The Role of Cognitive Data Management in Active Archive Architectures

By David Cerf

Today’s storage environments are comprised of a complex mix of file and object storage, and various file  systems each with their unique behaviors. Due to the dependencies between clients and storage resources, users can find it difficult to deploy an active archive for migrating or tiering to cost effective storage. Complex file storage infrastructure can result in low utilization and limit data mobility. The continued growth in unstructured data (files & objects) is increasing the complexity, costs, and operational overhead for data management.

How metadata is changing active archives

Embedded in every file is a treasure that can be used to improve data management and power an active archive, it is metadata. Metadata may be just a simple description of a file but it has very relevant information about the data. When combined with other metadata, policy engines and artificial intelligence, metadata becomes an incredibly valuable source of hidden information that can be used to drive data lifecycle management, improve workflow and enhance applications. Most importantly, this metadata can drive an active archive architecture, using metadata as the trigger to enable data defined tiering and data protection.

Data management is now “cognitive”

Most of today’s file-based workflows have no way of discovering or making use of this information in a simple to use, automated manner. But new cognitive data management solutions with their policy engines, are designed to complement any existing storage environment and enable an automated active archive. This means any file system can now add tape and object storage easily, improving data preservation and retention, search and collaboration, resulting in evergreen storage strategies for simplified migrations and tiering.