# Data Mesh: a Systematic Gray Literature Review

arXiv: https://arxiv.org/abs/2304.01062 · HTML: https://mlsanity.com/paper/2304.01062/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2023.

## Built on by (most cited first, 5 of 5)

- [Empowering Data Mesh with Federated Learning](https://mlsanity.com/paper/2403.17878/) (2024)
- [The Impact of Modern AI in Metadata Management](https://mlsanity.com/paper/2501.16605/) (2025)
- [Decentralized Data Governance as Part of a Data Mesh Platform: Concepts and Approaches](https://mlsanity.com/paper/2307.02357/) (2023)
- [Demonstrating Data-to-Knowledge Pipelines for Connecting Production Sites in the World Wide Lab](https://mlsanity.com/paper/2412.12231/) (2024)
- [WORKSWORLD: A Domain for Integrated Numeric Planning and Scheduling of Distributed Pipelined Workflows](https://mlsanity.com/paper/2603.12214/) (2026)

## Abstract (arXiv)

Data mesh is an emerging domain-driven decentralized data architecture that aims to minimize or avoid operational bottlenecks associated with centralized, monolithic data architectures in enterprises. The topic has picked the practitioners' interest, and there is considerable gray literature on it. At the same time, we observe a lack of academic attempts at defining and building upon the concept. Hence, in this article, we aim to start from the foundations and characterize the data mesh architecture regarding its design principles, architectural components, capabilities, and organizational roles. We systematically collected, analyzed, and synthesized 114 industrial gray literature articles. The review provides insights into practitioners' perspectives on the four key principles of data mesh: data as a product, domain ownership of data, self-serve data platform, and federated computational governance. Moreover, due to the comparability of data mesh and SOA (service-oriented architecture), we mapped the findings from the gray literature into the reference architectures from the SOA academic literature to create the reference architectures for describing three key dimensions of data mesh: organization of capabilities and roles, development, and runtime. Finally, we discuss open research issues in data mesh, partially based on the findings from the gray literature.