Utilization of Big Data in Improving the Efficiency of E-Business Systems in Indonesia
Abstract
The rapid growth of digital technology in Indonesia has fostered the expansion of e-business systems, which in turn has generated vast volumes of data. However, many e-business platforms still face challenges in utilizing this data effectively to improve operational efficiency and decision-making. This research was conducted to explore the utilization of big data in enhancing the efficiency of e-business systems in Indonesia. The main objective of the study is to analyze how the integration of big data analytics contributes to optimizing business processes, customer engagement, and overall system performance in the Indonesian digital commerce ecosystem.
A mixed-method approach was employed, combining quantitative surveys of 120 e-business practitioners with qualitative interviews involving 15 data analysts and IT managers from various sectors such as retail, fintech, and logistics. Data were analyzed using statistical tools and thematic coding to derive patterns and insights.
The findings indicate that e-businesses implementing big data strategies reported a significant improvement in system responsiveness, personalized customer services, and data-driven decision-making. Moreover, big data utilization has been linked to enhanced supply chain management and real-time monitoring capabilities. Despite these benefits, challenges such as data privacy concerns, lack of skilled personnel, and high infrastructure costs remain significant barriers.
In conclusion, the study confirms that the effective use of big data plays a crucial role in improving the efficiency and competitiveness of e-business systems in Indonesia. Future initiatives should focus on strengthening data governance and investing in human capital to maximize big data’s potential.
Full text article
References
Abdelaziz, S. (2024). Unveiling the Landscape of Sustainable Logistics Service Quality: A Bibliometric Analysis. Jurnal Optimasi Sistem Industri, 23(2), 227–265. https://doi.org/10.25077/josi.v23.n2.p227-265.2024
Akhmetshin, E. (2024). Intelligent Data Analytics using Hybrid Gradient Optimization Algorithm with Machine Learning Model for Customer Churn Prediction. Fusion: Practice and Applications, 14(2), 159–171. https://doi.org/10.54216/FPA.140213
Al, I. A. (2024). Large-scale Probabilistic Forecasting of Consumer Engagement of CPG Products using Heterogeneous Web Data. Procedia Computer Science, 237(Query date: 2025-05-05 13:47:53), 426–436. https://doi.org/10.1016/j.procs.2024.05.124
Ali, N. (2022). Fusion-based supply chain collaboration using machine learning techniques. Intelligent Automation and Soft Computing, 31(3), 1671–1687. https://doi.org/10.32604/IASC.2022.019892
Bagwari, A. (2022). CBIR-DSS: Business Decision Oriented Content-Based Recommendation Model for E-Commerce. Information (Switzerland), 13(10). https://doi.org/10.3390/info13100479
Bhardwaj, P. (2019). Big Data Analytics in Healthcare. Smart Healthcare Systems, Query date: 2025-05-05 13:47:53, 1–15. https://doi.org/10.1201/9780429020575-1
Cakir, A. (2022). Enabling real time big data solutions for manufacturing at scale. Journal of Big Data, 9(1). https://doi.org/10.1186/s40537-022-00672-6
Chen, C. (2020). HBD-Authority: Streaming Access Control Model for Hadoop. Proceedings - 2020 IEEE 6th International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, DependSys 2020, Query date: 2025-05-05 13:47:53, 16–25. https://doi.org/10.1109/DependSys51298.2020.00012
Corallo, A. (2023). Cybersecurity Challenges for Manufacturing Systems 4.0: Assessment of the Business Impact Level. IEEE Transactions on Engineering Management, 70(11), 3745–3765. https://doi.org/10.1109/TEM.2021.3084687
Das, S. K. (2023). Digital technologies for the sustainability of circular manufacturing processes: A review. Proceedings of the Summer School Francesco Turco, Query date: 2025-05-05 13:47:53. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85193711344&origin=inward
Elkmash, M. R. M. (2022). An experimental investigation of the impact of using big data analytics on customers’ performance measurement. Accounting Research Journal, 35(1), 37–54. https://doi.org/10.1108/ARJ-04-2020-0080
Jamwal, A. (2021). Industry 4.0 technologies for manufacturing sustainability: A systematic review and future research directions. Applied Sciences (Switzerland), 11(12). https://doi.org/10.3390/app11125725
Karim, A. (2021). Value Tracking Thru Digital Fields Countrywide Solution Big Data Analytics Project. Proceedings of the Annual Offshore Technology Conference, Query date: 2025-05-05 13:47:53. https://doi.org/10.4043/31046-MS
Kodali, R. K. (2022). Aqua Monitoring System using AWS. 2022 International Conference on Computer Communication and Informatics, ICCCI 2022, Query date: 2025-05-05 13:47:53. https://doi.org/10.1109/ICCCI54379.2022.9740798
Kolomiyets, G. (2024). The Impact of Digitalization on the Formation of new Business models in Electronic Commerce: Analysis and Trends. Salud, Ciencia y Tecnologia - Serie de Conferencias, 3(Query date: 2025-05-05 13:47:53). https://doi.org/10.56294/sctconf2024.652
Kruger, T. (2020). Big data and digital transformation summary three 3 years of panel discussions. Proceedings of the Annual Offshore Technology Conference, 2020(Query date: 2025-05-05 13:47:53). https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85086224180&origin=inward
Kumar, S. (2024). Correction to: Past, present, and future of sustainable finance: Insights from big data analytics through machine learning of scholarly research (Annals of Operations Research, (2022), 10.1007/s10479-021-04410-8). Annals of Operations Research, 332(1), 1199–1205. https://doi.org/10.1007/s10479-022-04535-4
Maran, K. (2022). Business Analytics Contribution in the Growth of Indian Digital Business. 2022 1st International Conference on Computational Science and Technology, ICCST 2022 - Proceedings, Query date: 2025-05-05 13:47:53, 497–500. https://doi.org/10.1109/ICCST55948.2022.10040343
Mohammed, A. (2022). Smart Project Management System (SPMS)—An Integrated and Predictive Solution for Proactively Managing Oil & Gas client Projects. Society of Petroleum Engineers - ADIPEC 2022, Query date: 2025-05-05 13:47:53. https://doi.org/10.2118/210877-MS
Moumen, Y. (2023). Study of the Impact of Industry 4.0 Tools in E-maintenance on the Performance of Industrial Companies. International Journal of Engineering Trends and Technology, 71(8), 66–75. https://doi.org/10.14445/22315381/IJETT-V71I8P206
Nikam, R. R. (2021). Data privacy preservation and security approaches for sensitive data in big data. Advances in Parallel Computing, 39(Query date: 2025-05-05 13:47:53), 394–408. https://doi.org/10.3233/APC210221
Pacheco-Velazquez, E. (2024). Exploring educational simulation platform features for addressing complexity in Industry 4.0: A qualitative analysis of insights from logistics experts. Frontiers in Education, 9(Query date: 2025-05-05 13:47:53). https://doi.org/10.3389/feduc.2024.1331911
Patel, H. (2019). Big Data Processing at Microsoft: Hyper Scale, Massive Complexity, and Minimal Cost. SoCC 2019 - Proceedings of the ACM Symposium on Cloud Computing, Query date: 2025-05-05 13:47:53, 490–490. https://doi.org/10.1145/3357223.3366029
Pughazendi, N. (2023). Graph Sample and Aggregate Attention Network optimized with Barnacles Mating Algorithm based Sentiment Analysis for Online Product Recommendation. Applied Soft Computing, 145(Query date: 2025-05-05 13:47:53). https://doi.org/10.1016/j.asoc.2023.110532
Raju, A. (2019). A Comparative Study of Spark Schedulers’ Performance. CSITSS 2019 - 2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution, Proceedings, Query date: 2025-05-05 13:47:53. https://doi.org/10.1109/CSITSS47250.2019.9031028
Sarni, W. (2021). Digital Water: Enabling a More Resilient, Secure and Equitable Water Future. In Digital Water Enabling a More Resilient, Secure and Equitable Water Future (p. 94). https://doi.org/10.4324/9780429439278
Sassi, I. (2019). Adaptation of Classical Machine Learning Algorithms to Big Data Context: Problems and Challenges: Odels under Spark. ICSSD 2019 - International Conference on Smart Systems and Data Science, Query date: 2025-05-05 13:47:53. https://doi.org/10.1109/ICSSD47982.2019.9002857
Singh, D. (2025). A combined AHP-DEMATEL model approach to build tech-enabled resilient supply chain. Journal of Enterprise Information Management, Query date: 2025-05-05 13:47:53. https://doi.org/10.1108/JEIM-03-2023-0166
Sivarajah, U. (2020). Role of big data and social media analytics for business to business sustainability: A participatory web context. Industrial Marketing Management, 86(Query date: 2025-05-05 13:47:53), 163–179. https://doi.org/10.1016/j.indmarman.2019.04.005
Sodhro, A. H. (2021). Toward ML-Based Energy-Efficient Mechanism for 6G Enabled Industrial Network in Box Systems. IEEE Transactions on Industrial Informatics, 17(10), 7185–7192. https://doi.org/10.1109/TII.2020.3026663
Sun, B. (2020). Business model designs, big data analytics capabilities and new product development performance: Evidence from China. European Journal of Innovation Management, 24(4), 1162–1183. https://doi.org/10.1108/EJIM-01-2020-0004
Turki, E. M. (2024). Enhancing E-Commerce Recommendations Through Data-Driven Approaches: A Case Study of Amazon Product Reviews. Journal of Information Systems Engineering and Management, 10(Query date: 2025-05-05 13:47:53), 269–279.
Wang, J. (2024). Overview of Data Quality: Examining the Dimensions,Antecedents, and Impacts of Data Quality. Journal of the Knowledge Economy, 15(1), 1159–1178. https://doi.org/10.1007/s13132-022-01096-6
Wang, S. (2022). Smart manufacturing business management system for network industry spin-off enterprises. Enterprise Information Systems, 16(2), 285–306. https://doi.org/10.1080/17517575.2020.1722254
Yadav, O. P. (2024). Fintech and Data Science: Shaping the Future of the Digital Economy. Synergy of AI and Fintech in the Digital Gig Economy, Query date: 2025-05-05 13:47:53, 332–349. https://doi.org/10.1201/9781032720104-21
Yiu, L. M. D. (2021). Firms’ operational and logistics characteristics and realisation of business analytics benefits: Evidence from stock markets. International Journal of Shipping and Transport Logistics, 13(6), 649–669. https://doi.org/10.1504/ijstl.2021.118531
Authors
Copyright (c) 2025 Agung Yuliyanto Nugroho, Rachmat Prasetio, Lucas Wong, Ananya Rao

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.