Today Veritas Technologies, announced the launch of Veritas Predictive Insights, a new solution that utilizes artificial intelligence (AI) and machine learning (ML) algorithms to reduce unplanned downtime and reduce total costs of ownership.
Veritas trained their Predictive Insights AI on encrypted event data from thousands of Veritas appliances. One of the major obstacles facing creators of AI is finding a good training data set; Veritas’ in house data collection over the past three years puts them on firm footing in this regard. Based on this accumulated experience, Predictive Insights monitors system health to detect potential issues. A major concern when developing the new system was to avoid alert fatigue and highlight significant incidents. Some of the potential issues it raises alerts are: proactive maintenance, performance and capacity forecasting, and compliance determination.
When a customer enables Predictive Insightson a supported Veritas appliance, telemetry is continuously collected and processed to generate a System Reliability Score (SRS) for each appliance. The score and related details are exposed via the dashboard. Based on the SRS and the details behind it, the system suggests proactive actions as identified by the analytics to improve system reliability. This could include a notification to install a patch to dispatching service personnel or making prescribed, on-site services. Data-driven metrics and recommendations like this can help IT teams make the case for essential maintenance while redirecting effort away from lower-priority areas in order to better serve the business and increase customer satisfaction.
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