It goes without saying that quality assurance (QA) is a critical component in any software development organization. QA establishes the quality standards and tactics for the validation and verification of software products, whether you are running an Agile or waterfall methodology. See Info-Tech’s Build a Strong Foundation for Quality blueprint for more information on QA practices. The insights generated from QA activities are critical to make informed, data-driven decisions for product deployment, enhancements, and product road mapping.
Automation, containers, behavior-driven development (BDD), and experienced-based testing are just a few tactics professed by the industry to overcome many of common challenges with QA. While these approaches are helpful, they do not directly address the foundations of effective QA:
Many of today’s testing tactics primarily focus on test design (e.g. BDD), test execution (e.g. automation), and test environment management (e.g. cloud and containers), assuming the right test data is available. However, this is not always the case.
The accuracy and relevance of your test data can make or break the success of your deployed product. Sufficient time and effort should be dedicated to ensuring the data you are preparing supports the test cases you want to execute. Unfortunately, we still see test data management (TDM) as a significant challenge that undermines the speed, rigor, and stability of test automation.
Many test data challenges can be rooted in the unavailability of on-demand test data due to costs, process handoffs (e.g. laborious sign-offs and wait times), functional siloes (e.g. separate test, data, and operations teams), and application system complexities (e.g. lack of holistic data management governance and distributed systems). These challenges are further exacerbated by the fact that many organizations are generating new test data manually despite the maturity of today’s toolsets. In fact, 69% of organizations employ spreadsheets to manually generate new test data and 59% create data manually with every test run (Continuous Testing Report, 2020).
TDM solutions can help overcome some of these challenges by automating the manual and error-prone tasks to generate and refresh your test data and by centrally managing your datasets for on-demand access.
TDM solutions create and manage non-production data that reliably mimics or resembles your production data, so that automation tools and testers can rigorously and accurately verify and validate your application systems. Today’s TDM solutions share several common elements:
Much like any other tool, you will need to evaluate how your TDM solution fits into your overall testing and product delivery environment and strategy:
Your TDM solution should not be done in isolation as local improvements can drive down the efficiency and performance of your entire delivery pipeline.
While it may be ideal to have a single vendor platform to manage all QA and testing activities, many TM vendors have some test data management features (e.g. Microsoft’s Azure DevOps, Parasoft’s Automated Software Testing Tool Suite, and Sauce Labs’ Continuous Testing Cloud Platform), but they are limited to access management and basic data editing and virtualization. In some cases, these basic capabilities are enough to meet testing requirements. However, some vendors offer complementary products within their portfolio to fulfill the TDM gap (such as Micro Focus’ Data Express and Broadcom’s Test Data Manager) or offer out-of-the-box integration with solutions specializing in TDM (such as Informatica’s Test Data Management).
Notable TDM vendors include:
Each TDM vendor has its own approach to the gathering, masking, and preparing of test data and out-of-the-box compatibility with specific industry standards and regulations. However, they share a common set of table stakes features:
TDM vendors try to position themselves as key differentiators through unique features in addition to positioning their table stakes features as best in class.
Test data management (TDM) solutions streamline test management workflows by removing the often manual, time-consuming, and laborious tasks of test data provisioning. While it may seem that TDM solutions are nice-to-have compared to other development, testing, and deployment priorities, the impacts of an automated TDM can be significant, especially when the system under test is distributed, large, and diverse. So, consider the following factors when deciding if a TDM solution is valuable in your organization:
Remember, TDM does not discount the importance of good data management and test management principles and the need to continuously improve your QA practices
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COVID-19 has forced software companies and their suppliers to refocus efforts around prioritizing systems and workflows that are nearly 100% digital in nature. As a result, Info-Tech has observed the quick emergence of six market themes that are highly relevant after COVID-19. This note series will profile key vendors and how they fit into the post-COVID-19 world.
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