Data migration is moving data from one system to another, usually because you are replacing a CRM, ERP, accounting package or database. A migration is not just a copy: the data has to be mapped onto the new system’s structure, cleaned, loaded and checked. Most failures come from skipping the mapping and checking, not from the transfer itself.
This guide covers what data migration is, the stages of a sound process, the tools people use, what goes wrong and where AI helps. If you are working on a specific move, our legacy system data migration mapping automation covers the step that causes the most trouble.
What is data migration?
It is the process of selecting, preparing, transferring and verifying data between systems, formats or storage. Common cases are moving to a new CRM, upgrading or switching an ERP, consolidating two companies’ systems after an acquisition, or retiring an old on-premises database.
It differs from integration (keeping two systems in sync over time) and from backup (a copy for recovery). A migration has an end date, and after cut-over the old system is retired or frozen.
The data migration process
A reliable migration follows roughly these stages:
- Scope and inventory. List what exists: objects, tables, files, attachments, volumes. Decide what moves, what is archived and what is dropped. Not everything deserves migrating.
- Profile the data. Look at completeness, duplicates, odd formats and orphaned records before touching anything. This tells you how big the cleaning job is.
- Map the fields. Match each source field to a target field, with transformation rules: date formats, picklist values, currency, status codes, ownership. This is the core of the project.
- Clean and transform. Deduplicate, standardise, fill gaps, and decide how to treat records that cannot be fixed.
- Test load. Run a migration into a test environment with a sample, then a full copy. Do it several times.
- Validate. Compare counts and totals, spot check records, test relationships (does every contact still belong to the right company?) and have business users confirm.
- Cut over. Freeze the old system, run the final delta load, switch users, and keep the old system read-only for a period.
- Reconcile and archive. Confirm final numbers, document decisions and retain the source data according to your retention policy.
Data migration tools
Tools fall into a few types, and the right one depends on volume and complexity.
- Built-in importers. Most platforms ship CSV or wizard-based importers. HubSpot and Salesforce both have import tools suitable for modest, clean datasets. Limits show up around relationships, custom objects, and updates to existing records.
- ETL and integration platforms. These extract, transform and load data between systems on a schedule or as a one-off. Workflow tools like n8n, Make and Zapier can handle small to medium migrations through APIs, though they are not designed for very large bulk loads. Dedicated ETL products and cloud data pipelines suit larger volumes.
- Custom scripts. Python or SQL scripts give full control over transformation and are common for ERP and database moves. They need testing, logging and someone who can maintain them.
- Vendor and partner tooling. ERP and accounting vendors such as SAP, NetSuite and Microsoft Dynamics 365 Business Central offer their own migration tools or partner services.
- Spreadsheets. Excel is still where mapping tables and cleansing are often done, and it is fine for small sets but weak at audit trails.
Data migration services, meaning a consultancy or implementation partner, are worth considering when the data is large, regulated or poorly documented, or when your team cannot spare the time. Ask for the mapping document and the validation plan up front.
Legacy data migration
Legacy migrations are harder because documentation is thin, field meanings have drifted, and the original developers have gone. Typical issues are fields reused for something else, free-text codes, mixed character sets, and business rules buried in old code. Start with interviews and sample data, not the schema alone. Our ERP data migration field mapping automation helps with this stage.
CRM data migration
CRM moves are mostly about relationships and history. Accounts, contacts, deals, activities and notes must keep their links, and owners must map to real users in the new system. Deal stages and picklists rarely match one for one. Duplicates multiply when two systems are merged. If you are moving between CRMs, read our guide to CRM automation with HubSpot and Salesforce, and look at CRM data migration and field mapping and CRM to CRM migration validation.
What goes wrong in data migration
- Mapping done late or informally. Fields get matched in someone’s head, and the rules are lost.
- Dirty data moved as is. Duplicates and bad records are loaded into a shiny new system and trust drops straight away.
- Broken relationships. Records load but lose the link to their parent, owner or history.
- No validation. Row counts match but totals, dates or statuses do not.
- One big-bang rehearsal. The first full load is the live one.
- Forgotten data. Attachments, email history, custom fields and permissions are left behind.
- Personal data surprises. Old data that should have been deleted is carried over. Check your retention rules before you move it.
- No rollback plan. The old system is switched off on day one.
Where AI helps in data migration
AI is useful in three places.
Field mapping. A model can read the source and target schemas, sample values and field names, and propose a mapping with a confidence level and an explanation. That speeds up the first draft considerably, and a person approves it.
Cleansing. AI can standardise names, addresses, company names and free-text categories, spot likely duplicates and suggest merges. See master data deduplication across systems and historic deal data cleanup during system migration.
Validation. It can compare source and target, explain mismatches in plain language and flag records that look wrong, as in bulk data import validation before go-live.
AI should not silently change data. Every transformation should be logged and reversible, the final mapping rules should be human-approved, and sensitive data should be processed within your own controlled environment.
Frequently asked questions
What is data migration?
It is moving data from one system to another, including mapping it to the new structure, cleaning it and checking the result.
How long does a data migration take?
It depends on data volume, quality and the number of systems. A clean CRM import can take days, whereas an ERP migration can take months, with most of that time spent on mapping and testing.
What are the best data migration tools?
There is no single best tool. Built-in importers suit simple moves, workflow platforms suit moderate ones, and scripts or dedicated ETL products suit large or complex ones.
Should we clean data before or during migration?
Before, where you can, and always in a test environment. Cleaning during the final load makes problems harder to trace.
Where Automata fits
We build ready-made automations for the hardest parts of a migration: field mapping, transformation rules, deduplication and validation reports. The starting point is legacy system data migration mapping, with CRM data migration and field mapping for CRM moves. We work with your platform’s own import tools or APIs, and a person approves the mapping before anything loads.
If you have a migration coming up, talk to us and we will tell you honestly what can be automated and what cannot.