Enterprise Information System and Data Mining

Enterprise Information System and Data Mining

Kenneth D. Lawrence (New Jersey Institute of Technology, USA), Dinesh R. Pai (Penn State Lehigh Valley, USA), Ronald Klimberg (Saint Joseph’s University, USA) and Sheila M. Lawrence (Rutgers University, USA)
Copyright: © 2010 |Pages: 8
DOI: 10.4018/jbir.2010070103
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Abstract

The advent of information technology and the consequent proliferation of information systems have lead to generation of vast amounts of data, both within the organization and across its supply chain. Enterprise information systems (EIS) have added to organizational complexity, and at the same time, created opportunities for enhancing its competitive advantage by utilizing this data for business intelligence purposes. Various data mining tools have been used to gain a competitive edge through these large data bases. In this paper, the authors discuss EIS-aided business intelligence and data mining as applicable to organizational functions, such as supply chain management (SCM), marketing, and customer relationship management (CRM) in the context of EIS.
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Eis And Business Intelligence

Over the last decade, the global business environment and its accompanying complexities has necessitated the use of BI tools. Comprehensive and timely information is a must for new product development and improving business operations. BI plays a crucial role in providing a sound DSS for operative and strategic decision making (Hannula & Pirttimaki, 2003).

Business intelligence can be defined as a process of acquiring, interpreting, collating, analyzing and exploiting information for business competitiveness (Chung, Chen, & Nunamaker Jr, 2005). Some of the tools used for business intelligence purpose and are discussed in this paper are: OLAP (Online Analytical Processing), and data mining.

Business intelligence (BI) has benefitted from the EIS and utilizes it to analyze huge amounts of data for making better decisions regarding customers, suppliers, supply chain, and infrastructure. Whereas EIS lays the technological platform to integrate various systems and coordinate business processes, BI is a data driven decision support system (DSS) that combines data gathering, data storage, and knowledge management with analysis for better managerial decision making. The primary objective of both is to support sound decision making.

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