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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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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

For all the attention AI receives in corporate slide decks and strategic roadmaps, many businesses are struggling to translate that ambition into something that holds up at scale. At least, that's the picture that emerged from a recent Forrester study commissioned by Tines ...

From smart factories and autonomous vehicles to real-time analytics and intelligent building systems, the demand for instant, local data processing is exploding. To meet these needs, organizations are leaning into edge computing. The promise? Faster performance, reduced latency and less strain on centralized infrastructure. But there's a catch: Not every network is ready to support edge deployments ...

Every digital customer interaction, every cloud deployment, and every AI model depends on the same foundation: the ability to see, understand, and act on data in real time ... Recent data from Splunk confirms that 74% of the business leaders believe observability is essential to monitoring critical business processes, and 66% feel it's key to understanding user journeys. Because while the unknown is inevitable, observability makes it manageable. Let's explore why ...

Organizations that perform regular audits and assessments of AI system performance and compliance are over three times more likely to achieve high GenAI value than organizations that do not, according to a survey by Gartner ...

Kubernetes has become the backbone of cloud infrastructure, but it's also one of its biggest cost drivers. Recent research shows that 98% of senior IT leaders say Kubernetes now drives cloud spend, yet 91% still can't optimize it effectively. After years of adoption, most organizations have moved past discovery. They know container sprawl, idle resources and reactive scaling inflate costs. What they don't know is how to fix it ...

Artificial intelligence is no longer a future investment. It's already embedded in how we work — whether through copilots in productivity apps, real-time transcription tools in meetings, or machine learning models fueling analytics and personalization. But while enterprise adoption accelerates, there's one critical area many leaders have yet to examine: Can your network actually support AI at the speed your users expect? ...

The more technology businesses invest in, the more potential attack surfaces they have that can be exploited. Without the right continuity plans in place, the disruptions caused by these attacks can bring operations to a standstill and cause irreparable damage to an organization. It's essential to take the time now to ensure your business has the right tools, processes, and recovery initiatives in place to weather any type of IT disaster that comes up. Here are some effective strategies you can follow to achieve this ...

In today's fast-paced AI landscape, CIOs, IT leaders, and engineers are constantly challenged to manage increasingly complex and interconnected systems. The sheer scale and velocity of data generated by modern infrastructure can be overwhelming, making it difficult to maintain uptime, prevent outages, and create a seamless customer experience. This complexity is magnified by the industry's shift towards agentic AI ...

In MEAN TIME TO INSIGHT Episode 19, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA explains the cause of the AWS outage in October ... 

The explosion of generative AI and machine learning capabilities has fundamentally changed the conversation around cloud migration. It's no longer just about modernization or cost savings — it's about being able to compete in a market where AI is rapidly becoming table stakes. Companies that can't quickly spin up AI workloads, feed models with data at scale, or experiment with new capabilities are falling behind faster than ever before. But here's what I'm seeing: many organizations want to capitalize on AI, but they're stuck ...