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SevOne Data Insight 3.0 Released

SevOne, a Turbonomic company, announced the launch of Data Insight 3.0, an integrated component of the company’s SevOne Network Data Platform.

This release of Data Insight 3.0 and availability of solutions for SD-WAN, Wi-Fi, and SDN completes the product transformation from a network monitoring appliance to an integrated network data platform to ensure continuous network performance.

Data Insight 3.0 allows users to easily find, use and share valuable insights hidden in network performance data. Data Insight 3.0 leverages real-time monitoring and offers simple, reusable and scalable reporting, and troubleshooting workflows that enable operational consistency, with a new system architecture and an enhanced user experience. Data Insight 3.0 delivers a series of new features and enhancements, including:

- Day One Report Library: Users can now leverage a library of more than a dozen auto-populating reports and templates for the most common network performance needs. Some of these customizable reports include: Alert dashboards, TopN views, and summary reports coupled with device, object and indicator templates.

- Metric Pivoting via Intelligent Chaining: IT Operations teams can now quickly pivot on a target metric and visualize dependent flow and alert data leveraging intelligent chaining.

- Embed Expertise in Troubleshooting Workflows: IT Operations teams can now increase operational consistency and minimize triage time by embedding expertise into troubleshooting workflows and sharing best practices across teams.

- In-Context Report Launching: Users can now set context across multiple widgets, simultaneously, to initiate parallel reporting, filter the necessary data, and obtain insights faster.

“To close the gap between modern networks and legacy monitoring, IT operations teams require network monitoring capabilities that are just as fast, flexible and scalable as their new networks,” said Jim Melvin, COO at SevOne. “More specifically, they require faster, smarter, and easier ways to gather and analyze performance data, shape the resulting operational insights, and share them with all types of users across their organizations.”

"Our recently published Network Management Megatrends 2020 research showed that customizable reporting, customizable dashboards, collaboration tools/workflows, network visualization and API integration with other tools were five of the top six features in a network management product,” said Shamus McGillicuddy, VP of Research for Network Management at Enterprise Management Associates. “The release of SevOne Data Insight 3.0, as part of the SevOne Network Data Platform, addresses these requirements directly. In particular, the ability to flexibly create workflows and share them across teams is crucial for continuous network performance as companies find their networks increasingly burdened.”

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SevOne Data Insight 3.0 Released

SevOne, a Turbonomic company, announced the launch of Data Insight 3.0, an integrated component of the company’s SevOne Network Data Platform.

This release of Data Insight 3.0 and availability of solutions for SD-WAN, Wi-Fi, and SDN completes the product transformation from a network monitoring appliance to an integrated network data platform to ensure continuous network performance.

Data Insight 3.0 allows users to easily find, use and share valuable insights hidden in network performance data. Data Insight 3.0 leverages real-time monitoring and offers simple, reusable and scalable reporting, and troubleshooting workflows that enable operational consistency, with a new system architecture and an enhanced user experience. Data Insight 3.0 delivers a series of new features and enhancements, including:

- Day One Report Library: Users can now leverage a library of more than a dozen auto-populating reports and templates for the most common network performance needs. Some of these customizable reports include: Alert dashboards, TopN views, and summary reports coupled with device, object and indicator templates.

- Metric Pivoting via Intelligent Chaining: IT Operations teams can now quickly pivot on a target metric and visualize dependent flow and alert data leveraging intelligent chaining.

- Embed Expertise in Troubleshooting Workflows: IT Operations teams can now increase operational consistency and minimize triage time by embedding expertise into troubleshooting workflows and sharing best practices across teams.

- In-Context Report Launching: Users can now set context across multiple widgets, simultaneously, to initiate parallel reporting, filter the necessary data, and obtain insights faster.

“To close the gap between modern networks and legacy monitoring, IT operations teams require network monitoring capabilities that are just as fast, flexible and scalable as their new networks,” said Jim Melvin, COO at SevOne. “More specifically, they require faster, smarter, and easier ways to gather and analyze performance data, shape the resulting operational insights, and share them with all types of users across their organizations.”

"Our recently published Network Management Megatrends 2020 research showed that customizable reporting, customizable dashboards, collaboration tools/workflows, network visualization and API integration with other tools were five of the top six features in a network management product,” said Shamus McGillicuddy, VP of Research for Network Management at Enterprise Management Associates. “The release of SevOne Data Insight 3.0, as part of the SevOne Network Data Platform, addresses these requirements directly. In particular, the ability to flexibly create workflows and share them across teams is crucial for continuous network performance as companies find their networks increasingly burdened.”

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In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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 ...