Data Warehousing Development Methodologies

Posted: August 27th, 2021

Data Warehousing Development Methodologies

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Data Warehousing Development Methodologies

Introduction

A data warehouse comprises a system that stores data from a functioning company database. In every Strategic Management process of an organization, a data warehouse is a major factor supporting the decision support system. A decision support system (DSS) is a computerized program used to analyze large amounts of data and compile information necessary for decision-making. On the other hand, business intelligence (BI) entails a group of technologies and applications used for gathering, storing, carrying out analysis, and offering data access. This empowers business users to make wise decisions regarding businesses. There exist many data warehousing methodologies supporting market growth. Previously, there has been a controversy over which data warehouse architecture is good for data warehousing. Hence, this paper provides a review and comparison of two methodologies which entail EDW (Inmon) and architected data marts (Kimball).

Inmon and Kimball’s architectures share a similar common feature. Every methodology comprises of one integrated repository of atomic data. Notably, the architecture of Inmon is referred to as, enterprise data warehouse, whereas the architecture of Kimball is called the dimensional data warehouse. They both offer an enterprise focus in their architecture to enhance company information analysis to address business needs. Therefore, there are proofs that Kimball and Inmon’s approaches are committed to delivering data warehouses successfully.

Differences between Kimball and Inmon Methodologies

On the Contrary, both data warehouse architectures have some differences. Inmon utilizes ER Model while Kimball applies the star schemas ’dimensional model.Unlike Kimball that uses a dimensional model for all data, Inmon utilizes a dimensional model fora data mart only.Additionally, Inmon is created to be used in departments, and it manipulates data marts as physical separation from the enterprise data warehouse. On the other hand, the separation of data marts from the dimensional data warehouse is not required in Kimball’s architecture.Further, Kimbal architecture design allows for direct access of data unlike in the Inmon’s architecture where data is accessed only through data marts. Thus, despite the similarities that are attributed to the architectural designs of the two systems, their performance mechanisms are fundamentally different. Hence, the selection of the methodologies should be done based on the organization needs to ensure proper utilization.

Factors to Consider While Selecting Methodologies

When selecting a particular methodology, it is vital to put various factors into consideration. These include resource availability, nature of the end-user activities, and the urgency of data warehouse needs. Equally, system compatibility and technical challenges are key issues to consider.Hence, the next step that Kath1yn Avery should take is to choose a suitable methodology to use in a given scenario. For businesses that are stable and can afford the costs, Inmon is the best method to use. This method is suitable since the design is not modified with changes in business conditions. If she is focused on winning quickly, and if the local optimization is good enough, she should use the Kimball approach. Hence, with this knowledge, she will select the best approach to use in developing Big Chain’s first data warehouse. 

Summary

Despite the differences between the two methodologies, the two approaches help in building a data warehouse. Different companies have used a combination of both. Also, the two have pros and cons, and they function well in different circumstances. Thus, it is not wise to conclude that one works better than the other. Additionally, while selecting the approach to use, Kath1yn Avery should consider several factors, including resource availability nature of the end-users activity, project urgency, and compatibility with the available system. Therefore, with these factors, she should carefully choose an approach that will satisfy the company’s business intelligence reporting needs.

Case of Successful Data Warehousing Applications

This paper uses the fluid motion company as a recent case of successful data warehousing applications. As a producer of chemicals, valves, and industrial pumps, it is a world market leader in the industry. With the help of various company resources, the data warehousing can collect data. Besides, to make wise decisions and enhance efficiency in companies, business users can create strong and solve complicated questions concerning the functionality of businesses. Likewise, the fluid motion company is permitted to combine data from different sources by Data warehousing. Thus, the company utilized the data warehouse as a decision support system siting organizational historical data.

Additionally, data warehouse application makes it possible to use data for business reports.The fluid company can speed up the process of data analysis and recovery using data warehousing applications. At the same time, the company can ask questions and keep a large quantity of data. Moreover, this market leader company exploited data warehousing as a de-normalized data structure offering good performance while carrying out analytical tasks. Still, the data warehouses can be utilized in enhancing real time information integration for development of different organizational reports. They also ease the flow of data when acquiring information.Therefore, the applications offer long-term impacts on the projects. Hence, data warehousing application is useful for organizational development.