Data Migration Testing: Methodology, Challenges & Techniques
Transferring data between systems is genuinely challenging if you manage large volumes of data. However, a well-considered test data migration approach can help avoid integrity errors, compatibility problems, and business disruptions.
But even with a robust data migration testing strategy, you cannot eliminate the risks of data quality issues and extended downtime. An abundance of customer data and corporate information also raises security and compliance concerns.
To help you minimize potential challenges or data migration errors, we’ll explain how to test data migration at each phase. We’ll also go over efficient testing methods and techniques to help you move data efficiently and compliantly. But first things first.
Table of Contents
What is Data Migration Testing?
The data migration process means transferring data from one environment to another. This can involve moving data from tables, databases, servers, and applications. Migrations typically happen when upgrading legacy systems, moving to a cloud-based platform, or consolidating companies after mergers and acquisitions.
Any change in the storage location, structure, or environment requires careful handling to avoid errors, data loss, or security risks. That’s what migration testing is about.
Data migration testing ensures the transferred data is accurate, complete, and functional in the new system. Essentially, testing lets you validate that the data retains its integrity, format, and usability across the migration process. Without a data migration test plan, companies risk data loss, repeated migrations, and operational disruptions.
Types of data migration
Data migration projects fall into several categories, depending on the type of data moved and the goals.
- Storage migration: Moving data between storage locations within an infrastructure. Storage data migration testing verifies that the data is correctly read, written, and managed in the post-migration environments.
- Database migration: Repositioning data to a new or updated database system, often with data format conversion. Testing should validate that all database tables are transferred with relationships and indexes intact.
- Application data migration: Transferring application data, often combining database and storage migrations. All functionalities, workflows, and data-related processes should work as expected in the new environment.
- Cloud data migration: Moving data or applications to the cloud or between cloud platforms. In addition to data integrity and compatibility, you must validate security configurations and compliance requirements.
- Business process migration: Transition of all kinds of data that support business processes, systems, and operations. Basic tests should confirm that every workflow integrates into the new systems.
Each type has unique considerations and testing requirements. And yet, the migration testing approach should be as thorough as possible.
How to Approach Test Data Migration: Guide & Best Practices
Of course, companies can just transfer all their databases and verify everything afterward. While that works for some enterprises, companies with complex workflows and legacy data are more likely to encounter issues.
Effective data migration testing involves a preparation stage, real-time migration, and post-migration tests. To simplify data migration testing, we divided each phase into steps highlighting the key points of interest and best practices along the way.
Pre-migration testing
Pre-migration testing lays the foundations for a successful migration. These steps help you understand the scope and identify data migration risks, preparing for potential issues during the transfer.
Step 1: Analyze source and target systems
Analyze the source and target systems to ensure completeness and accuracy of the migration.
- Identify data for migration: Identify what types of datasets you’ll move to the new system. To understand what to transfer, you should audit your application portfolio and business operations.
- Map the data relationships: Create a consistent mapping of data fields, types, formats, and other relationships between datasets.
- Assess data quality: Evaluate your databases to detect anomalies, errors, incomplete information, and duplicate records. If you can’t clean everything, prioritize cleansing for datasets that impact critical business processes.
- Compatibility checks: Confirm that the data types, formats, and structures are compatible between systems.
Step 2: Develop a data migration strategy
Establish a clear plan for transferring data between systems so stakeholders know what to expect.
- Outline objectives: Define measurable goals for the migration, including timelines, budgetary constraints, and success criteria. You should define quantifiable criteria for each test scenario to determine whether it has passed.
- Identify stakeholders: Establish a team that will be responsible for the data migration testing (IT project managers, migration specialists, engineers, testers, and data quality analysts). Each stakeholder should understand their responsibilities.
- Outline methodologies and tools: Determine the tools and methodologies suited for the data migration project. Specify the data transformation methods and techniques (incremental migration or parallel processing).
Step 3: Set up the testing environment
A controlled testing environment allows businesses to validate the migration processes without disrupting the operational environment.
- Establish a testing infrastructure: Prepare a controlled, isolated environment that mirrors the production setting. This includes setting up the same system architecture, network configurations, and load-balancing features.
- Establish rollback procedures: Rollback testing helps revert systems to a stable state in case of migration errors, which can prevent excessive downtime.
- Develop testing scenarios: These scenarios should test data integrity, application functionality, system performance, and security methods. Prioritize testing datasets responsible for business-critical functions.
- Calibrate the tools: The data migration platform, testing software, and security protocols should be reconfigured for the testing environment. Notably, you might calibrate the tool settings a few times after test migrations.
Step 4: Perform cybersecurity and privacy checks
Your migration process should comply with relevant data privacy regulations and security standards.
- Review compliance requirements: Ensure that all legal regulations are met. You should also get consent from users to share their data (especially if you are migrating to another company’s system).
- Form a compliance team: If the migration involves excessively sensitive data, include compliance officers who will oversee compliance with the legal requirements.
- Set strict access controls: Define user permissions, authentication mechanisms, and access levels to limit employee access to sensitive data. As a rule, the minimum number of people should have the minimal amount of access they need for the migration.
- Use data masking and tokenization: Apply data masking and tokenization techniques to protect sensitive information (like customer names or payment data) into fictitious but realistic values, making the datasets compliant with privacy laws. Here are 10 Best Data Masking Tools to consider. Similarly, synthetic data generation can create realistic datasets for pilot migrations, , and it’s important to evaluate utility and resemblance in synthetic data generators to ensure the generated data meets quality and compliance standards.
If you want to make your data compliant and enhance data security, consider getting a smart masking and synthetization tool.
After checking every box, you can proceed with the actual migration.
Migration testing
You should closely monitor and react to anomalies. It’s better to resolve issues as they happen rather than let them accumulate.
Step 1: Monitor the migration in real-time
Before initiating the live migration, a complete dry run is conducted in a staging environment. It’s basically a test drive that lets you validate the process before committing to the full migration.
Select datasets representative of your system or priority functions for this run. During the migration, keep all scripts and tools operational so the team can see how the tools catch issues related to mapping, configurations, and compatibility.
Step 2: Track the migration in real-time
Continuous tracking of the data transfer process ensures that it aligns with expected success criteria and timelines.
- Maintain detailed logs: Capture all activities in the log, such as successful transfers, errors, system responses, and latency increases. This will help find the root cause of issues if something goes wrong.
- Track resource allocation: Data migrations can be intensive and might require extra storage, CPU, memory, and bandwidth allocations.
- Set up notifications: Set up automated alerts for specific errors or anomalies you want to address immediately, such as data format mismatches.
Step 3: Validate data continuously
Periodically verify data integrity checks for referential integrity between tables to reduce the need for rework later. For example, if a customer’s record is lined with multiple orders, you should see if the relationships are preserved during migration.
Instead of verifying every record, verify representative data subsets to validate different categories of data. This can help you avoid repeating the migration all over again.
Step 4: Verify the data flow and synchronization
Confirm that data flows seamlessly between systems without interruptions or errors. This can include validating that customer data inputted into a front-end application flows correctly to the target system.
In many environments, certain systems must remain synchronized with the target system to ensure data consistency and availability. Synchronization tests confirm that the data updates without problem in the new system.
Post-migration testing
After the data migration, the testing strategy should be to confirm the system’s functionality, performance, reliability, and protection. This phase can be divided into several types of testing.
Step 1: Data reconciliation testing
Confirm that the migrated data matches the source data and that the tables’ relationships are intact.
- Verify the datasets: Compare and verify the volume and specifics of the data between systems (record counts, data fields, and other details).
- Check the integrity: Ensure that the primary and foreign keys are preserved (for example, that each record is still connected to tables with their transactional histories).
- Validate consistency: Files and data fields should have the correct format, type, and length to be compatible with the new system.
Step 2: Functional, performance, and user acceptance testing
Tests all applications, workflows, and business logic to see if it works correctly with the transferred data.
- Test edge cases: Run complex workflows or scenarios where several data points are processed to see how the system behaves.
- Validate the performance: Possible test cases should verify the application’s behavior under different business functions and load conditions. This includes uncommon use cases, high-volume transactions, and other complex workflows.
- Validate error handling: Ensure that the system accurately logs errors after the migration.
- Perform user acceptance testing: Encourage employees from your departments and end-users to test basic system functions.
Step 3: Cybersecurity testing
This phase goes further than pre-migration security analysis, as it now involves assessing the system’s vulnerabilities directly via different types of tests.
- Perform vulnerability scans: Automated tools can scan for security weaknesses, such as outdated software, poor configurations, or unsecured ports.
- Run penetration tests: Simulate attacks to see how malicious actors can bypass the authorization mechanism and access controls.
- Audit for compliance: Ensure all communications and data handling practices meet industry regulations and privacy laws. Verify that the system properly logs all transactions and interactions with data.
- Check data retention policies: Confirm that the new platform enforces proper data retention and disposal rules (such as maintaining medical records for at least six years after the last time they were used).
Step 4: Documentation and sign-off
The testing team should revise all technical documentation, user guidelines, and manuals to reflect the new architecture. Every step of the database migration testing strategy and all data transformations must be properly logged.
To finalize the test data migration process, organize a review with key stakeholders to verify all goals and acceptance criteria. Formalize the results of migration with a formal document signed by the IT department, compliance team, and other relevant units.
Data Migration Testing Techniques to Avoid Issues
You may encounter multiple complications and unexpected challenges during the migration project. For a successful data migration, you must understand these and be prepared to address them.
Data complexity
The data migration testing process is much more challenging when you’re moving data with numerous fields, complex relationships, and diverse formats. Different data points can contain interlinked tables, like customer records tied to transaction histories and product details. If the referential integrity isn’t maintained, you break links or lose important insights.
Companies should map data fields and relationships between primary and foreign keys (parent and child tables). Next, divide the migration into manageable segments and test each one independently to simplify troubleshooting. Use specialized migration software that can handle complex features like field mapping, integrity checks, and rollback.
Incompatibility
Data incompatibility arises when the source and target systems use different data structures, formats, or requirements. If compatibility isn’t addressed, migration can result in incorrect values, failed transfers, and truncated data.
Identify databases and applications that might require format changes, data value conversions, or other transformations early in the planning. Adjust schemas for data storage and retrieval so that they align between systems as much as possible. Set up validation rules and scripts to verify data compatibility and flag mismatches automatically during the migration.
Poor data quality
Inaccurate, duplicate, and outdated data, common in legacy systems, can negatively affect the new environment. For instance, low-quality datasets can lead to poor data analysis or reporting errors.
Datasets should be pre-processed before the migration to correct inaccuracies, update information, and remove duplicates. Ideally, data governance policies should be established to ensure that data remains accurate and consistent postmigration.
Security and compliance risks
Personally identifiable information creates risks of unauthorized access and misuse when being moved. You have to comply with all necessary data privacy laws, such as GDPR, CCPA, and HIPAA. Failure to implement proper safeguards during the data migration testing process can result in breaches and compliance violations.
It goes without saying that all transmitted data should be moved only through encrypted communication channels. You should always limit data access to authorized personnel only with role-based permissions and a trust security model.
Use data masking and tokenization to protect sensitive information in non-production environments. If you’re testing the migration process, you can use synthetic data that mimics your real data to eliminate all compliance risks.
Business disruptions
You’ll want to minimize downtime during migration to avoid stalling your business operations. However, tight deadlines usually increase the risk of human errors that may lead to even more disruption.
While it seems obvious, we cannot stress enough that you should avoid rushing. Allocate adequate time and resources for each phase of the migration, especially the pre-migration phase.
You can use strategies for live migration to move datasets while the system is still operational. Split data into smaller chunks that can be processed and migrated simultaneously to avoid congestion issues or failures.
If the migration is proving to be complex during the planning stage, you may need help from an experienced company.
Data Migration Test Cases: Checklist
The following checklist covers the things you should do during the pre-migration and migration processes we discussed above.
| Migration Phase | Test cases and considerations |
|---|---|
| Pre-Migration |
|
| Migration |
|
| Post-Data Migration |
|
Adopt an Efficient Data Migration Testing Methodology
Testing data migration is more effective if you divide the process into pre-migration, migration, and post-migration stages. Focusing on different sets of test cases and checks for each stage makes it much easier to deal with potential issues.
Data privacy and cybersecurity should be critical aspects of your data migration test strategy, too. To safeguard your datasets, consider leveraging Syntho’s synthetic data generation platform. It anonymizes data while preserving utility, ensuring compliance with regulations like GDPR, HIPAA, and more.
Want to learn more? Read our documentation or contact us to sign up for a demo.
Customer Service Engineer & Data Scientist
Shahin Huseyngulu has a strong academic foundation in Computer Science and Data Science and is an experienced Customer Service Engineer and Data Scientist. Shahin has held key roles in customer service, cloud solutions, and machine learning research, showcasing expertise in Python, SQL, and data analytics. Currently, Shahin excels as a Customer Service Engineer at Syntho, building and optimizing customer service operations while bringing a unique blend of technical and customer service skills to drive innovation and customer satisfaction in the tech industry.
Fuel innovation, unlock analytical insights, and streamline software development — all while maintaining the highest data privacy and security standards.