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Infovista Partners with Gigamon

Infovista announced its partnership with Gigamon to address digital transformation and next generation challenges in areas such as network slicing and SLA monetization across the 5G core, Open RAN, and Mobile Private Networks.

The partnership will see Communications Service Providers (CSPs) benefit from the combination of Infovista’s Ativa platform and the Gigamon Hawk Deep Observability Pipeline to maximize end-to-end visibility, analytics, and troubleshooting for 5G networks.

5G slicing is a key enabler for Industry 4.0 and is fast gaining momentum, with the market expected to reach $4.9 billion globally by 2027. However, CSPs are faced with a number of challenges when implementing network slicing, including proof of device stability, cost control and service reliability, making an assurance solution integral. The Ativa and Gigamon Hawk integration is perfectly placed to solve these issues, as it allows:

- Unprecedented network visibility: Customers can get unmatched comprehensive insights, with dashboards and analytics across all layers from the network, transport, application, subscriber, and device

- Reduced operational complexity: CSPs can overcome complexity and avoid repetitive tasks with advanced automation

- Automated troubleshooting: Automation capabilities allow ML/AI automated workflows for troubleshooting, hands-free zero-touch network configuration, and noise reduction

- Assured SLA delivery: Customers can ensure they avoid significant penalties and missed monetization opportunities by never missing cross-domain SLAs

- Boosted ROI: Costs are controlled and ROI is achieved by better preparing for the B2B monetization of 5G networks

Payam Maveddat, SVP Global Alliances at Infovista, says: “5G slicing promises CSPs an incredibly powerful low-latency, high-bandwidth platform for innovation and cross-sector optimization, which will accelerate digital transformation. Together, Infovista and Gigamon offer a compelling value proposition, where customers will get superb use case-based solutions with combined capabilities to handle the latest technologies, assisting with monetization and creation of business value, designed to address the most urgent operational challenges today, as well as preparing them for the future.”

Adrian Belcher, Director of Strategic Alliances at Gigamon adds, “We are thrilled to be partnering with Infovista to support CSPs and help deliver enhanced connectivity, flexibility, and visibility to our customers. We are in an incredibly promising time for digital transformation as our customers embrace the potential of 5G and recognize how network slicing can benefit their end-to-end 5G performance. The integration of Gigamon Hawk and Infovista’s Ativa platform can help CSPs overcome mission critical issues and help maintain business continuity.”

Beyond 360° assurance for 5G Slicing, Gigamon and Infovista are continuing to build use cases to benefits CSPs and are working to provide an end-to-end solution for user plane data analysis of applications, eliminate limitations of public cloud deployments and solve the challenge of encryption by providing visibility into TLS 1.3/DHE encrypted traffic.

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Infovista Partners with Gigamon

Infovista announced its partnership with Gigamon to address digital transformation and next generation challenges in areas such as network slicing and SLA monetization across the 5G core, Open RAN, and Mobile Private Networks.

The partnership will see Communications Service Providers (CSPs) benefit from the combination of Infovista’s Ativa platform and the Gigamon Hawk Deep Observability Pipeline to maximize end-to-end visibility, analytics, and troubleshooting for 5G networks.

5G slicing is a key enabler for Industry 4.0 and is fast gaining momentum, with the market expected to reach $4.9 billion globally by 2027. However, CSPs are faced with a number of challenges when implementing network slicing, including proof of device stability, cost control and service reliability, making an assurance solution integral. The Ativa and Gigamon Hawk integration is perfectly placed to solve these issues, as it allows:

- Unprecedented network visibility: Customers can get unmatched comprehensive insights, with dashboards and analytics across all layers from the network, transport, application, subscriber, and device

- Reduced operational complexity: CSPs can overcome complexity and avoid repetitive tasks with advanced automation

- Automated troubleshooting: Automation capabilities allow ML/AI automated workflows for troubleshooting, hands-free zero-touch network configuration, and noise reduction

- Assured SLA delivery: Customers can ensure they avoid significant penalties and missed monetization opportunities by never missing cross-domain SLAs

- Boosted ROI: Costs are controlled and ROI is achieved by better preparing for the B2B monetization of 5G networks

Payam Maveddat, SVP Global Alliances at Infovista, says: “5G slicing promises CSPs an incredibly powerful low-latency, high-bandwidth platform for innovation and cross-sector optimization, which will accelerate digital transformation. Together, Infovista and Gigamon offer a compelling value proposition, where customers will get superb use case-based solutions with combined capabilities to handle the latest technologies, assisting with monetization and creation of business value, designed to address the most urgent operational challenges today, as well as preparing them for the future.”

Adrian Belcher, Director of Strategic Alliances at Gigamon adds, “We are thrilled to be partnering with Infovista to support CSPs and help deliver enhanced connectivity, flexibility, and visibility to our customers. We are in an incredibly promising time for digital transformation as our customers embrace the potential of 5G and recognize how network slicing can benefit their end-to-end 5G performance. The integration of Gigamon Hawk and Infovista’s Ativa platform can help CSPs overcome mission critical issues and help maintain business continuity.”

Beyond 360° assurance for 5G Slicing, Gigamon and Infovista are continuing to build use cases to benefits CSPs and are working to provide an end-to-end solution for user plane data analysis of applications, eliminate limitations of public cloud deployments and solve the challenge of encryption by providing visibility into TLS 1.3/DHE encrypted traffic.

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Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...