Move the data, not just the rows
Migrate to Salesforce and keep every record's history intact
Most Salesforce migrations move records and quietly drop the part that matters: who changed what, when, and why. We carry the audit fields, ownership chains, and change timelines alongside the data, and reconstruct the history that won't transfer on its own, so your reports stay true and your AI agents have ground to stand on.
The history you lose isn't in the records. It's around them.
Insert a contact and the row arrives. But CreatedDate becomes today, the old owner is gone, the field history that showed a deal slipping three times is blank, and the audit trail compliance relies on starts at go-live. Standard imports stamp system fields with the load date by default. The data looks complete in a spreadsheet and is hollow the first time someone asks a question that depends on the past — last quarter's pipeline by rep, when an account went quiet, how long a case actually sat open. And unlike a bad report you can rebuild, a migration that flattened the timeline is hard to undo once people are working in the new org.
What 'history' actually means, field by field
- Audit context: CreatedDate, CreatedById, LastModifiedDate, LastModifiedById. Preservable through the 'Set Audit Fields upon Record Creation' permission (formerly 'Create Audit Fields'), but it's off by default, the source dates must be valid, and the referenced users must already exist in the target org or the write fails.
- Field History Tracking and Field Audit Trail: the row-level change log behind 'when did this stage move?' This does not travel with the record on insert. It has to be reconstructed from the source org's own history, exported before cutover.
- Ownership and territory chains: reassigning to a generic admin or integration user breaks pipeline-by-rep reporting and silently re-evaluates every sharing rule keyed on OwnerId.
- Relationships and parentage: cases tied to the right account, activities to the right contact, junction records intact. Broken lookups are the most common silent failure because the load 'succeeds' and the gaps only surface in reports.
- Derived signals reporting leans on: LastActivityDate, lead ConvertedDate, opportunity close and stage-change timestamps. These feed forecasts and SLAs, and they're the first things to quietly reset to launch day.
How we migrate without flattening the timeline
- Map before we move: a field-and-object inventory of the source that flags, per object, which historical fields exist and which can be carried versus must be reconstructed — so the gaps are decided up front, not discovered at cutover.
- Stage in a full sandbox: load, validate, and diff against source counts and totals before anything touches production, and prove the rollback path on that same stage so 'we can back out' is tested, not assumed.
- Preserve audit fields explicitly: enable audit-field write, sequence the load by dependency (users first, then accounts, then their children) so owner and CreatedById references resolve, and keep external IDs on every object so re-runs upsert instead of duplicating.
- Reconstruct what can't be carried: where Field History won't transfer, we export the source change log and load it into a custom history object, queryable in SOQL and reportable, so 'when did this change?' still has an answer after cutover.
- Reconcile, then sign off: record-count parity per object, control totals on the numbers that matter (pipeline, ARR, open cases), timeline spot-checks on a sample, and a reconciliation report you can hand to audit — not a vibe check.
Why this is the foundation, not the housekeeping
An AI agent on Agentforce is only as honest as the data underneath it. Ask an agent 'is this account at risk?' and it reasons over history — last touch, stage regressions, prior cases, time-in-stage. If the migration reset every timestamp to launch day, the agent will sound confident and be wrong, and you won't catch it until it's already told a rep the wrong thing about a live deal. This is why a migration done right pays off twice: once in reporting you can trust, again in agents you can actually deploy. It's the Agent Ready phase of our Data-to-Agent method, and it's the step everyone wants to skip because it doesn't demo well — until the org built on it does.
What you should ask any partner before they touch your data
- Will CreatedDate and the original owner survive, or reset to migration day? Get the answer in writing, per object.
- How do you handle Field History — carry it, reconstruct it into a history object, or shrug?
- What's the rollback plan, and have you run it in a sandbox before touching production?
- How do you prove parity at the end — counts, control totals, and timeline spot-checks, or just 'it loaded clean'?
- Who owns reconciliation if the numbers don't tie out — you, or us — and is that in the statement of work?
Frequently asked
Can Salesforce preserve the original CreatedDate and record owner on import?
Yes, but only if you set it up deliberately. The 'Set Audit Fields upon Record Creation' permission lets you write the original CreatedDate, CreatedById, LastModifiedDate, and LastModifiedById on insert. It's off by default, the source dates have to be valid, and the referenced users must already exist in the target org, which is why users load first. Skip the setup and every record silently stamps with go-live day.
Does Field History Tracking move with my records during migration?
No. The row-level change log behind questions like 'when did this opportunity slip?' does not transfer with the record on insert. If that timeline matters for reporting or compliance, it has to be exported from the source org before cutover and loaded into a custom history object you can query and report on. Plan for this explicitly, or you go live with a blank past.
How do you prove nothing was lost after the migration?
Reconciliation, not faith. We diff record counts per object and control totals against the source on the numbers that matter — pipeline, ARR, open cases — spot-check preserved timelines on a sample, and verify relationships resolved correctly. You get a reconciliation report you can hand to audit. If the numbers don't tie out, we find the gap before sign-off, not after go-live.
Why does migration history matter if we're moving to AI agents anyway?
Because the agents reason over that history. An Agentforce agent answering 'is this deal at risk?' reads stage changes, last activity, and prior cases. If the migration reset all of it to launch day, the agent gives confident, wrong answers and nobody flags it. Preserving history is what makes an agent deployable rather than dangerous, which is why we treat it as Agent Ready, the first phase of Data-to-Agent.
How long does a Salesforce migration with history preservation take?
It depends on object count, data volume, and how much history must be reconstructed versus carried. The load itself is rarely the long pole — mapping, reconstruction, and reconciliation are. We scope against your actual source schema instead of quoting a generic timeline, so the estimate reflects your data rather than an average, and you know before we start which history is preserved and which is rebuilt.
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