Concept: The Six Dimensions of Data Quality
Definition & Mental Model
The formal data governance framework established by DAMA International defining the six measurable facets required for data to be deemed fit for business decision-making.
The Six Core Dimensions
- Accuracy: The degree to which data correctly reflects real-world events or facts.
- Completeness: The proportion of expected data that is populated (audit of null/blank records).
- Consistency: Data values in one system do not contradict data values in another system.
- Validity: Conformance to strict business rules, data types, and syntax constraints.
- Timeliness: The degree to which data represents the current operational period.
- Uniqueness: Absence of duplicate records; each entity has a single primary key.
View Mermaid Syntax
mindmap
root((Data Quality))
Accuracy[Accurate values]
Completeness[Zero unplanned nulls]
Consistency[Uniform casing & naming]
Validity[Strict types & valid bounds]
Timeliness[Up-to-date data]
Uniqueness[Zero duplicate records]
Grounded Example: PwC Call Center Audit
- Completeness: 946 null values exist in
Speed of answer in seconds. Audit confirms these calls haveAnswered (Y/N) == "N". They are valid operational nulls. - Uniqueness: 5,000 calls tested; exactly 5,000 distinct
Call Idvalues.