The basic guiding principle for creating a data mart is the same as that of a data warehouse. That is, it is subject-oriented, integrated, time-variant, and non-volatile. The keys differences between data warehouses and data marts are size, content, technology, user-groups, development time (project duration), and the amount of resources required to implement (Tipton & Krause, 2001). However, today size is not a major differentiating factor because there are some data marts as large as data warehouses.

Compared to a data warehouse, a data mart costs less to implement and can be implemented in a fairly small amount of time (usually 90 days), and has more rapid response to queries because there is less but specific information to process, and thus the information is more easily understood. Technically, data marts bring data closer to small business units or departments that need it in terms of faster access to the data and with fewer restrictions than data warehouses (Westerman, 2001).

Most data marts contain data in hundreds of gigabytes compared to the terabytes in data warehouses; hence, they can be developed on less powerful hardware with small secondary devices, which equates to significant savings to an organization.  For example, most relational database software can be used to set up a data mart, but there are some vendors such as Microsoft, Oracle, IBM, Sybase, Greenplum, Infobright, and others that offer the so-called data mart in a box or data mart suites, which are cheaper to deploy and operate (Stair & Reynolds, 2006).

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