The Essence and Methods of Data Migration

What is Data Migration

Data migration is the process of transferring data from one system to another. It is a complex operation that may involve different types of data storage, formats, or IT environments. The main goal of migration is to ensure that existing data is transferred securely, completely, and consistently to a new system where it will continue to be processed and used.

Data migration may involve scenarios such as switching to new software, modernizing infrastructure, consolidating systems, or moving to the cloud. The migration process requires thorough planning, analysis, and execution, as poorly executed migration can lead to data loss, corruption, or misinterpretation.

Methods of Data Migration

Data migration can be carried out in several ways depending on technical capabilities, data volume, system availability requirements, and the criticality of the data itself. The most common methods include:

• Full Migration – A one-time transfer of all data from the old system to the new one at a specific time, often during a scheduled downtime (e.g., over a weekend).
• Phased (Incremental) Migration – Data is transferred in parts or logical units (e.g., by department, module, or date range), with the old and new systems possibly running in parallel for a period of time.
• Live (Real-Time or Continuous) Migration – Used in systems that require constant availability. Data is migrated continuously without interrupting operations, often using real-time replication.
• Hybrid Migration – A combination of the above approaches—for example, critical data may be migrated in a one-time operation, while less critical data is moved gradually. Hybrid approaches are common in complex projects or cloud migrations.

The choice of an appropriate migration method depends primarily on the volume and type of data, system availability requirements, technical and organizational capabilities, as well as the level of risk the organization is willing to accept.
It is crucial to select an approach that best balances security, efficiency, and operational continuity for the organization.

Typical Data Migration Process Consists of the Following Steps:

1. Data Analysis – includes assessment of the scope, volume, quality, and format of existing data. The goal is to understand what will be migrated, where the data is located, and in what condition it is.
2. Source and Target System Analysis – it is necessary to thoroughly understand the structure, logic, and technical capabilities of both the original and target systems. This includes consideration of data compatibility, data types, allowed values, and various constraints.
3. Definition of Migration Rules – based on the analyses, rules are defined that determine:
   • which data will be transferred and which will not,
   • how data formats and structures will be transformed,
   • what validation and cleansing rules will be applied.
4. Preparation of the Migration Environment – involves the development or configuration of tools and scripts for data export, transformation, cleansing, validation, and import. This may include custom scripts, ETL tools, or specialized migration platforms.
5. Migration Testing (Integration and Pilot Tests) – a small-scale data transfer verifies the functionality of the entire process – export, conversion, and import. Testing helps identify technical or logical errors in the process.
6. Data Backup – before the actual migration, a thorough backup of all relevant data is performed to ensure that the original state can be restored if necessary.
7. Data Cleansing – data is reviewed and cleaned before migration – duplicates, outdated or incorrect records are removed, and data consistency is ensured.
8. Migration Execution – the actual transfer of data (either as a one-time or phased operation) based on the prepared rules and verified migration process.
9. Post-Migration Validation and Testing – after migration, a thorough check of the correctness, completeness, and integrity of the transferred data is performed. The functionality of the new system with real data is also verified.

Conclusion

Data migration is a complex process that requires thorough preparation, a systematic approach, and continuous monitoring. Every step – from the initial data analysis to the final validation of migration results – plays a crucial role in ensuring a successful transfer of data to the new environment, while maintaining its quality, security, and consistency.