Understanding Data Migration
Data migration
The process of transferring data between different formats, storage types, or computer systems.
Data migration is a critical step in the installation process of new systems and change management.
Note- Data migration is not just about copying data.
- It involves transforming, validating, and ensuring the integrity of data in the new environment.
Common Data Migration Challenges
Incompatible File Formats: Different systems may use proprietary (unique to the system) or outdated file formats
Solution: Use data conversion tools to transform files into a compatible format
Example- Imagine migrating data from a legacy system that uses XML to a modern system that requires JSON.
- A conversion tool can automate this transformation, ensuring data integrity.
Diverse Data Structures: Data models can vary significantly between systems
Solution: Map data fields between systems and use ETL (Extract, Transform, Load) processes to restructure data
ExampleOne system might use a relational database, while another uses a NoSQL structure.
Analogy- Think of data migration as moving to a new house.
- Your furniture (data) may not fit perfectly in the new space (system), so you might need to rearrange or modify it to fit.
Validation Rules: Different systems may have distinct validation criteria
Solution: Align validation rules before migration and perform data cleansing to ensure compliance
ExampleA field that is required in the new system may be optional in the old one.
NoteIgnoring validation rules can lead to data corruption or loss, as incompatible data may be rejected by the new system.
Incomplete Data Transfers: Data may be lost or corrupted during the migration process, for instance, due to network failures, insufficient storage, or software bugs
Solution: Implement robust error-checking mechanisms and conduct post-migration validation to ensure data integrity
Self review- Have you considered how network stability might impact your data migration process?
- What steps can you take to mitigate this risk?
International Data Handling Conventions: Differences in date formats, currency symbols, and measurement units can lead to data misinterpretation.
Solution: Standardise data formats before migration and use localisation tools to adapt to regional conventions.
Example- Date Formats: MM/DD/YYYY vs. DD/MM/YYYY
- Currency Symbols: $ vs. €
- Measurement Units: Metric vs. Imperial
International data handling is especially critical for global organizations, where misinterpretations can lead to significant operational errors.
