This article outlines the 5 top test data management trends that will positively impact enterprises in the coming year.
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The Case for TDM
The business and technology arenas never stop moving forward, and test data management (TDM) better keep up. Traditional TDM methods are no longer suitable for today’s enterprise needs. Companies that continue to hold onto them suffer from issues impacting their efficiency and speed, privacy and security compliance, software quality, and more. As a result, multiple operational bottlenecks arise, and enterprises risk costly delays and data breaches.
TDM Trends for 2022
To stay ahead of the TDM game, here are five test data management trends and one novel TDM approach to consider.
The most prominent business driver today for test data management tools is legacy application modernization, typically cloud migrations.
Application modernization offers many benefits in terms of a company’s rate of innovation, agility, and cost efficiencies. IT teams looking for a low-cost, low-risk modernization strategy, should consider Gartner’s “5 Rs” model to application modernization:
Redeploy applications in a new, typically cloud, environment (a.k.a. “lift and shift”).
Restructure and optimize the existing code base, without changing its behavior, to remove technical debt, and improve non-functional capabilities.
Alter the code to shift it to a new application architecture (e.g., microservices).
Rewrite the application, while maintaining its scope.
Delete the previous code and install new software, while always taking new requirements into account
Regardless of the approach, an agile test data management framework is critical to the success of the application modernization project.
Provisioning test data into continuous integration and delivery pipelines is another key TDM trend. By doing so, software and testing teams can shift testing to the left, and alleviate the pains of synthesizing and provisioning compliant test data on demand.
Self-service is a growing trend in many data and analytics domains, including testing. Testing teams want to be able to provision test data independently, without relying on IT. The answer is a self-service portal, that allows them to request data subsets on demand, without overwriting any other tester’s data. A further requirement is the ability to roll back test data to prior versions for repeat tests.
The ever-increasing focus on online privacy and security, and the need to comply with regional regulations, have made this topic a top TDM trend. Data masking is applied to test data to protect people’s personal information, while still making it usable for testing purposes. Data privacy laws, like GDPR and CPRA, demand that the all test data be anonymized, in order to minimize the damage in case of a breach.
Data masking is used to disguise personally identifiable information.
Enterprises should replace personal data with realistic data that doesn’t expose anyone to risk. Their data masking tools should support in-flight masking of structured data (e.g., PII and financial transactions) and unstructured data (e.g., bank checks and voice calls), as well as maintain referential integrity of the masked data across the different applications and data stores.
The IT teams that provision test data increasingly function as a centralized service provider for the various lines of business in the enterprise. A recurrent theme is a central test data warehouse that would be available to everyone. This facility would be responsible for updating and masking data on the fly, while maintaining referential integrity, no matter how many data sources are involved.
More Than a TDM Trend: A Novel Approach to Test Data Management
These TDM trends are easier to contend with when taking a business entity approach to test data. The entity may be a specific customer, order, product, or any other business object that’s central to the application being tested. Test data is collected from the source systems by business entity, unified and masked as an entity, and then provisioned to the target test systems. This approach greatly simplifies test data management, ensuring referential integrity, efficiency, and control of the TDM process.
When taking an entity-based TDM approach, data for business entities is ingested into a centralized test data warehouse, enabling testing teams to subset data by applying selection criteria to the entities, and then provisioning it accordingly. The test data warehouse approach supports data versioning to enable test data rollbacks, as well as the segregation of test data by testers.