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Data mining is progressively used in information systems as a technology to support decision making on the tactical level, as well as to enable decision activities within operational business processes. In general there are three categories of business decision making approaches: (1) decision making based on precisely defined business rules, (2) analytical decision making based on the analysis of information and (3) decision making based on intuition. In many cases there are all three categories used at a time. Business rule based decision making is typical for operational business processes, whereas other two are typical for managerial processes. Data mining is predominantly used to support analytical decision making, which is typically based on models acquired from a huge quantities of data and therefore makes possible to acquire patterns and knowledge. Based on before introduced discussion one could assume that data mining can be used only in managerial processes. But, there are also operational business processes that require analytical decision activities, e.g. loan approval and classifying the set of customers for promotional mailings. Through data mining methods we can acquire patterns and rules, which can be used as business rules in operational processes. Thus, rules acquired through data mining methods can be used in operational business processes instead of or to support analytical decision activities. Data mining models should in such cases be acquired and used on a daily basis. Some recent technology achievements, such as JDM API (Java Data Mining Application Interface), enable the possibility to develop application systems which utilize data mining methods and as such do not demand expertise in data mining technology for business users. Through JDM API we can develop transactional application systems or any other application systems which create and use data mining models. It means that application systems which use JDM API can be used to support operational business process with the possibility to utilize business rules acquired through data mining methods. CRISP-DM 1.0 as the most used data mining methodology introduces four tasks within the deployment phase: plan deployment, plan monitoring and maintenance, produce final report and review project. None of those tasks provides detailed directions for the deployment of data mining models into business processes. The indicator that CRISP-DM 1.0 lacks such detailed directions mentioned is the fact that the CRISP-DM methodology update efforts intend to fulfill the following aim: “Integration and deployment of results with operational systems such as call centers and Web sites”. O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

The Deployment of Data Mining into Operational Business Processes