Copenhagen Airport (CPH) is the biggest and busiest airport in Scandinavia. In 2023, it rolled out an airport-wide AI-powered data analytics and intelligence project to improve its operational efficiency. The project was led and implemented by Carsten Jensen, the airport’s lead IT asset manager, and Mehdi Motaghiani, CEO of Smarter Airports, a venture co-created by CPH and Netcompany to drive digitalization.
The aim was to significantly increase the airport’s maximum passenger capacity and create new opportunities for airport mapping by integrating huge amounts of data on traffic handling, flight times, check-in and security to optimize processes.
“CPH strives to use cutting edge technology in our pursuit to operate the most efficient and sustainable airport operation possible,” Jensen tells PEX Network. “This is to the benefit of our passengers, key stakeholders and the environment around us.”
However, it lacked a central ecosystem that could provide the operational traffic system and modern, technology focused data and intelligence support needed for holistic optimization, says Motaghiani. “That’s where we started – to create that one source of truth when it comes to data – one that is punctual and reliable.”
Central data management
It is a significant advantage to have all data and information consolidated into one system, to which CPH and its numerous partners in the airport have shared access, adds Jensen. “The more business intelligence we have, the better and more efficient we can run our operations in cooperation with and to the benefit of our key stakeholders, but always with a strong focus on safety and the environment.”
Project AIRHART has enhanced CPH’s handling of the huge amount of data needed to run the very complex operation of the airport, involving a large number of stakeholders including air traffic control, airlines and ground handlers, Jensen says. “We can now optimize our turnaround times for airlines and handlers, improving total turnaround and travel time for passengers, but also reduce CO2 emissions while aircrafts are parked, landing or taking off in CPH.”
For example, the airport can use business intelligence to ensure that aircraft are only cleared for taxiing once the team is sure they won’t be parked on the apron, using unnecessary fuel. Likewise, it has a more efficient, automated process for verifying and linking key flight information, which was previously handled manually, as well as sharing information with the network of airports it collaborates with, Motaghiani says.
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Data quality the foundation of success
Access to clean, accurate and consistent data is the foundation of successful intelligence and analysis. Careful management of data quality and accuracy is an indispensable cornerstone. Through data quality management, organizations will be better positioned to not only maintain the integrity of their data but also strengthen the trust of their customers and stakeholders.
Data quality management also helps to ensure compliance with certain regulations and frameworks, another key piece of the business intelligence and data analytics success puzzle. CPH had to ensure that their endeavors were handled in line with airport collaborative decision-making (A-CDM), a European standard that aims to improve the efficiency and resilience of airport operations by optimizing the use of resources and improving the predictability of air traffic, Motaghiani says.
Change management should not be underestimated
With the benefit of large amounts of data being collected and analyzed comes the need to revisit and optimize standard operating procedures (SOP), says Jensen. “This takes some getting used to. Organizational change management should not be underestimated when you have thousands of users dependent on the data collected and shared over many entities.” CPH worked purposefully to implement its new traffic platform, but had a backup system running at all times that it could switch to if required. “In an airport, safety comes first, so there has been a significant focus on that.”
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AI balance is key
AI offers vast potential in business intelligence and data analytics. However, a carefully managed and balanced approach to implementing AI technology is important, says Motaghiani. Crucial to this is managing the extent to which you allow AI technology to make critical decisions, he adds. “The key is bringing AI across the board but to not let it make any significant decisions. For example, we apply AI-driven intelligence as recommendations for air traffic operators. Putting the suggestions to the operators and letting them make a decision about whether to trust the information coming from the AI.”
AI is just another source – another opinion, Motaghiani says. “That’s the approach we have taken as we didn’t want to pull anything over anybody’s head through automatic decision-making.” If, over time, you come to find that AI recommendations are always accepted and can be trusted, you can look to explore the option of AI-driven decision making, he adds.
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