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Frolick, 2000). Adding up, the complexity of an enterprise data warehouse can be reduced with the help of the data mart by rendering a part of the data, which has been tailored to meet the exact user requirements (Chenoweth, et al., 2006).

Coskun and Pohlen, emphasized and brought into light some of the disadvantages of an enterprise data warehouse in comparison to the data mart. They said that an enterprise data warehouse required long time for its development and also involved high cost (Coskun & Pohlen, 2002). While the data marts are quicker to implement and less costly, they also have the ability to carry specific data analyses (Coskun & Pohlen, 2002).

The data about a particular functional area or unit is contained in each data mart. As the data across the functional units and areas are not integrated, the data analysis to the functional areas cannot be done. Taking an example of the university setting, to study the cost per credit hour, there will be a requirement of integrating the data of students, financial and human resources. Credit hours will be known by looking at the student data. Data related to the departments of the faculty offering those credit hours will be provided by the human resource data; and the expenditures on those faculties and some other non-personal expenditures related with providing those credit hours is known through the financial data.

The data in the data mart may be at a detailed level or a summary and comprises of data of several months and years. Though its scope is limited to particular unit or functional area, yet it allows the trend analysis.

Enterprise Data Warehouse: The span and quantity of information incorporated in a data warehouse helps in distinguishing it from a data mart and an operational data store (ODS) (Benander, et al., 2000). It includes the data, which contains information of multiple years and crosses the functional areas, like human resources, students and finance (Benander, et al., 2000). In many cases, the end users will not be able to access the enterprise data warehouse because of the presence of many complexities (Benander, et al., 2000).

In order to help the corporations in assembling huge quantity of data, the data warehouses were developed. Due to alterations in the market and its effects on the business, together with the changing purchasing behaviors, consumption patterns, market saturation, growing competition, difficulty in differentiating the product line and emerging markets, the need for quick generation of information is growing (Coskun & Pohlen, 2002). The need for analytical databases along with the transactional databases has been created because of all the above factors (Coskun & Pohlen, 2002). The above mentioned data must be transformed into managerial information so that it can be easily retrieved and analyzed. In order to support decision-making, the data warehouse acts as a vehicle for summarizing and transforming the information (Coskun & Pohlen, 2002). The data warehouses, which contain data from the legacy systems, do not support the easy analysis and summarization of data. Thus, the data warehouse puts together all the data for answering the queries of multiple end-users (Coskin & Pohlen, 2002). Data acros本论文由英语论文网提供整理，提供论文代写，英语论文代写，代写论文，代写英语论文，代写留学生论文，代写英文论文，留学生论文代写相关核心关键词搜索。