Our Client

The client is a leading healthcare and medical research organization supporting medical education, scientific research, and institutional collaboration across the United States.

Its enterprise environment manages large volumes of research datasets, institutional documents, collaboration files, and operational records that support mission-critical academic and research activities. As data volumes continued to grow, the organization sought a more efficient way to optimize storage, strengthen governance, and establish an intelligent lifecycle strategy that could securely scale with future cloud modernization initiatives.

Key Challenges

 Healthcare and research organizations are rapidly modernizing storage environments by moving from legacy infrastructure to cloud-native platforms while adopting intelligent data lifecycle management. As enterprise data continues to grow, organizations must balance cost, governance, security, and scalability without compromising access to business-critical information. For the client, several challenges made this transformation increasingly urgent:

• Exponential Data Growth Without Business Value
  More than 40 TB of accumulated file data, much of which had not been accessed since before 2018, continued consuming premium storage without delivering ongoing business value.
• High Infrastructure and Storage Costs
  Inefficient storage utilization, redundant data, and inactive files significantly increased Amazon FSx storage costs, highlighting the need for ongoing cloud infrastructure optimization.
• Limited Data Lifecycle Governance
  The absence of automated policies for classification, archival, retention, and deletion resulted in uncontrolled data sprawl.
• Limited Data Ownership and Access Visibility
  Open-access shared drives reduced visibility into data ownership and usage, creating governance risks and limiting accountability.
• Security and Compliance Constraints
  Enterprise IAM policies prohibited archival approaches dependent on long-lived credentials, requiring a more secure architecture.
• Tooling and Architectural Limitations
  Existing tooling constraints, licensing incompatibilities, and migration performance issues identified during the proof of concept required architectural redesign before enterprise deployment.
• Scalability Bottlenecks
  Early proof-of-concept testing revealed migration performance challenges for large-scale datasets, highlighting the need for a more secure and scalable data lifecycle architecture.

To modernize enterprise data management, the organization required a secure, policy-driven, and cloud-native lifecycle strategy capable of reducing infrastructure costs while strengthening governance and supporting future data growth.

LTM’s Solution: Intelligent Data Lifecycle Management on AWS

LTM designed and implemented an Intelligent Data Lifecycle Management Framework through a proof-of-concept-led approach. Rather than deploying a predefined architecture, LTM evaluated multiple design options before implementing a secure, scalable framework aligned with the client's operational and security requirements.

Key capabilities included:

Policy-Driven Data Discovery and Archival

LTM initially designed a policy-based archival solution using Data Dynamics StorageX to automate the identification and movement of inactive data from Amazon FSx to Amazon S3.

• Automated classification of inactive data and aging enterprise data
• Policy-driven archival to Amazon S3 for cost optimization
• Simplified storage administration through automated lifecycle workflows
• Built-in restore capabilities and storage reclamation for business continuity

During implementation, enterprise IAM credential rotation policies made this architecture incompatible with the client's security standards, requiring the solution to be re-engineered without compromising automation or governance.

Secure Cloud-Native Migration Framework

LTM subsequently implemented a secure migration architecture using AWS Storage Gateway, delivering a compliant and scalable data lifecycle solution while eliminating dependency on long-lived IAM credentials. This approach also strengthened AWS infrastructure management by aligning storage operations with enterprise security and governance requirements.

• Secure migration of enterprise data from Amazon FSx to Amazon S3 using Active Directory-based authentication
• Elimination of IAM key dependency to align with enterprise security policies
• Custom Python automation for data validation, reconciliation, and cleanup
• Automated data integrity verification and audit reporting
• Controlled source-data deletion after validation to ensure zero data loss

Together, these capabilities established a secure, scalable, and policy-driven data lifecycle framework that optimized storage while improving governance, operational resilience, and long-term cloud infrastructure optimization.

Business Outcomes

The transformation delivered measurable operational and financial value while establishing a scalable foundation for enterprise data lifecycle management.

Significant Cost Optimization

• Reduced Amazon FSx storage footprint from ~39.4 TB to ~15 TB
• Lowered annual storage costs from ~$120K to ~$44K
• Reduced long-term archival costs by up to 95% through Amazon S3 archival tiers
• Generated estimated annual savings of $70K–$76K

Elimination of Dark Data

• Identified and archived 63% (approximately 24.87 TB) of inactive enterprise data
• Freed premium storage capacity for high-value business workloads

Improved Operational Efficiency

• Reduced backup volumes by approximately 60%, accelerating backup and recovery cycles
• Improved file system performance and search efficiency across millions of files

Enhanced Data Governance and Compliance

• Established visibility into data ownership, usage patterns, and data lifecycle
• Enabled policy-driven retention and regulatory compliance

Strengthened Security Posture

• Reduced exposure to outdated and inactive data assets
• Minimized the enterprise attack surface by eliminating unused data

Future-Ready Cloud Foundation

• Built a scalable foundation for automated lifecycle management and tiered storage adoption
• Enabled extensibility for future enterprise data modernization initiatives

Conclusion

More importantly, the engagement established enterprise data lifecycle management as a strategic business capability rather than an infrastructure function. With a secure, policy-driven framework now in place, the organization is well positioned to scale cloud modernization, automate lifecycle governance, and manage rapidly growing data volumes with greater efficiency, resilience, and control. As digital research and collaboration continue to expand, this intelligent foundation enables enterprise data to remain governed, accessible, and cost optimized throughout its lifecycle while supporting future innovation across research and institutional operations. 

As a trusted AWS Managed Services partner, LTM leveraged insights from ongoing operations of the customer’s AWS environment to identify storage optimization opportunities, proactively transform data lifecycles, and implement a secure, governance-driven archival strategy. This approach reduced storage costs by over 60%, while improving operational efficiency and compliance.

Learn more about LTM’s Amazon Web Services (AWS) cloud transformation capabilities

Contact us at SLSales.CIS@ltm.com to learn more.