Skip to main content

Optimizing Root Cause Analysis to Reduce MTTR

Ariel Gordon

Efficiently detecting and resolving problems is essential, of course, to continue supporting - and minimizing impact on - business services, as well as minimizing any financial impacts.

The goal is to turn the tables on IT problems so that 80 percent of the time is spent on the root cause analysis versus 20 percent on the actual problem fixing.

In resolving the issue, communication is a critical factor for integrating different expert groups towards a common goal. Because each team holds a narrow view of its own domain and expertise, there is always the danger lurking that the "big picture" angle will be missing. You don't want lack of communication to result in blame games and finger pointing.

Some problem detection methods include:

- Infrastructure Monitoring: specific resource utilization like disk, memory, CPU are effective for identifying availability failures – sometimes even heading those off before they happen.

- Domain or Application Tools: These help, but leave the issue that overall problem detection is still a game of hide-and-seek, a manually-intensive effort that comes under the pressure of needing a fix as quickly as possible.

- Dependency mapping tools, which map business services and applications to infrastructure components, can help you generate a topology map that will improve your root cause analysis process for the following reasons:

1. Connect Symptoms to Problems: A single map that relates a business service (user point of view) to its configuration items, will help you detect problems faster.

2. Common Ground: The map ties in all elements so that different groups can focus on a cross-domain effort.

3. High-Level, Cross-Domain View: Teams can view problems not only in the context of their domain, but in a wider view of all network components. For example, a database administrator analyzing a slow database performance problem can examine the topology map to see the effect of networking components on the database.

Root cause is a complex issue, so that no single tool or approach will provide you with full coverage. The idea is to plan a portfolio of tools that together deliver the most impact for your organization.

For instance, if you do not have a central event management console, then consider implementing a topology-based event management solution. If most of your applications involve online transactions, try to look for a transaction management product that covers the technology stack that is common in your environment. Put differently, select a combination of tools that are right for your environment.

Once you assess the tools that provide the most value, implement them in ascending order of value so that you get the biggest impact first.

Ariel Gordon is VP Products and Co-Founder of Neebula.

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Optimizing Root Cause Analysis to Reduce MTTR

Ariel Gordon

Efficiently detecting and resolving problems is essential, of course, to continue supporting - and minimizing impact on - business services, as well as minimizing any financial impacts.

The goal is to turn the tables on IT problems so that 80 percent of the time is spent on the root cause analysis versus 20 percent on the actual problem fixing.

In resolving the issue, communication is a critical factor for integrating different expert groups towards a common goal. Because each team holds a narrow view of its own domain and expertise, there is always the danger lurking that the "big picture" angle will be missing. You don't want lack of communication to result in blame games and finger pointing.

Some problem detection methods include:

- Infrastructure Monitoring: specific resource utilization like disk, memory, CPU are effective for identifying availability failures – sometimes even heading those off before they happen.

- Domain or Application Tools: These help, but leave the issue that overall problem detection is still a game of hide-and-seek, a manually-intensive effort that comes under the pressure of needing a fix as quickly as possible.

- Dependency mapping tools, which map business services and applications to infrastructure components, can help you generate a topology map that will improve your root cause analysis process for the following reasons:

1. Connect Symptoms to Problems: A single map that relates a business service (user point of view) to its configuration items, will help you detect problems faster.

2. Common Ground: The map ties in all elements so that different groups can focus on a cross-domain effort.

3. High-Level, Cross-Domain View: Teams can view problems not only in the context of their domain, but in a wider view of all network components. For example, a database administrator analyzing a slow database performance problem can examine the topology map to see the effect of networking components on the database.

Root cause is a complex issue, so that no single tool or approach will provide you with full coverage. The idea is to plan a portfolio of tools that together deliver the most impact for your organization.

For instance, if you do not have a central event management console, then consider implementing a topology-based event management solution. If most of your applications involve online transactions, try to look for a transaction management product that covers the technology stack that is common in your environment. Put differently, select a combination of tools that are right for your environment.

Once you assess the tools that provide the most value, implement them in ascending order of value so that you get the biggest impact first.

Ariel Gordon is VP Products and Co-Founder of Neebula.

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...