Data Migration Best Practices: Moving Data Between Applications Safely

Master the art of migrating data between applications during portfolio rationalisation. Avoid data loss, maintain integrity, and minimise business disruption.

Why Data Migration Is the Hardest Part of APR

The Hidden Complexity

Migrating data between applications is consistently the most underestimated aspect of portfolio rationalisation. What appears to be a simple transfer often involves complex transformations, validation rules, and business logic that can derail timelines and budgets.

Common Data Migration Challenges:

• Data format differences between source and target systems
• Incomplete or inconsistent data in source applications
• Complex relationships and dependencies between records
• Historical data that doesn't fit new application structures
• Business rules embedded in data that must be preserved
• Downtime requirements during cutover

Migration Failure Statistics

• 83% of data migration projects exceed their budget
• 68% experience significant delays
• 44% report data quality issues post-migration
• 33% suffer business disruption during migration
• Average cost overrun is 50-100% of original estimate

Data Migration Methodology

Phase 1: Assessment and Planning

Source Data Analysis:

• Inventory all data entities and volumes
• Document data formats, types, and structures
• Identify data relationships and dependencies
• Assess data quality (completeness, accuracy, consistency)
• Map source fields to target fields

Migration Strategy Definition:

• Big bang vs. phased migration approach
• Downtime requirements and business tolerance
• Parallel running period and validation
• Rollback plan and criteria
• Resource and timeline estimates

Phase 2: Design and Build

Mapping Specification:

• Field-by-field mapping from source to target
• Transformation rules for format differences
• Default values for missing required fields
• Validation rules for data integrity
• Exception handling for unmappable data

Migration Tool Selection:

• Custom scripts (Python, SQL) for complex transformations
• ETL tools (Talend, Informatica, Azure Data Factory)
• Application-specific migration utilities
• SaaS migration services
• Manual migration for small datasets

Phase 3: Testing

Test Migration Runs:

• Execute migration on a subset of data first
• Validate record counts match between source and target
• Verify data accuracy with business users
• Test edge cases and exception handling
• Measure migration performance and duration

User Acceptance Testing:

• Business users validate critical data in the target system
• Process testing with migrated data
• Report generation and accuracy check
• Integration testing with connected systems
• Performance testing under load

Phase 4: Execution

Pre-Migration Checklist:

• [ ] Final source data backup completed
• [ ] All migration scripts tested and approved
• [ ] Communication sent to all stakeholders
• [ ] Support team briefed and on standby
• [ ] Rollback plan reviewed and ready
• [ ] Downtime window confirmed

Migration Execution:

• Freeze source system for writes
• Execute migration scripts
• Validate record counts and checksums
• Run automated data quality checks
• Enable target system for users
• Monitor for issues

Post-Migration Validation:

• Verify critical records with business owners
• Run key reports and compare outputs
• Test end-to-end business processes
• Check integration data flows
• Document any known issues or exceptions

Data Quality Management

Pre-Migration Cleansing

Common Data Quality Issues:

• Duplicate records (same customer, different formats)
• Incomplete records (missing required fields)
• Invalid data (incorrect formats, out-of-range values)
• Orphaned records (references to deleted relationships)
• Inconsistent encoding (character sets, date formats)

Cleansing Strategies:

• Deduplicate using matching algorithms (fuzzy matching)
• Fill missing fields with defaults or best-guess values
• Validate formats and ranges against business rules
• Remove orphaned records or create placeholder references
• Standardise encoding to UTF-8 and consistent date formats

Data Quality Metrics

Track these during and after migration:

• Completeness: Percentage of required fields populated
• Accuracy: Percentage of records passing validation rules
• Consistency: Record counts matching across systems
• Timeliness: Data currency in the target system
• Uniqueness: Duplicate rate in the target system

Common Migration Scenarios

CRM to CRM Migration

Key Concerns:

• Contact and account deduplication
• Custom field mapping
• Activity history preservation
• Pipeline and opportunity migration
• Email template and workflow migration

ERP Migration

Key Concerns:

• Chart of accounts mapping
• Open transaction migration
• Historical data archival vs. migration
• Custom report recreation
• Approval workflow reconfiguration

Email/Collaboration Platform Migration

Key Concerns:

• Mailbox size and archival policies
• Calendar and contact migration
• Shared drive and permissions migration
• Group and distribution list recreation
• Third-party integration reconnection

Legacy Database Migration

Key Concerns:

• Schema differences and normalisation
• Stored procedure and business logic migration
• Character encoding conversion
• Index and performance optimisation
• Backup and recovery procedure updates

Risk Mitigation Strategies

Always Maintain Rollback Capability

• Keep source system operational until validation is complete
• Take full backups before migration execution
• Document rollback procedures step-by-step
• Set clear rollback criteria and decision points
• Test rollback procedures in staging environment

Parallel Running

• Run source and target simultaneously for 1-4 weeks
• Compare outputs from both systems daily
• Identify discrepancies and resolve before cutover
• Build user confidence in the new system
• Gradually shift traffic from old to new

Communication Plan

• Notify all stakeholders of migration timeline
• Provide user training before cutover
• Set up dedicated support channel during migration
• Send regular status updates during execution
• Document and share post-migration changes

Data migration is where portfolio rationalisation theory meets reality. By investing properly in planning, testing, and quality management, you protect your most valuable asset—your business data—while enabling the portfolio optimisation that drives long-term value.