The Hidden Cost of Data Debt in CRM Migrations

Duplicate records, inconsistent formatting, and orphaned data—how to identify and address data quality issues before they derail your project.

What Is Data Debt?

Every CRM accumulates data debt over time. It's the natural consequence of years of usage—different people entering data in different ways, integrations that broke and left orphaned records, mergers that combined databases without proper deduplication.

Data debt is invisible during normal operations. Your sales team learns to work around it. They know that "Acme Corp" and "Acme Corporation" and "ACME" are the same company. They know to check both the "Phone" and "Mobile" fields. They've developed workarounds for every data quality issue.

But migrations don't have that institutional knowledge. A migration sees three separate companies named Acme. It sees phone numbers in five different formats. And it faithfully moves all that mess into your shiny new CRM.

The Four Types of Data Debt

1. Duplicate Records
The most common form of data debt. The same contact exists multiple times with slight variations. The same company has three accounts because sales reps didn't check before creating new ones. Industry benchmarks suggest 10-30% of B2B CRM data is duplicated.

2. Inconsistent Formatting
Phone numbers stored as "(555) 123-4567" and "5551234567" and "+1-555-123-4567". State names as "California" and "CA" and "Calif." Dates as "12/25/2024" and "2024-12-25" and "December 25, 2024". These inconsistencies break reports, automations, and integrations.

3. Orphaned Records
Contacts without associated accounts. Opportunities linked to deleted contacts. Activities attached to records that no longer exist. These orphans cause errors during migration and create confusion in the new system.

4. Incomplete Data
Required fields that are blank. Email addresses that are clearly fake ("test@test.com"). Records created for testing that were never cleaned up. This incomplete data pollutes your new CRM from day one.

Why Data Debt Kills Migrations

Data debt compounds at every stage of a migration:

During Scoping: You can't accurately estimate effort when you don't know the true data complexity. A CRM with "50,000 contacts" might have 35,000 valid contacts after deduplication—or it might have 80,000 when you count all the related records.

During Mapping: Inconsistent data creates mapping nightmares. If the source system has 47 variations of "Industry" values, someone has to manually map each one. That's hours of tedious work that wasn't in the original estimate.

During Testing: Bad data causes test failures. You think there's a bug in your migration logic, but really it's just garbage in, garbage out. Debugging data issues is time-consuming and frustrating.

After Go-Live: Users immediately notice the problems. "Why do I have three records for this customer?" "Why is this report showing wrong numbers?" The migration is technically complete, but the complaints are just beginning.

Addressing Data Debt Before Migration

The best time to address data debt is before the migration starts. Here's how:

1. Run a Data Quality Assessment
Before quoting a migration project, analyze the source data. Count duplicates. Identify format inconsistencies. Map out orphaned records. This assessment should be a standard part of your discovery process.

2. Make Data Cleanup a Separate Phase
Don't bundle data cleanup into the migration quote. Make it a separate, billable phase. This sets proper expectations with the client and ensures you're compensated for the work.

3. Automate What You Can
Deduplication, format standardization, and orphan detection can all be automated. Manual review should be reserved for edge cases, not bulk processing.

4. Set Quality Gates
Define minimum data quality thresholds that must be met before migration proceeds. If duplicate rate is above 15%, pause and clean. If more than 20% of records have blank required fields, address that first.

How QuillSwitch Handles Data Debt

We built data quality tools directly into our migration platform:

QuillCleanse automatically detects duplicates using fuzzy matching across multiple fields. It doesn't just find exact matches—it identifies "John Smith" and "J. Smith" and "Johnny Smith" as potential duplicates for human review.

Pre-Migration Reports give you complete visibility into data quality before you commit to a timeline. You'll know exactly how much cleanup work is required, allowing you to quote accurately and set proper client expectations.

Data debt doesn't have to derail your migrations. But you have to see it before you can fix it.