A global enterprise managing thousands of employees across multiple regions faced increasing software licensing costs, underutilized SaaS subscriptions, and rapidly growing cloud infrastructure expenses. Limited visibility into actual usage patterns made it difficult to identify cost-saving opportunities, prepare for vendor audits, and accurately forecast IT spending.

To address these challenges, the organization implemented a synthetic IT Asset Management dataset that replicated its software estate, SaaS ecosystem, and cloud consumption patterns while eliminating exposure to confidential licensing agreements, vendor contracts, and financial data.

Using the synthetic dataset, ITAM, FinOps, and compliance teams were able to:

• Identify over-licensed and underutilized software assets

• Detect inactive SaaS subscriptions and orphaned user accounts

• Validate license optimization and reclamation workflows

• Test cloud cost allocation, chargeback, and forecasting models

• Simulate vendor audit scenarios and compliance reporting

• Evaluate AI-driven recommendations for software and cloud optimization

Business Outcomes

• Improved visibility into enterprise software and cloud utilization

• Reduced risk associated with vendor compliance audits

• Accelerated development of FinOps and cost optimization initiatives

• Enabled secure testing without exposing contractual or financial data

• Supported governance, budgeting, and strategic IT investment decisions