Planning a Legacy Data Migration? Here’s What You Need to Know
Many businesses assume a cloud data migration is a simple lift-and-shift process. The truth? Most of the work happens before any data moves.
At Dufrain, we’ve supported large-scale migrations for banks and insurers moving off legacy systems through our cloud migration services. Here’s what we’ve learned about the process of data migration and what you need to prepare for.
The Most Common Migration Question: How Long Will It Take?
Clients often ask: How long does a migration take? The short answer: it depends. The long answer: typically 2–3 months, but heavily influenced by:
- The number of source systems
- The complexity of data mapping
- The availability of SMEs (subject matter experts) who understand legacy systems
- The approach (Big Bang vs. phased migration)
Big Bang vs. Phased Migration
Big Bang: Done over a weekend, often with lower volumes or smaller data sets.
Phased: Used for complex or large-scale migrations. Runs on a cadence (e.g., monthly) to move different classes of data or business lines across in stages.
Both can be effective but your data strategy and tolerance for risk will guide the right model for overcoming cloud data migration challenges.
Where Most of the Work Happens: Upfront
Before migration begins, significant time goes into:
- Understanding source systems and fields
- Mapping source to target fields
- Validating transformation logic
- Building out test cycles (development, UAT, etc.)
Modern data platforms like Databricks and Microsoft Fabric supported by data engineering services, simplify the process of data migration by supporting repeatable logic, rules-based transformations, and rapid testing.
Why This Supports AI and Innovation
Legacy systems can’t support modern AI capabilities, not without extensive formatting, exporting, and restructuring. Once your data lives in a cloud-based, governed, and cleansed environment curated by data governance consultants with proper cloud migration plan, you’re free to explore:
- Predictive analytics
- Machine learning
- AI agents and chatbots
- Cross-platform insights
Migration isn’t just an IT task, it’s your AI enablement strategy.
5 Tips for a Successful Migration
- Engage SMEs early – Their knowledge of source systems is critical.
- Start with data mapping – This informs everything downstream.
- Use modern tools – Platforms like Databricks and Microsoft Fabric speed up the process of data migration and support reuse.
- Plan for testing – Multiple UAT cycles help ensure accuracy.
- Think beyond the move – Design your architecture to support future AI data analytics.
Ready to Migrate? Let’s Talk
Whether you’re consolidating insurance policy systems or modernising banking infrastructure, our team specialises in data migration in cloud computing- delivering fast, secure, and scalable migrations, built for today and ready for tomorrow.