A data migration can finish with the same number of records in the source and target systems and still contain serious quality problems.
Matching totals may confirm that records moved, but they do not prove that fields were mapped correctly, values were preserved, duplicates were handled properly, identifiers stayed connected or exceptions were resolved.
A stronger migration-control process therefore looks beyond record counts and validates what happened inside the records themselves.
Record Counts Are Useful, but They Are Only the First Check
Record-count reconciliation is important because it helps answer a basic question:
But even when the answer is yes, several problems can still exist.
- Fields may be mapped to the wrong destination
- Values may be truncated
- Date formats may change incorrectly
- Leading zeros may disappear
- Duplicate records may be introduced
- Existing records may be overwritten incorrectly
- Source-to-target relationships may be broken
- Blank or null values may be handled inconsistently
1. Validate Field Mapping, Not Just Record Movement
A migration workflow should define how each source field maps to the target structure before data is moved.
For example:
| Source Field | Target Field | Validation Question |
|---|---|---|
| Customer_ID | Account_ID | Was the identifier preserved correctly? |
| Company_Name | Organization_Name | Was the full value transferred? |
| Phone | Primary_Phone | Was formatting changed according to the approved rule? |
| Created_Date | Record_Date | Was the date format converted correctly? |
A matching row count cannot reveal a field-mapping error.
For mixed source formats, organizations may use data conversion services to prepare records for a target structure before migration.
2. Check Data Types and Formats
A value can move successfully and still become unusable if its format changes incorrectly.
Common examples include:
- Dates interpreted in the wrong format
- Numbers converted to text
- Text values converted to numbers
- Leading zeros removed from identifiers
- Decimal precision changed
- Special characters lost
- Phone formats altered inconsistently
These errors may not change the total number of records, but they can affect downstream processing.
3. Compare Critical Fields Between Source and Target
A stronger migration review compares selected critical fields at record level.
Depending on the dataset, this may include:
- Unique identifiers
- Names
- Dates
- Amounts
- Status fields
- Category codes
- Reference numbers
- Relationship identifiers
The objective is not necessarily to manually compare every field in every record. The validation design should focus on the fields most important to the client-defined workflow.
4. Duplicate Review Should Be Part of Migration Validation
Migration can introduce or expose duplicate records.
This can happen when:
- Multiple source systems contain the same entity
- Historical files overlap
- Identifiers are inconsistent
- Import batches are repeated
- Existing target records are not matched correctly
A duplicate review workflow should therefore be included where appropriate.
For a deeper explanation, see our guide: Duplicate Records Are Not Always Exact Copies.
5. Reconcile Exceptions Separately
Some records may fail, partially load or require manual review.
A controlled migration should keep those records visible.
Identify the original population expected to migrate.
Records that meet the defined source-to-target validation criteria.
Records with missing fields, format problems, mapping conflicts or other issues.
Items requiring clarification or a client-controlled decision.
Confirm that the source population is fully accounted for across all statuses.
6. Validate Parent-Child and Related Records
Some datasets contain relationships between records.
Examples may include:
- Customer and transaction records
- Company and location records
- Product and variant records
- Document and attachment records
- Account and contact records
The records may all be present after migration, but the relationships between them may be broken.
Where relationships matter, validation should confirm that the relevant source references remain correctly connected in the target structure.
7. Review Missing, Blank and Null Values
Blank values are another area where record-count matching can be misleading.
A migration may preserve every row but alter the way missing information is represented.
For example:
- Blank becomes zero
- Blank becomes “N/A”
- Null becomes an empty text value
- A required field becomes empty
- A default value is inserted automatically
The correct treatment should follow client-defined migration rules rather than assumptions.
8. Data Cleansing Before Migration Can Reduce Downstream Problems
Migration is often easier when existing data problems are reviewed before the move.
Potential pre-migration issues include:
- Duplicates
- Inconsistent formatting
- Invalid categories
- Missing required fields
- Outdated values
- Conflicting identifiers
Where these issues are present, data cleansing processing can help prepare the dataset before transformation or migration.
9. Use Reconciliation to Explain the Entire Migration Population
A useful migration report should explain what happened to every source record.
| Status | What It Explains |
|---|---|
| Source Records | Total population expected to migrate |
| Migrated | Records loaded into the target environment |
| Validated | Records that passed defined validation checks |
| Exceptions | Records requiring additional review |
| Rejected | Records not accepted by the target process |
| Pending | Records awaiting correction or clarification |
The goal is not simply:
A stronger control view is:
10. Migration Validation Should Be Based on Defined Rules
Different datasets require different validation criteria.
A client-defined migration plan may specify:
- Required fields
- Source-to-target maps
- Data-type rules
- Duplicate handling
- Identifier checks
- Allowed transformations
- Exception statuses
- Reconciliation requirements
This keeps routine processing consistent and prevents operators from making unsupported assumptions when source and target structures differ.
Data Migration Validation vs Data Entry Quality Control
The two processes are related, but they focus on different risks.
Data entry quality control focuses on whether values are captured correctly from source information.
Data migration validation focuses on whether existing records are transformed and moved correctly from one structure to another.
Both depend on defined rules, exception handling and reconciliation.
See our related article: Data Entry Quality Control: Why Accuracy Starts Before the First Field Is Entered.
How Outsourced Data Processing Can Support Migration Preparation
Migration projects often create large volumes of repetitive review work before and after the technical migration itself.
A structured data processing service can support administrative migration workflows such as:
- Source-data preparation
- Field mapping support
- Format normalization
- Duplicate review
- Pre-migration cleansing
- Source-to-target comparison
- Exception preparation
- Reconciliation reporting
The technical migration process and system architecture remain separate from the administrative data-processing work unless specifically scoped.
Frequently Asked Questions
Does a matching record count prove a successful data migration?
No. Matching record counts show that similar numbers of records exist in the source and target, but they do not prove that fields, formats, identifiers or relationships were migrated correctly.
What should be checked after a data migration?
Useful checks can include source-to-target field mapping, critical values, data types, duplicates, required fields, relationships, exceptions and reconciliation.
Why is reconciliation important in data migration?
Reconciliation confirms that the original source population is accounted for across migrated, validated, exception, rejected and pending statuses.
Should data be cleaned before migration?
Where duplicate, incomplete or inconsistent records exist, pre-migration cleansing can reduce avoidable downstream issues.
How should migration exceptions be handled?
Records with missing, conflicting or structurally unclear information should remain visible and be routed for review according to the client-defined migration procedure.
Final Thought: Migration Success Requires More Than Matching Totals
Record counts are useful, but they are not enough to prove that a migration produced reliable data.
A controlled migration-validation workflow should connect record counts with field mapping, data-type checks, duplicate review, relationship validation, exception management and reconciliation.
This follows the same operating principle discussed in our data entry outsourcing workflow guide: completion is more useful when the entire workload remains visible and reconcilable.
Need Support Preparing or Reviewing Data for Migration?
Global Data Entry Solutions supports structured data conversion, cleansing and administrative data-processing workflows using client-defined mappings, validation rules, exception handling and reconciliation.
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