Modeling-Centered Data Warehousing Learning: Methods, Concepts and Resources

Modeling-Centered Data Warehousing Learning: Methods, Concepts and Resources

Nenad Jukic (Quinlan School of Business, Loyola University Chicago, Chicago, IL, USA) and Boris Jukic (Operations & Information Systems, School of Business, Clarkson University, Potsdam, NY, USA)
Copyright: © 2012 |Pages: 22
DOI: 10.4018/jbir.2012100104
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Abstract

Though data warehousing is widely recognized in the industry as the principal decision support system architecture and an integral part of the corporate information system, the majority of academic institutions in the US and world-wide have been slow in developing curriculums that reflect this. The authors examine the issues that have contributed to the lag in the coverage of data warehousing topics at universities and introduce methods, concepts and resources that can enable business educators to deal with these issues and conduct comprehensive, detailed, and meaningful coverage of data warehouse related topics.
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Background Overview-Data Warehousing

A typical organization maintains and utilizes a number of operational databases. These operational databases are used to support the organization’s day-to-day operations. A data warehouse is created within an organization as a separate database, (using its own DBMS), whose primary purpose is data analysis for the support of management's decision making processes (Inmon, 2005). The data stored in the data warehouse captures many different aspects of the business process, such as production, supply-chain management, marketing, sales and customer service. This data reflects strategically important information such as customer behavior patterns, sales trends, outcomes of marketing strategies, and other characteristics. Therefore, this data is of vital importance to the success of the business whose state it captures. That is the reason why companies choose to engage in the relatively expensive and lengthy undertaking of creating and maintaining the data warehouse, often containing multiple terabytes of data. For example, one study (Gray, 2006) reports a typical cost of $3 million for creating a 1-terabyte data warehouse, with a typical implementation time of 2 years.

Often the same data can have both operational and analytical purposes, and subsequently can be stored in both an operational database and the data warehouse. For example, data describing that product A was bought by customer B in store C can be stored in an operational data store for business-process support purposes, such as financial transaction record keeping or inventory monitoring. That same piece of information can also be stored in a data warehouse where, combined with vast amounts of similar data accumulated over a time period, it is used to analyze important trends, such as sales patterns or customer behavior.

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