Data Migration: Moving From SaaS to Custom Software
Your data is your most valuable asset. Here's how to move it safely, completely, and with minimal disruption.
Data migration is often the scariest part of moving from SaaS to custom software. Years of business data locked inside a platform, in formats you didn't choose, with relationships you might not fully understand. It feels like defusing a bomb.
The reality is less dramatic. With proper planning, data migration is a well-understood process with established best practices. This guide walks you through the entire process from planning to validation.
Step 1: Data Audit and Discovery
Before migrating anything, you need to understand exactly what data you have, where it lives, and how it's structured. This means cataloging every data entity in your SaaS tool: contacts, deals, transactions, documents, attachments, custom fields, relationships, and metadata.
Most SaaS tools have export functionality (CSV, API) but it's rarely comprehensive. Salesforce gives you data export, but custom objects and relationships require the API. HubSpot exports contacts and companies easily, but workflow history and engagement data are harder to extract.
The audit should also identify: data quality issues (duplicates, incomplete records, inconsistent formatting), data you don't need to migrate (test data, obsolete records), and data relationships that need to be preserved (e.g., which contacts belong to which company).
Step 2: Schema Design for the New System
Don't just replicate your SaaS data model in the new system. The SaaS tool's data model was designed for generic use cases — your custom system should have a schema designed specifically for your data and your workflows.
This is an opportunity to fix data quality issues, normalize relationships, and add structure that the SaaS tool didn't support. For example, if your CRM has customer addresses stored as free-text fields, the new system can have properly structured address fields with validation.
Design the target schema first, then create the mapping from old to new. This mapping document becomes the specification for your ETL (Extract, Transform, Load) process.
Step 3: Extract, Transform, Load (ETL)
ETL is the actual migration process: extract data from the SaaS tool, transform it to match your new schema, and load it into the new database.
Extraction usually involves a combination of: data exports (CSV/Excel) for simple entities, API calls for complex or relationship data, and sometimes direct database access (if available, as with self-hosted tools).
Transformation is where the real work happens. This includes: reformatting dates, currencies, and phone numbers; mapping old field values to new ones (e.g., SaaS status codes to your custom status values); resolving duplicates; and generating new IDs while maintaining a mapping to old IDs for reference.
Extract
Pull data from the SaaS platform via exports and API. Verify completeness against the audit.
Transform
Reformat, clean, normalize, and map data to the new schema. Handle edge cases and exceptions.
Load
Insert transformed data into the new system. Validate relationships and referential integrity.
Validate
Compare record counts, spot-check data, and verify critical business logic. No shortcuts here.
Step 4: Validation and Cutover
Validation is the most important step and the one most often rushed. Before cutover, verify: record counts match (or differences are explained), a random sample of records matches the source exactly, all relationships are preserved, calculated fields and aggregations produce the same results, and critical business workflows function correctly with the migrated data.
The cutover itself should be planned to minimize disruption. Options include: big-bang (everything migrates at once, usually over a weekend), phased (migrate by department or data type over weeks), and parallel running (both systems run simultaneously for a validation period).
For most businesses, a weekend big-bang cutover works well. Export the final delta of data on Friday evening, run the migration Saturday, validate Sunday, and start on the new system Monday morning. Keep the old SaaS tool accessible (read-only) for 30-90 days as a reference.
Frequently Asked Questions
How long does a typical data migration take?
Will we lose any data during migration?
Can we keep using the SaaS tool during migration?
What if we need to rollback?
Planning a Data Migration?
We've migrated data from Salesforce, HubSpot, Airtable, and dozens of other platforms. Book a free assessment and we'll map out your migration plan.