To enable quick and accurate delivery of applications, software teams need on-demand access to the right testing data. Test Data Management (TDM) is the process of creating, handling, and provisioning test data to enable high-quality testing in non-production environments. The idea is to gather enough relevant and safe data, of sufficient quality, to mimic the many different scenarios that the system may encounter upon release.
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The Benefits of Test Data Management
When we ask, “What is test data management?”, what we are really asking is, “Which advantages will we receive by building the right test process around it?”
1. Boosting effectiveness: The effectiveness of both the testing process, and the complete product, rises significantly when proper test data management tools and methods are applied. Gathering relevant high-quality data, in a relatively short amount of time, enables development teams to increase test coverage, accelerate delivery, and improve the organization’s agility.
2. Reducing time and cost: By quickly provisioning the needed test data, teams are able to detect bugs early on in the software development process, and therefore fix them at a much lower cost. In addition, not having to look for relevant data frees development teams to focus on innovation and move the organization forward.
3. Preventing privacy and quality issues: When test data management is both safe, and of the highest quality, teams are able to prevent corruption and privacy compliance problems that can negatively affect the organization’s overall reputation. Reducing production defects and avoiding data breaches increase user trust levels, helping companies stay one step ahead of the competition.
The Challenges of Test Data Management
Testing teams have to contend with many data constraints, which typically slow down software delivery, while hindering quality, and agility.
1. Data accessibility: What is test data management, if not the process of quickly gathering the needed data for the task. Testing teams sometimes lack access to the necessary data, or the tools to extract it. For example, data is typically split between different data sources. A “customer” may be broken up between Customer Care, Billing, Ordering, Ticketing, and Collection systems. To run functional tests on a customer in an integrative testing environment, their data must be extracted from all relevant source systems.
Test data masking assures that sensitive information doesn’t wind up in the wrong hands.
2. Data quality: In many cases, the data may be available, but it fails to meet the required quality standards for different reasons. What is test data management’s biggest quality challenge? Let’s have a look at the following:
a. Corrupted data: Reused data often suffers from corruption problems. Relying on corrupted data could lead to severe problems that are only detected deep into the process, affecting every result.
b. Unmasked data: With data privacy headlining today’s news, there’s no doubt that any production data used in testing environment must be completely masked (made unidentifiable). Any mistakes in this area could lead to heavy fines as a result of non-compliance with GDPR and CCPA.
c. Irrelevant or incomplete data: Applications must be tested against specific data that simulates all required scenarios. Complete data with referential integrity is paramount to prevent test scenarios from breaking due to bad data.
Data synthesis is an invaluable capability, when artificially seeding a designated test population.
3. Data availability: Gathering enough production data to cover the required testing scenarios is often extremely challenging. For example, testers may have a population requirement of 300, but only 50 production samples are actually available. The right TDM tools need to be able to synthesize (generate) 250 data samples, based on the real-life samples, while maintaining data integrity across all systems.
The Right Test Data Management Solution
After answering the question “What is test data management?”, it’s now time to discuss the right solution. Instead of forcing development and testing teams to search for, and move, relevant, complete, and masked production data – from multiple systems, into multiple test environments – organizations need a test data automation tool that can do it for them.
When we set out to build our test data management tools, we focused on finding an answer to the above challenges and enhancing the advantages. The result is an on-demand test data provisioning solution that extends test data coverage, enhances quality, and accelerates software delivery cycles. Teams no longer have to go on a wild goose chase. Instead, they simply get the right data, delivered to their desktops in minutes, instead of days or weeks.
Complete test data sets are provisioned based on any production source, and in accordance with rules predefined by the company. This approach enables shift-left testing by making test data an accessible asset that companies can tap into early on in the development process.