Business rules in data modeling: Which characteristic best describes a well-written business rule that can be enforced in databases and applications?

Introduction / Context:
Business rules define constraints or derivations that the data must satisfy. Good rules guide schema design, validation logic, and process controls. Their quality greatly affects maintainability and enforceability across systems.

Given Data / Assumptions:

• A business rule should be understandable to both business and technical stakeholders.
• Rules should be testable and enforceable at the data and application layers.

Concept / Approach:
A declarative rule states what must be true without prescribing how to implement it. Declarative rules map well to constraints, triggers, checks, and validation logic. Imprecision, redundancy, and over-complication hinder governance and lead to inconsistencies.

Step-by-Step Solution:

Verification / Alternative check:
Data governance literature and modeling best practices consistently emphasize declarative, atomic rules that can be translated into constraints or validation checks.

Why Other Options Are Wrong:

• Imprecise: ambiguous, not enforceable.
• Redundant: duplicates logic, risking conflicts.
• Compound: multiple ideas in one rule complicate enforcement; better split into atomic rules.
• Hidden in code: reduces visibility and governance; rules should be documented and, where possible, enforced in the database.

Common Pitfalls:
Embedding business logic only in application code without documenting or enforcing it at the data layer, leading to data inconsistencies.

Final Answer:
Declarative