Modern equipment produces enormous amounts of operational data. The challenge is
turning that information into something useful before a failure occurs.
Predictive maintenance uses system data, monitoring, diagnostics, and analytics to
identify potential problems earlier and help organizations make more informed
maintenance decisions.

For automotive and industrial applications, this can mean monitoring system behavior,
identifying unusual signals, tracking performance changes, and using historical data to
understand when equipment may require attention.
As connected systems become more sophisticated, data becomes an engineering asset
rather than simply a diagnostic output.