TechMahindra's Major Incident Prediction in ITSM SNOW application helps organizations to identify major incidents using ML/AI as near real-time possible. It completely avoids the human effort of manually analyzing the incident trends/patterns based on the historical data. Also, it helps automate incident trend analysis, proactively signaling the possible outages. The main objective of the application is to reduce the major incident downtime and cut shot human dependency while identifying an outage. It Uses Celonis ML workbench capability to address the mentioned challenge. To begin with, workshops have been conducted with clients to capture the requirement and pain areas around major incident prediction as-is and followed by a flat-file extraction method from the SNOW Incident management process along with all the historical outage raw data to train the machine.
This is where TechMahindra and Celonis Combine their Strengths:
Reduction in Major incident downtime & human effort and Calls to the support desks.
Trend analysis is being performed Proactive & Automated. No/less dependency or manual intervention in predicting the possible outage.
Quicker to identify keywords contribution and relationship to incident trends.
Auto signal of the possible outages eliminating the tools & resource usage.
Helps the incident coordinator to better dwell into the analysis & investigate as quickly as possible to conclude an outage or reach out to the MI manager.
For the Incident Management process, this view provides an overall one stop shop visibility while investigating for patterns and anomalies in the incident trends that in return shortnes the process of identifying, confirming and declaring as an outage. As a result of it, overall major incident downtime & Resolution time decreases significantly along with increased operational efficiency.
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