Abstract—Business intelligence (BI) is the process of gathering enough of the right information in the right manner at the right time, and delivering the right results to the right people for decision-making purposes so that it can continue to yield real business benefits, or have a positive impact on business strategy, tactics, and operations in the enterprises. This paper was intended as a short introduction to the study of business intelligence in enterprise computing environment. In addition, the conclusions point out the challenges to broad and deep deployment of business intelligence systems, and provide the proposals of making business intelligence more effective. Keywords—Business Intelligence; Enterprise Intelligence Computing; Enterprise Information System I. INTRODUCTION he most common types of traditional information systems in enterprise computing environment are e-commerce systems, management information systems, and transaction processing systems, enterprise resource planning systems, and executive information systems. Together, these information systems help employees accomplish both routine and special tasks from recording sales, to processing payrolls, to supporting decisions in various departments, or to providing alternatives for large-scale projects. However, as businesses continue to use these systems for a growing number of functions in today’s competitive world, most enterprises are facing the challenges of processing and analyzing huge amounts of data and turning it into profits. They have many detailed operational data, yet can not get the satisfying answers they need from large volumes of operational data to react quickly to changing circumstances because the data are very likely distributed This work was supported by the Outstanding Overseas Chinese Scholars Fund of Chinese Academy of Sciences, the National Science Foundation of China (No. 60435010, 90604017, 60675010), 863 National High-Tech Program (No.2006AA01Z128), National Basic Research Priorities Programme (No. 2003CB317004) and the Nature Science Foundation of Beijing (No. 4052025). L. D. Xu, L. Zeng, Z.Z. Shi, Q. He, and M. G. Wang are with the Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, and Graduate University of Chinese Academy of Sciences. over many departments in the enterprises, or locked in a sluggish enterprise department. As a result, appropriate analyses on history data are unavailable and requisite to decision makers. For delivering the right information with the right format to the right people at the right time for decision-making purposes, the concept of business intelligence (BI) is presented, which is a set of tools, technologies or solutions designed for users to efficiently extract useful business information from oceans of routine data. The origin of business intelligence was firstly introduced by Garter Group in 1996, [1] and incipiently referred to some tools and technologies including data warehouses, reporting query and analysis. Today’s business intelligence is regarded as a very powerful solution, an extremely valuable tool, or a key approach to adding the value of the enterprise. A fast-growing-number of business sectors have deployed advanced business intelligence systems to enhance their competitiveness. II. TECHNICAL FRAMEWORK IN ENTERPRISE COMPUTING ENVIRONMENT The technology categories of business intelligence system mainly encompass data warehousing or data mart, OLAP, and data mining. [2] More specifically, data warehouse or data mart are the fundament infrastructure of business intelligence systems, and data mining is its core component that allow users to detect trends, identify patterns and analyse data, while OLAP is the sets of front-end analyzing tools. Constructing benign enterprise intelligence computing environment should consider at least the following two points. One is correct, valid, integrated, and in-time data, and another is the means which can transform the data into decision information. However, neither satisfied data nor effective means are easily acquired. Strong technical framework can be used to solve these two questions above. The framework consists of operational applications tier, data acquisition tier, data warehouse tier, platforms and enterprise L. D. Xu is also with the Department of Information Technology and Decision Sciences, Old Dominion University (e-mail: lxu@odu.edu). Research on Business Intelligence in Enterprise Computing Environment Lida Xu, Li Zeng, Zhongzhi Shi, Qing He, Maoguang Wang T 3270 1-4244-0991-8/07/$25.00/©2007 IEEE