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. 24, 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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References

  1. Khatri, V., & Brown, C. V. (2010). Designing data governance. Communications of the ACM, 53(1), 148-152.
  2. Paik, H. Y., Xu, X., Bandara, H. D., Lee, S. U., & Lo, S. K. (2019). Analysis of data management in blockchain-based systems: From architecture to governance. Ieee Access, 7, 186091-186107.
  3. Abraham, R., Schneider, J., & Vom Brocke, J. (2019). Data governance: A conceptual framework, structured review, and research agenda. International journal of information management, 49, 424-438.
  4. Frankel, D. S. (2003). Model driven architecture applying MDA. John Wiley & Sons.
  5. Soley, R. (2000). Model driven architecture. OMG white paper, 308(308), 5.
  6. Carney, D., Çetintemel, U., Cherniack, M., Convey, C., Lee, S., Seidman, G., ... & Stonebraker, M. (2002, January). Monitoring streams—a new class of data management applications. In VLDB'02: Proceedings of the 28th International Conference on Very Large Databases (pp. 215-226). Morgan Kaufmann.
  7. Allan, C., Burel, J. M., Moore, J., Blackburn, C., Linkert, M., Loynton, S., ... & Swedlow, J. R. (2012). OMERO: flexible, model-driven data management for experimental biology. Nature methods, 9(3), 245-253.
  8. Hoschek, W., Jaen-Martinez, J., Samar, A., Stockinger, H., & Stockinger, K. (2000). Data management in an international data grid project. In Grid Computing—GRID 2000: First IEEE/ACM International Workshop Bangalore, India, December 17, 2000 Proceedings 1 (pp. 77-90). Springer Berlin Heidelberg.
  9. Demchenko, Y., De Laat, C., & Membrey, P. (2014, May). Defining architecture components of the Big Data Ecosystem. In 2014 International conference on collaboration technologies and systems (CTS) (pp. 104-112). IEEE.
  10. Sakr, S., Liu, A., Batista, D. M., & Alomari, M. (2011). A survey of large scale data management approaches in cloud environments. IEEE communications surveys & tutorials, 13(3), 311-336.
  11. Ceri, S., Fraternali, P., Bongio, A., Brambilla, M., Comai, S., & Matera, M. (2003). Morgan Kaufmann series in data management systems: Designing data-intensive Web applications. Morgan Kaufmann.
  12. Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological forecasting and social change, 126, 3-13.
  13. Watson, H. J. (2014). Tutorial: Big data analytics: Concepts, technologies, and applications. Communications of the Association for Information Systems, 34(1), 65.
  14. Weill, P., Subramani, M., & Broadbent, M. (2002). IT infrastructure for strategic agility. Available at SSRN 317307.
  15. Weiss, C., Karras, P., & Bernstein, A. (2008). Hexastore: sextuple indexing for semantic web data management. Proceedings of the VLDB Endowment, 1(1), 1008-1019.
  16. Thumburu, S. K. R. (2021). Optimizing Data Transformation in EDI Workflows. Innovative Computer Sciences Journal, 7(1).
  17. Thumburu, S. K. R. (2021). Integrating Blockchain Technology into EDI for Enhanced Data Security and Transparency. MZ Computing Journal, 2(1).
  18. Gade, K. R. (2021). Data Analytics: Data Democratization and Self-Service Analytics Platforms Empowering Everyone with Data. MZ Computing Journal, 2(1).
  19. Gade, K. R. (2021). Migrations: Cloud Migration Strategies, Data Migration Challenges, and Legacy System Modernization. Journal of Computing and Information Technology, 1(1).
  20. Katari, A., Muthsyala, A., & Allam, H. HYBRID CLOUD ARCHITECTURES FOR FINANCIAL DATA LAKES: DESIGN PATTERNS AND USE CASES.
  21. Katari, A. Conflict Resolution Strategies in Financial Data Replication Systems.
  22. Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening
  23. Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.
  24. Gade, K. R. (2019). Data Migration Strategies for Large-Scale Projects in the Cloud for Fintech. Innovative Computer Sciences Journal, 5(1).