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The 2026 Test Data Management Report for AI-Ready Enterprises
- Letter from the Authors
- Ranking Test Data Priorities: Why Data Quality Claims the #1 Spot
- The Data Control Paradox: Top Priorities vs. Biggest Barriers
- The Problem with Process: Test Data Bottlenecks Are Stalling Workflows
- Solving for Modern Test Data Management Challenges
- Key Takeaways: How You Can Make the Next Step Toward AI-Ready Test Data Management
- Respondents Snapshot: Segments, Industries, & Job Titles
- Key Terms to Know
Report > The 2026 Test Data Management Report for AI-Ready Enterprises
The Data Control Paradox: Top Priorities vs. Biggest Barriers
Enterprises Want High-Quality Data — But Don’t Always Get It
While data quality is a top priority, it’s also enterprises’ greatest challenge. Our survey found that 39% of enterprises face data quality or validity issues — again potentially leading to issues like software defects and application quality concerns. This concern is followed by testing across complex systems (30%) and sensitive data/personally identifiable information compliance (26%).
Lack of Governance & Control Causes Major Problems Across the Board
These top challenges — data quality/validity issues, testing across complex systems, sensitive data/PII compliance, as well as relying heavily on production data copies (25%) and long lead times for getting access to test data (22%) — all paint a bigger picture about test data management.
We’re seeing a trend here that depicts “data chaos,” a lack of data governance and control over data. When organizations don’t have complete control of their data, it means they can struggle to get the compliant, high-quality data they need. Additionally, they may struggle to protect complex enterprise environments and ensure compliance.
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Top Challenges by Role
We asked specific groups working in IT operations, security and compliance, analytics and data engineering, and application development about their biggest challenges. For most of the groups, data quality once again rose to the top.
For application development leaders, testing across complex systems was the #1 priority, followed closely by data quality. Testing across and among complex systems is a priority as it is at this point that problems that emerge at scale begin to appear.
For a tester, the problems of integrity that they may have solved locally return as the integration of different systems presents both the problems that these new additions may use different representations of data, and that the test data in use by System A may has to synchronized with System B.
This seems simple to describe but can be incredibly laborious to implement. Hard-coded values can result in a full review of huge swaths of use cases. Identifier-dependent automated tests can suffer the same problem if they need to be synced and each system chooses different identifiers for similar cases.
Reconciling business logic also turns into a real effort at scale. Downstream systems that rely on the state of data from upstream systems can create the need for net-new cases or can require test cases that need to be harmonized to work with both sets of business logic (among other concerns).
And finally, there is the impact of complex change management. At a small scale, potential secondary and tertiary testing effects are few. But as scale-up occurs, more and more effort has to be placed on making sure that changes in the testing of one system doesn’t impact testing in another.
Testing across complex environments is the #1 priority for software roles.
Why Data Quality Makes Protecting Sensitive Data More Complex
Organizations are continuing to make trade-offs in the name of data quality — sacrificing compliance because they’re worried data protection will degrade data integrity. Quality is the #1 barrier to protecting sensitive data. This issue compounds, as sensitive data continues to increase exponentially. Out of those surveyed, 57% report an increase in the volume of sensitive data in non-production environments.
Enterprises fear that masking or protecting their data will degrade its quality, though that’s not true if you have the right solution. (For more information on approaches to data protection, see our “Solving for Modern Test Data Management Challenges” section.)
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