4 Best Practices for Data Migration Validation

To ensure successful data and content migrations, life sciences organizations must follow the right steps. In this edition, we discuss four data migration validation best practices to maximize data integrity and compliance.

Moving data and content from one location, format or application to another as a result of M&A activity, initiatives to move to the cloud, or the need to replace outdated technology is a rigorous undertaking. But it’s unavoidable as technology and corporate strategies continue to evolve (and fast). How do you change solutions or environments while maintaining regulatory compliance?

The answer lies in your data migration validation process.

What Is Data Migration Validation?

Data migration validation is an essential step in the new system implementation process to ensure success and compliance. It involves defining how the elements of your system are supposed to function and how data should work in the new system, and then developing and deploying tests to validate that it actually functions as defined.

Validation is the first requirement identified in 21 CFR Part 11 for compliance. 21 CFR Part 11, the FDA’s regulations on electronic records and electronic signatures, requires validation for systems that create, modify, maintain, archive, retrieve or transmit electronic records. Validated systems demonstrate fitness of use, consistency and reliability.

When performing any type of data migration, validation and testing are imperative to minimize the risk of unplanned downtime, loss or corruption of data. This is one of the biggest challenges in a migration project because it can involve thousands of fields and millions of records.

To keep your data and content compliant and secure during a new system implementation or upgrade, follow these 4 data migration validation best practices.

Data Migration Validation: 4 Best Practices

1. Define Testing Strategy Early

We continue to beat the drum that planning is fundamental to a successful data migration.

Data migration validation begins well in advance of technical testing. Define your overall testing strategy as early as possible in the project—at minimum, when the business and technical requirements are near finalized.

If you don’t define a testing approach and scope early enough in the project, you will end up with higher costs and longer timelines—and the potential for insufficient validation and lack of compliance.

2. Follow a Three-Prong Testing Approach

Speaking of testing strategy, we recommend a three-prong approach to migration testing, including:

1. Count-based testing: Check that the number of records in the target matches those migrated from the source.
2. Functional testing with migrated content: Ensure the target application works correctly with migrated data.
3. Manual business verification: Have end-users randomly sample migrated data to verify it migrated per the requirements.

3. Test Before and After the Migration

In the broad scope of a data migration, we typically follow a 5-step process that aligns with the ETL (extract-transform-load) process.

1. Plan: Address key objectives, approaches, roles and responsibilities. This step coincides with the Extraction of content and metadata from the source system(s).
2. Analyze: Analyze source system data and identify mapping rules upfront. This step coincides with the Transformation step as you transform, clean and enrich information to match the source to the target system.
3. Prototype: This is essentially a dry run of the Load step before formal testing, allowing you to visualize your data in the context of the new application and make any necessary updates.
4. Test: Formally test the migration before loading/deployment to Production.
5. Deploy: Import (Load) content and transformed metadata into the target system.

After migration deployment, we recommend a Post-Load Processing step in which you verify the accuracy of the migration through a combination of count queries and manual verification to confirm the transformations were completed accurately.

In this process, you are planning your testing approach, prototyping the test and formally testing – all before you actually deploy the migration – followed by testing after the migration happens. This level of testing ensures a compliant validated data migration.

4. Assign Experienced Quality Assurance Resources

Ultimately, data migration validation is all about ensuring the quality and integrity of your data and system. So, get Quality Assurance (QA) team members involved early and, where possible, have prior experience in Data Migration projects.

Assign QA resources to review and test business and technical requirements—again before the migration even begins. These specialists should work with business analysts to review detailed transformation and mapping specifications, which are important components of validation. Have them meet regularly with the technical team to assure test scripts and transformation requirements are aligned.

Conclusion

Moving data in the life sciences space is serious business with high stakes. A validated data and content migration is essential to a successful and compliant outcome. It’s important to follow a process that already has validation efforts built-in, allowing for less risk and swifter project completions.

Daelight Solutions helps pharmaceutical, biotech and medical device companies, CROs and other players in the life sciences industry plan and execute validated data and content migrations.

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