Meethun Panda¹ and Soumyodeep Mukherjee², ¹Bain & Company, UAE, ²Genmab, USA

As organizations aim to become data-driven, they face a persistent paradox: centralization enables control and consistency, while decentralization fosters scalability and innovation. This "Data Platform Unification Paradox" creates a dynamic tension that challenges the design and management of modern data platforms. This paper introduces Data Mesh as a solution to this paradox, combining domain-driven decentralization with federated governance to balance these competing demands. The study demonstrates that Data Mesh not only addresses scalability and agility challenges but also enables organizations to enhance data quality, governance, and collaboration. It highlights the transformative impact of Data Mesh in fostering domain-centric innovation, streamlining data ownership, and supporting advanced analytics, including AI and generative AI applications. Furthermore, the findings suggest that emerging technologies like quantum databases and multi-agent frameworks powered by Large Language Models (LLMs) require robust decentralized architectures to achieve their full potential. These insights provide actionable pathways for organizations aiming to modernize their data ecosystems while navigating the complexities of centralization and decentralization.

Data Mesh, Data Governance, Centralization, Decentralization, Data Paradox, AI, Quantum Databases, LLM Multi-Agent Systems, Distributed Data Platforms