CRM Migration & Enrichment: Transfer Data Without Losing Quality

Somewhere between 47% and 70% of CRM implementations fail, depending on which study you read. The scary part? Most of those failures aren't software problems. They're data problems that started during migration and never got fixed.

CRM migration is supposed to be a fresh start - a chance to move from a system that's not working to one that will. But too often, teams spend months preparing for the switch only to discover they've imported the same bad data into a shiny new interface. Duplicate records, outdated contacts, missing fields, broken relationships between accounts and opportunities. The new CRM inherits every problem the old one had, plus a few new ones created during transfer.

This doesn't have to happen. A well-planned CRM data transfer can actually improve your data quality rather than just preserve it. The migration becomes an opportunity for cleanup, enrichment, and starting fresh with records that are complete, accurate, and ready to drive revenue.

Why CRM Migrations Fail (And It's Usually the Data)

Most teams underestimate what CRM migration actually involves. They think of it as a technical exercise - export data from system A, import into system B, done. In reality, it's a data quality project that happens to involve changing platforms.

The problems typically start before anyone touches a migration tool.

Data decay has already happened. Research suggests over 70% of CRM records become inaccurate within a year. Job changes, company acquisitions, email bounces, phone number rotations. By the time you're ready to migrate, your source data is already degraded. Moving that data as-is means importing decay.

Duplicates have multiplied. Most organizations have 30-50% redundancy in their CRM databases. Marketing imports a trade show list. Sales manually adds contacts they've been emailing. An integration creates records automatically. The same person exists three times with slight variations in their name, and no one noticed until the new CRM's deduplication flagged thousands of potential matches.

Field structures don't align. Your old CRM had "Lead Source" as a free-text field where reps typed whatever they wanted. Your new CRM wants a standardized dropdown. Those 47 different variations of "Google" and "google" and "Google Search" and "google ads" need to map to something consistent - or you lose reporting capability entirely.

Relationships break. In the old system, contacts connected to companies through lookup fields that made sense. In the new system, the data model works differently. Without careful mapping, you end up with orphaned contacts, broken account hierarchies, and deals that don't link to anything.

The teams that succeed at migration treat it as a data transformation project, not just a data transfer project.

The Migration Strategy That Actually Works

A successful migration strategy happens in phases, and the actual data transfer is one of the later steps, not the first.

Phase 1: Audit What You Actually Have

Before deciding what to migrate, you need to understand your current state. This means actually looking at your data, not just assuming it's fine.

Run data quality reports on your existing CRM. How many records have missing email addresses? Missing phone numbers? How many contacts aren't linked to accounts? How many accounts have no activity in the past two years? How many obvious duplicates exist?

This audit usually surfaces uncomfortable truths. Teams discover that half their "leads" are actually outdated emails or spam form submissions. They find thousands of records that nobody has touched in years. They realize their prize "database of 50,000 contacts" is really maybe 15,000 usable records once you strip out the garbage.

Good. Better to know now than after you've paid to migrate and clean up junk you should have deleted.

Phase 2: Define What Needs to Move

Not everything deserves migration. This is where organizations make costly mistakes - either moving too much (paying to store and clean worthless data) or too little (losing historical context that matters).

Essential data categories typically include:

• Active customers and their complete history
• Open opportunities and pipeline records
• Contacts with recent engagement (email opens, meetings, deals)
• Accounts with current or recent activity

Candidates for archival or deletion:

• Contacts with no activity in 24+ months
• Bounced email addresses
• Obvious test records and duplicates
• Incomplete records missing critical fields

The cost of migration often scales with data volume. More records mean more complexity, more potential errors, and more cleanup afterward. Migrating 20,000 high-quality records costs less and causes fewer problems than migrating 100,000 records where 80% are junk.

Phase 3: Clean Before You Move

The data cleanup migration principle is straightforward: garbage in, garbage out. Whatever state your data is in when you export it is the state it'll be in when you import it - only worse, because the transfer process introduces its own errors.

Deduplication should happen before migration, not after. Identify duplicate contacts and companies in your source system. Decide which record to keep as the master. Merge the others. This is tedious but critical - trying to dedupe after migration is harder because you've lost some of the contextual information that helps identify true duplicates.

Standardize field values while you still can. Those 47 variations of "Google" as a lead source? Pick the one you want and update the others. Industry classifications that evolved organically over years? Rationalize them into a clean taxonomy. Date formats, phone number formats, address formats, standardize everything now.

Validate contact information. Email addresses that have bounced should be marked or removed. Phone numbers should be verified where possible. This is also the moment to run enrichment on sparse records - fill in missing company data, update job titles that have changed, add firmographic details you never had.

Platforms like Databar can accelerate this pre-migration cleanup significantly. Instead of manually researching each incomplete record, you can run batch enrichment across your database, pulling current information from 90+ data providers to fill gaps and validate existing data. A record that was incomplete in your old CRM becomes complete before it ever touches your new one.

Phase 4: Map Fields With Surgical Precision

Field mapping is where many migrations fall apart. The fields in your old CRM won't match the fields in your new one, not in name, not in type, not in structure.

Create a detailed mapping document that includes:

• Every field from the source system
• The corresponding field in the target system (or "do not migrate")
• Any transformation required (text to dropdown, date format conversion, etc.)
• Validation rules (required fields, format constraints)

Pay special attention to:

Custom fields. Your old CRM probably has dozens of custom fields that various teams created over the years. Some are critical. Some haven't been used since 2019. Map the ones that matter; skip the rest.

Picklist values. Standard fields like industry, country, and lead source need value-by-value mapping if the options differ between systems.

Relationships. How contacts link to accounts. How deals link to contacts and accounts. How activities connect to records. These relational mappings are more complex than field mappings and more likely to cause problems if done wrong.

Phase 5: Test Before You Commit

Never migrate everything at once without testing first. Run pilot migrations with subsets of your data.

Start with a small representative sample. Maybe 500 records across different object types. Migrate them into a sandbox or test environment. Then manually check: Did all fields transfer correctly? Are relationships intact? Do the numbers match?

Test your edge cases. Records with special characters in names. Records with multiple relationships. Records with attachment files. These are where migration tools often fail.

Involve actual users. Have sales reps look at their accounts. Have marketing check their campaign data. They'll spot issues that technical validation misses because they know what the data is supposed to look like.

Phase 6: Execute and Validate

When you've tested thoroughly and cleaned systematically, the actual migration becomes almost anticlimactic. Export, transform, import - with confidence that you've addressed the risks.

Post-migration validation should include:

• Record count reconciliation (did everything make it?)
• Random sample verification (spot-check records against source data)
• Relationship validation (are connections intact?)
• User acceptance testing (do the people who use this data trust it?)

Expect to find some issues. No migration is perfect. The goal is catching problems quickly and fixing them before anyone makes business decisions based on bad data.

The Enrichment Opportunity Most Teams Miss

Here's the thing about CRM migration that most guides don't emphasize: migration is the single best time to enrich your data. You're already touching every record. You're already investing in data quality. Why not come out the other side with better data than you've ever had?

During migration, you can:

Fill missing firmographic data like industry, employee count, and revenue. Your old CRM probably has records with just a company name and website - no context for segmentation or prioritization. Enrich those records during migration so they arrive complete.

Update stale contact information. Job titles change. People move companies. Phone numbers rotate. Running enrichment as part of migration catches updates that accumulated while the data sat in your old system.

Add data points you never had. Maybe your old CRM didn't have technographic information or intent signals. Your new one can. Migration is the time to populate those fields for existing records so you start with a complete dataset.

Validate and verify at scale. Email validation, phone number verification, company domain matching. These checks can run programmatically across your entire database during the migration process.

Databar's waterfall enrichment is particularly useful here - you can pull from multiple data providers automatically, getting the most complete record possible by combining sources. A contact that one provider has email for but no phone number, and another has phone but no email, becomes a contact with both.

Common Migration Mistakes (And How to Avoid Them)

Mistake: Treating migration as a one-weekend IT project. Migration affects sales, marketing, customer success, finance - everyone who touches customer data. Involving only IT or only ops means missing requirements and creating adoption problems.

Fix: Build a cross-functional team and communicate constantly.

Mistake: Migrating historical data you'll never use. That seven-year-old contact who opened one email in 2017 and never engaged again doesn't need to exist in your new CRM.

Fix: Define retention policies and be willing to archive aggressively.

Mistake: Cleaning up "later" after migration. Later never comes. The new system goes live, everyone is busy, and the data quality project gets deprioritized indefinitely.

Fix: Make pre-migration cleanup a non-negotiable gate before the transfer happens.

Mistake: Assuming the migration tool handles everything. Tools help, but they don't make decisions. They can't tell you which duplicate to keep or how to map "Hot Lead" in your old CRM to the new lead scoring system.

Fix: Migration tools are execution mechanisms, not strategy replacements.

Mistake: Not having a rollback plan. Sometimes migrations fail. Systems don't connect. Data corrupts. Users revolt.

Fix: Keep your old system accessible for a defined period post-migration. Know how you'd restore if needed.

After Migration: Keeping Data Clean

A successful migration is a starting point, not an ending. The same data decay that degraded your old CRM will start working on your new one immediately.

Build ongoing hygiene into your operations:

• Automated deduplication rules that flag potential matches as they're created
• Regular enrichment workflows that keep records current
• Validation on data entry to prevent bad data from entering
• Scheduled audits that catch decay before it compounds

The migration itself took months of effort. Don't let that investment decay within a year because you stopped paying attention to data quality after go-live. Get started enriching your CRM data with Databar for free today!

FAQ

What is CRM migration?

CRM migration is the process of transferring customer data, business processes, customizations, and integrations from one CRM platform to another. It involves more than just moving records - it includes mapping fields between systems, transforming data to fit new structures, cleaning and validating information, and ensuring relationships between records remain intact. A well-executed migration improves data quality; a poorly executed one imports existing problems into the new system.

How long does a typical CRM migration take?

Simple migrations with clean data and standard field structures might complete in a few weeks. Complex migrations involving large datasets, custom fields, multiple integrations, and significant data cleanup can take three to six months or longer. The actual data transfer is usually the shortest phase - planning, cleanup, field mapping, and testing consume most of the timeline.

Should I clean data before or after migration?

Before. Cleaning after migration is harder because you've lost context from the source system and introduced potential errors during transfer. Pre-migration cleanup also reduces migration complexity and cost since you're moving fewer records. The principle is "garbage in, garbage out", the quality of your source data determines the quality of your migrated data.

How do I handle duplicates during CRM migration?

Deduplicate before migration whenever possible. Use your source CRM's duplicate detection or a dedicated deduplication tool to identify potential matches. Establish merge rules - which record becomes the master based on completeness, recency, or activity. After merging in the source system, your migration transfers clean, deduplicated records rather than importing the same duplicates you're trying to escape.

Can I enrich data during CRM migration?

Yes, and you should. Migration is the ideal time for enrichment because you're already touching every record. Running batch enrichment fills missing fields, updates stale information, and validates contact data before it enters the new system. Platforms like Databar can pull from multiple data providers to fill gaps, so records that were incomplete in your old CRM arrive complete in your new one.

What causes CRM migrations to fail?

Most failures trace to data issues, not technical problems. Common causes include: underestimating data cleanup requirements, poor field mapping that loses information, broken relationships between records, lack of user testing before go-live, and insufficient training on the new system. Studies suggest failure rates between 47-70% - the vast majority preventable with proper planning and pre-migration cleanup.