CRM Data Migration and Field Mapping
A company decides to switch CRM platforms, and the migration project quickly becomes the most dreaded initiative on the ops roadmap: years of custom fields, workflow-dependent picklist values and free-text data that only made sense in the old system's context all need to map to a new schema, and a naive field-by-field export/import either drops data that doesn't have an obvious home or maps it incorrectly in ways nobody notices until a report comes back wrong months later. Teams often end up either scoping down what actually migrates, quietly losing historical context, or spending months of manual reconciliation work that delays the whole switch.
STARTING PRICE
From €799
Complex tier · Multi-system orchestration, custom logic, and higher-volume or higher-risk processing.
Get a quote →Saves roughly Weeks of manual reconciliation avoided per migration project.
How the automation works
We build an AI-assisted mapping layer that analyzes both the source and destination schemas, proposes field mappings based on field type, naming similarity and actual data patterns (not just label matching), and flags anything without a confident destination home for a human decision rather than dropping it silently. Custom fields with business-specific meaning get explicit review rather than being auto-mapped to the nearest similarly-named field, and the migration runs with validation checks at each stage so a mapping error surfaces before it's buried in ten thousand migrated records.
Process flow
- 01
Audit source schema and data trigger
The full source CRM schema is inventoried, including custom fields, picklist values actually in use versus defined-but-unused, and data quality patterns that will affect mapping decisions.
- 02
Propose field mappings ai
Source fields are matched to destination schema fields using type compatibility, naming similarity and sampled data pattern analysis, producing a confidence-scored mapping proposal rather than a rigid one-to-one guess.
- 03
Human review of ambiguous fields output
Custom fields, low-confidence matches and anything with business-specific meaning route to a human reviewer for an explicit mapping decision instead of being auto-mapped to the nearest match.
- 04
Migrate with validation checks integration
Data moves in staged batches with validation checks comparing record counts, key field distributions and relationship integrity (contacts still linked to the right accounts) between source and destination at each stage.
- 05
Reconciliation report output
A final reconciliation report compares source and destination record counts and flags any discrepancy, giving the team a concrete list to check rather than trusting the migration blindly.
Inputs
- Source CRM full schema and data export
- Destination CRM schema
- Custom field business context
- Human mapping decisions for ambiguous fields
Outputs
- Confidence-scored field mapping proposal
- Migrated records in destination CRM
- Relationship integrity validation report
- Final reconciliation report with discrepancy list
Works with
Prefer a fully custom build instead of an off-the-shelf integration? We scope both options during your free consultation — most jobs like this one work fine on standard connectors, but higher-volume or non-standard systems sometimes need bespoke API work, reflected in the complex tier.
Where this goes wrong if you get it wrong
- Picklist values that look identical between systems can carry different business meaning built up over years of workflow automation in the source system — a stage called 'Negotiation' in the old CRM might trigger specific downstream automations that don't exist in the new one, so a literal value-to-value mapping can silently strip business logic that depended on that exact label, not just move the data.
- Free-text fields that were used inconsistently over the years (a 'notes' field that sometimes held structured data reps typed as a workaround for a missing proper field) don't map cleanly to a structured destination field, and force-mapping them loses the structure that existed informally, while leaving them as free text loses the ability to report on that data going forward — this genuinely needs a human decision on a field-by-field basis, not a blanket rule.
- Relationship integrity between objects (which contacts belong to which accounts, which activities are linked to which opportunities) is the single most common silent failure point in CRM migrations — a migration that validates field values but not relationship links can produce a destination CRM that looks complete at a glance while actually having orphaned or misattributed records that only surface as a problem months later.
- Migrating historical closed deals with the same rigor as active pipeline is often unnecessary and expensive — treating every historical record as requiring full field-level validation slows the migration and inflates cost for data that mostly needs to exist for reference, not to drive live workflows; scoping validation depth by record recency and business criticality keeps the project proportionate.
Frequently asked questions
Will this preserve custom fields and workflow-dependent data?
Custom fields and anything tied to business-specific workflow logic get explicit human review during mapping rather than being auto-mapped to the nearest similarly-named field, specifically to avoid silently stripping meaning that mattered in the source system.
How do you handle data that doesn't have an obvious destination field?
It's flagged for a human decision — whether to create a new field, map to an existing one with a caveat, or intentionally leave it as historical reference data — rather than being dropped silently during migration.
What happens if the migration reveals a mapping error after data has moved?
Staged batches with validation checks at each step are designed to surface mapping errors early, and the final reconciliation report gives a concrete discrepancy list to review before the migration is considered complete.
How long does a typical CRM migration take with this approach?
It depends heavily on schema complexity and data volume — a full migration with schema audit, mapping review and staged validation for a mid-sized CRM typically runs 4-8 weeks from kickoff.