Data Migration
De-risk data migration with nightly automation
Our proven approach runs the full migration pipeline every night with anonymised production data. By go-live day, the migration has been executed and validated hundreds of times. No surprises, no weekend heroics.
Data migration capabilities
Automated, repeatable, validated. Every aspect of the migration is code-driven and testable.
Automated nightly migration runs
Our data migration framework runs a full extract-transform-load cycle every night against your production data. Each morning you see exactly what succeeded, what failed, and what changed. No manual scripts, no guesswork.
Anonymised test data
Production data is anonymised automatically during extraction. Personal identifiers are replaced with realistic but fictional equivalents, so your team can test against representative data without privacy or compliance risk.
Validation and reconciliation
Every migration run produces a reconciliation report: row counts, checksum comparisons, referential integrity checks, and data-type validation. Discrepancies are flagged with drill-down detail so they can be resolved before go-live.
Schema mapping and transformation
We map source schemas to target schemas, handling column renames, type conversions, default values, and business-rule transformations. Mapping rules are version-controlled and testable.
Incremental and delta migration
After the initial full load, subsequent runs process only changed records. This reduces run time dramatically and allows continuous synchronisation between old and new systems during a parallel-running period.
AI-enhanced data cleansing
We use AI to detect and correct common data quality issues: duplicate records, inconsistent formatting, missing values, and obsolete entries. AI suggestions are flagged for human review, not applied blindly.
Why nightly automation changes everything
Most data migrations fail because they are tested once and executed once. Ours is tested every night.
Nightly automation
The migration pipeline runs unattended every night. By the time your team arrives, the latest migration results are ready for review. Issues found today are fixed in code and re-validated tonight.
Risk reduction through repetition
Each nightly run is a dress rehearsal for go-live. By the time you cut over, the migration has been executed and validated hundreds of times. There are no surprises on migration day because every scenario has already been tested.
Safe testing with real data shapes
Anonymised production data preserves the structure, distribution, and edge cases of your real data. Testing against synthetic data misses the problems that only appear with production volumes and complexity.
Continuous improvement
Every failed validation is a test case. The migration codebase accumulates knowledge: edge cases, business rules, and data quirks that would otherwise be discovered during a high-pressure cutover.
From assessment to go-live
A proven process refined across multiple large-scale data migration projects.
Data assessment
We profile your source data: volumes, schemas, relationships, data quality issues, and business rules. You receive a migration complexity report with a realistic timeline and risk assessment.
Pipeline build
We build the automated migration pipeline: extraction, anonymisation, transformation, loading, and validation. The first nightly run produces a baseline reconciliation report. We iterate on mapping rules and transformations based on validation results.
Nightly validation
The pipeline runs every night. Your team reviews reconciliation reports each morning. Transformation bugs, missing mappings, and data quality issues are fixed in code and re-validated the same night. Confidence builds with every successful run.
Cutover and go-live
On migration day, the same pipeline that has been running nightly executes the final cutover. The process is identical to every rehearsal, just with the "anonymise" step turned off. Post-cutover validation confirms data integrity in the live system.
Go deeper
Legacy Application Modernisation
Our full legacy modernisation service: assessment, re-platforming, and strangler fig migration.
AI Integration
AI-enhanced data cleansing, classification, and enrichment for migration workloads.
DevOps & Modernisation
CI/CD pipelines that automate migration runs and validation checks.
Custom Software Development
The new system your data is migrating into, built by the same team.
Frequently asked questions
How does the nightly automation work?
A scheduled pipeline (typically an Azure DevOps or GitHub Actions workflow) runs after business hours. It connects to your source database, extracts data, applies anonymisation and transformation rules, loads into the target system, and runs validation checks. Results are published to a dashboard and emailed to stakeholders. If critical validations fail, the pipeline alerts the team immediately.
How is data anonymised?
We replace personally identifiable information (names, email addresses, phone numbers, national insurance numbers, etc.) with realistic fictional equivalents. The anonymisation is deterministic: the same source value always produces the same anonymised value, preserving referential integrity across tables. Anonymisation rules are configurable per column and per table.
What databases do you support?
We work with SQL Server, Azure SQL, PostgreSQL, MySQL, Oracle, and Cosmos DB as both source and target. We also migrate from legacy systems (Access, flat files, custom formats) and SaaS platforms via API extraction. The migration framework is database-agnostic at the transformation layer.
How long does a data migration take?
It depends on data volume, complexity, and quality. A straightforward migration (clean data, similar schemas) can be production-ready in 4-6 weeks. Complex migrations (multiple sources, heavy transformation, poor data quality) typically take 8-16 weeks. The nightly automation means you get early visibility into the timeline: if validation pass rates plateau, we know where effort is needed.
Can you run the old and new systems in parallel?
Yes. Incremental/delta migration keeps the target system synchronised with the source during a parallel-running period. Users can operate in both systems while you validate the new system. When you are confident, the cutover is a planned, low-risk event.
How does AI improve data migration?
AI helps with data cleansing tasks that are tedious and error-prone for humans: detecting duplicate records across inconsistent formats, standardising addresses, inferring missing values from context, and classifying unstructured text. AI suggestions are flagged for review rather than applied automatically, so your team retains control over data quality decisions.
Ready to de-risk your data migration?
Book a free migration assessment. We will profile your source data, estimate complexity, and show you how nightly automation removes the risk from go-live.
Book a free consultationor call 01202 375647