9.8 Cross-Concern Relationships

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The concerns in this model interact, but they remain distinguishable.

For example, a Data Structure Definition combines structure with references to semantic meaning. A validation process may interpret data using semantic definitions and governance-approved rules. A release decision may depend on data values, semantic classification, policy authority, and evidence requirements. A provenance record may connect data, interpretation, governance, and evidence into a single reviewable chain.

To identify a cross-concern relationship, record:

Concerns involvedElement, artefact, rule, or decision creating the relationshipAuthority or governance mechanism controlling the relationship, where applicableTraceability relationship preserving the connectionEvidence supporting later review, where applicableTable 9.8-1 provides examples of cross-concern relationships.

Table 9.8-1: Examples of cross-concern relationships in structured information processing

Originating concern	Related concern	Example
Structure	Semantics	A field definition references the governed meaning of a currency code.
Semantics	Interpretation	A semantic classification provides context for a validation rule.
Interpretation	Evidence	A derived analytical assertion records the rule version and input data used to produce it.
Governance and Authority	Semantics	A semantic authority approves a change to a definition or classification.
Governance and Authority	Traceability and Evidence	A release policy requires evidence of the release decision and handling obligations.
Data	Traceability and Evidence	A persisted transaction record links to provenance and audit records.

Cross-concern relationships support a governable architecture. They allow concerns to interact without allowing one concern to absorb or obscure another.