Data Warehouse a Strategic Weapon of an Organization Term Paper

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Data Warehousing: A Strategic Weapon of an Organization.

Within Chapter One, an introduction to the study will be provided. Initially, the overall aims of the research proposal will be discussed. This will be followed by a presentation of the overall objectives of the study will be delineated. After this, the significance of the research will be discussed, including a justification and rationale for the investigation.

The aims of the study are to further establish the degree to which data warehousing has been used by organizations in achieving greater competitive advantage within the industries and markets in which they operate. In a recent report in the Harvard Business Review (2003), it was suggested that companies faced with the harsh realities of the current economy want to have a better sense of how they are performing. With growing volumes of data available and increased efforts to transform that data into meaningful knowledge that can be used to aid in gaining competitive advantage, companies are increasingly recognizing how knowledge can be used as business intelligence to reduce risk and to accomplish business outcomes (Harvard Business Review, 2003). More importantly, companies are wanting to make certain that enterprise data is integrated to the point that it can be used to guide the business in making critical decisions at the right time and right place in relation to customers.

As noted by Foote and Krishnamurthi, (2001), until very recently, the forecasting process used by companies was relatively subjective and was dependent upon the opinions of company executives, sales force analysts, and industry analysts, who were not always extremely reliant in aiding the company to in the production of satisfactory outcomes. Quite frequently, as reported by Foote and Krishnamurthi, companies found that they had missed the mark in forecasting and consequently had failed in achieving profitability, reliability and a competitive vantage position in their industry. Thus, companies are increasingly recognizing the value of investing in an information system to support their forecasting process. According to Foote and Krishnamurthi, a data warehouse has come to be identified as assuming a pivotal role in gaining the knowledge needed by companies to implement reliable systems for forecasting. It enables companies to collect data from many sources, perform analyses, and make informed decisions in real time for the purposes of achieving competitive advantages and accuracy in its forecasting operations in an unprecedented manner.

As data warehousing has been identified as offering extensive promise to companies in improving and gaining greater accuracy in forecasting, it is the intent of the study to further examine the documented experiences of companies who have implemented data warehousing in order to gain a better understanding as to whether improved forecasting has been obtained within these companies. As well, it is the aim of the study to further determine whether companies have been able to achieve greater competitive advantage.

Objectives of the Study

The overall objective of the study is to further explore the degree to which data warehousing has been effective in assisting companies with the process and activities of forecasting as well as in gaining competitive advantage

Significance of and Justification for the Study

As evidenced within the current literature, some companies have reported success with data warehousing while others have not. While Foote and Krishnamurthi (2001) developed a model for understanding the stages of a data warehouse for the purposes understanding and predicting how companies data warehouses change over time, it would appear that this model may also offer utility in determining why some companies are more successful and gain greater competitive advantage than other companies. The proposed study offers the opportunity to examine the success or lack of success of data warehouses through the systematic examination of a number of different variables (i.e., those identified in objective 4 above). As well, Foote and Krishnamurthi's stages model of data warehousing has not of yet been tested in this manner and the results of the study may offer further opportunity to further validate the model while demonstrating its potential utility in examining the degree of competitive advantage achieved by companies on the basis of the stages model.



Data Warehousing: Background

During the 1990s, data warehousing emerged as one of the most important developments in the information systems field. Prior research has suggested that 95% of the Fortune 1000 companies either have a data warehouse in place or are planning to develop one (META Group, 1996). Predictions had suggested that the data warehousing market
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would grow to a $113.5 billion market by the year 2002, including the sales of systems, software, services, and in-house expenditures (Eckerson, 1998). Such predictions have not been surprising as research findings had suggested that company executives had identified data warehousing and electronic commerce as most critical for their company's strategic initiatives (Eckerson 1999).

As explained by Wixom and Watson (2001), data warehousing emerged largely in response to business need and technological advances. As the business environment has become more global, competitive, complex, and volatile, there has been a greater demand for data warehousing. Customer relationship management and e-commerce initiatives are creating requirements for large, integrated data repositories and advanced analytical capabilities. More data are captured by organizational systems (e.g., barcode scanning, clickstream) or can be purchased from companies. As further explained by Wixom and Watson, through hardware advances such as symmetric multi-processing, massive parallel processing, and parallel database technology, it has now become now possible to load, maintain, and access databases of terabyte size. As a result of these changes, organizations have changed significantly in the manner in which they conduct business, particularly in sales and marketing, allowing companies to analyze the behavior of individual customers rather than demographic groups or product classes (Wixom & Watson, 2001).

As data warehousing has emerged, a number of definitions have been applied to describe the activities and tasks involved in the construction of a data warehouse. As defined Inmon (1992), a data warehouse is a managed database in which the data is:

Subject oriented: There is a shift from application-oriented data (i.e., data designed to support application processing) to decision-support data (i.e., data designed to aid in decision making). If designed well, subject-oriented data provides a stable image of business processes, independent of legacy systems. In other words, it captures the basic nature of the business environment.

Integrated: The database consolidates application data from different legacy systems (usually means old-style mainframe databases) which use different encoding, measurement units, and so on, and eliminates inconsistencies in the data.

Time-variant: Informational data has a time dimension: each data point is associated with a point in time, and data points can be compared along that time axis unlike operational data which is valid only at the moment of access capturing a moment in time.

Nonvolatile: New data is always appended rather than replaced. The database continually absorbs new data, integrating it with the previous data.

As Inmon suggested, data warehousing has been and continues to be an evolving concept. A data warehouse (or smaller-scale data mart) has been described as a specifically prepared repository of data created to support decision making (Gray & Watson, 1998). Data are extracted from source systems, cleaned/scrubbed, transformed, and placed in data stores (Gray & Watson 1998). As further explained by Gray and Watson, a data warehouse has data suppliers who are responsible for delivering data to the ultimate end users of the warehouse, such as analysts, operational personnel, and managers. The data suppliers make data available to end users either through SQL queries or custom-built decision-support applications.

According to Foote and Krishnamurthi (2001), by definition, a data warehouse is a subject oriented (i.e., by product, store and department), integrated, time-variant and non-volatile collection of data to support decision-making. Conceptually, a data warehouse is created as data from older systems are copied into a new computer system dedicated completely to analyze the data. The purpose behind data analysis is to better understand what is happening, or what did happen within a company. The value of better understanding is translated into better decision-making. As further conceptualized by Foote and Krishnamurthi, there are four commonly used terms to describe the architecture and the functionality of a data warehouse. They are:

Data Mart: A data warehouse about a particular subject such as a store, a department and a product.

Data Warehouse: A repository of data from older systems and other sources that has been cleaned, transformed and duplicated into a data warehouse database.

Enterprise Data Warehouse: A data warehouse built for an entire company.

Operational Data Store: A data warehouse that requires faster response time and update capability. It is extensively used to provide an up-to-date view of data.

According to Babcock (1995), data warehousing tasks are oriented towards information, analysis and decision-making rather than operation or transaction processing. As suggested by Whitten, Bentley and Barlow (1994), data warehouses are best understood as stored data that has been extracted from production databases and conventional files. Kador (1995) emphasized that a complete data warehouse is not possible as the opportunities for…

Sources Used in Documents:


Agosta, L. (2003). Ask the Expert. Harvard Business Review, 81(6), 1.

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Babcock, Charles (1995). Slice, dice & deliver. Computerworld, 29, 46, 129 -132.

Beitler, S.S., & Lean, R. (1997). Sears' EPIC Transformation: Converting from Mainframe Legacy Systems to Online Analytical Processing (OLAP). Journal of Data Warehousing (2:2), 5-16.

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