Do not release synthetic relational data after a single foreign-key query. Validate it in layers: freeze the schema and relationship contract, verify keys and constraints, test every parent-child relationship, enforce business rules, compare fidelity and utility, assess privacy, and gate publication on a versioned report. Any unexplained referential-integrity violation is a release defect.
Synthetic data is useful for testing and research, but it can omit, distort, or introduce properties that do not exist in the source. The Office for National Statistics says its production method and appropriate uses must be documented, while UK Government Digital Service guidance updated 3 August 2026 warns that synthetic data is vulnerable to weakness, bias, and omission just as real-world data is. Apply the same seriousness of evaluation you would apply to production-like data.
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What referential integrity means in synthetic data
Referential integrity means that every foreign-key value in a child table identifies the intended row in its parent table, subject to the relationship’s nullability and business rules. In generated data, this is more than checking whether two columns happen to contain matching strings. The parent population must be the correct one, composite keys must match as a set, cardinalities must be plausible, and rows must obey the same edit rules used to validate the source data.
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Freeze the validation contract before generating data
Validation is reproducible only when the expected state is versioned before the release candidate is created. Store these artifacts together:
- The source schema version, including table and column names, data types, lengths, precision, primary keys, unique constraints, nullability, and foreign-key actions.
- A data dictionary defining allowed domains, units, formats, and semantic meaning.
- A relationship map listing every parent and child table, the columns that form each key, optionality, and intended one-to-one or one-to-many cardinality.
- The generator version, configuration, random seed or other reproducibility settings, and any transformation rules.
- Acceptance thresholds for structural checks, relationship counts, fidelity metrics, utility tests, and privacy review.
Give the contract its own version identifier and record it in every validation report. A schema change, generator change, constraint change, or threshold change should create a new validation run rather than silently overwriting the old one.
Define the population boundary
Write down which parent population a foreign key is allowed to reference. This matters in multi-tenant, regional, or time-partitioned data: an identifier can exist in the database and still be invalid for a particular child row. Validation must test membership in the intended parent population, not merely a coincidental value match.
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Validate structure before loading rows
Compare the generated structure with the frozen contract before inserting data. Reject a release candidate when it has missing columns, incompatible types, unexpected nullable changes, or extra required columns without defaults. Also verify precision and scale for numeric fields, maximum lengths for strings, and the exact columns participating in primary, unique, and foreign-key constraints.
| Structural check | What to compare | Failure to reject |
|---|---|---|
| Tables and columns | Names, presence, and order-independent identity | Missing or unexpected objects |
| Data types | Type, length, precision, and scale | Values that truncate, coerce, or compare differently |
| Nullability and defaults | Required versus optional columns and default expressions | Nulls in required fields or required columns with no load value |
| Keys and constraints | Primary keys, unique constraints, foreign-key columns, and actions on delete/update | Duplicate identifiers or relationships enforced differently from the contract |
Run these checks against the destination definition, not only against a generator configuration file. Deployment tooling can alter a column or omit a constraint even when the generator settings are correct.
Load tables in dependency order
Insert parent tables before children so that foreign-key checks operate on the complete intended population. Build a dependency graph from the relationship map and use a topological order where possible. Record the order in the job log so a failed load can be reproduced.
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Handling circular references
Circular references cannot be resolved by a simple parent-first order. Use a documented two-phase load: insert rows with a permitted temporary null or placeholder, then update the deferred foreign key after both sides exist. If the database supports deferrable constraints, defer them only inside the controlled test transaction. Temporarily disabling constraints can be appropriate for a test-only load when it is explicitly documented and followed by the complete audit suite; never publish an artifact produced with checks silently disabled.
Check primary keys, unique constraints, and required key columns
Run key checks before relationship checks. An orphan query can miss the root cause when a parent key is duplicated or a composite key is incomplete.
- Assert that every primary-key value is present when the contract requires it.
- Assert that no primary key occurs more than once.
- Assert that every declared unique constraint is unique, including combinations of columns.
- For composite keys, reject rows with only part of the key populated.
- Reject nulls in non-nullable foreign-key columns; permit null only when the relationship is explicitly optional.
SELECT parent_id, COUNT(*) AS n
FROM parent
GROUP BY parent_id
HAVING COUNT(*) > 1;Any returned row indicates a duplicate parent key. Add equivalent queries for each unique constraint and for composite keys using all key columns in the GROUP BY.
Run an orphan check for every foreign key
For each child foreign key, left join the child to the parent on the complete key and fail when a non-null child key has no matching parent. Treat returned rows as release defects unless a documented, risk-approved exception explains them.
Single-column relationship
SELECT c.child_id, c.parent_id
FROM child c
LEFT JOIN parent p ON p.parent_id = c.parent_id
WHERE c.parent_id IS NOT NULL
AND p.parent_id IS NULL;This query identifies child rows whose non-null parent_id has no parent match. Capture the count and a sample of offending values in the report, but do not replace the full check with sampling.
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SELECT c.*
FROM child c
LEFT JOIN parent p
ON p.account_id = c.account_id
AND p.region_code = c.region_code
WHERE p.account_id IS NULL;Joining on only one component can produce false matches. The parent row must match every component of the declared key. Add explicit handling for nullable composite keys according to the contract: either all components are required, or the relationship is considered absent when the optional key is null.
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Validate intended parent membership
After confirming that a key exists, check scope. For example, join a child account to the parent account and also assert the tenant, region, effective date, or partition condition required by the relationship. This catches a foreign key that points to a valid identifier belonging to another population.
Check cardinality and cross-table business rules
Foreign-key existence is necessary but not sufficient. Validate the relationship’s expected shape:
- One-to-one relationships have at most one child for each parent and do not leave required counterparts missing.
- One-to-many relationships respect documented minimum and maximum child counts.
- Optional relationships allow zero children where specified, while mandatory relationships do not.
- High-fan-out parents and empty parents are deliberately represented when the use case requires them.
Then run edit rules that involve several columns or tables. Examples include status transitions that follow an allowed sequence, an end date that is not earlier than a start date, invoice totals that equal the sum of detail rows, and child dates that fall within the parent’s active period. The Office for National Statistics describes this class of sanity and edit check as necessary because a valid-looking synthetic row can still violate the rules applied to real data.
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Exercise both valid and invalid cases
Include fixtures that should pass and fixtures that should fail: a parent with zero children, a parent at the maximum child count, a boundary date, a null optional key, a rare category, and an intentionally orphaned child. A test suite that never demonstrates a failure can be wired incorrectly. Keep the invalid fixtures outside the release dataset and assert that each relevant check catches them.
Assess fidelity and utility after integrity passes
Once structure and relationships pass, compare the generated data with the source or an approved reference range. Select metrics for the intended use case rather than applying one universal threshold; the cited guidance does not provide a single numeric cutoff.
- Compare row counts by table and by important time, tenant, or geographic slice.
- Compare marginal distributions such as null rates, numeric summaries, and category frequencies.
- Compare correlations and other dependencies that downstream analyses rely on.
- Compare relationship distributions, including child counts per parent and the frequency of optional relationships.
- Run the actual analytical, application, or test workload for which the dataset is being released and record whether it produces the expected outputs.
Keep the reference data used for these comparisons independent from the synthetic test fixtures where possible. UK Government Digital Service guidance notes that synthetic-only test configurations can pass while failing on real-world production data; independent validation data and distribution comparisons reduce that false confidence.
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Test realism at boundaries and rare cases
Average rows can look correct while edge cases are absent. Deliberately inspect nulls, rare categories, maximum field lengths, boundary dates, empty parent populations, high-fan-out parents, and records near numeric limits. Verify that these cases retain valid keys and relationships, not just plausible individual values.
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For systems that enforce formats or edit rules, apply the same sanity checks used for the real dataset. Australian guidance specifically calls out type, format, range, outlier, completeness, and diversity validation, and recommends keeping a live test dataset for ongoing monitoring. Use that labelled set to detect drift when a generator or schema changes.
Assess privacy and preserve provenance
Referential integrity does not make synthetic data safe by itself. Singapore’s Personal Data Protection Commission recommends validating format and structure against the documented data dictionary, selecting fidelity and utility metrics, and assessing re-identification risk. Perform the organisation’s disclosure or re-identification assessment before release, including checks for unusual combinations that could identify a person or household.
Retain the generator and schema versions, configuration, reproducibility settings, source-data version, validation thresholds, complete results, exceptions, and approvals. Provenance lets an investigator explain how a value was produced and reproduce a failed run without retaining more source data than necessary.
Automate the release gate
Put structural checks, key checks, orphan queries, cardinality rules, business rules, fidelity comparisons, and privacy review status into the data-generation job or CI pipeline. Every check should emit a machine-readable result containing:
- Contract, schema, and generator version identifiers.
- Pass or fail status and execution timestamp.
- Total rows examined and violation count.
- A bounded sample of offending keys for diagnosis.
- Thresholds used and links or identifiers for the supporting evidence stored in your controlled repository.
- Exception ID, approver, expiry, and risk rationale when a release is allowed to proceed with a known issue.
Fail publication when any referential-integrity check has an unexplained violation. Do not convert an error into a warning merely because the synthetic database is for testing; a test that depends on valid relationships can hide defects when its fixtures are invalid.
Monitor after release
Keep a small labelled test set and rerun the suite after every schema, generator, constraint, or relationship-map change. Separate synthetic data from live data and production credentials. NHS England recommends purpose-built test environments where possible, auditing synthetic-data use, clear environment separation, and removal of synthetic records after testing. Apply those controls to prevent test records from being mistaken for real activity.
Troubleshoot common failures
Orphans appear after a successful load
First check whether the parent load was filtered by tenant, region, or date while the child load was not. Then verify that key values were not truncated, coerced in type, normalized differently, or generated before the final parent population was known. For composite keys, inspect every component. Re-run the duplicate-parent check before changing the generator.
Only some composite relationships fail
Look for null or blank components, inconsistent casing or padding, and a generator that samples each component independently. Generate the composite key as an atomic value or derive the child key from an existing parent row, then rerun the full relationship query.
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Do not remove the relationship from the release schema. Use the documented two-phase or deferrable-constraint procedure, run all audits after the second phase, and preserve evidence that the final state satisfies the constraint.
Fidelity is acceptable but application tests fail
Check relationship distributions and business rules rather than only table-level counts. A dataset can match marginal category frequencies while missing rare combinations, valid status transitions, or the expected number of children per parent. Add the failed production-like scenario to the independent labelled test set.
Quick Recap
Production checklist
- Version the schema, data dictionary, relationship map, generator configuration, and acceptance thresholds.
- Compare destination structure and constraints with that contract.
- Load parents before children, using a documented two-phase process for cycles.
- Check primary keys, unique constraints, composite-key completeness, and required-key nulls.
- Run a complete orphan query for every single-column and composite foreign key.
- Verify intended parent scope, cardinality, optionality, and cross-table edit rules.
- Exercise boundary, rare, empty-parent, high-fan-out, and intentionally invalid cases.
- Compare counts, distributions, dependencies, and relationship shapes for the intended use case.
- Complete privacy, provenance, and environment-separation controls.
- Publish only when all violations are resolved or covered by an explicit, approved exception, then retain the report and monitor after changes.
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