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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

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Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

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Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

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 ...

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

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

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 ...