World leading research university

How a leading UK university modernized Test Data Management with AI-driven automation

Overview

A leading UK university needed to modernize test data provisioning for application testing while ensuring faster, more automated, and scalable workflows. The university previously relied on a traditional Test Data Management (TDM) tool, but found it unable to keep pace with modern development cycles and automation needs

To address these challenges, the university replaced Delphix with Synthesized, an AI-powered, agent-based test data infrastructure that enables real-time, automated test data provisioning at scale. Unlike legacy tools built for static environments, Synthesized’s cloud-ready, AI-driven architecture streamlined test data workflows, reduced manual intervention, and accelerated application testing.

Background

Synthesized’s AI-powered test data infrastructure enables universities and research institutions to automate data masking, provisioning, and discovery at scale. By leveraging agent-based processing and machine learning, synthesized ensures secure, compliant, and production-like test data without exposing sensitive information. Unlike traditional manual de-identification methods, synthesized automates PII discovery, classification, and anonymization, eliminating bottlenecks in test data access while improving governance and visibility.

For this leading UK university, the challenge was clear: slow, manual test data workflows were hindering application testing, compliance, and overall operational efficiency. Synthesized’s solution enabled the university to deploy scalable, automated test data pipelines while maintaining complete oversight and security controls. Through this collaboration, the university modernized its test data strategy, ensuring faster, safer, and more reliable application testing.

Test data challenges

The university faced critical bottlenecks in test data management, impacting application testing speed, security, and compliance:

SLOW, MANUAL TEST DATA PROVISIONING

Test teams lacked fast, automated access to production-like test data across 1000s of test cases. Existing processes required manual intervention, leading to delays and inconsistencies in test data delivery

LIMITED SCALABILITY & AUTOMATION

Without an automated test data pipeline, teams relied on ad-hoc requests, creating inefficient workflows that disrupted continuous testing and DevOps integration. As applications grew, manual intervention was needed to maintain up-to-date test environments, slowing development cycles.

NO SEAMLESS DEVOPS INTEGRATION

Without automated test data refreshes, provisioning was disconnected from CI/CD workflows, preventing agile development teams from running frequent, reliable tests.

PERFORMANCE LIMITATIONS IN TEST DATAPROVISIONING

Delphix could not efficiently process large volumes of data across multiple databases, leading to bottlenecks that delayed application testing. The university needed a scalable, automated solution to handle real-time, high-volume test data provisioning.

Solution

Synthesized powered multiple critical use cases for the university:

ON-DEMAND TEST DATA PROVISIONING
AI-driven test data discovery and generation eliminated manual requests by enabling instant access to up-to-date test data. Automated workflows ensured reliable, production-like datasets were always available for testing

ACCELERATED TEST DATA DELIVERY
Synthesized’s agent-based horizontal scaling enabled parallel data processing across multiple databases, significantly reducing provisioning time. Integrated directly into CI/CD workflows, test data refreshes were automated, removing delays in testing cycles.

EASE OF USE & OPERATIONAL VISIBILITY
A centralized dashboard provided real-time insights into test data usage, automation workflows, and provisioning status, allowing teams to access and manage test data efficiently without infrastructure complexity.

END-TO-END TEST DATA AUTOMATION
Synthesized powered multiple critical use cases for the university:

01 Automated test data pipelines:
AI-powered test data generation, masking, and provisioning integrated into CI/CD workflows.

02 High-performance test data processing:
Parallelized processing across multiple databases, enabling large-scale test data generation and performance testing.

03 Seamless DevOps integration:
Fully automated test data refreshes triggered on demand through DevOps pipelines, eliminating reliance on manual updates.

04 Sensitive data discovery & classification:
Continuous scanning of databases to identify and categorize PII, ensuring compliance while improving data visibility across departments.

Implementation

01 Discovery and assessment:
The university partnered with Synthesized to evaluate existing test data workflows, identify inefficiencies, and define performance benchmarks for automation.

02 Proof of Concept (PoC):
A local installation and POC validated Synthesized’s automated PII discovery, data masking, and test data provisioning across a subset of university databases.

03 Architecture planning & design:
A horizontally scalable, agent-based processing model was designed to enable parallelized test data generation across 10+ databases.

04 Deployment & training:
Synthesized was fully integrated into the university’s test data infrastructure, with scheduled automation workflows and training for application testing teams.

05 Continuous feedback & optimization:
Real-time usage insights and feedback loops allowed synthesized to refine workflows, enhance governance controls, and improve automation efficiency.

Use cases

Synthesized’s platform has been applied to multiple scenarios within the university, enabling faster, more secure, and scalable test data management across teams.

END-TO-END TEST DATA AUTOMATION
Replacing manual test data provisioning with an AI-driven, scalable test data infrastructure that automates data discovery, masking, and provisioning, enabling continuous test environment readiness across all applications.

AI-POWERED DATA DISCOVERY& RISK MITIGATION
Implement automated PII scanning, classification, and policy enforcement to proactively identify sensitive data, enforce compliance, and mitigate security risks, ensuring adherence to GDPR and institutional governance.

SCALABLE, AGENT-BASED TEST DATAPROCESSING
Deploying an agent-based architecture to parallelize test data generation and provisioning across 10+ databases, enabling real-time, on-demand test data access without performance bottlenecks.

Results

The university successfully modernized its test data strategy by replacing a legacy TDM tool with Synthesized’s AI-powered test data infrastructure, achieving faster, more secure, and scalable data provisioning across its application ecosystem.

Test data provisioning 10x faster

AI-driven automation eliminated manual workflows, reducing provisioning time by 80% and ensuring instant access to high-quality test data for development and testing teams.

Scalable, high-performance test data processing

Synthesized’s agent-based architecture enabled parallelized test data generation across 10+ databases, eliminating performance bottlenecks and supporting multiple applications simultaneously.

Seamless DevOps integration & operational visibility

A centralized test data infrastructure provided real-time insights into test data usage, allowing teams to provision and refresh datasets on demand while improving efficiency across environments in a secure and compliant way.

Lower operational costs & increased productivity

By automating pii discovery, data masking, and test data provisioning, the university reduced overhead, freed up engineering resources, and accelerated development cycles.

Future directions

Scaling across more applications & university systems
Extending Synthesized test data infrastructure beyond the initial 10 databases to support new applications, research initiatives, and operational systems.

AI-powered test data generation for ai/ml initiatives
Leveraging AI-powered test data generation to accelerate AI/ML model training, predictive analytics, and data-driven research, ensuring realistic, high-fidelity synthetic datasets.

Optimizing test data workflows & automation
Expanding DevOps integration and workflow automation to reduce manual dependencies, improve test coverage, and enable faster application releases.

Enterprise-wide test data infrastructure
Building a scalable, self-service test data platform to support distributed development teams, continuous testing, and AI-driven automation across the organization.

Conclusion

The university’s transition to Synthesized’s AI-powered test data infrastructure has eliminated manual bottlenecks, accelerated provisioning, and improved scalability. By adopting fully automated, high-performance test data workflows, the university is reducing operational complexity and enabling faster application development. As they expand their use of Synthesized, the university is building a foundation for more efficient, secure, and automated testing to support future innovation.