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Tujuan dari penelitian ini adalah untuk mengeksplorasi bagaimana integrasi data secara real-time dan pemodelan yang adaptif dapat mendukung pengambilan keputusan berbasis data yang lebih cepat dan akurat dalam lingkungan bisnis yang dinamis. Dengan metode Agile BI memungkinkan organisasi atau perusahaan untuk merespon perubahan kebutuhan bisnis dengan cepat melalui siklus iterative yang fleksibel. Hasil dari penelitian ini menunjukkan bahwa penggunaan pendekatan Agile BI dalam integrasi data real-time dapat dikembangkan dengan menghasilkan produk dashboard visual untuk kebutuhan analisis bisnis dan pengambilan keputusan. Penelitian ini juga memberikan saran praktis terkait penguatan infrastruktur teknologi, peningkatan kapasitas sumber daya manusia, serta pengelolaan keamanan data untuk mendukung implementasi Agile BI secara optimal.
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References
- D. Larson and V. Chang, “A review and future direction of agile, business intelligence, analytics and data science,” Int J Inf Manage, vol. 36, no. 5, pp. 700–710, 2016, doi: 10.1016/j.ijinfomgt.2016.04.013.
- J. McPadden et al., “Health care and precision medicine research: Analysis of a scalable data science platform,” J Med Internet Res, vol. 21, no. 4, pp. 1–11, 2019, doi: 10.2196/13043.
- A. O. Asare, P. C. Addo, E. O. Sarpong, and D. Kotei, “COVID-19: Optimizing Business Performance through Agile Business Intelligence and Data Analytics,” Open Journal of Business and Management, vol. 08, no. 05, pp. 2071–2080, 2020, doi: 10.4236/ojbm.2020.85126.
- C. R. Sahara and A. M. Aamer, “Real-time data integration in smart warehouse as a contemporary approach,” AIP Conf Proc, vol. 2646, no. January, 2023, doi: 10.1063/5.0112745.
- N. Biswas, A. S. Mondal, A. Kusumastuti, S. Saha, and K. C. Mondal, “Automated credit assessment framework using ETL process and machine learning,” Innov Syst Softw Eng, 2022, doi: 10.1007/s11334-022-00522-x.
- M. Kostov and K. Kaloyanova, “Real-time data integration in information systems using stream processing for medical data,” Annual of Sofia University St. Kliment Ohridski. Faculty of Mathematics and Informatics, vol. 110, pp. 101–110, 2023, doi: 10.60063/gsu.fmi.110.101-110.
- F. Hassan, M. E. Shaheen, and R. Sahal, “Real-time healthcare monitoring system using online machine learning and spark streaming,” International Journal of Advanced Computer Science and Applications, vol. 11, no. 9, pp. 650–658, 2020, doi: 10.14569/IJACSA.2020.0110977.
- A. S. Paramita, H. Prabowo, A. Ramadhan, and D. I. Sensuse, “Modelling Data Warehousing and Business Intelligence Architecture for Non-profit Organization Based on Data Governances Framework,” Journal of Applied Data Sciences, vol. 4, no. 3, pp. 276–288, 2023, doi: 10.47738/jads.v4i3.117.
- A. M. Awasthi and D. Pandita, “Role of business intelligence and analytics: Analysis of data driven decision,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 12, pp. 1506–1510, 2019, doi: 10.35940/ijitee.L3101.1081219.
- R. Hartono and T. I. Ramadhan, “Pengembangan Prototipe Real Time Data Integration Model Untuk Kebutuhan Business Intelligence,” Seminar Nasional Penelitian (SEMNAS CORISINDO 2024), pp. 191–197, 2024.
- M. Nur Fauziah, R. Hartono, and A. Supriatman, “Pengembangan Data Warehouse Pada Apotek X Untuk Kebutuhan Business Intelligence,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 4, pp. 8075–8082, 2024, doi: 10.36040/jati.v8i4.10646.
- V. B. Munagandla and I. Developer, “Cloud-Based Real-Time Data Integration for Scalable Pooled Testing in Pandemic Response,” vol. 01, pp. 485–504, 2023.
- A. Ambasht, “Real-Time Data Integration and Analytics: Empowering Data-Driven Decision Making,” International Journal of Computer Trends and Technology, vol. 71, no. 7, pp. 8–14, 2023, doi: 10.14445/22312803/ijctt-v71i7p102.
- B. J. Chang, “Agile business intelligence: Combining big data and business intelligence to responsive decision model,” Journal of Internet Technology, vol. 19, pp. 1699–1706, 2018, doi: 10.3966/160792642018111906007.
- N. Biswas, A. Sarkar, and K. C. Mondal, “Efficient incremental loading in ETL processing for real-time data integration,” Innov Syst Softw Eng, vol. 16, no. 1, pp. 53–61, 2020, doi: 10.1007/s11334-019-00344-4.
References
D. Larson and V. Chang, “A review and future direction of agile, business intelligence, analytics and data science,” Int J Inf Manage, vol. 36, no. 5, pp. 700–710, 2016, doi: 10.1016/j.ijinfomgt.2016.04.013.
J. McPadden et al., “Health care and precision medicine research: Analysis of a scalable data science platform,” J Med Internet Res, vol. 21, no. 4, pp. 1–11, 2019, doi: 10.2196/13043.
A. O. Asare, P. C. Addo, E. O. Sarpong, and D. Kotei, “COVID-19: Optimizing Business Performance through Agile Business Intelligence and Data Analytics,” Open Journal of Business and Management, vol. 08, no. 05, pp. 2071–2080, 2020, doi: 10.4236/ojbm.2020.85126.
C. R. Sahara and A. M. Aamer, “Real-time data integration in smart warehouse as a contemporary approach,” AIP Conf Proc, vol. 2646, no. January, 2023, doi: 10.1063/5.0112745.
N. Biswas, A. S. Mondal, A. Kusumastuti, S. Saha, and K. C. Mondal, “Automated credit assessment framework using ETL process and machine learning,” Innov Syst Softw Eng, 2022, doi: 10.1007/s11334-022-00522-x.
M. Kostov and K. Kaloyanova, “Real-time data integration in information systems using stream processing for medical data,” Annual of Sofia University St. Kliment Ohridski. Faculty of Mathematics and Informatics, vol. 110, pp. 101–110, 2023, doi: 10.60063/gsu.fmi.110.101-110.
F. Hassan, M. E. Shaheen, and R. Sahal, “Real-time healthcare monitoring system using online machine learning and spark streaming,” International Journal of Advanced Computer Science and Applications, vol. 11, no. 9, pp. 650–658, 2020, doi: 10.14569/IJACSA.2020.0110977.
A. S. Paramita, H. Prabowo, A. Ramadhan, and D. I. Sensuse, “Modelling Data Warehousing and Business Intelligence Architecture for Non-profit Organization Based on Data Governances Framework,” Journal of Applied Data Sciences, vol. 4, no. 3, pp. 276–288, 2023, doi: 10.47738/jads.v4i3.117.
A. M. Awasthi and D. Pandita, “Role of business intelligence and analytics: Analysis of data driven decision,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 12, pp. 1506–1510, 2019, doi: 10.35940/ijitee.L3101.1081219.
R. Hartono and T. I. Ramadhan, “Pengembangan Prototipe Real Time Data Integration Model Untuk Kebutuhan Business Intelligence,” Seminar Nasional Penelitian (SEMNAS CORISINDO 2024), pp. 191–197, 2024.
M. Nur Fauziah, R. Hartono, and A. Supriatman, “Pengembangan Data Warehouse Pada Apotek X Untuk Kebutuhan Business Intelligence,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 4, pp. 8075–8082, 2024, doi: 10.36040/jati.v8i4.10646.
V. B. Munagandla and I. Developer, “Cloud-Based Real-Time Data Integration for Scalable Pooled Testing in Pandemic Response,” vol. 01, pp. 485–504, 2023.
A. Ambasht, “Real-Time Data Integration and Analytics: Empowering Data-Driven Decision Making,” International Journal of Computer Trends and Technology, vol. 71, no. 7, pp. 8–14, 2023, doi: 10.14445/22312803/ijctt-v71i7p102.
B. J. Chang, “Agile business intelligence: Combining big data and business intelligence to responsive decision model,” Journal of Internet Technology, vol. 19, pp. 1699–1706, 2018, doi: 10.3966/160792642018111906007.
N. Biswas, A. Sarkar, and K. C. Mondal, “Efficient incremental loading in ETL processing for real-time data integration,” Innov Syst Softw Eng, vol. 16, no. 1, pp. 53–61, 2020, doi: 10.1007/s11334-019-00344-4.