Outcreating Enterprise Test Data Management

Our Client

The client is a leading U.S.-based multinational hospitality company operating a global portfolio of over 30 brands and thousands of properties across more than 140 countries and territories. Recognized as an industry leader, the organization caters to a wide range of travel segments, including luxury, premium, and extended stays. They have a strong global presence through managed, franchised, and licensed properties, and they deliver consistent guest experiences. However, they are also manage large-scale data across distributed systems and geographies, facing challenges in ensuring data consistency, quality, and compliance across their extensive portfolio. With millions of guests served annually, the organization is focused on delivering exceptional travel experiences, operational excellence, and strong customer loyalty.

Challenges

The client's rapid digital transformation across multiple IT programs created significant complexity in managing test data at enterprise scale. Fragmented processes, manual provisioning, and insufficient masking increased delays, inconsistencies, and PII exposure risk, while poor preservation of data relationships affected test reliability and reporting quality.

To support secure, efficient, and compliant testing across transformation programs, the organization needed to:

• Centralize test data management to eliminate fragmentation and inconsistencies across programs
• Automate PII identification, classification, and dynamic masking to mitigate data exposure risks
• Ensure regulatory compliance with robust governance controls and complete audit trails
• Preserve data integrity and referential relationships across datasets during masking and anonymization
• Scale test data provisioning to support large-volume datasets and multiple data formats across the enterprise

LTM's Solution

LTM designed and implemented a centralized Test Data Management solution using LTM AI TDM to automate and secure test data provisioning across the enterprise. The solution enabled automated PII discovery, rule-based dynamic masking, and strong governance controls to protect sensitive data while maintaining data integrity and usability for testing.

Built to support multiple data formats, the framework delivered reusable, high-quality test datasets across environments. By replacing fragmented manual processes with an AI-driven approach, LTM helped the client accelerate test data provisioning, strengthen compliance, and establish a scalable foundation for ongoing cloud transformation and analytics initiatives.

Beyond implementation, the solution enabled faster onboarding of new data sources and established a reusable, extensible foundation to support ongoing cloud transformation and advanced analytics initiatives across the organization

Business Benefits

The transformation delivered measurable business impact while modernizing the test data management foundation:

• Achieved 70% reduction in manual effort through automated Test Data Management (TDM)
• Improved speed and efficiency of test data availability across business programs
• Strengthened data security and compliance with PII regulations across distributed systems
• Delivered a scalable solution supporting large-volume datasets and multiple data formats
• Ensured high-quality test data with preserved relationships and integrity across programs
• Reduced data-related defects and ambiguities by more than 95% through consistent masking implementation and governance controls

Conclusion

The successful deployment of a centralized, AI-driven Test Data Management framework marked a significant governance and efficiency milestone for our client. By moving beyond fragmented, manual data provisioning, LTM delivered a robust, scalable, and automated TDM solution that streamlined test data provisioning across multiple business programs. With AI-driven masking, regulatory compliance, and a reusable foundation in place, the organization is positioned to accelerate its cloud transformation, improve testing efficiency, and confidently manage data governance at enterprise scale.