BACKGROUND

Data analytics is a broad term that encompasses many diverse types of data analyses.  Any type of information can be subjected to data analytics techniques to gain insights that can be used for decision-making. Complicated airport organizational structures make it difficult to identify the primary areas of concern, prioritize actions, and make effective changes for the future. Yet with the introduction of data analytics, airports can better understand not only their day-to-day operations, but ultimately address their challenges by monitoring key performance indicators (KPIs) and using evidence-based decision-making. Data is abundantly available throughout the airport setting and can be found in areas such as operations, finance, retail, and passenger movement, but the limiting factor is often collecting and analyzing the data to empower airport decision-makers. Other impediments to data analytics include resource limitations, data contained in silos, inconsistent metrics, and lack of training in the use of modern data science techniques.

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.

OBJECTIVE

The objective of this research is to develop an ACRP WebResource to help airports leverage data more effectively by building or enhancing their data analytic capabilities.

The ACRP WebResource should be written for all types and sizes of airports and should include and/or address, at a minimum:

• A primer, written for employees at all levels of the organization, including the benefits of a data analytics program, the requirements (effort/cost/employees) for a data analytics operation, the importance of engaging airport stakeholders in a data analytics program, and simple steps to begin a data analytics program including the capabilities and skill sets needed to manage such a program.
• Glossary of terms specific to data analytics.
•  An overview of typical data architecture and governance and examples of comprehensive systems in use at various sizes of airports. The overview should include a discussion of methods for ensuring that datasets are available across an organization and that silos are minimized. 
• An overview of how data-sharing relationships are developed and managed, including sample contractual language.
• Identification of data messaging standards available to all sizes of airports.
• Listing of potential data sources according to their data sophistication/complexity and capability.
• Listing of potential data analytics applications and/or services including dashboard and reporting functionality.
• Listing of end users of data (audience) and examples of how the data is being used (i.e., airport departments, airlines, tenants, concessionaires, government entities, business partners, third parties, etc.).
• Identification of how airports are using data analytics to help them with future planning, revenue generation, efficiency in airport management, and effectiveness of day-to-day operations.
• Identification of existing systems used by airports, the primary purpose of the system(s), and how the system(s) categorizes data (i.e., airport operations, life safety, finance, etc.).
• Discussion on data privacy and security (i.e., confidentiality, integrity, and availability of data), including applicable standards and best practices.
• Identification of training and resource requirements.
• Discussion of current obstacles and challenges to creating a data analytics program and recommendations to mitigate them.
• Discussion of strategies and action plans for understanding and improving data analytics maturity.
• At least six case studies of airports of various sizes and a method for evaluating how best to leverage standard datasets available at each airport taking into consideration needs, range of system users, cost, effort, etc.