A range of business trends, from the ongoing digital transformation push and trend toward cloud services to increasingly complex IT environments and the need to ensure data privacy and compliance, are fueling rapid expansion of the test data management market.
According to a recent report from analysts with Custom Market Insights, the global market will grow an average of 12% a year over the next 10 years, from more than $1.1 billion last year to more than $2.5 billion by 2032.
“The market’s growth is fueled by the increasing complexity of IT landscapes, the need for compliance with data privacy regulations, and the rising emphasis on quality assurance in software development,” the market research firm wrote, adding that the space “is driven by the growing recognition of the importance of high-quality test data in ensuring the reliability and performance of software applications. Organizations are increasingly adopting test data management solutions to address the challenges of data privacy, data security, and the need for realistic test scenarios.”
High-quality test data is a critical part of the overall software development lifecycle (SDLC) process, giving organizations a way of ensuring the applications they’re creating are reliable and secure when they’re deployed by catching, tracking and fixing defects. It’s important in a world where time-to-market is key for staying competitive, privacy regulations like the European Union’s GDPR are becoming more common and the costs for not detecting defects rises at each stage of the software development process.
Production Data Won’t Do for Testing
Production data can’t be used in a test system because of security and regulatory concerns, so enterprises need to create test data that is as similar as possible to the actual data that will be used by the application, according to cloud data management vendor Informatica.
“Data that has personally identifiable information must be altered in order to protect people from having sensitive data exposed to the development and testing teams,” Informatic states on its website. “Test data management uses data masking techniques to obfuscate personally identifiable information while still retaining the formatting and other data properties that are important for testing.”
“Software testers are the ones who are responsible for producing software test data,” wrote Cem Dilmegani, principal analyst with research firm AIMulitple. “In some cases, they work in coordination with software developers.”
Dilmegani noted a Delphix survey that found that 50% of respondents felt that QA teams were responsible for test data management, followed by project teams, at 16%, and IT operations (10%). That said, 5% said it should be a centralized and collaborative task in their organizations.
It’s a crowded market, with dozens of vendors fighting for space, including such names as HP, CA, IBM, Informatica, Compuware, Delphix, and SAP, and the market is continuing to be shaped by ongoing trends, according to Custom Market Insights analysts.
The Cloud is the Place
Key among those is the growing popularity of cloud-based test data management offerings, due to their flexibility, scalability and cost efficiencies. In 2022, cloud-based deployment of test date management solutions accounted for almost 80% of the space, and it will continue to grow almost 12% a year through 2032, they wrote.
On-premises deployments were the second-largest market segment in 2022.
“Cloud-based deployment offers a range of services, including storage, computation, and networking capabilities,” they wrote. “Given the often extensive and sensitive nature of data required for proper application testing, cloud-based test environments have gained popularity for their ability to facilitate on-demand testing of various products.”
Third-party cloud providers are address security concerns via rigorous testing, which will help organizations feel more comfortable adopting cloud services for test data management.
AI and Automation
Another trend is that enterprises are pushing to automate the provision of test data processes to speed up the testing cycles and improve efficiency, an effort that raises the increasing role of AI in the space, including using generative AI tools to more quickly create the test data.
“Acquiring data that produces impactful results is a serious challenge for software developers and testers and often includes a good amount of manual labor,” wrote John Rhodes, CTO, architect, and managing director of CM First Group, a company that helps organizations modernize their legacy applications. “AI algorithms have emerged as powerful tools for automating test data generation, harnessing machine learning techniques to analyze existing data sets and generate new test cases based on patterns and trends.”
Using AI models not only means an organization can quickly generate large volumes of diverse test data – which is useful when dealing with complex applications or tight deadlines – but also improves the efficiency of the data-generation processes, according to Rhodes.
“No more creating the test data manually, which can be time-consuming and prone to human errors,” he wrote. “Simply automate the process and proceed forward with fewer errors to work around.”
Challenges Still Remain
That said, there are challenges in the test data management space, according to Custom Markets Insights. Key among them are the growing concern about the security of test data, which may make some organizations reluctant to adopt such management solutions, and interoperability between test data management systems that could hinder integration with existing IT infrastructures, the analysts wrote.
There also are challenges “in maintaining and provisioning relevant test data in dynamic and evolving testing environments,” they wrote.
