Data Mesh versus Central Warehouses for Trustworthy AI Pipelines

Abstract

Data-mesh and central-warehouse architectures allocate ownership and governance responsibilities in different ways. The difficulty is particularly visible when a system is transferred to a new setting. The central research objective is to compare their implications for provenance, access control and reproducible AI training across enterprise units. Previous work provides context for sampling, input validation and study boundaries [3–7]. To answer the question, the study proposes a design-science comparison using matched data-product scenarios, architecture diagrams and traceability tests. Related enterprise-storage studies inform the assessment of operational reliability [1] and the deployment assumptions adopted for system-level comparison [2]. Under matched evaluation conditions, the protocol compares domain-owned data products, a central warehouse and a hybrid governance model. Methodological sources are used to specify transparent comparison and reporting practices [8–12]. The design prioritises precise operational definitions and a straightforward baseline before introducing more complex alternatives. Performance will be judged using dataset lineage completeness, policy violations, integration effort and model retraining reproducibility. Interpretation will account for conflicting data contracts, institutional access rules, ownership disputes and cross-domain schema changes. Primary analyses would be reported by the main deployment or participant subgroup so that averaged results do not conceal systematic failures. The supplementary technical literature is used to identify plausible failure mechanisms [13–17]. No study result is assumed in advance; the analysis is designed to show both when the method helps and when it fails, with reporting context from the remaining sources [18–23].