Data Mesh in Practice: How Organizations are Decentralizing Data Ownership

Authors

  • Naresh Dulam Vice President Sr Lead Software Engineer, JP Morgan Chase, USA Author
  • Abhilash Katari Engineering Lead, Persistent Systems Inc, USA Author
  • Karthik Allam Big Data Infrastructure Engineer, JP Morgan & Chase, USA Author

Keywords:

Data Mesh, Decentralized Data Ownership, Domain-Driven Design

Abstract

Data Mesh is an emerging approach to data architecture that aims to overcome the limitations of centralized systems by decentralizing data ownership and treating data as a product. Unlike traditional data platforms that often struggle with scalability, Data Mesh emphasizes a shift in how organizations manage and utilize data, empowering domain-oriented teams to take full responsibility for the data they produce. This model advocates for domain-driven design, where data is owned and managed by cross-functional teams that understand the specific needs of their business unit, creating a more agile and scalable data ecosystem. The core principles of Data Mesh—decentralized ownership, self-serve infrastructure, & a product-focused approach to data—allow organizations to scale data operations more effectively while maintaining high-quality data easily accessible to those who need it. Adopting Data Mesh requires technical changes & significant cultural shifts within organizations. A key challenge organizations face when adopting Data Mesh is overcoming resistance to change, as this new model requires teams to embrace new ways of thinking about and using data. This paper explores how various organizations have successfully implemented Data Mesh, focusing on real-world case studies demonstrating the benefits and the challenges associated with its adoption. By decentralizing data ownership, organizations can improve collaboration, reduce bottlenecks in data access, and foster a more substantial alignment between data & business goals. However, the transition to a Data Mesh model has challenges. Organizations must invest in the right technology and infrastructure, train teams to work in this new decentralized way and redefine how data is governed and secured. Ultimately, the shift to Data Mesh can lead to greater flexibility and innovation in how data is utilized, but it requires careful planning and an openness to cultural change. This paper provides practical guidance for organizations transitioning from traditional, centralized data systems to a decentralized Data Mesh architecture, offering insights into the strategies, tools, & practices that can facilitate a successful implementation.

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References

Esposito, C. (2018). Interoperable, dynamic and privacy-preserving access control for cloud data storage when integrating heterogeneous organizations. Journal of Network and Computer Applications, 108, 124-136.

Pala, S. K. (2017). Advance Analytics for Reporting and Creating Dashboards with Tools like SSIS, Visual Analytics and Tableau.

Kostić, D., Rodriguez, A., Albrecht, J., & Vahdat, A. (2003, October). Bullet: High bandwidth data dissemination using an overlay mesh. In Proceedings of the nineteenth ACM symposium on Operating systems principles (pp. 282-297).

Raval, S. (2016). Decentralized applications: harnessing Bitcoin's blockchain technology. " O'Reilly Media, Inc.".

Wright, A., & De Filippi, P. (2015). Decentralized blockchain technology and the rise of lex cryptographia. Available at SSRN 2580664.

Casino, F., Dasaklis, T. K., & Patsakis, C. (2019). A systematic literature review of blockchain-based applications: Current status, classification and open issues. Telematics and informatics, 36, 55-81.

Ruj, S., Stojmenovic, M., & Nayak, A. (2013). Decentralized access control with anonymous authentication of data stored in clouds. IEEE transactions on parallel and distributed systems, 25(2), 384-394.

Kshetri, N. (2017). Blockchain's roles in strengthening cybersecurity and protecting privacy. Telecommunications policy, 41(10), 1027-1038.

Demchenko, Y., Cushing, R., Los, W., Grosso, P., de Laat, C., & Gommans, L. (2019, July). Open data market architecture and functional components. In 2019 International Conference on High Performance Computing & Simulation (HPCS) (pp. 1017-1021). IEEE.

Naraine, M. L. (2019). The blockchain phenomenon: Conceptualizing decentralized networks and the value proposition to the sport industry. International Journal of Sport Communication, 12(3), 313-335.

Mougayar, W. (2016). The business blockchain: promise, practice, and application of the next Internet technology. John Wiley & Sons.

Kleppmann, M., Wiggins, A., Van Hardenberg, P., & McGranaghan, M. (2019, October). Local-first software: you own your data, in spite of the cloud. In Proceedings of the 2019 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software (pp. 154-178).

Hitt, L. M., & Brynjolfsson, E. (1997). Information technology and internal firm organization: An exploratory analysis. Journal of management information systems, 14(2), 81-101.

Raman, A., Joglekar, S., Cristofaro, E. D., Sastry, N., & Tyson, G. (2019, October). Challenges in the decentralised web: The mastodon case. In Proceedings of the internet measurement conference (pp. 217-229).

Asharaf, S., & Adarsh, S. (Eds.). (2017). Decentralized computing using blockchain technologies and smart contracts: Emerging research and opportunities: Emerging research and opportunities.

Gade, K. R. (2017). Integrations: ETL vs. ELT: Comparative analysis and best practices. Innovative Computer Sciences Journal, 3(1).

Gade, K. R. (2019). Data Migration Strategies for Large-Scale Projects in the Cloud for Fintech. Innovative Computer Sciences Journal, 5(1).

Gade, K. R. (2018). Real-Time Analytics: Challenges and Opportunities. Innovative Computer Sciences Journal, 4(1).

Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.

Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.

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Published

30-07-2020

How to Cite

[1]
Naresh Dulam, Abhilash Katari, and Karthik Allam, “Data Mesh in Practice: How Organizations are Decentralizing Data Ownership ”, Distrib Learn Broad Appl Sci Res, vol. 6, Jul. 2020, Accessed: Dec. 23, 2024. [Online]. Available: https://dlabi.org/index.php/journal/article/view/248

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