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A data dictionary provides authoritative definitions for data elements: names/aliases, descriptions, data types, lengths, formats, units, domains (allowed values), default/nullable rules, and stewardship. It ensures consistent interpretation across requirements, design, development, and testing, reducing defects caused by semantic mismatches (e.g., what exactly is “customer ID”?). User journeys (Option A) are captured with story maps or use cases. Interfaces (Option C) are modeled with context diagrams, interface specifications, or sequence diagrams. Decision logic (Option D) is best represented with decision tables/trees or business rules catalogs. CAPM business analysis guidance emphasizes traceability from data requirements to design (ERDs), validation (test cases), and governance (data quality rules). A well-maintained dictionary supports regulatory compliance, integration projects, and analytics by aligning meaning across systems and teams. It also aids change impact analysis: when a data element changes, downstream consumers can be identified quickly.