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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

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...