Data Mesh Best Practices: Governance, Domains, and Data Products

Authors

  • Naresh Dulam Vice President Sr Lead Software Engineer, JP Morgan Chase, USA Author
  • Abhilash Katari Engineering Lead, Persistent Systems Inc, USA Author
  • Venkataramana Gosukonda Senior Software Engineering Manager, Wells Fargo, USA Author

Keywords:

Data Mesh, data domains, decentralized data architecture

Abstract

Data Mesh is revolutionizing how organizations approach data architecture by addressing the challenges of scalability, agility, and centralized bottlenecks inherent in traditional data platforms. Rooted in decentralization, Data Mesh shifts ownership and responsibility to domain-oriented teams, empowering them to manage data as a product focusing on usability, accessibility, and value. At its core, Data Mesh is built on three key pillars: governance, domains, and data products. Governance ensures that standards, compliance, and security are consistently upheld without stifling innovation, relying on automated policies and tooling to enforce consistency across decentralized teams. Domains enable teams with the most profound understanding of their data to take ownership, fostering accountability & eliminating delays caused by relying on centralized data teams. Treating data as a product shifts the perspective to prioritize the end consumer's needs, ensuring that data is discoverable, reliable, and purpose-built for solving business challenges. Implementing Data Mesh requires more than technical changes; it demands a cultural shift where domain teams are empowered and supported with the right tools, practices, and training. Organizations must define clear domain boundaries, create reusable & interoperable data products, and invest in infrastructure that supports self-service data management and real-time monitoring. Effective governance should be embedded into the process with automation, ensuring that policies are enforced seamlessly without adding overhead. Collaboration between domains is equally critical to avoid the creation of new silos, requiring strong communication channels & shared standards. By fostering a product mindset, teams can continuously improve their data offerings, respond to feedback, and evolve with business needs. As organizations transition to Data Mesh, they must prioritize scalability, interoperability, and robust automation to maintain efficiency while unlocking the full potential of decentralized data management. This approach addresses traditional bottlenecks and empowers teams to innovate and deliver value faster. By embracing the principles of Data Mesh and adopting practical best practices, organizations can create a resilient and adaptive data ecosystem that supports their long-term goals while ensuring the scalability and agility needed in today's fast-paced digital landscape.

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Published

11-05-2022

How to Cite

[1]
Naresh Dulam, Abhilash Katari, and Venkataramana Gosukonda, “Data Mesh Best Practices: Governance, Domains, and Data Products”, Australian Journal of Machine Learning Research & Applications, vol. 2, no. 1, pp. 524–547, May 2022, Accessed: Dec. 22, 2024. [Online]. Available: https://sydneyacademics.com/index.php/ajmlra/article/view/210

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