Lifecycle
Lifecycle of a Data Migration
A structured migration lifecycle that guides planning, extraction, transformation, validation and deployment from start to finish.
Overview
Managing every stage of migration delivery
A successful migration depends on visibility, planning and repeatable execution. The lifecycle below outlines the major stages involved in moving data from a legacy platform into a modern target system while maintaining quality, integrity and business continuity.
Each stage builds on the previous one, allowing data owners, SMEs and technical teams to validate progress before moving toward deployment cutover.
Lifecycle stages
- Planning and scoping
- Migration strategies
- Data mapping
- Extraction and transformation
- Loading and validation
- Check, cleanse and repeat
Why it matters
Structured migration stages reduce risk, improve traceability and ensure that every data object is validated before deployment.
Process
The Migration Lifecycle
Planning and Scoping
Define project scope, identify legacy systems, assess risks and establish migration objectives before delivery begins.
Migration Strategies
Determine object-level migration requirements, cutover approaches, data quality concerns and expected volumes.
Data Mapping
Map legacy data structures to the target system while resolving formatting, validation and field alignment issues.
Extract and Transform
Extract required data from source systems and apply transformation rules needed for the target environment.
Load and Validate
Load prepared data into the target system and validate completeness, accuracy and functional integrity.
Check and Cleanse
Refine failed data objects, repeat validation cycles and prepare the migration process for final deployment cutover.
DM Planning and Scoping
Time spent planning early in the migration helps uncover risks, dependencies and technical constraints before delivery.
- Investigating legacy systems
- Defining migration scope
- Hardware and security requirements
- Data quality concerns
- Expected data volumes
DM Strategies
Migration strategies define how each object will be migrated and validated during delivery.
- Object-level migration scope
- Cutover strategies
- Data quality management
- Duplicate handling
- Volume expectations
Mapping
Mapping aligns source system data with the target structure while resolving formatting and validation issues.
- Missing values
- Incorrect formatting
- Invalid values
- Source-to-target alignment
Extract
Required data is extracted from legacy systems and verified for completeness and correctness.
- SME collaboration
- Select statements
- DM REVOLVE extraction
- Verification checks
Transformation
Legacy data is transformed into formats suitable for the target platform and validated by data owners.
- Field restructuring
- Reference mapping
- Format standardisation
- Static value assignment
Load
Validated and transformed data is loaded into the target environment through repeatable test cycles.
- Load simulation
- Iterative migration runs
- Integration testing
- Target validation
Validate
Data owners validate migrated data to ensure it is accurate, complete and functionally correct.
- Missing record checks
- Duplicate validation
- Financial verification
- Functional verification
Check and Cleanse
Failed objects are refined and cycled back through the workflow until validation and signoff are achieved.
- Refinement cycles
- Data cleansing
- ROM reporting
- Repeatability assurance
Next step
Keep your migration controlled from start to finish
A repeatable migration lifecycle improves visibility, reduces risk and helps ensure successful deployment cutovers for complex business systems.
