Unified Tools Improve Response Time

Pete Goldin
APMdigest

According to the 2014 Application Troubleshooting Survey, conducted by Stackify, 37% of respondents rely on user notifications to identify issues, and many problems take more than a half day to rectify.

However, the survey also revealed that adoption of next generation unified application troubleshooting tools drastically improves response times and minimizes customer impact. The survey found that the most sophisticated companies used integrated tools to simplify and speed up the process of troubleshooting application issues.

Organizations which implemented integrated troubleshooting tools were able to identify and resolve issues without impacting their users in significantly less time than those using standalone tools only.

“In the past several years, applications have come to play a more centric part of many businesses, even those that traditionally were not software players,” said Matt Watson, founder and CEO of Stackify. “Until now, the evolution of application development has historically outpaced an organization’s ability to support and troubleshoot those very same applications – resulting in costly business disruptions as the root causes of issues were identified and resolved. Our survey shows that this is beginning to change.”

Key findings from the report include:

■ 85% of organizations are utilizing multiple internally developed applications, with more than one-third developing and supporting over 10 applications.

■ While logs and errors topped the list of data sources used to troubleshoot application issues, error aggregation tools fell behind infrastructure monitoring and notification tools in a list of the top tools.

■ Even with log management tools at the top of the list, a full one-third of organizations or more aren’t using any tools, making application troubleshooting largely a manual process of collecting and correlating error, log and supporting data.

■ While 46% of developers find out about application issues via application monitoring, 32% still find out from users calling the helpdesk.

■ For organizations using integrated tools, 46% of issues take only an hour to resolve, compared to only 32% of issues when using standalone tools.

■ Organizations with standalone troubleshooting tools cited that 52% of issues taking a half of a day to find the root cause, whereas those with integrated tools only cited 37%.

■ Organizations using integrated tools are able to resolve issues a full 80% of the time without impacting users, whereas those using standalone tools only do so 48% of the time.

The report is based on survey responses from 172 IT operations and development professionals around the world, across companies of all sizes.

Pete Goldin is Editor and Publisher of APMdigest

Hot Topics

The Latest

As AI Scales Across Enterprises, Breaking Points Emerge

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

Preparing Network Infrastructure for AI Starts with the Basics

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Agentic Operations Begin with Governed Middleware Access

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

The Network Data Problem Slowing Down Enterprise AIOps

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

The Governance Gap Behind AI's Infrastructure Problem

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

MEAN TIME TO INSIGHT Podcast - Episode 27: Observability Unification

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

5 Reasons Traditional VPNs Struggle in a Cloud-First World

Virtual Private Networks became a cornerstone of enterprise security at a time when corporate infrastructure looked very different from today ... For years, this model worked well. But the architecture behind VPNs assumed a centralized corporate environment—one where the network itself was the hub of activity. In a cloud — first world, that assumption no longer holds ...

What 1.8 Million Outages Say About Who Is Really On Call in August

Website outages get resolved just as fast in August as they do in November. I went looking for the opposite: the summer slowdown everyone assumes is there once the people who fix things are away. It isn't in the data we collected, covering 1.8 million confirmed outages across tens of thousands of websites ...

Why Enterprises Are So Unhappy with Their IT Infrastructure

This year, many of the cloud infrastructure contracts signed in the early days of the AI boom will come up for renewal. As the year goes on, I anticipate we'll see a significant amount of cloud vendor swapouts and multi-cloud adoption, and the reason isn't just GPU depreciation. It's because they're tired of their current cloud providers ...

Agentic AI Just Became Your Newest Production Dependency. Are You Tracking It Like One?

There's a moment the many observability teams have experienced days into bringing a new service into production: you realize that the vendor's claims of "intelligent" behavior included a large serving of hype. Their dashboards look nice until they don't, the failure modes are a black box, and no one on the team can confidently explain why the system did what it did at 2 am. Agentic AI is about to force every Ops team to relive that moment at web-scale until they start treating these systems as the dependencies they actually are ...

Unified Tools Improve Response Time

Pete Goldin
APMdigest

According to the 2014 Application Troubleshooting Survey, conducted by Stackify, 37% of respondents rely on user notifications to identify issues, and many problems take more than a half day to rectify.

However, the survey also revealed that adoption of next generation unified application troubleshooting tools drastically improves response times and minimizes customer impact. The survey found that the most sophisticated companies used integrated tools to simplify and speed up the process of troubleshooting application issues.

Organizations which implemented integrated troubleshooting tools were able to identify and resolve issues without impacting their users in significantly less time than those using standalone tools only.

“In the past several years, applications have come to play a more centric part of many businesses, even those that traditionally were not software players,” said Matt Watson, founder and CEO of Stackify. “Until now, the evolution of application development has historically outpaced an organization’s ability to support and troubleshoot those very same applications – resulting in costly business disruptions as the root causes of issues were identified and resolved. Our survey shows that this is beginning to change.”

Key findings from the report include:

■ 85% of organizations are utilizing multiple internally developed applications, with more than one-third developing and supporting over 10 applications.

■ While logs and errors topped the list of data sources used to troubleshoot application issues, error aggregation tools fell behind infrastructure monitoring and notification tools in a list of the top tools.

■ Even with log management tools at the top of the list, a full one-third of organizations or more aren’t using any tools, making application troubleshooting largely a manual process of collecting and correlating error, log and supporting data.

■ While 46% of developers find out about application issues via application monitoring, 32% still find out from users calling the helpdesk.

■ For organizations using integrated tools, 46% of issues take only an hour to resolve, compared to only 32% of issues when using standalone tools.

■ Organizations with standalone troubleshooting tools cited that 52% of issues taking a half of a day to find the root cause, whereas those with integrated tools only cited 37%.

■ Organizations using integrated tools are able to resolve issues a full 80% of the time without impacting users, whereas those using standalone tools only do so 48% of the time.

The report is based on survey responses from 172 IT operations and development professionals around the world, across companies of all sizes.

Pete Goldin is Editor and Publisher of APMdigest

Hot Topics

The Latest

As AI Scales Across Enterprises, Breaking Points Emerge

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

Preparing Network Infrastructure for AI Starts with the Basics

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Agentic Operations Begin with Governed Middleware Access

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

The Network Data Problem Slowing Down Enterprise AIOps

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

The Governance Gap Behind AI's Infrastructure Problem

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

MEAN TIME TO INSIGHT Podcast - Episode 27: Observability Unification

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

5 Reasons Traditional VPNs Struggle in a Cloud-First World

Virtual Private Networks became a cornerstone of enterprise security at a time when corporate infrastructure looked very different from today ... For years, this model worked well. But the architecture behind VPNs assumed a centralized corporate environment—one where the network itself was the hub of activity. In a cloud — first world, that assumption no longer holds ...

What 1.8 Million Outages Say About Who Is Really On Call in August

Website outages get resolved just as fast in August as they do in November. I went looking for the opposite: the summer slowdown everyone assumes is there once the people who fix things are away. It isn't in the data we collected, covering 1.8 million confirmed outages across tens of thousands of websites ...

Why Enterprises Are So Unhappy with Their IT Infrastructure

This year, many of the cloud infrastructure contracts signed in the early days of the AI boom will come up for renewal. As the year goes on, I anticipate we'll see a significant amount of cloud vendor swapouts and multi-cloud adoption, and the reason isn't just GPU depreciation. It's because they're tired of their current cloud providers ...

Agentic AI Just Became Your Newest Production Dependency. Are You Tracking It Like One?

There's a moment the many observability teams have experienced days into bringing a new service into production: you realize that the vendor's claims of "intelligent" behavior included a large serving of hype. Their dashboards look nice until they don't, the failure modes are a black box, and no one on the team can confidently explain why the system did what it did at 2 am. Agentic AI is about to force every Ops team to relive that moment at web-scale until they start treating these systems as the dependencies they actually are ...