There is nothing new about this. People have been seeking patterns in data since human life began. Hunters seek patterns in animal migration behavior, farmers seek patterns in crop growth, politicians seek patterns in voter opinion, and lovers seek patterns in their partners’ responses. A scientist’s job is to make sense of data, discover the patterns that govern how the physical world works, and encapsulate them in theories that can be used for predicting what will happen in new situations.
What is new is the staggering increase in opportunities for finding patterns in data. The rapid expansion of databases in recent years, particularly those related to everyday activities like customer preferences, has made data mining a leading technology in the business world. With the world becoming more complex and generating massive amounts of data, data mining is our only chance to understand the underlying patterns. Intelligently analyzed data is a valuable resource. It can lead to new insights, and in commercial settings, to competitive advantages.
Useful patterns allow us to make non-trivial predictions on new data. There are two extremes for the expression of a pattern: as a black box whose innards are effectively incomprehensible and as a transparent box whose construction reveals the structure of the pattern. Both, we are assuming, make good predictions. The difference is whether the patterns that are mined are represented in terms of a structure that can be examined, reasoned about, and used to inform future decisions. Such patterns we call structural because they explicitly capture the decision structure. They help to explain something about the data.
Recognizing patterns in data is fundamental for informed decision-making. While a black box model might accurately predict customer churn, it doesn't explain the reasons behind it. In contrast, a structural model not only predicts churn but also reveals the underlying factors, such as dissatisfaction with customer service or pricing issues. This deeper understanding enables businesses to address the root causes rather than merely treating the symptoms. For instance, by identifying that customer service quality is a key driver of churn, a company can implement targeted improvements, ultimately reducing churn and enhancing customer loyalty. Structural models thus offer actionable insights beyond mere predictions.