How a Major North American Bank Cut Retrieval Times from Weeks to Hours with CC1
Legacy compliance systems can slow audits, increase costs, and limit visibility. This customer story shows how a major North American bank modernized its compliance environment using Custodia Compliance Cloud One (CC1). By migrating over 8 PB of legacy recordings into a secure cloud archive, the bank reduced retrieval times from weeks to hours, lowered storage and operational costs, and improved audit readiness. Read the story to see how CC1 enabled faster investigations, stronger governance, and a future-ready compliance foundation. Contact HPE to discuss how these outcomes could apply to your organization.
What challenges did the bank face with its legacy systems?
The bank struggled with over 8 petabytes of legacy call data locked in outdated systems, which resulted in retrieval times that delayed audits and investigations for weeks. Additionally, they faced high storage and maintenance costs associated with aging on-premises hardware, fragmented compliance visibility, inconsistent governance, and heavy operational burdens from manual workflows.
How did CC1 improve the bank's compliance processes?
CC1 modernized the bank’s compliance data estate by capturing and unifying legacy communication records into a single cloud environment. This led to a significant reduction in retrieval times from weeks to hours, allowing for faster audits and investigations. The bank also compressed and migrated over 8 PB of legacy recordings into a secure 110 TB cloud archive, standardized governance with enriched metadata, and ensured end-to-end data integrity.
What were the outcomes of adopting CC1?
After adopting CC1, the bank reduced storage costs by migrating to a cloud archive and lowered operational costs by eliminating maintenance-heavy legacy systems. They achieved total compliance visibility across all communication channels through a unified view, improved audit-readiness with rich metadata and detailed audit trails, and established a foundation for future AI, analytics, and automation initiatives.
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