5 Best Practices for Data Governance and Quality in Cloud Migration

The global cloud migration services market is expected to grow at an impressive rate of 24% annually from 2024 to 2032. As enterprises increasingly move to the cloud, new databases are emerging that will exist solely in cloud environments. While this trend is promising for organizations eager to leverage cloud technology, it comes with significant challenges. According to the Bloor Group, 83% of data migration projects either fail or exceed their budgets and schedules, a concern echoed by Oracle regarding the high failure rates of cloud migrations.

One critical factor that can ensure failure during cloud migration is poor data quality. Even if you successfully manage other complexities—such as budget constraints, privacy, security, and the intricate processes of planning, preparation, review, and testing—your migration will only be effective if your data quality is up to par.

Given the complexity of cloud migration, it’s crucial to address the following data quality risks: low-quality existing data, merging data from multiple sources, human error, transfer errors, configuration issues, external threats, insider risks, and hardware failures.

So, how can you overcome these challenges and maintain high-quality data during your cloud migration? Read on for the best practices. 

Conduct a Thorough Data Audit

Before migrating data to the cloud, it’s crucial to audit for quality issues like duplicates, inaccuracies, or missing information. This ensures that only clean, high-quality data is moved, reducing the risk of post-migration complications.

Given below is a list of KPIs for data quality during pre-migration, along with a use-case that describes each metric:

• Error rates: A healthcare provider identifies frequent data entry errors during an audit. By addressing these issues, they can improve patient record accuracy, ensuring reliable medical histories post-migration.
• Duplication rates: A retail company finds duplicate customer records during an audit. Eliminating these duplicates before migration helps streamline marketing efforts and enhance customer experience.
• Completeness: A financial institution discovers many customer records with missing addresses. By filling in these gaps before migration, they can ensure accurate customer communications and compliance with regulations.
• Accuracy percentage: An e-commerce business corrects outdated product information, improves inventory accuracy, and prevents stock issues post-migration.
• Timeliness: A logistics firm ensures all shipment data is current before migration, enabling real-time tracking and reducing delivery errors in the new system.

Identifying and addressing these data quality issues upfront leads to smoother cloud migrations and more reliable post-migration data.

Prioritize and validate data for seamless migration

Once you've resolved data quality issues, you must classify your data based on its importance and sensitivity. Critical data, such as financial records or customer information, should be prioritized to maintain business continuity and reduce risks. For instance, during a migration, a healthcare provider would prioritize transferring patient records first to avoid service disruptions.

Next, involve key stakeholders—like IT, data analysts, and business users—in validating the audit findings. This collaborative approach ensures you identify all critical data accurately and securely handle them.

KPIs such as data importance scores and stakeholder feedback accuracy help measure how well you’re doing. These indicators ensure that crucial data is moved securely and efficiently. 

Nail Your Pre-Migration Phase

 Before initiating the migration process, establishing a solid foundation is critical. The pre-migration phase is where you build this foundation, ensuring that every step of your migration is well-organized and efficient. By meticulously planning, mapping your data, and running test migrations, you can minimize risks and pave the way for a successful transition.

Plan meticulously

A solid migration plan is essential to get your migration phase right. For example, a major global financial institution can develop a detailed migration strategy for moving its customer data and financial records to the cloud. They must outline a clear timeline, define roles and responsibilities, and identify key milestones. Such careful planning would help them coordinate across various departments, mitigate risks, and stay on schedule. By proactively addressing potential issues such as data security and compliance requirements, they would most likely avoid costly delays and bring forth a smooth transition. 

Map your data

Creating a detailed data mapping plan helps to ensure smooth integration into the cloud environment. The same financial institution would need to map out how their legacy data structures and formats would translate to the new cloud system. They must identify which data would need transformation—such as converting data formats and integrating disparate data sources. For instance, they could map how transactional data from different regions would be unified under a single system to maintain accuracy and consistency. This thorough mapping process helps prevent data loss and ensures all critical data is correctly aligned with the new cloud architecture. 

Conduct test migration

Running a pilot migration helps to identify and resolve issues before full-scale migration. The financial institution can conduct a test migration with a subset of data that resembles their customer data and financial records. During this pilot, they might discover issues such as data inconsistencies and integration problems. They could find that some data fields from their old system did not map correctly to the new cloud environment. By addressing these issues during the test phase, they can refine their migration strategy and ensure that the final migration would proceed without major problems, thus safeguarding data integrity and continuity. 

Choose the Best Migration Strategy for You

Selecting the right migration strategy is pivotal for a seamless cloud transition. Let’s see how to get it right with a relevant use case:

Evaluate your cloud service provider's security and scalability

When moving from on-premise to the cloud, IT teams are often concerned with two key factors: security and scalability. Top cloud service providers like AWS, Azure, and GCP offer stellar services in these areas, but it's crucial to thoroughly assess your provider's specific offerings. Start by evaluating their security measures, certifications, and scalability options. While scalability is a primary reason companies opt for cloud services, it's important to ensure that the provider can scale according to your needs without exceeding your budget.

For example, a healthcare provider might assess potential cloud providers not only for compliance with healthcare regulations like HIPAA but also for their ability to scale as the organization grows. They might find that while one provider meets security standards, their scalability pricing model doesn’t fit within the company’s budget. This careful evaluation helps safeguard sensitive patient data, ensures regulatory compliance, and guarantees that the provider can support the organization’s future growth cost-effectively.

Evaluate various methods of migration

Different migration methods come with their own sets of pros and cons. You might choose to copy your existing database, which could mean downtime, or replicate it to the cloud while keeping the on-premise version, which requires careful syncing. Another option is to update both environments simultaneously, avoiding downtime but potentially increasing costs. For instance, the healthcare provider could decide on a hybrid approach, which allows them to test their new cloud setup while still using their on-prem systems, ensuring they didn’t lose access to crucial patient information during the transition.

Monitor KPIs during the move

Keeping a close eye on KPIs throughout the migration is essential. Track metrics like data transfer speeds, error rates, and system performance to ensure everything is running smoothly. The healthcare provider would have to monitor these KPIs in real time. They could use this data to quickly address any issues that come up, making sure the migration didn’t disrupt patient care or system access. 

Validate Your Data Post-Migration

Once your data migration is complete, it’s essential to verify that data has been transferred accurately and is working as expected. Here’s how to ensure a smooth transition:

1) Validation checks: Start with comprehensive validation checks to confirm that your data has been accurately and completely transferred. This includes verifying data formats, relationships, and values. For instance, a retail company migrating its customer and sales data should ensure that customer profiles, purchase histories, and transaction details are correctly represented in the new cloud system. Validation tools can help automate these checks, identifying any discrepancies or missing information efficiently.

2) Consistency checks: Conduct consistency checks by comparing data from before and after the migration. This helps ensure that there are no discrepancies between the old and new systems. For example, if the retail company migrated its sales records, it should compare total sales figures, customer data, and inventory levels between the legacy system and the new cloud environment. Automated tools can be used to quickly spot inconsistencies that need to be resolved.

3) Implement data governance mechanisms: Establish strong data governance mechanisms to maintain data quality post-migration. This includes setting up policies for data management, continuous monitoring, and ensuring compliance with relevant standards.

Establish a Robust Documentation Practice

Effective documentation is crucial for ensuring that your cloud migration is successful and that all steps are properly recorded for future reference. Here’s how to set up a robust documentation practice:

Document the migration process

Create comprehensive records of every step in the migration process, including initial planning, data audit results, chosen migration strategy, and any issues encountered.

Maintain data mapping and transformation logs

Clearly document how data from legacy systems like AS400/iSeries is mapped to the new cloud environment, including any transformations applied. For example, during the migration of a retail company's sales data, document how data fields from the old CRM system map to the new cloud-based CRM. In the case of hospitals, they might choose to keep detailed logs of data mapping and transformation processes, which would prove invaluable for troubleshooting and ensuring data accuracy in the new system.

Create post-migration review documents

• Validation results: Record the results of validation and consistency checks, including any discrepancies found and how they were resolved.
• Ongoing documentation: Establish a protocol for ongoing documentation to track data quality and system performance post-migration.

The Final Word

Successful cloud migration hinges on thorough planning and adherence to best practices. Conducting detailed data audits, meticulously managing pre-migration phases, selecting the right migration strategy, verifying data post-migration, and maintaining robust documentation are crucial steps.

Equally important is choosing a reliable platform to guide your migration and ensure data quality throughout the process. For a seamless transition, check out Acceldata's observability platform, designed to support and enhance your cloud migration journey.

Summary

Cloud migration is growing rapidly, but many projects fail due to poor data quality and management. To ensure a successful transition, businesses should follow best practices like conducting thorough data audits to clean up issues before migrating, prioritizing critical data, and involving key stakeholders in validation. Careful planning and mapping of data, running test migrations, and choosing the right migration strategy are essential steps to minimize risks. Finally, post-migration validation and consistent documentation help maintain data integrity and smooth cloud integration.

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