Peran Manajemen Strategi dalam Mengintegrasikan Big Data untuk Keputusan Keuangan Digital: Studi Kasus pada Perbankan Digital di Indonesia
DOI:
https://doi.org/10.35870/emt.v9i4.4771Keywords:
Big Data Analytics, Digital Banking, Digital Financial Decision, Digital Transformation, Strategic ManagementAbstract
The digital transformation in Indonesia's banking sector has created a new paradigm that requires big data analytics integration for optimizing digital financial decision-making. This research aims to analyze the role of strategic management in integrating big data to support digital financial decisions, identify strategic factors affecting implementation effectiveness, and evaluate its impact on banking institution performance. The research method employs a quantitative approach with descriptive-analytical design through structured surveys to 90 respondents from three leading digital banks (Jenius, Bank Neo Commerce, and SeaBank). Data was analyzed using Structural Equation Modeling (SEM) with AMOS software. Research results indicate that strategic management significantly influences digital financial decision-making (β=0.347, p<0.001) and big data analytics (β=0.612, p<0.001), while big data analytics significantly affects decision-making performance (β=0.428, p<0.001). Key strategic factors include technology infrastructure readiness, human resource competency, data governance, organizational culture, and strategic partnerships. Big data integration provides transformative impact through improved risk prediction accuracy, product development acceleration, service personalization, and operational efficiency optimization. The research conclusion emphasizes the importance of a holistic approach in integrating strategic, technological, and human resource aspects to achieve competitive advantage in Indonesia's digital banking sector.
Downloads
References
Andrias, A., Aisah, S., Yusuf, Y., Rumpoko, R., Putra, T. R. A., & Ollivia, J. (2025). Integrasi Aspek Sumber Daya Manusia Terhadap Regulasi Dan Kebijakan Keuangan Digital. Jurnal Neraca Peradaban, 5(2), 87-97. https://doi.org/10.55182/jnp.v5i2.565.
Aurellia, O. A., Kirana, L. P., & Kaltum, U. (2022). Digital Transformation Strategies In The Banking Industry: A Case Study of The Implementation of New Technology at PT. Bank Mandiri (Persero) TBK. Ganaya: Jurnal Ilmu Sosial dan Humaniora, 5(4), 474-481. https://doi.org/10.37329/ganaya.v5i4.2812.
Diener, F., & Špaček, M. (2021). Digital transformation in banking: A managerial perspective on barriers to change. Sustainability (Switzerland), 13(4), 1–26. https://doi.org/10.3390/su13042032.
Febrianti, A., & Shadiq, S. (2025). ANALISIS PERAN FINANCIAL TECHNOLOGY DALAM MENDUKUNG EFISIENSI OPERASIONAL DAN MANAJEMEN PERBANKAN. Musytari: Jurnal Manajemen, Akuntansi, dan Ekonomi, 18(5), 31-40.
Feriyanto, O., Ilmi, A. N., Aulia, V., Jandriani, L. H., Safitri, S., & Irmayanti, E. (2024). Peran akuntansi terhadap pengambilan keputusan bisnis melalui analisis big data (studi literatur). JURNAL ILMIAH EKONOMI, MANAJEMEN, BISNIS DAN AKUNTANSI, 1(2), 602-613.
Hu, S., Xiong, C., Chen, P., & Schonfeld, P. (2023). Examining nonlinearity in population inflow estimation using big data: An empirical comparison of explainable machine learning models. Transportation Research Part A: Policy and Practice, 174, 103743. https://doi.org/10.1016/j.tra.2023.103743.
Indarto, M., & Santoso, B. (2024). Efektivitas Pemanfaatan Big Data dalam Pengambilan Keputusan Strategis di Industri Perbankan. Jurnal Bisnis dan Manajemen (JURBISMAN), 2(4), 1159-1182. https://doi.org/10.61930/jurbisman.v2i4.900.
Khusna, T., Fransiska, M., Karimah, N. A., & Arwani, A. (2025). Peran Big Data Analytics Dalam Meningkatkan Transparansi Laporan Keuangan Syariah. Sahmiyya: Jurnal Ekonomi dan Bisnis, 13-26.
Mhlanga, D. (2024). The role of big data in financial technology toward financial inclusion. Frontiers in Big Data, 7. https://doi.org/10.3389/fdata.2024.1184444.
Poth, C. N., & Shannon-Baker, P. (2022). State of the methods: Leveraging design possibilities of qualitatively oriented mixed methods research. International Journal of Qualitative Methods, 21, 16094069221115302. https://doi.org/10.1177/16094069221115302.
Rahmadany, R., & Yanti, E. M. (2025). INOVASI CAPITAL, ASSET QUALITY, MANAGEMENT, EARNINGS, AND LIQUIDITY DALAM TRANSFORMASI DIGITAL PERBANKAN MODERN. Jurnal Ekobismen, 5(1), 13-21.
Reyhan, M., Ahmad, D. R., Ramadhan, N. A., & Kusumasari, I. R. (2024). Penggunaan Data Analisis dan Big Data dalam Strategi Pengambilan Keputusan Keuangan. Jurnal Akuntansi, Manajemen, dan Perencanaan Kebijakan, 2(2), 9-9. https://doi.org/10.47134/jampk.v2i2.540.
Sari, M. M., & Avrianto, R. P. (2025). Big Data dalam Bisnis: Studi Literatur dan Penerapannya di Indonesia. Jurnal Inovasi Informatika, 7(2), 25-36.
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial least squares structural equation modeling. In Handbook of market research (pp. 587-632). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-57413-4_15.
Tedi, E., & Wiyono, D. (2025). Transformasi Digital dalam Layanan Keuangan: Menilai Peran Kecerdasan Buatan dalam Meningkatkan Manajemen Risiko Kredit. Cakrawala Management Business Journal, 8(1), 1-31. https://doi.org/10.30862/cm-bj.v8i1.374.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Masno Marjohan, Hadi Supratikta, Reza Nugraha, Rika Septiani, Siti Patmah, Sakdanur Sakdanur, Anton Antoniii, M. Mahsus
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.
