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Netuitive Enhances IT Analytics Platform for APM with New UI: Netuitive Health Assistant

Responding to increasing demand for faster, more intelligent, and more actionable identification of IT and application anomalies that can impact the business, Netuitive announced a new user interface (UI) and key enhancements to its IT Analytics Platform for application performance management (APM).

With the release of the new UI (Netuitive Health Assistant), Netuitive has significantly improved its IT analytics platform with new capabilities that simplify the process of proactively detecting, interpreting, and resolving performance alarms as part of large scale, enterprise APM deployments.

Unlike conventional monitoring typically involving multiple UIs, Netuitive Health Assistant enables support staff to easily interpret and see – in a single screen – the business impact by incorporating the intelligence of business users into the analysis. Core advancements include:

• Higher Accuracy: The new UI uses enhanced analytics to accelerate the problem diagnostic workflow, helping support staff separate minor anomalies from real problems, and matching the problem to solutions in the Netuitive Knowledge Base.

• Smarter Analysis: The new alarm UI does the work of multiple screens in one – making it easier for users to understand what factors are contributing to degradation in the “health” index of a service or component as well as how this correlates to metrics that indicate business impact.

• More Actionable: Knowledge Base entries are easier to create with a user friendly UI and improved alarming logic presents Level 1 IT support staff with clearer instructions on what action to take for the most common performance problems.

“Netuitive’s 20 years of experience with large scale customer deployments combined with our advanced R&D in IT analytics and Behavior Learning technology has led us to develop Netuitive Health Assistant,” said Marcus Jackson, Director of Product Management at Netuitive.

“It’s no longer about simply identifying an anomaly that might be good or bad," Jackson continued. "Incident NG automates and tailors analysis based on key data and idiosyncrasies associated with your environment. The result is much more intelligent and actionable insight that is particularly helpful for Level 1 IT operators responsible for interpreting increasing volumes of IT and application performance data and metrics from across the enterprise.”

Netuitive’s IT analytics fall squarely into Gartner’s categorization of IT Operations Analytics (ITOA).

“IT operations analytics tools enable CIOs and senior IT operations managers to monitor their business operational data and metrics," adds Jackson. "The tools are similar to a business intelligence platform that business unit managers use to drive business performance. IT operations analytics tools enable users to assess efficiency, optimize IT investments, correlate trends, and understand and maximize IT opportunities that support the business.”

Related Links:

www.netuitive.com

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Netuitive Enhances IT Analytics Platform for APM with New UI: Netuitive Health Assistant

Responding to increasing demand for faster, more intelligent, and more actionable identification of IT and application anomalies that can impact the business, Netuitive announced a new user interface (UI) and key enhancements to its IT Analytics Platform for application performance management (APM).

With the release of the new UI (Netuitive Health Assistant), Netuitive has significantly improved its IT analytics platform with new capabilities that simplify the process of proactively detecting, interpreting, and resolving performance alarms as part of large scale, enterprise APM deployments.

Unlike conventional monitoring typically involving multiple UIs, Netuitive Health Assistant enables support staff to easily interpret and see – in a single screen – the business impact by incorporating the intelligence of business users into the analysis. Core advancements include:

• Higher Accuracy: The new UI uses enhanced analytics to accelerate the problem diagnostic workflow, helping support staff separate minor anomalies from real problems, and matching the problem to solutions in the Netuitive Knowledge Base.

• Smarter Analysis: The new alarm UI does the work of multiple screens in one – making it easier for users to understand what factors are contributing to degradation in the “health” index of a service or component as well as how this correlates to metrics that indicate business impact.

• More Actionable: Knowledge Base entries are easier to create with a user friendly UI and improved alarming logic presents Level 1 IT support staff with clearer instructions on what action to take for the most common performance problems.

“Netuitive’s 20 years of experience with large scale customer deployments combined with our advanced R&D in IT analytics and Behavior Learning technology has led us to develop Netuitive Health Assistant,” said Marcus Jackson, Director of Product Management at Netuitive.

“It’s no longer about simply identifying an anomaly that might be good or bad," Jackson continued. "Incident NG automates and tailors analysis based on key data and idiosyncrasies associated with your environment. The result is much more intelligent and actionable insight that is particularly helpful for Level 1 IT operators responsible for interpreting increasing volumes of IT and application performance data and metrics from across the enterprise.”

Netuitive’s IT analytics fall squarely into Gartner’s categorization of IT Operations Analytics (ITOA).

“IT operations analytics tools enable CIOs and senior IT operations managers to monitor their business operational data and metrics," adds Jackson. "The tools are similar to a business intelligence platform that business unit managers use to drive business performance. IT operations analytics tools enable users to assess efficiency, optimize IT investments, correlate trends, and understand and maximize IT opportunities that support the business.”

Related Links:

www.netuitive.com

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 ...