Applying Intelligent Big Data Analytics in a Smart Airport Business: Value, Adoption, and Challenges

Applying Intelligent Big Data Analytics in a Smart Airport Business: Value, Adoption, and Challenges

Desmond Narongou, Zhaohao Sun
DOI: 10.4018/978-1-7998-9016-4.ch010
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Airports have always been one of the biggest contributors of big data to the aviation ecosystem. With the abundance of data available, big data analytics can help transform the airports to smart ones. This chapter examines airport analytics from a business process viewpoint. It explores the value of applying intelligent big data analytics in an airport from an operations perspective and strategic differentiation perspective. This chapter also discusses the challenges faced when adopting intelligent big data analytics in a smart airport paradigm from the perspective of PNG's National Airports Corporation (NAC). This chapter then looks at how these challenges can be overcome to realize the true value of applying intelligent big data analytics in an airport. The approach proposed in this chapter might contribute to expediting research of future development of intelligent big data analytics solutions that are customizable to an airport to recognize the real value of intelligent big data analytics in all facets of its operations.
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Big data has become the strategic resource of an organization and a country (McKinsey, 2011). Big data analytics is at the heart of any business, management, decision making, and socio-economic development (Sun & Wu, 2021). Big data analytics is revolutionizing businesses and different industries around the world (Weber, 2020) (Ghavami, 2020). In the aviation industry, the business value of smart airports that encompasses the application of intelligent big data analytics is expected to grow exponentially over the next few years (Narongou & Sun, 2021) even after the COVID-19 pandemic subsides and far beyond. The global aviation analytics market is expected to grow from USD 1.7 billion in 2020 to USD 3 billion in 2025 at a compound annual growth rate (CAGR) of more than 11 percent (Globe Newswire, 2021).

One of the biggest challenges within the aviation industry is to optimally utilize and maximize the big volume (abundance) of data to realize the real business value in the sector (Oldenburg, 2020). There are silos of information lying around the industry that need to be properly captured, organized, and analyzed using intelligent big data analytics tools to manage costs, improve revenues, and provide a strategic differentiation to the industry (Ghavami, 2020). Oftentimes the available data and information are too broad and inadequate to provide sufficient insights at the airports for decision making (Lees, 2016). This has given airport executives limited insights into understanding the opportunities for growth and improvement as well the challenges faced in an airport from all its operation functions (Narongou & Sun, 2021). The application of intelligent big data analytics incorporates data, information, and big data itself through advanced ICT tools aims to address these challenges to provide endless opportunities for refining business processes in the airport (Sun Z., 2021).

Following one of the biggest falls in the history of the aviation industry as we face the COVID-19 pandemic head-on, the aviation industry players are looking into sustainable business solutions that will survive and sustain its continuity. The aviation industry players are seeking different business and revenue models to increase their business value while mitigating the challenges faced when adopting and integrating big data analytics into their decision-making models. The big data market grows significantly (Sun, Strang, & Li, 2018). This growth comes with challenges that need to be well analyzed and be prepared to adopt and implement the change in a smart airport ecosystem.

Based on the above analysis, there are still three research issues:

  • 1.

    How do airports best utilize big data for their strategic differentiation and operational improvement?

  • 2.

    What is the market and business value for applying big data analytics in a smart airport ecosystem?

  • 3.

    What are some challenges that impede the adoption of intelligent big data analytics in a smart airport?

This chapter will address these issues by firstly looking into the available related literature to intelligent big data analytics. This will then be able to provide a better direction to discuss airport analytics, which is one of the smart solutions to penetrate each stage of the airport business process as a service. By looking into intelligent big data analytics as a business service, we can best describe the business value of applying intelligent big data analytics in a smart airport from different perspectives. This chapter also discusses the challenges faced when adopting intelligent big data analytics in a smart airport paradigm from the perspective of PNG’s National Airports Corporation (NAC). This chapter then looks at how these challenges can be overcome to realize the true value of applying intelligent big data analytics in an airport.

Key Terms in this Chapter

Big Data: Is data with at least one of the ten big characteristics, consisting of big volume, big velocity, big variety, big veracity, big intelligence, big analytics, big infrastructure, big service, big value, and big market.

Smart Airport: Smart airport is an integrated airport environment that interconnects all ICT systems and related sources of data and information smartly for optimizing customer satisfaction, operational efficiency, strategic differentiation, and economic diversity, underpinned by advanced digital technologies and intelligent systems.

Airport Services Chain: Refers to activities and services chain operating in an airport from landside to airside, or vice versa. The airport services chain comprises primary and secondary services or support services.

Artificial Intelligence (AI): Is science and technology concerned with imitating, extending, augmenting, automating intelligent behaviors of the human beings.

Cloud Computing: Is a computing paradigm based on the demand for resources and services in the cloud. It is a special distributed computing that introduces utilization models for remotely provisioning scalable and measured resources.

Aviation Ecosystem: Refers to processes within which all players from airport operators, airlines, government authorities, and other stakeholders are involved in the operations, conduct, and function of the aviation-related activities.

Data Science: Is a field that builds on and synthesizes a number of relevant disciplines and bodies of knowledge, including statistics, informatics, computing, communication, management, and sociology to translate data into information, knowledge, insight, and intelligence for improving innovation, productivity and decision making.

Intelligent Big Data Analytics: Is science and technology about collecting, organizing, and analyzing big data to discover patterns, knowledge, and intelligence as well as other information within the big data based on artificial intelligence and intelligent systems.

Internet of Things (IoT): Refers to systems that involve computation, sensing, communication, and actuation. It involves the connection between humans, non-human physical objects, and cyber objects, enabling monitoring, automation, and decision making.

Airport Analytics: Refers to the application of big data analytics to data generated from activities conducted within an aerodrome. All data from passenger processing activities including aircraft movement are also part of the airport analytics platform.

Smart Airport Management: Is a special form of airport management that integrates and shares key Information Communication Technology (ICT) systems, data, and information to optimize performance and capacity, passenger experience, and customer service for the entire aviation ecosystem.

Big Data Analytics: Big data analytics is a science and technology about organizing big data, analyzing and discovering knowledge, patterns, and intelligence from big data, visualizing and reporting the discovered knowledge for assisting decision making (Sun, Sun, & Strang, 2016). The main components of big analytics include big data descriptive analytics, predictive analytics, and prescriptive analytics (Sun, Sun, & Strang, 2018), which correspondingly address the three questions of big data: when and what occurred? what will occur? and what is the best answer or choice under uncertainty? All these questions are often encountered in almost every part of science, technology, business, management, organization, and industry.

PMIA: Port Moresby Jackson’s International Airport (PMIA) is managed by the National Airports Corporation (NAC), a state-owned entity entrusted to manage all airport infrastructure services throughout Papua New Guinea (PNG).

Intelligent System: Is a software system that can imitate, automate some intelligent behaviors of human beings. Expert systems and knowledge-based systems are examples of intelligent systems.

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