Question 1: Case One –eBay Q1.1. Discuss the relationships between business intelligence, data warehouse, data mining, text and web mining, and knowledge management. Justify and synthesis your answers/viewpoints with examples (e.g. eBay case) and findings from literature/articles. To understand the relationships between these terms, definition of each term should be illustrated. Firstly, business intelligence (BI) in most resource has been defined as a broad term that combines many tools and technologies, used to extract useful meaning of enterprise data in order to help the decision maker. Turban, Sharda, Aronson, and King (2008) said: ‘Business Intelligence is an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies. It is a content-free expression, so it means different things to different people. BI’s major objective is to enable interactive access (sometimes in real time) to data, to enable manipulation of data, and to give business managers and analysts the ability to conduct appropriate analysis. By analysing historical and current data, situations, and performances, decision makers acquire valuable insights that enable them to make more informed, timely, and consequently better decision’ p.28. The EBSP glossary defines business intelligence as ‘a broad term for software reporting tools that pull data from various sources to generate customizable reports’ (EBSP, 2009). Rud (2009) also defined BI as a set of architectures, methodologies, theories, processes and technologies that aims to deliver meaningful and useful information for business purposes. He argues that BI is a gate of new opportunities that bring a business into a competitive market advantage and ensures long-term stability. However, from the above definitions it is clear that BI is capable of providing a holistic view of the business by utilizing organisation resources such as database (i.e., data warehouse and data mart) and data analytics tools and techniques (i.e., data, text and web mining). Secondly, according to Turban et al. (2008), ‘data warehouse (DW) is a pool of data produced to support decision making’. Data warehouse is a repository of internal, external, current and historical data that is supposed to facilitate analytical activities (i.e., data mining, reporting, querying and OLAP). The major advantage of DW is that it makes data quickly and easily reachable by end users. Moreover, it provides corporate data of different formats as a consolidated view, making this data ready for analysis. Therefore, DW is the foundation that BI built upon or the back office of BI. Indeed, data warehousing can be seen as a subcomponent of BI and at the same time a vehicle for delivering BI. The third term to be discussed is data mining (DM), text and web mining. The word ‘mining’ means the process of extracting valuable minerals from the earth; the main objective of DM is to find useful and meaningful information by exploring hidden patterns and relationships between 1
collated data records. Jackson (2002) defined data mining as ‘an extension of traditional data analysis and statistical approaches in that it incorporates analytical techniques drawn from a range of disciplines’, whereas Turban et al. (2008) simply defined it as the act of discovering or mining knowledge from a large set of data by using mathematical, statistical and artificial intelligence techniques. Data mining uses data warehouse as the source of information that helps to perform business intelligence activities. This relationship can be interpreted from another perspective where data warehouse involves data cleaning and data integration, which are an important pre-processing step for data mining (Jackson, 2002). Figure 1 shows the sequence of data processing from its original source though data warehouse until final reports.
Figure 1. Sequence of data processing (Jackson, 2002).
On the other hand, text mining and web...
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