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Review Article

A Systematic Review of Big Data Analytics in Aviation Operations and Decision-Making

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Abstract

This systematic review examines the impact of Big Data Analytics (BDA) on airline operations and decision-making, with an emphasis on its applications, problems, and future prospects. The aviation sector generates massive and complicated datasets from various sources, including flight operations, maintenance logs, passenger records, and air traffic control systems. BDA allows greater operating efficiency, predictive maintenance, increased safety, and data-driven decision-making in a variety of aviation disciplines. Key applications investigated include route profitability analysis, air traffic flow optimization, supply chain resilience, and real-time monitoring via the integration of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML). A systematic review technique was used to maintain academic rigor. A qualitative analysis was performed on 19 peer-reviewed research chosen from over 200 papers obtained from major academic databases such as Scopus, Web of Science, IEEE Xplore, and ScienceDirect. The inclusion criteria were aviation-specific BDA applications published between 2010 and 2024. Thematic analysis was performed to extract insights and organize the findings into important areas of effect. The findings show that BDA adoption has tremendous benefits, but it also faces continuing challenges such as data complexity, high implementation costs, ethical concerns, and a need for qualified people. This review establishes a platform for future research and practical implementation strategies in aviation analytics.

Keywords:

Air Traffic Management Aviation Operations Big Data Analytics Data-Driven Decision-Making Predictive Maintenance

Article information

Journal

Scientific Journal of Engineering, and Technology

Volume (Issue)

2(2), (2025)

Pages

31-37

Published

02-08-2025

How to Cite

Gonzalo, J. O. (2025). A Systematic Review of Big Data Analytics in Aviation Operations and Decision-Making. Scientific Journal of Engineering, and Technology, 2(2), 31-37. https://doi.org/10.69739/sjet.v2i2.743

References

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