Abstract
Big data analytics has become one of the most significant frontiers in academia and one of the most successful applications in industry. Globally, big data analytics are becoming increasingly crucial for driving the business performance of enterprises on global markets. Having thriving business models for airports is crucial to enhancing a commercial airport's viability. Utilizing big data analytics and its services remains a significant challenge for airports in developing countries. This chapter uses big data-driven research as a search methodology through various literature sources including practical case studies in airport and aviation analytics and their applications to airport business process and services. With this understanding to improve airport business models and to enhance airport services, the objective of this chapter is to examine how to enhance airport business services with big data analytics.
TopIntroduction
Big data analytics has become one of the most important research frontiers in academia and one of the most significant applications in industries (Sun & Stranieri, 2021) (Gehavami, 2020) (Sallam, Richardson, Kronz, Sun, & Schlegel, 2021). The Gartner report identifies 12 strategic technology trends shaping the future of digital business, and data fabric deployments are among the first to be mentioned, doubling data utilization efficiency by 2024 while cutting the amount of time employees spend on managing data in half (Gartner, 2022).
According to a report titled “Big Data and Analytics Services Global Market Report 2022” by ReportLinker, it is projected that at a compound annual growth rate (CAGR) of 12.8%, the global big data and analytics services market is expected to grow from $107.85 billion in 2021 to $121.65 billion in 2022. Businesses are experiencing growth as a result of resuming operations and adapting to the new normal after recovering from COVID-19, which had previously caused restrictive containment measures such as social distancing, remote working, and the closure of commercial activities, posing operational challenges. In 2025, the market is expected to reach $196.95 billion at a CAGR of 12.8% (Globe Newswire, 2022). COVID-19 has drastically impacted all business operations since 2020, and revolutionized how businesses operate and conduct their activities and to be self-sustaining in the long run. Diversifying its business streams is a direction to take when establishing resilient business models during such times as this.
Big data analytics (BDA) is a science and technology for organizing and analyzing big data, and discovering knowledge, insights, patterns and intelligence from big data, visualizing and reporting the discovered knowledge and insights for assisting decision making (Sun, Sun, & Strang, 2018).
The traditional airport's primary functions of aeronautical business have been enhanced by a variety of commercial services, digital services, and equipment. In general, airports provide complementary commercial services, digital services, and follow parallel business paths - aviation and non-aviation, or aeronautical and non-aeronautical (Brilha & Nobre, 2019). Enhancing airport business services using BDA is a bigger part of the whole airport digital transformation process, enabled by digitization, connectivity, and data (Nau & Benoit, 2017) (Pell & Blondel, 2018).
Based on the above analysis, there are still research issues that needs to be addressed:
- 1.
How can big data analytics (BDA) be a key partner in driving a sustainable airport business model?
- 2.
How do airport business models and airport business services integrate with big data analytics?
- 3.
What are the implications and challenges faced in a real business world in enhancing airport business services with BDA services?
This chapter will address these three issues. To this end, the remainder of this chapter is organized as follows: Section 2 reviews and discusses how airport big data and airport data analytics support airport business services and processes through a literature review based on big data driven small data analysis. Section 3 discusses aviation ecosystem, airport analytics, aviation analytics and discuss their interrelationships. Section 4 explores how to enhance airport business services with intelligent big data analytics as a service. Section 5 looks at the challenges for implementing and adopting big data analytics from a PNG perspective. Section 6 provides discussions and limitations for this research. Section 7 provides a conclusion to this chapter with future research work.
TopLiterature Review
In this section, we use ”research as a search” and “big data-driven small data analysis” (Sun & Huo, 2021) to conduct the literature review for this research.
Key Terms in this Chapter
Intelligent Big Data Analytics: 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.
Cloud Computing: 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.
Airport Services Chain: This 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.
Big Data: 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.
Artificial Intelligence (AI): Science and technology concerned with imitating, extending, augmenting, automating intelligent behaviors of the human beings.
Aviation Analytics: The use of big data analytics services to extract transform and load all data from stakeholders in the aviation sector for decision making
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 AU74: The in-text citation "Sun, Sun, & Strang, 2016" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ). 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.
Airport Analytics: This 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.
Airport Business Services: This refers to key functions, processes and activities that are necessary to sustain airport business model from aeronautical to non-aeronautical commercial activities