The data avalanche is here. To get through it, you need to tap into AI-powered capabilities for IT operations. To successfully implement AIOps, you need to balance AI power with a hefty dose of pragmatism. You need the right tools, skills, maturity, and context, to enrich data quality and drive business outcomes.
- 65% of businesses report challenges with ineffective visibility across infrastructure and applications
- Legacy manual operations can pose problems while integrating distributed systems to provide seamless experiences
- 47% of IT leaders admit their teams are unable to adopt mature machine learning and AI capabilities
- Lack of advanced analytics and automation toolset leads to firefighting and fatigue
TUNE OUT THE TRIVIAL
Eliminate alert noise and fatigue by integrating raw alerts to tell a story. Leverage algorithmic baselining, automate incident identification and resolve business-critical issues faster.
- Prioritize alerts, avoid false alarms, and focus on what matters with dynamic thresholds.
- Combine machine-learning with human intel to empower teams with enriched alerts.
- Identify ranges of normalcy, subdue fake events, and analyze true abnormalities to make faster decisions.
SWIFT AND SEAMLESS
Reduce resolution time and increase the reliability and availability of your systems. Netenrich’s platform leverages machine learning-based problem scoring to automate incident classification and provide service agents with actionable guidance from industry experts.
- Identify patterns of anomalous behavior with better operational efficiencies. Eliminate guesswork with actionable conclusions for performance incidents.
- Highlight and channel critical events to support teams and drive lower MTTR. Leverage escalation workflows based on dependencies and monitoring systems, backed by skilled analysts.
- Minimize disruptions to service delivery with early detection for potential risks.
OPTIMIZED FOR ORDER
Manual processes can’t get to the root causes of failures quickly and accurately. Netenrich’s automatic and adaptive learning algorithms can reduce noise and get to the critical.
- Design and orchestrate response for various impact levels—mobilize responders, engage stakeholders, and send status updates.
- Drive down MTTR, seek out anomalies and unusual changes with optimized workflows for diagnosis and resolutions.
- Improve incident response time SLA, increase operational efficiency, and reduce error rates through automation.
Eliminate alert noise and fatigue.
Reduce resolution time.
Gain actionable recommendations.
Prioritize new capabilities.
Automate incident management.
Reduce firefighting to focus on criticals.
Increase turnaround time.
Higher service adoption.
Improve reliability of services.