Root Cause Analysis, powered by AIOps
Speed up IT incident resolution
Without an automated system to detect the root cause of IT incidents, responders have to go through the grueling (and time-consuming) process of manually sifting through hundreds of thousands of IT alerts and changes to find the reason their services are degraded.
There is good news. The days of manually sifting through applications to determine the cause of outages are over. BigPanda is purpose built to root out the cause through artificial intelligence.
The key to low MTTR
BigPanda helps IT Ops, NOC, DevOps and SRE teams quickly isolate incident root cause. The platform pinpoints the earliest symptoms and identifies issues and changes that may have caused them. Instead of burning hours on bridge calls, IT can identify root cause and take action.
What prolongs incident investigations?
What prolongs incident investigations?
IT stacks have become incredibly diverse and short-lived making traditional dependency-driven root cause analysis ineffective. An order-of-magnitude increase in the pace of change, thanks to practices such as continuous delivery, leads to even more complexity.
When an incident forms, it often manifests with tens or even hundreds of alerts. Even when grouped, it is difficult to determine which ones point to the scope, impact or root cause of the issue.
Inaccurate infrastructure topology
Without an up-to-date, real-time topology of affected services, organizations can’t identify the probable root cause of incidents. This limits their ability to rapidly investigate and resolve incidents before they escalate into crippling outages.
An incident is often presaged by a distinct order of events. When simply aggregating alerts received in a specific window of time, clues get lost, making it harder to visualize and understand incident evolution over time.
How BigPanda’s Root Cause Analysis helps
Highlights root cause changes
BigPanda aggregates change data, allowing the Root Cause Changes feature to analyze changes against existing incidents in real-time. It then automatically identifies and surfaces suspicious looking recent changes that may be causing the incident.
Dynamically updates incident title
Dynamic incident titles display the probable root cause at a glance. BigPanda surfaces the common denominator of incidents in real-time, and as new alerts are added to the incident, BigPanda dynamically updates the incident title.
BigPanda’s Real-Time Topology Mesh creates a full-stack topology model showing dependencies between networks, servers, clouds and applications for every incident. BigPanda uses this map to correlate IT events and surface probable root cause.
Visualizes events in sequence
BigPanda provides an Incident Timeline to show when an incident started and how it evolved. This shows when each correlated event occurred in sequence, so users can trace the probable root cause more quickly.
Root cause analysis with AIOps for modern, complex IT environments
Modern IT environments are complex and chaotic, making it impossible for a single root cause analysis (RCA) technique to address all types of incidents. That’s why BigPanda provides a range of features and capabilities that together provide a comprehensive RCA solution for any scenario.
BigPanda’s RCA capabilities use Open Box Machine Learning to help organizations identify changes in infrastructure and applications that cause the majority of today’s incidents and outages. In addition, BigPanda also surfaces low-level infrastructure issues that cause problems.
Domain agnostic AIOps is a necessity for diagnosis
BigPanda CTO Elik Eizenberg explains domain-centric and domain-agnostic AIOps, and why the latter helps IT leaders understand how their IT environments are functioning.
How to pick the right AIOps solution for you
The good news: AI is real, it can dramatically improve IT Ops, and it’s becoming obvious that IT Ops cannot succeed without it. These are the must-have features of an AIOps solution.
Rethink root cause analysis for fast-moving IT stacks
Learn how legacy incident management tools and approaches are not working in fast-moving IT stacks, and why finding the root cause change is essential to root cause analysis.