In aviation, data is not just a tech subject. It’s a culture of safety. A model for the entire industrial sector.
Every plane crash makes the news. However, the statistical reality commands admiration: in 2025, commercial air transport recorded 1.32 accidents per million flights, compared to 1.42 in 2024 according to data published in March 2026 by the International Air Transport Association (IATA). Over the long term, the progress is even more spectacular: the rate of fatal accidents has increased from one per 3.5 million flights between 2012 and 2016 to one per 5.6 million today.
This performance is not the result of chance. It is the result of a culture of continuous improvement supported by a data culture which has taken hold in aviation. Black boxes which record information related to the flight, in-flight surveillance systems (ACARS, QAR) in the event of an abnormal situation, transmission of security event reports via the ECCAIRS platform… Each flight generates thousands of technical parameters, each incident is the subject of feedback, each anomaly is analyzed and shared. Aviation safety has become a discipline based on continuous improvement in which systematic data analysis is one of the key factors. Result: anticipated breakdowns, optimized maintenance, risks identified before takeoff.
Aviation, a laboratory for predictive AI
However, the risk does not disappear: it evolves. The increase in traffic, the explosion of cyberattacks, the effects of climate change on flight conditions, the increasing digitalization of on-board systems and even geopolitical tensions are making the operational environment more complex. And in this context, each accident remains a human tragedy but also a major economic shock for the entire aeronautical ecosystem (companies, manufacturers, insurers and maintenance players). For example, aircraft repair costs increased by up to 30% between 2024 and 2025 (Source: Lee, 2025). Thus, security becomes a strategic economic and reputational issue as much as a human imperative.
To contain these risks, data science and artificial intelligence are emerging as decisive tools. In aircraft design, they optimize the choice of materials and the design of devices. In training, they enrich the simulators with complex incident scenarios. Their impact is particularly visible in predictive maintenance: the continuous analysis of data from sensors and the history of each aircraft makes it possible to anticipate failures before they occur. This approach not only improves safety, but also reduces technical expenses: It has reduced maintenance costs (-30%), breakdowns (-75%) and downtime (-40%) (Source: Lee, 2025).
Technology alone is not enough. Safety is based on a collective culture in which pilots, technicians, engineers and authorities contribute to enriching shared knowledge of risks. Data science does not replace human vigilance: it makes it more powerful, by making visible what the eye cannot perceive in the mass of data.
Institutional players like OSAC (Organization for Civil Aviation Safety) are the perfect illustration of this. By controlling the selection of aircraft to be monitored using statistical sampling, data science makes it possible to target the highest risk profiles and the results speak for themselves: between 2024 and 2025, this risk-based monitoring approach (Risk Based Oversight) made it possible to identify 40% additional anomalies at constant scope.
The regulatory framework pushes in the same direction. European Regulation (EU) No. 376/2014 requires collecting and analyzing security events in a proactive manner. The Safety Management System (SMS) approach required by ICAO requires identifying, measuring and anticipating risks, an approach impossible without analytical tools. Complying with these growing demands without accessible data science solutions will soon become an insurmountable challenge.
Use of data and AI: accelerating the transformation of critical industries
However, aviation shows that a shared data culture can radically transform safety management. By combining operational data, feedback and artificial intelligence, it is constantly reinventing itself to offer an ever safer and more efficient transport system. This lesson extends well beyond the airline industry. Wherever complex systems intersect with major security issues, such as in heavy industry, nuclear energy, transport infrastructure or health, the same question arises: how to transform dispersed data into preventive decisions?
Data science and predictive AI now offer the tools to answer them. Building this data culture in each critical sector is no longer a forward-looking ambition: it is the condition for risk management in the 21st century.
