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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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Payment system failures are putting $44.4 billion in US retail and hospitality sales at risk each year, underscoring how quickly disruption can derail day-to-day trading, according to research conducted by Dynatrace ... The findings show that payment failures are no longer isolated incidents, but part of a recurring operational challenge that disrupts service, damages customer trust, and negatively impacts revenue ...

For years, the success of DevOps has been measured by how much manual work teams can automate ... I believe that in 2026, the definition of DevOps success is going to expand significantly. The era of automation is giving way to the era of intelligent delivery, in which AI doesn't just accelerate pipelines, it understands them. With open observability connecting signals end-to-end across those tools, teams can build closed-loop systems that don't just move faster, but learn, adapt, and take action autonomously with confidence ...

The conversation around AI in the enterprise has officially shifted from "if" to "how fast." But according to the State of Network Operations 2026 report from Broadcom, most organizations are unknowingly building their AI strategies on sand. The data is clear: CIOs and network teams are putting the cart before the horse. AI cannot improve what the network cannot see, predict issues without historical context, automate processes that aren't standardized, or recommend fixes when the underlying telemetry is incomplete. If AI is the brain, then network observability is the nervous system that makes intelligent action possible ...

SolarWinds data shows that one in three DBAs are contemplating leaving their positions — a striking indicator of workforce pressure in this role. This is likely due to the technical and interpersonal frustrations plaguing today's DBAs. Hybrid IT environments provide widespread organizational benefits but also present growing complexity. Simultaneously, AI presents a paradox of benefits and pain points ...

Over the last year, we've seen enterprises stop treating AI as “special projects.” It is no longer confined to pilots or side experiments. AI is now embedded in production, shaping decisions, powering new business models, and changing how employees and customers experience work every day. So, the debate of "should we adopt AI" is settled. The real question is how quickly and how deeply it can be applied ...

In MEAN TIME TO INSIGHT Episode 20, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA presents his 2026 NetOps predictions ... 

Today, technology buyers don't suffer from a lack of information but an abundance of it. They need a trusted partner to help them navigate this information environment ...

My latest title for O'Reilly, The Rise of Logical Data Management, was an eye-opener for me. I'd never heard of "logical data management," even though it's been around for several years, but it makes some extraordinary promises, like the ability to manage data without having to first move it into a consolidated repository, which changes everything. Now, with the demands of AI and other modern use cases, logical data management is on the rise, so it's "new" to many. Here, I'd like to introduce you to it and explain how it works ...

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