Data Science is developing at a breakneck pace, and many companies contact Big Data specialists and Data scientists for making vital decisions and business insights. According to IBM predictions, the demand for data scientists and data engineers will rise by 28% by 2020, and annual demand for these experts will amount to around 700,000 job openings.

Big Data, Data Science, and Machine Learning are the skills which are the hardest for recruiters to hunt for, and the lack of these specialists may cause disruption in future productivity of many companies.

Data Science in Aviation

Source: IBM report 

Data Science is used most frequently in Professional Services, Finance&Insurance, and Manufacturing. However, the demand for data engineers in such industries as Biotech, Energy, and Aviation is also soaring for these industries call for specific domain expertise, which is especially hard to find.

Data Science in Aviation

Modern planes generate a lot of data pertinent to fuel usage, characteristics of the engine systems, weather systems, crew activity, in-flight connectivity, and much more. The latest generation of jets collects data via thousands of sensors. In fact, each flight produces more than 30 times the amount of data the previous generation of planes used to generate.

According to Oliver Wyman, the amount of data produced by jets annually should be around 98 billion gigabytes, or 98 million terabytes by 2026.

Data Science in Aviation

Such amount of data streaming from jets calls for effective ways to process it and use it to solve a couple of critical problems. In fact, the biggest challenge in the aviation industry is that there is no physical access to testing environment, and in case of a tech failure, companies may incur great expenses due to down time. Thus, Data Science is used for precise prediction of the lifetime of a device and accurate anticipation of the time when it needs repairing or replacing.

Some of the key problems Data Science helps to solve in Aviation:

  • Data Science is used to optimize operations and improve maintenance. It is often used for static modeling and anticipating different equipment failures, including the critical ones, as they can’t be fixed during the flight by the cabin crew. What’s more, only major airports provide maintenance teams.
  • It helps to find interconnection between different factors, such as how a certain type of equipment works depending on a specific type of a plane, and its location in a specific part of the plane. That aids in enhancing customer experiences.
  • The data from weather systems can be leveraged to modify flight routes and avoid storms.
  • Aviation businesses may also use data science in the price formation process. For example, price for the access to in-flight internet can vary depending on the timing, what day of week this is, distance between cities, and other factors.

Due to the limited access to the testing environment, Data Scientists use logs to simulate and analyze equipment performance and user experiences.

However, it is crucial to have a solid domain knowledge to perform quality Data Science in Aviation. There are a lot of different equipment types, and meer knowledge of algorithms and stats won’t do here. You need to understand the very specifics of the equipment, how it interoperates with other devices and whether it works well on a certain type of planes or under specific conditions. For instance, The WAP access point may have a low bandwidth if it is located in a specific part of the plane. Furthermore, you must have a clear understanding of different types of data which are generated by various types of devices.

Data Science in Aviation. Gogo Use Case

Gogo is a North American in-flight Internet market leader which offers its services to many global airlines.The project was growing, and the client wanted to transition it to a new cloud-based big data solution and perform a lot of Data Science and BI reporting. Thus, Gogo has decided to extend their capacity with N-iX team in Eastern Europe. N-iX was able to gather a team of specialists with extensive expertise in AWS, Hadoop, Apache Spark, Tableau, and other technologies. The team has considerably grown in size ever since.

Currently, the dedicated development team is engaged in storing and processing a large amount of data about the quality and speed of the company’s services. Also, N-iX Data Scientists analyze the data and turn it into actionable insights and predictions. The team provides 24/7 support to enable uninterrupted and quality performance of the client’s solutions. Furthermore, N-iX experts use BI analytics tools and present the results in a clear and understandable way.

The team has contributed a lot to data engineering and performed complete migration from the legacy system to cloud. N-iX Data engineers have developed a Big Data solution and data warehouse system to enable storing and processing of a large amount of data.

We’ve built a pipeline: from the moment when the logs come from the equipment to the moment when they are completely analyzed, processed, and stored in the data warehouse. Our Data engineers are working on ensuring longevity of the equipment. They have improved the system of timely replacing devices and reducing the number of not-fault-founds. In fact, the team has decreased the number of not-fault-founds by 8 times since the new system was implemented. N-iX software developers have also built a platform for measuring availability and implemented a system for WAP points analysis and in-cabin performance.

Data Analysts working on the project have built a platform for analyzing and finding reasons for incidents and equipment failures. For instance, a certain type of equipment may be not compatible with certain types of planes. Also, we’ve built a platform demonstrating problems or ill-performance of specific maintenance teams. All that helps to predict the problems and ensure quality and uninterrupted performance.

Wrap-up

Many businesses use Data Science in Aviation to ensure continuous maintenance, timely prediction of faults, and uninterrupted performance of their solutions. However, to implement effective Data Science solutions, a company needs solid technological expertise and domain knowledge. Gogo extended its development team with qualified Big Data engineers, Data scientists, Data Analysts, and BI specialists in Ukraine. As a result, the company has accelerated the adoption of innovations and obtained valuable insights for improving its service.

To learn more on Data Science in Aviation, please, contact our experts.

The Rise of Data Science in Aviation: How to Unlock Hidden Value