Our built-in ML model provides automated data insights through statistical rules, Machine Learning and Natural Language Processing. Actionable insights and recommendations are displayed on the dashboard without the user having to navigate through TBs of data.
KPIs computed by our ML model include: The Operations Performance Index (OPI) measures how robust current operations are, with a score ranging 0 to 10. Overall IT operations are gauged by this index. User Experience Index (UEI) measures the end user’s experience of business services, ranging from 0 to 10. Customer satisfaction is measured by this index. These KPIs helps in better capacity planning and preparing for any seasonal trends.
With our built-in machine learning model, we detect anomalous behavior by considering seasonality, past trends, user feedback, inter-metric correlations, that identify anomalous points in real-time, eliminating the need for static rule-based alerts.
The platform provides real-time contextual and correlated alerts with slice & dice and granular data to get context across usage patterns, uptime, downtime, and many more metrics across multiple dimensions such as business/peak hours, time, geographies, business impact, etc. It also provides static and dynamic alerts with threshold and support for alert suppression, deduplication, unified KPI views, and more.
With this platform, you can aggregate, normalise, correlate and analyse event log data from various devices. In addition to supporting temporal and topology correlation, it supports alert suppression, alert deduplication, and reduces alert fatigue.