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Identify IT changes across hybrid-cloud environments

BigPanda correlates multisource alerts with change data so NOC or ITSM teams can identify and roll back specific actions that cause incidents.

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Changes cause 85% of incident-impacting alerts

Hybrid-cloud environments undergo constant changes — a primary cause of incidents. Legacy tools can’t collect multisource data. BigPanda can.

Reveal change data linked to IT incidents in real-time

Correlate multi-source alerts with change data to identify the probable change that caused an incident. The application of pragmatic AI explains in natural language the reasoning behind the matches and how statistical confidence is achieved.

Multi-source change aggregation

Powered by a broad range of out-of-the-box integrations for common change feeds such as ServiceNow, JIRA, Jenkins and CloudTrail, along with a powerful Changes REST API, BigPanda can connect to all change feeds and tools, and aggregate their data.

Real-time IT Incident correlation

Correlates multi-source alerts with change data to identify incident-impacting changes across hybrid cloud environments. BigPanda’s Incident 360 console reveals statistically significant changes linked to each incident, enabling users to confirm matches before initiating incident triage or roll-back.

Pragmatic, explainable AI

BigPanda’s pragmatic AI provides explanations in natural, easy-to-understand language why suspected changes were linked with an incident.

Collaboration with change tools and teams

If a potential change is matched, users can explain their choice and propose next steps, such as roll-back. This message is communicated to the tool that initiated the modification, notifying the responsible team for prompt action.

Reportable analytics

A new integration with Unified Analytics enhances efficiency in measuring, improving, and operationalizing root cause change investigation across all applications and services.

Hear from our customers

“With BigPanda, our IT noise is not only reduced, but we are able to identify root cause in real-time. We see who the responsible team is, who owns the service that’s alerting, etc., which is significantly reducing our MTTR. One of the biggest drivers that we have right now is auto-remediation.”

Priscilliano Flores

Staff Software Systems Engineer at Sony Interactive Entertainment

“With BigPanda, we’ve automated our alert process by 83%, enabling root cause identification of critical alerts within 30 seconds.”

Mark Peterson

SPV IT Operations, Cambia Health Solutions

FAQ

What is the Root Cause Changes feature?

The BigPanda Root Cause Changes (RCC) feature utilizes advanced Artificial Intelligence to achieve accurate and automatic identification of change data associated with an incident. This is critical given that up to 85% of performance-related incidents are caused by a change.

 With BigPanda RCC, ITOps, DevOps, and SRE teams gain fast, precise root cause identification at the time of the incident, resulting in up to 50% MTTR reduction and instantly uncovering crucial details for incident resolution. Read more about Root Cause Changes in our blog.

Can you determine the root cause of failures faster using Root Cause Changes?

BigPanda Root Cause Changes provides instant insights into change data impact. RCC uses advanced AI to identify and correlate real-time change data with incidents. BigPanda uses 29 unique vector dimensions to identify high-confidence alerts and change data matches associated with incident creation, providing users with a comprehensive view of suspected changes that are statistically relevant.

How do I know whether Root Cause Changes is accurate?

Root Cause Changes provides high-confidence accuracy in determining root cause anchored in customer validation. BigPanda improves RCC AI algorithms by incorporating impactful change tags used successfully across customer deployments. New dimensions and categories deliver greater statistical precision and confidence when analyzing high-ranking suspected changes linked to incidents. This ensures consistency, reliability, and reduced toil during incident triage.

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