Veritas Technologies, the worldwide leader in enterprise data protection and the software-defined storage market, today announced the launch of Veritas Predictive Insights, a new solution that utilizes artificial intelligence (AI) and machine learning (ML) algorithms to deliver always-on proactive support.
Utilizing years of encrypted event data from thousands of Veritas appliances, Veritas Predictive Insights’ cloud-based AI/ML Engine monitors system health, detects potential issues and creates proactive remediation before problems can occur. Predictive Insights also enhances Veritas product availability and customer satisfaction by helping businesses reduce unplanned downtime, ensure faster time to fault resolution and reduce overall total costs of ownership.
Ever-changing business requirements are placing a stronger emphasis on storing, managing, accessing and protecting data. But businesses today must also complete these tasks while ensuring the least possible downtime, avoiding unplanned maintenance and ensuring service level agreements (SLAs) are met. This latest offering from Veritas addresses the growing concern from customers to simplify their data and infrastructure management while reducing the risks and costs associated with downtime and access to critical business data.
Veritas Predictive Insights processes millions of events, providing IT administrators with the ability to avoid alert fatigue and focus on significant incidents.
“Having consistently available IT systems is increasingly important to organizations that are responsible for managing, analyzing and protecting more and more data points every day,” said David Noy, vice president and general manager, Product Management and Alliances, Veritas. “This new technology harnesses the power of AI and continuous ML models to provide predictive analytics to IT staff. Administrators can then proactively support and remediate a wide range of potential issues before they occur, react much quicker and allow for less costly resolution.”
Providing analytic insights into a range of potential issues
Veritas Predictive Insights provides prescriptive support services, such as proactive maintenance, performance and capacity forecasting, as well as compliance determination. The services are driven from the power of AI and continuous ML models that utilize years of collected data points from tens of thousands of Veritas customer installations. Combined with real-world input from service personnel, the Veritas AI/ML Engine delivers predictive insights about a customer’s environment, resulting in proactive recommendations and actions to improve their business operations.
“Advances today in machine learning that use statistical techniques to give IT systems the ability to "learn" from data without being explicitly programmed, can be useful to organizations to detect potential issues and remediate them quickly,” said Christophe Bertrand, senior analyst for Data Protection at ESG. “Veritas Predictive Insights is one such solution that can help customers reduce risks and costs associated with downtime and access to critical data and improve their operational efficiency.”
Veritas Predictive Insights “Always On” feature allows for intelligent, data driven decision-making capabilities and optimized services ensuring that customers have an enriched support experience, which is fast, proactive and prescriptive. Veritas appliance customers that have this auto-support feature turned on, can maximize the benefits of Veritas Predictive Insights instantly and achieve improved ROI on their appliances and reduce the costs associated with downtime.
When a customer enables the auto-support feature on a Veritas appliance, telemetry is continuously collected and processed by the AI/ML Engine, generating a System Reliability Score (SRS) for each appliance. The information generated is visible in a dashboard and can be used by Veritas appliances services personnel and is also accessible to customers. Based on the SRS and the details behind it, the support and customer teams can take proactive actions as identified by the analytics. This could include a notification to install a patch to dispatching service personnel or making prescribed, on-site services.