What is Data Migration and how to create the perfect process

Moving a spreadsheet from one folder to another takes seconds. Moving a company's live operational data from one system to another can take months, and a mistake can mean lost or corrupted records.

Companies take on data migration to replace a legacy platform, consolidate systems after an acquisition, or move workloads to the cloud. The cloud alone is a large driver: in November 2024, Gartner forecast that public cloud spending would reach $723.4 billion in 2025, up 21.5% on 2024, and that 90% of organisations would adopt a hybrid cloud approach through 2027. Every workload that moves to the cloud brings a data migration with it.

Data migration explained

Data migration means transferring data from one storage system, format, or application to another, permanently and without any loss of accuracy along the way. Businesses usually take this on when they introduce a new platform, retire legacy infrastructure, or restructure how information moves between departments.

Extraction, transformation, and loading sit at the centre of almost every migration project. Get those three steps wrong and everything downstream, from reporting to compliance, inherits the error.

The main types of data migration

Most migration projects fall into one of six categories. They overlap in practice, but each carries its own risks and its own checklist.

Storage migration

Moving data from one physical or virtual medium to another, for example, from on-premises hard drives to cloud-based storage. The driver is usually modernisation rather than a lack of space; legacy storage tends to degrade in performance long before it runs out of capacity.

Application migration

Switching software vendors or platforms, which forces data out of one format and into another. Differences in data models are the main obstacle here, and APIs are commonly built to bridge the gap and protect data integrity during the switch.

Data centre migration

Relocating physical infrastructure, servers, storage arrays, and network hardware to a new site, or moving the data housed there onto new infrastructure without changing location. Data centres concentrate a business's most sensitive information in one place, so both the physical move and the digital one need equal scrutiny.

Database migration

Database migration means shifting data between versions of the same database, or switching to a different database management system entirely. Schema differences between the old and new systems are where most database migrations go wrong.

Cloud migration

Cloud migration covers moving data from on-premises infrastructure to the cloud, or shifting it between cloud providers. This is the category most organisations are actively working through right now, and Flexera's 2025 State of the Cloud Report found that more than half of enterprise workloads already run in public cloud, with further migration planned over the next year.

Business process migration

Business process migration moves the data behind entire business processes, including product, customer, and operational metrics, into a new environment. A reorganisation or an optimisation drive usually triggers it, and it needs the same GDPR discipline as any other migration involving personal data.

The data migration process

A migration project succeeds or fails on planning, not on the technology doing the moving. Most well-run projects follow the same five phases, often structured around an ETL (extract, transform, load) workflow.

Pre-migration planning

Analyse the existing data alongside the target system's requirements, and put a written data migration plan in place, covering schedule, budget, and method, before touching a single record.

Data inspection

Check the data for quality issues and conflicts here. Cleanup tools earn their cost at this stage, because fixing a formatting problem before migration is far cheaper than fixing it after.

Data backup

Back up everything being migrated and test that the backup restores. Treat this as part of your disaster recovery strategy, not a separate task.

Migration process design

An ETL developer or migration engineer defines testing procedures, acceptance criteria, and who owns what, usually alongside a business analyst who understands what “correct” looks like from the business side.

Execution and validation

Data moves under constant monitoring, with validation running in parallel to catch discrepancies before they reach production.

Example: Notion's database re-shard

Notion's infrastructure team showed why that last phase matters. In 2023, the company re-sharded its core Postgres database, which stores every workspace's blocks and comments, from 32 to 96 machines with zero downtime. The first sync attempt took three days. Once the team skipped index creation during the copy and rebuilt the indexes afterwards, the same sync took twelve hours.

Big bang or trickle: which approach fits your migration?

Every migration uses one of two delivery models, and the choice shapes the rest of your data migration strategy.

The big bang approach

All data moves in a single operation, from the current environment straight to the target one, which is why big bang migrations are usually scheduled for planned downtime or public holidays.

Why teams choose it

Planning costs less and takes less time, which is what makes this approach attractive when budgets are tight or the dataset is modest.

Where it bites

Fewer moving parts means fewer places for something to break, but it also means everything breaks at once if it does. Every system tends to go offline for the duration, so this approach suits smaller businesses that can absorb a short, contained outage far better than large enterprises can.

The trickle approach

Data moves in smaller phases, with the old and new systems running in parallel until the switch is complete. Downtime approaches zero, but the project itself runs longer and costs more, in both time and money.

What it demands

Trickle migrations trade downtime risk for coordination overhead. Stack Overflow's first attempt to move Stack Overflow for Teams to Azure between 2021 and 2023 took three tries before it succeeded. The lesson the infrastructure team carried into their larger 2023-2025 project, moving the entire public Stack Overflow platform off physical data centres and onto Google Cloud, was to build a dedicated migration team and run the move in phases rather than as one event (source: Stack Overflow, August 2025). The public sites were fully decommissioned from physical infrastructure in mid-2025.

Who it suits

Larger businesses with large data volumes and little tolerance for downtime usually choose trickle and accept a longer, more expensive project.

Data migration best practices

Build a dedicated migration team

A team assigned specifically to the project, rather than one juggling it alongside other work, is what makes cleanup, profiling, and scripting happen properly instead of under time pressure.

Restrict what actually needs to move

Not every historical record earns its place in the new system. Archiving stale data before migration reduces both the cost and the risk of the project.

Profile and back up before writing a single script

Profiling shows what your scripts need to handle, and a tested backup is what lets you roll back a failed load.

Go Wombat's engineers run every migration against a checklist built from these three points, and every project starts with a structured discovery phase before any code is written. If you are comparing data migration services and want a second opinion on scope before signing anything, start with a discovery session.

What a data migration tool needs to do

Building migration tooling from scratch is expensive and slow. Off-the-shelf data migration software saves that work, but tools vary widely. Check any candidate against these five criteria.

Connectivity

Does the tool support your current systems, and will it scale as the business grows?

Transformation

Can it handle structured, unstructured, and semi-structured data without manual rework? Most enterprise datasets are a mix of all three, so this is rarely optional.

Scalability

Does performance hold up at petabyte scale, not just in a demo environment? Plenty of tools look impressive on a small dataset and fall over on a real one.

Security

Does it meet the same cybersecurity standard as the rest of your infrastructure? Data needs the same protection in transit as at rest. A tool that is weak on security should not make the shortlist, whatever its other features.

Speed

How much does processing speed degrade as data volume increases? Test throughput on a copy of your largest table before you commit.

Data migration risks and how to manage them

Most migration problems start with a planning phase that was skipped or rushed to reach execution sooner.

Data loss

Data loss usually traces back to one thing: a backup that either doesn't exist or was never actually tested.

How to reduce it

Take a full backup and pair it with a documented disaster recovery plan before migration starts, not after something goes wrong.

Prolonged timelines

Prolonged timelines tend to come from poor connectivity, unclear scope, or a team stretched too thin to do the job properly.

How to avoid it

Scope realistically during pre-migration planning. Timelines slip during execution far more often than they slip during a well-run planning phase.

Security exposure

Security exposure happens when data sits unencrypted, either in transit or at rest.

How to reduce it

Encrypt data before, during and after the move, and check the settings against your security baseline rather than trusting the defaults.

Budget overrun

Budget overruns usually come from scope creep and rework nobody planned for.

How to fix it

Agree on fixed acceptance criteria before execution begins, so “done” means the same thing to everyone on the project.

Data quality drift

Data quality drift creeps in when a migration runs without full validation at every stage.

How to reduce it

Run continuous data migration testing and a QA process through design, execution, and post-migration, not just at the very end.

How long does a data migration actually take in 2026?

There is no fixed answer until someone has seen your data. A big bang migration on a modest dataset can finish in days. A trickle migration across a large enterprise, the kind Stack Overflow ran over roughly two years, can take considerably longer once phased rollouts, parallel validation, and dependency mapping are factored in.

What determines the timeline is rarely the technology. Data volume, the number of dependent systems, and how much cleanup the source data needs before it's fit to move matter far more.

Key takeaways

Whatever the type of migration, the fundamentals are the same: plan before you touch the data, back it up before you move it, and validate throughout rather than once at the end.

Go Wombat has built migration strategies for clients across manufacturing and Industry 4.0, AdTech and MarTech, sports technology, and PropTech and real estate, alongside experience in BI and data visualisation and custom ERP systems. Share your project brief if you are planning a migration and want a second opinion on the approach before committing to one.