Cloud-Based Big Data Analytics Frameworks for Strategic Business Intelligence and Decision Support
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I3P118Keywords:
Cloud Computing, Big Data Analytics, Business Intelligence, Decision Support Systems, Strategic Analytics, Distributed Computing, Data-Driven Decision MakingAbstract
The multifold increase in digital information created by enterprise applications, social media, Internet of Things (IoT) operational apparatus, and transactional software applications has altered the way organizations gain strategic intelligence. The scope, speed, diversity, accuracy and worth of the present-day big data cannot be dealt with by traditional data processing and decision supporting systems. Cloud computing stands out as a paradigm shift that comes with more scalable, elastic, and cost-effective infrastructures that can fulfill the large-scale data analytics. To this end, big data analytics frameworks in the form of clouds have been enablers of core strategy business intelligence (BI) and decision support systems (DSS). This essay is a thorough and methodical discussion of cloud-based big data analytics designs and their implication on improving strategic business intelligence and business decision-making. The paper will examine the architecture features, analytics tiers, and processing frameworks that can allow organizations to transform raw and heterogeneous data into actionable insights. The combination of distributed storage system, parallel processing engines, real time streaming analytics, and advanced machine learning models in the cloud environment is discussed in detail. Special consideration is made as to how these frameworks aid descriptive, diagnostic, predictive and prescriptive analytics in several business areas, such as finance, marketing, supply chain management and customer relationship management. The fine literature review encompasses previous research before 2024, which shows the development of the framework of big data analytics, the model of cloud services, and the business intelligence system. The survey explains why traditional on-premise analytics systems have serious drawbacks like the lack of scalability, the high cost of purchasing and installing systems and poor flexibility that cloud-based systems can handle. Moreover, issues regarding data security, privacy, governance, latency, and interoperability are also discussed its most critical. The team of the proposed methodology presents a layer-based cloud-based analytics system that encompasses the data ingestion, storage, processing, analytics, visualization and decision-support layers. The key features proposed in mathematical models and performance metrics are aimed at checking the efficiency of the system, its scalability and accuracy rate of decision making. The analysis of the effect of the cloud-based analytics on strategic decision-making is presented in the results and the discussion sections, where the authors observe the increase in the agility and accuracy in forecasting and responsiveness of the organization. The paper will end up by summarizing the important findings and explaining the future research directions of cloud-native analytics, explainable AI and intelligent decision support.
References
1. Dean, J., & Ghemawat, S. (2008). MapReduce: simplified data processing on large clusters. Communications of the ACM, 51(1), 107-113.
2. Ghemawat, S., Gobioff, H., & Leung, S. T. (2003, October). The Google file system. In Proceedings of the nineteenth ACM symposium on Operating systems principles (pp. 29-43).
3. Khan, Z., Anjum, A., & Kiani, S. L. (2013, December). Cloud based big data analytics for smart future cities. In 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing (pp. 381-386). IEEE.
4. White, T. (2012). Hadoop: The definitive guide. "O'Reilly Media, Inc.".
5. Zaharia, M., Chowdhury, M., Franklin, M. J., Shenker, S., & Stoica, I. (2010). Spark: Cluster computing with working sets. In 2nd USENIX workshop on hot topics in cloud computing (HotCloud 10).
6. Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R. H., Konwinski, A., ... & Zaharia, M. (2009). Above the clouds: A berkeley view of cloud computing.
7. Mell, P., & Grance, T. (2011). The NIST definition of cloud computing.
8. Wang, L., Zhan, J., Shi, W., & Liang, Y. (2011). In cloud, can scientific communities benefit from the economies of scale?. IEEE Transactions on Parallel and Distributed Systems, 23(2), 296-303.
9. Botta, A., De Donato, W., Persico, V., & Pescapé, A. (2016). Integration of cloud computing and internet of things: a survey. Future generation computer systems, 56, 684-700.
10. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 1165-1188.
11. Eckerson, W. W. (2010). Performance dashboards: measuring, monitoring, and managing your business. John Wiley & Sons.
12. Turban, E. (2011). Decision support and business intelligence systems. Pearson Education India.
13. Kambatla, K., Kollias, G., Kumar, V., & Grama, A. (2014). Trends in big data analytics. Journal of parallel and distributed computing, 74(7), 2561-2573.
14. Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation computer systems, 25(6), 599-616.
15. Niu, Y., Ying, L., Yang, J., Bao, M., & Sivaparthipan, C. B. (2021). Organizational business intelligence and decision making using big data analytics. Information Processing & Management, 58(6), 102725.
16. Miah, S. J. (2015). A Cloud-based Business Analytics for Supply Chain Decision Support. Journal of Information Sciences and Computing Technologies, 4(1), 274-280.
17. Vögler, M., Schleicher, J. M., Inzinger, C., & Dustdar, S. (2017). Ahab: A cloud‐based distributed big data analytics framework for the Internet of Things. Software: Practice and Experience, 47(3), 443-454.
18. M Alasiri, M., & Salameh, A. A. (2020). The impact of business intelligence (BI) and decision support systems (DSS): Exploratory study. International Journal of Management, 11(5).
19. Müller, O., Junglas, I., Brocke, J. V., & Debortoli, S. (2016). Utilizing big data analytics for information systems research: challenges, promises and guidelines. European Journal of Information Systems, 25(4), 289-302.
20. Fernández, A., Del Rio, S., López, V., Bawakid, A., Del Jesus, M. J., Benítez, J. M., & Herrera, F. (2014). Big Data with Cloud Computing: an insight on the computing environment, MapReduce, and programming frameworks. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 4(5), 380-409.
21. Khan, Z., Anjum, A., Soomro, K., & Tahir, M. A. (2015). Towards cloud based big data analytics for smart future cities. Journal of Cloud Computing, 4(1), 2.
