The week's pick
Random Articles
Reseach Article
Data Mesh Adoption in Regulated Enterprises: A Systematic Review of Principles, Constraints & Architecture Patterns
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 82 |
| Year of Publication: 2026 |
| Authors: Sumanth Singh |
10.5120/ijca2026926426
|
Sumanth Singh . Data Mesh Adoption in Regulated Enterprises: A Systematic Review of Principles, Constraints & Architecture Patterns. International Journal of Computer Applications. 187, 82 ( Feb 2026), 9-16. DOI=10.5120/ijca2026926426
Abstract
This study examines the use of data mesh in regulated businesses between 2019 and 2025, taking into account scholarly and professional sources. The state-of-the-art findings about domain-led, decentralized data environments, including federated governance and implementation techniques from rigorous industries including healthcare, finance, and telecommunications, are combined in this study. Fifteen peer-reviewed papers and their referenced sources are discussed. It emphasizes how difficult it is for regulated firms to connect governance with automated compliance and implement organizational changes because of the scalability and operational advantages that data mesh promises. Four architectural patterns—pure, semi-pure, hybrid, and distributed implementations—that are typical in regulated situations that are highlighted in the literature. In controlled settings, each pattern has pros and cons. In this regard, a number of shortcomings are found, including sector-specific approaches to regulatory compliance, management of interorganizational data sharing, and automation of procedures for compliance verification. In conclusion, a deliberate trade-off between strong center-led governance and domain autonomy, backed by cutting-edge technologies and related organizational reforms, is necessary for successful adoption.
References
- Z. Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale," O'Reilly Media, Inc., 2022. [Online]. Available: https://www.oreilly.com/library/view/data-mesh/9781492092384/
- A. Wider, S. Verma, and A. Akhtar, "Decentralized data governance as part of a data mesh platform: Concepts and approaches," in Proceedings of the 2023 IEEE International Conference on Web Services (ICWS), Chicago, IL, USA, 2023, pp. 746–754. https://doi.org/10.1109/ICWS60048.2023.00101 .
- S. Driessen, G. Monsieur, and W. J. van den Heuvel, "Data mesh: A systematic gray literature review," ACM Computing Surveys, vol. 57, no. 1, pp. 1–44, 2024. https://doi.org/10.1145/3687301.
- I. A. Machado, C. Costa, and M. Y. Santos, "Data mesh: Concepts and principles of a paradigm shift in data architectures," Procedia Computer Science, vol. 196, pp. 263–271, 2022. https://doi.org/10.1016/j.procs.2021.12.013.
- D. van der Werf, J. L. Rebelo Moreira, and J. P. S. Piest, "Towards a data mesh reference architecture," in Enterprise Design, Operations, and Computing. EDOC 2024 Workshops, Springer, 2025, vol. 537, pp. 339–353. https://doi.org/10.1007/978-3-031-07481-3.
- A. Wider and S. Werner, "From data mesh to intermesh: A platform-driven approach to govern inter-organizational data sharing," in Service-Oriented Computing. SummerSOC 2025, Springer, Cham, 2025, vol. 2602. https://doi.org/10.1007/978-3-032-07313-6_5.
- D. Joshi, S. Pratik, and M. P. Rao, "Data governance in data mesh infrastructures: The Saxo bank case study," in Proceedings of the International Conference on Electronic Business (ICEB), 2021, vol. 21, pp. 599–604. [Online]. Available: https://aisel.aisnet.org/iceb2021/52/
- Loe, A., Medley, L., O’Connell, C., Quaglia, E.A. (2023). Applications of Timed-Release Encryption with Implicit Authentication. In: El Mrabet, N., De Feo, L., Duquesne, S. (eds) Progress in Cryptology - Africacrypt 2023. Lecture Notes in Computer Science, vol 14064. Springer, Cham. https://doi.org/10.1007/978-3-031-37679-5_21
- V. K. Butte and S. Butte, "Enterprise data strategy: A decentralized data mesh approach," in Proceedings of the 2022 International Conference on Data Analytics for Business and Industry (ICDABI), IEEE, 2022, pp. 62–66. https://doi.org/10.1109/ICDABI56818.2022.10041672.
- S. W. Driessen, G. Monsieur, and W. J. van den Heuvel, "Data market design: A systematic literature review," IEEE Access, vol. 10, pp. 33123–33153, 2022. https://doi.org/10.1109/ACCESS.2022.3161478
- Endress F, Kipouros T, Buker T, Wartzack S, Clarkson PJ. The Value of Information in Clustering Dense Matrices: When and How to Make Use of Information. Proceedings of the Design Society. 2022;2:703-712. https://doi.org/10.1017/pds.2022.72
- M. Tonnarelli, I. Kumara, S. Driessen, D. Tamburri, W. van den Heuvel, and P. Oor, "Data catalog tools: A systematic multivocal literature review," Journal of Systems and Software, vol. 230, p. 112584, 2025. https://doi.org/10.1016/j.jss.2025.112584.
- L. Schuiki, C. Giebler, E. Hoos, and H. Schwarz, "Unraveling data mesh: Current state, challenges and research gaps," in Service-Oriented Computing, Springer, 2025, pp. 59–79. https://doi.org/10.1007/978-3-032-07313-6_4
- R. Suguimoto, P. Meirelles, and K. Braghetto, "Metadata management in data mesh: Toward federated discovery and governance," in Anais do XL Simpósio Brasileiro de Banco de Dados (SBBD 2025), 2025, pp. 823–829. doi: https://doi.org/10.5753/sbbd.2025.247722
- J. Li, S. Cai, L. Wang, M. Li, J. Li, and H. Tu, "A novel design for data processing framework of park-level power system with data mesh concept," in Proceedings of the 2022 IEEE International Conference on Energy Internet (ICEI), IEEE, 2022, pp. 153–158. doi: https://doi.org/10.1109/ICEI57064.2022.00032
Index Terms
Keywords