Big Data and Air Traffic Management

According to webopedia ( Big Data is a buzzword, or catch-phrase, used to describe a massive volume of both structured and unstructured data that is so large that it's difficult to process using traditional database and software techniques.

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In fact I am using it exactly as that: As a buzzword in my title to try to catch your attention.

Today I have attended a very interesting workshop about Data Science in aviation organized by Innaxis ( But what is data science? Ï have checked a few definitions, and the one I like best is the one given by which says that: "Data science is the study of where information comes from, what it represents and how it can be turned into a valuable resource…"

So, what about data science and air traffic management?

Well, if we think about it, increased awareness of data science (and the use of buzzwords like ‘big data’) in the last few years is one of the next (or present) natural evolutions of the greater digital revolution: first we accelerate the generation of data, and commodatise its storage and processing power, and next we think of generating value from all this data.

But indeed it makes sense. To take other industries as a benchmark, companies are aware nowadays not only that the data they have collected and are collaterally collecting can have value to generate new business and to render current operations more efficient, but they now have started to pro-actively look for ways of capturing even more data so as to accelerate further this value creation - Thinking about how companies like google, facebook, credit card and telecommunication companies, just to mention a few, are gathering our data on purpose (and for free) to generate further revenue for themselves is scary and somewhat perverse... However, it is reality and the aim of this workshop was to see how all the data that we are gathering in aviation (accidentally or purposefully (structured or unstructured, if I understand the jargon well)) can be used to generate more value for us.

As one of the presenters put it, the value could be one of three types:

  1. to generate new income,
  2. to help in decision making and to render the system more efficient, and
  3. to reduce risk, thus making it more safe.

And the three objectives are applicable for aviation: In ATM, we are generally looking at making the system safer and more efficient. Other segments of aviation are looking for new income.

During the workshop we listened to 4 different operationally-concerned presenters (2 from the airline world, another from an aircraft manufacturer and one from ATM) explain that they are collecting a lot of data and that some of it is being used to create value, for example to create capacity, to compute the best flying profiles or to improve the airline safety records. Yet, I felt that the underlying thread was one which said: we have a lot of data which we are under using: Data Scientists, please come help us find ways of how we can generate more benefit from this data. (and here we are back to the big data definition above…)

The workshop continued then with a series of other presentations, this time from professionals in the field of data science whose objectives was to educate us and to give us more background information and to inspire us.

I am sure that the day was fruitful for many, as the idea of gathering aviation operations and data scientists in a room will give many of us ideas for the future.

As for me, apart from thanking once more Innaxis for making this workshop a reality, I come home convinced that data science is a necessity in ATM, if we want to move ahead, if we want to better understand our complex ATM system and if we want to be wise about tomorrow’s decisions on how to enhance safety and efficiency in our industry.

It also gave me an appetite for those mathematical formulas I left 19 years ago; but that is already another story…

...(by the way, can anyone predict the next evolution within the digital revolution? Working on this from now will be worth a rich gold mine...Anyone? Data scientist? Let me know ;)