Benefits
- Real-time causality: Identify change-related root causes as soon as the incident populates for a faster, clearer understanding of why incidents occurred.
- Instant deployment: From day one, enhance correlation using default change tags sourced from statistically relevant, real-world deployment data.
- Analytics for root cause: Measure and optimize interrelated change data to gain insight into where and how to increase root-cause detection and incident-response workflows.
Up to 85% of performance-related incidents are caused by a system change. The third generation of BigPanda Root Cause Changes uses advanced artificial intelligence to identify change data associated with an incident. This information gives ITOps, DevOps, and SRE teams fast, precise root-cause identification at the time of the incident, resulting in up to 50% mean time to resolution (MTTR) reduction and instantly uncovering crucial details for incident resolution.
- Instant insights into the impact of changes BigPanda Root Cause Changes identifies and correlates real-time change data with incidents. It uses 29 unique vector dimensions to identify high-confidence alerts and change-data matches associated with incident creation, providing a comprehensive view of statistically relevant suspected changes.
- High-confidence accuracy anchored in customer validation BigPanda improves root-cause change AI algorithms by incorporating impactful change tags used across real-world deployments. New dimensions and categories increase statistical precision and confidence when analyzing high-ranking suspected changes. This ensures consistency, reliability, and reduced effort during incident triage.
- Advanced analytics improve insights A new Unified Analytics dashboard allows you to measure, improve, and operationalize root-cause change investigation across all applications and services. Interactive dashboards show change tag details, total alerts, and incidents, allowing you to optimize out-of-the-box RCC configurations and make operational improvements.
Key capabilities
- Correlate incident alerts with multisource change data: Identify infrastructure, application, security, third-party, and more change data across hybrid-cloud environments. Correlate with performance-impacting incidents in real-time to accurately detect and resolve the likely cause.
- Instantly deploy impactful change correlation patterns: BigPanda Root Cause Changes automatically surfaces statistically relevant change tags used across customer deployments to perform reliable change correlation with incidents from day one.
- Access change data analytics: Measure alert, change, and incident metrics over multiple time dimensions to help identify high-confidence change-match data and support ongoing configuration enhancements tailored to products, services, and infrastructure.
AI-generated incident root cause
Unified change tag taxonomy
Change analysis within a hybrid cloud
Challenge
A majority of service-impacting incidents can be attributed to dynamic infrastructure changes, resulting in manual user effort to identify specific changes that likely caused an incident.
Each change management tool uses different syntax to describe tag categories, making it labor-intensive to manually link interrelated change alerts at scale.
Organizations must monitor and track changes across infrastructure and identify the impact on applications. A lack of change-data analytics makes it difficult to improve incident management.
How BigPanda helps
Eliminate statistically irrelevant change and alert matches to ensure incident responders can promptly pinpoint probable root cause in real time.
AI algorithms automatically aggregate and normalize impactful change and alert tags across deployments to make configuration seamless.
Unified Analytics provides interactive dashboards so you can explore, filter, and investigate change data to improve overall incident-management workflows.
Business value
Use technology to reduce manual effort and scale ITOps workflows.
Achieve quick time-to-value and reduce mean time to resolution (MTTR).
Optimize costs through operational improvements.
AI-generated incident root cause
Challenge
A majority of service-impacting incidents can be attributed to dynamic infrastructure changes, resulting in manual user effort to identify specific changes that likely caused an incident.
How BigPanda helps
Eliminate statistically irrelevant change and alert matches to ensure incident responders can promptly pinpoint probable root cause in real time.
Business value
Use technology to reduce manual effort and scale ITOps workflows.
Unified change tag taxonomy
Challenge
Each change management tool uses different syntax to describe tag categories, making it labor-intensive to manually link interrelated change alerts at scale.
How BigPanda helps
AI algorithms automatically aggregate and normalize impactful change and alert tags across deployments to make configuration seamless.
Business value
Achieve quick time-to-value and reduce mean time to resolution (MTTR).
Change analysis within a hybrid cloud
Challenge
Organizations must monitor and track changes across infrastructure and identify the impact on applications. A lack of change-data analytics makes it difficult to improve incident management.
How BigPanda helps
Unified Analytics provides interactive dashboards so you can explore, filter, and investigate change data to improve overall incident-management workflows.
Business value
Optimize costs through operational improvements.
“Change-related incidents are one of the biggest generators of unnecessary alert noise. We’ll use BigPanda Root Cause Changes tool to gain a clearer understanding of the underlying causes behind incidents so we can respond more effectively.”
Mark Peterson
IT Operations Supervisor, Cambia Health Solutions
