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Measure data quality against the needs of a specific decision or service—not against an assumed universal standard. Start by defining who will use the data and what they need it to do, then turn those needs into explicit checks, targets and a repeatable assessment. A dataset can be suitable for one use and unsuitable for another.
What does a data quality assessment measure?
A data quality assessment checks how well data meets defined requirements for a particular use. It is not simply a search for errors or a one-time cleaning exercise. Its results should help people decide whether data is appropriate for a decision or service, what limitations matter, and where improvement effort will have the greatest effect.
There is no single universal list of dimensions, threshold or scoring formula. The UK Government Data Quality Framework is useful guidance beyond government, though it was developed in a public-sector context. It defines quality in relation to fitness for purpose and says assessment should reflect user needs. Read the framework overview and its detailed guidance.
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How do you assess the quality of your data?
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Define the purpose and users
Write down the decision, service or analysis the data supports, who relies on it, and what they need to know. Identify the critical data assets and the fields or tables that could change the outcome. Quality requirements follow from this use: a delay or missing value that is tolerable for one purpose may make the same data inadequate for another.
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Choose relevant dimensions and define rules
Select the quality dimensions that matter to the use, then express priority needs as checks that can be measured. For example, a timeliness rule might specify how soon a record must enter the system after collection. Set realistic targets, define legitimate exceptions, and make clear which records and time period are in scope. An unusual value is not automatically an error if the rule permits it.
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Measure a baseline
Choose a metric that matches each rule. Government guidance gives percentages, counts, true-or-false checks and ratios as possible measures. Different fields and checks can use different metrics. Where practical, automate repeat checks to make the method more consistent; automation does not by itself improve the underlying data.
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Document and communicate the results
Record the method, scope, target, measurement scale, results and confidence. State strengths and limitations, including areas not checked, so users can judge whether the data is appropriate for their purpose. ISO 8000-8:2015 addresses concepts and measurement of information and data quality, including documenting methods and tailoring, stating thresholds and scales, and communicating confidence. See the ISO 8000-8:2015 overview.
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Prioritise improvements and investigate causes
Rank problems by the importance of the affected data, how much data is affected, the risk and the cost of repair. Look for systemic causes in collection or processing, and address issues at their source where possible. Set measurable improvement goals rather than treating each assessment as an isolated clean-up. The UK Government’s data quality action-plan guidance covers critical assets, standards, assessment and prioritisation.
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Repeat the assessment
Reassess with the same rules and method so you can compare results over time and see whether changes worked. If the intended use or requirements change, document the change rather than presenting results from different scopes as directly comparable.
Which data quality dimensions and metrics should you use?
The UK Government Data Quality Framework identifies six core dimensions, attributed to DAMA UK. It explicitly treats these as adaptable rather than prescriptive: add or exclude dimensions according to user needs and context.
| Dimension | What to assess |
|---|---|
| Completeness | Whether required data is present. Complete data can still be wrong; completeness is not accuracy. |
| Uniqueness | Whether records that should be distinct are duplicated. |
| Consistency | Whether data agrees across records, fields or sources where it should. |
| Timeliness | Whether data is available within the time required for its use. |
| Validity | Whether data conforms to defined formats, rules or permitted values. |
| Accuracy | Whether data correctly represents the real-world facts or events it is intended to describe. |
For every metric, define its denominator, population, time window, exception handling, target and interpretation. For instance, a percentage is hard to interpret unless readers know which records were eligible to be counted and which were excluded. The official guidance does not prescribe a universal threshold or single scoring formula; ISO 8000-8:2015 likewise says the business must establish thresholds and that the measurement scale should be stated.
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Dimensions beyond the six may matter, particularly for AI and other digital-data uses. On 3 September 2026, ETSI announced ETSI TR 104 180, which specifies 18 data quality metrics and mathematical measurement methods. ETSI groups examples under fundamental quality, usability, fairness, and privacy and responsible use. Its announcement describes a proof of concept involving two contrasting datasets—industrial IoT sensor data and demographic data—and says that metric combinations and weights vary by use case. This development does not establish a universal score or indicate that the report replaces government guidance or ISO standards. Read ETSI’s announcement.
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How should you interpret a quality score?
A score is meaningful only in relation to the rules, data and use it represents. Before combining measures, state what each one covers and how it is calculated. A single aggregate can conceal important differences—for example, strong completeness alongside weak accuracy—or imply comparability where scopes, exceptions or measurement methods differ. Use a score only when its construction and intended interpretation are transparent; report the underlying measures and limitations needed to make a decision.
How is data quality different from data maturity?
A dataset-level quality assessment asks whether particular data meets requirements for a defined use. A maturity assessment asks how capable an organisation is at managing data. The UK Government’s Data Maturity Assessment for Government framework, updated 23 July 2026, uses a matrix of topics and themes rather than a single score. It can help organisations identify priorities and track improvement over time; it does not replace checks on whether a particular dataset is fit for purpose.
What should an assessment report include?
- The decision or service, intended users, and data assets and fields in scope.
- The dimensions, rules, targets, denominators, time windows and exception handling used.
- The measurement method, scale, results and confidence.
- Known strengths, limitations, and checks or data that were out of scope.
- Prioritised issues, their likely impact and extent, and planned actions to address root causes.
- When the assessment was performed and how results compare with prior assessments using the same method.
These details let readers judge the evidence without mistaking a measured result for a guarantee. A domain-specific regulatory assessment or benchmark may impose additional requirements; this practical approach is not a substitute for either.
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