{"id":3734,"date":"2026-06-08T13:17:03","date_gmt":"2026-06-08T18:17:03","guid":{"rendered":"https:\/\/cloudamize.com\/?p=3734"},"modified":"2026-06-08T13:21:16","modified_gmt":"2026-06-08T18:21:16","slug":"how-ingram-micro-replaced-migration-guesswork-with-real-environment-data","status":"publish","type":"post","link":"https:\/\/cloudamize.com\/en\/how-ingram-micro-replaced-migration-guesswork-with-real-environment-data\/","title":{"rendered":"How Ingram Micro Replaced Migration Guesswork with Real Environment Data"},"content":{"rendered":"

Cloud migrations fail when they’re built on assumptions. When application dependencies are mapped manually, when TCO estimates come from spreadsheets rather than instrumented infrastructure, and when migration waves are sequenced by instinct rather than connectivity data, risk accumulates quietly until it surfaces at the worst possible moment.<\/span><\/p>\n

That’s the scenario an Ingram Micro end customer was working hard to avoid. Facing a large, highly interdependent on-premises environment and a mandate to move to AWS, the team needed more than a migration plan. They needed a plan they could actually trust.<\/span><\/p>\n

Without that level of visibility, planning would have depended too heavily on assumptions rather than operational data.<\/span><\/p>\n

The challenge: three gaps, one complex environment<\/span><\/h2>\n

Before a single workload could move, three critical questions needed answers, and the existing environment made each one hard to answer well.<\/span><\/p>\n

1. COST VISIBILITY<\/b><\/p>\n

The customer lacked a clear picture of current total cost of ownership and had no reliable projection of what the equivalent workloads would cost on AWS. Without that financial baseline, it was impossible to build a business case or validate the economics of migration.<\/span><\/p>\n

2. APPLICATION DEPENDENCY MAPPING<\/b><\/p>\n

The environment contained many interconnected applications and servers. Understanding which workloads were tightly coupled \u2014 and which could be decoupled safely \u2014 was essential for avoiding failures mid-migration. That understanding simply didn’t exist at the required level of detail.<\/span><\/p>\n

3. MIGRATION SEQUENCING<\/b><\/p>\n

Even with dependency data, organizing workloads into realistic, low-risk migration waves requires a systematic approach. The team needed a way to separate non-production from production migrations, identify which applications needed to move together, and sequence everything in an order that minimized downstream disruption.<\/span><\/p>\n

The solution: Cloudamize across two AWS program phases<\/span><\/h2>\n

Cloudamize’s DISCOVER, ANALYZE, and PLAN capabilities were deployed across both the OLA \/ Cloud Viability Assessment and MAP Assess phases, turning raw environment data into actionable migration intelligence.<\/span><\/p>\n

OLA \/ CLOUD VIABILITY ASSESSMENT<\/b><\/p>\n