Intelligent Data Management

Intelligent data management is the process of managing unstructured data throughout its lifecycle with analytics and intelligence.

The criteria for a solution to be considered as Intelligent Data Management includes:

  • Analytics-Driven: Is the solution able to leverage analysis of the data to inform its behavior? Is it able to deliver analysis of the data to guide the data management planning and policies?
  • Storage-Agnostic: Is the data management solution able to work across different vendor and different storage platforms?
  • Adaptive: Based on the network, storage, usage, and other conditions, is the data management solution able to intelligently adapt its behavior? For instance, does it throttle back when the load gets higher, does it move bigger files first, does it recognize when metadata does not translate properly across environments, does it retry when the network fails?
  • Closed Loop: Analytics feeds the data management which in turn provides additional analytics. A closed loop system is a self-learning system that uses machine learning techniques to learn and adapt progressively in an environment.
  • Efficient: An intelligent data management solution should be able to scale out efficiently to handle the load, and to be resilient and fault tolerant to errors.

Intelligent data management solutions typically address the following use cases:

  • Analysis: Find the what, who, when of how data is growing and being used
  • Planning: Understand the impact of different policies on costs, and on data footprint
  • Data Archiving: Support various forms of managing cold data and offloading it from primary storage and backups without impacting user access. Includes: Archive data by policy – move data with links for seamless access, Archive project data – archive data that belongs to a project as a collection, Archive without links – move data without leaving a link behind when data needs to be moved out of an environment
  • Data Replication: Create a copy of data on another location.
  • Data Migration: Move data from one storage environment to another
  • Deep Analytics: Search and query data at scale across storage

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