Whether you’re working with jet engines, oil and gas wells, or heavy machinery, maintenance is a top priority and an important operational expense. With cognitive search, you can leverage all structured data and text-based information to get a complete view of the system.

Consider this. The world’s leading producer of jet engines uses the Attivio Platform to aggregate and correlate the structured data from sensors and the text-based maintenance notes so they can more efficiently manage maintenance and safety.

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With cognitive search to manage the massive amounts of data produced by your systems, you have automated, self-learning techniques to separate the signal from the noise.

To make sense of maintenance data streams, the Attivio Platform uses cognitive capabilities, such as:

  • Text analytics to analyze unstructured data and identify key phrases
  • Machine learning to detect trends and patterns, so that events and failures can be detected before they happen
  • Knowledge graphing, to map out the disparate sources of information and infer connections and relationships.

By fully understanding all the data signals – whether machine-generated IoT data or human-generated maintenance notes – you can protect your systems from safety risks and unexpected failures.


Maintenance-related data is a strategic asset that’s hard to gather, combine, and analyze, but highly valuable. With modern, cognitive search, structured and unstructured data is aggregated and correlated – from any source, no matter where it resides.

Case study from Oil & Gas. There's a significant opportunity to save billions of dollars of nonproductive time (NPT) by applying advanced analytics techniques to data and content captured relative to oil and gas wells. Tapping into unstructured content, such as maintenance notes, drilling reports, and other sources, yields the qualitative insights associated with NPT.

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