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Organizational data is very core in management of operations and achievement of competitive edge. Graettinger states that, an organization can ‘mine’ data in order to create information assets that it can use to achieve its strategic objectives. According to Graettinger, data mining is the process of discovering data, and modeling hidden patterns in large volume of data. Data mining is part of the larger decision support system (DSS) architecture. Unlike other components of DSS, which mainly display information, data mining produces models, which capture and represent the hidden patterns in the data. Through data mining, a user can build models automatically from hidden patterns. Models produced through data mining are both descriptive and prospective. That is, they address the ‘why’ and the ‘what’ concerns of an organization.
An organization, which employs data mining, has a potential of achieving greater profitability and efficiency in production compared to an organization, which do not utilize this feature. These include, business expansion, achieving sales effectiveness and profitability, and reduction of production costs. For instance, a bank can use data mining to sift through various customer characteristics, and identify the most significant ones. Then, utilize data mining models based on significant customer characters to send mails to prospective customers, inviting them to purchase the bank’s credit offers.
Graettinger explains that, an organization can use a pilot project to introduce data mining in its operations. This can take 1 to 3 months, and 4 to 10 people to execute the project. Before rolling-out the project, the executive should set the goals and objectives, as well as the methods of project evaluation. An organization should ensure that, both the executives and the users, work in liaison during execution of the pilot project; otherwise, the organization may fail to achieve the set objectives of data mining.