Business Modeling and Data Mining demonstrates how real world business problems can be formulated so that data mining can answer them. The concepts and techniques presented in this book are the ...essential building blocks in understanding what models are and how they can be used practically to reveal hidden assumptions and needs, determine problems, discover data, determine costs, and explore the whole domain of the problem. This book articulately explains how to understand both the strategic and tactical aspects of any business problem, identify where the key leverage points are and determine where quantitative techniques of analysis -- such as data mining -- can yield most benefit. It addresses techniques for discovering how to turn colloquial expression and vague descriptions of a business problem first into qualitative models and then into well-defined quantitative models (using data mining) that can then be used to find a solution. The book completes the process by illustrating how these findings from data mining can be turned into strategic or tactical implementations.·Teaches how to discover, construct and refine models that are useful in business situations ·Teaches how to design, discover and develop the data necessary for mining ·Provides a practical approach to mining data for all business situations ·Provides a comprehensive, easy-to-use, fully interactive methodology for building models and mining data ·Provides pointers to supplemental online resources, including a downloadable version of the methodology and software tools.
Experience is a great teacher—it is a well-known aphorism. In fact, experiences of the world and sense impressions are also fall in the category of good teachers. Ideas about the world keep on ...changing. As situations and circumstances change, the ideas about the world and about the situations also change. Ideas are referred as mental furniture. With many experiences in mind, even secondhand ones that come from analyzing the experiences of others through reading or formal study, the minds of the human population are well furnished and allow recognizing and appropriately responding to many and various situations. When less experienced and less mental furniture, it recognizes fewer situations and is unaware of appropriate actions. However in every case, novel situations require a rearrangement of mental furniture to encompass and incorporate a new situation. New situations demand new ideas —new thinking. Today, the world is changing at faster pace, especially in the corporate world The rapid pace of change demands a constant rearranging of ideas and a constant search for new ideas–new relationships, new ways of looking at the world, new strategies.
This chapter focuses on the issues that are important for the modeling effort. People always maintain the practice of any business process. It is supported by written rules, implicit and explicit ...expectation, corporate culture, tradition, explicit incentives, familiarity, and emotional motivation and it is entangled in formal and informal web of internal interactions that serve to maintain and alter it. As a result, it has an "inertia" that results from the influences just mentioned, which is to say that changing any system takes effort. In case the existing system is left to itself, it tends to react so as to achieve itself to an unchanged state. Successful model deployment has to recognize this from the outset; recruiting and maintaining support from all of the stakeholders from the beginning of the project through deployment is a key to success. A modeler is responsible for delivering the model in an appropriate form for implementation. By some considerations, the role of the modeler comes to an end with the delivery of the model, although the person, who is a modeler, may take another role.
This chapter discusses some technical issues concerned with deploying a mined model. A mined model must solve, or at least address business issues, and the business issues have to be so framed such ...that they lead to a solution through data mining. The structure of a delivered model has to be kept in mind at some point in a model creation. Some data mining tools require a modeler to deploy a model by calling a run-time version (or perhaps even a full version) of the modeling tool. Making a generic form of the model is either hard or impossible to retrieve from the modeling environment. It is important to have a neural network, decision tree, or clusters as a model that works correctly, but if a set of equations or rules cannot be produced that expresses the model, then it can't be transferred into a different environment. Some models are created in one environment (usually some form of MS Windows) and have to be deployed elsewhere—perhaps on a mainframe or running under some version of UNIX, or as a distributed application on multiple systems. On the other hand, sometimes it is most convenient to have the model as a callable executable routine embedded in a library, a COM object, or some other form provided by the tool vendor.
This chapter focuses on practical examples mainly on one data set, and they are explored in some details using numerous ways. An interested reader can duplicate most of the example explorations. In ...addition, the chapter discusses and provides downloads of some of the actual models created to get the appropriate results. Thus, this chapter offers the opportunity for an interested reader to duplicate and extend on the explorations presented. Even without active participation, the reader should find that the continued focus on exploring a single data set leads to some degree of familiarity with its content. The repeated variety of explorations will provide same information in different layouts, showing merits and demerits of each tool and technique. One of the biggest problems in mining data, particularly for an inexperienced miner, is that the miner can easily deviate from the actual goal. To avoid this problem, one should make sure that the results achieved from mining are actually worth having. This means that the results, in whatever form they appear, should be applicable to the business problem.
This chapter discusses art rather than science to discover the right model. It is believed to be an art, because there is no single method to discover the right model—although there is always a right ...model to be discovered. And the right model can be discovered only by practice. All else consists of rules of thumb and helpful hints. A modeler personally and interactively explores the territory to be modeled. Sometimes this may be quite cursory, as when a business manager requests a tactical model based on a preconceived notion of the problem that needs to be solved (e.g., “Build a customer segmentation model for me!”). Sometimes exploring the territory to be modeled may turn out to be the most important and time taking part of the whole discovery effort, as when a modeler assists a senior management team facing a serious strategic challenge under novel circumstances. Despite of the level of difficulty of the model discovery challenge, a modeler must know some usual threads that he/she needs to apply to all discovery situations.
