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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesMeasure data quality against a defined decision or service—not against an abstract idea of perfect data. Start by identifying who will use the data and what they need it to do, then set checkable rules, record a baseline, and repeat the same assessment to see whether quality is improving.
What does data quality mean?
Data quality is fitness for purpose. A dataset can be suitable for one task and inadequate for another: a monthly report may tolerate a delay that would make the same information unsuitable for a real-time service. Assessment therefore begins with the intended use, not a universal pass mark.
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The UK Government Data Quality Framework offers useful cross-sector guidance, although it was developed in a government context. Its approach is to define user needs, establish rules and measure how well data meets them. The framework also stresses that quality is not perfection: it is managed through continuous improvement. See the framework overview and its detailed guidance.
How to assess the quality of a dataset
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Define the decision, users and scope
Write down the decision or service the data supports, who relies on it, and what could go wrong if the data is late, missing or incorrect. Identify the critical data assets and the specific tables, fields or records that affect that use. Keep the scope explicit; an assessment of a few high-impact fields is not an assessment of every field in the system.
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Choose relevant dimensions and turn needs into rules
Use quality dimensions to identify possible risks, then express priorities as rules that can be checked. A timeliness rule, for example, might define the maximum acceptable delay between collection and entry into the system. Set a realistic target for each rule and document legitimate exceptions. An unusual value is not automatically an error if it is allowed by the rule and valid in context.
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Measure a baseline
Select a metric that matches each rule. The UK guidance gives examples including percentages, counts, true-or-false checks and ratios. A single dataset can use different measures for different fields and checks. Where practical, automate repeat checks so they run consistently, but do not assume automation by itself fixes quality problems.
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Record the method and communicate the result
Document the scope, rule, population assessed, measurement period, denominator, exception handling, target and result. State how the scale should be interpreted, how confident you are in the result, and what was not checked. ISO 8000-8:2015 provides guidance on documenting measurement methods and tailoring, stating thresholds and scales, and communicating confidence. See the ISO 8000-8:2015 standard page.
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Prioritise fixes and investigate causes
Rank issues by the importance of the affected data, how much data is affected, the resulting risk and the cost of repair. Look for systemic causes in collection, entry, transformation or processing rather than repeatedly correcting only downstream symptoms. Where possible, improve the process at the source and set measurable goals. The UK Government’s data quality action-plan guidance covers assessment, standards and prioritisation.
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Repeat the assessment
Run the same checks using the same definitions and comparable scope. Keep results over time so you can tell whether a change reflects an actual improvement, a changed method or a different population. Reassessment also shows whether a source-level fix is working.
Which data quality dimensions and metrics should you use?
The UK Government Data Quality Framework identifies six core dimensions, attributed to DAMA UK. It treats them as a guide rather than a mandatory checklist: add or omit dimensions according to the user’s needs and the data’s context.
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| Dimension | Question to make measurable |
|---|---|
| Completeness | Are required values or records present? |
| Uniqueness | Are duplicate records present where one record should represent one entity or event? |
| Consistency | Do values agree across records, systems or representations where they should? |
| Timeliness | Is the data available within the time required for its use? |
| Validity | Do values conform to defined formats, ranges or business rules? |
| Accuracy | Does the data correctly represent the real-world fact or event it describes? |
Completeness and accuracy are different: every required field can be populated while some values are still wrong. Likewise, a value that passes a format check may be valid in form but inaccurate in fact. Treat each dimension as a prompt for a specific rule rather than a score in itself.
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For each metric, define its denominator, population, time window, exceptions, target and interpretation. For example, a completeness percentage is not meaningful unless it is clear which records and required fields count in the denominator. Official guidance does not set one universal threshold or scoring formula; the business context must determine what is acceptable.
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How to interpret newer data-quality standards
On 3 September 2026, ETSI announced ETSI TR 104 180, a report specifying 18 data quality metrics and mathematical measurement methods. ETSI groups the metrics into 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. The examples use different metric combinations and weights, underscoring that measurement depends on the use case. Read ETSI’s announcement for details and its link to the technical report.
ETSI’s development is relevant to AI and digital data ecosystems; the announcement does not establish that it replaces ISO or government guidance, or that it is mandatory. For any use case, select metrics that reflect the actual risks and requirements rather than adopting a broad list or weighting scheme without justification.
Dataset quality and organisational maturity are different
A dataset-level assessment asks whether particular data meets the requirements of 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 one overall score. It can help an organisation prioritise resources and track improvement over time, but it does not replace checks on whether a particular dataset is fit for a particular decision.
What a useful assessment report should disclose
- Purpose and audience: the decision or service supported and who will rely on the data.
- Scope: assets, fields, records and time period included, plus any exclusions.
- Rules and thresholds: the checks used, their targets and how exceptions were handled.
- Methods and results: metric definitions, denominators, scales and results that can be compared over time.
- Confidence and gaps: how dependable the findings are and which areas were not assessed.
- Action: priority issues, their likely impact, root causes, owners and measurable improvement goals.
These disclosures let users judge whether the data is suitable for their own use, rather than mistaking a passing score on a limited set of checks for a guarantee of overall quality.
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