Data-Quality and Single-Source-Of-Truth Frameworks for Inter Agency Reporting: A Review

Abstract

Inter-agency reporting, the routine exchange of administrative data among public bodies to produce shared statistics, dashboards, and compliance returns, depends on the assumption that a figure reported by one agency will mean the same thing when consumed by another. In practice, that assumption is fragile. Divergent definitions, duplicated master records, and the absence of an authoritative reference source produce reconciliation disputes, delayed reporting cycles, and eroded institutional trust. This review synthesises three literatures that jointly address the problem: data quality dimensions and assessment methodologies, master data management (MDM) and single source-of-truth (SSOT) architectures, and data governance and stewardship. It also examines interoperability standards that bind these elements together across organisational boundaries. Drawing on foundational and pre-2019 scholarship, we organise the field around a fitness-for-use conception of quality in which multiple dimensions (accuracy, completeness, timeliness, consistency, and others) are evaluated relative to the reporting task. We describe a consolidated table of data quality dimensions and their relevance to inter-agency contexts, and we compare architectural patterns for establishing an authoritative record. Our synthesis identifies recurring gaps: quality frameworks that stop at the enterprise boundary, MDM programmes treated as technology projects rather than governance commitments, and governance models that under-specify cross-boundary accountability. We argue that data quality, single-source-of-truth architecture, and governance are mutually reinforcing rather than substitutable, and that inter-agency reporting fails when any one is pursued in isolation. We close with future directions emphasising semantic interoperability, contingency-based governance design, and quality measurement adapted to shared data.