What is a CRM data migration and how do you avoid losing data?

A CRM data migration is moving your records from wherever they live now into the new system with the relationships between them intact, and you avoid losing data by mapping and cleaning before you load, testing in a sandbox, migrating in waves, and reconciling counts afterwards. Most data loss is not deletion. It is silent: a field that did not map, an association that did not carry, a duplicate that overwrote the good record.

Step 1: inventory and decide what travels

List every source. The old CRM, the spreadsheets, the email tool, the shared inbox, the one person's personal file. For each, decide what is coming. Contacts with no activity in years, addresses you cannot legally email, and fields nobody has read since 2019 do not need to make the trip. Archive them somewhere retrievable and move a smaller, better dataset.

Step 2: map the fields

Every source field gets a destination field, a type, and a transformation rule if the values need normalizing. This is where you standardize state and country values, phone formats, and picklists. Inconsistent state values are the classic one, since a list that contains Texas, TX, Tx and texas will not group in any report. A clean state abbreviation reference helps here. Anything with no destination is a decision, not an accident: either create the field deliberately or drop it deliberately.

Step 3: clean and deduplicate before load

Deduplicate in the export, not in the new system. Merging afterwards is slower, riskier, and often loses activity history. Pick your match key deliberately: email for contacts, domain for companies, and a documented rule for what happens when two records match on one and disagree on another. Validate email addresses at this stage too, because migrating a list full of dead addresses damages your sending reputation on day one. Our email list cleaning process covers that part.

Step 4: sandbox, then waves

Load a small representative sample into a test environment first. Check associations, not just records: does the contact still attach to the right company, does the deal still attach to the contact, did the owner come across, did the created date survive or did everything become today.

Then migrate in waves. Companies first, then contacts, then deals and activities, so the things that get associated already exist. A single all at once load is how you discover a mapping error after two hundred thousand records.

Step 5: reconcile

Count records in the source. Count records in the destination. Explain every difference. "We expected 41,200 and loaded 39,850 because 1,350 were merged duplicates" is a reconciliation. "It looks about right" is not. Then spot check twenty records by hand across different types, and keep the source system read only for a while rather than cancelling it the same week.

See our data and integration work.