Evaluating Data Mesh and Traditional Data Architectures: Implications for Governance, Scalability, Cost, and Enterprise Adoption

Abstract

This paper compares Data Mesh with traditional data architectures, emphasizing aspects of governance, scalability, cost, and enterprise adoption. Although conventional approaches have long been dominant, they often suffer from slow responsiveness, limited innovation at the domain level, and scalability issues. In contrast, the Data Mesh paradigm envisions a distributed framework in which multiple domain teams own their data, thereby improving data quality and manageability. This study outlines the essential characteristics of both architectures, addressing the limitations of one while highlighting the advantages of the other particularly the cost benefits and flexibility of Data Mesh when integrating diverse data sources. While the advantages of Data Mesh are clear, barriers such as skill deficiencies and the need for a cultural transformation remain. This paper discusses strategies to mitigate these barriers and recommends further empirical studies on costs, productivity, and hybrid models that combine traditional data architectures with Data Mesh principles.