Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

by Foster Provost

There is a direct trade-off in where and how effort is expended in the data mining process. For the supervised problems, since we spent so much time defining precisely the problem we were going to solve, in the Evaluation stage of the data mining process we already have a clear-cut evaluation question: do the results of the modeling seem to solve the problem we have defined? For example, if we had defined our goal as improving prediction of defection when a customer’s contract is about to expire, we could assess whether our model has done this. In contrast, unsupervised problems often are much more exploratory. We may have a notion that if we could cluster companies, news stories, or whiskeys, we would understand our business better, and therefore be able to improve something. However, we may not have a precise formulation. We should not let our desire to be concrete and precise keep us from making important discoveries from data. But there is a trade-off. The tradeoff is that for problems where we did not achieve a precise formulation of the problem in the early stages of the data mining process, we have to spend more time later in the process — in the Evaluation stage.