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A professional-level summary covering key definitions, frameworks, and exam-relevant points.
Data Integrity in the DMBOK
The DAMA DMBOK v2 treats data integrity as a fundamental requirement of data quality. It is enforced through a combination of database constraints (technical enforcement), data validation rules (application-level enforcement), and data governance policies (organisational enforcement). The DMBOK emphasises that technical enforcement alone is insufficient — governance and stewardship are required to maintain integrity at the organisational level.
Types of Data Integrity
| Type | Definition | Enforcement Mechanism |
|---|---|---|
| Entity integrity | Each row is uniquely identifiable | Primary key constraints |
| Referential integrity | Foreign key references are valid | Foreign key constraints |
| Domain integrity | Values conform to defined domain | Data type, check constraints, reference data |
| User-defined integrity | Business-specific rules are enforced | Triggers, application logic, governance processes |
Data Integrity vs Data Quality
Data integrity is a subset of data quality. A dataset can have perfect integrity (no constraint violations) but still be poor quality (inaccurate, incomplete, or outdated). Conversely, a dataset with integrity violations is always poor quality. The CDMP exam tests whether candidates understand this relationship and can identify which type of integrity violation is present in a given scenario.
CDMP Exam Relevance
Data integrity concepts appear in both the Data Quality and Data Modeling knowledge areas. Key exam topics: the four types of integrity, the difference between integrity and quality, and the mechanisms used to enforce each type of integrity. Referential integrity questions are particularly common in the Data Modeling domain.