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List of Conformed Dimensions of Data Quality

The following is the current version of the Conformed Dimensions of Data Quality (r4.3) and their underlying concepts. Each Dimension has one or more underlying concepts. The definitions of each of those are available here. The following is a PDF format document of the Conformed Dimensions level of detail.

Conformed Dimension

Conformed Dimension Definition

Underlying Concepts

Non Standard Terminology for Dimension


Completeness measures the degree of population of data values in a data set.

Record Population, Attribute Population, Truncation, Existence

Fill Rate, Coverage, Usability, Scope


Accuracy measures the degree to which data factually represents its associated real-world object, event, concept or alternatively matches the agreed upon source(s).

Agree with Real-world, Match to Agreed Source



Consistency measures whether or not data is equivalent across systems or location of storage.

Equivalence of Redundant or Distributed Data, Format Consistency, Logical Consistency, Temporal Consistency

Integrity, Concurrence, Coherence


Validity measures whether a value conforms to a preset standard.

Values in Specified Range, Values Conform to Business Rule, Domain of Predefined Values, Values Conform to Data Type, Values Conform to Format

Accuracy, Integrity, Reasonableness, Compliance


Timeliness is a measure of time between when data is expected versus made available.

Time Expectation for Availability, Manual Float, Electronic Float

Currency, Lag Time, Latency, Information Float, Cadence


Currency measures how quickly data reflects the real-world concept that it represents.

Current with World it Models



Integrity measures the structural or relational quality of data sets.

Referential Integrity, Uniqueness, Cardinality

Validity, Duplication, Coherence


Accessibility measures how easy it is to acquire data when needed, how long it is retained, and how access is controlled.

Ease of Obtaining Data, Access Control, Retention

Availability, Security


Precision is the measurement or classification detail used in specifying an attribute's domain.

Precision of Data Value, Granularity, Domain Precision

Coverage, Detail


Lineage measures whether factual documentation exists about where data came from, how it was transformed, where it went and end-to-end graphical illustration.

Source Documentation, Segment Documentation, Target Documentation, End-to-End Graphical Documentation



Representation measures ease of understanding data, consistency of presentation, appropriate media choice, and availability of documentation (metadata).

Easy to Read & Interpret, Presentation Language, Media Appropriate, Metadata Availability, Includes Measurement Units



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