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The Scope of Data Mining

Data mining derives its name from the similarities between searching for valuable business information in a large database - for example, finding linked products in Nielsen's gigabytes of store scanner data - and mining a mountain for a vein of valuable ore. Both processes require either sifting through an immense amount of material, or intelligently probing it to find exactly where the value resides.

Given databases of sufficient size and quality, data mining technology can generate new business opportunities by providing these capabilities:

Automatic prediction of trends and behaviors. Data mining automates the process of finding predictive information in large databases. Questions that traditionally required extensive hands-on analysis can now be answered directly from the data - quickly. A typical example of a predictive problem is targeted marketing. Data mining uses data on past promotional mailings to identify the targets most likely to maximize return on investment in future mailings. Other predictive problems include forecasting bankruptcy and other forms of default, and identifying segments of a population likely to respond similarly to given events.

Data mining tools sweep through databases and identify previously hidden patterns in one step. An example of pattern discovery is the analysis of retail sales data to identify seemingly unrelated products that are often purchased together. Other pattern discovery problems include detecting fraudulent credit card transactions and identifying anomalous data that could represent data entry keying errors.

Data mining techniques can yield the benefits of automation when implemented on existing software and hardware platforms at SBS Data, and can be implemented on new systems as existing platforms are upgraded and new products developed. When data mining tools are implemented on high performance parallel processing systems, they can analyze massive databases in minutes. Faster processing means that users can automatically experiment with more models to understand complex data. High speed makes it practical for users to analyze huge quantities of data. Larger databases, in turn, yield improved predictions. Databases can be larger in two senses:

Higher dimensionality. In hands-on analyses, analysts must often limit the number of variables they examine because of time constraints. Yet variables that are discarded because they seem unimportant may carry information about unknown patterns. High performance data mining allows users to explore the full dimensionality of a database, without preselecting a subset of variables.

Larger samples. Larger samples yield lower estimation errors and variance, and allow users to make inferences about small segments of a population.

A recent Gartner Group Advanced Technology Research Note listed data mining and Artificial Intelligence at the top of the five key technology areas that "will clearly have a major impact across a wide range of industries within the next three to five years." Gartner also listed parallel architectures and data mining as two of the top ten new technologies in which companies will invest during the next five years. According to a recent Gartner HPC Research Note, "With the rapid advance in data capture, transmission and storage, large-systems users will increasingly need to implement new and innovative ways to mine the after-market value of their vast stores of detail data, employing MPP [massively parallel processing] systems to create new sources of business advantage (0.9 probability)."