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Only 57% of Critical IT Infrastructure Issues Are Detected Before Business Impact

Pete Goldin
APMdigest

Organizations large and small are struggling to meet their Key Performance Indicator (KPI) goals and prevent IT issues before they adversely impact the business — in fact, organizations detect and address an average of only 57% of critical IT issues before they impact the business — according to Continuity Software's 2015 IT Operations Analytics Survey.

Additional key findings include:

■ Uptime is the leading KPI for IT operations, used by 89% of respondents — 51% of respondents track uptime in real time, while 19% track it daily.

■ Organizations are least likely to monitor KPIs in the cloud environment (only 20% do so). In contrast, more than 70% of organizations monitor KPIs across their networks, databases, applications and storage.

■ Only 29% of respondents consistently meet or exceed their KPI goals. 46% percent of respondents meet their goals most of the time, and 13% meet some of them.

■ Organizations are more likely to meet their KPI goals if they frequently track configuration consistency across more IT domains.

■ IT Operations Analytics tools are more commonly found in large organizations. 47% of organizations with over 10,000 employees use them, compared to 32% of smaller companies.

■ 76% of executives who use IT Operations Analytics find these solutions helpful or extremely helpful in early detection of IT issues.

■ IT executives believe the best way to improve operations excellence is to use better tools for measurement and analysis (28%), use tools to enforce IT best practices (28%), or use tools to detect cross-domain IT configuration issues (22%).

"Although KPI tracking is becoming more and more sophisticated, the fact that less than a third of organizations are meeting their KPI goals is concerning," said Doron Pinhas, CTO, Continuity Software. "Organizations could meet their KPI goals more consistently - and avoid critical issues - if they used better analytics tools more often and more widely, particularly in the cloud."

The 2015 IT Operations Analytics Survey is based on responses from over 200 IT professionals from various industries.

Pete Goldin is Editor and Publisher of APMdigest

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Only 57% of Critical IT Infrastructure Issues Are Detected Before Business Impact

Pete Goldin
APMdigest

Organizations large and small are struggling to meet their Key Performance Indicator (KPI) goals and prevent IT issues before they adversely impact the business — in fact, organizations detect and address an average of only 57% of critical IT issues before they impact the business — according to Continuity Software's 2015 IT Operations Analytics Survey.

Additional key findings include:

■ Uptime is the leading KPI for IT operations, used by 89% of respondents — 51% of respondents track uptime in real time, while 19% track it daily.

■ Organizations are least likely to monitor KPIs in the cloud environment (only 20% do so). In contrast, more than 70% of organizations monitor KPIs across their networks, databases, applications and storage.

■ Only 29% of respondents consistently meet or exceed their KPI goals. 46% percent of respondents meet their goals most of the time, and 13% meet some of them.

■ Organizations are more likely to meet their KPI goals if they frequently track configuration consistency across more IT domains.

■ IT Operations Analytics tools are more commonly found in large organizations. 47% of organizations with over 10,000 employees use them, compared to 32% of smaller companies.

■ 76% of executives who use IT Operations Analytics find these solutions helpful or extremely helpful in early detection of IT issues.

■ IT executives believe the best way to improve operations excellence is to use better tools for measurement and analysis (28%), use tools to enforce IT best practices (28%), or use tools to detect cross-domain IT configuration issues (22%).

"Although KPI tracking is becoming more and more sophisticated, the fact that less than a third of organizations are meeting their KPI goals is concerning," said Doron Pinhas, CTO, Continuity Software. "Organizations could meet their KPI goals more consistently - and avoid critical issues - if they used better analytics tools more often and more widely, particularly in the cloud."

The 2015 IT Operations Analytics Survey is based on responses from over 200 IT professionals from various industries.

Pete Goldin is Editor and Publisher of APMdigest

Hot Topics

The Latest

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 ...

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? ...

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 ...

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 ...

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 ...

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 ... 

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 ...

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 ...

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 ...

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 ...