Datasphere and SAP: How Data Integration Can Drive Business Value

Authors

  • Arunkumar Pasumarthi Technical Specialist, HCL AMERICA Author
  • Balamuralikrishnan Anbalagan Microsoft Corp, USA Author

DOI:

https://doi.org/10.32628/CSEIT25113472

Keywords:

SAP, Datasphere, Data Integration, Business Value, Digital Transformation, Enterprise Analytics

Abstract

At the time of the relevance of information and data as the foundation of current strategic business making, it becomes important to connect unrelated systems to draw relevant and practical business intelligence (Walha et al., 2024; Egbert & Ulbricht, 2024). This paper discusses SAP-Datasphere collaboration, and it emphasizes the revolution entering enterprise activities; modern data integration architecture. It considers the feasibility of a standardized data environment through consideration of the functionalities of the systems involved, mode of integration and relative deployment rates (Li et al., 2024). Using the combination of qualitative approach and thematic analysis, the research herein depicts a systematic report on the effect SAP Datasphere has on enhancing the organizational performance, scalability, and regulatory compliance of companies (Zhou et al., 2024). It also involves compilations of comparative tables and visual presentations which illustrate metric of performance, ROI and automation metrics (Visani et al., 2024). The results show that businesses that use SAP Datasphere realise greater flexibility in operations and determinable competitive edge in data-intensive markets. Finally, the paper will provide a set of effective recommendations to every enterprise willing to achieve significant business value using advanced data integration (Chatterjee et al., 2024; Gao & Sarwar, 2024).

📊 Article Downloads

References

Gu, Y., Mohseni, E., Farzadnia, N., & Khayat, K. H. (2024). An overview of the effect of SAP and LWS as internal curing agents on microstructure and durability of cement-based materials. Journal of Building Engineering, 95, 109972. https://doi.org/10.1016/j.jobe.2024.109972 DOI: https://doi.org/10.1016/j.jobe.2024.109972

Chetty, A., & Blekhman, R. (2024). Multi-omic approaches for host-microbiome data integration. Gut Microbes, 16(1), 2297860. https://doi.org/10.1080/19490976.2023.2297860 DOI: https://doi.org/10.1080/19490976.2023.2297860

Walha, A., Ghozzi, F., & Gargouri, F. (2024). Data integration from traditional to big data: main features and comparisons of ETL approaches. The Journal of Supercomputing, 80(19), 26687-26725. https://doi.org/10.1007/s11227-024-06413-1 DOI: https://doi.org/10.1007/s11227-024-06413-1

Li, Z., Sun, W., Zhan, D., Kang, Y., Chen, L., Bozzon, A., & Hai, R. (2024). Amalur: The convergence of data integration and machine learning. IEEE Transactions on Knowledge and Data Engineering, 36(12), 7353-7367. https://doi.org/10.1109/TKDE.2024.3357389 DOI: https://doi.org/10.1109/TKDE.2024.3357389

Egbert, S., & Ulbricht, L. (2024). Data integration and analysis platforms as digital platforms: a conceptual proposal. Information, Communication & Society, 1-22. https://doi.org/10.1080/1369118X.2024.2442394 DOI: https://doi.org/10.1080/1369118X.2024.2442394

Zhou, H., Zhou, F., Zhao, C., Xu, Y., Luo, L., & Chen, H. (2024). Multimodal data integration for precision oncology: Challenges and future directions. arXiv preprint arXiv:2406.19611. https://doi.org/10.48550/arXiv.2406.19611

Bhat, S., & Kavasseri, A. (2024). Multi-source data integration for navigation in gps-denied autonomous driving environments. International Journal of Electrical and Electronics Research, 12(3), 863-869. https://doi.org/10.37391/IJEER.120317 DOI: https://doi.org/10.37391/ijeer.120317

Dablanc, A., Hennechart, S., Perez, A., Cabanac, G., Guitton, Y., Paulhe, N., ... & Marti, G. (2024). FragHub: a mass spectral library data integration workflow. Analytical chemistry, 96(30), 12489-12496. https://doi.org/10.1021/acs.analchem.4c02219 DOI: https://doi.org/10.26434/chemrxiv-2024-dc48x-v3

Zheng, Y., Liu, Y., Yang, J., Dong, L., Zhang, R., Tian, S., ... & Shi, L. (2024). Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials. Nature biotechnology, 42(7), 1133-1149. https://doi.org/10.1038/s41587-023-01934-1 DOI: https://doi.org/10.1038/s41587-023-01934-1

Jacobides, M. G., Ma, M. D., Trantopoulos, K., & Vassalos, V. (2024). The business value of gamification. California Management Review, 66(2), 91-107. https://doi.org/10.1177/00081256231218469 DOI: https://doi.org/10.1177/00081256231218469

Chatterjee, S., Rana, N. P., & Dwivedi, Y. K. (2024). How does business analytics contribute to organisational performance and business value? A resource-based view. Information Technology & People, 37(2), 874-894. https://doi.org/10.1108/ITP-08-2020-0603 DOI: https://doi.org/10.1108/ITP-08-2020-0603

Etienne Fabian, N., Dong, J. Q., Broekhuizen, T., & Verhoef, P. C. (2024). Business value of SME digitalisation: when does it pay off more?. European Journal of Information Systems, 33(3), 383-402. https://doi.org/10.1080/0960085X.2023.2167671 DOI: https://doi.org/10.1080/0960085X.2023.2167671

Gao, J., & Sarwar, Z. (2024). How do firms create business value and dynamic capabilities by leveraging big data analytics management capability?. Information Technology and Management, 25(3), 283-304. https://doi.org/10.1007/s10799-022-00380-w DOI: https://doi.org/10.1007/s10799-022-00380-w

Visani, F., Raffoni, A., & Costa, E. (2024). The quest for business value drivers: applying machine learning to performance management. Production Planning & Control, 35(10), 1127-1147. https://doi.org/10.1080/09537287.2022.2157776 DOI: https://doi.org/10.1080/09537287.2022.2157776

Kim-Duc, N., & Nam, P. K. (2024). Earnings growth rates in business valuation models: The impossible quaternity. Global Finance Journal, 60, 100930. https://doi.org/10.1016/j.gfj.2024.100930 DOI: https://doi.org/10.1016/j.gfj.2024.100930

Fauzi, Q., Ulfah, U., & Wijayanti, I. (2024). Ethical challenges in transportation: A study on the implementation of Islamic business values. Al-Uqud: Journal of Islamic Economics, 8(2). https://doi.org/10.26740/al-uqud.v8n2.p287-301

Collier, C. A., & Powell, A. L. (2024). Data analyst competencies: a theory-driven investigation of industry requirements in the field of data analytics. Journal of Information Systems Education, 35(3), 325-376. https://doi.org/10.62273/SPYC4248 DOI: https://doi.org/10.62273/SPYC4248

Wang, L., & Zhao, J. (2024). Investment strategy. In Strategic Blueprint for Enterprise Analytics: Integrating Advanced Analytics into Data-Driven Business (pp. 139-158). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-55885-6_7 DOI: https://doi.org/10.1007/978-3-031-55885-6_7

Canay, Ö., & Kocabıçak, Ü. (2024). CAWAL: A novel unified analytics framework for enterprise web applications and multi-server environments. Information Processing & Management, 61(3), 103617. https://doi.org/10.1016/j.ipm.2023.103617 DOI: https://doi.org/10.1016/j.ipm.2023.103617

Downloads

Published

25-12-2024

Issue

Section

Research Articles

How to Cite

[1]
Arunkumar Pasumarthi and Balamuralikrishnan Anbalagan, “Datasphere and SAP: How Data Integration Can Drive Business Value”, Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, vol. 10, no. 6, pp. 2512–2522, Dec. 2024, doi: 10.32628/CSEIT25113472.