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 and Authority-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 Authority, and Evidence into a single reviewable chain.

To identify a cross-concern relationship, record:

Concerns involved
Element, artifact, rule, or decision creating the relationship
Governance and Authority mechanism controlling the relationship, where applicable
Traceability relationship preserving the connection
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.