In aviation, data is not just a technical subject. It is a culture of safety. A model for the entire industrial sector.
Each airplane accident makes headlines. However, statistically, the reality is admirable: 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 decreased from one for every 3.5 million flights between 2012 and 2016 to one for every 5.6 million flights today.
This performance is not random. It is the result of a culture of continuous improvement supported by a data culture that has been established in aviation. Black boxes that record flight-related information, in-flight monitoring systems (ACARS, QAR) in case of abnormal situations, transmission of safety event reports via the ECCAIRS platform, each flight generates thousands of technical parameters, each incident undergoes an experience 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. The result: anticipated breakdowns, optimized maintenance, risks identified before takeoff.
Aviation, a predictive AI laboratory
Nevertheless, the risk does not disappear; it evolves. The increase in air traffic, the explosion of cyberattacks, the effects of climate change on flight conditions, the increasing digitization of onboard systems, and geopolitical tensions complicate the operational environment. And in this context, each accident remains a human tragedy but also a major economic shock for the entire aviation ecosystem (airlines, manufacturers, insurers, and maintenance actors). For example, the costs of repairing aircraft have increased by up to 30% between 2024 and 2025 (Source: Lee, 2025). Thus, aviation safety becomes an economic and strategic reputational issue as well as a human imperative.
To contain these risks, data science and artificial intelligence are essential tools. In aircraft design, they optimize the choice of materials and the design of the devices. In training, they enrich simulators with scenarios of complex incidents. Their impact is particularly visible in predictive maintenance: continuous analysis of data from sensors and the history of each aircraft allows anticipating failures before they occur. This approach not only improves safety but also reduces technical expenses: it has reduced maintenance costs by 30%, breakdowns by 75%, and downtime by 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 the shared knowledge of risks. Data science does not replace human vigilance; it makes it more powerful by making visible what the human eye cannot perceive in the mass of data.
Institutional actors like OSAC (Civil Aviation Safety Organization) are a perfect illustration of this. By statistically sampling the selection of aircraft to control, data science allows targeting the highest-risk profiles, and the results are significant: between 2024 and 2025, this risk-based surveillance approach led to identifying 40% more anomalies with a constant scope.
The regulatory framework is moving in the same direction. The European Regulation (EU) No. 376/2014 requires collecting and analyzing safety events proactively. The Safety Management System (SMS) approach mandated by ICAO requires identifying, measuring, and anticipating risks, a process impossible without analytical tools. Complying with these growing requirements without accessible data science solutions would pose an insurmountable challenge.
Use of data and AI: accelerating the transformation of critical industries
Despite everything, aviation shows that a shared data culture can radically transform safety management. By combining operational data, feedback, and artificial intelligence, it constantly reinvents itself to offer a transport system that is increasingly safe and efficient. This goes beyond the aviation sector. Wherever complex systems intersect with major security issues such as in heavy industry, nuclear energy, transport infrastructure, or healthcare, the same question arises: how to transform scattered data into preventive decisions?
Data science and predictive AI tools now offer the means to answer this question. Building this data culture in every critical sector is no longer a prospective ambition: it is the key to risk management in the 21st century.
