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What are the four pillars of data quality?
The model below groups commonly used dimensions into four decision-oriented pillars. It maps to established guidance while keeping related checks together.
| Pillar | Core question | Typical checks |
|---|---|---|
| Accuracy and validity | Does the data describe reality and follow required rules? | Reference-data matching, range checks, type and format validation |
| Completeness and uniqueness | Are required information and entities present once, without unintended duplicates? | Null rates, expected-record coverage, duplicate-entity detection |
| Consistency and integrity | Do values agree across records and systems, and are changes controlled? | Cross-field rules, reconciliation, referential integrity, transformation controls |
| Timeliness, context, and fitness for use | Is the data current and appropriate for this decision and period? | Freshness, latency, reporting-period checks, documented limitations |
Use the pillars as a starting model, then add dimensions that your domain requires. The UK Government Data Quality Framework identifies six core dimensions—completeness, uniqueness, consistency, timeliness, validity, and accuracy—and says its list is not prescriptive. Government of Canada guidance (2024) uses nine: access, accuracy, coherence, completeness, consistency, interpretability, relevance, reliability, and timeliness.
Pillar 1: Accuracy and validity
Accuracy means truth
Accuracy asks whether a value represents the real-world object, event, or person it claims to represent. A customer’s address can be syntactically valid yet wrong because the customer moved or entered a different address.
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Validity means conformance
Validity checks whether data follows defined formats, types, ranges, code lists, and business rules. A date must parse as a date; a percentage may need to fall between 0 and 100; a country code must come from an approved list. Passing these checks proves conformance, not truth.
How to measure it
- Define approved formats, ranges, enumerations, and cross-field rules for critical fields.
- Test samples or records against authoritative reference data where possible.
- Report the rule, population tested, failure count, and measurement date; do not collapse different error types into one unexplained score.
Pillar 2: Completeness and uniqueness
Completeness is coverage
Completeness asks whether expected records and essential values are present. Set the denominator first: completeness may mean all expected transactions arrived, or that a required field is populated for every in-scope record. A complete dataset can still be inaccurate; the UK framework explicitly warns that completeness does not establish accuracy.
Uniqueness protects entity counts
Uniqueness asks whether each real-world entity or event is represented once when the use case requires one representation. Duplicate customers, repeated invoices, or replayed events can inflate totals while every individual row appears valid.
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How to measure it
- Compare received records with an independently defined expected population.
- Calculate missingness for priority fields and distinguish “unknown,” “not applicable,” and blank values.
- Define duplicate keys or matching rules, then review false matches and missed matches before automating merges.
Pillar 3: Consistency and integrity
Consistency within and across assets
Consistency means values agree where they should. A line-item total should reconcile to an invoice total; a customer identifier should map to the same customer across systems; units and code meanings should not change silently between datasets.
Integrity requires controlled change
Integrity extends consistency to relationships, transformations, and history. Protect referential links, version schemas, record who changed a transformation, and preserve enough lineage to explain how a reported value was produced. A pipeline that produces the same answer by accident is not reliable if its logic cannot be traced or reproduced.
How to measure it
- Write reconciliation rules for totals, counts, keys, units, and allowed state transitions.
- Run referential-integrity checks after loads and transformations.
- Track schema, mapping, and business-rule changes alongside the data and its quality results.
Pillar 4: Timeliness, context, and fitness for use
Timeliness is relative to the decision
Freshness is not a universal number. A fraud decision may need seconds-old events; an annual demographic report may be fit for purpose when updated yearly. Define the required period, delivery deadline, and maximum acceptable age for each important use.
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Context makes quality claims meaningful
Record collection method, coverage, units, time zone, reference period, known gaps, and intended population. Users need to know whether a value is current, revised, estimated, or carried forward. Faster publication can require less validation or fewer late records, creating a trade-off with completeness or accuracy; disclose that trade-off instead of presenting “real time” as automatically better.
Fitness for use is the decision test
Quality is judged against intended use and user needs. A dataset can be acceptable for trend analysis but unsuitable for an individual eligibility decision. ISO/IEC 25024:2015 provides quantitative data-quality measures, but it does not set universal rating ranges; organizations must establish thresholds in context.
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What are the established dimensions of data quality?
There is no single canonical list or universal definition of “good” data. The frameworks below demonstrate why the four-pillar model should be treated as an editorial synthesis.
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| Framework | Dimension count | What it establishes |
|---|---|---|
| UK Government Data Quality Framework (2020) | Six core dimensions | Completeness, uniqueness, consistency, timeliness, validity, and accuracy; the framework says the list is non-prescriptive. |
| Government of Canada guidance (2024) | Nine dimensions | Access, accuracy, coherence, completeness, consistency, interpretability, relevance, reliability, and timeliness. |
| ISO/IEC 25024:2015 | Quantitative measures | Measurement guidance without universal score ranges or thresholds. |
| ETSI TR 104 180 (announced September 3, 2026) | 18 metrics | Metrics grouped around fundamental quality, usability, fairness, and privacy/responsible use; proof-of-concept work included industrial IoT sensor and demographic data. |
For AI and cross-domain data, traditional correctness checks are not enough. Consider representation bias, lineage and traceability, anonymity, confidentiality, and responsible-use controls. ETSI DATA committee chair Diego Lopez said: “It is essential that data quality is measurable, especially for organisations who need to establish whether its data is fit to essential intents, like it would be the case of trustworthy AI,” in the September 3, 2026 announcement.
How do you measure data quality?
- Define the use. Name the decision, users, population, time period, and harm caused by an error or delay.
- Identify critical fields and relationships. Prioritize fields that drive decisions, joins, totals, compliance, or model outcomes rather than measuring everything equally.
- Turn expectations into observable rules. Specify formats, ranges, allowed values, requiredness, duplicate keys, reconciliations, freshness limits, and lineage requirements.
- Set contextual thresholds. Establish acceptable failure levels with data users and owners. ISO/IEC 25024 does not supply universal ranges for you.
- Assign ownership. Name the producer, steward, approver, and escalation path for each critical rule.
- Run checks across the lifecycle. Test at collection, ingestion, transformation, publication, and consumption stages where a defect can be detected or prevented.
- Report results with context. Include scope, denominator, date, rule version, exceptions, and known limitations. Keep metadata and published quality measures synchronized with the dataset.
How can you improve data quality?
Prevent errors at capture
Use constrained inputs, reference lists, clear definitions, and immediate validation for high-impact fields. Prevention is usually cheaper than repairing ambiguous records downstream, but do not reject values merely because they are unusual when legitimate exceptions exist.
Profile before you remediate
Profile distributions, missingness, duplicates, invalid codes, outliers, and freshness across the actual data. Separate systematic defects from one-off exceptions and preserve the original value when corrections must be auditable.
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Fix the producing process
Assign defects to the step or owner that created them. Add tests to pipelines, monitor rule failures, version mappings, and document approved exceptions. A dashboard that only reports defects without changing the source process will not improve quality.
Use tools for repeatable work
Data profiling software can discover patterns and missingness; validation tools can enforce schemas and business rules; monitoring platforms can alert on drift, freshness, reconciliation failures, and duplicate spikes. Select tools that integrate with your storage, pipelines, catalog, and access controls, and verify that they preserve lineage and sensitive-data protections.
How to compare two datasets or data products
Compare like with like, using the same population, period, definitions, and test rules. Weight each axis according to the intended use rather than creating one universal score.
- Coverage and completeness, including expected-population definition
- Accuracy evidence and validation method
- Freshness and delivery latency relative to the decision
- Consistency across sources and reconciliation performance
- Duplicate detection and entity-resolution handling
- Lineage, traceability, and change documentation
- Bias, privacy, confidentiality, and responsible-use controls where relevant
- Transparency about exceptions, estimates, revisions, and known gaps
Practical conclusion
Use the four pillars to organize a quality program, not to claim that every dataset needs the same scorecard. Start with purpose and users, measure the few dimensions that affect their decisions, set explicit contextual thresholds, and keep evidence about exceptions and changes with the data. For sensitive, AI, or cross-domain uses, extend the scorecard beyond correctness to include fairness, traceability, privacy, and responsible use.
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ISO/IEC 25024:2015 is the directly relevant data-quality measurement standard; its measures still require organization-specific thresholds. The UK Government Data Quality Framework and Government of Canada guidance provide practical dimension and implementation models. ETSI TR 104 180 illustrates how newer measurement work incorporates usability, fairness, and privacy alongside fundamental quality.
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