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Reliable synthetic data requires two different tests. Validity asks whether every row obeys the dataset contract: schema, types, constraints, relationships, and volume. Utility asks whether the generated data preserves the distributions, correlations, temporal sequences, and other patterns that the intended use requires. A dataset can pass validity checks and still be statistically unhelpful, unfair, or too revealing.
The practical approach is a layered quality contract. Check inexpensive structural rules first, then missingness, uniqueness, distributions, integrity, freshness, and volume. Reconcile independent counts and aggregates between pipeline stages. Finally, evaluate synthetic-data utility, privacy, fairness, and sequential behavior with thresholds defined for your use case.
What synthetic-data quality checks must prove
Synthetic data is generated rather than copied directly from operational records, so quality has two dimensions:
- Validity: each record and table conforms to explicit technical and business rules.
- Utility: the generated dataset retains the relationships and patterns needed for analytics, testing, model development, or other stated purposes.
Validity checks include required columns, data types, null policy, accepted values, numeric bounds, key uniqueness, foreign-key coverage, temporal ordering, row counts, and freshness. Utility checks compare distributions, correlations, category proportions, and sequence logic with the intended source or specification. Risk checks consider privacy exposure and fairness for the population and decisions involved.
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No universal pass-rate percentage establishes that synthetic data is “good.” Thresholds depend on the use case, the generator, the source specification, and whether a rule is a hard gate, a warning, or a monitoring metric.
A layered validation matrix
| Layer | Example checks | Failure meaning | Suggested action |
|---|---|---|---|
| Schema | Required columns, data types, column order where a contract requires it | Generator or mapping drift | Fail the batch and inspect the schema version |
| Constraints | Not-null, accepted values, ranges, checks, primary keys, foreign keys | Values violate the contract | Quarantine invalid rows; fix the generator or mapping |
| Missingness | Null rate by column and partition | Incomplete generation or masking | Compare with the specification and baseline |
| Uniqueness | Key uniqueness, compound-key uniqueness, duplicate-entity heuristics | Collisions or duplicated entities | Re-key, deduplicate, or revise the generator |
| Distribution | Minimum and maximum, quantiles, category proportions, correlations | Synthetic utility drift | Tune the generator and document tolerated error |
| Integrity | Foreign-key coverage, temporal ordering, cross-column rules | Broken relationships or impossible states | Run relationship checks and repair upstream logic |
| Volume | Total rows, partition counts, expected ranges | Dropped, duplicated, or overproduced records | Reconcile source, staging, and target counts |
| Reconciliation | Counts, sums, balances, checksums, source-target joins | Transformation or loading mismatch | Stop downstream publication and investigate |
| Freshness | Last-update timestamp and arrival service-level agreement | Stale or missing batch | Alert and hold dependent outputs |
| Synthetic risk and utility | Similarity to intended distributions, sequence logic, fairness, privacy exposure | Data may be valid but unsafe or unfit | Add use-case-specific evaluation and governance |
Great Expectations describes data quality as multidimensional, covering distribution, freshness, integrity, missingness, schema, uniqueness, volume, and metadata for unstructured data. Treat each dimension as a separate signal so a passing schema check does not hide a failing distribution or freshness check.
Write the data contract before generating rows
A check is actionable only when the expected state is explicit. Document the following before generation or loading:
- Dataset grain: what one row represents, such as one order, event, account, or account-day.
- Schema: required columns, data types, precision, permitted column order, and schema version.
- Null policy: which fields may be null and the allowed null rate by partition.
- Accepted values: enumerations, code sets, case rules, and whether unknown values are allowed.
- Numeric and date bounds: inclusive minimums and maximums, timezone, and valid date windows.
- Keys: the single-column or compound key that identifies a row and the attributes that identify a real-world entity.
- Relationships: foreign-key mappings, cardinality, and cross-column rules.
- Cadence and volume: expected arrival time, partitioning scheme, and whether row count is fixed or variable.
- Utility tolerances: acceptable error for proportions, quantiles, correlations, totals, and sequence behavior.
- Severity: which rules block publication, which create warnings, and which are tracked for trend monitoring.
Keep business rules in executable form where possible. “A shipment date cannot precede an order date” is more useful than a general statement that dates should be reasonable.
Run cheap structural checks first
Execute low-cost checks before expensive distribution comparisons or cross-table joins. This ordering shortens feedback loops and prevents wasted compute on an invalid batch.
Schema presence and types
Confirm that the expected file or table exists, all required columns are present, and each column has the contract type. Check precision and scale for decimals, timezone handling for timestamps, and whether a platform requires a particular column order. A changed schema usually indicates generator drift, a mapping change, or an incompatible version. Fail the batch rather than silently coercing values.
Required fields and accepted values
Apply not-null rules to mandatory fields. Validate categorical columns against their allowed set, including case and whitespace normalization. Decide explicitly whether an empty string, a null, and an “unknown” code are distinct states. Reject out-of-set values or route them to quarantine so they cannot become unnoticed new categories.
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Ranges and cross-column checks
Check inclusive numeric and date bounds, then evaluate rules involving multiple columns: a minimum must not exceed a maximum, a refund cannot be positive when the order is cancelled, and an end timestamp cannot precede a start timestamp. Range limits should come from the contract or a documented baseline; do not invent a universal tolerance.
Constraints: what the platform can enforce
Constraints define the allowed shape and values. dbt documents not_null, unique, primary_key, foreign_key, check, and custom constraints. They may be declared at column or model level, but support and enforcement vary by warehouse and materialization. An enforced model contract is required where the platform supports contract enforcement.
When an enforced dbt constraint fails, table creation or update fails, the operation is rolled back, and an error is returned. Other platforms may treat the same declaration as informational, so verify actual enforcement rather than assuming that a declared rule blocks bad data. In a warning-only environment, pair the constraint with an explicit validation job and publication gate.
Primary keys are not enough
A surrogate identifier can be unique while two rows describe the same customer, device, or event. Define uniqueness at the business grain. For an order-line table, the compound key might be order identifier plus line number; for a daily account table, account identifier plus business date may be required. Add duplicate-entity heuristics when several attributes together should identify one real-world entity.
Counts and volume checks
Row counts are a first-class quality signal, not merely an operational statistic. Great Expectations documents three useful patterns:
| Pattern | Use it when | Action on failure |
|---|---|---|
| Range check | Volume naturally varies and you need to detect unusual spikes or drops | Alert, inspect the affected partition, and decide whether to hold publication |
| Exact count | A fixed batch contract requires a known number of rows | Fail the batch because missing or extra records violate the contract |
| Equality with another table | Reconciling source, staging, and target representations | Stop downstream use and locate the stage that changed the count |
Run total counts and partitioned counts. Daily or hourly partitions localize a failure that a whole-table total can conceal. Record the batch identifier, partition, expected count, observed count, and the code and schema versions that produced it.
Normalize before comparing counts
Define whether tombstoned, rejected, late-arriving, or quarantined rows belong in each count. Compare the same population at every stage. A source count that includes soft-deleted records cannot be reconciled directly to a target count that excludes them.
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Missingness and uniqueness
Measure null rates by partition
Compute null rates for every column that has a null policy, both overall and by time or business partition. A stable overall rate can conceal a broken day if later partitions dominate the total. Compare each result with the contract and a documented baseline. Unexpectedly low nullness can also indicate that the generator filled values that should remain missing.
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Test keys at the correct granularity
Check single-column keys, compound keys, and acceptable uniqueness proportions. Normalize case, whitespace, null representation, and data types before comparing values; otherwise formatting differences can create false duplicates or false uniqueness. For generated identifiers, verify both uniqueness and relationship coverage. If entity-level duplication matters, use a deterministic composite fingerprint or a domain-specific heuristic in addition to the row key.
Distribution and utility checks
Structural validity does not prove that synthetic data is useful. Compare generated values with the intended source or specification using measures appropriate to each field:
- Numeric fields: minimum, maximum, quantiles, dispersion, and meaningful totals.
- Categorical fields: category proportions, rare-category retention, and allowed-value coverage.
- Relationships: correlations or conditional distributions that the use case depends on.
- Time fields: event spacing, seasonality, ordering, and lag relationships.
- Partitions: distribution drift by day, region, cohort, or other business slice.
Define tolerated error before looking at results. A model-training dataset may require preservation of minority-category relationships, while a user-interface test fixture may need only valid values and realistic volumes. Treat distribution metrics as warnings or monitoring signals unless the use case makes a particular relationship essential.
Do not collapse utility into one score
Competing quality taxonomies use different metrics. A single similarity score can hide a broken tail, a missing rare category, or an impossible sequence. Keep the individual measures visible, record the population and partition used, and explain which deviations are acceptable.
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Foreign-key coverage
For every child record, verify that the referenced parent exists in the permitted parent population. Measure coverage, not just whether a constraint was declared. Also check cardinality: if each account may have one current profile, multiple generated profiles indicate a different defect from a missing parent.
Cross-table business rules
Some rules cannot be expressed as a single-column expectation. Compare order totals with payment totals, ensure account balances reconcile with ledger entries, and test that category totals add to the overall amount. Use custom SQL or an equivalent cross-table validation when a built-in check cannot represent the rule.
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Temporal and sequential behavior
Check that events follow valid order, timestamps use the intended timezone, and intervals are plausible for the use case. A 2024 review by Vallevik and colleagues found that sequential-logic detection was scarce in existing synthetic-data evaluation frameworks. Add explicit sequence tests for workflows, event streams, and longitudinal records instead of assuming that distribution similarity proves temporal realism.
Reconciliation: go beyond row counts
Reconciliation compares independent measurements at pipeline stages. Counts can match while values are wrong, duplicated, or shifted between categories. At minimum, reconcile:
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- Sums of additive measures such as amounts, quantities, and balances.
- Category totals and subtotals by key dimensions.
- Minimum and maximum dates and other boundary values.
- Foreign-key coverage and source-target join results.
- Checksums or deterministic fingerprints when a stable row representation exists.
Use the same inclusion rules, rounding, currency, timezone, and null handling at both sides. A one-cent difference can be a rounding policy; a large delta may indicate a dropped partition or duplicate load. Record expected and observed values together so the failure is diagnosable.
Cross-table amount matching
For a synthetic order and payment dataset, group both tables by the reconciliation key, sum amounts using the same precision, and compare the results. Investigate keys present on only one side separately from keys whose totals differ. This distinguishes missing relationships from incorrect values and supports targeted repair.
Freshness and arrival checks
Validate the last-update timestamp and the arrival service-level agreement for every expected batch. A complete but stale dataset is not fit for a time-sensitive output. Alert when a partition is late or absent, and hold dependent reports, models, or test environments until the freshness policy is met or an approved exception is recorded.
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Great Expectations
Great Expectations organizes checks with batches, expectation suites, validation definitions, and validation results. Its documentation summarizes the workflow as: “Validate your Expectations against your data and explore the results.” Use built-in expectations for schema, volume, uniqueness, and common integrity rules. Add custom SQL expectations when a rule spans tables or cannot be expressed by a built-in expectation, such as matching financial totals across source and target.
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Declare supported constraints in dbt models and test them in development, pull requests, and production after refreshes. Because enforcement varies by warehouse, document whether each constraint blocks table creation, produces a test failure, or is informational. Pair failures with alerts, ownership, and a remediation procedure so a red check does not become ignored noise.
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Custom SQL
Custom SQL is appropriate for multi-column sums, cross-table joins, conditional rules, and reconciliation logic. Keep queries versioned with the model, expose observed and expected values in the result, and include a stable batch or partition identifier. Compare tools on rule expressiveness, cross-table capability, enforcement versus warning behavior, batch support, execution cost, CI/CD integration, alerting, lineage, result detail, and exception documentation.
Where checks belong in the pipeline
- Ingestion: verify file or table presence, schema version, encoding, and basic row count.
- Generation output: apply not-null, accepted-value, range, key, and duplicate-entity checks before transformation.
- Staging: validate partition counts, null rates, and type coercions.
- Transformation: run cross-column rules, foreign-key coverage, temporal ordering, and business calculations.
- Target publication: reconcile counts, sums, balances, category totals, and checksums against the prior stage.
- Post-refresh monitoring: evaluate freshness, distribution drift, utility metrics, privacy controls, and fairness slices.
Run high-impact uniqueness and non-null checks first, then layer accepted values and referential integrity. Execute expensive distribution and cross-table checks after structural checks pass. Keep results linked to code, schema, generator, and batch versions.
Failure handling runbook
- Record the failing rule, batch identifier, partition, observed value, expected value, and code and schema versions.
- Classify the cause as generator defect, transformation defect, load defect, or legitimate business change.
- Quarantine invalid rows or block downstream publication for high-severity failures.
- Preserve unexpected rows, join exceptions, and aggregate deltas for debugging.
- Fix the upstream cause and rerun the affected batch rather than patching only the target.
- Document any approved exception with an owner, rationale, scope, and expiry date.
Do not silently widen a threshold to make a failure disappear. If the business rule changed, update the contract, version it, and communicate the new gate.
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Synthetic-data fairness, privacy, and governance
A valid synthetic dataset can still be unsafe or unfit. Compare utility by relevant population slices rather than only in aggregate. Check whether minority categories, rare combinations, and outcome relationships are preserved enough for the stated use. Conversely, inspect whether generated records are too close to source records, which can create privacy exposure.
The 2024 review by Vallevik and colleagues reported that existing frameworks often emphasize statistical similarity while fairness and carbon-footprint dimensions remain scarce. Add fairness measures that match the decisions or models the data will support, document the protected or vulnerable groups considered, and record the environmental cost of generation when it matters to governance. For sequential data, test event order and transition behavior explicitly because sequence evaluation is often missing from generic scorecards.
Define release gates
- Hard gates: schema, mandatory fields, key integrity, forbidden values, critical foreign keys, and reconciliation mismatches that make downstream results unreliable.
- Warnings: moderate distribution drift, unusual but explainable volume, or a noncritical category shift.
- Monitoring metrics: long-term utility, fairness, privacy exposure, carbon footprint, and trend changes that require review rather than immediate blocking.
Record the intended use, allowed users, retention period, generator version, source specification, and approved exceptions with the validation results.
Quick Recap
Practical checklist before release
- The dataset grain and schema version are documented.
- Required fields, accepted values, ranges, and null policies pass.
- Single-column and compound keys are unique at the required grain.
- Foreign-key coverage, cardinality, cross-column rules, and temporal ordering pass.
- Overall and partitioned counts match their range, exact, or source-target contract.
- Sums, balances, category totals, dates, and checksums reconcile where applicable.
- Freshness meets the arrival service-level agreement.
- Distributions, correlations, and sequence behavior meet use-case tolerances.
- Fairness, privacy, and other synthetic-data risk checks are recorded.
- Failures have owners, severity, preserved evidence, and documented remediation.
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