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In the current arena where companies face extreme competiveness and continuous changes, a rapid and flexible capability to respond to market dynamism is a key factor for the success or failure of any organization. In this context, the development of efficient strategic and operational decision-making support systems is essential for guaranteeing business success and survival. Nowadays, data mining systems are an effective technology for supporting organizational decision-making processes. From the viewpoint of data mining development, the year 2000 marked the most important milestone: CRISP-DM (CRoss-Industry Standard Process for Data Mining) was published (Chatam, et al., 2002), (Piatetsky-Shaphiro, 2000). CRISP-DM is the most used model for developing data mining projects (Kdnuggets, 2007). Data mining had been successfully applied to many real problems. As a result, data mining has been popularized as the business intelligence tool with the greatest growth projection. In recent years, data mining technology has moved out of the research labs and into companies on the ‘Fortune 500’ list (Kantardzic & Zurada, 2005). Even so, the scientific literature is dotted with many examples of failed projects, project planning delays, unfinished projects, or budget overruns (Eisenfeld et al., 2003), (Meta Group Research, 2003), (Maciaszek, 2005). There are two main reasons for this. On the one hand, there are no standard development processes to implement an engineering approach in data mining project development (Marban, 2008). On the other hand, requirements are not properly specified. One of the critical success factors of data mining projects is the need for a clear definition of the business problem to be solved, where data mining is considered to be best technological solution (Hemiz, 1999). This indicates the need for a proper definition of project requirements that takes into account organizational needs based on a business model. Historically, research in data mining has focused on the development of algorithms and tools, without any detailed consideration of the search for methodological approaches that ensure the success of a data mining project. In this paper, we propose a methodological approach to guide the development of a business model of the decision-making process within an organization. The business decision-making model (represented in i* notation) is translated into use cases based on

From the Business Decision Modeling to the Use Case Modeling in Data Mining Projects