ERP Data Cleansing Before Migration
Migrating dirty data into a new ERP is like pouring contaminated fuel into a new engine—it will run, but poorly and not for long. Gartner estimates that poor data quality costs organizations an average of $12.9 million annually, and an ERP migration is both the best opportunity and the last affordable chance to fix systemic data issues. Data cleansing before migration is not optional—it is the single highest-ROI activity in any ERP project.
Data Profiling and Quality Assessment
Before cleansing can begin, you must understand the current state of your data through systematic profiling. Data profiling analyzes completeness, accuracy, consistency, and uniqueness across every data object in migration scope. Tools like Informatica Data Quality, Talend Data Preparation, or open-source solutions like Great Expectations automate profiling across millions of records and surface issues that manual review would miss.
- Completeness analysis: identify fields with null or missing values—typical legacy systems have 15-30% incomplete master records
- Accuracy validation: cross-reference key fields (addresses, tax IDs, bank details) against authoritative external sources
- Consistency checks: identify conflicting data across systems (e.g., customer address differs between ERP and CRM)
- Uniqueness profiling: detect duplicate records using fuzzy matching algorithms—average legacy systems contain 10-25% duplicates
- Data quality scorecard: assign a 0-100 quality score per data object to prioritize cleansing effort and track improvement
Systematic Cleansing Techniques
Data cleansing follows a top-down approach: fix structural issues first (format standardization, field mapping), then address content issues (missing values, incorrect entries), and finally resolve relational issues (orphaned records, broken references). Each cleansing rule must be documented and repeatable—you will run the cleansing process multiple times as source data continues to change during the implementation period.
- Standardization: normalize formats for addresses, phone numbers, dates, and units of measure across all source systems
- Enrichment: populate missing fields using internal cross-references, external data providers, or business rule defaults
- Deduplication: merge duplicate records using survivorship rules that retain the most complete and recent data per field
- Validation: apply business rules to flag impossible values (negative quantities, future dates on historical records, invalid codes)
- Archival: identify and separate inactive or obsolete records that should not migrate to the new system
Organizational Ownership and Governance
Data cleansing fails when IT owns it alone. Business process owners must define data quality standards, validate cleansing results, and make decisions about ambiguous records. Establish data stewards for each major data domain (customer, vendor, item, financial) who have both the authority and accountability to approve cleansed data for migration.
- Assign data stewards per domain: minimum one business-side steward per major data object (customer, vendor, item, GL)
- Define quality thresholds: target quality score per data object that must be achieved before migration begins
- Cleansing sprints: 2-week sprint cycles with measurable targets, progress reviews, and escalation of business decisions
- Business rule documentation: every cleansing decision must be documented for audit trail and future reference
Automate data profiling and cleansing with Netray's AI-powered data quality agents—schedule a data health check.
Related Resources
ERP Data Migration Strategy: Planning, Execution & Validation
Plan a successful ERP data migration with a phased strategy covering extraction, transformation, loading, and validation. Reduce migration risk by 80%.
ERPERP Duplicate Data Identification & Resolution Strategies
Identify and resolve duplicate records in your ERP with fuzzy matching, survivorship rules, and merge strategies. Reduce duplicates by 90% before migration.
ERPMaster Data Management (MDM) Strategy for ERP Systems
Implement a master data management strategy for your ERP. Establish golden records, governance policies, and data stewardship for lasting data quality.