Predictive analytics—sometimes used synonymously with predictive modeling—is not synonymous with statistics, often requiring modification of functional forms and use of ad hoc procedures, making it a part of data science to some degree. This is used for developing models that may be used to predict future events or behaviors. There are different forms of predictive models, which vary based on the event or behavior that is being predicted. Nearly all predictive models produce a score; a higher score indicates that a given event or behavior is very likely to occur.

Predictive analytics, along with data mining techniques and predictive models, relies on multivariate analyzing techniques, including time-series or advanced regression models. These techniques allow organizations to decide on relationships and trends and predict future behaviors or events.

In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making. Predictive models are not restricted to business, for they are used to predict anything from the reliability of an electronic component to the success of a manned lunar landing. These model, however, are usually stochastic models that can be used in a simulation.

The way predictive models produce value is simple in concept; they make it possible to make more right decisions, more quickly, and with less expense. They can provide support for human decisions, making them more efficient and effective, or in some cases, they can be used to automate an entire decision-making process.

Arrelic developed APRANA (Asset Predictive Analytics) and ARLYTIC (Asset Reliability Analytics) with leveraging analytics capabilities along with consulting experience and deep expertise in predictive maintenance.