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www.AviationPros.com 11 of data that this generates, has enabled the increased targeting of predictive analysis to certain areas, components, and systems to better inform the engineers on the ground. Right now, basically every part of a plane can tell crews on what needs replacing, or if something is faltering. Ironically, this is also where the issues of information overload arise as thousands of data nodes can overwhelm teams and deci- sion makers who look to streamline processes and implement accurate models. But finding the true signal of a datapoint generated by machine sensors and hidden in a diverse pool of sources — all of which are constantly generating information at different times — has become impossible for a human team to process. Even with the resources leading airlines have to store and analyze this information, much of the big data generated by sensors on a plane remains largely unexploited, according to the Financial Times. Paul Stein, chief scientific officer of Rolls- Royce, identified the industry's lack of sufficient communications infrastructure to harvest and transmit the data as the industry's leading short-term obstacle, the FT stated. However, it's not about constantly analyzing every line of data as it comes in. It is important to note that the terabytes gathered by millions of parts on a daily basis are not all necessarily useful. At the 2014 "The Data Driven Business of Winning" summit, managing director of CMS Motor Sports Ltd., Mark Gallagher stated that Formula 1 teams efficiently analyze data to win races by identifying anomalies, reported TechRadar. "99 percent of the information we get, everything is fine [...] we're looking for the data that tells us there's a problem or that tells us there's an opportunity," Gallagher was quoted as saying. Due to the degradation of monitored equipment and varia- tions in sensory output, identifying these anomalies by a human team can be exceptionally complex. Furthermore, rules are hard to implement as the natural lifecycle of a component within an aircraft's ecosystem can be unpredictable. To reduce the loss of valuable data, which can be timely and therefore perishable, data automation solutions for areas like pre- dictive maintenance can be invaluable. More than just analyzing the data, being able to interact with the information and extrapo- late particular anomalies that can offer differential perspectives on the state of a component, engine, or aircraft navigation board, can answer the important questions. MONITORING EACH SENSOR IN ISOLATION DOESN'T WORK Components, whether in an aircraft or in a car, often fail due to numerous factors within the entire ecosystem. Because of this, monitoring just one sensor within an aircraft is unlikely to produce a complete dataset that depicts an accurate view of what is actually occurring. What's more, the manual effort required to combine a series of individually monitored sensors to successfully extrapolate alerts and filter critical signals from large amounts of data is very high. Not only is this method inefficient and expensive, but it also fails to successfully scale in the long run. But just like finding the needle in the noise of data stack, this level of data generation falls into the realm beyond human teams due to the sheer volume of information and the ambiguity that the raw data can produce. However, through automated predictive maintenance, decision makers can successfully leverage part har- monization to gain a clearer overview of specific sensor insights. These help create accurate predictive models that can show the parts that are set to fail first and help schedule replacements, which in turn improves the management of part inventories. Combining all these factors helps teams successfully imple- ment complete and functional predictive maintenance, which according to an International Journal of Applied Mathematics and Informatics study, can decrease total maintenance costs by 30 percent and reduce stationary time of aircraft by up to 45 percent. Additionally, the successful application of predictive mainte- nance can avoid the knock on effects that unscheduled emergency maintenance can have. Increased downtime generated by this spontaneous maintenance, for example, can have detrimental effects on customer views and company reputation. BEYOND HUMAN-SCALE Although data analysis as a process requires the input of human teams and professionals, the sphere of industrial data has already surpassed dated models that revolved around reports and charts. It is here that the timely analysis of datasets is crucial. By having a machine do the job, for example, companies can see a problem within an aircraft before it occurs, a task that is now practically impossible for data science teams with the advent of increased component connectivity. Furthermore, and not to definitively relegate humans to the corner, the mass shortage of data scientists simply requires the automation of certain processes. Plain and simple. Not only are these solutions able to collate a week's (or a year's) worth of data to build the most optimal model for the job, but they can facili- tate the real time execution of decisions based on timely data. For predictive maintenance in the aviation sector, the ability of these automated machine solutions to compare, contrast, and segment massive aircraft datasets for more accurate predictions is a near perfect fit. Unlike human teams that can take weeks to analyze just one segment of a dataset in order to accurately install predictive maintenance models, automated solutions can discover critical points in the data in mere seconds. As industrial data continues to evolve and expand, predictive maintenance will require the agile analysis afforded by automated systems and retreat from human engagement sphere. For the aviation industry, the ability to execute practical business solu- tions in this area will require automated near real-time analysis and accurate interpretation of aircraft machine and sensor data conversations.