Data Quality

Data Cleansing Strategies: Preparing Your Data for MDM Migration

Proven strategies for cleansing enterprise data before migrating to an MDM platform — ensuring your golden records start clean and stay clean.

August 14, 2025

8 min read

The Golden Rule: Clean Before You Migrate

Migrating dirty data into a shiny new MDM platform is like moving clutter into a new house. The mess follows you. Invest in data cleansing before migration and your MDM implementation will succeed faster.

Assessment Phase

Data Profiling First

Before cleansing, understand what you're dealing with:

Run profiling tools across all source systems

Document data quality issues by category and severity

Quantify the cleansing effort required

Prioritize based on business impact

Common Data Quality Issues

Duplicates: Same entity represented multiple times

Incomplete records: Missing critical attributes

Inconsistent formats: Phone numbers, addresses, dates

Obsolete data: Inactive products, closed accounts

Invalid values: Out-of-range, impossible combinations

Cleansing Strategies

Standardization

Normalize formats across all records:

Address standardization using postal databases

Name parsing and standardization (prefixes, suffixes)

Phone number formatting to E.164 standard

Date format harmonization

Deduplication

Identify and consolidate duplicate records:

Exact match on unique identifiers (email, GTIN)

Fuzzy matching on name + address combinations

ML-based matching for complex scenarios

Human review for ambiguous matches

Enrichment

Fill gaps in your data:

Third-party data append services

Reverse lookup for missing contact details

Industry classification enrichment

Geolocation data from address records

Validation

Verify data accuracy:

Address verification against postal databases

Email deliverability checking

Phone number validation and carrier lookup

Business entity verification (DUNS, tax ID)

Archival

Remove data that shouldn't migrate:

Inactive records beyond retention requirements

Test and dummy data

Superseded historical records

Orphaned child records

Cleansing Workflow

**Profile** → Understand data quality baseline
**Classify** → Categorize issues by type and severity
**Automate** → Apply rules-based cleansing at scale
**Review** → Human validation for complex cases
**Verify** → Re-profile to confirm improvements
**Document** → Record all transformations applied

Post-Migration Quality Maintenance

Cleansing isn't a one-time event. Establish:

Intake quality rules to prevent dirty data entry

Ongoing monitoring dashboards

Regular data quality audits

Stewardship workflows for issue resolution

eTron Digital's Data Cleansing Practice

We've cleansed millions of records across MDM migrations for global brands. Our methodology combines automated tooling with domain expertise to deliver migration-ready data that meets the highest quality standards.