Increase reliability of data protection solutions with predictive analytics

Use Case Descriptions

Integration Environment

A data protection solution for on-premises or cloud applications.

Customer / Partner Types
A software provider of data protection solutions that has expanded to provide BaaS and DRaaS.

Company and Solution Background
The company has been providing enterprise grade solutions for cloud, virtual and physical environments. The company software solutions deliver data availability capability, ranging from long-term archive to disaster recovery and high availability. Integrating with cloud based data centers, the company also offers BaaS and DRaaS to customers as extensions of same software platforms.

Solution Architecture

Requirements and Challenges

As a data protection software and service company, the most important objective of a solution is to make secondary copies of production data reliably, no matter on-premises or in the cloud. The lack of insight to the primary production environment makes it difficult for the software to adapt to the dynamic changes in the protected systems. It also imposes challenges to prepare resources with proper sizing in the cloud for disaster recovery exercises.


In addition to a holistic view of the whole infrastructure of the protected system,® provides insight and predictive analytics for the company’s software to take precise and proactive actions to the events in the protected systems. The company’s software and® communicate through Predictive Data Adapter (PDA), an extension of the®, providing policy based action with standardized common data protection operations and event triggers of protected systems.  

Solution Benefits

Reliably protect primary data by adaptive scheduling from predictive analytics
Respond precisely to unexpected events with full stack visibility and object correlations of protected environment
Adaptively adjust protection priority for vulnerable systems with intelligent analytics from impact analysis and anomaly detection
Properly size instance resources for disaster recovery with load analytics and prediction based on primary systems