Salesforce Migration for SaaS Leaders: The Data Cleanliness Framework That Prevents a New Org From Inheriting Old Problems

Most Salesforce migration failures are not migration failures. They are data quality failures that the migration makes visible. The old org had duplicate records, inconsistent field values, and orphaned contacts attached to inactive accounts. The migration process copies all of it faithfully into the new org — at scale.

A Salesforce migration for mid-market SaaS leaders who want a genuinely clean start requires a data cleanliness framework that runs before migration begins, not alongside it.

The Four-Phase Data Cleanliness Framework for Salesforce Migration

Phase 1: Object-Level Inventory (Pre-Migration)

Before any data moves, run a full inventory of every Salesforce object that will be migrated: number of records, percentage of records with required fields populated, number of duplicate records by matching rule, field population rate for the top 20 fields by report usage frequency.

This inventory tells you where the data quality problems are concentrated. It also tells you which cleanup work produces the highest downstream impact per hour of effort. Prioritize the cleanup based on the inventory, not on intuition.

Phase 2: Duplicate Resolution (Pre-Migration)

Duplicate records in Salesforce fall into three categories: true duplicates that represent the same entity with different creation dates, near-duplicates that represent the same entity with slight field variation, and phantom duplicates that represent what looks like the same entity but are actually different contacts at the same company.

Each category requires a different resolution approach. True duplicates: merge and preserve the record with the most complete field data and the oldest creation date. Near-duplicates: standardize the differing fields against the more authoritative source, then merge. Phantom duplicates: review against external data sources before merging — these are the false positives that create merge errors.

Phase 3: Field Value Standardization (Pre-Migration)

Picklist field values that were created without governance accumulate over three to five years into a semantic mess. Industry: 'Technology' and 'Tech' and 'Software' as separate values. Lead Source: 'Web' and 'Website' and 'Organic Web' as separate values. These variations produce fragmented reports and unreliable segmentation.

Standardize all picklist values against a defined taxonomy before migration. This is also the right moment to deprecate picklist values that haven't been used in more than 12 months.

Phase 4: Record Validation and Writeback Testing (Post-Migration)

After migration, run a validation pass that compares record counts, required field population rates, and duplicate rates between the old org and the new org. Any metric that is worse in the new org than in the old org represents a migration process failure that needs to be investigated before the old org is decommissioned.

If your current Salesforce migration plan doesn't include this four-phase framework, TeraQuint can run a pre-migration data cleanliness assessment that identifies the cleanup scope before any data is moved.

Sudhanshu Gupta | Former Salesforce Technical Consultant | TeraQuint INC