The $25M Case for Data Minimization

How One Enterprise Eliminated 35+ Petabytes of ROT

In this one-pager, you’ll learn:

  • How one Fortune 100 enterprise eliminated 35+ petabytes of ROT from a 500+ petabyte estate.
  • How data minimization delivered $25M+ in annualized ROI.
  • How federated remediation eliminates cleanup bottlenecks.
  • How Securiti, a Veeam company, helps minimize redundant, obsolete, and trivial (ROT) data & accelerate safe AI adoption.

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The enterprise data footprint is expanding at an unprecedented pace as SaaS, multi-cloud, and AI adoption grow. As a result, redundant, obsolete, and trivial (ROT) data is growing too—hidden across a multi-petabyte data estate. This drives up compliance risk and pollutes AI pipelines with bad data.

One Fortune 100 enterprise managing a 500+ petabyte estate across cloud, on-premises, and distributed systems turned that runaway growth into a measurable win, eliminating 35+ petabytes of ROT and generating $25M+ in annualized ROI, without slowing its AI roadmap.

This case study breaks down exactly how: the discovery approach that uncovered dark data no one knew existed, the automation that flagged risks before they became violations, and the remediation model that got cleanup done without a single bottleneck. Download it to see the full breakdown and how the same approach could benefit your data estate.

 


Frequently Asked Questions (FAQs):

ROT stands for redundant, obsolete, and trivial data—duplicate, outdated, or low-value information stored across enterprise systems. It consumes storage and resources without serving a meaningful business purpose.

ROT accumulates as SaaS, multicloud, and AI adoption expand the enterprise’s data footprint. Without effective discovery and cleanup, duplicate and outdated information can remain hidden across cloud, on-premises, and distributed systems.

ROT increases unnecessary storage and management costs, raises compliance risks, and can introduce poor-quality data into AI pipelines. When it remains undiscovered, organizations struggle to identify and address these risks.

Minimizing ROT helps reduce storage costs, lower compliance exposure, and improve the data foundation for AI. The featured case study reports eliminating 35+ petabytes of ROT and achieving $25M+ in annualized ROI without slowing the enterprise’s AI roadmap.

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