Empowering Decision-Making through Big Data Analytics: A Narrative Review of Techniques, Tools, and Industrial Applications
DOI:
https://doi.org/10.61978/digitus.v2i3.836Keywords:
Big Data Analytics, Decision Support Systems, Machine Learning, Predictive Analytics, Industry Applications, Data-Driven Decision-Making, Organizational FactorsAbstract
Big Data Analytics (BDA) has become a pivotal enabler of data-driven decision-making across various industrial sectors. This narrative review aims to synthesize existing literature on BDA techniques, tools, and applications to identify their role and impact in decision support systems. The review draws upon scholarly databases such as Scopus, IEEE Xplore, and Google Scholar, utilizing a systematic search strategy with Boolean keyword combinations to retrieve relevant literature. Studies were screened based on inclusion and exclusion criteria, focusing on empirical findings and practical applications of BDA across domains. Findings reveal that techniques such as data mining, predictive analytics, and machine learning offer enhanced accuracy and real-time capabilities, leading to better outcomes in healthcare diagnostics, manufacturing efficiency, and logistics optimization. The utilization of platforms like Hadoop, Spark, and Tableau demonstrates both functional versatility and implementation challenges, influenced by cost, infrastructure, and human capital readiness. Furthermore, the success of BDA initiatives is closely linked to organizational factors including data quality and workforce expertise. Systemic barriers such as strict data policies, fragmented IT infrastructures, and limited data access in low-resource settings impede optimal BDA deployment. This review underscores the need for strategic policy reforms, technological investments, and capacity building to realize the full potential of BDA. By addressing existing limitations and supporting future research directions, organizations can harness BDA to enable informed, agile, and sustainable decision-making.
References
Akinsete, O. and Oshingbesan, A. (2019). Leak detection in natural gas pipelines using intelligent models. https://doi.org/10.2118/198738-ms DOI: https://doi.org/10.2118/198738-MS
Anand, M. and Babu, S. (2025). Digital twin for IoT healthcare system, 143–174. https://doi.org/10.4018/979-8-3693-4199-5.ch006 DOI: https://doi.org/10.4018/979-8-3693-4199-5.ch006
El-Helw, A., Raghavan, V., Soliman, M., Caragea, G., Gu, Z., & Petropoulos, M. (2015). Optimization of common table expressions in MPP database systems. Proceedings of the VLDB Endowment, 8(12), 1704–1715. https://doi.org/10.14778/2824032.2824068 DOI: https://doi.org/10.14778/2824032.2824068
Kumar, S. and Singh, M. (2019). Big data analytics for healthcare industry: impact, applications, and tools. Big Data Mining and Analytics, 2(1), 48–57. https://doi.org/10.26599/bdma.2018.9020031 DOI: https://doi.org/10.26599/BDMA.2018.9020031
Mowrer, M., Roberts, B., & Paula, H. (2017). Tapping into your current data reserves. https://doi.org/10.4043/27736-ms DOI: https://doi.org/10.4043/27736-MS
Naik, N., Rallapalli, Y., Krishna, M., Vellara, A., Shetty, D., Patil, V., … & Somani, B. (2022). Demystifying the advancements of big data analytics in medical diagnosis: an overview. Engineered Science. https://doi.org/10.30919/es8d580 DOI: https://doi.org/10.30919/es8d580
Haleem, A., Javaid, M., Singh, R., Rab, S., & Suman, R. (2022). Perspectives of cybersecurity for ameliorative Industry 4.0 era: A review-based framework. Industrial Robot: The International Journal of Robotics Research and Application, 49(3), 582–597. https://doi.org/10.1108/ir-10-2021-0243 DOI: https://doi.org/10.1108/IR-10-2021-0243
Javed, A., Ahmed, W., Alazab, M., Jalil, Z., Kifayat, K., & Gadekallu, T. (2022). A comprehensive survey on computer forensics: State-of-the-art, tools, techniques, challenges, and future directions. IEEE Access, 10, 11065–11089. https://doi.org/10.1109/access.2022.3142508 DOI: https://doi.org/10.1109/ACCESS.2022.3142508
Kalinaki, K., Shafik, W., Masha, M., & Alli, A. (2024). A review of artificial intelligence techniques for improved cloud and IoT security, 38–68. https://doi.org/10.4018/979-8-3693-0766-3.ch002 DOI: https://doi.org/10.4018/979-8-3693-0766-3.ch002
Khatana, S., & Kulshrestha, S. (2025). International law and cybersecurity in the era of cloud computing, 251–270. https://doi.org/10.4018/979-8-3693-9581-3.ch013 DOI: https://doi.org/10.4018/979-8-3693-9581-3.ch013
S, J., Ravimaran, S., & Sathish, A. (2021). Robust security with strong authentication in mobile cloud computing based on trefoil congruity framework. Journal of Organizational and End User Computing, 33(6), 1–28. https://doi.org/10.4018/joeuc.20211101.oa11 DOI: https://doi.org/10.4018/JOEUC.20211101.oa11
Sasada, T., Kawai, M., Masuda, Y., Taenaka, Y., & Kadobayashi, Y. (2023). Factor analysis of learning motivation difference on cybersecurity training with Zero Trust architecture. IEEE Access, 11, 141358–141374. https://doi.org/10.1109/access.2023.3341093 DOI: https://doi.org/10.1109/ACCESS.2023.3341093
Taneja, S., Shukla, R., & Singh, A. (2024). Embracing digital transformation, 83–93. https://doi.org/10.4018/979-8-3693-2019-8.ch005 DOI: https://doi.org/10.4018/979-8-3693-2019-8.ch005
Vakaliuk, T., & Семеріков, С. (2023). Introduction to DOORS workshops on edge computing (2021–2023). Journal of Edge Computing, 2(1), 1–22. https://doi.org/10.55056/jec.618Pawar, S. and Dhumal, V. (2024). The role of technology in transforming leadership management practices. Multidisciplinary Reviews, 7(4), 2024066. https://doi.org/10.31893/multirev.2024066 DOI: https://doi.org/10.31893/multirev.2024066
Protopsaltis, A., Sarigiannidis, P., Margounakis, D., & Lytos, A. (2020). Data visualization in Internet of Things, 1–11. https://doi.org/10.1145/3407023.3409228 DOI: https://doi.org/10.1145/3407023.3409228
Redondo, R., Herrero, Á., Corchado, E., & Sedano, J. (2020). A decision-making tool based on exploratory visualization for the automotive industry. Applied Sciences, 10(12), 4355. https://doi.org/10.3390/app10124355 DOI: https://doi.org/10.3390/app10124355
Sugumaran, V., Sangaiah, A., & Thangavelu, A. (2017). Computational intelligence applications in business intelligence and big data analytics. https://doi.org/10.1201/9781315180748 DOI: https://doi.org/10.1201/9781315180748
Tripathi, M., Goswami, I., Haralayya, D., Roja, M., Aarif, M., & Kumar, D. (2024). The role of big data analytics as a critical roadmap for realizing green innovation and competitive edge and ecological performance for realizing sustainable goals., 260–269. https://doi.org/10.2174/9789815256680124010021 DOI: https://doi.org/10.2174/9789815256680124010021
Udegbe, E., Morgan, E., & Srinivasan, S. (2019). Big-data analytics for production-data classification using feature detection: application to restimulation-candidate selection. SPE Reservoir Evaluation & Engineering, 22(02), 364–385. https://doi.org/10.2118/187328-pa DOI: https://doi.org/10.2118/187328-PA
Zhu, J., Zhuang, E., Jian, F., Baranowski, J., Ford, A., & Shen, J. (2016). A framework-based approach to utility big data analytics. IEEE Transactions on Power Systems, 31(3), 2455–2462. https://doi.org/10.1109/tpwrs.2015.2462775 DOI: https://doi.org/10.1109/TPWRS.2015.2462775
