Predictive modeling is the process of using known results to create a statistical model that can be used for predictive analysis, or to forecast future behaviors. It’s a tool within predictive analytics, a field of data mining that tries to answer the question: “What is likely to happen next?”
Digitization has created enormous volumes of real-time data in virtually every industry. This data can be used to analyze historical events to help forecast future ones, such as financial risks, mechanical breakdowns, customer behavior and other outcomes. However, the data produced by digital products is often unstructured — (i.e., not organized in a predefined manner) — making it too complex for human analysis. Instead, companies use predictive modeling tools that employ machine learning algorithms to parse and identify patterns in the data that can suggest what events are likely to happen in the future.
This “crystal ball” capability has applications across the enterprise; businesses use predictive modeling to make their operations more efficient, get their products to market more quickly and improve their relationships with their customers, to name just a few. It is an especially powerful tool in ITOps and software development, where it can help predict system failures, application outages and other issues.
Below, we’ll look at how predictive models work, the various predictive modeling techniques, the benefits of predictive analytics, and how to choose the right predictive model for your organization.