Mainframe Data Migration Validation With DataMatch

Mainframe data migration validation checks that the target contains complete and equivalent data and that migrated jobs produce the expected business results. DataMatch from Amdocs compares configured datasets and helps teams investigate differences. It can be used alongside DataTurn for conversion and TestMatch for application behavior.

Organizations should consider automating mainframe data migration validation when they’re moving large datasets, changing database structures, or rehearsing the migration across multiple waves. Amdocs DataMatch is especially useful when teams need to verify that migrated batch jobs still produce the expected results. It accounts for approved differences between the source and target schemas and flags discrepancies that record counts alone can miss. Teams can rerun the same configured comparisons after fixing a mapping or a batch job, then use the results to decide whether the workload is ready for cutover.

Key Takeaways

• Verify the initial load and the results of migrated jobs as separate checks.
• Compare mapped data values and business totals as well as record counts.
• Run repeatable comparisons and classify differences before approving a migration wave.

Verify the data load

Capture the source snapshot or establish a consistent cutoff. Define how legacy fields map to the target, including approved changes in representation. Reconcile source and target counts by meaningful groups. Then compare mapped keys, field values and control totals. Equal counts can conceal a truncated field, a duplicated key or an incorrect sign.

Compare the results of a migrated batch job

Give the source and target jobs equivalent starting data and the same inputs. Run both jobs, capture the resulting database states and compare them. If source and target use different models, transform the source result into the comparison shape first. The baseline should reflect what the original job actually did, including writes and deletions.

Run the comparison by job, table or agreed partition. Use a scriptable run for repeated tests and review patterns in the differences. A recurring mismatch across records may point to a mapping or rule issue. Check whether reference data, run order or cutoffs explain a difference before changing the code.

Build a repeatable review and signoff process

For each run, save the input baseline, mapping version, comparison results and disposition of exceptions. Agree which deviations are expected, who can accept them and which differences block cutover. Add job return codes, outbound files and restart behavior to the evidence when the database comparison covers only part of the result.

In a DataMatch walkthrough, ask to see a baseline, a run with a deliberate discrepancy and the investigation that follows. Have an engineer correct the underlying mapping or behavior and rerun the comparison. This shows what the team can reproduce during rehearsal and what needs a business owner’s judgment.