Research Projects

Assessing and Improving Data Analytic Capabilities in Airports In Reserve

Airports are currently facing numerous organizational challenges, and it is often difficult to identify the primary areas of concern, prioritize effectively, and make effective changes for the future. Improved data analytic capabilities can help airports make better informed decisions to improve their organizational issues. This research will result in a user-friendly guidebook that will provide simple and effective methods for airports to improve their data analytics capability.

Background (Describe the current situation or problem in the industry, and how your idea would address it.)

Today's airports stand at the cusp of a data analytic revolution. The possibilities available to airports are growing rapidly in terms of the data available to support evidence-based decision making and improve airport effectiveness. For example, data analytics can help airport operators easily visualize the busiest spikes for their gates and security checkpoint which can allow the airport to better plan for and allocate resources where they are needed most (Kohli, 2018). Through data analytics, airports can perform valuable tasks such as better visualizing their data to gain a more comprehensive understanding of their operations, developing predictive analysis and what-if scenarios, backing up their management decisions with hard data, identifying operational inefficiencies, and leveraging historical information and/or real-time data to better plan for the future (Weinberg, 2016).

The Dublin Airport serves as one example of an airport that has gained insight into airport operations using data analytics. The airport has introduced a new initiative to improve the flow of air traffic at the airport and reduce flight delays using Airport Collaborative Decision Making (A-CDM). With greater data-sharing it will be possible to review the process end to end and identify points of congestion that lead to delays (Mullan, 2018). However, as airports evolve in their thinking about data management, analytics, and governance, some of the shortcomings of their current data situation are becoming pain points. For example:

• A wealth of available data without the time, knowledge, or skills required to effectively analyze it – In modern airports, a wealth of data is often readily available; however, many airports simply lack the resources needed to use it effectively. The challenges of heavy workloads and limited budgets across airports have been well documented (e.g., ACRP Report 186; ACRP Web-only Document 28), and innovative data analytics may seem like an unrealistic goal under these constraints.

• Poor visibility of data to their own users – In many cases, data sets may operate within functional silos, with limited awareness of the data available across the airport.

• Incompatibility of analysis methods and tools – A lack of common data structures, formats, and interfaces often makes integrated data analytics exceedingly difficult within airports or through cross-airport collaboration.

• Lack of awareness of modern data science techniques – Data science is not the core mission of most airport staff. Generating greater value from data sets that are varied in their composition, are growing at ever-increasing rates, and can no longer be properly supported through legacy business intelligence and data visualization is a task that requires a change to how staff are trained and supported.

Through this effort, airports can modernize and make more effective and efficient their data management, analysis, and reporting to better achieve their mission. The results of this project will allow a diverse range of airports improve their data analytic capability and to more effectively use their data for operational success.

Objective (What is the desired product or result that will help the airport industry?)

The objective of this research is to help airports leverage their data more effectively and enhance their data analytic capabilities. The main outcome will be a user-friendly guidebook to assist airports with assessing and improving data analytic capabilities. The guidebook will include background information on the opportunities and challenges associated with airport data analytics, a data analytics maturity model assessment that can be flexibly implemented depending upon individual airport needs, detailed action plans, and real-world case studies of airports using data analytics to support implementation of the strategies within the action plans. For example, this may include guidance on how to produce an inventory of data, data environments, and data analytics within an airport; detailed strategies and action plans for improving data analytics maturity; guidance for establishing a data analytics team; and resources for analytics training. The goal is to provide airports with guidance to arm them with the ability to enact meaningful improvements to their data analytics capabilities.

Approach (Describe in general terms the steps you think are needed to achieve the objective.)

The proposed research will comprise three main phases.

Phase 1: Literature Review to Understand Airport Data Opportunities and Challenges. The goal of the literature review will be to develop a deeper and broader understanding of the opportunities and challenges surrounding data analytics in the airport industry. The review will focus on identifying emerging data possibilities and anticipated future trends, as well as the common obstacles airports face in effectively leveraging their data.

Phase 2: Stakeholder Interviews to Uncover Data Analytics Needs and Best Practices in the Airport Industry. The objective of Phase 2 will be to build on the literature review results by collecting qualitative data from stakeholders. This will involve conducting interviews with professionals in the airport industry with data analytics expertise, as well as those who would be the intended users for the guidebook. Through this process, more detailed information regarding data analytics needs and best practices specifically within the airport industry will be identified.

Phase 3: Guidebook for Airport Data Analytics. Based on the research described above, a guidebook for improving data analytics capabilities will be created. The guidebook will include background information on the opportunities and challenges associated with airport data analytics, a data analytics maturity model assessment that can be flexibly implemented depending upon individual airport needs, detailed action plans, and real-world case studies of airports using data analytics to support implementation of the strategies within the action plans. For example, this may include guidance on how to produce an inventory of data, data environments, and data analytics within an airport; detailed strategies and action plans for improving data analytics maturity; guidance for establishing a data analytics team; and resources for analytics training.

Cost Estimate and Backup (Provide a cost estimate and support for how you arrived at the estimate.)

Recommended Funding: The estimated funding for this proposed effort is $300,000.

The estimated time needed to complete this research is 18 months, including 3 months for review and revision of a draft final report. These estimates are based on the complexity of information to be gathered as well as the high level of detail that will be expected in the final products.

Related Research - List related ACRP and other industry research; describe gaps (see link to Research Roadmaps above), and describe how your idea would address these gaps. This is a critical element of a synthesis topic submission.

After examining ACRP studies conducted, there are currently no projects related to data analytics and the proposed idea to use data analytics to assess and improve capabilities in airports. There is much data available for airports to collect, analyze, and use to make decisions and improve their operations, and the development of this user-friendly guidebook will assist airports with leveraging their data more effectively.

1. Andersson, K., Carr, F., Feron, E., & Hall, W. D. (2001). Analysis, Modeling, and Control of Ground Operations at Hub Airports. Air Transportation Systems Engineering, 305–341. doi: 10.2514/5.9781600866630.0305.0341

2. Ferri, R. (n.d.). People Analytics: Overcoming HR Data Challenges for Program Success. Retrieved from https://www.gr8people.com/blog/talent-acquisition/people-analytics-overcoming-hr-data-challenges-for-program-success

3. Kohli, D. (2018). The Important Role of Big Data in Airport Transformation. Retrieved from https://www.aviationpros.com/airports/article/12434059/the-important-role-of-big-data-in-airport-transformation

4. Lees, Eliot. (2016) A Better Way to Manage Airports: Passenger Analytics [White Paper]. ICF. http://www.crowdvision.com/wp-content/uploads/2016/10/A-BETTER-WAY-TO-MANAGE-AIRPORT.pdf

5. Mullan, M. (2019). How digitalisation is accelerating opportunities with data and analytics. Retrieved from https://www.internationalairportreview.com/article/101763/data-analytics-dublin-airport-digitialisation/

6. Nash, M., et al. (2012). ACRP Report 65: Guidebook for Airport Irregular Operations (IROPS) Contingency Planning. Washington, D.C.: Transportation Research Board of the National Academies.

Annotations
Idea No. 476