Ranking Test Data Priorities: Why Data Quality Claims the #1 Spot

Main Test Data Priorities: Test Data Quality is Top of Mind

What do enterprises want when it comes to test data automation? In our survey, we found that data quality is top of mind. Out of all the respondents across industries and job titles, 43% cited consistent, high-quality data as one of their main priorities related to test data automation. It’s the #1 priority, followed by flexibility (39%) and scalability (27%).

It’s not surprising that data quality rises above the rest. High-quality test data is foundational for delivering reliable, production-ready applications.  

Organizations that struggle with test data quality often experience: 

• Increased production defects and application instability. 
• Slower release cycles due to rework and retesting. 
• Higher operational costs and inefficiencies. 
• Greater compliance and data exposure risk. 
• Reduced customer satisfaction and higher churn. 

5 Ways Data Quality Improves Your Operations

When test data accurately reflects real-world conditions, teams can confidently validate functionality, performance, and security before release. High-quality data supports organization operations in these five ways: 

1. Improves Defect Detection Early 

Realistic, complete data surfaces edge cases and integration issues that synthetic or incomplete datasets often miss. 

2. Reduces Downstream Risk & Rework  

Poor data leads to missed defects, which are significantly more expensive to fix in production and can impact customer experience. 

3. Enables Trustworthy Results 

Test outcomes are only as good as the data behind them — consistent, high-quality data ensures decisions are based on reality, not false positives or false confidence. 

4. Supports Modern Delivery Velocity 

As AI and automation accelerate development, high-quality data ensures testing keeps pace without becoming the bottleneck. 

5. Strengthens Compliance & Security Validation 

Accurate, properly masked data allows teams to validate privacy controls and regulatory requirements without introducing risk. 

How Quality Impacts AI-Assisted & Agentic Development 

As organizations ramp up velocity, the errors caused by poor data quality magnify in scale and impact. A test data strategy that can’t keep up with the velocity of code is doomed to fail. 

This becomes even more important in the context of AI-assisted and agentic development. AI may help teams generate more code, tests, and application changes faster, but poor or inconsistent test data will limit the value of that acceleration. Faster code creation without reliable test data could simply mean faster defect creation.

Test data is becoming a quality gate for AI-era software delivery. As development accelerates, organizations need confidence that their test environments reflect real-world data conditions. Otherwise, they risk validating AI-generated or AI-assisted code against incomplete, stale, or unrealistic data.

Flexibility Makes Test Data Management Easier

Test data is used for a variety of use cases, including agentic development. Having flexibility for test data automation will ensure that it can fit all the necessary use cases a given organization may have. Many modern enterprises need flexible tools — masking, data delivery, synthetic, or otherwise — to ensure their needs are met effectively. (For more on this, see the “Solving for Modern Test Data Management Challenges” section.)

How Scalability Simplifies Handling Large, Complex Datasets

Organizations are managing increasingly large and complex datasets. Databases continue to grow and accumulate terabytes of data. Scalability, the third priority of surveyed enterprises, ensures that these organizations can be more effectively managed.

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