Skip to main content

Measurement and Analysis Across Entire IT Infrastructure Is Key

Doron Pinhas

Continuity Software announced the results of the Continuity Software IT Operations Analytics Benchmark. Based on results collected across a variety of industry verticals - including financial services, healthcare, manufacturing, and retail - the benchmark underscores the importance of operational analytics in meeting IT performance goals.

The IT Operations Analytics Benchmark survey's key findings include:

- Large organizations are the most common users of analytical tools to monitor and measure IT performance goals: 57% of the large organizations surveyed use analytical tools to monitor, and measure IT performance goals (versus just 29% of small companies).

- Cross-domain operational excellence is mostly measured by uptime: 89% of the organizations surveyed measure uptime across most or all IT domains; 66% measure performance; 51% measure the number of open issues.

- Frequently tracking configuration consistency helps organizations meet their goals: 53% of the organizations that track configuration consistency on a daily basis across the IT infrastructure are meeting or exceeding their goals, compared to 31-33% of the organizations that track only portions of the infrastructure.

- Better measurement and analysis tools are required for IT operations excellence: 40% of organizations surveyed cited better measurement and analysis tools as the most effective means for achieving operations excellence, followed by tools to detect cross-domain IT configuration issues (22%) and tools to enforce IT best practices (19%).

- Storage and network performance rank highest: 71% of the organizations surveyed monitor storage and network key performance indicators (KPIs); other areas of IT operations that are commonly monitored and measured include applications (69%), databases (66%), and clusters (49%).

- Cloud environments continue to lag behind: Only 14% of the organizations surveyed monitor and measure cloud KPIs, and 43% of the organizations surveyed never analyze configuration consistency in their cloud environment.

Few would argue that business organizations that deliver IT operational excellence enjoy a distinct advantage over their competitors. As this survey reveals, organizations that are successful in achieving this goal invest in measurement and analysis of KPIs and are able to transform the collected insights into immediate actions.

It is also interesting to note that while the push to move data and applications into the cloud continues to escalate, most cloud infrastructure remains under-monitored, and consequently at great risk of unplanned downtime and service disruption.

Doron Pinhas is CTO of Continuity Software.

Related Links:

www.continuitysoftware.com

Hot Topics

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 ...

Measurement and Analysis Across Entire IT Infrastructure Is Key

Doron Pinhas

Continuity Software announced the results of the Continuity Software IT Operations Analytics Benchmark. Based on results collected across a variety of industry verticals - including financial services, healthcare, manufacturing, and retail - the benchmark underscores the importance of operational analytics in meeting IT performance goals.

The IT Operations Analytics Benchmark survey's key findings include:

- Large organizations are the most common users of analytical tools to monitor and measure IT performance goals: 57% of the large organizations surveyed use analytical tools to monitor, and measure IT performance goals (versus just 29% of small companies).

- Cross-domain operational excellence is mostly measured by uptime: 89% of the organizations surveyed measure uptime across most or all IT domains; 66% measure performance; 51% measure the number of open issues.

- Frequently tracking configuration consistency helps organizations meet their goals: 53% of the organizations that track configuration consistency on a daily basis across the IT infrastructure are meeting or exceeding their goals, compared to 31-33% of the organizations that track only portions of the infrastructure.

- Better measurement and analysis tools are required for IT operations excellence: 40% of organizations surveyed cited better measurement and analysis tools as the most effective means for achieving operations excellence, followed by tools to detect cross-domain IT configuration issues (22%) and tools to enforce IT best practices (19%).

- Storage and network performance rank highest: 71% of the organizations surveyed monitor storage and network key performance indicators (KPIs); other areas of IT operations that are commonly monitored and measured include applications (69%), databases (66%), and clusters (49%).

- Cloud environments continue to lag behind: Only 14% of the organizations surveyed monitor and measure cloud KPIs, and 43% of the organizations surveyed never analyze configuration consistency in their cloud environment.

Few would argue that business organizations that deliver IT operational excellence enjoy a distinct advantage over their competitors. As this survey reveals, organizations that are successful in achieving this goal invest in measurement and analysis of KPIs and are able to transform the collected insights into immediate actions.

It is also interesting to note that while the push to move data and applications into the cloud continues to escalate, most cloud infrastructure remains under-monitored, and consequently at great risk of unplanned downtime and service disruption.

Doron Pinhas is CTO of Continuity Software.

Related Links:

www.continuitysoftware.com

Hot Topics

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 ...