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TrueSight Infrastructure ManagementBMC

Enable IT operations with cloud- and vendor-agnostic infrastructure monitoring and management

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Vendor

BMC

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Company Website

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Product details

TrueSight Infrastructure Management brings together enterprise-class monitoring, event management, and operational analytics data, giving IT teams a centralized, 360-degree view of the health and performance of their IT infrastructure.

Enable greater efficiency when you move from the traditional bottom-up approach to IT infrastructure management

  • Enterprise monitoring and event management – Detect, analyze, and act on events impacting the business
  • IT operations analytics – Use behavioral learning and log analytics to reduce MTTR
  • Broad and deep coverage – Out-of-the-box coverage for key technology vendors, and customizable integrations via APIs, SDKs, scripts, and Sentry Monitoring Studio
  • ITSM integration – Integrate TrueSight with BMC Helix ITSM for automated ticket creation, enrichment, and routing
  • Cloud-ready – Monitor the health and performance of IT infrastructure and application resources across a multi-cloud environment

Use the power of analytics to proactively manage your multi-cloud infrastructure

Monitoring and Event Management: Get a bird’s eye view of your IT infrastructure

Define and perform telemetry from the end user perspective to solve business problems, rather than blindly reacting to state changes in infrastructure components. TrueSight empowers you to troubleshoot the root cause of degraded application performance by diving into underlying infrastructure metrics, events, and logs.

  • Reduce event volume by decreasing event noise through analytics
  • Decrease false alarms by up to 90%
  • Lower MTTR (mean time to repair) by up to 75%
  • Warn IT when a metric is trending out-of-band, up to 3 hours before a baseline is breached, with predictive analytics
  • Resolve 40% more incidents automatically

Behavioral Learning: Reduce event noise through dynamic baselining

TrueSight uses patented self-learning, self-configuring analytics that automatically discover and learn the behavioral and performance trends for monitored infrastructure and application components. Understanding their relationships over time better identifies normal and abnormal behavior rather than relying on static thresholds that don’t reflect business reality.

  • See events only when there is significant abnormal behavior and reliably predict potential problems
  • Detect events that might otherwise be missed by traditional static thresholds
  • Pinpoint and prioritize your most critical business issues—regardless of source—significantly streamlining subsequent event and impact management efforts

Probable Cause Analysis: Uncover underlying evidence

TrueSight Infrastructure uses machine learning to continuously correlate millions of data points collected from every monitored application component and automatically identifies a small number of potential causes. Applying intelligent filtering and leveraging service relationships further reduces the list and ranks them according to their likely contribution to the problem being diagnosed.

  • Automatically collect detailed diagnostic data across data sources to accelerate problem diagnosis
  • Get visibility into problems as they develop, allowing you to diagnose intermittent performance issues on-demand
  • Define custom diagnostics for KPIs and trigger actions to collect additional input and initiate automated workflows, such as checking for recent changes recorded in your change management system
  • Use probable cause analysis as a spring-board for further root cause analysis by launching directly into log analytics within the context of the event being explored

Log Analytics: Machine learning mines log data to reveal root cause

TrueSight IT Data Analytics uses machine-assisted analysis for log data, metrics, events, changes, and incidents. You can automatically sift through millions of messages with a single click to solve problems faster.

  • Automate cross-domain collection and indexing of log and other machine data
  • Search for anomalous behavior and identify patterns and deviations with behavioral learning
  • Create proactive alerts when pattern deviations are detected
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