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Data Analysis

  • Once the data are ready, the analysis phase generally consists of two different steps with a reporting period in between. The first step in the analysis is called knowledge discovery. In this phase, ‘smart’ algorithms search through the data looking for patterns or relationships. These algorithms are typically Chaid (Chi Square Automatic Interaction Detection) or Cart (Classification And Regression Trees) procedures, though Neural Nets, Genetic Algorithms, and other hybrid systems are also used. They generally take one user-specified variable called the ‘dependent variable’, and try to relate every variable in the file to that variable. Some algorithms can look for linear, and non-linear relationships, as well as transform the variables in a variety of ways to maximize their relationships. Relationships are generally reported as decision trees, which are an easily understood way of presenting information.
  • Data Mining analyses typically relate hundreds and even thousands of variables to several dependent variables of key interest. Since many algorithms are free to manipulate the variables to maximize their relationships, it is not uncommon for an analysis to yield hundreds of ‘significant’ relationships. These relationships are simply measures of statistical association, and are often spurious or otherwise of little importance, and they are therefore considered to be hypotheses about relationships in the data, which need to be studied further.
  • This information is generally discussed with the ‘hands on’ users or other researchers, and the number of hypotheses is filtered down to focus on the most promising avenues for further analysis. This second step in the analysis is generally called validation, and usually relies on common statistical techniques like regression, discriminant analysis, and cluster analysis. This step usually includes some form of quantification of trends or market opportunities, prediction, segmentation, or response modeling. The ultimate goal of the analysis is generally to either increase revenues through a better understanding of the customer, or else to develop better predictive models to use as forecasting tools.
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