Many organizations now deal with thousands, if not millions, of events taking place across their systems every day. But what about your capacity to deal with that noise? With the rise of digital transformation, the scale of operations complexity has increased to a point no longer manageable by humans, creating a need for automation, machine learning, and predictive capabilities.
Emerging AIOps providers are trying to combat the noise with analytics and machine learning, but most still hold on to traditional bottlenecked command-and-control workflows, and don’t integrate human response data that forms the real source of truth for incident resolution. In this e-book, we introduce a new approach to AIOps that eliminates silos between event and incident management, has helped customers reduce noise on average by 98%, and supports both centralized and distributed workflows in harmony.
Many organizations now deal with thousands, if not millions, of events taking place across their systems every day. But what about your capacity to deal with that noise? With the rise of digital transformation, the scale of operations complexity has increased to a point no longer manageable by humans, creating a need for automation, machine learning, and predictive capabilities.
Emerging AIOps providers are trying to combat the noise with analytics and machine learning, but most still hold on to traditional bottlenecked command-and-control workflows, and don’t integrate human response data that forms the real source of truth for incident resolution. In this e-book, we introduce a new approach to AIOps that eliminates silos between event and incident management, has helped customers reduce noise on average by 98%, and supports both centralized and distributed workflows in harmony.
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