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Why Incident Triage is a Key Element in Your MTTR

Yoram Pollack
BigPanda

One of the key performance indicators for IT Ops is MTTR (Mean-Time-To-Resolution). MTTR essentially measures the length of your incident management lifecycle: from detection; through assignment, triage and investigation; to remediation and resolution. IT Ops teams strive to shorten their incident management lifecycle and lower their MTTR, to meet their SLAs and maintain healthy infrastructures and services. But that's often easier said than done, with incident triage being a key factor in that challenge.


Why Incident Triage is Critical for Lowering MTTR

One of the main side effects of today's increasingly complex, hybrid and constantly changing IT environments is the proliferation of disparate ops teams, tools, apps and environments. This in turn leads to high volumes of IT incidents that lack full business context.

As a result, it has become increasingly difficult for first incident responders to triage incoming incidents: Without the ability to understand the incidents' severity based on their business priorities and their impact on services or customers, their routing information, and more — IT Ops teams often waste valuable time determining what to do next, and in doing so, lengthen the incident management lifecycle.

In essence, incident triage has grown to play a key role in determining MTTR in modern, hybrid environments.

Manual Incident Triage Can Be Painful

Because different applications and services have different impacts on customers, availability and revenue, when several incidents occur at the same time it is imperative for incident responders in IT Ops and NOC teams to identify the priority in which these incidents need to be dealt with, and how best to deal with each of them. For teams to be able to rapidly perform this triage, they need access to critical business context and business metrics:

■ The business severity of each incident

■ The services each of them impact

■ Whom to route them to

■ In which priority to do so

■ And other context based on the organization's relevant processes and services.

Without easy access to this information, the teams waste precious time tracking down relevant spreadsheets, runbooks, and other sources of tribal knowledge, as well as manually calculating the business metrics needed to help them understand the incidents' implications.

The more time that is spent on these manual steps, the longer the incident triage lasts.

And the longer that takes, the higher the probability that SLAs are violated, MTTR is kept high, and costs associated with high MTTR rapidly increase.

The solution? Automating incident triage.

Automating Incident Triage

Incident triage can be automated by following several key guidelines:

■ The first step is to allow relevant business context information to reside on the incident level, rather than on the alert level. This can be done by creating custom tags for incidents that can hold this information and be acted upon (filtering, sorting etc).

■ The next step is to create simple yet robust formulas that allow operators to automatically calculate the values and metrics held by these tags. For example — calculate the SLA values in an SLA tag, based on the customer and the service to which the incident is referring. By automatically calculating the values and attaching them to the incident by using tags, the need to search for this information manually within tribal knowledge sources is eliminated, as is the need to calculate the values manually when the incident happens.

■ Now — provide filtering and sorting capabilities based on these tag values, and facilitate effective visualization of these tags alongside the incidents, so teams can easily make decisions and act on the incidents based on what they are seeing.

■ Finally — allow routing automation based on the tag values, so large volumes of incidents can be dealt with by relevant teams or automated resolution processes.


The Short and Long Term Advantages of Automating Incident Triage

The first advantage of incident triage automation is self-evident in all that was just discussed, mainly a shorter incident lifecycle — leading to improved performance and availability for apps and services. It's simple — lower MTTR equals better service.

But let's not forget two additional, substantial gains.

First — improved NOC productivity. By providing the above-mentioned capabilities, a substantial part of the incident lifecycle becomes simpler, and teams can collaborate better — lowering stress and effort across the board. Over time, the collected information can also be used for ongoing improvements in tools and processes.

And second — reclaimed FTE hours, an often “hidden” cost-reducer and revenue-generator. By reclaiming thousands of operational “fire-fighting” man-hours and utilizing them to improve and develop new services, enterprises not only reduce costs but also accelerate their business.

Yoram Pollack is Director of Product Marketing at BigPanda

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Why Incident Triage is a Key Element in Your MTTR

Yoram Pollack
BigPanda

One of the key performance indicators for IT Ops is MTTR (Mean-Time-To-Resolution). MTTR essentially measures the length of your incident management lifecycle: from detection; through assignment, triage and investigation; to remediation and resolution. IT Ops teams strive to shorten their incident management lifecycle and lower their MTTR, to meet their SLAs and maintain healthy infrastructures and services. But that's often easier said than done, with incident triage being a key factor in that challenge.


Why Incident Triage is Critical for Lowering MTTR

One of the main side effects of today's increasingly complex, hybrid and constantly changing IT environments is the proliferation of disparate ops teams, tools, apps and environments. This in turn leads to high volumes of IT incidents that lack full business context.

As a result, it has become increasingly difficult for first incident responders to triage incoming incidents: Without the ability to understand the incidents' severity based on their business priorities and their impact on services or customers, their routing information, and more — IT Ops teams often waste valuable time determining what to do next, and in doing so, lengthen the incident management lifecycle.

In essence, incident triage has grown to play a key role in determining MTTR in modern, hybrid environments.

Manual Incident Triage Can Be Painful

Because different applications and services have different impacts on customers, availability and revenue, when several incidents occur at the same time it is imperative for incident responders in IT Ops and NOC teams to identify the priority in which these incidents need to be dealt with, and how best to deal with each of them. For teams to be able to rapidly perform this triage, they need access to critical business context and business metrics:

■ The business severity of each incident

■ The services each of them impact

■ Whom to route them to

■ In which priority to do so

■ And other context based on the organization's relevant processes and services.

Without easy access to this information, the teams waste precious time tracking down relevant spreadsheets, runbooks, and other sources of tribal knowledge, as well as manually calculating the business metrics needed to help them understand the incidents' implications.

The more time that is spent on these manual steps, the longer the incident triage lasts.

And the longer that takes, the higher the probability that SLAs are violated, MTTR is kept high, and costs associated with high MTTR rapidly increase.

The solution? Automating incident triage.

Automating Incident Triage

Incident triage can be automated by following several key guidelines:

■ The first step is to allow relevant business context information to reside on the incident level, rather than on the alert level. This can be done by creating custom tags for incidents that can hold this information and be acted upon (filtering, sorting etc).

■ The next step is to create simple yet robust formulas that allow operators to automatically calculate the values and metrics held by these tags. For example — calculate the SLA values in an SLA tag, based on the customer and the service to which the incident is referring. By automatically calculating the values and attaching them to the incident by using tags, the need to search for this information manually within tribal knowledge sources is eliminated, as is the need to calculate the values manually when the incident happens.

■ Now — provide filtering and sorting capabilities based on these tag values, and facilitate effective visualization of these tags alongside the incidents, so teams can easily make decisions and act on the incidents based on what they are seeing.

■ Finally — allow routing automation based on the tag values, so large volumes of incidents can be dealt with by relevant teams or automated resolution processes.


The Short and Long Term Advantages of Automating Incident Triage

The first advantage of incident triage automation is self-evident in all that was just discussed, mainly a shorter incident lifecycle — leading to improved performance and availability for apps and services. It's simple — lower MTTR equals better service.

But let's not forget two additional, substantial gains.

First — improved NOC productivity. By providing the above-mentioned capabilities, a substantial part of the incident lifecycle becomes simpler, and teams can collaborate better — lowering stress and effort across the board. Over time, the collected information can also be used for ongoing improvements in tools and processes.

And second — reclaimed FTE hours, an often “hidden” cost-reducer and revenue-generator. By reclaiming thousands of operational “fire-fighting” man-hours and utilizing them to improve and develop new services, enterprises not only reduce costs but also accelerate their business.

Yoram Pollack is Director of Product Marketing at BigPanda

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For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

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Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

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