What This Is
Data migration is moving data from one system to another: extracting it from the source, transforming it to fit the destination’s structure, validating it for accuracy and completeness, and loading it into the new system. This is the work that happens when you switch platforms, replace a legacy system, consolidate databases, or bring a new application online that needs to start with existing data rather than an empty database.
The risk in data migration is not the technology. It is the data itself. Source systems have inconsistencies, duplicate records, missing fields, and encoding issues that only surface when you try to move the data somewhere structured. A migration that works perfectly on a test sample can fail on production data because the 50,000th record has a character encoding that breaks the import, or a date format that was entered differently in 2019 than in 2024.
We handle data migrations as part of system builds (custom software development, legacy modernisation) and as standalone projects when a client is switching platforms. Our own platform manages data across two database engines (MySQL and PostgreSQL), and we have migrated client data between CRMs, accounting systems, and custom applications, each with its own schema assumptions and data quality challenges.
When You Need This
Data migration is required whenever you are moving to a new system and your existing data needs to come with you. Specific scenarios:
- You are replacing a legacy system and need years of historical data transferred to the new platform without loss
- You are switching SaaS providers (CRM, accounting, project management) and need to export from one and import to another with field mapping and data transformation
- You are consolidating multiple data sources into a single system: merging records from separate databases, spreadsheets, or platforms
- You are splitting a database: extracting a subset of data from a monolithic system into a new dedicated application
- A system build we are delivering needs to launch with existing data. The migration is part of the go-live plan
This is not the right service if you need ongoing data synchronisation between two live systems. That is system integration or API integration. Data migration is a one-time (or phased) transfer, not a continuous flow.
How We Work
Data migration projects follow a structured, multi-pass process designed to catch problems before they affect production data.
Step one is source analysis. We examine the source data in detail: schema, record counts, data types, null rates, duplicate patterns, encoding, and referential integrity. This analysis reveals the real shape of the data. Not what the documentation says it should look like, but what it actually looks like. Most migration failures are caused by assumptions about source data quality that turn out to be wrong.
We build the migration as a repeatable script, not a manual process. The extraction, transformation, and loading steps are codified so they can be run multiple times: first on a test sample, then on a full copy, then on production data. Each run produces a report showing records processed, records skipped, records that need manual review, and validation results.
Validation is built into every stage. Record counts are compared between source and destination. Key fields are spot-checked. Referential integrity is verified. Business rules are tested (totals still add up, relationships are preserved, dates are correct). The migration is not considered complete until the destination data has been validated against the source by both automated checks and manual review.
What You Get
- Source data analysis documenting schema, data quality issues, duplicates, and transformation requirements
- Field mapping document showing how source fields map to destination fields, including transformations, defaults, and business rules
- Migration scripts that are repeatable, logged, and capable of running in test mode before touching production data
- Data cleansing as part of the transformation: deduplication, format normalisation, encoding fixes, and null handling
- Validation report comparing source and destination data: record counts, field-level spot checks, and referential integrity verification
- Rollback plan so production data can be restored if the migration reveals issues post-go-live
- Cutover plan defining the sequence, timing, and verification steps for the production migration
- Post-migration audit confirming data integrity after the system goes live
Technologies We Use
- Laravel for migration script orchestration: artisan commands, database connections, and job queues for large datasets
- PostgreSQL / MySQL for destination databases, with support for cross-engine migrations
- Python (pandas) for complex data transformation, deduplication, and analysis when the dataset demands it
- CSV / JSON / XML parsing for flat-file exports from legacy or SaaS systems
- API extraction when source systems offer programmatic data access rather than file exports
Related Systems
Data migration is often the bridge between a legacy system and the new system we are building to replace it. A migration might populate a new client portal with existing client records, seed a reporting dashboard with historical data, or transfer contracts into a contract management system. The quality of the migration determines whether the new system starts with trustworthy data. You can also see how data migration fits within all our services.
Talk to Us About Your Data
If you need to move data between systems and cannot afford to lose records, corrupt relationships, or discover problems after go-live, get in touch and we will start with a source data analysis.
Why This Is The Riskiest Part Of Any Replacement
Data that has accumulated over years is never as clean as anyone expects. That is not a criticism of the business; it is what happens to records maintained by many people over a long period.
Duplicates that were tolerable in a permissive system break a strict one. Fields used for something other than their label. Dates in three formats. Notes carrying information that never had a field of its own. Records referring to other records deleted years ago.
None of that is visible from outside, which is why a migration estimate made without looking at the actual data is a guess. The single most reliable predictor of a difficult migration is that nobody has yet run a trial import.
The Part Everyone Forgets
Attachments, documents and free-text notes.
Structured records move cleanly because they map field to field. The eight years of context living in uploaded files and typed notes often has nowhere obvious to go, and it tends to be dealt with last, under time pressure, when the budget is spent.
If that material matters to how the business operates, and it usually does, it should be scoped at the start rather than treated as a tidy-up task. It is frequently the reason a business cannot leave a system at all.
What A Safe Migration Looks Like
The pattern is unexciting and worth insisting on.
Profile before planning. Look at the real data and find the problems rather than discovering them.
Trial run into a copy of the target, checked by people who know what the data should look like rather than only by developers.
Reconcile counts on both sides. How many went in, how many arrived, and an explanation for every one that did not.
Run it again, because the first attempt always surfaces something. Migrations that succeed are usually rehearsed two or three times.
Keep the old system readable for a period afterwards. Cheap, and the difference between an awkward week and a crisis.
What Moves The Price
Data quality, which nobody knows until somebody looks.
Whether attachments and notes are in scope.
How different the two data models are. Same shape is quick. Reconciling two systems that disagree about what a customer is takes design.
Whether the business can stop. A migration with a weekend window is a different job from one that must run alongside live use.
How much history you keep. Everything is expensive and often unnecessary. This is a decision worth making deliberately.
Where Migrations Go Wrong
No trial run before the price was agreed. The origin of most overruns here.
Verification by record count alone. Counts matching proves nothing about whether the contents are right. Somebody who knows the business has to look at real records.
No rollback point. There must be a moment where you decide to abandon and go back, and it must be a decision rather than a surprise.
Silent truncation. Fields longer than the destination allows, quietly cut. Discovered months later, usually by a customer.
Questions To Ask Whoever Quotes For This
- Have you run a trial import, and what did it find? Before agreeing a price.
- How will we verify the data is right? Counts are not verification.
- What is the rollback plan and how long do we have to use it?
- What are you deliberately not migrating? There is always something.
- How long does the old system stay readable? Longer than feels necessary.
- What happens to attachments and notes?
A Brief You Can Send Anyone
From and to: [systems and versions]
Volume: [records, roughly, and how far back]
What we know is messy: [duplicates, gaps, inconsistent formats]
Attachments and notes: [how much, and whether they matter]
Downtime we can tolerate: [hours, or none]
Who can verify the data looks right: [a name, not a role]
What we are happy to leave behind: [decide this early]
The deadline, and who set it: [vendor, contract, or preference]
How We Run Them
Profile, trial, verify with your people, rehearse, then run with a defined abandon point. The old system stays readable afterwards.
Where the migration is part of moving off an ageing platform rather than a straight system swap, legacy modernisation covers the wider decision, and the export question there is the one that decides whether you have options at all.
Get in touch and the first thing we will ask for is a copy of the data.
