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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

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Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

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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 ...

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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

The Latest

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

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 ...