Über die Autorin bzw. den Autor

Carlo Vercellis - School of Management, Politecnico di Milano, Italy

As well as teaching courses in Operations Research and Business Intelligence, Professor Vercellis is director of the research group MOLD (Mathematical Modeling, Optimization, Learning from Data). He has written four book in Italian, contributed to numerous other books, and has had many papers published in a variety of international journals.

Von der hinteren Coverseite

Business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better business decisions. The term implies having a comprehensive knowledge of all factors that affect a business, such as customers, competitors, business partners, economic environment, and internal operations, therefore enabling optimal decisions to be made.

Business Intelligence provides readers with an introduction and practical guide to the mathematical models and analysis methodologies vital to business intelligence.

This book:

• Combines detailed coverage with a practical guide to the mathematical models and analysis methodologies of business intelligence.
• Covers all the hot topics such as data warehousing, data mining and its applications, machine learning, classification, supply optimization models, decision support systems, and analytical methods for performance evaluation.
• Is made accessible to readers through the careful definition and introduction of each concept, followed by the extensive use of examples and numerous real-life case studies.
• Explains how to utilise mathematical models and analysis models to make effective and good quality business decisions.

This book is aimed at postgraduate students following data analysis and data mining courses. Researchers looking for a systematic and broad coverage of topics in operations research and mathematical models for decision-making will find this an invaluable guide.

Aus dem Klappentext

Business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better business decisions. The term implies having a comprehensive knowledge of all factors that affect a business, such as customers, competitors, business partners, economic environment, and internal operations, therefore enabling optimal decisions to be made.

Business Intelligence provides readers with an introduction and practical guide to the mathematical models and analysis methodologies vital to business intelligence.

This book:

• Combines detailed coverage with a practical guide to the mathematical models and analysis methodologies of business intelligence.
• Covers all the hot topics such as data warehousing, data mining and its applications, machine learning, classification, supply optimization models, decision support systems, and analytical methods for performance evaluation.
• Is made accessible to readers through the careful definition and introduction of each concept, followed by the extensive use of examples and numerous real-life case studies.
• Explains how to utilise mathematical models and analysis models to make effective and good quality business decisions.

 This book is aimed at postgraduate students following data analysis and data mining courses. Researchers looking for a systematic and broad coverage of topics in operations research and mathematical models for decision-making will find this an invaluable guide.

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Business Intelligence

John Wiley & Sons

Chapter One

The advent of low-cost data storage technologies and the wide availability of Internet connections have made it easier for individuals and organizations to access large amounts of data. Such data are often heterogeneous in origin, content and representation, as they include commercial, financial and administrative transactions, web navigation paths, emails, texts and hypertexts, and the results of clinical tests, to name just a few examples. Their accessibility opens up promising scenarios and opportunities, and raises an enticing question: is it possible to convert such data into information and knowledge that can then be used by decision makers to aid and improve the governance of enterprises and of public administration?

Business intelligence may be defined as a set of mathematical models and analysis methodologies that exploit the available data to generate information and knowledge useful for complex decision-making processes. This opening chapter will describe in general terms the problems entailed in business intelligence, highlighting the interconnections with other disciplines and identifying the primary components typical of a business intelligence environment.

1.1 Effective and timely decisions

In complex organizations, public or private, decisions are made on a continual basis. Such decisions may be more or less critical, have long- or short-term effects and involve people and roles at various hierarchical levels. The ability of these knowledge workers to make decisions, both as individuals and as a community, is one of the primary factors that influence the performance and competitive strength of a given organization.

Most knowledge workers reach their decisions primarily using easy and intuitive methodologies, which take into account specific elements such as experience, knowledge of the application domain and the available information. This approach leads to a stagnant decision-making style which is inappropriate for the unstable conditions determined by frequent and rapid changes in the economic environment. Indeed, decision-making processes within today's organizations are often too complex and dynamic to be effectively dealt with through an intuitive approach, and require instead a more rigorous attitude based on analytical methodologies and mathematical models. The importance and strategic value of analytics in determining competitive advantage for enterprises has been recently pointed out by several authors, as described in the references at the end of this chapter. Examples 1.1 and 1.2 illustrate two highly complex decision-making processes in rapidly changing conditions.

The main purpose of business intelligence systems is to provide knowledge workers with tools and methodologies that allow them to make effective and timely decisions.

Effective decisions. The application of rigorous analytical methods allows decision makers to rely on information and knowledge which are more dependable. As a result, they are able to make better decisions and devise action plans that allow their objectives to be reached in a more effective way. Indeed, turning to formal analytical methods forces decision makers to explicitly describe both the criteria for evaluating alternative choices and the mechanisms regulating the problem under investigation. Furthermore, the ensuing in-depth examination and thought lead to a deeper awareness and comprehension of the underlying logic of the decision-making process.

Timely decisions. Enterprises operate in economic environments characterized by growing levels of competition and high dynamism. As a consequence, the ability to rapidly react to the actions of competitors and to new market conditions is a critical factor in the success or even the survival of a company.

Figure 1.1 illustrates the major benefits that a given organization may draw from the adoption of a business intelligence system. When facing problems such as those described in Examples 1.1 and 1.2 above, decision makers ask themselves a series of questions and develop the corresponding analysis. Hence, they examine and compare several options, selecting among them the best decision, given the conditions at hand.

If decision makers can rely on a business intelligence system facilitating their activity, we can expect that the overall quality of the decision-making process will be greatly improved. With the help of mathematical models and algorithms, it is actually possible to analyze a larger number of alternative actions, achieve more accurate conclusions and reach effective and timely decisions. We may therefore conclude that the major advantage deriving from the adoption of a business intelligence system is found in the increased effectiveness of the decision-making process.

1.2 Data, information and knowledge

As observed above, a vast amount of data has been accumulated within the information systems of public and private organizations. These data originate partly from internal transactions of an administrative, logistical and commercial nature and partly from external sources. However, even if they have been gathered and stored in a systematic and structured way, these data cannot be used directly for decision-making purposes. They need to be processed by means of appropriate extraction tools and analytical methods capable of transforming them into information and knowledge that can be subsequently used by decision makers.

The difference between data, information and knowledge can be better understood through the following remarks.

Data. Generally, data represent a structured codification of single primary entities, as well as of transactions involving two or more primary entities. For example, for a retailer data refer to primary entities such as customers, points of sale and items, while sales receipts represent the commercial transactions.

Information. Information is the outcome of extraction and processing activities carried out on data, and it appears meaningful for those who receive it in a specific domain. For example, to the sales manager of a retail company, the proportion of sales receipts in the amount of over 100 [euro] per week, or the number of customers holding a loyalty card who have reduced by more than 50% the monthly amount spent in the last three months, represent meaningful pieces of information that can be extracted from raw stored data.

Knowledge. Information is transformed into knowledge when it is used to make decisions and develop the corresponding actions. Therefore, we can think of knowledge as consisting of information put to work into a specific domain, enhanced by the experience and competence of decision makers in tackling and solving complex problems. For a retail company, a sales analysis may detect that a group of customers, living in an area where a competitor has recently opened a new point of sale, have reduced their usual amount of business. The knowledge extracted in this way will eventually lead to actions aimed at solving the problem detected, for example by introducing a new free home delivery service for the customers residing in that specific area. We wish to point out that knowledge can be extracted from data both in a passive way, through the analysis criteria suggested by the decision...