Data quality reality check: Is incorrect (but plausible-looking) data generally easy to detect in operational databases?

Introduction / Context:
This question addresses data quality. Many real-world errors are not syntactic violations; they are semantic issues that pass basic constraints yet remain wrong from a business perspective, making detection challenging.

Given Data / Assumptions:

• Databases enforce structural constraints (types, NOT NULL, keys, FK).
• Incorrect data may still conform to types and constraints.
• Detection often requires domain rules and cross-record validations.

Concept / Approach:
While constraint violations are easy to detect, semantically incorrect data is hard. Example: a valid date of birth stored as yesterday for an adult, or a price that matches numeric constraints but violates contract terms. Detecting such issues requires business rules, profiling, anomaly detection, and sometimes human review. Thus, claiming incorrect data is “easy to detect” is misleading.

Step-by-Step Solution:
Differentiate structural validity from business correctness.Apply validation rules beyond schema (checks, triggers, reference tables).Use data profiling and statistical baselining to detect outliers.Implement data stewardship workflows for remediation.

Verification / Alternative check:
Run profiling on production data; many issues (duplicates, inconsistent codes, implausible values) will not be flagged by the schema alone.

Why Other Options Are Wrong:
Keys and FK constraints help structural integrity but do not guarantee correctness of values. Marking “Correct” would underestimate the complexity of data quality.

Common Pitfalls:
Assuming that passing validation equals correctness; lack of reference data management; ignoring time-based validity (effective dates).

Final Answer:
Incorrect