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In nearly every instance, the data analyst will, along with staff, be repeating Steps 2–13. When initiating use of predictive analytics, conventional wisdom says that at least 50% of the variance should be explained using regression analysis, but it is the experience of this author that explained variance of 70–75% for a variable of interest can be achieved with a good fitting model, using 10 predictor variables or fewer, in a regression analysis.

As practice changes are implemented based on the information that emerges, variables from the initial model will no longer predict the variable of interest because the problem (or part of the problem) will have been solved by the practice changes. Traditionally, the analyst would then have to start over and develop a new model, but in this case, much of the work has already been done when developing the initial full model that is graphically depicted in Figure 1.1. As you return to Step 2, you will review the existing full model and rerun all the analytics to identify existing predictor variables that have now become an issue due to the new practice changes and/or identify new variables that relate to the variable of interest.