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9.8 Cross-Concern Relationships
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:
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Concerns involved
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Element, artefact, rule, or decision creating the relationship
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Authority or governance mechanism controlling the relationship, where applicable
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Traceability relationship preserving the connection
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Evidence supporting later review, where applicable
Table 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.