Overview

Reduce event noise, raise fewer incidents, recover from faults faster

A unified AIOps platform, allied to the power of machine learning algorithms - leveraging end-user, incident, performance data and metrics from any source, any perspective. 

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Integrations driven: assimilate data from a diverse range of IT management tools and applications

  • Collect, correlate events from application performance, network, server, storage and infrastructure monitoring tools
  • Visualize and manage IT health data in a single-pane-of-glass
  • Cross-stack, cross-domain contextual awareness for real-time, predicted business impact 
  • Open API support to integrate with anything with a digital pulse - TCP/UDP, REST and SOAP web services, SNMP, log files, database, CLI, …

Assimilate data from a diverse range of IT tools

Eliminate event storms and reduce event volumes. See the health of your enterprise from the data center to the cloud

  • Automatic suppression of event storms and noise – eliminate manual processing of up to 99% of downstream and sympathetic events
  • Automatically de-duplicate, correlate and group events into manageable, relevant, actionable scenarios, adding business context
  • Enrich events with incident/change management data, location and dependency information
Eliminate Storm Events

Prevent service impact: advanced warning indicators

  • Automatically identify hidden event patterns that typically lead to critical service performance degradation and outages
  • Visualizations plot severity and time to failure - intuitive reports connect related attributes
  • Prevent future service disruption with accurate service forecasting
Prevent Service Impacts

Remove the guess-work: build operational knowledge

  • Iterative algorithms model your environment – adding historical context, dependencies, and predictive service fingerprinting, resulting in early warnings before service is impacted
  • Prioritize events based on recent incidents and changes - dynamically organized by service impact, severity, and priority
  • Model policies and algorithms formulate future predictions
  • Service Model queries pinpoint related infrastructure objects that may also be impacted
Build Operational Knowledge

Root cause analysis: automate service restoration

  • Automatically identify hidden event patterns that typically lead to critical service performance degradation and outages
  • Visualizations plot severity and time to failure - intuitive reports connect related attributes
  • Prevent future service disruption with accurate service forecasting
Root Cause Analysis

Visualize infrastructure-to-business dependencies; leverage existing data sources via import and federation capabilities

  • Model the impact of IT changes on services and customers before changes are made
  • Identify and re-schedule conflicting changes
  • Identify unauthorized changes to IT infrastructure
  • Deliver change scheduling by business service, customer or technology
  • Audit & Compliance: automated audit trail of IT infrastructure changes; verify disaster recovery systems are in-line with production
Visualize infrastructure to business dependencies
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