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Case StudyFinancial Services
Financial Services Firm (Confidential)

Cloud Migration: Reducing Timelines by 50% via Automated Frameworks

A mid-market financial services firm needed to migrate 120 workloads from on-premises data centers to AWS. DiscoverCloud's automated migration framework compressed a projected 18-month program to 9 months.

6 min readSep 2025
Primary Impact
50%
Reduction in Migration Timeline
9 mo
Actual migration duration (vs. 18-month estimate)
120
Workloads migrated successfully
100%
Compliance validation pass rate at cutover
0
Regulatory findings from post-migration audit

The Challenge

A mid-market financial services company with 120 application workloads spread across two owned data centers needed to exit both facilities within 18 months as part of a real estate consolidation. An independent estimate projected the migration at 18-24 months. The combination of timeline pressure, regulatory requirements for financial data handling in AWS, and the organization's limited internal cloud expertise created a high-risk program that required a migration partner with both technical depth and program management capability.

The Solution

DiscoverCloud delivered the migration using an automated migration factory model: standardized migration patterns for common workload types (web/app tier, databases, file services), automated discovery and assessment tooling, parallel workstream execution, and continuous compliance validation against the organization's financial services regulatory requirements.

Implementation

Discovery and Migration Sequencing

DiscoverCloud's discovery tooling deployed lightweight agents to all 120 workloads, automatically collecting configuration, dependency maps, and performance baselines. The discovery output populated a migration backlog in DiscoverCloud's migration management platform, with each workload assessed against migration pattern fit (lift-and-shift, re-platform, refactor), dependency analysis (ordered sequencing to avoid migrating workloads before their dependencies), and regulatory classification (which workloads handled regulated financial data and required enhanced controls). The dependency analysis revealed 14 critical sequencing constraints that would have caused migration failures if not addressed—constraints that the manual assessment had not fully documented.

Automated Migration Execution

The migration factory model standardized execution for the three most common workload patterns, which together accounted for 85 of the 120 workloads. Standard pattern migrations used pre-built CloudFormation templates, automated cutover runbooks, and validated testing procedures—reducing per-workload migration time from 2-3 weeks (full custom migration) to 3-5 days. Parallel execution of up to 12 workloads simultaneously was enabled by the factory model's standardization; custom migrations would have required serialized execution due to the coordination overhead. The combination of acceleration per workload and parallel execution accounted for the 50% overall timeline compression.

Regulatory Compliance Validation

Financial services regulatory requirements—data residency, encryption standards, access logging, change management documentation—were embedded as automated compliance checks in the migration validation pipeline. Each workload cutover required passing a compliance gate before being marked migration-complete. The compliance gate checked: data residency (workload deployed in configured AWS regions only), encryption (EBS encryption enabled, RDS encryption enabled, S3 bucket encryption configured), access logging (CloudTrail enabled, S3 access logging configured), and IAM (no root account access keys, MFA enabled on all human accounts). Automated compliance checking eliminated the manual compliance audit that had been estimated as a significant timeline contributor in the original 18-24 month projection.