With the rapid growth and increasing complexity of data, organizations struggle to manage it effectively, thereby revealing the limitations of traditional data architectures.
Data Mesh offers a solution through a decentralized, domain-oriented approach that treats data as a product and is supported by federated governance models and self-service infrastructure. Despite its increasing adoption, discussions of its governance within academic circles remain scattered and unstructured. The main objectives of this article are to provide an in-depth overview, share valuable insights into research paths and trends within the field of
Data Mesh architecture, and synthesize conclusions from previous studies. This is due to limited domestic research, the fragmented nature of existing studies, and the absence of systematic analysis in this field. Through a comprehensive literature review, this study aims to elucidate key
Data Mesh concepts, including distributed data architectures and decentralized governance. By highlighting significant publications, authors, and journals influencing the discourse on Data Mesh, it establishes a benchmark for measuring the breadth and depth of research in this area. One of the aims of this study is to enhance future research trajectories addressing relevant issues. Using VOSviewer software, the methodology integrates bibliometric analysis and systematic review to identify and examine research trends in
Data Mesh architecture. Purposive sampling was employed to identify relevant research from the Scopus and WOS databases, with Google Scholar incorporated as a supplementary database for the study's statistical population. The analysis of keyword co-occurrence networks constitutes one of the study’s main findings. Furthermore, four clusters based on citation counts were identified in the co-citation network of sources, with the Computer Science Information Systems and Computer Science Theory and Methods journals forming the largest clusters. A key finding of this research is the identification of significant indicators of
Data Mesh architecture through an extensive literature analysis aimed at developing a framework for decentralized data governance. This study provides practitioners and policymakers with evidence-based recommendations for effectively implementing
Data Mesh principles. The study also promotes interdisciplinary collaboration by demonstrating the connections between
Data Mesh and related domains such as blockchain. Finding future research avenues on the application of decentralized technology in data governance-related concerns is one of the most important results.
