B. Lekay 2973630
We swear that this assignment is our original work. All information obtained directly or indirectly from other sources has been fully acknowledged. All members of the group contributed equally and fairly to the completion of this project.
Signed: BJ Le Kay
Date: April 2013
|Table of Contents |Page |
|1. Introduction |
|1.1 Problem definition and background to the problem ...view middle of the document...
|4.1 Management strategies |19 |
|4.2 Possible new research questions |20 |
|5. List of references |20 |
|6. Appendix |20 |
1. Problem definition and background to the problem
Business intelligence (BI) is used to create value for organisations by enabling a company to increase its revenues, reduce its costs, or both- thus leading to higher profits.
Various researches indicate that Business Intelligence Systems (BIS) can provide the ability to analyze business information in order to support and improve management decision making across a broad range of business activities. They leverage the large data infrastructure investment, for example, in ERP systems made by firms, and have the potential to realize the substantial value locked up in a firm’s data resources. However, research has identified, while substantial business investment in BI systems is continuing to accelerate, there is a complete absence of a specific and rigorous method to measure the realized business value, if any.
A BI application does not analyse the information from once source but from different applications, as shown in the diagram below.
BI does not produce data; it uses the data created by enterprise applications, i.e. ERP, CRM, SCM, etc.
Figure 1 describes how BI is used in business to provide tools to assist in decision making. Databases (i.e. ERP, SCM, etc.) keep track of transactions captured by users of these applications. Laudon and Laudon (2006: 254) define OLAP as tools which enable users to analyze multidimensional data interactively from multiple perspectives. According to Laudon and Laudon (2006: 254) OLAP consists of three basic analytical operations: consolidation (roll-up), drill-down, and slicing and dicing. Consolidation involves the aggregation of data that can be accumulated and computed in one or more dimensions. For example, all sales offices are rolled up to the sales department or sales division to anticipate sales trends. By contrast, the drill-down is a technique that allows users to navigate through the details. For instance, users can view the sales by individual products that make up a region’s sales. Slicing and dicing is a feature whereby users can take out (slicing) a specific set of data of the OLAP cube and view...