1. Introduction

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The Federated Data Interpretation Systems Reference Architecture (FDIS-RA) defines a common architectural framework for environments that interpret, validate, and compare data produced through statutory and regulatory reporting regimes. The Financial Data Transparency Act (FDTA) provides the primary motivating case, but the architecture applies more broadly across jurisdictions, domains, and regulatory contexts that require transparent, comparable, and governable data interpretation.

Regulated data interpretation systems operate under conditions that differ materially from those of typical information systems. These systems support statutory and supervisory mandates, influence enforcement and policy outcomes, and provide information used in legal, audit, and cross-jurisdictional contexts. Interpretive behavior in such systems is institutional and consequential, rather than merely operational.

These systems frequently persist for decades. During that time, reporting definitions evolve, validation criteria change, analytical techniques mature, and supervisory expectations shift. Technology platforms, vendors, and organizational responsibilities also change. Despite these changes, organizations need to reconstruct and defend prior interpretations under the semantic and contextual assumptions that applied when those interpretations were produced.

Many existing environments embed semantic interpretation implicitly within code, configuration, documentation, or tooling conventions. Interfaces often support structural exchange without preserving governed meaning. Systems may treat changes in semantic definitions as ordinary software releases rather than as changes in interpretive authority. These conditions make cross-period comparison difficult, complicate inter-agency coordination, and increase maintenance and audit burdens.

FDIS-RA addresses this architectural gap by treating meaning, semantic version context, and interpretive authority as explicit architectural concerns. The architecture separates evidence custody, observation generation, semantic canonicalisation, interpretation, validation, and analytical consumption into defined responsibilities with governed boundaries. This separation supports long-term interpretive stability while preserving implementation diversity and controlled evolution.

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