Solomon Negash Computer Science and Information Systems Department Kennesaw State University email@example.com ABSTRACT Business intelligence systems combine operational data with analytical tools to present complex and competitive information to planners and decision makers. The objective is to improve the timeliness and quality of inputs to the decision process. Business Intelligence is used to understand the capabilities available in the firm; the state of the art, trends, and future directions in the markets, the technologies, and the regulatory environment in which the firm competes; and the actions of competitors and the implications of these actions. The emergence of the data warehouse as a repository, advances in data cleansing, increased capabilities of hardware and software, and the emergence of the web architecture all combine to create a richer business intelligence environment than was available previously. Although business intelligence systems are widely used in industry, research about them is limited. This paper, in addition to being a tutorial, proposes a BI framework and potential research topics. The framework highlights the importance of unstructured data and discusses the need to develop BI tools for its acquisition, integration, cleanup, search, analysis, and delivery. In addition, this paper explores a matrix for BI data types (structured vs. unstructured) and data sources (internal and external) to guide research. KEYWORDS: business intelligence, competitive intelligence, unstructured data I. INTRODUCTION
Demand for Business Intelligence (BI) applications continues to grow even at a time when demand for most information technology (IT) products is soft [Soejarto, 2003; Whiting, 2003]. Yet, information systems (IS) research in this field is, to put it charitably, sparse. While the term Business Intelligence is relatively new, computer-based business intelligence systems appeared, in one guise or other, close to forty years ago.1 BI as a term replaced decision support, executive information systems, and management information systems [Thomsen, 2003]. With each new iteration, capabilities increased as enterprises grew ever-more sophisticated in their computational and analytical needs and as computer hardware and software matured. In this paper BI systems are defined as follows:
For a history of business intelligence, see [Power 2004]
Business Intelligence by S. Negash
Communications of the Association for Information Systems (Volume 13, 2004)177-195
BI systems combine data gathering, data storage, and knowledge management with analytical tools to present complex internal and competitive information to planners and decision makers. Implicit in this definition is the idea (perhaps the ideal) that business intelligence systems provide actionable information delivered at the right time, at the right location, and in the right form to assist decision makers. The objective is to improve the timeliness and quality of inputs to the decision process, hence facilitating managerial work. Sometimes business intelligence refers to on-line decision making, that is, instant response. Most of the time, it refers to shrinking the time frame so that the intelligence is still useful to the decision maker when the decision time comes. In all cases, use of business intelligence is viewed as being proactive. Essential components of proactive BI are [Langseth and Vivatrat, 2003]: • • • • • • • real-time data warehousing, data mining, automated anomaly and exception detection, proactive alerting with automatic recipient determination, seamless follow-through workflow, automatic learning and refinement, geographic information systems (Appendix I)
• data visualization (Appendix II) Figure 1 shows the variety of information inputs available to provide the intelligence needed in...