Data Warehousing for Decision Support

Data Warehousing for Decision Support

John Wang (Montclair State University, USA), James Yao (Montclair State University, USA) and Qiyang Chen (Montclair State University, USA)
Copyright: © 2008 |Pages: 10
DOI: 10.4018/978-1-59904-843-7.ch015
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Today’s business environment is dynamic and uncertain. Competition among business organizations is becoming more intensified and globalized. These business organizations’ demand for both internal and external information is growing rapidly. This rapidly growing demand to analyze business information has quickly led to the emergence of data warehousing (Finnegan, Murphy, & O’Riordan, 1999). The strategic use of information from data warehousing assures the solution of the negative effects of many of the challenges facing organizations (Love, 1996). When the data warehousing technologies are well positioned and properly implemented, they can assist organizations in reducing business complexity, discovering ways to leverage information for new sources of competitive advantage, realizing business opportunities, and providing a high level of information readiness to respond quickly and decisively under conditions of uncertainty (Love; Park, 1997).

Key Terms in this Chapter

Business Intelligence: Business intelligence is a corporation’s ability to access and employ information usually contained in a data warehouse. With the information, the corporation can analyze and develop insights and understanding that lead to improved and informed business decision making.

Metadata: Metadata are data about data. They include the attributes of and information about each piece of data that will be contained in the data warehouse.

Data Warehouse: This is a database built to support information access. Typically, a data warehouse is fed from one or more transaction databases. The data need to be cleaned and restructured to support queries, summaries, and analyses.

Database Management System (DBMS): DBMS is computer system software that manages the physical data.

Geographic Information System (GIS): A GIS is a computer system designed to allow users to collect, manage, and analyze large volumes of spatially referenced information and associated attribute data.

Data Warehouse Life Cycle Management (DWLM): DWLM is the creation and ongoing management of a data warehouse throughout its operational life. DWLM delivers enterprise-scale data warehouses that adapt efficiently to change at lower cost than traditional software development methodologies.

Online Analytical Processing (OLAP): OLAP is a database designed to support analytical processing such as decision support.

Data Mart: It is a subset of a data warehouse that focuses on one or more specific subject areas. The data usually are extracted from the data warehouse and further denormalized and indexed to support intense usage by targeted customers.

Online Transaction Processing (OLTP): OLTP is a database designed to support transactional processing.

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