Essay on Evaluation Of User Requirement Analysis Of Data Warehouse Design

Essay on Evaluation Of User Requirement Analysis Of Data Warehouse Design

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Evaluation of User Requirement Analysis in Data Warehouse Design
1.0 Introduction
A data warehouse (DW) can be acknowledged as one of the most complex information system modules available and it is a system that periodically retrieves and consolidates data from the sources into a dimensional or normalized data store. It is an integrated, subject-oriented, nonvolatile and a time-variant collection of data in support of management’s decisions (Inmon, 1993).
1.1 Data Warehouse Design Process
When considering the suggestions of various authors who are well known in the field, such as William Inmon and Ralph Kimball the DW design process can be divided into three main stages (Figure 1).

The first phase which is DW planning is aimed at determining the scope and the objectives of the DW. The second phase will be concentrating on individual data marts (DM) and will be repeated for each DM to be implemented. And according to the Figure 1, the second phase can be further divided into five distinct main phases. The final phase will focus on performance optimization and keeping the DW schema up-to-date.
In DW lifecycle, one of the most crucial phases that might affect all other phases of DW development is the Requirement Analysis phase. It is a process of collecting requirements from end user. The goal of this phase is to identify organizational objectives and elaborate requirements that could measure organizational performance. However, DW projects are complex, inherently risky and some of them fail in the implementation. According to studies more than 80% of DW projects fail to fulfill business goals.
One of the main reasons behind the failure of DW is the incompetence in analyzing user requirements. Kimball et al. (2008) indicated...

... middle of paper ...

...ta in data source. So it is easier to construct DW schema and at the same time ensure the availability of data. Since data-driven approaches start with the available data, this methodologies are suitable when detailed knowledge of data sources is available. The data-driven approaches require normalized data, so in order to successfully use these methods the source schema should exhibit a decent degree of normalization.
In practice, when the chosen data source for the DW relies on a reconciled, well-designed and not very large single relational operational database these requirements are largely satisfied: in fact, normalization and detailed knowledge are guaranteed by the source integration process. The same holds, thanks to a careful source recognition activity, in the frequent case when the source is a single relational database, well-designed and not very large.

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