Established model
The six data quality dimensions
DAMA International · 2013
Quality is not one property but several — completeness, uniqueness, timeliness, validity, accuracy, consistency.
Its place in the frameworkData Core›Data Collection
What it does
Splitting quality into named dimensions makes it arguable and measurable. It also settles a common confusion: data can be entirely valid and entirely wrong, since validity asks whether a value is of the right form and accuracy asks whether it describes reality, and only one of the two is cheap to check.
- Reach for it when
- When setting up collection, and when a dataset is described as bad without anyone saying in what way.
- Where it stops
- Measuring the dimensions costs real effort, and perfection in all six is neither affordable nor necessary. Which dimensions matter depends on what the data is for.
DAMA UK Working Group, The Six Primary Dimensions for Data Quality Assessment, 2013.
Why it sits at Data Collection
Where the numbers come from: which sources, how they are captured, whether they can be trusted, and on what basis the organisation is entitled to hold them.
A model is only useful when you reach for it at the right moment. This one answers a question that arises here — so it is filed here, and nowhere else. These are the working areas it serves:
- The sourcesMaster-data practice on source-of-truth designation, and the reconciliation problem where several systems hold the same fact.
- InstrumentationMeasurement-design literature: the distinction between data that accumulates and data that is captured on purpose.
- Data qualityThe data-quality dimensions used in information management: completeness, accuracy, timeliness, consistency and validity.
- Consent and basisData-protection principles of purpose limitation and data minimisation, which constrain collection independently of technical capability.
What it touches elsewhere
Nothing in a business is decided on its own. A conclusion reached with this model at Data Collection lands in these other cores, whether or not anyone follows it there.
- KPI managementA measure is only as good as the collection behind it, and the definition has to be collectable.
- Data GovernanceWhat may be collected and how long it may be held is settled in governance.
- Omni CoreMuch behavioural data is collected at touchpoints, and instrumentation is part of designing them.
- Business CoreRegulatory obligations on data collection are compliance obligations like any other.
Filed at the same place
These answer questions that arise at Data Collection too. Where they disagree with this one, the disagreement is the useful part.
- Non-response bias and total survey errorEvery source of error in a survey, gathered into one frame so they can be traded off against each other.
Elsewhere in Data Core
These are other people’s models, named here so you can go to the source and use them properly. The Omnigoal is not affiliated with their authors and is not endorsed by them; nothing of theirs is reproduced here — no canvas, no diagram, no wording. Each is described in our own words, with the originator credited, because the framework is a place to put thinking, not a replacement for the people who did it. Model names and trademarks belong to their respective owners and are used here only to refer to the work itself.
All 125 models