Testing & QA

Managing test data in enterprise environments is a complex and often painful process. Legacy systems, fragmented data landscapes, and deeply integrated components make it difficult to provide high-quality, compliant data for development and testing. Today, most organizations rely on one of two traditional methods to generate test data: extracting data from production environments or manually creating rule-based mock data.

Using production data may provide realistic scenarios, but it comes with serious privacy, compliance, and security risks. Attempts to mitigate these risks through anonymization or de-identification are often inadequate and leave organizations vulnerable to data breaches and regulatory penalties.

Rule-based mock data is safer from a privacy perspective and widely used for its speed and convenience. However, it often lacks the depth and variability needed for robust testing. Creating accurate rules is time-consuming and requires deep understanding of complex schemas—something test engineers may not always have. As a result, this data often fails to reflect real-world conditions, leading to lower test quality and missed bugs.

These limitations are clear. Organizations need a better way to generate safe, high-quality test data at scale.

Recent advances in generative AI have introduced two powerful approaches to synthetic test data that overcome the shortcomings of traditional methods. Both are supported by the MOSTLY AI Data Intelligence Platform.

1. AI-Generated Synthetic Mock Data

This approach enables the creation of schema-compliant test data without relying on any production data. By using AI to generate synthetic values based on structural definitions and business logic, teams can quickly produce meaningful mock data for common testing scenarios. This method is especially useful during early development or when access to production systems is restricted. It reduces manual rule-writing and offers a faster, more flexible alternative to static test datasets.

2. Production-Based Synthetic Data

When realism is critical, synthetic data can be generated by learning directly from production data. MOSTLY AI’s advanced generative models replicate the structure, relationships, and statistical distributions of the original data while ensuring complete privacy. The resulting synthetic dataset behaves like the real thing but contains no information that could be linked to actual individuals.

This approach enables safe and compliant testing that closely mirrors real-world conditions and is ideal for high-impact testing scenarios, especially in regulated industries.

Synthetic test data offers a range of benefits that transform the way teams build, test, and deliver software.

• Accelerated development cycles
  By generating realistic, privacy-safe data on demand, development teams can eliminate bottlenecks and start testing immediately.

• Improved test coverage and quality
  Synthetic data supports rich, diverse, and representative test scenarios that uncover edge cases and prevent costly bugs in production.

• Seamless support for complex environments
  The MOSTLY AI Data Intelligence Platform supports both single-table and multi-table datasets, preserving referential integrity across relational databases and ensuring consistent, high-fidelity data.

• Privacy compliance by design
  Synthetic data is fully anonymous and exempt from data protection regulations. It can be safely shared and used in non-production environments, even in highly regulated sectors such as banking, insurance, and healthcare.

Not all synthetic data is created equal. Its value depends entirely on how well it reflects the complexity, variability, and patterns of real-world datasets. Without this level of accuracy, synthetic data loses its usefulness for testing, development, and analysis.

At MOSTLY AI, accuracy is our foundation. Our proprietary generative AI technology is designed to capture even the most intricate data relationships and reproduce them with exceptional precision. The result is synthetic data that mirrors real-world behavior and supports confident, insight-driven decision-making.

The MOSTLY AI Data Intelligence Platform empowers teams to generate high-quality, privacy-compliant synthetic data tailored to their needs. Whether you're building with mock data or testing against real-world patterns, our solution helps you move faster, improve quality, and stay fully compliant.

Start exploring for free and see how synthetic data can transform your development and testing workflows.