This evidence-synthesis article examines enterprise data intelligence through the focal contribution “SiriusDeliver: Automating Data Warehouse Delivery at Tencent” and nine author-disjoint, topically matched studies. The analysis is organized around resource efficiency and performance trade-offs. Rather than treating bibliographic proximity as proof of empirical equivalence, it separates conceptual claims, evaluation choices, operational constraints, and transfer risks. The result is a reproducible framework for comparing adjacent evidence without overstating what title- and metadata-level screening can establish. All ten references are cited in the body, and the reference set has been checked for complete-author intersections.
enterprise data intelligenceresource efficiency and performance trade-offsevidence synthesisreproducibilityresearch evaluation
- Xie, H., Zhou, X., Yang, J., Shen, S., Wang, Z., Zheng, Y., Xu, T., Shi, Y., Zong, Z., Li, Y., Chen, P., Jiang, J., He, D., Yan, X., & Jiang, J. (2026). SiriusDeliver: Automating Data Warehouse Delivery at Tencent. arXiv. https://doi.org/10.48550/arXiv.2608.09185 DOI
- Martin, S. (2011). Annual International Conference on Mobile Communications, Networking and Applications / Business Intelligence & Data Warehouse - Special Track: Data Analysis, Data Quality & Metadata Management. Annual International Conference on Mobile Communications, Networking and Applications / Business Intelligence & Data Warehouse - Special Track: Data Analysis, Data Quality & Metadata Management. https://doi.org/10.5176/978-981-08-9266-1_mobicona-bidw-damd-2011 DOI
- Gunduz, D., Ergul Azizler, M., & Arslan, E. (2015). Implementation Scenarios of Reporting from Data Warehouse for Business Intelligence. International Journal of Modeling and Optimization, 5(3), 211-215. https://doi.org/10.7763/ijmo.2015.v5.464 DOI
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- Sá, J.-O.-E., & Santos, M.-Y. (2017). Process-driven data analytics supported by a data warehouse model. International Journal of Business Intelligence and Data Mining, 12(4), 383. https://doi.org/10.1504/ijbidm.2017.086986 DOI
- Aspin, A. (2015). SQL Server Reporting Services as a Business Intelligence Platform. Business Intelligence with SQL Server Reporting Services, 1-17. https://doi.org/10.1007/978-1-4842-0532-7_1 DOI
- Rudy, R., & Limantara, N. (2011). Model Data Warehouse dan Business Intelligence untuk Meningkatkan Penjualan pada PT. S. ComTech: Computer, Mathematics and Engineering Applications, 2(1), 418. https://doi.org/10.21512/comtech.v2i1.2774 DOI
- Kurze, C., & Gluchowski, P. (2010). Business Intelligence: Model Driven Architecture für Data Warehouses: Computer Aided Warehouse Engineering - CAWE. Multikonferenz Wirtschaftsinformatik 2010, 217-218. https://doi.org/10.17875/gup2010-1568 DOI
- Togatorop, P.-R., Sitorus, D., Purba, Y., & Tarigan, A.-M.-F. (2022). Twitter Data Warehouse and Business Intelligence Using Dimensional Model and Data Mining. 2022 IEEE International Conference of Computer Science and Information Technology (ICOSNIKOM), 1-6. https://doi.org/10.1109/icosnikom56551.2022.10034904 DOI
- Batalla, S. (2024). From data to value: turn unstructured data into a dimensional data model using Data Warehouse (SAP BW/4HANA) and Business Intelligence (SAP Lumira designer) visualizations. Proceedings of the 2024 10th International Conference on Computer Technology Applications, 103-108. https://doi.org/10.1145/3674558.3674572 DOI
- Journal
- Markets, Organizations & Public Value
- Volume
- 1 (2026)
- Article number
- mv20260062
- License
- CC BY 4.0
