Capabilities that your Data Migration Framework Must Have

Kuppusamy Gopalasamy Synthesis

Kuppusamy Gopalasamy, Technical Manager, Synthesis Systems

Data migrations is no longer just about moving data from one system to another. It encompasses the migration of customers from one system to another, ensuring a seamless transition that enhances customer comfort with their accounts and profiles in the new system. Achieving this goes beyond relying solely on data migration tools such as bulk loaders, SQL loaders, and data pumps. It necessitates the utilization of a comprehensive migration framework tailored for customer migration.

The data migration framework should possess several essential capabilities to facilitate the smooth transition of customers from a legacy system to a well-designed target system:

ETL Capability

The foundation of a migration framework is its ability to perform Extract, Transform, and Load (ETL) operations, seamlessly transferring data from the source system to the destination.

Domain-specific Templates

A well-defined data migration template for each domain is crucial. These templates aid in field mapping during extraction and data transformation, ensuring accurate data migration.

Flexible Data Extraction

The framework should offer flexibility in extracting data from the legacy system in various formats, such as CSV files, databases, and Excel sheets.

Robust Data Validation

The responsibility of data validation should rest with the migration framework, not the target system. The framework should include robust data validation capabilities to ensure high-quality data reaches the target system for loading.

Access to Data Validation failures

The migration team should have immediate access to data validation failures. This access facilitates collaboration with clients to address data cleansing and reprocessing needs.

Support for Data Lookup

Different systems may have varying acceptable values for attributes like Types, Categories, and Statuses. The framework should support lookup features to validate, map, and transform data accordingly.

Support for Data Transformation

The framework should support merging and splitting data to align with target system requirements. Mapping from input to output can vary from one-to-one, one-to-many, many-to-one, to many-to-many relationships.

Versatile Loading Techniques

The target system may offer diverse methods for bulk loading data, such as file-based, API-driven, and direct database loading. The framework should support a range of loading techniques, even enabling a mix of these methods in a single migration.

Comprehensive Logging

Logging is critical for identifying and rectifying issues. The framework should maintain detailed logs, enabling the migration team to analyze and address problems.

Real-time Migration Statistics

Providing migration statistics and success rates is vital. The framework should offer real-time statistics to executives, facilitating informed decision-making.

Insightful Data Analysis

Beyond simple statistics, clients expect meaningful insights, such as failure breakdowns by type, bill cycle, and customer status. The framework should support the configurable execution of these scenarios and promptly present the results post-migration.

User-Friendly Interface

A user interface should be available to display and download statistics and detailed reports. This empowers executives to access reports conveniently while the team is engaged in migration efforts.

By incorporating these capabilities, a data migration framework can streamline the process of moving customers from legacy systems to modern, user-friendly platforms while ensuring data accuracy, customer comfort, and executive decision support.