The CRM Migration Checklist: Cleaning Data Before the Move
Your new CRM is going to be exactly as good as the data you put in it.
That sounds obvious. And yet, most CRM migrations fail for exactly this reason. Teams spend months evaluating platforms, negotiating contracts, planning rollouts, and training users. Then they import their existing data, unchanged, and wonder why the new system has the same problems as the old one.
The migration itself isn't the hard part. Cleaning your data before you move it is.
Why CRM Migrations Fail
Most migration failures trace back to a single assumption: "We'll fix the data after we move it."
This never happens. Once the new system goes live, everyone is too busy learning the interface, rebuilding reports, and managing the inevitable user complaints. Data quality gets pushed to "next quarter," which becomes "next year," which becomes "we've always had duplicate records."
The other common mistake is trusting exports. Your old CRM will happily export everything you ask for: all 47,000 contacts, including the ones from 2018 that bounced years ago, the duplicates created when your sales team merged with the acquisition's sales team, and the test records someone forgot to delete.
Every piece of bad data you migrate becomes someone else's problem to discover.
The Clean Before You Move Principle
Data cleaning before CRM migration isn't optional. It's the difference between a fresh start and an expensive lateral move.
Think of it like moving apartments. You could pack everything you own, including the broken lamp and the expired spices and the clothes that haven't fit since 2019. Or you could sort through things first, toss what doesn't serve you anymore, and arrive at your new place with only what you actually need.
The second approach takes more effort upfront. It also means you're not unpacking boxes of junk for the next three years.
Pre-Migration Data Cleaning Checklist
Before exporting a single record from your old system, work through this checklist. Each step uncovers problems that are dramatically easier to fix now than after migration.
1. Audit Your Current State
Run a data quality assessment on your existing CRM. You need to know:
- Record count by object: How many contacts, accounts, opportunities, and custom objects exist?
- Completeness rate: What percentage of required fields are actually filled in?
- Duplicate estimate: How many records might be duplicates based on email, company name, or phone?
- Age distribution: How many records haven't been touched in 12+ months?
This audit establishes your baseline. It also usually reveals that the situation is worse than anyone thought, which is valuable information to have before stakeholders start asking why migration is taking so long.
2. Define Your Source of Truth
If you're migrating from multiple sources (old CRM plus spreadsheets plus marketing automation), decide now which system wins conflicts.
When Salesforce says the company name is "Acme Corp" and HubSpot says "ACME Corporation," which one goes into the new system? When the phone number exists in three places with three different formats, which source do you trust?
Document these decisions before you start. Trying to make them record by record during migration leads to inconsistency and frustration.
3. Handle Duplicates Before Migration
Duplicate records are the most common data quality issue, and CRM migration makes them worse. Every duplicate in your old system becomes a duplicate in your new system. And if you're combining data from multiple sources, you'll create new duplicates when the same person exists in both.
Duplicate detection requires more than exact matching. "Jon Smith" and "John Smith" at the same company are probably the same person. "Robert" and "Bob" with matching emails definitely are. Traditional string matching misses these.
Your options:
- Merge before export: Resolve duplicates in the old system, then export clean data
- Merge during staging: Export everything to a staging area, deduplicate there, then import
- Merge in the new system: Import duplicates and use the new CRM's deduplication tools (not recommended, as you're fighting against established records)
Most teams find the staging approach works best. It gives you a clean environment to work in without affecting production systems.
4. Standardize Formats
Format inconsistencies multiply during migration. Phone numbers stored as "(555) 123-4567" in one system and "5551234567" in another create downstream problems for dialers, routing, and validation.
Fields that typically need standardization:
- Phone numbers: Pick a format (E.164 is the safest choice) and apply it everywhere
- Addresses: State names vs. abbreviations, street vs. St., suite formatting
- Company names: Legal suffixes (LLC, Inc.), capitalization, punctuation
- Dates: Ensure consistent formatting across systems
- Names: Title case, handling of credentials (MD, PhD, CPA)
This is tedious work. It's also the kind of work that compounds: fix it once now, or fix it repeatedly in every report and integration for the life of the system.
5. Map Fields Between Systems
Field mapping is where migrations get complicated. Your old CRM's "Industry" dropdown has 47 options. Your new CRM has 23 different ones. What happens to records tagged "Manufacturing, Heavy Equipment"?
Create a complete field mapping document that includes:
- Source field name → Destination field name
- Data type compatibility (text to text, picklist to picklist)
- Value transformations (old value → new value)
- Fields that don't have a destination (decide: migrate to notes field, custom field, or drop)
Don't skip custom fields and custom objects. These often contain the most valuable business data and the most creative data entry.
6. Test With a Subset First
Never migrate your entire database on the first attempt.
Export a representative sample: maybe 500 records that include different record types, different sources, edge cases you're worried about. Run them through your full migration process. Then have actual users verify the results.
Questions to answer with your test batch:
- Did records arrive with the expected field values?
- Are relationships preserved (contacts linked to accounts, activities linked to contacts)?
- Can users find what they expect to find?
- Do integrations and workflows trigger correctly?
Fix problems in your test environment. Then test again. Repeat until a batch completes without surprises.
7. Validate After Migration
Migration isn't done when the import completes. It's done when you've verified the data arrived correctly.
Post-migration validation includes:
- Record counts: Do the numbers match what you expected?
- Spot checks: Pull 20 random records and verify every field
- Relationship integrity: Are records still connected to their related objects?
- Search tests: Can users find records they know should exist?
- Report verification: Do key reports produce expected numbers?
Budget time for this. Rushing through validation guarantees you'll discover problems at the worst possible moment, usually when a sales rep can't find their largest account.
The Downloadable Checklist
We've compiled this process into a printable CRM Migration Data Cleaning Checklist. It includes specific tasks, fields to check, and sign-off boxes for each phase.
Download the CRM Migration Data Cleaning Checklist →
What CleanSmart Does for Migrations
This is exactly the problem we built CleanSmart to solve.
Our platform runs your data through a complete cleaning process before migration: SmartMatch finds duplicates using semantic similarity (catching "Jon" and "John" and "Robert" and "Bob"), AutoFormat standardizes phone numbers and addresses and names, SmartFill predicts missing values based on patterns in your existing data, and LogicGuard flags records that look suspicious.
You get a staging environment where you can review every change before committing, an audit trail of what was modified and why, and export files formatted specifically for your destination CRM.
Clean your data before the migration. You'll thank yourself later.
How long does CRM data cleaning take before a migration?
Timeline depends on data volume and quality. A 10,000-record database with moderate quality issues typically takes 2-3 weeks to properly audit, clean, and validate. Larger databases or those with significant duplicate problems may need 4-6 weeks. The investment upfront saves months of cleanup work after migration.
Should I clean data in the old CRM or export first?
Export to a staging environment first. Working in your production CRM risks disrupting current operations, and you lose the ability to compare original vs. cleaned data. A staging approach lets you clean without pressure and provides a clear audit trail of changes.
What's the biggest mistake companies make during CRM migration?
Assuming data quality will improve automatically in the new system. New software doesn't fix old data. Every duplicate record, every formatting inconsistency, every outdated contact you migrate becomes permanent in your new CRM unless you clean it first. The migration itself is the forcing function to fix years of accumulated data quality issues.
William Flaiz is a digital transformation executive and former Novartis Executive Director who has led consolidation initiatives saving enterprises over $200M in operational costs. He holds MIT's Applied Generative AI certification and specializes in helping pharmaceutical and healthcare companies align MarTech with customer-centric objectives. Connect with him on LinkedIn or at williamflaiz.com.
