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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

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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 ...