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 is nothing like that, and this is exactly where most of the risk in modern IT projects hides.

Data migration means moving data between storage systems, formats, or applications. Businesses take this on for a handful of reasons: replacing a legacy platform, consolidating systems after an acquisition, or shifting workloads to the cloud. Public cloud spending alone is forecast to reach $723.4 billion in 2025, up 21.5% from the year before, and Gartner expects 90% of organisations to be running a hybrid cloud setup through 2027 (source: Gartner, November 2024). Most of that spending is really just data moving from where it already lives to somewhere new.

What follows covers what data migration actually involves, the main types you'll run into, and the process that separates a controlled migration from a chaotic one.

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. This step sits inside the wider disaster recovery strategy, never bolted on alongside it as an afterthought.

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.

What Notion's re-shard proves about validation

Notion's infrastructure team learned the value of that last phase the hard way. In 2023, the company re-sharded its core Postgres database, the system storing every workspace's blocks and comments, from 32 to 96 machines with zero downtime. Their first sync attempt took three days; once they worked out that skipping index creation during the copy and rebuilding indexes afterwards, the same sync dropped to twelve hours. Validation and iteration, not the initial plan, got them there.

Big bang or trickle: which approach fits your migration?

Every migration project chooses between two delivery models, and that choice is the single biggest decision in your data migration strategy.

The big bang approach

All data moves in a single operation, from the current environment straight to the target one. Big bang migrations are usually scheduled during downtime or public holidays for a reason.

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 found this out directly. Its 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 substantial data volumes and less appetite for inconvenient downtime tend to default to trickle. Longer and more careful, not cheaper, is the trade-off.

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

Skipping this step is not a shortcut, and treating it as one is the line between a recoverable mistake and a permanent one.

Go Wombat's engineers run every project against a data migration checklist built from exactly these three points, backed by a structured discovery phase work before any code gets written. If you're 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 solves that, but not all data migration tools solve it equally well. Five capabilities separate the ones worth paying for from the ones that create more work later.

Connectivity

Does the tool support your current systems, and will it scale with the business as it grows? A tool that only fits today's stack is a short-term fix, not a real answer.

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? Your data is one of the business's most valuable assets, and it deserves the same scrutiny in transit as it gets at rest.

Speed

How much does processing speed degrade as data volume increases? A tool that passes the first four checks and fails on security isn't a shortlist candidate. Call it a liability with a good user interface instead.

Data migration risks and how to manage them

Most migration risk traces back to the same root cause: skipping the planning phase to reach execution faster. That instinct is understandable, and it's also how a six-week migration turns into a six-month one.

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, checked against your cybersecurity consulting baseline rather than assumed to be covered by default settings.

Budget overrun

Budget overruns are almost always a scope creep problem, made worse by 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, and anyone who gives you one without seeing your data first is guessing. 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

Data migration is not a single technical task. Treat it as a sequence of decisions, about scope, method, timing, and risk tolerance, that decides whether a business ends up with a cleaner, faster system or a more expensive version of the same problems.

Whether you are running a database migration, a full cloud migration, or a business process overhaul, the fundamentals hold: plan before you touch the data, back it up before you move it, and validate constantly 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 deep 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.