Most Salesforce data migrations fail quietly. The load completes, the record counts look right, and three weeks later someone notices that every close date is wrong, the ownership history is gone, and half the accounts have two versions.
That happens because the hard part is not moving rows. It is deciding what each field means in the new system, which records deserve to survive, what order objects have to load in so relationships resolve, and how you prove afterwards that nothing was lost.
We do that work first, rehearse the migration in a full sandbox, and only then cut over. Our data migration checklist covers the same ground if you would rather run it yourself.
We profile the source system before promising anything — record volumes, field fill rates, duplicate density, orphaned relationships, and the fields people actually use versus the ones that were created and abandoned. This is what tells you whether the migration is a two-week job or a two-month one.
Every field in the legacy system is mapped, transformed or explicitly dropped, with a named owner for each decision. Picklist values get reconciled, free-text fields that were doing the job of a picklist get normalised, and anything ambiguous is escalated rather than guessed.
Duplicates are matched on rules you approve, not on a vendor default. Merge decisions are logged so they can be reversed. Addresses and formats are standardised before load, because fixing them afterwards means touching live records your team is already working in.
We build the load in a full-copy sandbox and run it end to end, in sequence, against production-scale volumes — Data Loader and the Bulk API for standard loads, ETL tooling where the transformation logic justifies it. Load order, external IDs, record types and sharing rules are all proven before anyone touches production.
Cutover runs to a written runbook with a rollback point at every stage. Where history matters, we preserve created and modified dates and record ownership rather than stamping every record with the migration date — a small detail that decides whether your reporting is usable the day after go-live.
Counts reconciled object by object, relationship integrity checked, spot-checks run by the people who use the data daily, and a signed record of what moved and what did not. Then the documentation goes to your team, so the next migration does not have to start from nothing.
Org consolidation after a merger, or splitting one org into several.
Lifecycle stages and associations remapped onto the Salesforce object model.
Customer and order data out of systems built long before CRM existed.
Entity and relationship mapping, including custom entities.
The systems nobody documented, and the files that quietly became the system.
Three things we optimise for on every migration.
Teams abandon a CRM they cannot trust. Clean, complete, correctly attributed data is what keeps them in it after week one.
Preserved history and audit fields mean your pipeline trends and ownership reports are usable immediately, not after a quarter of rebuilding.
Reconciliation at object level, with a written record of every dropped field and merged duplicate. If something did not come across, you know before your customers do.
Load order, audit fields and the failures that surface after go-live.
The seven variables that move the number, and what a low quote usually leaves out.
Implementation, integration, customisation and ongoing support around the migration.