Data warehouses and data marts: Can these tools be implemented too early in the development of a company?
Moyle, Sam A
This document will discuss the small business and its particular needs when considering data warehouses or data marts in conjunction with business intelligence tools. We will discuss some of the history of the data warehouse genre, and how it relates to the small business. Issues of data model architecture and development strategies are also discussed with the purpose of discovering techniques that help the developer maximise return from their time investment. Proposed or expected benefits should be ascertained before the data warehouse is built. This provides management with information upon which to base decisions, particularly important where resources are tight. Especially important is time till cost recovery, which allows financial benefit to be calculated. The process (or methodology) by which data warehouses are developed is discussed. Current practice is shown, and it is noted that there are no real ‘hard and fast’ rules that govern this process. Industry-accepted ‘Do’s and Don’ts’ are listed for discussion. There are two questions being asked by this document. The first, “Can these tools be implemented too early in the development of a company”, is answered by implementing a pre-packaged data warehouse, with associated business intelligence tools, as a pilot project for a small company. From this implementation conclusions will be drawn about whether the company is mature enough to warrant such a tool. The second question asked is “Can we generate heuristics that will enable small business to assess the value of a data warehouse, before implementation?” Through this implementation it will be established that heuristic rules may be derived to guide small-business people in their pre-implementation decision making. However, a number of implementations will need to be completed before similarities can be accurately identified and rules derived.
Degree Name: Bachelor of Commerce with Honours
Degree Discipline: Information Science
Research Type: Dissertation