Big Data Analytics (BDA) is transforming industries worldwide, including the airline sector, by enabling data-driven decision making that improves operational efficiency and profitability. This research focuses on the application of BDA in Biman Bangladesh Airlines, a key player in Southeast Asia’s aviation market where adoption of such technologies is still emerging. Revenue management systems, crucial for airline profitability, benefit significantly from big data insights that influence fleet planning, fare structuring, and operational management. Using a mixed-methods approach, data were gathered from airline professionals and managers across Bangladesh, complemented by detailed case studies of Biman Bangladesh Airlines. The study examines how Biman leverages BDA to enhance flight punctuality, optimize passenger and cargo load factors, improve fuel efficiency, and increase customer satisfaction. Results demonstrate that effective use of big data analytics enables better decision-making, helping airlines respond flexibly to operational challenges and market demands. Despite these advantages, the research also highlights challenges related to building analytics capabilities and managing complex data environments in the airline industry. Overall, this study contributes to a better understanding of how airlines in developing markets can harness BDA strategically to boost revenue, improve competitiveness, and support sustainable growth.
References
[1]
Akerkar, R. (2014) Analytics on Big Aviation Data: Turning Data into Insights. InternationalJournalofComputerScienceandApplications, 11, 116-127.
[2]
Burmester, G., Ma, H., Steinmetz, D. and Hartmannn, S. (2018) Big Data and Data Analytics in Aviation. In: Durak, U., Becker, J., Hartmann, S. and Voros, N., Eds., AdvancesinAeronauticalInformatics, Springer, 55-65. https://doi.org/10.1007/978-3-319-75058-3_5
[3]
Hausladen, I. and Schosser, M. (2020) Towards a Maturity Model for Big Data Analytics in Airline Network Planning. JournalofAirTransportManagement, 82, Article 101721. https://doi.org/10.1016/j.jairtraman.2019.101721
[4]
Odarchenko, R., Hassan, Z. and Zaman, A. (2019) Use of Big Data in Aviation. In: Shmelova, T., Sikirda, Y., Rizun, N., Kucherov, D. and Dergachov, K., Eds., Automated Systems in the Aviation and Aerospace Industries, IGI Global, 436-452. https://doi.org/10.4018/978-1-5225-7709-6.ch017
[5]
Feliu, C. (2021) Big Data in the Airline Industry: 5 Case Studies from the Industry Leaders. https://jaauth.journals.ekb.eg/
[6]
Altexsoft (2021) 10 Ways Airlines Use Artificial Intelligence and Data Science to Improve Operations. https://www.altexsoft.com/
[7]
Kasturi, E., Devi, S.P., Kiran, S.V. and Manivannan, S. (2016) Airline Route Profitability Analysis and Optimization Using Big Data Analyticson Aviation Data Sets under Heuristic Techniques. ProcediaComputerScience, 87, 86-92. https://doi.org/10.1016/j.procs.2016.05.131
[8]
Creswell, J.W. (2014) Research Design. SAGE Publications, Inc.
[9]
Yin, R. (2009) Case Study Research: Design and Methods. Sage Publications.
[10]
Merriam, S. (2009) Qualitative Research: A Guide to Design and Implementation. Jossey-Bass.
[11]
Kothari, C. (2004) Research Methodology: Methods and Techniques. New Age International (P) Ltd.
[12]
McKinney, W. (2017) Python for Data Analysis. O’Reilly Media, Inc.
[13]
Ahlgren, A. (2022) Fuel Efficiency in Airlines: Cost Reduction and Environmental Impact. Aviation Journal, 15, 123-128.
[14]
Altexsoft (2021) How Predictive Analytics Is Transforming Airline Maintenance and Operations. https://www.altexsoft.com
[15]
Bahadur, S. (2022) Leveraging Big Data Analytics to Increase Airline Ancillary Revenue. InternationalJournalofAviation, 14, 112-125.
[16]
Balm, M. (2018) Advanced Analytics in Airline Customer Segmentation Using Machine Learning. JournalofAirTransportManagement, 70, 123-130.
[17]
BytesView. (2022) What Is Sentiment Analysis? How Companies Use It for Better Customer Insights. https://www.bytesview.com
[18]
Carifio, J. and Perla, R.J. (2007) Ten Common Misunderstandings, Misconceptions, Persistent Myths and Urban Legends about Likert Scales and Likert Response Formats and Their Antidotes. JournalofSocialSciences, 3, 106-116. https://doi.org/10.3844/jssp.2007.106.116
[19]
Delahaye, D., Acuna-Agost, R. and Bondoux, M. (2017) Dynamic Pricing Optimization in Airline Revenue Management. JournalofRevenue&PricingManagement, 16, 122-137. https://doi.org/10.1057/s41272-017-0095-z
[20]
ECA (2017) Definition and Calculation of Block Fuel Consumption in Commercial Aviation. European Civil Aviation Conference Report.
[21]
Franko, G. (2020) The Impact of Big Data Analytics on Airline Cost Savings and Revenue Growth. JournalofAviationEconomics, 12, 33-41.
[22]
IBM (2021) Social Network Analysis (SNA): Visualizing Relationships and Connections. IBM Knowledge Center. https://www.ibm.com
[23]
Kaduc, S. (2014) Revenue Management Software and Its Role in Airline Profitability. AviationTechnologyJournal, 11, 23-32.
[24]
McAndrew, C. (2021) Big Data in Airlines: Challenges and Opportunities. AviationToday, 17, 58-64.
[25]
Metzger, K. and Balakrishnan, H. (2022) Fuel Efficiency and Cost Management in Commercial Aviation. TransportationScience, 56, 275-290.
[26]
Meerendonk, D. (2022) The Evolution of Revenue Management Systems in Airlines. RevenueManagementReview, 18, 44-59.
[27]
Optimizely (2020) How Airlines Use Data Analytics for Pricing and Demand Forecasting. https://www.optimizely.com
[28]
Sean, L. (2015) Data-Driven Marketing Strategies in Airlines: A Case Study of Delta Airlines. JournalofAirlineMarketing, 7, 14-22.
[29]
Soliman, M., O’Connell, J. and Tamaddoni-Nezhad, A. (2022) Commercial Data Sources for Airline Analytics: Sabre MIDT and OAG. TransportationDataScience, 4, 99-110.
[30]
Visinescu, L. (2017) Big Data Analytics for Improved Decision-Making in Aviation. JournalofDecisionSupportSystems, 12, 15-27.
