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Trends and Challenges in Database Management Systems for Business Analytics
Subject area: Science,Engineering and Technology · Area of research: Database Management Systems
Abstract
The growing usage of data-driven decision-making has boosted the role of Database Management Systems (DBMS) in the framework of efficient business analytics. The growing big data, cloud computing, and artificial intelligence necessitate spectacular opportunities and challenges that organizations face while handling, storing, and analyzing vital information in the business. The paper discusses the modern tendencies and issues of DBMS for business analytics with regards to scalability, real-time processing, interoperability, and security. A literature search was performed in articles published between 2020 and 2025 with the aim of identifying: the existing technologies, patterns of their use, and areas of research and development. The report elucidates the importance of a growing trend towards the use of cloud-native databases, NoSQL and NewSQL databases, in-memory computing, and the use of AI to assist in query optimization as major trends that are changing the market. Although there are drawbacks like data governance, complexity in integration of data, high costs of implementation, and skills shortages, which may still pose obstacles to full adoption. The paper can add value to the literature by charting the recent developments in DBMS, discussing the essential shortcomings and providing suggestions fororganizations desiring to improve their analytics. The insights have academic and practical implications for the researchers, technology providers, and businesses looking forward to deriving a competitive advantage out of an advanced database solution.
Keywords
Database Management Systems, Business Analytics, Data Warehousing, Cloud Databases, Scalability.
References
[1] Aversa, D. (2024). Scenario analysis and climate change: a literature review via text analytics. British Food Journal, 126(1), 271–289. https://doi.org/10.1108/BFJ-08-2022-0691
[2] Azeem, M. (2024). The relationship between warehousing, transportation, and halal food exports: an empirical case. South Asian Journal of Social Review, 3(1), 1–14. https://doi.org/10.57044/sajsr.2024.3.1.2413
[3] An, K., Sun, X., Song, Y., Lu, Y., & Shangguan, Q. (2024). A DenseNet-based feature weighting convolutional network recognition model and its application in industrial part classification. IET Image Processing, 18(3), 589–601. https://doi.org/10.1049/ipr2.12971
[4] Cattaneo, C., Tambuzzi, S., De Vecchi, S., Maggioni, L., & Costantino, G. (2024). Consequences of the lack of clinical forensic medicine in emergency departments. International Journal of Legal Medicine, 138(1), 139–150. https://doi.org/10.1007/s00414-023-02973-8
[5] Dr. Alok Singh Chauhan, Dr. B., & Dr. Mukta Makhija, D. A. A. (2024). Industry 4.0 and Indian Manufacturing Companies: Barriers towards sustainability. Journal of Informatics Education and Research, 4(1). https://doi.org/10.52783/jier.v4i1.646
[6] Ejaz, Umair & Islam, S A Mohaiminul & Sarkar, Ankur & Imashev, Aidar. (2024). AI-Enabled Diagnostic Platforms For Real-Time Disease Detection In Remote And Underserved Areas. IOSR Journal of Mechanical and Civil Engineering. 2652-2663. https://doi.org/10.9790/1684-2001034756.
[7] Filippou, G., Georgiadis, A. G., & Jha, A. K. (2024). Establishing the link: Does web traffic from various marketing channels influence direct traffic source purchases? Marketing Letters, 35(1), 59–71. https://doi.org/10.1007/s11002-023-09700-8
[8] Ghosh, D. (2024). Data Warehousing in Microsoft Fabric. In Mastering Microsoft Fabric (pp. 131–165). Apress. https://doi.org/10.1007/979-8-8688-0131-0_5
[9] Gitinavard, H., Mohagheghi, V., Mousavi, S. M., & Makui, A. (2024). A new bi-stage interactive possibilistic programming model for perishable logistics distribution systems under uncertainty. Expert Systems with Applications, 238. https://doi.org/10.1016/j.eswa.2023.122121
[10] Huang, C. Y., Peng, Q., Zhang, F. X., Wang, S. Y., Luo, C., Zhang, Y. F., & Yu, G. (2024). Survey on Key Techniques of Multi-replica Distributed Transaction Processing and Representative Database Systems. Ruan Jian Xue Bao/Journal of Software, 35(1), 455–480. https://doi.org/10.13328/j.cnki.jos.006822
[11] Haque, N. T., Tasnim, Z., Chowdhury, A. R., & Reno, S. (2024). Securing Farm Insurance Using a Private-Permissioned Blockchain Driven by Hyperledger Fabric and IPFS. In Lecture Notes in Networks and Systems (Vol. 738 LNNS, pp. 347–359). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-4433-0_29
[12] Jimenez, S. L. R., Perez, M. A. G., Gallegos, E. N. C., Jimenez, L. A. V., Cueva, N. A. A., & Cabrera, M. B. S. (2024). Application of remote monitoring of biosignals and geolocation with a wearable for patients with sequelae of the coronavirus. Indonesian Journal of Electrical Engineering and Computer Science, 33(1), 135–150. https://doi.org/10.11591/ijeecs.v33.i1.pp135-150
[13] Li, Z., Chen, H., Guan, W., Geng, S., & Chen, X. (2024). An Integrated Data Analysis and Application Service Platform for Power Grid Engineering. In Mechanisms and Machine Science (Vol. 146, pp. 793–810). Springer Science and Business Media B.V. https://doi.org/10.1007/978-3-031-44947-5_61
[14] Lindawati, A. S. L., & Meiryani. (2024). A bibliometric analysis on the research trends of global climate change and future directions. Cogent Business and Management. Cogent OA. https://doi.org/10.1080/23311975.2024.2325112
[15] Merlo, T. R. (2024). Foundations of Artificial Intelligence and the Ever-Evolving Technological Landscape in Business (pp. 1–18). https://doi.org/10.4018/979-8-3693-1058-8.ch001
[16] Mrs. Nayan Ahire, Ms. Vaishnavi Adhangle, Mr. Nikhil Handore, Ms. Chandrakala Gaikwad, & Mr. Umesh Hinde. (2024). OMR Sheet Evaluation using Image Processing. International Journal of Advanced Research in Science, Communication and Technology, 334–338. https://doi.org/10.48175/ijarsct-15660
[17] Monteiro Cordeiro, N., Facina, G., Pinto Nazário, A. C., Monteiro Sanvido, V., Araujo Neto, J. T., Rodrigues dos Santos, E., … Elias, S. (2024). Towards precision medicine in breast imaging: A novel open mammography database with tailor-made 3D image retrieval for AI and teaching. Computer Methods and Programs in Biomedicine, 248. https://doi.org/10.1016/j.cmpb.2024.108117
[18] Maharaj, K. D., Dass, J., Ibrahim, M., Mahmood, T., & Rowshanfarzad, P. (2024, January 1). Peripheral Doses Beyond Electron Applicators in Conventional C-Arm Linear Accelerators: A Systematic Literature Review. Technology in Cancer Research and Treatment. SAGE Publications Inc. https://doi.org/10.1177/15330338241239144
[19] Mohasseb, A. M. A. (2024). The Impact of Big Data Predictive Analytics on Firm Performance: The Role of Cloud ERP and Business Intelligence Integration. المجلة العلمية للدراسات والبحوث المالية والتجارية, 5(1), 917–947. https://doi.org/10.21608/cfdj.2024.329290
[20] Patel, U., Patel, R., & Kadecha, P. (2024). AN IOT-AIDED SMART AGRITECH SYSTEM FOR CROP YIELD OPTIMIZATION. In Precision Agriculture for Sustainability: Use of Smart Sensors, Actuators, and Decision Support Systems (pp. 433–452). Apple Academic Press. https://doi.org/10.1201/9781003435228-26
[21] Richer, G., Pister, A., Abdelaal, M., Fekete, J. D., Sedlmair, M., & Weiskopf, D. (2024). Scalability in Visualization. IEEE Transactions on Visualization and Computer Graphics, 30(7), 3314–3330. https://doi.org/10.1109/TVCG.2022.3231230
[22] Sharma, S. (2024). Big Data Analytics in Business Process: Insights and Implications. In Lecture Notes in Networks and Systems (Vol. 837 LNNS, pp. 112–118). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-48465-0_15
[23] Weissgold, B. J. (2024). US wildlife trade data lack quality control necessary for accurate scientific interpretation and policy application. Conservation Letters, 17(2). https://doi.org/10.1111/conl.13005
[24] Wanye, F., Gleyzer, V., Kao, E., & Feng, W. C. (2024). SamBaS: Sampling-Based Stochastic Block Partitioning. IEEE Transactions on Network Science and Engineering, 11(3), 3053–3065. https://doi.org/10.1109/TNSE.2024.3358301
[25] Yang, J., & Kim, K. S. (2024). Exploring the Security Vulnerability in Frequency-Hiding Order-Preserving Encryption. Security and Communication Networks, 2024, 1–11. https://doi.org/10.1155/2024/2764345
[26] Zamboni, K., Schellenberg, J., Hanson, C., Betran, A. P., & Dumont, A. (2019). Assessing scalability of an intervention: Why, how and who? Health Policy and Planning, 34(7), 544–552. https://doi.org/10.1093/heapol/czz068
How to cite this paper
@article{1710321,
author = {Jyothi Swaroop Myneni},
title = {Trends and Challenges in Database Management Systems for Business Analytics},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {7},
pages = {784-791},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1710321.pdf},
abstract = {The growing usage of data-driven decision-making has boosted the role of Database Management Systems (DBMS) in the framework of efficient business analytics. The growing big data, cloud computing, and artificial intelligence necessitate spectacular opportunities and challenges that organizations face while handling, storing, and analyzing vital information in the business. The paper discusses the modern tendencies and issues of DBMS for business analytics with regards to scalability, real-time processing, interoperability, and security. A literature search was performed in articles published between 2020 and 2025 with the aim of identifying: the existing technologies, patterns of their use, and areas of research and development. The report elucidates the importance of a growing trend towards the use of cloud-native databases, NoSQL and NewSQL databases, in-memory computing, and the use of AI to assist in query optimization as major trends that are changing the market. Although there are drawbacks like data governance, complexity in integration of data, high costs of implementation, and skills shortages, which may still pose obstacles to full adoption. The paper can add value to the literature by charting the recent developments in DBMS, discussing the essential shortcomings and providing suggestions fororganizations desiring to improve their analytics. The insights have academic and practical implications for the researchers, technology providers, and businesses looking forward to deriving a competitive advantage out of an advanced database solution.},
keywords = {Database Management Systems, Business Analytics, Data Warehousing, Cloud Databases, Scalability.},
month = {January},
}