Discover the top 7 test data management trends for 2024, and how a business entity approach to test data management can leverage them quickly and easily.
The Case for Test Data Management
Top Test Data Management Trends
Entity-Based Test Data Management is Trending
Software development technology never stops progressing, and test data management must keep up. Homegrown, patchwork solutions simply aren’t suitable for enterprise needs.
Companies that continue to hold onto antiquated tools suffer from poor efficiency, slow provisioning speeds, inconsistent privacy compliance, inadequate software quality, and more. Test data bottlenecks are status quo, and enterprises unwittingly experience costly delays and risk non-compliance with data privacy regulations.
Get the latest Gartner report on test data management.
To make sure your test data management tools are what you need, familiarize yourself with the following 7 trends (and afterwards, 1 novel approach) for 2024.
The most prominent business driver today for enterprise test data management is legacy application modernization, including straightforward data migration to the cloud.
Not only is more data moving to the cloud, but it’s also moving to a greater variety of cloud platforms. The adoption of hybrid and multi-cloud deployments is rising. While each organization is at a different stage in its modernization journey, what they all have in common are the growing size and complexity of their data.
Legacy application modernization tools positively impact a company’s rate of innovation, agility, and cost efficiencies. Data teams looking for a low-cost, low-risk modernization strategy, should consider Gartner’s “5 Rs” model:
Re-host
Redeploy applications in a new, typically cloud, environment (a.k.a. “lift and shift”).
Refactor
Restructure and optimize the existing code base, without changing its behavior, to remove technical debt, and improve non-functional capabilities.
Rearchitect
Alter the code to shift it to a new application architecture (e.g., microservices).
Rebuild
Rewrite the application, while maintaining its scope.
Replace
Delete the previous code and install new software, while always taking new requirements into account
Regardless of the model, an agile test data management framework is critical to the success of the project.
The increasing number and types of databases and technologies, including NoSQL, have led to greater data complexity in the sense that a dataset may follow its own logic. So, before the data can be used, data teams need to understand the way it was extracted and structured.
Shift-left testing is an approach to software testing in which testing is performed as early as possible in the process – i.e., shifting testing to the left in the Software Development Life Cycle (SDLC). The idea is to enhance agility by the continuous testing and deployment of parts (e.g., features) of the application as they become available (in short sprints).
By testing early and often, development and QA teams reduce the number of bugs and increase the quality of the code. The goal is to avoid dealing with defects in production, when fixes are much more costly (both in terms of company resources and customer frustration).
Self-service is a growing trend in many data and analytics domains, including testing. Testing teams should be able to provision test data independently, without relying on IT. The solution is a self-service portal, that allows them to provision data subsets on demand, without overwriting any other tester’s data. Another requirement is the ability to roll back test data to prior versions for regression testing.
The ever-increasing focus on data privacy and security, and the need to comply with data privacy laws (like GDPR, CPRA, and HIPAA), have made data masking tools an integral part of the test data management toolbox. Test data masking safeguards Personally Identifiable Information (PII), while allowing the data to be used for legitimate purposes.
Enterprises should replace personal data with realistic data that doesn’t expose anyone to risk. Their data anonymization tools should support in-flight masking of structured data (e.g., PII and confidential records) 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.
Synthetic data generation is the process of creating fake data that imitate the statistical characteristics of real-world data, without compromising data privacy. Synthetic data generation tools are useful in software testing because they allow data teams to test new functionality when production data is non-existent, inaccessible, insufficient, or non-compliant.
Although fake data is compliant with data privacy regulations, by definition, there are potential points of failure that need to be monitored to prevent PII from penetrating into the lower environments.
Provisioning test data into Continuous Integration / Continuous Delivery (CI/CD) pipelines is another emerging trend. Automation tools can be used to manage test data provisioning, reducing the time and effort to create test datasets.
All test data management trends are easier to contend with when taking a business entity approach, where business entities can be customers, orders, or devices.
Test data is collected from any production source, unified, and masked inflight, and then provisioned to the target test systems. Using this approach, development and QA teams can version test data to enable rollbacks, and reserve specific datasets for specific testers.
An entity-based test data management approach features a centralized test data warehouse, allowing for data subsetting – and the subsequent re-use of the subsets across domains – as needed.
Learn more about entity-based test data management tools.