Data migration: move your data without losing trust in it
Data migration is where system projects succeed or fail. Veratas runs migration as a disciplined workstream with documented field mapping, repeatable ETL, full reconciliation, and a validated cutover, so the data lands in the new system complete, correct, and trusted from day one.
Why migration sinks system projects
The new system is rarely what fails at go-live. The data is.
When a major ERP or CRM implementation goes badly at go-live, the cause is usually not the software. It is the data. Records arrive incomplete, balances do not match the old system, duplicate customers appear, and historical transactions are missing or wrong. Users open the new system, see numbers they do not recognise, and lose confidence in the whole project on the first morning. Confidence, once lost that way, is very hard to win back, which is why migration deserves to be run as a workstream in its own right.
The damage is rarely caused by one big mistake. It is caused by skipped discipline: source data that was never profiled, so its quality problems surfaced at cutover instead of months earlier; mapping decisions made informally and undocumented, so nobody could check them; a migration script written quickly for one test and then quietly changed before go-live, so the final run behaved differently from anything that was validated. Each shortcut is small. Together they produce a migration nobody can trust.
We treat migration as a controlled programme with its own plan, environments, and acceptance criteria. The data is profiled before mapping begins. Every transformation rule is documented and signed off. The migration is built as repeatable ETL, and the same pipeline runs every test load, the dry run, and the final cutover, so go-live behaves exactly as the rehearsals did. Nothing about the production run is a first attempt.
Single-source data migration, from $8,000
A fixed-scope migration of one source, extracted, transformed, validated, and loaded. You see exactly what the price covers, and a fair quote for anything more.
Fixed fee from $8,000 | Live in 3 to 5 weeks | Price shown up front
One source, migrated and verified
A clean, reconciled migration of one source into your target.
- Extraction from one source system
- Transformation and cleansing rules
- Load into your target system
- Validation and reconciliation
- A cutover plan and rehearsal
- Documentation and handover
Target platform or licence costs are billed separately. The $8,000 covers the implementation services.
What is in the $8,000, and what we quote separately
Anything beyond the standard package is optional, and always quoted before you commit.
| In your $8,000 | Beyond the package, quoted at a fair rate |
|---|---|
| One source system | Multiple sources |
| Transformation and cleansing | Complex transformation logic |
| Validation and reconciliation | Large historical data volumes |
| Cutover plan and rehearsal | Ongoing synchronisation |
| Documentation | Master data management |
You pay $8,000 for the standard migration. Everything else is optional, scoped and quoted transparently at a reasonable rate, and always shown before you decide.
Migration delivered as a controlled workstream
We treat data migration as a programme in its own right, with its own plan, environments, and acceptance criteria.
Mapping and profiling
Source-data profiling to expose the real state of the data, followed by documented field-level mapping. Transformation rules, default values, and exception handling are agreed and signed off before any load is built.
ETL build
Repeatable, re-runnable migration pipelines, so test loads, dry runs, and the final cutover all use the same validated process. The production run is never code that has only been executed once.
Reconciliation
Record counts, control totals, and sample-level validation after every load, with a reconciliation report reviewed and signed off before cutover. You see proof the data is complete, not an assurance.
Cutover and parallel run
A documented cutover runbook with a planned downtime window, optional parallel-run validation against the old system, clear go and no-go criteria, and a tested rollback plan.
Profiling, reconciliation, and the case for a parallel run
Three practices carry most of the risk reduction in a migration.
Source profiling is the first and most undervalued step. Before any mapping is written, we analyse the source data as it actually is: how many records exist, which fields are populated, what formats appear, where duplicates and orphaned records hide, and where values fall outside what the target system will accept. Profiling turns vague worry into a concrete remediation list while there is still time to act on it. The alternative, discovering these problems during cutover weekend, is how migrations overrun and how trust is lost.
Reconciliation is what converts a migration from an act of faith into something demonstrable. After every load we compare source and target on record counts, on control totals such as the sum of open balances, and on sampled individual records checked field by field. The results go into a reconciliation report. Before cutover, that report is reviewed and signed off by the people who own the data, so go-live proceeds on evidence the data is complete and correct, not on a hope that it is.
A parallel run adds another layer for high-stakes migrations. Both the old and new systems run together for a defined period, processing the same activity, and the two are reconciled against each other before the old system is retired. It costs time and effort, and it is not needed for every migration. But in regulated environments, or where a wrong balance has serious consequences, a parallel run is the difference between hoping the new system is right and proving it. We help you judge honestly whether your migration warrants one.
A migration you can trust
Migrations run through five phases, with the same pipeline used from the first test load through to go-live.
Profile
Source-data profiling to expose quality issues, gaps, duplicates, and true volume early, when there is time to remediate, rather than discovering them as surprises during cutover.
Map
Field-level mapping, transformation rules, default values, and a data-quality remediation plan, all documented and signed off by the people who own the source data.
Build
Migration ETL development, followed by iterative test loads into a target environment, with full reconciliation after each load so issues are found and fixed in cycles.
Validate
User acceptance testing on the migrated data, defect remediation, and a complete cutover dry run in a pre-production environment, timed end to end so the real window is known.
Cutover
The final migration over a planned downtime window, reconciliation sign-off, optional parallel run, and hypercare support while users settle into the new system.
Why clients choose Veratas for data migration
Most migration pain comes from skipping discipline. We do not skip it, and the result is a calm go-live.
Reconciled, not assumed
Every load is reconciled with record counts, control totals, and sampling. You see documented proof the data is complete and correct, rather than being asked to trust the migration blind.
Repeatable pipelines
The same migration pipeline runs every test load, the dry run, and the final cutover. Go-live behaves exactly as the rehearsals did, because the production run is validated code, not a fresh attempt.
Quality surfaced early
Source profiling exposes data-quality issues at the start of the project, when there is time and budget to remediate them, rather than during the pressure of cutover weekend.
Rollback ready
Every cutover has documented go and no-go criteria and a tested rollback plan. The decision to proceed is deliberate and reversible, even though rollback is rarely needed in practice.
FAQ
Frequently asked questions
Quick answers to questions you may have. Can't find what you're looking for? Check out our full documentation.
Migrate your data with confidence
A disciplined migration workstream is the difference between a smooth go-live and a painful one. Start with a conversation about your source systems and timeline.