Data science in aviation; high potential slow progress

Data science provides great opportunities for the aviation industry to improve products and processes. Despite the abundance of data and tools the application of data science in aviation is still limited. Biggest constraints are the lack of recognition that data can be used more effectively and there is a shortage of data scientists that have the required skills and possess domain knowledge.

What is data science?

There are many definitions of data science. One definition reads: “Data science is the study of where information comes from, what it represents and how it can be turned into a valuable resource in the creation of business strategies. Mining large amounts of structured and unstructured data to identify patterns can help an organization rein in costs, increase efficiencies, recognize new market opportunities and increase the organization’s competitive advantage.”

Data science and the airline industry

The aviation sector gathers and stores a large amount of unstructured, heterogeneous data – safety data and reports, flight plans, navigation data, airport data, radar tracks – from multiple sources – airlines, ANSPs and airports. While the collection of information through different data sensors has been growing exponentially over the last years, the application of data science to the data has not. This conclusion of the 2013 workshop on Data Science in Aviation is still valid today.

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Some progress has been made by applying data science to marketing needs – analysis of IFE preferences. This sector may be more advanced at airline marketing departments than at some airports. In airport and airspace operations data science applications have a lot of potential, but data is not being capitalised fully. Organizations are unaware of the potential value or don’t have the expertise required to turn data into actionable knowledge.

A successful example of data science enhanced operations

Data science can increase predictability and efficiency of operations, even when confronted with unpredictable things like weather. While few successful applications can be found beyond the proof of concept stage, there are some data science enhanced operations already active. An example is our runway and capacity forecasting system developed for KLM, see

Organizations cannot risk losing any competitive advantage they have by not investing in their data. Therefore, organizations should assess how data science can be applied to their data. This requires data science expertise that may not be readily available within the own organization. Attracting data scientist can be difficult, because they are much sought after in all industries.

About To70. To70 is one of the world’s leading aviation consultancies, founded in the Netherlands with offices in Europe, Australia, Asia, and Latin America. To70 believes that society’s growing demand for transport and mobility can be met in a safe, efficient, environmentally friendly and economically viable manner. For more information, please refer to

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12 thoughts on “Data science in aviation; high potential slow progress

    1. Thank you for the comment on the blog. We agree with your reaction. Also in other sectors, Data Science has a lot of potential. Feel free to contact us if you would like to know more about this topic.

  1. This article is quite revealing to me. I have 7 years of experience as Database Administrator using Microsoft technology and I’m trying to enter into the world of data science. In fact, I’m doing a Masters of IT with majors in Data Analytics, at Swinburne University, Australia. I’m also starting to read books about statistics because I don’t have a background on this. I’m not sure about this, but I think that in order to be a good data scientist it is also key to learn about the business. So, I think I will also have to learn about one particular industry. Based on this, I was thinking about orientating my data science learning to sports, because I have been watching them since I am a kid and I mostly know how they are played (espacially soccer and tennis). But it seems data science has a great potential for such an exciting industry like aviation. I’m starting to consider this as an alternative.

  2. As this article clearly mentions, there is quite a lot of potential of data science and analysis in Aviation industry due to the Paramount data that’s generated every minute in the industry. Just as an example, a Dreamliner aircraft sends nearly 10GB of data per second as it covers the skies. Similarly, prediction of the whole scenario of aircraft movement can be aided by data analytics and prove to be a big boon in air traffic management (ATM) while coordinating with respective stake holders at the airport ground.

    Kindly mention ways by which data science can practically be used by the industry in Layman’s terms. Finally, A great article, Thank You.

  3. Can you also provide some insights into the future of using this machine learning in Operations Dept or Air traffic and further future .

  4. Hi Arjen,
    Thanks for sharing this great information about data science in aviation, my brother also want to become an Aeronautical Engineer and I think he will do well in data science.

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