Abstract
Since the computer age dawned on mankind, one of the most important areas in information technology has been that of “decision support.” Today, this area is more important than ever. Working in dynamic and ever-changing environments, modern-day managers are responsible for an assortment of far reaching decisions: Should the company increase or decrease its workforce? Enter new markets? Develop new products? Invest in research and development? The list goes on. But despite the inherent complexity of these issues and the ever-increasing load of information that business managers must deal with, all these decisions boil down to two fundamental questions: What is likely to happen in the future? What is the best decision right now? Whether we realize it or not, these two questions pervade our everyday lives — both on a personal and professional level. When driving to work, for instance, we have to make a traffic prediction before we can choose the quickest driving route. At work, we need to predict the demand for our product before we can decide how much to produce. And before investing in a foreign market, we need to predict future exchange rates and economic variables. It seems that regardless of the decision being made or its complexity, we first need to make a prediction of what is likely to happen in the future, and then make the best decision based on that prediction. This fundamental process underpins the basic premise of Adaptive Business Intelligence.
TopIntroduction
Since the computer age dawned on mankind, one of the most important areas in information technology has been that of “decision support.” Today, this area is more important than ever. Working in dynamic and ever-changing environments, modern-day managers are responsible for an assortment of far reaching decisions: Should the company increase or decrease its workforce? Enter new markets? Develop new products? Invest in research and development? The list goes on. But despite the inherent complexity of these issues and the ever-increasing load of information that business managers must deal with, all these decisions boil down to two fundamental questions:
Whether we realize it or not, these two questions pervade our everyday lives — both on a personal and professional level. When driving to work, for instance, we have to make a traffic prediction before we can choose the quickest driving route. At work, we need to predict the demand for our product before we can decide how much to produce. And before investing in a foreign market, we need to predict future exchange rates and economic variables. It seems that regardless of the decision being made or its complexity, we first need to make a prediction of what is likely to happen in the future, and then make the best decision based on that prediction. This fundamental process underpins the basic premise of Adaptive Business Intelligence.
TopBackground
Simply put, Adaptive Business Intelligence is the discipline of combining prediction, optimization, and adaptability into a system capable of answering these two fundamental questions: What is likely to happen in the future? and What is the best decision right now? (Michalewicz et al. 2007). To build such a system, we first need to understand the methods and techniques that enable prediction, optimization, and adaptability (Dhar and Stein, 1997). At first blush, this subject matter is nothing new, as hundreds of books and articles have already been written on business intelligence (Vitt et al., 2002; Loshin, 2003), data mining and prediction methods (Weiss and Indurkhya, 1998; Witten and Frank, 2005), forecasting methods (Makridakis et al., 1988), optimization techniques (Deb 2001; Coello et al. 2002; Michalewicz and Fogel, 2004), and so forth. However, none of these has explained how to combine these various technologies into a software system that is capable of predicting, optimizing, and adapting. Adaptive Business Intelligence addresses this very issue.
Clearly, the future of the business intelligence industry lies in systems that can make decisions, rather than tools that produce detailed reports (Loshin 2003). As most business managers now realize, there is a world of difference between having good knowledge and detailed reports, and making smart decisions. Michael Kahn, a technology reporter for Reuters in San Francisco, makes a valid point in the January 16, 2006 story entitled “Business intelligence software looks to future”:
“But analysts say applications that actually answer questions rather than just present mounds of data is the key driver of a market set to grow 10 per cent in 2006 or about twice the rate of the business software industry in general.
‘Increasingly you are seeing applications being developed that will result in some sort of action,’ said Brendan Barnacle, an analyst at Pacific Crest Equities. ‘It is a relatively small part now, but it is clearly where the future is. That is the next stage of business intelligence.’”
Key Terms in this Chapter
Data Mining: The application of analytical methods and tools to data for the purpose of identifying patterns, relationships, or obtaining systems that perform useful tasks such as classification, prediction, estimation, or affinity grouping.
Optimization: Process of finding the solution that is the best fit to the available resources.
Knowledge: “Integrated information,” which includes facts and relationships that have been perceived, discovered, or learned.
Business Intelligence: A collection of tools, methods, technologies, and processes needed to transform data into actionable knowledge.
Adaptive Business Intelligence: The discipline of using prediction and optimization techniques to build self-learning ‘decisioning’ systems”.
Information: “Organized data,” which are preprocessed, cleaned, arranged into structures, and stripped of redundancy.
Prediction: A statement or claim that a particular event will occur in the future.
Data: Pieces collected on a daily basis in the form of bits, numbers, symbols, and “objects.”