⎯ A business intelligence system provides decision-makers with information and knowledge extracted from data, through the application of mathematical models and algorithms.
In some instances, this activity may reduce to calculations of totals and percentages, graphically represented by simple histograms, whereas more elaborate analyses require the development of advanced optimization and learning models.
⎯ In general terms, the adoption of a business intelligence system tends to promote a scientific and rational approach to the management of enterprises and complex organizations. Even the use of a spreadsheet to estimate the effects on the budget of fluctuations in interest rates, despite its simplicity, forces decision-makers to generate a mental representation of the process of the financial flow.
⎯ Classical scientific disciplines, such as physics, have always resorted to mathematical models for the abstract representation of real systems. Other disciplines, such as operations research, have instead exploited the application of scientific methods and mathematical models to the study of artificial systems, for example, public and private organizations. Part II of this book will describe the main mathematical models used in business intelligence architectures and decision support systems, as well as the corresponding solution methods, while PartIIIwill illustrates several related applications.
⎯ The rational approach typical of a business intelligence analysis can be summarized schematically in the following main characteristics.
⎯ First, the objectives of the analysis are identified and the performance indicators that will be used to evaluate alternative options are defined. ⎯ Mathematical models are then developed by exploiting the relationships among system control variables, parameters, and evaluation metrics. ⎯ Finally, what-if analyses are carried out to evaluate the effects on the performance determined by variations in the control variables and changes in the parameters.
⎯ Although their primary objective is to enhance the effectiveness of the decision-making process, the adoption of mathematical models also affords other advantages, which can be appreciated particularly in the long term. First, the development of an abstract model forces decision-makers to focus on the main features of the analyzed domain, thus inducing a deeper understanding of the phenomenon under investigation. Furthermore, the knowledge about the domain acquired when building a mathematical model can be more easily transferred in the long run to other individuals within the same organization, thus allowing sharper preservation of knowledge in comparison to empirical decision-making processes. Finally, a mathematical model developed for a specific decision-making task is so general and flexible that in most cases it can be applied to other ensuing situations to solve problems of similar type.
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