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Find dirty data by starting with the decision or process the database supports, not by scanning every column indiscriminately. Identify the tables and fields that matter to that use, define measurable quality rules, profile those fields to establish a baseline, and rank failures by business importance, impact, and distance from the target. Then fix the cause, rerun the same checks, and report the change.
In this guide, “dirty data” means data that fails a rule required for a specific use. A null can be an error in a required customer identifier, but perfectly valid for a field that does not apply. A populated value can also be dirty if it is inaccurate, contradictory, stale, or outside the permitted domain.
Start with the use case, not the whole database
The importance of a field depends on how the data is used. A missing postal code may be harmless for an internal employee directory but block a regulated mailing process. The Government Digital Service (GDS) puts it plainly: “The importance of fields or tables will vary depending on how the data is used.” Its guidance also says you should measure only items identified as critical and tied to a specific data use.
- Name the decision or process. Examples include regulatory reporting, billing, fraud review, inventory planning, customer support, or a machine-learning feature pipeline.
- Map the data path. List the source systems, tables, fields, joins, transformations, and outputs involved in that use.
- Mark critical fields. Record which fields can change a decision, cause a failed control, misidentify an entity, or prevent a transaction.
- Record acceptable exceptions. A value that is absent because a question is not applicable should not be counted as a defect.
Keep a short scope statement for each assessment: the use, population or time period, tables and columns included, exclusions, and the person responsible for the result. This prevents a profile from being mistaken for a statement about the quality of the entire database.
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Define what “fit for purpose” means
Write the rules before looking at the results. Rules should be specific enough that two analysts would classify the same record in the same way, and each rule should have a target or tolerance.
Rules to establish with data users
- Requiredness: which records and values must exist, and when a missing value is structurally allowed.
- Format and domain: permitted data types, patterns, codes, enumerations, units, and ranges.
- Uniqueness: which keys must be unique and whether repeated rows represent valid events rather than duplicates.
- Relationships: required parent records, valid foreign-key relationships, and cross-table referential conditions.
- Cross-field logic: conditions such as an end date not preceding a start date, or a status agreeing with a cancellation timestamp.
- Freshness: how recent a value or record must be for the stated use.
- Accuracy evidence: the authoritative source, reconciliation, or domain review that can establish whether a plausible value is actually true.
Set realistic thresholds. A rare exception may not make a dataset unfit for purpose, while a tiny error rate in a safety-critical field may require immediate action. Capture the rule, target, measurement method, population, owner, and review date in a data-quality register.
Use a practical vocabulary of quality dimensions
The GDS implementation guide (2024) uses six core dimensions. They are a useful starting vocabulary, not a checklist to apply blindly.
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|---|---|---|
| Completeness | Are required records and values present? | Null, blank, or absent required values; missing expected records |
| Uniqueness | Are there unintended duplicate records or entities? | Repeated keys, duplicate entity candidates, repeated submissions |
| Consistency | Do values agree within a record and across related assets? | Contradictory statuses, totals, units, or parent-child relationships |
| Timeliness | Is the data current enough for the use? | Age of timestamps compared with the agreed freshness rule |
| Validity | Does each value conform to its permitted type, format, range, or domain? | Invalid codes, malformed identifiers, out-of-range numbers |
| Accuracy | Does the value correspond to the real-world entity or event? | Reconciliation with a trusted source, provenance, or expert review |
The Government of Canada’s 10 January 2024 guidance describes nine dimensions: access, accuracy, coherence, completeness, consistency, interpretability, relevance, reliability, and timeliness. The difference is useful: dimensions such as relevance, access, and interpretability depend strongly on user need and the data lifecycle. Choose the dimensions and operational rules that fit your use instead of declaring one list universal.
Profile the critical data to establish a baseline
Profiling produces a factual snapshot of the selected data. Microsoft’s documented Unified Catalog workflow, for example, lets an authorized user select columns and run a statistical profiling snapshot; that is an example of a workflow, not evidence that one platform is best for every database.
Completeness checks
Measure the proportion and count of null, blank, or absent values for each required field. Separate expected or structural missingness from errors. Report the denominator and population: “94.2% of active policy records had a policyholder identifier during January” is actionable; “5.8% missing” without scope is not.
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Uniqueness checks
Test declared keys and likely business keys, then investigate candidates rather than deleting them automatically. Two rows with the same customer name may be different people; two rows with the same event identifier may be an ingestion replay. Decide whether repeated rows violate the data model before labeling them dirty.
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Compare values with documented formats, enumerated codes, units, and ranges. Look for values that are technically parseable but outside the domain, such as a future birth date or a status code retired by the source system. Keep the rule version with the result when domains change over time.
Consistency checks
Compare related fields in the same record and related records across tables. Examples include a closed account with an open balance, an order line whose total disagrees with quantity multiplied by price, or a child row pointing to a parent that should not exist. Cross-table checks often reveal defects that column-level statistics miss.
Timeliness checks
Measure record or attribute age against the freshness requirement for the actual use. A timestamp that is acceptable for a monthly report may be too old for fraud detection. Check time zones, late-arriving data, and the difference between event time and load time before interpreting an age distribution.
Accuracy checks
Profiling can show that a value is present and plausible; it cannot prove that the value is true. Compare with an authoritative reference, reconcile totals, inspect provenance, or involve a domain expert. Statistics Canada’s process-quality guidance and the Canadian data-quality guidance both emphasize that accuracy requires evidence beyond appearance.
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For every measure, retain the query or rule definition, run date, data slice, exclusions, sample or full-population method, and permissions or performance constraints. The exact query syntax, indexes, sampling strategy, and safe execution plan depend on the database engine and production environment, so adapt the checks to your schema and change-control process.
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Turn measurements into a priority order
Do not rank issues by raw defect count alone. A few errors in a critical field can matter more than thousands of harmless exceptions in an optional field. Conversely, a severe failure in a low-impact field may wait behind a modest failure in a field that controls a major decision.
A transparent scoring model
The GDS Data quality issues framework, updated 16 April 2026, offers one example. It combines an importance-impact score with a performance band:
- Assign an importance score for how essential the affected data is to the use.
- Assign an impact score for the consequence if the issue is not corrected.
- Multiply importance by impact to obtain the issue’s importance-impact score.
- Classify the measured performance against its target using the framework’s performance band.
- Multiply the importance-impact score by the performance-band score.
- Use the resulting total to order urgency and resources.
| Total score | Example band in the GDS framework |
|---|---|
| 1–7 | Low |
| 8–16 | Medium |
| 17–32 | High |
| 33–64 | Critical |
These bands are a proposed method from the UK framework, not a mandatory rule or cross-industry benchmark. Publish your scoring definitions so that a reviewer can reproduce the result.
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In the GDS example, completeness has a target of 95% and an observed result of 94.2%. The importance score is 2 and the impact score is 2, giving an importance-impact score of 4. The 94.2% result falls only slightly short and receives a performance score of 1 in that example. The final calculation is 2 × 2 × 1 = 4, classified as Low priority under that method. A different use, threshold, or impact assessment could produce a different priority for the same measurement.
Improve the ranking with context
- Record the number and proportion of affected rows, but do not treat either as the priority by itself.
- Note whether the issue is growing, stable, or concentrated in a new source, region, product, or time period.
- Consider legal, safety, financial, customer, operational, and reputational consequences.
- Include the effort and reversibility of a correction, while avoiding a cheap fix that leaves the source defect intact.
- Escalate issues that compromise the reliability of other quality measurements, such as a broken reference table used by many checks.
Investigate the root cause before changing rows
A profile tells you where a rule fails; it does not explain why. For each high-value issue, trace the data from capture through validation, transformation, loading, and reporting.
- Reproduce the exception. Save representative record identifiers and the exact rule result without exposing unnecessary personal data.
- Locate the first divergence. Compare the source value, ingestion payload, staging record, transformed record, and published value.
- Classify the cause. Common categories include source-entry error, missing validation, mapping or code-set drift, parsing failure, duplicate delivery, late arrival, join logic, or an incorrect business rule.
- Choose the correction path. Correct source capture or validation when possible; otherwise apply a controlled remediation with provenance and an audit trail.
- Assign ownership and a due date. The owner should control the process that can prevent recurrence, not merely the report that exposes it.
Do not overwrite evidence. Keep original values, corrected values, correction reason, actor or process, timestamp, and rule version where governance requires an audit trail. If a value cannot be established accurately, use an explicit unknown state rather than inventing a plausible replacement.
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Track issues in a data-quality register
A register turns profiling into managed work. Use one row per rule failure or coherent issue, with fields such as:
- Issue identifier and concise description
- Use case, table, field, and affected population
- Dimension and rule version
- Target, current measurement, denominator, and run date
- Importance, impact, performance score, and resulting priority
- Business and technical owner
- Root cause, proposed action, due date, and status
- Correction method, validation evidence, and residual limitations
Separate an issue from an individual bad row. One root cause may affect millions of records; thousands of rows may represent one harmless, documented exception. This distinction keeps remediation focused on prevention.
Rerun comparable checks and communicate change
After a correction, rerun the same rule against a comparable population. Keep the prior result so you can show the target, baseline, current value, absolute change, and remaining exceptions. If the population or rule changed, label the comparison as non-equivalent rather than presenting a false trend.
Each report should state the rule, target, current result, affected data, owner, run date, comparison period, exclusions, and limitations. A concise dashboard can show priority, trend, and open actions, while a detailed appendix preserves exception samples and method versions. Repeated measurement is what distinguishes a one-time cleanup from ongoing quality control.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose a profiling or monitoring approach
You can implement checks in database jobs, a data-quality service, an orchestration pipeline, or a catalog product. Select an approach against the work you actually need:
| Decision factor | Questions to answer |
|---|---|
| Source support | Can it read every required database, file, API, and warehouse safely? |
| Rules and dimensions | Can it express requiredness, uniqueness, validity, consistency, timeliness, and accuracy evidence? |
| Scope control | Can you profile selected critical columns and partitions rather than scanning everything? |
| Business logic | Can it test cross-field and cross-table conditions? |
| Scheduling and history | Can it rerun checks, retain versions, and show trends? |
| Security and permissions | Does it respect least privilege, masking, residency, and retention requirements? |
| Performance | What load, indexes, replicas, sampling, or maintenance windows are required? |
| Explainability | Can owners see the rule, failed records or aggregates, and reason for the score? |
| Operating cost | What are the licensing, compute, storage, engineering, and support costs? |
For small or infrequent assessments, carefully reviewed database checks may be sufficient. Recurring, multi-source monitoring usually benefits from centralized scheduling, history, ownership, and alerting. In either case, preserve the rule definitions and execution context so results remain auditable.
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Common mistakes and how to avoid them
Scanning every column first
Broad scans create noise, consume resources, and produce findings unrelated to a decision. Start with critical fields tied to a named use, then expand when evidence shows another dependency.
Calling every null an error
Model optionality and structural missingness explicitly. Measure required values against the correct denominator.
Confusing completeness with accuracy
A filled field can be wrong, and a missing field can be appropriate. Use reference data, provenance, reconciliation, or expert review for accuracy.
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Assuming duplicate-looking rows are invalid
Event and transaction models often permit repeated entities. Confirm the key and grain before deleting or merging records.
Using a universal threshold
Targets depend on the use, risk, lifecycle, and cost of failure. Document why a threshold is acceptable and revisit it when the process changes.
Fixing the output but not the source
One-time cleansing will be undone by the next load if the capture, mapping, or transformation defect remains. Prefer controls at the earliest practical point and monitor the source after remediation.
Running heavy profiles in production without a plan
Coordinate with database owners, choose safe windows or replicas, limit columns and partitions, and document sampling. A quality check that disrupts the operational system creates a new risk.
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The GDS score bands make trade-offs visible; they do not replace judgment. Explain the assumptions behind importance, impact, targets, and performance bands.
A repeatable operating checklist
- Name the decision or process and its acceptable outcomes.
- Map source, critical tables, fields, relationships, and owners.
- Define requiredness, domains, keys, relationships, freshness, and accuracy evidence.
- Choose the dimensions that matter; record targets and exceptions.
- Run a baseline profile and retain scope, method, date, and limitations.
- Validate surprising results with data users and source-system owners.
- Score issues by importance, impact, and measured performance.
- Investigate the first point of failure and assign corrective ownership.
- Apply controlled fixes with provenance and an audit trail.
- Rerun equivalent checks, compare trends, and communicate open risk.
For further detail, see the Government Data Quality Framework guidance, the GDS implementation guide, and the GDS data quality issues framework. The framework example used here was updated 16 April 2026. Microsoft’s profiling workflow is documented in Configure and Run Data Profiling in Unified Catalog. For alternative dimension definitions and accuracy context, consult the Government of Canada guidance on data quality and Statistics Canada’s process quality guidelines.
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