Unified Semantic Layers: Eliminating Metric Inconsistency Across Distributed Enterprise Analytics

Abstract

Distributed enterprise analytics has become the default operating condition for large organizations rather than an exception, as firms decentralize data ownership across business units, regional operations, and product lines. A recurring consequence of this decentralization is that the same underlying business concept, such as revenue, active users, or churn, is routinely defined, calculated, and reported differently by different teams querying different systems, even when the row-level data feeding those calculations is fully accurate. This paper argues that this inconsistency is best understood as a governance and semantics defect rather than a traditional data quality defect and that a unified semantic layer, a governed, queryable abstraction that encodes metric definitions, business logic, and access rules in one place rather than in each downstream tool separately, is the systems-engineering mechanism that resolves it. The paper synthesizes foundational data quality and query-view theory, the empirical data mesh and data governance literature, and recent practitioner-facing research on semantic layers and business intelligence adoption into a single framework of four design principles that a unified semantic layer must satisfy: architecturally enforced single definitional authority, domain-federated authorship with centrally governed publication, view-based logical abstraction over physically distributed sources, and consumer-agnostic accountability extending to both human and AI-mediated consumers. The synthesis further shows that the technical mechanism alone is insufficient without organizational conditions long identified in the literature on business intelligence adoption, including perceived trustworthiness of system output and baseline governance maturity. This paper is presented explicitly as an integrative literature synthesis grounded in verified published findings rather than an original instrumented deployment study. The analysis concludes that metric inconsistency in decentralized enterprise architectures is fundamentally a governance problem implemented through a technical mechanism and that the semantic layer's distinct contribution is converting metric governance from a voluntary organizational practice into an architectural constraint.