… and how you can handle it with XDM’s Test Data Automation.

The rapid evolution of software development and testing will continue in 2025. And there will continue to be existing and new challenges for software developers to overcome. What are the top 7 challenges in test data procurement and how does UBS Hainer’s XDM address them?

Challenge 1 – Data Privacy and Security Concerns

As data privacy regulations like GDPR, CCPA, and emerging laws continue to evolve, organizations face significant challenges in ensuring that test data complies with these regulations. The need to protect sensitive information while maintaining realistic test scenarios is becoming increasingly complex.

XDM addresses the growing concerns around data privacy and security through several key features:

• Data Masking and Anonymization: XDM’s Masking Tool automatically identifies and masks sensitive data using predefined modification functions and look-up tables. This ensures compliance with data protection regulations like GDPR while maintaining data realism for testing purposes.
• Robust Permission System: XDM includes a comprehensive permission system that controls access to configuration objects, supporting OpenID, LDAP, and a built-in identity provider. This feature ensures that only authorized personnel can access sensitive test data.
• Secure Cross-Environment Management: The solution manages data across different environments, preventing accidental overwriting of test data in use and allowing for secure data resets for repeated tests. This capability helps maintain data integrity and security across various testing stages.

Challenge 2 – Volume and Complexity of Data

With the rise of big data and IoT devices, the volume and complexity of data required for testing are growing exponentially. Managing and processing these large datasets for testing purposes will become more challenging, requiring advanced solutions for data handling and analysis.

XDM is well-equipped to handle large volumes and complex data structures:

• Scalability and Performance: The system’s architecture supports scalability, ensuring it can accommodate increasing data volumes without compromising performance.
• Cloud-Ready Solution: Being cloud-ready, XDM can leverage cloud infrastructure to scale resources dynamically as data volumes grow.
• Advanced Data Modeling: XDM offers robust data modeling capabilities, allowing for the design and management of complex data structures across database boundaries. This feature is crucial for managing intricate data relationships in modern applications.

Challenge 3 – Data Quality and Relevance

Ensuring that test data is of high quality and relevant to testing scenarios remains a critical challenge. As software systems become more complex, maintaining data that accurately represents real-world scenarios becomes increasingly difficult.

Ensuring high-quality and relevant test data is a core strength of XDM:

• Test Data Finder: This feature automates the process of finding high-quality, realistic test data. It provides advanced Business Intelligence (BI) capabilities to analyze key statistics, helping teams uncover potential edge cases and ensure comprehensive test coverage.
• Multi-dimensional Edge Case Identification: XDM can surface specific outliers by combining statistics from different attributes, ensuring tests account for rare but critical scenarios.
• Cross-Environment Searches: The ability to search across multiple environments, including both test and production systems, helps testers understand discrepancies and identify the best test cases for migration.

Challenge 4 – Cross-Border Data Transfers

With data flowing across international borders, organizations must navigate complex regulations governing cross-border data transfers. This adds another layer of complexity to test data procurement, especially for global organizations.

XDM has all the practical requirements to meet this challenge:

• Compliance with Regulations: XDM ensures that test data complies with data protection and security standards, which is crucial for managing cross-border data transfers.
• Data Residency and Sovereignty: With the solution’s ability to manage data across different environments it can enforce data residency requirements, ensuring data remains within specified geographic boundaries unless explicitly authorized for transfer.

Challenge 5 – Integration with Emerging Technologies

As new technologies like AI, ML, and blockchain become more prevalent in software development, test data procurement must adapt to support these technologies. This includes generating and managing test data that can effectively validate AI algorithms or blockchain-based applications.

XDM’s features in this context:

• Data Augmentation: Combining the capabilities for subsetting data and doing realistic data manipluations, training and verification data can be replicated from existing data sets
• State-of-the-Art Technology: XDM supports a variety of databases and data storage systems, so it will adapt to emerging technologies as they become more prevalent in software development.

Challenge 6 – Shift-Left and Shift-Right Testing Approaches

The adoption of shift-left and shift-right testing approaches requires early and continuous access to relevant test data throughout the development lifecycle and into production environments. This necessitates more dynamic and flexible test data management strategies.

XDM supports both shift-left and shift-right testing approaches:

• Early Data Provisioning (Shift-Left): The Test Data Shop and automated workflows allow for quick and efficient provisioning of test data early in the development process, supporting shift-left testing practices.
• Realistic Data Simulation (Shift-Right): XDM’s ability to provide realistic test data that closely mimics production scenarios supports shift-right testing by enabling validation of performance and functionality under real-world conditions.
• Continuous Integration Support: The Jenkins plug-in and API support facilitate integration with CI/CD pipelines, supporting both shift-left and shift-right testing throughout the development lifecycle.

Challenge 7 – Increased Compliance Overhead

The complexity of complying with multiple data privacy laws increases the operational overhead for organizations. This requires more sophisticated data governance frameworks and potentially increases the cost and time associated with test data procurement.

XDM helps reduce the operational overhead associated with compliance:

• Automated Compliance Checks: XDM’s data masking and privacy features automate compliance checks, reducing manual effort in ensuring adherence to regulatory requirements.
• Centralized Data Management: XDM’s centralized approach to test data management simplifies compliance efforts by providing a single point of control for data-related activities.
• Configuration as Code: Support for configuration management through a Git source code repository allows for automated application of changes, which can include compliance-related configurations.

Conclusion

UBS-Hainer’s XDM solution offers a comprehensive set of features that address the major challenges in future test data procurement. Its strengths lie in ensuring data privacy and security, managing complex data structures, providing high-quality and relevant test data, and supporting modern testing approaches.

By automating many aspects of test data procurement and management, XDM significantly reduces operational overhead, improves testing efficiency, and helps organizations maintain compliance with evolving data protection regulations.

As software development continues to evolve, solutions like XDM will play a crucial role in enabling organizations to meet the challenges of test data procurement in an increasingly complex and regulated digital landscape.