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Managing Network Performance and Visibility in an Era of Hyperconnectivity

Tim Mullahy
Liberty Center One

The Internet of Things (IoT) is changing the world. From augmented reality advanced analytics to new consumer solutions, IoT and the cloud are together redefining both how we work and how we engage with our audiences. They are changing how we live, as well.


Per analyst firm IDC, worldwide spending on IoT will reach $1.1 trillion by 2022. In a separate brief, the firm also predicted that the number of connected devices, including machines, sensors, and cameras, will top 41 billion and generate approximately 79 zettabytes of data. In short, we stand at the precipice of a hyperconnected world.

A world of smart city initiatives and connected homes. A world where advanced insights pertaining to everything from product performance to customer behavior are just a few clicks away. A world where everything is online.

There's a lot to be gained from this kind of environment. However, IoT and hyperconnectivity are not without their challenges and risks. Far from it.

For one, there's the matter of performance and bandwidth management. The traditional centralized computing model simply doesn't work for networks of sensors and devices which, more often than not, are distributed across vast geographic distances. The successful configuration of network devices within your organization requires a different networking model and hardware.

■ Data should be analyzed at the “edge,” or as close to it as possible. Each sensor and device should either be connected to a nearby processing node or capable of processing data on its own. This saves bandwidth and reduces latency, as the network doesn't get clogged by information processing requests.
 
■ In lieu of traditional network infrastructure, organizations that seek to leverage hyperconnectivity should instead deploy a software-defined wide area network (SD-WAN). This technology uses artificial intelligence and machine learning to intelligently map a network and route traffic. Most SD-WAN platforms also include functionality to allow IT to visualize the network's layout.

■ Incorporate big data and analytics expertise. You can gain considerable insights from the massive volume of data generated by connected endpoints, but only if you have people who understand how this data is analyzed, collected, and utilized.

Having an optimized network does your organization no good if it cannot actually see what's happening on that network. Moreover, having a fleet of connected endpoints you cannot manage monitor or control puts sensitive assets under direct threat of cyberattack. Managing data security requires both visibility and control.

■ Advanced endpoint management software is a must, as is a solid mobility strategy. Your organization cannot effectively make the leap to IoT without first having control over its smartphones and wearable devices. IT should, with relative ease, be able to view everything they need from a single interface.

■ Consider deploying an AI-based cybersecurity solution. Acting more as digital immune systems than traditional reactive solutions, these platforms are uniquely-suited for the expansive, constantly-evolving nature of IoT networks.

■ Network segmentation is similarly critical. IoT traffic should be carried over its own separate network for both security and efficiency. For consumer IoT devices in the workplace, deploy a guest network that is completely disconnected from critical architecture. 

The Internet of Things is one of the most disruptive technologies the business world has ever seen. Hyperconnectivity represents a considerable challenge from a performance, security, and management standpoint. However, this challenge is far outshone by the benefits should you overcome it.

Tim Mullahy is Managing Director at Liberty Center One

Hot Topics

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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 ...

Managing Network Performance and Visibility in an Era of Hyperconnectivity

Tim Mullahy
Liberty Center One

The Internet of Things (IoT) is changing the world. From augmented reality advanced analytics to new consumer solutions, IoT and the cloud are together redefining both how we work and how we engage with our audiences. They are changing how we live, as well.


Per analyst firm IDC, worldwide spending on IoT will reach $1.1 trillion by 2022. In a separate brief, the firm also predicted that the number of connected devices, including machines, sensors, and cameras, will top 41 billion and generate approximately 79 zettabytes of data. In short, we stand at the precipice of a hyperconnected world.

A world of smart city initiatives and connected homes. A world where advanced insights pertaining to everything from product performance to customer behavior are just a few clicks away. A world where everything is online.

There's a lot to be gained from this kind of environment. However, IoT and hyperconnectivity are not without their challenges and risks. Far from it.

For one, there's the matter of performance and bandwidth management. The traditional centralized computing model simply doesn't work for networks of sensors and devices which, more often than not, are distributed across vast geographic distances. The successful configuration of network devices within your organization requires a different networking model and hardware.

■ Data should be analyzed at the “edge,” or as close to it as possible. Each sensor and device should either be connected to a nearby processing node or capable of processing data on its own. This saves bandwidth and reduces latency, as the network doesn't get clogged by information processing requests.
 
■ In lieu of traditional network infrastructure, organizations that seek to leverage hyperconnectivity should instead deploy a software-defined wide area network (SD-WAN). This technology uses artificial intelligence and machine learning to intelligently map a network and route traffic. Most SD-WAN platforms also include functionality to allow IT to visualize the network's layout.

■ Incorporate big data and analytics expertise. You can gain considerable insights from the massive volume of data generated by connected endpoints, but only if you have people who understand how this data is analyzed, collected, and utilized.

Having an optimized network does your organization no good if it cannot actually see what's happening on that network. Moreover, having a fleet of connected endpoints you cannot manage monitor or control puts sensitive assets under direct threat of cyberattack. Managing data security requires both visibility and control.

■ Advanced endpoint management software is a must, as is a solid mobility strategy. Your organization cannot effectively make the leap to IoT without first having control over its smartphones and wearable devices. IT should, with relative ease, be able to view everything they need from a single interface.

■ Consider deploying an AI-based cybersecurity solution. Acting more as digital immune systems than traditional reactive solutions, these platforms are uniquely-suited for the expansive, constantly-evolving nature of IoT networks.

■ Network segmentation is similarly critical. IoT traffic should be carried over its own separate network for both security and efficiency. For consumer IoT devices in the workplace, deploy a guest network that is completely disconnected from critical architecture. 

The Internet of Things is one of the most disruptive technologies the business world has ever seen. Hyperconnectivity represents a considerable challenge from a performance, security, and management standpoint. However, this challenge is far outshone by the benefits should you overcome it.

Tim Mullahy is Managing Director at Liberty Center One

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 ...