Kudyba: That's a great question to begin, simply because there continues to exist a great deal of uncertainty as to what data mining is. Some factions include techniques such as OLAP (online analytical processing) and simply data query and navigation as components of data mining; however, this is not accurate. What data mining really entails is the utilization of mathematical and statistical applications that process and analyze data. Mathematics refers to equations or algorithms that process data to discover patterns and relationships among variables. Statistics generally shed light on the robustness and validity of the relationships that exist in the data mining model. Leading methods of data mining include regression (logit regression), segmentation classification, neural networks, clustering and affinity analysis. All of the major data mining software vendors incorporate these into their platforms. There are some other quantitative-based techniques that could be included in the data mining family, but the ones I just mentioned are the real core components.

Kudyba: This question helps extend the answer to the previous question. Other components of BI generally involve data management, reporting and analytic activities. Data management and reporting can involve general query techniques to access data variables, ETL (extract, transform and load) tools that also enhance data access and storage, the creation of reports or fine-tuned dashboards containing customized performance indicators and, of course, the generation of OLAP cubes. Visualization is a component that enhances these various data and information reporting mechanisms. All of these technologies provide an essential platform to answer more retrospective questions about businesses, where retrospective refers to what happened to the various business processes of the organization. Data mining, on the other hand, also involves data management activities including variable and format selection and transformation and normalization of variables, which must be addressed before processing data with mining methodologies.

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