Most of the risk in a cloud migration isn't the destination platform — it's the move itself. This guide covers how a properly planned migration to Azure, AWS or Google Cloud avoids the data loss, application breakage and cutover disasters that give cloud migration its bad reputation.
- Why migrations go wrong more often than they should
- Assessment before anything moves
- Migration planning and sequencing
- Data migration and reconciliation
- Cutover strategy: big bang vs staged
- The post-migration period nobody plans for
- Platform-specific migration considerations
- Choosing who does the migration
Why migrations go wrong more often than they should
Cloud migration has an outsized reputation for disaster relative to how straightforward it should theoretically be, and the reason is almost always process, not technology. Data gets left behind or duplicated. An application that worked fine on-premise behaves differently once network latency and cloud-specific permissions enter the picture. A cutover gets scheduled for a Friday evening with insufficient testing, and Monday morning arrives with email down and nobody quite sure why. None of this is inherent to cloud migration — it's what happens when migration is treated as a weekend project rather than a properly sequenced piece of work.
Assessment before anything moves
A migration that starts with moving things is starting in the wrong place. It should start with a full assessment: what applications and data actually exist, what depends on what, which systems have hard compliance or latency requirements, and which ones are candidates for retirement rather than migration at all. This step routinely surfaces genuine surprises — forgotten dependencies, unofficial shadow systems, data nobody remembered existed — and finding them before migration begins is considerably cheaper than finding them during cutover.
Migration planning and sequencing
Group by dependency, not convenience
Applications that depend on each other should move together or in a defined sequence, not simply in whatever order is easiest to schedule.
Identify low-risk pilots
Migrate a lower-stakes system first to validate the process before touching anything business-critical.
Build a rollback plan for every stage
Every migration step needs a defined way back if something doesn't work as expected — not as a last resort, but as a standard part of the plan.
Schedule around the business, not around convenience
A cutover timed for genuinely quiet periods, with stakeholders informed in advance, causes a fraction of the disruption of one scheduled purely for engineer convenience.
Data migration and reconciliation
Moving data isn't the hard part — confirming it arrived correctly is. Every proper migration includes a reconciliation step: comparing record counts, checksums or specific business-critical figures (an opening trial balance, a customer count, an order total) between source and destination before the old system is switched off. Skipping this step in the interest of speed is the single most common cause of "we didn't notice the data was wrong until three weeks later."
Cutover strategy: big bang vs staged
A "big bang" cutover — switching everything over at once — is faster but concentrates all the risk into a single moment. A staged migration, moving workloads incrementally with both environments running in parallel for a period, takes longer and costs more to run temporarily but drastically reduces the blast radius of anything going wrong. For most business-critical systems, staged is worth the extra time; for lower-stakes or simpler systems, a well-tested big bang cutover is often entirely reasonable.
The post-migration period nobody plans for
The weeks immediately after go-live are where most unresolved issues actually surface — performance quirks under real load, an integration that behaves subtly differently, users hitting an edge case nobody tested for. A migration project that ends the moment the cutover completes, with no hypercare period afterward, leaves the business to discover and fix these issues alone at exactly the point their attention has moved on to something else.
Platform-specific migration considerations
The fundamentals above apply regardless of destination, but each platform has its own practical wrinkles. Migrating to Azure often means untangling Microsoft licensing alongside the technical move, since the two get resolved together more often than not. Migrating to AWS typically involves more upfront network architecture decisions — VPC design, IAM structure — since AWS gives you more raw building blocks to assemble correctly. Migrating to Google Cloud is comparatively rare as a whole-estate move and more commonly a targeted migration of a specific data or analytics workload, which changes the risk profile considerably.
Choosing who does the migration
The single best predictor of a smooth migration isn't the destination platform — it's whether the team doing it has actually done this before, on this specific type of system, and has a documented process rather than improvising. Ask any prospective provider what their reconciliation process looks like and what their rollback plan is for a failed cutover. If the answer is vague, that's the answer.
