BigPanda’s Root Cause Changes

Correlating IT incidents to the changes that caused them

Hybrid cloud organizations experience thousands of infrastructure and software changes daily, whether they are planned, ad-hoc, are related to continuous delivery or caused by automated orchestration. These changes are the leading cause of incidents and outages.

Legacy IT Operations tools and techniques cannot provide the answer to this challenge, as they are not built to collect data from different change tools, let alone analyze and correlate the changes to the incidents and outages they may have caused.

BigPanda Root Cause Changes correlates IT incidents to the changes in infrastructure and software that caused them

Root Cause Changes

Big Panda Root Cause Changes presents IT Ops, NOC, DevOps and SRE teams with all the changes related to incidents they are working on within BigPanda’s Incident 360 Console and notifies them which of these changes are suspected to be the root cause.

Ingesting data from all major change management, CI/CD, orchestration and change audit tools, BigPanda’s Root Cause Changes uses the power of Open Box Machine Learning to correlate these changes to the incidents they have caused, in real-time, allowing teams to quickly resolve the incidents and/or roll back the change.

Read a quick blog to learn how BigPanda’s Root Cause Changes helps IT Operations teams manage fast-moving IT stacks.

Main features of BigPanda’s Root Cause Changes

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 correlation

BigPanda uses Open Box Machine Learning to correlate incidents with the aggregated changes, in real-time, to identify each incident’s related changes and surface the changes that likely caused it. The incident’s Related Changes tab in the Incident 360 Console displays all the changes which BigPanda’s Open Box Machine Learning has determined are related to that incident. Suspected root cause changes – BigPanda marks the changes suspected by the Open Box Machine Learning technology to be the cause of an incident and allows users to decide which of them are indeed a match.

Explainable Machine Learning

Within the suspected change description, BigPanda provides the reasoning behind the suggestion in simple, easy-to-understand language.

Collaboration with change tools and teams

When a suspected change is marked as a match, users can add an explanation for their decision and a recommendation for further actions, for example rolling the change back. This comment is sent to the originating change tool so the team that made the change is alerted and can quickly take action.

Analyze information from your CI/CD and change tools, and match it to your monitoring alerts, to quickly identify the root cause changes.

BigPanda’s Root Cause Changes provides IT Operations, NOC and DevOps teams with critical capabilities

Root Cause Changes

Powerful, transparent root cause analysis capabilities

Root Cause Changes harnesses the power and transparency of BigPanda’s Open Box Machine Learning to provide teams with real-time, explainable root cause change determination, allowing them to make quick and confident decisions.


Work in context

All the related changes and the suspected root cause changes are displayed in context to the incident the user is working on, simplifying and reducing the time needed for root cause analysis.


Supports any dynamic infrastructure

BigPanda ingests changes from both modern tools such as Jenkins and CloudTrail, as well as traditional tools like ServiceNow and JIRA. The Changes REST API also makes it easy to integrate change feeds from any other change tool. Furthermore, it supports continuous integration/continuous development pipelines. Developer operations teams now have the ability to easily troubleshoot and roll back changes generated by their CI/CD pipelines.

So, what are you waiting for?