PagerDuty Analytics and Visibility

PagerDuty’s analytics and visibility capabilities unlock actionable insights into your operational maturity and effectiveness so you can effect necessary change. Granular views and metrics help teams proactively understand why and how to decrease incident frequency, impact, and duration. And in the middle of a firefight, teams are empowered with powerful, cross-cutting visualizations that support rapid triage and root cause identification.

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analytics and visibility

Understand Patterns, Detect Anomalies

PagerDuty turns the entire universe of data into real-time visibility, so you can quickly assess issues and figure out what’s going on when every second counts.

Measure to Take
Action

Tune and improve your alerting mechanisms with infrastructure visibility and uplevel your operations by tracking metrics such as incident frequency over time.

Improve Business Outcomes

Understand how your operations impact your business outcomes and get better insight into the factors that result in fewer incidents and happier teams.

Customer Love

"The Operations Command Console drives down MTTR as it is a critical tool in helping us assess incident blast radius, and provides enhanced situational awareness throughout the organization."

— Nestor Camacho, Oracle

Product Capabilities

Leverage granular insights into incident frequency, reporting by team, responder, service, and much more. Views can be customized by timescale.

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Extend rich PagerDuty data to the rest of your toolchain with CSV exports and incident APIs.

Visualize and relate the health of your services and infrastructure to real-time incident response workflows, all from a single pane of glass.

The “focus” view draws relationships between previously siloed contexts so that teams can gain 360-degree visibility into major incidents and down-to-the-second impact.

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Actionable, time-series visualizations of all the correlated events in your entire infrastructure make it easy to quickly identify service dependencies and root cause.

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Machine learning algorithms identify similar incidents, group related alerts, and uncover ways to prevent simmering incidents based on historical data.