MIS Research Center Seminar Series
8:30 AM - 11:15 AM
2-260T, 2nd Floor, Carlson School of Management

Abstract

Successfully supporting managerial decision making has become critically dependent upon the acquisition of integrated, high quality and actionable information organized and presented to managers in a timely and easily understood manner. Data warehouses have emerged as the foundation upon which to meet this need. They serve as an integrated repository for internal and external data critical to understanding and evaluating the position of the business within its environmental context. With the addition of data mining algorithms, predictive models, analytic tools, and user-friendly interfaces, data warehouses have the potential to provide actionable information resources -- business intelligence that supports effective problem and opportunity identification and critical decision making as well as strategy formulation, implementation, and evaluation.

In this presentation, we explore the nature of business intelligence applications utilizing a design science framework. This framework positions business systems within a general system perspective emphasizing organizational goals, the generation of design alternatives, and the development of evaluation criteria for selecting among generated alternatives. Data warehousing applications focus on data acquisition, integration and management of data quality. First-generation business intelligence applications focus on data presentation utilizing report generation and online analytical processing (OLAP) tools. They emphasize data exploration, effectively “hiding the SQL,” and enabling managers a simple mechanism for the evaluation of business performance against established metrics. Second-generation business intelligence applications focus on data mining, utilizing statistical and artificial intelligence techniques. They emphasize the identification of patterns and factors influencing performance. Third-generation business intelligence business intelligence applications focus on predictive analytics utilizing management science and operations research techniques. They emphasize the identification and evaluation of decision alternatives and the establishment of business performance metrics and goals. These “close the loop” on business intelligence projects providing quantitative mechanisms for the evaluation of business decisions and for the evaluation of business intelligence projects developed to support them.

Biography

Salvatore T. March is the David K. Wilson Professor of Management at the Owen Graduate School of Management, Vanderbilt University. He received a B.S. in Industrial Engineering and M.S. and Ph.D. degrees in Operations Research from Cornell University. His research interests are in conceptual modeling, data warehousing and business intelligence, distributed database design, and electronic commerce. His published research articles have appeared in journals such as Communications of the ACM , Decision Support Systems , IEEE Transactions on Knowledge and Data Engineering , Information Systems Research , ACM Transactions on Database Systems , and MIS Quarterly . He has served as the Editor-in-Chief of ACM Computing Surveys , a Senior Editor for Information Systems Research and an associate editor for Decision Sciences Journal and MIS Quarterly . He served three years on the advisory board for the Teradata University network. He is currently a Program Committee Co-Chair for the International Conference on Information Systems.