Data mining and knowledge discovery techniques can help advance the business intelligence field. For example, data and text mining methods can inductively determine relationships and rules for improved business intelligence. They could also assist in generating new cases. Neural networks and genetic algorithms could enhance business intelligence applications by helping to weed out rules and cases, looking for inconsistencies within knowledge repositories, and helping to filter noisy data.
Business rules and business rule engines, which can be partly derived from AI and business intelligence techniques, are an excellent application of how AI has blended and synergized with business intelligence. For example, on September 1, 2004, Fair Isaac Corporation, a leading provider of analytics and decision technology, and Lombardi Software, provider of business process management software, announced a partnership to provide organizations with an integrated solution incorporating Fair Isaac’s Blaze Advisor business rules management software and Lombardi’s TeamWorks business process management software. Fair Isaac Corporation, which has previously used neural networks in their products, is combining its business rules technology with business process management at the policy and decision making levels of the organization. There is an active community of practitioners and researchers in business rules, as evidenced by the annual Business Rules conference (November 2004 - Las Vegas).
Other conferences, such as the Knowledge Management and Business Intelligence workshop (KMBI 2005, http://wm2005.iese.fraunhofer.de) to be held in April 2005 in Germany, are promoting the integration of AI, knowledge management, and business intelligence. Business intelligence is being defined in this context as an active, model-based and prospective approach to discover and explain hidden, decision-relevant aspects in large amounts of business data to better inform business decision processes.
Business intelligence needs to embed AI methods within its tool suite. According to Judith Lamont’s article, “Competitive Intelligence (CI): Ingredients for Success” (KMWorld, Nov/Dec. 2002), she states that “many CI initiatives leave a big gap between information and action…in addition, the corporate hierarchy does not always integrate CI into the top ranks.” If business intelligence does not incorporate such active components as AI and KM, business intelligence may suffer the same fate as CI, as described by Lamont.
Perhaps, the problem is that there is too much “intelligence” around—artificial intelligence, competitive intelligence, business intelligence, and other intelligentsia. Maybe it’s time to package all these types of intelligence into one. We could call it “ABC Intelligence” to denote the basic building blocks (i.e., the ABC’s) of non-human intelligence. Or alternatively, the ABC Intelligence could refer to “Artificial, Business, and Competitive” Intelligence. I’ll let you decide what’s best!
About the AuthorDr. Jay Liebowitz is a Full Professor in the Graduate Division of Business and Management at Johns Hopkins University. He is also the Program Director of a new Graduate Certificate in Competitive Intelligence at Johns Hopkins University (http://business.jhu.edu). He previously served as the first Knowledge Management Officer at NASA Goddard Space Flight Center, and as the Robert W. Deutsch Distinguished Professor of Information Systems at University of Maryland-Baltimore County. He has written 30 books, and is the Founding Editor-in-Chief of the leading international journal, Expert Systems With Applications. He can be reached at 301-315-2893 or .
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