Naming guidance for attributes Which guideline set is most appropriate when choosing an attribute name in a data model?

Introduction / Context:
Good attribute names improve readability, reduce onboarding time, and minimize misinterpretation in analytics and applications. Naming is a low-cost design decision with high downstream impact. Standards provide consistency across teams and systems, especially in regulated environments where clarity is critical.

Given Data / Assumptions:

• The goal is a clear, maintainable data model.
• Names should reflect business meaning and be stable over time.
• Developers, analysts, and stakeholders share and reuse these names extensively.

Concept / Approach:

Best practice is to use singular nouns (for example, amount, order_date), apply a consistent naming convention (for example, snake_case or lowerCamelCase, standardized abbreviations), and avoid aliases that obscure meaning (for example, amt vs amount unless amt is a defined standard). These rules keep schemas predictable and self-documenting, which supports code generation, query reuse, and data governance.

Step-by-Step Solution:

Verification / Alternative check:

Many modeling style guides from industry and academia recommend singular nouns and consistent conventions; they discourage ad-hoc aliases that fragment understanding and lineage.

Why Other Options Are Wrong:

• E: Verbs describe actions; attributes describe facts, so nouns are preferred.

Common Pitfalls:

• Using abbreviations that are not standardized, leading to inconsistent naming.
• Embedding units or formatting in names (for example, amount_in_usd) without a clear convention for units metadata.

Final Answer:

All of the above