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BigPanda Launches Automatic Incident Triage

New Capability Gives IT Ops Teams Business Context to Inform Incident Triage, Increase Availability of Applications and Services and Dramatically Reduce Incident Resolution Cycles

BigPanda announced the availability of Automatic Incident Triage, a new platform component that significantly reduces the manual toil associated with the triage phase of incident management.

Automatic Incident Triage reduces the “mean-time-to-resolve” (MTTR) for applications and services by enabling IT Ops and NOC teams to quickly triage incidents by reducing the steps required to fully understand the business context of an incident and assign it to the right response team within their desired collaboration platforms.

“Streamlining processes is a critical component of technology operations,” said Rob Scarmuzzi, Executive Director of Operations Technology at E*TRADE Financial. “Automating tasks, like consolidating events, equips us with the tools to manage our workflow efficiently and ultimately freeing up time to deploy manpower to areas that require attention. BigPanda’s Automatic Incident Triage enhancements put additional firepower behind these automated capabilities.”

Enterprises with complex hybrid IT infrastructures and organization structures face a growing number of challenges, including centralized and decentralized Ops teams, and hybrid environments with on-prem and cloud-based applications and tool sprawl, making it difficult to rapidly understand, investigate, remediate and resolve incidents.

According to Gartner, “organizations are struggling to reduce incident response times because of delays around manual incident routing and cross-team collaboration challenges with incident response.” Gartner goes on to state, “Depending on the organization, gathering the context of the incident often takes 15 to 30 minutes, which significantly impacts mean time to resolve (MTTR).”*

An inability to quickly gather business context in the incident triage phase delays incident response times, which negatively impacts service availability and reliability, creates user satisfaction issues, and drives up operational costs. BigPanda’s Automatic Incident Triage helps IT Ops and NOC teams solve this pain point, improve NOC productivity and reclaim high-value L3 and DevOps FTE hours.

“Time is one of the biggest enemies of IT Ops and NOC teams. Incident responders know all too well how long it takes to answer the ‘What next?’ question once they’re presented with an incident,” said Elik Eizenberg, Co-Founder and CTO at BigPanda. “Automatic Incident Triage turns what used to be a technical incident into a business incident automatically, helping incident responders rapidly triage and handle more incidents than before and quickly route critical incidents to the right teams for follow-up and resolution.”

With Automatic Incident Triage, BigPanda customers can:

- Automatically calculate and incorporate detailed business context into incidents, such as validated incident severity, impacted services, business priority and routing information using easy-to-create custom incident tags.

- Quickly and easily sort, filter, visualize and respond to the incidents, prioritizing those with either the most pressing validated incident severity or the number of impacted services.

- Bi-directionally sync custom incident tags with collaboration tools such as ServiceNow or Jira to deliver easier mapping of fields and trigger workflows within those tools.

Automatic Incident Triage allows Ops teams to handle higher volumes of actionable incidents themselves, without having to escalate as frequently. And when they do escalate, the additional business context makes it easy to prioritize and route incidents to the right teams for faster resolution.

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BigPanda Launches Automatic Incident Triage

New Capability Gives IT Ops Teams Business Context to Inform Incident Triage, Increase Availability of Applications and Services and Dramatically Reduce Incident Resolution Cycles

BigPanda announced the availability of Automatic Incident Triage, a new platform component that significantly reduces the manual toil associated with the triage phase of incident management.

Automatic Incident Triage reduces the “mean-time-to-resolve” (MTTR) for applications and services by enabling IT Ops and NOC teams to quickly triage incidents by reducing the steps required to fully understand the business context of an incident and assign it to the right response team within their desired collaboration platforms.

“Streamlining processes is a critical component of technology operations,” said Rob Scarmuzzi, Executive Director of Operations Technology at E*TRADE Financial. “Automating tasks, like consolidating events, equips us with the tools to manage our workflow efficiently and ultimately freeing up time to deploy manpower to areas that require attention. BigPanda’s Automatic Incident Triage enhancements put additional firepower behind these automated capabilities.”

Enterprises with complex hybrid IT infrastructures and organization structures face a growing number of challenges, including centralized and decentralized Ops teams, and hybrid environments with on-prem and cloud-based applications and tool sprawl, making it difficult to rapidly understand, investigate, remediate and resolve incidents.

According to Gartner, “organizations are struggling to reduce incident response times because of delays around manual incident routing and cross-team collaboration challenges with incident response.” Gartner goes on to state, “Depending on the organization, gathering the context of the incident often takes 15 to 30 minutes, which significantly impacts mean time to resolve (MTTR).”*

An inability to quickly gather business context in the incident triage phase delays incident response times, which negatively impacts service availability and reliability, creates user satisfaction issues, and drives up operational costs. BigPanda’s Automatic Incident Triage helps IT Ops and NOC teams solve this pain point, improve NOC productivity and reclaim high-value L3 and DevOps FTE hours.

“Time is one of the biggest enemies of IT Ops and NOC teams. Incident responders know all too well how long it takes to answer the ‘What next?’ question once they’re presented with an incident,” said Elik Eizenberg, Co-Founder and CTO at BigPanda. “Automatic Incident Triage turns what used to be a technical incident into a business incident automatically, helping incident responders rapidly triage and handle more incidents than before and quickly route critical incidents to the right teams for follow-up and resolution.”

With Automatic Incident Triage, BigPanda customers can:

- Automatically calculate and incorporate detailed business context into incidents, such as validated incident severity, impacted services, business priority and routing information using easy-to-create custom incident tags.

- Quickly and easily sort, filter, visualize and respond to the incidents, prioritizing those with either the most pressing validated incident severity or the number of impacted services.

- Bi-directionally sync custom incident tags with collaboration tools such as ServiceNow or Jira to deliver easier mapping of fields and trigger workflows within those tools.

Automatic Incident Triage allows Ops teams to handle higher volumes of actionable incidents themselves, without having to escalate as frequently. And when they do escalate, the additional business context makes it easy to prioritize and route incidents to the right teams for faster resolution.

The Latest

IT organizations have historically measured success by how quickly they can respond when something goes wrong. The entire discipline of Incident Management has been optimized around mean time to resolution, first-response SLAs and ticket closure rates. But new research suggests that even though this is a well-executed playbook, it's no longer enough to retain customers ...

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...