Through daily operations, airports are producing vast amounts of data. However, for many airports the use of data remains an untapped potential. COVID-19 and its severe effect on aviation has highlighted a need for airports to be more agile when planning operations. Optimized utilization of existing data will become the foundation of COVID-19 recovery. Airports will need to adopt a data-driven approach to operations ensuring compliance with guidelines and the need for physical distancing, while simultaneously adapting to an evolving demand of the use of infrastructure.
The first step towards becoming a more data-driven organization is to realize the potential of available data, more so than considering the lack of data as a challenge. Even the very best airports in the world do not have full data coverage. Airports that are successful work with what they have, understand what they need, and how they can improve for the future.
Industry knowledge, benchmark data, alternative data sources, and the like can produce planning, analytics, and improvement initiatives that significantly improve and prepare operations for COVID-19 recovery.
Airports should still strive to improve the collection and quality of data, however, this process is only reinforced by gaining knowledge and experience by working with what is presently available.
To demonstrate the variety of data sources available, let us consider planning for physical distancing at check-in. To do so, we need to understand:
• How many passengers will arrive at check-in, when will they arrive, and at what check-in area?
• How fast are passengers being processed?
• What is the required distance between passengers?
• What is the size of the queuing areas?
If we focus on the first point, there are numerous ways of establishing when passengers arrive at a check-in area:
• Automated passenger tracking provides valuable information on passenger arrival and transaction times as well as use of infrastructure.
• Surveys are utilized by many airports and they are a good way of collecting data for processing rates, and knowing when passengers arrive. Depending on the methodology, they can also define which flight each passenger is on.
• Baggage tag scans provide an additional way of determining passenger arrivals as they are collected once the bag enters the baggage system at check-in. While the show-up profiles generated need to be adjusted for any wait time, they produce a passenger arrival for every single flight.
If all of the above data sources are available, they provide the opportunity to cross-validate as shown in the example below. Data validation is a topic on its own, but the importance must not be underestimated.
FIGURE 1: SHOW-UP VALIDATION
Figure 1: Comparison of three different data sources for passenger show-up. The correlation between baggage tag scans (grey line) and passenger surveys (teal) indicates that the baggage tag scans could be used as an alternative source for passenger arrival at check-in and validates the findings of the survey. The security passenger arrival is used to further validate the survey and baggage data.
Add to this the flight schedule, a qualified assumption on load factors, transaction times at check-in, the required distance between passengers, and the size of queue areas. All this information will enable an airport to run scenarios on how many check-in counters an airline will need in the COVID-19 recovery phase, what queue space is required, as well as allowing the airport to dispatch staff to potentially challenging areas as forecasted by the analysis.
Will the first analysis be spot on? Probably not. But it will be a lot better than no analysis, and airports can continuously adjust their input parameters based on how they matches operational reality. More importantly, it will give the airport something to work with, a tool to communicate to key stakeholders, and provides the foundation for evolving airport data analytics.
As stated above, using analysis and scenario planning as a means to handle COVID-19 is an opportunity to showcase the power of data and analytics at airports, while also supporting changing legacy processes. It will not be possible to obtain complete data coverage on all affected areas during the crisis. Still, it is possible to deliver tangible results, initiatives, and recommendations to support your airport operation by using already existing data.
FIGURE 2: THE SKATEBOARD MODEL
Figure 2: The skateboard model. Same end state but different ways of getting there. It is possible and important to deliver value from the beginning and continuously build upon your capabilities. Source: Implement Consulting Group with SAS
The Skateboard Model is taken from process change and project management. As Rasmus Waldemar, Implement Consulting Group explains: “Successful airport transformation delivers value in small releases. It makes the change process tangible, engages stakeholders, and creates results much faster. We have seen airport operation projects analyzing data for more than 12 months. This means no change and no impact in those 12 months. The great change projects in airport operations manage to analyze and implement in small releases e.g. a monthly improvement.”
The same applies to data. Recommendations include:
• Leveraging the data already availablet o create tangible results for the organization – this will drive the demand for additional analytics
• Looking for alternative data sources when the primary source is not available
• Stakeholders will often hold additional data. Engage with them and showcase the value added that data sharing can generate
• Validating all data
• Using the data to implement small and continuous improvements
There are several challenges on the journey towards data excellence and operational optimization. Data is often collected for ad-hoc purposes with no enterprise data strategy to ensure effective and planned collection and storage of data. Data warehouses are in process but far from complete, making a central point of data collection and utilization inaccessible. This makes information sharing difficult with the risk of reinforcing organizational silos. Lastly, a critical challenge to achieve data driven decision making, is to ensure that all data is validated before using it as the foundation for operational decisions.
Despite these challenges, the data coverage of any airport will be sufficient to get started. On average, 40-60% data coverage is adequate to create value. Gaps can be covered using benchmarks, until the organization evolve, and data coverage and quality improve.
The only way to achieve data excellence is through experience and curiosity but too often, analytics and operationalization of data are postponed due to a perceived lack of data coverage. Instead of holding back, doubting if data coverage is sufficient, start by using what is available and build on that. This is how the journey towards unleashing data and creating value for both airport and passengers begins.
Rasmus Kaster holds an MA in International Studies from Durham University. Before joining Copenhagen Optimization, he has worked as a military linguist (Persian) for the Danish Armed Forces and with strategy and controlling for the police. He is a firm believer that data, and the optimal visualization thereof, is the foundation for good governance and decision making on all levels of an organization.
The article was provided by a third party and, as such, the views expressed therein and/or presented are their own and may not represent or reflect the views of ACI, its management, Board, or members. Readers should not act on the basis of any information contained in the blog without referring to applicable laws and regulations and/or without appropriate professional advice.