Technology used: Splunk ML Toolkit and Splunk IT Service Intelligence, Salt Stack, Control-M, NOI, ICD, Tomcat.
Prediction of job failures and automation of corrective actions in response to batch job failures.
Automation of alerts and historical tickets; correlation and identification of resolution to improve resolution time.
Reduction of alert noise level using AIOPs-based approach to automate the creation of maintenance windows in event management tools.
Predicts job failures and minimizes the impact of batch job failures by automating corrective action
Automation of ticket creation in service management tool from event management for alerts generated by critical devices/ servers; correlate with historical ticket information and update new ticket with past ticket resolution
Reduce alert noise level by creating maintenance windows in event management to suppress alerts during the Change window
Reduction in manual efforts by operation personals to identify and fix issues
Insights into failure causes which help in understanding repeated issues and trends
Centralized view of real-time/ historical IT operations and service management data trends and patterns
Improved resolution and issue detection times
Reduction in alert noise level
Reduced impact on business service and improved availability
Trianz knew exactly what to do in terms of streamlining our incident and service request management process. The way Trianz provided for better, more efficient API-based interaction was exceptional.
It was outstanding – Trianz setting up a managed services center to support our IT infrastructure on AWS, thereby proactively and reactively addressing and resolving issues.
Partnering with Trianz has proved to be a one-of-its-kind experience. Especially because of