Enhancing Agile Project Management with Big Data Analytics: A Data-Driven Framework
DOI:
https://doi.org/10.37376/fesj.vi19.7432Keywords:
Agile Project Management, Big Data Analytics, Data-Driven Decision-Making, Software Development, Project Outcomes, Framework IntegrationAbstract
In order to improve the Agile project management process, this article offers a data-driven framework that integrates Big Data analytics with the phases of Agile project management. The suggested framework offers an organized method for data-driven decision-making throughout all five essential Agile phases: Envision, Speculate, Explore, Adapt, and Close. This fills a gap in previous research, which frequently lacks ways for integrating Big Data tools into Agile Project management. In order to improve project results and empower Agile teams, it uses Big Data analytics to change project management from intuition-based to evidence-supported decision-making.
A survey of 101 Agile professionals and evaluations from seven project managers were used to assess the usefulness and potential impact of this methodology. The framework's potential to enhance sprint predictability, software quality, team reactivity, and organizational learning was highly valued, according to the survey results. . This study offers a useful guide for incorporating Big Data into Agile project management, promoting continuous optimization and evidence-based decision-making in contemporary software development.
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Okeleke, P., Ajiga, D., Folorunsho, S., & Ezeigweneme, C. (2024). Leveraging big data to inform strategic decision making in software development. International Journal of Applied Research in Social Sciences. https://doi.org/10.51594/ijarss.v6i8.1429.
Rahaman, M., Rozony, F., Mazumder, M., & Haque, M. (2024). BIG DATA-DRIVEN DECISION MAKING IN PROJECT MANAGEMENT: A COMPARATIVE ANALYSIS. ACADEMIC JOURNAL ON SCIENCE, TECHNOLOGY, ENGINEERING & MATHEMATICS EDUCATION. https://doi.org/10.69593/ajsteme.v4i03.88.
Daraojimba, E., Nwasike, C., Adegbite, A., Ezeigweneme, C., & Gidiagba, J. (2024). COMPREHENSIVE REVIEW OF AGILE METHODOLOGIES IN PROJECT MANAGEMENT. Computer Science & IT Research Journal. https://doi.org/10.51594/csitrj.v5i1.717.
Latypova, V. A. (2024, May). Decision Support for Documentation Control in Project Management on the Basis of Association Rule Mining. In 2024 XXVII International Conference on Soft Computing and Measurements (SCM) (pp. 333-337). IEEE.
Pantović, V., Vidojević, D., Vujičić, S., Sofijanic, S., & Jovanović-Milenković, M. (2024). Data-Driven Decision Making for Sustainable IT Project Management Excellence. Sustainability. https://doi.org/10.3390/su16073014.
Biesialska, K., Franch, X., & Muntés-Mulero, V. (2020). Big Data analytics in Agile software development: A systematic mapping study. Inf. Softw. Technol., 132, 106448. https://doi.org/10.1016/j.infsof.2020.106448.
Saltz, J., & Krasteva, I. (2022). Current approaches for executing big data science projects—a systematic literature review. PeerJ Computer Science, 8. https://doi.org/10.7717/peerj-cs.862.
Rosen, C., Grawi, B., & Shihab, E. (2015). Commit guru: analytics and risk prediction of software commits. Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering. https://doi.org/10.1145/2786805.2803183.
Mazrae, P., Mens, T., Golzadeh, M., & Decan, A. (2023). On the usage, co-usage and migration of CI/CD tools: A qualitative analysis. Empirical Software Engineering, 28, 1-45. https://doi.org/10.1007/s10664-022-10285-5.
Shah, H., Syed, Q., Shankaranarayanan, B., Palit, I., Singh, A., Raval, K., Savaliya, K., & Sharma, T. (2023). Mining and Fusing Productivity Metrics with Code Quality Information at Scale. 2023 IEEE International Conference on Software Maintenance and Evolution (ICSME), 563-567. https://doi.org/10.1109/icsme58846.2023.00073.
Begel, A., & Vrzakova, H. (2018, June). Eye movements in code review. In Proceedings of the Workshop on Eye Movements in Programming (pp. 1-5).
Thiruvathukal, G., , S., Hayward, N., & Läufer, K. (2018). Metrics Dashboard: A Hosted Platform for Software Quality Metrics. ArXiv, abs/1804.02053.
Simpson, B., Johnson, E., Adeleke, G., Amajuoyi, C., & Lande, O. (2024). Leveraging big data for agile transformation in technology firms: Implementation and best practices. Engineering Science & Technology Journal. https://doi.org/10.51594/estj.v5i6.1216.
Maurer, F., Cohn, M., Griffiths, M., Highsmith, J., Schwaber, K., & Kruchten, P. (2004). Agile Project Management. , 201. https://doi.org/10.1007/978-3-540-27777-4_29.
Khalajzadeh, H., Simmons, A. J., Abdelrazek, M., Grundy, J., Hosking, J., & He, Q. (2020). An end-to-end model-based approach to support big data analytics development. Journal of Computer Languages, 58, 100964.
Fawzy, A., Tahir, A., Galster, M., & Liang, P. (2025). Exploring data management challenges and solutions in agile software development: a literature review and practitioner survey. Empirical Software Engineering, 30(3), 1-61.
Ogbuke, N. J., Yusuf, Y. Y., Dharma, K., & Mercangoz, B. A. (2022). Big data supply chain analytics: ethical, privacy and security challenges posed to business, industries and society. Production Planning & Control, 33(2-3), 123-137.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 1165-1188.
Raj, P. (2018). A detailed analysis of NoSQL and NewSQL databases for bigdata analytics and distributed computing. In Advances in Computers (Vol. 109, pp. 1-48). Elsevier.
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