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Data quality dimensions assist organizations in systematically assessing and improving the quality of their data. They encompass various aspects such as accuracy, completeness, and consistency.
Use this profile to understand the building block briefly, place it in the model, and switch to the 360° assessment when needed.
Theoretical construct: explains a term, principle, or mental model.
What organizes, connects, or makes decisions possible.
Data quality dimensions are different lenses for judging whether data is fit for a particular purpose.
The approach grew from quality management and information-systems research. ISO 8000 and data-management bodies of knowledge established recurring dimensions such as accuracy, completeness, timeliness, consistency, and uniqueness.
Like a toolbox, data has properties suited to the job: a measuring tape must be accurate, readable, and available to serve its purpose.
Agreement between a value and reality or a reference.
The share of expected values that are present.
Whether the data’s time state fits the intended use.
Dimensions turn the vague verdict “good data” into testable criteria. Their priority depends on purpose: timeliness may matter more than absolute completeness for a particular decision.
Where this building block is located in the topic model.
Explore how this building block connects to concepts, methods, technologies, and tools.
These sources establish the term and its professional meaning.
All direct connections of the current building block in a compact text view.
This classification shows where the building block typically matters, how demanding it is, and what kind of impact it has in the model.
The level within the organization (enterprise, domain, team) at which the AssetBlock is applied.