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representatives, quality assurance managers, security officers, and so forth) who work in industry are only interested in data mining insofar as it helps them do their job better. They are uninterested in technical details and do not want to be concerned with integration issues; a successful data mining application has to be integrated seamlessly into an application. Bringing an algorithm that is successful in the laboratory to an effective data-mining application with real-world data in industry or scientific community can be a very long process. Issues like cost effectiveness, manageability, maintainability, software integration, ergonomics, and business process reengineering come into play as significant components of a potential data-mining success.

Data mining in a business environment can be defined as the effort to generate actionable models through automated analysis of a company’s data. In order to be useful, data mining must have a financial justification. It must contribute to the central goals of the company by, for example, reducing costs, increasing profits, improving customer satisfaction, or improving the quality of service. The key is to find actionable information, or information that can be utilized in a concrete way to improve the profitability of a company. For example, credit-card marketing promotions typically generate a response rate of about 1%. The praxis shows that this rate is improved significantly through data-mining analyses. In the telecommunications industry, a big problem is the concept of churn, when customers switch carriers. When dropped calls, mobility patterns, and a variety of demographic data are recorded, and data-mining techniques are applied, churn is reduced by an estimated 61%.

Data mining does not replace skilled business analysts or scientists but rather gives them powerful new tools and the support of an interdisciplinary team to improve the job they are doing. Today, companies collect huge amounts of data about their customers, partners, products, and employees as well as their operational and financial systems. They hire professionals (either locally or outsourced) to create data-mining models that analyze collected data to help business analysts create reports and identify trends so that they can optimize their channel operations, improve service quality, and track customer profiles, ultimately reducing costs and increasing revenue. Still, there is a semantic gap between the data miner who talks about regressions, accuracy, and ROC curves versus business analysts who talk about customer retention strategies, addressable markets, profitable advertising, and so on. Therefore, in all phases of a data-mining process, a core requirement is understanding, coordination, and successful cooperation between all team members. The best results in data mining are achieved when data-mining experts combine experience with organizational domain experts. While neither group needs to be fully proficient in the other’s field, it is certainly beneficial to have a basic background across areas of focus.

Introducing a data-mining application into an organization is essentially not very different from any other software application project, and the following conditions have to be satisfied:

There must be a well-defined problem.

The data must be available.

The data must be relevant, adequate, and clean.

The problem should not be solvable by means of ordinary query or OLAP tools only.

The results must be actionable.

A number of data mining projects have failed in the past years because one or more of these criteria were not met.

The initial phase of a data-mining process is essential from a business perspective. It focuses on understanding the project objectives and business requirements, and then converting this knowledge into a data-mining problem definition and a preliminary plan designed to achieve the objectives. The first objective of the data miner is to understand thoroughly, from a business perspective, what the client really wants to accomplish. Often the client has many