Benefits
- Improve decision-making: Automatically combine multi-source, historical, and change data to optimize situational awareness. Deliver AI-powered insights directly within your workflows for smarter, more informed incident investigations.
- Maximize value: Combine siloed data and processes with data-driven analytics and customizable out-of-the-box dashboards to identify gaps and maximize the value of existing tools.
- Increase operational visibility: Integrate fragmented tools to create a cohesive view. This allows teams to enhance performance monitoring, identify issues quickly, and uncover optimization opportunities.
- Lower costs: Aggregate, normalize, filter, and correlate data and knowledge across silos into a unified view to drive efficiencies across response teams and their workflows.
Maintaining service reliability is business-critical for IT operations and incident management teams. IT teams must detect, triage, and investigate incidents efficiently and consistently. Failing to do so can result in poor customer experiences, SLA penalties, and damage to the brand.
These processes are becoming increasingly challenging for IT teams due to the growth in scale, complexity, and fragmentation associated with hybrid infrastructures. Traditional processes cannot keep pace with this growth, leaving L1 Operations and Service Desk teams unable to respond quickly due to a lack of visibility and context.
- Overwhelming data scale & velocity L1 Operations and Service Desk teams are overwhelmed, impeding incident detection and triage. IT applications and services produce more data than ever, surpassing human capacity to understand and manage it.
- Trapped, siloed knowledge and data Essential knowledge and data for incident response is scattered across various tools, teams, and organizations. Gathering this knowledge is time and resource-intensive, and consolidating this information slows investigation and remediation processes.
- Fragmentation slows down automation initiatives Inconsistent data and manual workflows hinder implementing automation in incident response workflows. A lack of situational awareness, combined with siloed knowledge and data, complicates the process of determining what and where to automate.
Why BigPanda
Operations and Incident Management teams during incident response. Combine IT operational data, service knowledge, and institutional knowledge into a standard data model, to deliver rich, accurate insights that help teams detect issues, resolve incidents, and prevent service disruptions.
Integrate data and knowledge across silos
Combine machine data (operational, infrastructure, and observability) and human knowledge (service history, SOPs, and chat transcripts) to break down knowledge gaps. Provide an unprecedented understanding of your IT infrastructure and operations, both past and present.
Standardize fragmented data into a single data model
BigPanda standardizes and processes all data types so they can be analyzed for trends and patterns associated with active incidents. These insights are surfaced to operations and response teams so they can resolve incidents faster.
Enrich and share IT knowledge
Give every user and IT team involved in incident management processes access to relevant, critical insights. Enrich and expand team knowledge so they can better understand issues and accelerate investigation and response.
