Every data migration carries the same quiet risk: that records will leave the source system and arrive at the target subtly changed, incomplete, or missing altogether. A migration that runs to completion without error is not the same as a migration that has moved every record faithfully. The difference between the two is proof. This checklist sets out how to plan, test, and evidence a migration so that when you sign off, you can show that nothing was lost and nothing was corrupted.
Why migrations go wrong
Most migration failures are not dramatic. They are quiet, and they surface weeks later when a report does not reconcile or a customer record cannot be found. The common causes are well understood. Incorrect field mapping sends data into the wrong column. Truncation and type mismatches silently clip values when a target field is shorter or of a different type than the source. Character-encoding issues turn accented names and currency symbols into unreadable sequences. Records are dropped when a load fails partway, or duplicated when a job is re-run without a clean restart. Broken referential integrity leaves orphaned child records pointing at parents that never arrived. Most dangerous of all are silent transformation errors, where the logic that reshapes data during the move produces plausible but wrong results that no exception ever flags.
A migration is not done until it is proven
The governing principle is simple. A migration is complete only when you can demonstrate, with evidence, that the target holds a correct and complete copy of what the source contained. “The load finished” is a statement about a process. “Every row reconciles and every checksum matches” is a statement about the data itself, and only the second one is worth signing off against. Structured data migration testing exists to turn confidence into evidence, so that proof, not optimism, carries the project across the line.
The testing checklist
The following steps run in sequence, each one building the evidence base for sign-off. Treat every item as a gate: you do not move on until the current step has passed and its results are recorded.
- Pre-migration data profiling and cleansing. Profile the source before you move anything. Understand cardinality, null rates, formats, and outliers, and cleanse known defects at source so you are not migrating problems into a new home. Sound data quality and validation at this stage prevents a great deal of rework later.
- Row-count and checksum reconciliation. Compare record counts between source and target for every table, and go further by generating checksums or hash totals over key columns and full rows. A matching checksum is your strongest single piece of evidence that a record crossed intact.
- Field-level and sampling validation. Counts prove presence, not correctness. Validate individual field values, and take representative and edge-case samples across the full range of data, comparing them field by field against the source.
- Referential-integrity and relationship checks. Confirm that every foreign-key relationship holds after the move. Look explicitly for orphaned child records and for parents that lost their children, since these are the failures that break applications downstream.
- Transformation-logic verification. Wherever data is reshaped, recalculated, or merged in transit, test the logic against known inputs and expected outputs. This is where silent errors hide, so verify the rules independently rather than trusting that the code was written correctly.
- Performance and cutover-window testing. Run the migration at full production volume to confirm it completes inside the available cutover window. A migration that is correct but too slow to finish overnight is still a failed migration.
- A tested rollback plan. Have a documented, rehearsed way to return to the source system if reconciliation fails on the night. An untested rollback is a hope, not a plan; prove it works before you need it.
- Post-cutover business validation. Before sign-off, have the people who use the data confirm that reports, balances, and day-to-day workflows behave correctly in the target. Technical reconciliation and business acceptance together close the loop.
Make the proof checksummed and evidenced
The thread running through the checklist is evidence you can hand to an auditor or a nervous sponsor. Row counts, checksums, sampling results, and integrity reports should be captured, dated, and retained, not merely observed and forgotten. When a checksum computed over the source matches the checksum computed over the target, you have mathematical evidence that the content is identical, down to the byte. That is a far stronger position than a tester reporting that things “looked fine”. Evidenced proof also protects the project after go-live, when questions about a particular record can be answered from the reconciliation record rather than from memory.
Scaling the approach
The same discipline serves an SME moving a single CRM and an enterprise consolidating several ERP instances into a cloud warehouse. What changes is scale, not principle. Smaller projects may run the checklist manually against a handful of tables; larger ones automate reconciliation and checksum comparison so that thousands of tables can be evidenced consistently. In every case the test is the same question asked with rigour: can you prove that every record arrived, and arrived correctly? If the answer is yes, and you can show the working, the migration is genuinely done.
How Baknet can help
Baknet helps SME and enterprise teams plan and prove their migrations, building reconciliation and checksum evidence into the process so sign-off rests on proof rather than optimism. Whether you are moving a single system or consolidating many, we can help you migrate without data loss and demonstrate it. Book a consultation to discuss your migration.