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Everything you need to know about a Data Migration Testing Strategy

hemanthyamjala profile image Hemanth Yamjala Updated on ・4 min read

Data Migration Testing
Businesses are going for digital transformation in a big way to stay competitive. The process involves replacing the legacy systems with the latest ones including migrating to the cloud. However, replacing such legacy systems can have its set of challenges including the transfer of data sets. This is because data migration can be a hugely risky venture given its critical nature and high volume. Further, data needs to be delivered to the right person(s) in the right format and in the time when needed. So, what makes data such an important element for any business or organization? Well, data is arguably everything that any business would need to remain operational. It helps a business to understand the ‘health’ of its various wings. In its absence, any business would be struggling to stay afloat let alone remain competitive. No wonder data migration testing has assumed such a significant role.

To explain the criticality of data for an organization with an example, let us consider a large multinational company having many branches, departments, processes, supply networks, and employees spread across geographies. Each process of a department within a branch in a city captures data across touch points. The same is sent across the organization’s hierarchies for analysis and generating results. Since data sets are integrated and help stakeholders to derive new insights, any error can cause misleading information to emerge. Consequently, business stakeholders who are wont to make decisions based on the sanctity of data will be at a loss or worse, may end up making wrong decisions. The example puts into focus the criticality of data for any organization and calls for maintaining its security, accuracy, and integrity at all costs through data migration testing.

Data migration challenges

Data migration can face a slew of challenges, which if not addressed in time, can lead to erroneous results.

  • The mapping of data types should be precise. For example, numeric should be mapped with floating types.
  • Data can be lost during migration possibly due to a faulty process or erroneous analysis. Only a proper migration testing strategy can identify such cases.
  • Since different data types can be stored in multiple tables, they need to be translated correctly when transferred to a modern system.
  • Corrupted data, when migrated to a new system, can cause the latter to crash.
  • The presence of logical errors can hamper the process of data transformation. If not fixed, they can generate unexpected results in the new system.
  • Since the storage of data can be different across systems (legacy and new,) they need to be made compatible with the new system.

To mitigate such challenges and ensure the safe migration of data, businesses should implement a robust test data migration strategy.

Implementing an effective data migration testing strategy

The first step is about determining the scope of data migration. Thereafter, the Subject Matter Experts or SMEs can list out the components to effectively plan for a migration testing approach. The plan may include deciding the phases of migration and allocating resources such as tools and datasets for transfer. Remember, during the actual migration of data, the new system may face unexpected system or performance issues. So, effort should be made in such a way that business continuity does not get hit.

The development, QA, operations, and business analysts should first analyze the new system and understand its functioning. Importantly, the migration team should take the backup of data to be migrated. This is to pre-empt any failure issues later. Again, two major things should be considered during data migration:

Data volume: The initial data comprising historical data should be migrated in a single step. Effort should be made to load huge volumes of data by changing the design architecture of the system. This can pre-empt system failure, which otherwise can be a serious issue for Fintech and banking applications.

Data value: It relates to various types of datasets and their formats. During data migration, the data types should be mapped properly so that there are no compatibility issues between the source and target fields.

Phases in data migration testing

  • Discuss the benefits of data migration with every stakeholder including the time of migration, the usage of new system, and the process to raise tickets for any unexpected issues.
  • Build a comprehensive application migration testing plan by analyzing the business requirements.
  • Create a data migration team with representatives from development, QA, management, and end-users.
  • Set up the criteria to identify the success or failure of migration.
  • Take a backup of existing environments including databases. Post a successful data migration, the original data source should be deleted.
  • Identify the common sources of errors and define a suitable migration testing strategy to counter the same.
  • Build a process to record the results of every iterations including the mismatches.

Conclusion:

Data migration has become a critical requirement for businesses to stay competitive and deliver superior customer experiences. However, suitable data migration testing exercise should be executed to ensure that the accuracy, security, and integrity of data remain intact.

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