Selector Analytics

Selector employs machine learning and NLP-driven, self-serve analytics to provide access to actionable insights. Selector Analytics uses artificial intelligence and machine learning to conduct three essential functions and provide actionable insights to network, cloud, and application operators.

Collect: Selector Analytics collects any data (including configurations, alerts, metrics, events, and logs), from various heterogeneous data sources. For example, Selector Analytics may harvest data from router logs, device or network metrics, or device configurations.

Correlate: Once collected, Selector Analytics normalizes, filters, clusters, and correlates metrics, events, and alarms using pre-built workflows to draw actionable insights. Selector Analytics then uses machine learning-based data analytics to compare metrics and events and conduct automated anomaly detection. If an anomaly is uncovered, Selector’s AIOps solution issues an automatic alert and uses a ranking system to shortlist factors potentially responsible for the network outage.

Collaborate: Business and technical leaders want instant, concise status reports without writing SQL queries or building new dashboards. Selector Analytics supports Natural Language Queries directly integrated into common messaging platforms such as Slack and Microsoft Teams, providing a conversational chatbot experience. Selector’s AIOps solution infers natural language queries, finds the closest match, and can share query outputs and dashboards with larger workflows such as IT-service management ticketing, alerting, and automation tools.