Data quality is deteriorating. Timelines have slipped twice. Your underwriters have stopped trusting the migrated data. The board wants answers. You need a rescue plan – not another restart.
Endorsement chains are breaking during UAT. The third endorsement references terms from the first, but the migration tool treats each as an independent record. Reconciliation errors compound downstream – one broken chain poisons every subsequent endorsement. Your underwriters have gone back to their spreadsheets because the migrated data can't be trusted for risk decisions.
In the Major Insurer programme (2016-2019), endorsement chain reconciliation failures were the single largest source of data quality defects. The migration consumed three years and eight figures because the fundamental problem – treating migration as data movement rather than business logic preservation – was never addressed.
Every time a validation issue surfaces, the timeline extends. The SI adds resources. Costs compound. But the fundamental problem – the migration tool doesn't understand your data model – isn't being addressed. You're throwing people at a tooling problem. We've seen this pattern: the SI's generic ETL approach works for the first 70% of records, then stalls on the complex specialty classes that carry the most premium.
Timeline overruns on failed insurance migrations are common because the complexity is non-linear. The last 30% of a book – the endorsement-heavy, multi-year, bespoke policies – takes longer than the first 70%.
They've been asked to validate migrated data three times now. Each time, they found errors. They've lost confidence in the programme and are actively resistant. The compliance team is raising concerns about regulatory reporting during the transition. The board wants to know if they should kill the project.
Underwriter cooperation is the single largest non-technical risk factor in insurance migrations. Once lost, it takes evidence – not promises – to rebuild.
The original SI engagement consumed the approved budget. Requesting more funding means going back to the board with a failure report. But abandoning the migration means staying on a legacy PAS that's costing the business in underwriter productivity, regulatory risk, and broker relationships. You're stuck between a failed programme and a platform you can't afford to stay on.
The cost of staying on legacy PAS compounds quarterly: manual processes, regulatory non-compliance risk, underwriter attrition, and broker frustration.
Before proposing a new approach, we audit the failed migration. What was the SI's methodology? Where did endorsement chain reconciliation fail? What data model assumptions were wrong? We don't start from scratch – we start from what's been learned. KeystoneMigrate's DataArc mapping rebuilds the source-to-target model from the actual data, not the data dictionary that turned out to be incomplete. Then we build a corrected blueprint that your underwriters can validate before a single record moves.
KeystoneMigrate runs in your data platform (Databricks, Snowflake, Fabric, or similar) alongside whatever the current state is – whether that's a partially migrated dataset, a suspended migration, or a rollback to the source. We don't need a clean starting point. We need your actual data, in its actual state. Every migration rule is versioned and auditable – your compliance team can trace exactly what happened to every record.
The biggest damage in a rescue is trust. Underwriters don't believe the data will be right. The board doesn't believe the timeline will hold. KeystoneMigrate's checkpoint validation produces reconciliation reports at each phase – not just at the end. Your underwriters can check the evidence themselves. We don't ask for trust. We earn it with evidence at every stage.
The migration failed due to tooling limitations rather than book complexity. The SI's generic ETL approach couldn't handle endorsement chain reconciliation – a problem KeystoneMigrate's domain-aware model solves natively. Typical rescue timeline: 3-4 months from engagement to full-book reconciliation.
100% programmatic — zero manual rekeying
100% programmatic methodology eliminates the manual rekeying bottleneck that stalls rescue engagements. Every record migrated through versioned, auditable rules — not spreadsheet workarounds.
96% source-to-target data mapping
96% source-to-target data mapping achieved from a black-box source with no documentation. Failed migrations typically break down at the mapping stage — KeystoneMigrate's AI-powered discovery rebuilds what generic tools miss.
6 months from engagement to completion
6 months from engagement to completion — from a completely unknown source system. Rescue engagements from partially-mapped sources can move faster because discovery work already exists.
Based on engagement data. Exact figures pending final verification.
We've recovered migrations that were shelved, over-budget, and losing stakeholder support. The data is still there. The business case is still valid. You need a different approach, not a different team.