Data Management Glossary
Data Storage Management Services (DSMS)
Data Storage Management Services (DSMS) is a term used by Gartner to refer to storage agnostic unstructured data management software solutions like Komprise. IDC estimates that 90% of the data generated by organizations today is unstructured. And Gartner predicts (subscription required) that by 2028, large enterprises will triple their unstructured data capacity across their on- premises, edge and public cloud locations, compared to mid-2024.
Common DSMS Use Cases
- Storage Optimization: Read 8 Ways to Save on Storage and
Backup Costs - Data Lifecycle Management: Read How Lummus Technologies Saves 80% and Improves Data Lifecycle Management
- Data Governance and Reporting: Read New Reports on Unstructured Data and Storage Costs
- Data Classification: Read Why Data Classification Matters
- Data Migration: Read the Unstructured Data Migration Guide
- Data Tiering: Read the Unstructured Data Tiering Guide
- Ransomware Protection: Read How to Protect File Data from Ransomware at 80% Lower Costs
Why Storage-Agnostic Unstructured Data Management Services?
Enterprise data will outlive your data storage, so increasingly enterprise organizations are looking for data storage agnostic approaches to managing and delivering data services. This is only going to become more essential as organizations not only see to optimize costs but also to harness the potential of their unstructured data as part of a broader AI strategy. According to Komprise cofounder and CEO Kumar Goswami:
“Komprise is on a mission to change the way the world manages unstructured data, which is growing exponentially in the enterprise.”
He noted when reviewing the 2024 State of Unstructured Data Management report:
“Our latest survey reveals a pivotal moment in enterprise IT as organizations grapple with the transformative potential of AI while balancing fiscal responsibility. IT leaders will also need to factor in critical data governance and security capabilities. Managing unstructured data strategically to optimize costs and use data workflows to enrich metadata is a great place to start an AI initiative.”
