design methodologies developed by Kimball and Inmon have lines drawn in the sand. 

Both Kimball and Inmon view data warehousing as separate from OLTP and Legacy applications.

Kimball views data warehousing as a constituency of data marts. Data marts are focused on delivering business objectives for departments in the organization. And the data warehouse is a conformed dimension of the data marts. Hence a unified view of the enterprise can be obtain from the dimension modeling on a local departmental level.

Figure 1. Kimball's Data Warehousing Design Methodology

Inmon beliefs in creating a data warehouse on a subject-by-subject area basis. Hence the development of the data warehouse can start with data from the online store. Other subject areas can be added to the data warehouse as their needs arise. Point-of-sale (POS) data can be added later if management decides it is necessary.

The data mart is the creation of a data warehouse's subject area.

Figure 2.  Inmon's Data Warehouse Design Methodology

There are pros and cons to both approaches. And there are third ways that can be unique to an enterprise's needs. Please contact us if you would like to have more information.