In systems analysis, what does a data dictionary typically document to standardize definitions across the project?

Introduction / Context:
A data dictionary is the authoritative catalog of data elements in a system or enterprise. It enforces consistent meanings, naming, formats, and relationships so analysis, design, build, and operations all refer to data in the same way.

Given Data / Assumptions:

• We are building or analyzing an information system.
• Artifacts like DFDs and ERDs reference shared data elements.
• We need a single source of truth for definitions and structures.

Concept / Approach:
A comprehensive data dictionary spans: (1) data structures (fields, records, composite elements), (2) data flows (what data moves between processes/entities), and (3) data stores (logical files/tables where data persists). Including all three aligns analysis models and design schemas.

Step-by-Step Solution:
Identify the scope of documentation needed across artifacts.Confirm that structures, flows, and stores are each documented to prevent ambiguity.Conclude that the correct answer must include all listed categories.

Verification / Alternative check:
When onboarding new team members, the data dictionary is referenced to understand element names, allowed values, formats, and where elements travel or persist—evidence that it encompasses structures, flows, and stores.

Why Other Options Are Wrong:
Focusing on only one dimension (structures, flows, or stores) produces gaps and inconsistent interpretations. Hence, “All of the above” is the only complete answer. “None of the above” is incorrect because all belong in a robust dictionary.

Common Pitfalls:
Letting the dictionary drift out of date or failing to align it with physical schemas during implementation. Keep it version-controlled and synchronized.

Final Answer:
All of the above