FAQ

How Do You Migrate Data Off a Legacy Platform to Custom Software?

SaaS Displacement

Key Takeaways
  • PRJ-01 was designed from the start with a multi-source import system.
  • For any business migrating off a multi-vendor SaaS stack, the sequence follows a pattern:

You migrate data off a legacy platform by building an import layer that can ingest data from each source in its native format -- CSV exports, API pulls, webhook feeds -- then mapping that data into a unified schema in the new system. In a documented migration, 12 inbound data sources were consolidated into a single platform with 135 database tables, resolving 958,937 contact points from 616,543 leads into one unified identity system (portal_stealth_locked_values, February 2026).

The real problem is not moving data -- it is cleaning it

Every platform stores data differently. Field names vary. Formats are inconsistent. Duplicates exist across systems because each vendor tracked the same customer independently. The migration is not a simple copy-paste. It is a reconciliation.

According to a 2023 Gartner estimate, poor data quality costs organizations an average of $12.9 million per year. When data lives in six separate platforms -- CRM, affiliate tracking, email, marketing automation, social management, and phone system -- the inconsistencies multiply. Each vendor has its own customer ID, its own activity log, and its own version of the truth.

How the documented migration worked

PRJ-01 was designed from the start with a multi-source import system. Rather than migrating all data in one bulk operation, the platform ingests data through 12 inbound sources and normalizes everything into a unified identity model (portal_stealth_locked_values, audited):

Inbound sources (12): Konnektive, Shopify, WooCommerce, Everflow, CAKE, Zapier, Waypoint, BobGo, Klaviyo, Dripcel, Stripe (payment events), and CSV upload.

What happens on import:

  1. Ingestion: Data arrives via webhook, API call, or CSV upload -- each source has a dedicated adapter that normalizes the raw data into the platform's internal format.
  2. Identity resolution: The system runs a three-tier matching process (unique ID, then email, then phone) to determine whether an incoming record is a new lead or an existing one. This prevents duplicate records from multiplying across the migration.
  3. Contact point consolidation: Each email address, phone number, and identifier gets linked to a single lead profile. In the documented system, 958,937 contact points were resolved against 616,543 lead profiles -- meaning the average lead had data from 1.5 sources that needed consolidation.
  4. Enrichment: After identity resolution, profiles are enriched with demographic and behavioral data, producing a complete record rather than fragments spread across six dashboards.

The practical migration sequence

For any business migrating off a multi-vendor SaaS stack, the sequence follows a pattern:

Phase 1: Run parallel. Keep the old platforms running. Begin ingesting new data into the custom system. This lets you validate that the new system is capturing everything correctly without shutting anything down.

Phase 2: Backfill historical data. Export CSV files from each legacy platform and import them through the custom system's import layer. The documented platform processes CSV uploads with auto-column mapping, handling format variations across source systems.

Phase 3: Verify and reconcile. Compare lead counts, transaction totals, and attribution data between the old platforms and the new unified system. In the documented build, 31 analytics dimensions were consolidated into a single view, replacing the six separate dashboards that previously had to be manually reconciled (CS10).

Phase 4: Cut over. Redirect all inbound data sources (webhooks, API integrations, e-commerce events) to the new platform. Deactivate the old SaaS subscriptions.

In the documented case, the SaaS subscriptions went from $1,565/month across six platforms to $0/month after the migration was complete (28_month_financial, QB-verified February 2026).

Related: How long does it take to build a SaaS replacement? | What does it cost per month to run custom-built software after launch?

References

  1. Gartner (2023). "Data Quality Market Survey." Cost of poor data quality benchmarks ($12.9M average annual impact).
  2. Keating, M.G. (2026). "Case Study: The Platform Displacement." Stealth Labz. Read case study