The challenge was to harvest and sift through this data, recognize the patterns that indicate a high likelihood of asset failure, identify the most urgent issues, and get the right information to its engineers with enough lead time for them to take effective action.“Before, we only used between 10 and 12 percent of the operational data we collected, which is the industry average,” comments Benn. “By the time we had searched for, collated and forwarded the right information to the right people, we might respond too late to avoid impact to operations, or have to make last-minute changes to our maintenance schedule, which reduces efficiency. Our challenge was to provide right-time, actionable, effective information proactively, rather than in a reactive or look-back assessment.”“What we wanted was a way to identify patterns in that sensor data that would give us an early warning of asset failure. We saw an opportunity to use analytics technology to extract greater value from the systems and data we already possessed, which would help us to, for example, avoid preventable failures and potentially save millions of dollars.