Did you know, according to research by the Bloor Group, over 80% of data migration projects exceed their planned timelines and budgets? Cost overruns average 30%. Time overruns average 41%. This statistic demonstrates the difficulty level of data migration validation processes in an organization and underscores the significance of best data migration validation practices. Implementing well-defined data migration validation best practices serves as the cornerstone for successful and cost-effective moves. Data migration demands meticulous attention to planning, execution, and validation which will drive the confident success of business data transfers within the systems and applications of the organization.
This article is a technical and informative piece of content that will enable decision-makers and C-suite executives to learn practical data migration validation best practices.
What will you learn from this article?
What is Data Migration and Data Migration Validation?
Data migration is the process of transferring data from one source system to another system that is named the target system. These data migrations happen between legacy systems to modern platforms and on-premises infrastructure to the cloud.
On the other hand, data migration validation is the practice of validating the completeness and accuracy of migrated data. This validation process will identify the completeness, consistency, and integrity of the data throughout the migration from the source system to the destination system.
6 Key Data Migration Validation Best Practices
A well-designed data migration validation practice plan will guarantee that the data migration in a data ecosystem during digital transformation is successful to its fullest. Here are the significant data migration testing practices that any data expert needs to know to cite the accountability of migrated data.
1. Meticulous planning on data migration validation methods
A well-planned data validation strategy is the key to maximizing the success rate of migration projects. This practice involves defining and identifying clear objectives, scope, timelines, budget, level of validation, tools used, specific methods, and understanding validation checkpoints. This includes analyzing and assessing potential risks during migration projects.
This planning stage will decide the resource allocation and assemble multidisciplinary teams according to their expertise and skills. It is crucial to establish suitable and easy communication channels to align the projects with multiple levels of stakeholders.
2. Data profiling and cleaning
Data profiling is the process of identifying and understanding the format, content, type, structure, and quality of data and defining the suitability for migration. This process involves calculating missing data, incorrectness, duplications, and inconsistency at the source system level.
Data profiling and cleaning will be conducted on the source data to prevent them from being migrated to the target system. Resolving and cleaning data and standardizing the data format will ensure the discrepancies are not propagating through the data migration project.
3. Automated data validation tools and data comparison tools
Automated data validation and data comparison tools utilizing advanced machine learning technology will identify the data discrepancies between the source system and the target system. You can train these tools to own your data and let them work on comparing the data before and after migration and work on those data inconsistencies that are missed through narrow cracks.
These tools will conduct validation and comparison tasks to ensure precision, reliability, and integrity to the utmost level waiving off human errors and manual efforts.
4. Parallel real-time testing in data migration validation
As the migration runs between the source system and the destination system, the data migration project team can conduct parallel real-time testing on the data. This will empower the team to identify the data errors and allow them for immediate correction.
This parallel testing will analyze the inputs and outputs between legacy systems and modern systems and enable organizations to make corrections simultaneously before the complete implementation of data migration.
5. Establish a robust data backup plan in data migration validation
A robust data backup plan is crucial before executing the data migration to safeguard the source data that needs to be transferred to the targeted storage. This includes implementing regular backup plans, such as full and incremental backups to protect the data in case of any data loss or corruption during the migration process.
It is recommended to build a backup for the whole data and create another copy of the data set for migration. This will avoid the risks associated with data migration project failures and the organization can restart the project with clean data from scratch. Storing multiple copies of data in various locations will facilitate data recovery and rollback to clean data for any unforeseen situations.
6. Stay informed on data security and compliance
Stay up to date with the advanced and latest data security practices and policies during data migration processes. Enable data access controls, encryption techniques, and all essential data security measures adhering to the regulatory data policies.
Prioritizing data security during data migration is crucial to save sensitive data sets from unauthorized access, breaches, and cyber threats. This practice will protect organizations from regulatory and reputational risks associated with data security.
Ultimate Data Migration Validation Testing Checklist- A Quick Glance
1. Pre-Migration Validation
- Source data integrity and completeness
- Data profiling and cleansing
- Data mappings and transformations
- Compatibility between source and target systems
2. Migration Process Testing
- Data extraction from the source system
- Data transformation and mapping rules
- Data loading into the target system
- Data migration performance and scalability
3. Data Quality Assurance
- Data accuracy, completeness, and consistency
- Referential integrity and constraints
- Data anomalies, duplicates, and outliers
- Compliance with quality standards
4. Functional Testing
- Business logic and rules
- Data manipulation operations
- Data integrity across related tables/entities
- End-to-end testing of critical processes
5. Integration Testing
- Data integration with downstream systems
- Data synchronization and replication
- Compatibility with third-party applications/APIs
- Data availability and accessibility
6. Performance Testing
- Data migration performance under varying loads
- Data throughput, latency, and response times
- Performance bottlenecks and scalability
- Meeting defined performance objectives
7. Security Testing
- Data encryption, masking, and anonymization
- Access controls and permissions
- Vulnerability assessments and penetration testing
- Compliance with data protection regulations
8. Disaster Recovery Testing
- Data backup and recovery procedures
- Failover and disaster recovery mechanisms
- Data integrity and availability in backup repositories
- Timely restoration of data in case of failures/disasters
9. User Acceptance Testing (UAT)
- Stakeholder and end-user engagement
- Data accuracy and usability from the user’s perspective
- Gathering feedback and addressing concerns
- Sign-off for migration readiness
10. Post-Migration Validation
- Data consistency and integrity in the target system
- Reconciliation between source and target environments
- Regression testing for data loss or corruption
- Monitoring system performance and stability
Frequently Asked Questions about Data Migration Validation Best Practices
1. How will data validation be done after migration into cloud?
Data validation after migration to the cloud will typically involve a combination of automated tools and manual checks. Automated scripts can verify data integrity, consistency, and completeness, while manual validation ensures that the data meets specific business requirements. Additionally, cloud-native tools and services may offer built-in validation features to streamline the process.
2. What are the few data validation tools after migration?
After migration, some popular data validation tools include Apache Nifi, Talend Data Quality, Informatica Data Quality, and Trifacta. Each offers robust features for ensuring data accuracy and integrity post-migration.
3. What should I test after data migration?
After data migration, you should test data integrity, accuracy, completeness, and performance. Ensure that all migrated data is present, correctly formatted, and consistent with the original dataset. Verify that applications and systems reliant on this data function as expected and conduct stress tests to assess performance under various loads. Additionally, validate that any dependencies or integrations with other systems remain intact post-migration.
Heliosz.Ai: Your Trusted Partner for Data Migration Validation Best Practices
Successful data migration and data migration validation demands a qualified and experienced data solutions partner. Heliosz.ai brings a wealth of experience and expertise in data migration and validation, empowering businesses to reap the full potential of their data assets.
Our partnership with advanced technologies coupled with a customer-centric approach, ensures seamless transitions and tangible business outcomes. From data profiling and cleansing to automated validation and continuous monitoring, we offer end-to-end solutions tailored to your unique needs.
Final Thoughts
The ability to manage and leverage data effectively is the game-changer strategy for businesses for unimaginable successful outcomes. With the evolution of cloud storage, modernizations of systems and applications, and evolving needs for scalability and agility in business results in frequent data migration projects. Needless to mention agile data migration projects in an organization should be paired with best practices in data migration validation which will decide the overall success rate.
With a trusted and expert partner like Heliosz.ai, data migration, validation, management, and the entire data lifecycle may look like a walk in the park! Schedule a call with our team and kickstart the successful data migration journey!
