Mastering Data Migration Testing

07 Mar 2024
Mastering Data Migration Testing

There’s little doubt that our modern world is built on vast amounts of data. The advent of easily accessible technology made our day-to-day lives easier and enabled us to collect and collate mammoth amounts of data. The world is no longer limited to paper records and the headaches and limitations of paper storage. As technology advances, the meticulous transfer of data from one medium to a more modern solution is becoming increasingly important. When data migration goes wrong, it can lead to a complete systems meltdown, as experienced by Scotland’s TBS bank in 2018. Disaster struck when 1.9 million customers were affected by botched data migration to transfer the records and accounts of 5.2 million customers from an old system to a newly designed one. Some customers were locked out of the system, while others could see accounts belonging to other customers or were seeing incorrect balances. Nearly a month after the botched migration, some customers were still unable to make payments or access accounts. The damage to TSB’s reputation is immeasurable, apart from the £ 48.65 million fine they received.

Data migration stands as a critical operation in the Information and Communication Technology (ICT) landscape as organisations undergo digital transformation, system upgrades, or simply aim to improve their data management strategies. Data migration involves transferring data from one storage system, database, or format to another. This process is often fraught with potential risks including data loss, corruption, and downtime. Failures in this process can have devastating consequences, as can be seen in the TSB example.

Data migration testing serves as a safeguard to ensure that data is accurately and effectively transferred with minimal risks. This practice is especially crucial in the context of big data systems and the complex ETL (Extract, Transform, Load) processes that underpin data migration efforts. Data migration testing is a pivotal phase in the data migration process, ensuring that data transferred across systems is accurate, intact, and performs optimally within its new environment. Through meticulous testing, organisations can safeguard against data anomalies, data loss and corruption, while ensuring seamless integration of new applications and systems.

What is Data Migration Testing?

Data migration testing is a comprehensive process that involves verifying the successful transfer of data from the source to the destination system without data loss or corruption and ensuring that all functional and non-functional aspects of the application that interact with the data work as expected after migration. This process includes several key activities such as data quality testing, data integrity testing, and system testing, among others, to ensure the data's usability, integrity, and security post-migration.

Who Needs Data Migration Testing?

Considering the disastrous effects data migration errors may have, organisations should carefully consider if Data Migration Testing might benefit them. Consultation with software testing experts will ensure that the risk of data migration is minimised, but businesses in the following scenarios should keep data migration testing front of mind:

  • Organisations Undergoing Digital Transformation - Companies moving from legacy systems to modern IT infrastructures require data migration testing to ensure seamless data transfer.
  • Businesses Implementing New Systems - Organisations introducing new software systems (e.g., ERP, CRM) need this testing to ensure data compatibility and performance in the new environment.
  • Companies Merging or Acquiring - During mergers and acquisitions, data consolidation from different entities necessitates rigorous testing to ensure data integrity and compliance.
  • Cloud Migration Projects - With the increasing shift towards cloud storage and computing, data migration testing is crucial for organisations transitioning to cloud platforms.
  • ETL Developers and Data Engineers - Professionals responsible for managing data pipelines and ensuring the smooth transition of data between systems.

Benefits of Data Migration Testing

Here are 5 key benefits of data migration testing:

  • Ensures Data Quality and Integrity - Critical for big data systems where the volume, variety, and velocity of data can introduce complex data quality issues.
  • Accuracy and Integrity - Ensures data being migrated retains its accuracy and integrity, minimising risks of data loss or corruption.
  • Performance Validation - Validates that the migrated data does not adversely affect the performance of the new system, ensuring that application response times and data processing capabilities meet expected standards.
  • Compliance and Security - Helps in adhering to data protection regulations by ensuring that the migration process does not compromise data security and privacy.
  • Confidence and Reliability - Provides stakeholders with confidence that the migrated data is reliable, accurate, and ready for use in the new environment, facilitating smoother transitions and reducing downtime.

Step-by-step Data/Cloud Migration Testing

Planning and Analysis

Clearly articulate the objectives, considering the specific challenges posed by big data volumes and complexity. Also ensure that the ETL Process Mapping is done correctly by documenting the ETL processes involved, including data extraction methods, transformation logic, and loading techniques. Clearly outline the goals of the migration, including which data needs to be migrated and identify potential risks associated with the migration process, including data sensitivity, volume, and compatibility issues.


Establish environments that can handle big data workloads, simulating real-world data volumes and processing conditions. It is also pertinent to establish a detailed mapping guide that outlines how data from the source system will be transformed, transformed (ETL)and loaded into the target system.


Develop comprehensive test cases that cover all aspects of data validation, including but not limited to data completeness, accuracy, consistency, and performance; ensure that you utilise specialised testing tools which can handle large-scale data migration testing, taking into account compatibility and performance across diverse data sources and targets.

Validation and Verification

Implement data quality frameworks suited for big data, focusing on scalability and the ability to process data in various formats and from different sources. Employ tools and techniques to check the quality of the migrated data, ensuring it meets predefined criteria and standards. It is also crucial to form end-to-end testing to verify the functionality and performance of applications using the migrated data in the target environment.

Reporting and Documentation

Document any issues encountered during the migration testing process, along with their resolution steps and prepare a comprehensive report detailing the testing process, findings and confirmations that the data migration meets all objectives and requirements.


The digital landscape is expanding at a mind-blowing rate, and data integrity will remain crucial as the axis on which so many systems are built. Data migration testing within the realms of big data and ETL processes is a complex yet indispensable part of ensuring data integrity and operational efficiency in modern data ecosystems. By adopting a methodical approach, supported by using specialised testing tools and open-source alternatives for ETL processes, organisations can navigate the challenges of data migration. The successful transfer of vast data volumes and the optimisation of ETL processes enable data-driven innovation and give a competitive edge in an environment that demands agile reliability.

Mathew Zungu

Test Architect

Mathew Zungu is a Test Architect at Inspired Testing. His passions include Business Agility, Continuous Quality, Test Automation and Design Thinking. He holds a Masters in Business Administration and his experience in software quality spans nearly two decades.

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