Vol. 2 No. 1 (2022): Journal of AI-Assisted Scientific Discovery
Articles

A domain driven data architecture for data governance strategies in the Enterprise

Sarbaree Mishra
Program Manager at Molina Healthcare Inc., USA
Vineela Komandla
Vice President - Product Manager, JP Morgan & Chase, USA
Srikanth Bandi
Software Engineer, JP Morgan & Chase, USA
Sairamesh Konidala
Vice President, JP Morgan & Chase, USA
Jeevan Manda
Project Manager, Metanoia Solutions Inc, USA
Cover

Published 05-04-2022

Keywords

  • Data governance,
  • domain-driven architecture

How to Cite

[1]
Sarbaree Mishra, Vineela Komandla, Srikanth Bandi, Sairamesh Konidala, and Jeevan Manda, “A domain driven data architecture for data governance strategies in the Enterprise”, Journal of AI-Assisted Scientific Discovery, vol. 2, no. 1, pp. 543–567, Apr. 2022, Accessed: Dec. 27, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/242

Abstract

Managing data effectively has become a pressing challenge as organizations face an ever-expanding, diverse, and complex data landscape. Enterprises are tasked with ensuring data quality, maintaining regulatory compliance, and aligning data use with strategic business objectives. This is where a domain-driven data architecture offers a powerful approach to tackle these challenges. By anchoring the technical design of data systems within the context of business domains, organizations can create a framework that facilitates better collaboration between business and technical teams. Such alignment ensures that data is managed systematically and leveraged effectively to drive decision-making and innovation. A domain-driven approach encourages accountability by clearly defining ownership and stewardship for data within respective business areas, reducing ambiguity & enhancing governance. This architecture allows enterprises to design scalable and flexible data governance strategies that adapt to changing business needs while promoting trust in data use. Moreover, it emphasizes clear communication, shared understanding, and active stakeholder collaboration, ensuring that data governance is a collective effort rather than a siloed task. By implementing this approach, organizations can foster a culture of responsibility & transparency, ensuring data is treated as a strategic asset rather than a mere operational resource. This article explores domain-driven data architecture's fundamental principles and practices, illustrating how they enable effective governance in large and complex enterprises. From defining business-driven data ownership models to leveraging automation for policy enforcement, this framework equips organizations with the tools to navigate the intricacies of modern data environments. The focus is on practical and actionable insights that can be adapted to suit specific organizational contexts, helping businesses comply with regulations & unlock the full potential of their data assets. This approach to data governance is essential for companies seeking to thrive in today’s data-rich world while staying ahead of compliance and operational challenges.

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