Predictive analytics is an area of statistical analysis that deals with extracting information from data and using it to predict future trends and behavior patterns. The core of predictive analytics relies on capturing relationships between explanatory variables and the predictive variables from past occurrences, and exploiting it to predict future outcomes.

Predictive modeling draws from statistics and optimization techniques to extract accurate information from large volumes of data. Modeling techniques produce interpretable information allowing maintenance personnel to understand the implications of events, enabling them to take action based on these implications.

Predictive analytics builds on prior investments in enterprise asset management (EAM) systems, combines real-time data from sensors and other acquisition techniques with historical data to predict potential asset failures, and enables the move from reactive (scheduled, break-fix) to proactive (condition-based, preventive) maintenance.

Predictive analytics can be used to analyze the real-time data from the sensors in the context of historical data and asset information held in the EAM system to predict future conditions such as faults or failures and produce alarms or schedule maintenance or replacement. Predictive analytics also complement other existing systems such as data historians and SCADA systems.