Tiered files still appear in the same folder structure and can be opened like normal, but the actual data is stored elsewhere. This means a user may never notice the difference. This transparency eliminates the need to change applications or user workflows.
The Financial and Security Benefits
Offloading cold files from high-performance storage to a more cost-effective archive platform offers dramatic savings. By reducing the active storage, backup, and disaster recovery (DR) footprint, organizations can reduce their total storage-related costs by more than 70% annually.
Here’s an example based on a 1-PB environment:
- Cold Data Identified: 80% of total volume (819TB)
- Annual Storage Costs (Traditional Setup): $2.59 million
- Annual Costs with Tiering in Place: $770,000
- Annual Savings: $1.82 million, or 70%
Beyond cost savings, tiering also shrinks the ransomware attack surface. Since cold files are no longer actively stored in the primary environment, they are effectively removed from the reach of most threats. This shift can reduce the volume of vulnerable data by up to 80%, significantly lowering both the probability and impact of an attack. Additionally, immutable storage options can be used for cold data, preventing modifications and deletions and further enhancing ransomware resilience.
There is the added benefit that one can deploy a more extensive (and likely more expensive) ransomware solution, as it’s only needed for 20% of the data.
Tiering Without Disruption Boosts Productivity and Departmental Buy-In
Unlike traditional data migrations or storage refreshes, file data tiering operates quietly in the background, maintaining unchanged file access for users and applications, ensuring operational continuity while improving security and reducing costs.
Ideally, an unstructured data management solution can meet the organization’s needs, allowing IT to establish policies for identifying which files to tier based on factors such as age, usage patterns, file types, or ownership. However, there is even more power and potential cost savings when authorized departmental users review their own files and tag data sets that they deem ready for tiering.
This allows IT leaders to optimize tiering strategies collaboratively, maximizing cost savings to the larger organization--and departments specifically--if they are on a chargeback plan. Additionally, a non-disruptive tiering technique that enables direct, native access at the destination means that researchers, data scientists, and other data stakeholders can rest assured; the data they designate for archives will be readily available when needed for future AI or big data analytics projects.
In an era where CIOs and other IT leaders face pressure to be more efficient, avoiding costs wherever possible while mitigating security risks and preparing data for AI, there is a significant amount at stake. The practical idea of file-level data tiering offers a high-impact solution that can preserve the data estate for competitive advantage.
To review, this delivers:
- Cost Optimization: By removing inactive data from expensive storage environments and backup workflows and by reducing the amount of data to be protected from ransomware, you can save 70-80% on storage and backups annually.
- Cost Avoidance: By reducing the capacity of data stored on expensive primary storage, you avoid the need to purchase additional primary storage. In today’s market with rising prices due to tariffs, this is a nice additional insurance to have.
- Risk Reduction: By limiting the attack surface and exposure of sensitive or stale files, you have an expanded ransomware defense.
- Future-Readiness: By enabling the secure and cost-effective retention of data, you create valuable repositories for unstructured data, supporting AI and analytics initiatives.
Final Thoughts
File data has quietly become one of the most costly and risky assets in the enterprise. But with smart, automated strategies like file-level data tiering, CIOs can control dramatically reduce costs, boost security, and prepare their organizations for AI.
