Iron Mountain

The problem

Iron Mountain manages billions of physical and digital records across 50+ countries under regulatory regimes that overlap, conflict, and change. Their digital transformation required migrating legacy data infrastructure to modern, scalable systems without disrupting daily operations that customers depend on for compliance-relevant record retrieval. The existing architecture had accumulated decades of technical debt. Data was siloed across business units, each with its own conventions, retention policies, and integration surface. Compliance requirements (GDPR, HIPAA, SOC 2, industry-specific rules per jurisdiction) added complexity to every architectural decision — schemas, retention windows, data-residency guarantees, and encryption boundaries had to hold across geographies simultaneously.

Key challenges

Enterprise data migration at this scale is not a technical problem — it's a portfolio of technical, organizational, regulatory, and vendor problems that all have to be solved in order. Downtime windows measured in minutes have customer contract implications. Compliance failures have material business consequences. And the org has hundreds of downstream systems depending on the data being migrated — every one of them has to keep functioning through the transition, which usually means running the old and new architectures in parallel for a nontrivial window. On top of that, the vendor and tooling landscape is fragmented. Every business unit had accumulated its own preferences over decades. Rationalizing that without breaking working systems requires patience, careful sequencing, and honest engineering judgment about what to preserve versus rewrite.

What we built

AR Data delivered data infrastructure engineering across the migration — designing migration paths from legacy systems, building modern data pipelines, and implementing governance frameworks that met enterprise compliance requirements. The engagement focused on: data pipeline modernization, cloud migration architecture, compliance-aware data governance, and infrastructure automation. The approach favored incremental cutover over big-bang migration. Every legacy system was mirrored into the modernized platform before it was retired, so business units could validate the new system with real workloads before committing. Governance was designed as code — data classification, retention, and residency policies enforced at the pipeline layer rather than as documentation asking teams to comply. This is what made the migration auditable at every step.

Our approach

1. 1

   Inventory before touching anything

   The first phase was a full inventory of data assets, downstream dependencies, and compliance obligations per system. Skipping this phase is the most common reason large migrations fail; doing it well is what makes the rest of the work sequencable.

2. 2

   Mirror-then-cutover per system

   Every legacy system was mirrored into the modernized platform in parallel. Business units validated the new system against real workloads before the legacy was decommissioned. Zero "big bang" cutovers.

3. 3

   Governance as code

   Retention, classification, and residency policies live in the pipeline layer as enforceable code, not as PDFs in a compliance folder. Every dataset carries its policy metadata; every pipeline enforces it.

4. 4

   Compliance verification at each phase

   GDPR, HIPAA, and SOC 2 verification happened at each migration phase, not just at the end. Compliance failures caught early cost hours; caught late they cost weeks.

Key architectural decisions

Results

• Modernized data infrastructure for global operations across 50+ countries
• Zero downtime during phased migration
• Compliance verified across GDPR, HIPAA, SOC 2, and jurisdiction-specific regimes
• Governance as code — data policies enforced at the pipeline layer
• Ongoing infrastructure support and optimization post-migration
• Downstream systems continued operating without disruption throughout the transition
• Auditable migration history — every phase gate documented and verified

Impact

The Iron Mountain engagement is the pattern we bring to every enterprise data engagement: inventory before touching, mirror before cutover, encode compliance instead of documenting it, and treat downstream systems as first-class stakeholders. The work is unglamorous compared to greenfield builds, but it's where enterprise trust is actually earned — and it's what informs how we approach every large migration since.

Tech stack