Did you know that Gartner predicts 75% of businesses will use generative AI to create synthetic customer data by 2026— up from less than 5% in 2023? Organizations must embrace this shift or risk falling behind due to change anxiety.
As GenAI adoption accelerates and industries adapt to new possibilities, testing processes are also poised for transformation. A key aspect of this evolution is synthetic data generation, which not only enhances test data preparation but also helps organizations navigate complex data laws and regulations.
Synthetic data plays a crucial role in testing and training machine learning models as well as in regular testing, particularly in scenarios where real data is scarce or highly sensitive, such as in finance and healthcare. In fact, Gartner predicts that by 2030, synthetic data will surpass real data in AI training, underscoring its growing importance.
Challenges for Test Data Preparation
Test data preparation plays a crucial role in IT and D&A operations, ensuring software applications and security systems undergo rigorous testing before deployment. However, traditional methods present significant challenges.
Many organizations still rely on manual data entry, which, while customizable, is slow, error-prone, and lacks scalability. Others extract data from production environments, providing realistic datasets but posing security and compliance risks. Automated tools offer efficiency but often fail to generate dynamic and contextually accurate data.
A Gartner report highlights that 60% of organizations adopt synthetic data due to real-world data accessibility issues, 57% cite complexity, and 51% struggle with data availability. Quality and consistency remain a concern—poor test data can introduce bias, leading to unreliable test cases. The challenge grows as IT teams manage massive datasets across multiple environments.
Data privacy laws like GDPR and HIPAA further restrict access to real-world data, complicating compliance. Without centralized test data management, inefficiencies, redundancies, and security vulnerabilities emerge. Additionally, IT workflows involve structured and unstructured data—ranging from text to images—adding complexity. Traditional approaches also risk overlooking critical edge cases, leading to unexpected failures.
As IT and Analytics landscapes evolve, organizations must explore innovative solutions like Generative AI to streamline test data preparation while ensuring security and compliance.
Synthetic data: A compelling solution for all
Generative AI (Gen AI) offers an innovative approach to test data preparation by leveraging deep learning and automation. Here’s how it addresses the key challenges:
Improving Data Quality and Consistency: AI-driven test data generation ensures accuracy, completeness, and realism, eliminating inconsistencies across different environments for large volume of data. Special measures can be introduced to eliminate biases in data. This makes handling test data management more feasible.
Enhancing Data Privacy and Regulatory Compliance: Our experience with Trust Your Supplier (TYS) and other platforms enabled us to create synthetic test data that mimics real-world data without exposing sensitive information, ensuring privacy compliance. For instance, it enabled us to adhere to GDPR for TYS, HIPAA for one of our US based healthcare organizations. We use AI to automate data masking, anonymization, and compliance tracking to meet industry regulations.
Facilitating Centralized Test Data Management: AI-powered platforms centralize test data storage, improve access control, and reduce redundancy. This helps in saving efforts for preparing data for each test environment. AI-based encryption and anomaly detection enhance data protection for stored test data
Managing Complex Business Processes in Multiple Data Modes: AI-generated test data accurately captures real-world IT scenarios, enhancing test coverage. AI can generate structured and unstructured test data, ensuring comprehensive testing across diverse IT environments from multiple sources.
Uncovering Overlooked Insights: AI identifies hidden patterns and edge cases, improving test scenario coverage and reducing unexpected failures.
With synthetic data, as an organization for our product Trust Your Supplier(TYS) and clients we achieved efficiency and speed as AI automates test data generation significantly reducing manual effort and accelerating the testing process. As it reduces dependency on production data by generating near to realistic data, compliance risk, exposure risk and operational costs is reduced. It can handle large-scale data generation without human intervention, adapting to testing requirements dynamically. This data includes diverse scenarios, improving software reliability and performance. With these benefits, technological advancements and data regulations, it’s evident that organizations will be compelled to shift to use of synthetic data sooner or later-with organizations dealing with sensitive data leading the front.
Current Limitations and Redressal While Using Generative AI
While Generative AI (Gen AI) offers transformative potential in test data preparation, its implementation comes with notable challenges that organizations must navigate carefully.
One key concern is biased performance—AI models trained on skewed datasets risk reinforcing inaccuracies. To ensure fairness, regular retraining with diverse and representative data sets is essential.
Additionally, ethical and legal constraints pose hurdles. AI-generated test data must align with regulatory frameworks such as GDPR and HIPAA, requiring transparent governance policies to maintain accountability and compliance.
Security risks also loom large. AI models handling sensitive test data can become targets for cyber threats. Implementing robust encryption, secure training methodologies, and stringent access controls is crucial to mitigating vulnerabilities.
Moreover, model performance issues can arise when AI struggles with domain-specific complexities. Fine-tuning models with industry-specific datasets and continuous monitoring can significantly enhance reliability and precision.
As organizations increasingly integrate Gen AI into IT workflows, a balanced approach—leveraging its strengths while proactively addressing these challenges—will be key to unlocking its full potential in test data preparation.
Future Scope of Generative AI in Test Data Preparation
The future of Generative AI (Gen AI) in test data preparation is poised for rapid advancements, promising to revolutionize workflows with enhanced security, efficiency, and compliance.
One key innovation is Explainable AI, which aims to increase transparency by providing insights into how test data is generated, ensuring greater trust in AI-driven processes. Federated Learning is another breakthrough, allowing AI models to learn from multiple sources without compromising data privacy, significantly bolstering security measures.
AI Agent driven compliance auditing is set to automate regulatory tracking, reducing the burden of manual oversight in test data management. Meanwhile, self-adaptive test data generation will enable AI models to evolve dynamically based on real-time feedback, improving the accuracy and relevance of generated datasets.
Seamless integration with DevOps pipelines will further streamline test data provisioning within CI/CD workflows, minimizing deployment risks and accelerating software delivery cycles.
As organizations continue to embrace Gen AI, these innovations will drive greater efficiency, stronger security, improved ML model performance, and seamless regulatory compliance. With AI technology rapidly evolving, its role in IT and data & analytics (D&A) will only expand, transforming test data management for the better.
Author
Shubhangi Singh, Sr. Business Analyst
Shubhangi Singh is a seasoned business analyst with
Chainyard with over 10 years of experience in business analysis and digital transformation. She holds an MBA from IIM Lucknow and a BE in Electronics and Communication from VTU, Belgaum. She is also a Certified Scrum Product Owner (CSPO) and AWS Certified Cloud Practitioner.
