Cloud & Infrastructure

Exiting your data centre

A structured, tool-led migration from on-premises to AWS. Every workload is discovered, planned, and moved with the tool built for it.

Discovery · Wave planning · Migration factory

Why it matters

Understand the estate before you move it.

The biggest risk in a data centre exit is starting to migrate before you understand your environment. A disciplined, tool-assisted approach shows you what you have, what depends on what, and how each workload should move, before a single server is touched.

Understand

Know exactly what you have and how it connects.

De-risk

Surface dependencies and challenges before migration starts, not midway through it.

Plan

Move each workload the right way, in the right order.

The migration journey

Three phases. Each one feeds the next.

01

Discovery and portfolio assessment

Before choosing how to migrate, we build a complete, data-backed picture of your environment. This is what makes timelines and effort estimates reliable.

What we capture

  • Servers
  • Applications
  • Dependencies
  • Utilisation
  • Network rules
  • Boot and configuration

Discovery tooling

We select the discovery tool that fits your environment.

ToolBest for
AWS TransformRecommendedAWS’s discovery and assessment capability. Inventories servers and applications, captures utilisation data, and maps dependencies.
Modelize.ITDeep application-dependency mapping and modelling for complex, tightly coupled environments.
Device42Agentless discovery with a strong CMDB, dependency mapping, and application affinity grouping.
FlexeraIT asset management and discovery with detailed software and licence insight for large estates.
CloudamizeAnalytics-led discovery with right-sizing and cost modelling for cloud planning.
MovereFull-environment discovery with configuration, utilisation, and security posture snapshots.

Output: a validated portfolio, with dependencies identified, challenges surfaced, and workloads grouped for planning.

02

Strategy and wave planning

Using the assessment data, each workload is assigned a migration strategy, grouped into a wave, and sequenced by dependency.

Migration strategies

Rehost

Move as-is.

Replatform

Optimise with minimal change.

Refactor

Re-architect for the cloud.

Retire

Decommission what is no longer used.

Retain

Keep on-premises for now.

Grouped into waves

Sequenced by dependency for a safe, orderly cutover.

Stakeholder alignment

Plan, timelines, and effort are agreed up front, grounded in the discovery data.

03

Migration

Different workloads move with different purpose-built AWS services.

What you’re movingAWS toolHow it helps
ServersAWS Transform and AWS Application Migration Service (MGN)Rehosts servers at scale using automated, block-level replication, keeping cutover downtime short.
DatabasesAWS Database Migration Service (DMS)Migrates databases with continuous replication, between the same or different database engines.
Storage and file dataAWS DataSyncMoves large volumes of file and object data to AWS securely.

Execution model

The migration factory.

Migration runs as a factory: a repeatable process that moves servers in standardised waves rather than one-off manual efforts. Each wave follows the same sequence, so quality stays consistent, effort becomes predictable, and throughput rises with every cycle.

Standardised runbooks

Every server follows the same documented steps: checks, agent install, replication, testing, cutover.

Repeatable waves

Workloads move through the pipeline on a fixed cadence, and each cycle runs faster than the last.

Automation at scale

MGN, DMS, DataSync, and Cloud Migration Factory (CMF) orchestration handle bulk migration with little manual effort.

Continuous improvement

Lessons from each wave feed back into the runbooks, so speed and reliability improve as the programme runs.

The outcome

Discovery, a clear plan, and the right tool.

A successful data centre exit follows that sequence. Together, they turn the exit into a well-governed, low-risk move to AWS.

Reliable timelines

Estimates are built on discovery data, not guesswork.

Faster, cleaner moves

Each workload moves with the tool built for it.

Less wasted spend

Right-sized strategy and tooling cut wasted effort and cost.

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