Predictive Analytics vs Machine Learning in Aviation

What is Predictive Analytics?

Predictive analytics is a step before prescriptive analytics. Predictive analytics uses both current and prior data to model future outcomes and catch problems before they actually occur. This is key in the aircraft industry, and predictive analytics has gotten better and better as planes have become more technologically connected.Airline companies use predictive analysis to change marketing strategies based on consumer forecasting, as well as using it to keep their technology in top condition, reducing the need for additional maintenance and detailed borescope inspections.

Because the assumption of safety is vital to consumers, being able to find the early signs of equipment failure is crucial. Not only is it safer, but it also prevents extra work. That’s because the failure of one component can often cause problems to connected parts that were otherwise working well. 

Predictive analytics are also key in allowing maintenance to keep every component up-to-date without worrying about sudden malfunctions or breakdowns. This prevents delays and keeps maintenance processes running smoothly and able to deal with minor issues as they arise. 

What is Machine Learning?

Another important part of predictive maintenance in the airplane business is machine learning. With predictive analytics, sensory equipment gathers information from each aircraft’s systems, and sends that information to a cloud. That data is then analyzed and used to determine everything from fleet maintenance schedules to marketing strategies. Machine learning in predictive analytics takes this one step farther. 

Machine learning is what most people envision when they think of Artificial Intelligence. Not only do the computers spit out information and identify trends in the data, but the computer comes to a conclusion about the data, and then trains itself on how to respond to that data next time. This involves using algorithms when sifting through data to identify larger statistical trends. Once those trends have been identified, the computer essentially makes decisions about what future algorithms and models should look like, and trains them accordingly. 

This is key in the airline industry where computers must deal with massive amounts of data pouring in. Sifting through such large quantities of information allows AI to determine large-scale trends that may affect something as big as an entire fleet. Determining these trends then allows machine learning to set up replicable processes in place, ensuring that predictive maintenance can happen whenever necessary.

Predictive Analytics vs. Machine Learning

The difference between these two technological terms lies in their roles. Predictive analytics is a big term that refers to a field in analytics technology. Machine learning is a key component within that field of predictive analytics. Technically, predictive analytics could exist without machine learning in aviation, but it would require significant work from humans to decipher all the data and draw conclusions that could apply to large-scale problems. These larger trends would often be found only by people looking at the big picture, and would take longer to discover, if they were discovered at all. Machine learning allows computers to quickly process massive amounts of data that humans simply cannot process, and then make decisions about how to respond to trends. This makes predictive analytics the game-changing technology that it is in the aircraft industry.

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