Top Data Migration Considerations for Data Teams

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Read more about author Mav Turner.

Organizations have become highly data-centric in the past years, increasing complications and costs as the volume of data rose. However, data integrity issues alone cost organizations $12.9 million annually, on average, according to Gartner. Because of this, data professionals have spent valuable time, resources, and brainpower on identifying and solving data issues rather than higher-value tasks. Moving into Q4, the opportunity presents itself for data professionals to significantly improve their processes, by implementing automated testing as a part of the data migration, pipeline, and lifecycle. 

Data Migration Best Practices 

Data migration projects can be complicated. First, the upfront risk and associated costs of the project must be evaluated to begin a data migration journey. This is the key to the project’s success. Additional considerations in this process include defect reporting requirements, incident- and error-handling systems integration, and a configuration management process for testing. The responsible party must be able to ensure high-quality testing from the very beginning of the planning process. Equally important are testing staff resources estimates and training needs: Clear roles and responsibilities will ensure an optimal migration. Finally, entrance and exit criteria should be established before formal testing commences to ensure a verifiably successful project.

Even once risks and costs are evaluated, data cannot be instantly migrated. Rather, ensure you have a complete strategy for automated data checks as part of your data pipeline. These kinds of plans allow organizations to start their migration with confidence and ensure the right data gets migrated in a way that ensures the proper context is migrated with the data, or appended to it. Moving unorganized data creates massive waste, as it costs teams additional time and money when they have to go back and re-run the process, or end up storing duplicates because they don’t know what the data is – so they err on the side of caution by keeping it and loading another, potentially duplicate, dataset. They need a plan before moving.

Next, it’s important to build data dependencies and security matrices that look at data’s priority, relationships, integrity, consistency, size, and its metadata. Most importantly, data teams must use automated tests to validate and verify the metadata before moving it. This can be automatically built into the matrices, generally choosing priority based on business value and starting with a smaller subset. With automated data checking, this complicated process of validating and verifying metadata can run more efficiently, resulting in a smoother migration process that could not be as easily achieved if run manually.

Next in this process, teams can overlay their plan with mappings and environments. Check the plan to make sure processes and testing environments have coverage from the lowest level schema to the highest reporting level. I encourage my customers to implement a data testing report card that includes:

  • Accuracy: Do data objectives correctly represent the values?
  • Completeness: Is data missing?
  • Conformity: Does the data match the specified format?
  • Consistency: Are there data conflicts related to an object?
  • Integrity: Are data relationships maintained?
  • Timeliness: Is data up to date?
  • Uniqueness: Does data only repeat where it should?

In the final step of data migration, the entire set must be verified and synced with all dependencies and changes. Verifying data should take place throughout the migration process and in all ongoing external integrations. As experienced as a data professional may be, “staring and comparing” allows room for errors, creating data that cannot be used, or worse, creates issues for your business and customers.

The Challenges of Manual Data Integration Testing

Consider data integration from a migration perspective. First, data teams must replicate the data, which is vital when upgrading or moving databases or migrating from on-premises to cloud. Next, ongoing testing must be conducted at all levels. Only then is it ready to move to the next phases, including data ingestion, data storage, extraction, load transformation, marts and methods, and reporting and analytics. Each of these layers presents its unique challenges. If data teams do not have best practices, they can become time-intensive and costly, and ultimately affect the result of the initiative. 

For example, public cloud has become the most common environment due to its flexible cost structure and speed. However, to realize these advantages organizations must evolve their data testing processes to achieve scale and pace. There’s no point in moving to the cloud if teams cannot test their data with the velocity it requires. As data teams look to manual versus automated data integration testing in their cloud environments, there are six major considerations, including:

  • Agile and DevOps practices: These teams mandate frequent changes that require constant testing.
  • Complex transformation: There are no “big bang” approaches. Instead, iterative development must be supported.
  • End-to-end testing: Testing is required for all data warehouses in business intelligence layers and integrations with target applications and operations source systems.
  • Domain knowledge required: Test management skills of data warehouses are typically limited as the industry is evolving quickly. 
  • Data Management: Robust Data Governance management is a must for most organizations, but not existent in all. 
  • High-testing cost: Tests that are done manually on the business intelligence report layer are expensive.

These complex dependencies make manual testing difficult and especially prone to human error. Migration and integration errors impact the final business unit, causing cascading effects and risking an inability to retrieve or trust your data at all. When the stakes for failure are this high, it is irresponsible to leave the process to human hands alone. 

Automated Testing: A Data Team’s Best Friend

A defined automated testing strategy goes hand in hand with a data team’s end project goals. Automated solutions are the future of testing environments, as they reduce bottlenecks, increase accuracy, and bring precision to manual processes. Data professionals can no longer succeed without a successful test automation practice in today’s dynamic digital environment.