Data migration is one of the highest-risk stages of any system change. Moving from legacy systems, switching vendors or moving to the cloud puts critical records at risk of loss, corruption and duplication, and problems often surface only after go-live. We plan, test and validate migrations end to end, using AI and our own accelerators to profile, cleanse and reconcile data at full volume.
What Is Data Migration Testing?
Data migration testing confirms that data moved from a source system to a target system is complete, accurate and usable. It covers profiling and cleansing the source data, validating each transformation, reconciling every record after each load, and testing that the new system works correctly with the migrated data.
Why Data Migrations Go Wrong
Most migration problems are predictable, and avoidable with the right testing:
- Poor-quality source data – duplicates, gaps and inconsistencies discovered late, during trial loads.
- Mapping and transformation errors – business rules applied incorrectly between source and target.
- Sample-based checks – errors in the records nobody checked reach the live system.
- Unstructured data – documents and free text that cannot be mapped reliably by hand.
- Compressed timelines – too few rehearsal runs before cutover.
We test, validate and safeguard every stage, so you go live knowing your data is right.
How We Use AI in Data Migration
- Data profiling – AI scans source data to find duplicates, gaps and inconsistencies before mapping starts.
- Mapping – AI suggests source-to-target field mappings for our consultants to review and approve.
- Cleansing rules – AI proposes automated cleansing rules, reducing manual correction between runs.
- Unstructured data – AI extracts and structures information from documents, free text and legacy records, such as university curriculum and module data.
- Full-volume reconciliation – every record is checked, not a sample, with results reported for every run.
Every AI suggestion is reviewed by a consultant before it is applied to your data.
Our Data Migration Testing Approach
We follow a four-stage method, refined over many migrations, that reduces risk at every step.
1. Plan and Govern
- Define a data management strategy aligned to your business goals.
- Agree clear responsibilities with a RACI matrix for every stage.
- Set a realistic schedule with rehearsal runs and contingency built in.
2. Profile, Cleanse and Validate
- Profile source data with AI to find duplicates, gaps and inconsistencies early.
- Cleanse data with automated rules, so only accurate, high-quality data is migrated.
- Validate every transformation with ETL testing and our Validation Manager accelerator.
- Report results in real time, so decisions are based on evidence.
3. Migrate and Reconcile
- Test extraction, transformation and loading on every run, including trial loads.
- Reconcile 100% of records to find corrupt, missing or duplicated data.
- Confirm compliance with security, privacy and regulatory requirements, including UK GDPR.
4. Prove the Target System
- Run user acceptance testing with migrated data.
- Performance test the new system with production-scale data volumes.
- Support cutover and early life, with monitoring after go-live.
Migrations We Test
- ERP and finance – SAP S/4HANA, Oracle and Workday
- CRM – Salesforce and Microsoft Dynamics 365
- Student records – Ellucian Banner, Tribal SITS and SAP Student Lifecycle Management
- Curriculum management – programme specifications and module descriptors migrated from unstructured documents into new curriculum systems
- Legal systems – practice management and document management platforms, including iManage, Intapp and Aderant
- Cloud and data platforms – data warehouses, analytics and cloud migrations
Why Choose Prolifics Testing for Data Migration
- Specialists since 1999 – data migration and validation for universities, law firms and enterprises.
- AI and proven accelerators – AI-assisted profiling and cleansing, plus our Validation Manager accelerator, which has validated more than 3 billion fields in hours.
- Full-volume reconciliation – every record checked, not a sample.
- Independent assurance – we can assure a migration run by your team or implementation partner, or carry it out ourselves.
Case Study: AI Curriculum Data Migration at the University of Lancashire
We used AI to migrate curriculum data from more than 10,000 unstructured Word documents into the University of Lancashire's new curriculum management system. AI-generated extraction scripts and automated AI validation, combined with proven ETL tooling, achieved over 99% accuracy and created more than 10,000 course and module records without manual data entry.
Read the full case study and watch the webinar
Data Migration Testing FAQs
What is the difference between data migration and data migration testing?
Data migration is the process of moving data between systems. Data migration testing proves that the move was complete and accurate, and that the new system works correctly with the migrated data. We can provide either or both.
How does AI help with data migration?
AI speeds up profiling, mapping and cleansing, and structures unstructured data such as documents and free text. Our consultants review every AI suggestion before it is applied.
Do you check every record or a sample?
Every record. We reconcile 100% of migrated data on each run, so errors cannot hide in the records a sample would miss.
Can you assure a migration being run by another supplier?
Yes. We often provide independent assurance alongside an implementation partner, testing their extracts, transformations and loads against agreed acceptance criteria.
How do you protect personal data during migration testing?
We work within your security controls and can use masked, GDPR-compliant test data generated by our Tidium platform where production data cannot be used.
Can AI migrate data from unstructured documents accurately?
Yes, with the right approach. For the University of Lancashire, AI-generated extraction scripts and automated AI validation achieved over 99% accuracy across more than 10,000 Word documents, with transformation handled by proven ETL tools.
