Incident Contextualization

Solution Capabilities & Features

Overview

One of your key IT operations challenges is Making Sense of Your Data so that you can determine the root cause of systems performance problems quickly and recommend the right fixes more efficiently. Issues with data consolidation and contextualization result in a time-consuming and error-prone process. An AI-powered Incident Contextualization process can help you scale your limited resources with a repeatable process for identifying a curated set of recommended resolutions.

Incident Contextualization Business Drivers

Process efficiency and
productivity improvement

Limited dependence on
tribal knowledge silos

Need for rapid
root cause isolation

Key AIOps Capabilities & Features

Policy-based Escalation
  • Automates management of hand-off from the anomaly detection process to the incident investigation team
  • Enforces rules that are continually tuned leveraging user domain knowledge
  • Optionally adjusts rules based on the cumulative industry experience and learnings of the users of the tool
  • Utilizes correlation to determine appropriate resolution team (e.g. Apps, DB, infrastructure)
Accelerated Root Cause Analysis ​
  • Leverages machine learning algorithms to identify potential root cause alternatives
  • Determines metric correlation algorithmically (e.g. service-level KPIs) and incident co-occurrence (i.e. what other incidents occurred in the same time interval)
  • Considers seasonality effects based on time-series and log analysis
Automated Recommendations
  • Links incidents to the relevant SAP knowledge base of suggested best practices and remediation actions.
  • Focuses the remediation process on a narrower “curated” set of probable root cause options

Next Steps

1.  Check out the Incident Contextualization Solution eBrief

2. Contact us to initiate a needs discovery dialogue

3. Request a demo

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