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A defensible migration does more than produce the same row count. Define what must survive, capture a source baseline, version every transformation, reconcile the target with checks appropriate to the data, and preserve time-correlated evidence that remains readable after the legacy platform is retired. Set acceptance thresholds for your own risk, policy and legal obligations; no universal error rate or retention period applies to every migration.
What “data integrity” must cover
Integrity is the confidence that records are complete, accurate for their intended use, unaltered except through authorized processing, and still understandable in context. A migration can copy every value and still damage a record set by dropping metadata, breaking relationships, changing time meaning, losing provenance or making the format impossible to interpret later.
U.S. federal records rules illustrate the breadth of the concept. 36 CFR § 1236.10 identifies reliability, authenticity, integrity, usability, content, context and structure as records-management concerns; it describes integrity controls such as audit trails that ensure records are complete and unaltered. Those rules apply to federal agencies, not automatically to private organizations, but they are a useful design test.
| Integrity question | Evidence to seek | What a passing result does not prove |
|---|---|---|
| Completeness | Counts, control totals, key-range coverage, relationship counts and exception reconciliation | That every value was mapped to the right field |
| Accuracy | Field comparisons, domain checks, business-rule tests and approved transformation results | That omitted records were discovered if the scope was defined incorrectly |
| Authenticity and provenance | Source manifests, actor or service identity, chain-of-custody events and versioned mappings | That a trusted actor made a semantically correct decision |
| Context and structure | Metadata, schema, relationships, code lists, units, time-zone rules and interpretive documentation | That future software will natively read the format |
| Usability after retirement | Retrieval tests, readable exports, retained documentation and documented conversion history | That users can infer undocumented business meaning |
Set scope and acceptance criteria before extraction
Inventory records and dependencies
List every source dataset, record class, table or file, including archives, attachments, indexes, reference data and audit logs. Identify the business owner, authoritative fields, primary and foreign keys, parent-child relationships, calculated values, units, code sets, date and time semantics, formats, system dependencies and known defects. Include interfaces that will continue to send or receive data during the transition.
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Map each record class to its retention schedule, legal holds, access restrictions and intended use in the destination. If a field is being retired, document why and where its meaning is preserved. A data dictionary should explain abbreviations, status codes, null meanings, encoding, rounding and whether a value is entered, derived or imported.
Decide what stops release
Write measurable acceptance criteria before anyone sees the migration results. Examples include zero unexplained missing records in a critical class, no duplicate values for a key that must be unique, all foreign keys resolving, and all financial or operational control totals reconciling within an owner-approved tolerance. Define which discrepancies require a rerun, which can be corrected manually, who may approve an exception and what evidence is retained for that decision.
Do not import a threshold from another project. Criticality, business purpose, transformation complexity, applicable contracts and law determine tolerances, sampling depth, recovery choices and retention duration. NIST SP 800-53 is a control catalog rather than an automatically binding rule for every organization. Check your sector requirements and internal policy.
Assign accountable roles
- Data owner: defines intended use and accepts residual discrepancies.
- Migration lead: controls the plan, mappings, batches and reruns.
- Control or records officer: verifies retention, legal holds, metadata and disposition requirements.
- Independent reviewer: examines evidence and approvals rather than approving their own work.
- System and security administrators: protect source, tooling, target and evidence stores and manage clock synchronization.
Capture a source baseline you can defend
Take a read-consistent snapshot or otherwise record exactly what the extraction represents. Preserve the extraction manifest and a copy of the mapping specification in a protected location. At minimum, record:
- Snapshot date and time, time zone or clock basis, extraction method and software version.
- Dataset and record-class scope, schema or file-format version, and source-system identifier.
- Total records and, where useful, counts by partition, status, date range, owner and key distribution.
- Control totals for financial amounts, quantities, balances or other domain measures.
- Null, duplicate and referential-integrity findings already present in the source.
- Known source defects, excluded records, legal holds and concurrent changes.
- Cryptographic digests or other manifest identifiers for unchanged files and exported packages.
A baseline distinguishes a pre-existing defect from a migration defect. If the source contains duplicate customer numbers, for example, the destination should preserve the documented condition or apply an approved remediation—not silently “fix” it without a traceable decision.
Control mappings, transformations and provenance
Version every rule that can change a result
Store approved versions of field mappings, conversion code, configuration, lookup tables, reference-data snapshots, unit conversions, character-set rules and date/time logic. Record the effective version used for each batch. A mapping that says “status” becomes “state” is not enough when codes, defaults or historical meanings also change; document the complete rule and its owner.
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Log processing at batch and record level
For each extraction, transfer, transformation, load, validation and rerun, capture the batch or source range, actor or service identity, start and completion times, tool and configuration version, outcome, exception count and links to input and output manifests. For high-value or regulated records, retain a record-level lineage identifier that connects the destination record to its source key and transformation version.
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Never overwrite source evidence
Keep the original extract, its digest, the transformation inputs and the resulting exception list under access control. Corrections should produce a new version or linked remediation event. An operator who changes a destination value should not be able to erase the prior value, reason, approval and timestamp.
Reconcile the destination with tests suited to the data
Run the same checks during rehearsal and production so that a result is comparable. Record the method, population, query or script version, execution time, result, discrepancies, disposition and retest evidence.
Counts and control totals
Compare source-to-target counts by dataset, partition and important business dimensions—not only a grand total. Reconcile sums, balances, quantities or event totals using the same inclusion rules. Investigate a matching total when individual records could still be swapped or duplicated.
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Test uniqueness for primary and business keys, required-field null rates, orphaned foreign keys, parent-child counts and many-to-many link tables. Compare distributions for statuses, dates, regions and other fields whose unexpected shifts indicate a mapping or filter error.
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Domains and business rules
Validate enumerated codes against the approved reference-data version. Check units, sign conventions, decimal precision, rounding, date ranges, time zones, daylight-saving transitions, character encoding and normalization. Recalculate derived values where the destination is expected to calculate them, and compare against the source value when it is retained for historical reporting.
Record comparisons and sampling
For unchanged structures, compare normalized rows or canonical exports. For transformed structures, compare semantically equivalent fields rather than raw bytes. Use a documented sample for large populations, stratified to include old and recent records, edge dates, null-heavy records, unusual codes, high-value transactions and records touched by remediation. Sampling supplements population-level controls; it does not excuse missing scope definitions.
Checksums: useful but narrow
NIST identifies cryptographic checksums as an example for validating that information remains bound to its producer. A digest can show that an unchanged file or byte sequence was altered during transfer. It cannot show that the wrong source file was selected, that a field was mapped correctly, that a transformation retained every intended record or that a new representation has the same meaning. Hash the canonical representation agreed for the test, and document the algorithm, input and time.
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| Check | Best use | Typical failure it reveals |
|---|---|---|
| File or package digest | Unchanged exports and transfer integrity | Truncated, modified or substituted bytes |
| Row and partition counts | Population completeness | Filters, paging or failed batches |
| Control totals | Amounts, quantities and operational measures | Sign, precision, duplication or omission errors |
| Key and relationship tests | Structural integrity | Duplicates, orphan records and broken links |
| Domain and semantic comparisons | Meaning after transformation | Code, unit, time-zone and conversion errors |
Build an audit trail that works across systems
Place migration and validation events in an access-controlled evidence store separate from the system being migrated when feasible. NIST addresses audit generation, time correlation, standardized formats, retention and long-term retrieval in SP 800-53 Rev. 5. Correlate clocks across source, migration tooling, target and logging infrastructure; record the time zone or UTC basis and handle clock corrections explicitly.
Minimum event fields
- Event identifier and event type.
- Source and destination system identifiers.
- Dataset, batch, record range or lineage identifier.
- Human actor or service identity and authorization context.
- Action and transfer purpose.
- Timestamp, time zone or synchronized-clock basis.
- Tool, code, schema and configuration versions.
- Input and output manifest identifiers or digests.
- Validation checks executed, population and result.
- Exception identifiers, remediation or rerun links.
- Reviewer and approval identity, decision and time.
- Retention classification, access restrictions and disposition status.
Use a standardized, exportable format so events from different systems can be correlated and searched. Protect logging configuration and evidence from the operators whose actions are being recorded. Define what happens when logging fails or storage is exhausted: fail the migration, pause it, queue events durably or invoke an approved emergency procedure. The response and subsequent reconciliation belong in the trail.
Example event record
{
"event_id": "mig-2026-10-17-00042",
"action": "validate_batch",
"source": "legacy-orders",
"destination": "orders-archive",
"batch": "orders-2018-Q1",
"actor": "migration-service",
"timestamp": "2026-10-17T14:22:31Z",
"tool_version": "loader-4.3.1",
"mapping_version": "orders-map-7",
"checks": ["count", "amount_total", "foreign_keys", "sample_compare"],
"result": "passed_with_exception",
"exception_ids": ["EX-1182"],
"reviewer": "records-reviewer-2",
"approval": "approved_after_remediation"
}Run a rehearsal, cut over, and verify recovery
- Load a representative rehearsal set. Include ordinary, historical, boundary, malformed, null-heavy and high-value records, plus attachments and linked metadata.
- Execute the production reconciliation suite. Use the pre-approved queries, scripts and acceptance criteria; do not substitute an easier check after seeing results.
- Investigate every exception. Classify it as source defect, mapping defect, tool failure, expected transformation or approved exclusion. Correct the cause, rerun the affected scope and link both attempts.
- Test retrieval and interpretation. Have an authorized user locate a record, follow its relationships, read its metadata and understand its historical meaning without undocumented legacy knowledge.
- Approve the release. Obtain sign-off from the accountable owner and independent reviewer against the recorded criteria. Record unresolved risk, compensating controls and rollback authority.
- Execute production cutover. Freeze or coordinate source changes as planned, capture the final baseline, run the same controls and preserve the cutover decision and timing.
- Perform post-cutover reconciliation. Check late-arriving changes, interfaces, permissions, reports, indexes and audit-event continuity. Keep the source available for the defined recovery period rather than deleting it immediately.
A rollback or recovery plan should specify the trigger, decision maker, data needed to restore service, treatment of records created after the migration point and how the recovery itself is logged. A technically successful load is not complete until users can retrieve both records and their evidence.
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Make records readable after the legacy platform is gone
Before retirement, prove that required records, metadata, relationships, audit logs and documentation can be retrieved without undocumented software, unavailable credentials or a running legacy database. Preserve the data dictionary, schema and code lists alongside the records, not in a location that will disappear with the old application.
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- Choose durable, documented export formats and retain the conversion specification.
- Keep the relationship between each record, its metadata, attachments, audit events and source identifier.
- Retain a readable copy of reference data needed to interpret historical codes.
- Document any format conversion, loss of display behavior or change in calculated fields.
- Test retrieval on the retained platform or reader, including a record from each class and period.
- Maintain retained reading equipment or a supported conversion path when required by the format.
- Store evidence under the approved retention schedule, legal holds and access controls.
NIST SP 800-53 AU-11 states: “Retain audit records for [Assignment: organization-defined time period consistent with records retention policy] to provide support for after-the-fact investigations of incidents and to meet regulatory and organizational information retention requirements.” The bracketed assignment is deliberate: set the period through your records policy and obligations, not a universal number.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose migration tooling by control capability
No single product is established by the available standards as the required choice. Evaluate tools against the evidence you must produce:
| Capability | Questions to ask |
|---|---|
| Source and target coverage | Can it read every record class, attachment and historical partition, and export results independently? |
| Transformation validation | Can it test field mappings, domains, units, time zones, keys and referential integrity? |
| Metadata and lineage | Can it preserve relationships and link each destination record to its source and mapping version? |
| Evidence protection | Can it produce exportable, tamper-evident or access-controlled manifests and logs? |
| Correlation | Can events use synchronized timestamps and a common event or batch identifier? |
| Exceptions and replay | Can failed ranges be isolated, corrected and rerun without silently duplicating successful data? |
| Separation of duties | Can operators, reviewers and approvers have distinct permissions? |
| Long-term retrieval | Can evidence be exported in documented formats and read after the product is retired? |
Treat vendor-generated “success” status as one signal, not acceptance evidence. Your independent counts, semantic checks, exception disposition and retrieval test establish whether the migration meets its purpose.
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Matching row counts but missing meaning
Symptom: totals match, but status codes, units, time zones, attachments or relationships do not. Recovery: compare metadata and semantic rules, restore the required context, rerun affected transformations and document any approved limitation.
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Checksums pass while mappings are wrong
Symptom: destination files have the expected digest, yet values are in the wrong columns or records came from the wrong export. Recovery: verify the source manifest and scope, then run field-level, key and business-rule comparisons. A digest cannot answer a semantic question.
Audit logs cannot be correlated
Symptom: source, tool and target show conflicting times or incompatible identifiers. Recovery: synchronize clocks, record the clock basis, add a shared batch or event ID and preserve the reconciliation of previously captured timestamps.
Exceptions are fixed without history
Symptom: a corrected record has no original value, reason or approver. Recovery: restore the source evidence, create linked remediation events, restrict direct edits and rerun validation with an independent review.
Retirement removes the reader
Symptom: files exist but staff cannot interpret codes, attachments or audit events. Recovery: retain documentation and reference data, provide a supported reader or conversion, test retrieval and delay disposition until the retention and legal-hold checks pass.
U.S. federal references and limits
The regulatory examples above are specifically U.S. federal. FAR Part 4 contains computer-data retention and transfer provisions in a federal-contractor context; contract clauses and agency instructions determine what applies. The Government Accountability Office lists Federal Information System Controls Audit Manual, GAO-26-108633, published June 29, 2026, as a revision replacing the 2024 edition: confirm the current edition before relying on it. Organizations outside federal scope should map equivalent duties from their own statutes, contracts, records schedules, privacy requirements and internal controls.
The practical controls remain the same in principle: define the record and its meaning, preserve provenance, reconcile with tests that fit the transformation, protect a correlated audit trail, and demonstrate retrieval after retirement. The evidence package—not a single checksum or row count—is what supports the integrity claim.
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