Skip to main content

What IoT Challenges are Coming to Your Network Teams?

Chris Bihary

Self-driving cars, integrated toys, smart home appliances, and even critical infrastructure have all become part of the ecosystem of Internet of Things (IoT) devices, which begs the concerning question, "How will network administrators process all the data generated?"

IoT has introduced new pathways into data centers because the technology relies on TCP/IP communications that may have a detrimental impact on traffic and, more important, data center security. And while a significant amount of the data that is collected at the edge is managed and manipulated there, eventually the data, in some form, makes its way back to a central location. 

Managing a data centers IoT might appear simple, with many of the devices performing simple processes such as turning lights on or off or perhaps even monitoring temperature. The very simplicity of this challenges larger issues involving security, connectivity, and operational concerns.

As IoT devices are feeding data into data centers, from both internal and external devices, while also introducing new requirements and new types of data, we need to ready for the exponential growth in the market and the astonishing number of IoT devices expected to be nearly triple the planet’s human population by 2020.

With each added device will come increased data and increased requirements for security and management of the devices onto the networks, providing critical operational information and potentially transforming data center operations.

IoT will eventually provide data streams between each asset and the data center, allowing those assets to be integrated into new and existing organizational processes, thus, having access to real-time information via IoT devices.

A greater understanding of operational status would allow network administrators to enhance productivity through optimized models, bring more IoT devices into the data center, and incorporate IoT analytics into business planning and processes giving insights into overall business requirements, which ultimately would help predict any fluctuations of operational data. 

With all the benefits of IoT, network administrators and teams are still faced with the sheer volume of devices and the structure of IoT data, showcasing itself in areas such as security, data, storage management, servers, and the data center network. This ultimately means that network administrators need to deploy more aggressive capacity management to align business priorities associated with IoT.

Data center professionals are quickly discovering that IoT consists of a lot of individual devices with their own specifications, but over time, a lack of standardization will become a much bigger problem, as more of our devices seek to communicate with each other and are forced to meet compliance standards to include GDPR.

IoT is growing and IT teams are bearing the brunt of the increased data and concerns generated by IoT, but there is also no denying the potential of IoT to deliver new insights, improve business drivers and operations, and growing services is on the horizon, and having the right infrastructure in your data center to adopt to the changes will remain vital to success.

Hot Topics

The Latest

64% of enterprise networking teams use internally developed software or scripts for network automation, but 61% of those teams spend six or more hours per week debugging and maintaining them, according to From Scripts to Platforms: Why Homegrown Tools Dominate Network Automation and How Vendors Can Help, my latest EMA report ...

Cloud computing has transformed how we build and scale software, but it has also quietly introduced one of the most persistent challenges in modern IT: cost visibility and control ... So why, after more than a decade of cloud adoption, are cloud costs still spiraling out of control? The answer lies not in tooling but in culture ...

CEOs are committed to advancing AI solutions across their organization even as they face challenges from accelerating technology adoption, according to the IBM CEO Study. The survey revealed that executive respondents expect the growth rate of AI investments to more than double in the next two years, and 61% confirm they are actively adopting AI agents today and preparing to implement them at scale ...

Image
IBM

 

A major architectural shift is underway across enterprise networks, according to a new global study from Cisco. As AI assistants, agents, and data-driven workloads reshape how work gets done, they're creating faster, more dynamic, more latency-sensitive, and more complex network traffic. Combined with the ubiquity of connected devices, 24/7 uptime demands, and intensifying security threats, these shifts are driving infrastructure to adapt and evolve ...

Image
Cisco

The development of banking apps was supposed to provide users with convenience, control and piece of mind. However, for thousands of Halifax customers recently, a major mobile outage caused the exact opposite, leaving customers unable to check balances, or pay bills, sparking widespread frustration. This wasn't an isolated incident ... So why are these failures still happening? ...

Cyber threats are growing more sophisticated every day, and at their forefront are zero-day vulnerabilities. These elusive security gaps are exploited before a fix becomes available, making them among the most dangerous threats in today's digital landscape ... This guide will explore what these vulnerabilities are, how they work, why they pose such a significant threat, and how modern organizations can stay protected ...

The prevention of data center outages continues to be a strategic priority for data center owners and operators. Infrastructure equipment has improved, but the complexity of modern architectures and evolving external threats presents new risks that operators must actively manage, according to the Data Center Outage Analysis 2025 from Uptime Institute ...

As observability engineers, we navigate a sea of telemetry daily. We instrument our applications, configure collectors, and build dashboards, all in pursuit of understanding our complex distributed systems. Yet, amidst this flood of data, a critical question often remains unspoken, or at best, answered by gut feeling: "Is our telemetry actually good?" ... We're inviting you to participate in shaping a foundational element for better observability: the Instrumentation Score ...

We're inching ever closer toward a long-held goal: technology infrastructure that is so automated that it can protect itself. But as IT leaders aggressively employ automation across our enterprises, we need to continuously reassess what AI is ready to manage autonomously and what can not yet be trusted to algorithms ...

Much like a traditional factory turns raw materials into finished products, the AI factory turns vast datasets into actionable business outcomes through advanced models, inferences, and automation. From the earliest data inputs to the final token output, this process must be reliable, repeatable, and scalable. That requires industrializing the way AI is developed, deployed, and managed ...

What IoT Challenges are Coming to Your Network Teams?

Chris Bihary

Self-driving cars, integrated toys, smart home appliances, and even critical infrastructure have all become part of the ecosystem of Internet of Things (IoT) devices, which begs the concerning question, "How will network administrators process all the data generated?"

IoT has introduced new pathways into data centers because the technology relies on TCP/IP communications that may have a detrimental impact on traffic and, more important, data center security. And while a significant amount of the data that is collected at the edge is managed and manipulated there, eventually the data, in some form, makes its way back to a central location. 

Managing a data centers IoT might appear simple, with many of the devices performing simple processes such as turning lights on or off or perhaps even monitoring temperature. The very simplicity of this challenges larger issues involving security, connectivity, and operational concerns.

As IoT devices are feeding data into data centers, from both internal and external devices, while also introducing new requirements and new types of data, we need to ready for the exponential growth in the market and the astonishing number of IoT devices expected to be nearly triple the planet’s human population by 2020.

With each added device will come increased data and increased requirements for security and management of the devices onto the networks, providing critical operational information and potentially transforming data center operations.

IoT will eventually provide data streams between each asset and the data center, allowing those assets to be integrated into new and existing organizational processes, thus, having access to real-time information via IoT devices.

A greater understanding of operational status would allow network administrators to enhance productivity through optimized models, bring more IoT devices into the data center, and incorporate IoT analytics into business planning and processes giving insights into overall business requirements, which ultimately would help predict any fluctuations of operational data. 

With all the benefits of IoT, network administrators and teams are still faced with the sheer volume of devices and the structure of IoT data, showcasing itself in areas such as security, data, storage management, servers, and the data center network. This ultimately means that network administrators need to deploy more aggressive capacity management to align business priorities associated with IoT.

Data center professionals are quickly discovering that IoT consists of a lot of individual devices with their own specifications, but over time, a lack of standardization will become a much bigger problem, as more of our devices seek to communicate with each other and are forced to meet compliance standards to include GDPR.

IoT is growing and IT teams are bearing the brunt of the increased data and concerns generated by IoT, but there is also no denying the potential of IoT to deliver new insights, improve business drivers and operations, and growing services is on the horizon, and having the right infrastructure in your data center to adopt to the changes will remain vital to success.

Hot Topics

The Latest

64% of enterprise networking teams use internally developed software or scripts for network automation, but 61% of those teams spend six or more hours per week debugging and maintaining them, according to From Scripts to Platforms: Why Homegrown Tools Dominate Network Automation and How Vendors Can Help, my latest EMA report ...

Cloud computing has transformed how we build and scale software, but it has also quietly introduced one of the most persistent challenges in modern IT: cost visibility and control ... So why, after more than a decade of cloud adoption, are cloud costs still spiraling out of control? The answer lies not in tooling but in culture ...

CEOs are committed to advancing AI solutions across their organization even as they face challenges from accelerating technology adoption, according to the IBM CEO Study. The survey revealed that executive respondents expect the growth rate of AI investments to more than double in the next two years, and 61% confirm they are actively adopting AI agents today and preparing to implement them at scale ...

Image
IBM

 

A major architectural shift is underway across enterprise networks, according to a new global study from Cisco. As AI assistants, agents, and data-driven workloads reshape how work gets done, they're creating faster, more dynamic, more latency-sensitive, and more complex network traffic. Combined with the ubiquity of connected devices, 24/7 uptime demands, and intensifying security threats, these shifts are driving infrastructure to adapt and evolve ...

Image
Cisco

The development of banking apps was supposed to provide users with convenience, control and piece of mind. However, for thousands of Halifax customers recently, a major mobile outage caused the exact opposite, leaving customers unable to check balances, or pay bills, sparking widespread frustration. This wasn't an isolated incident ... So why are these failures still happening? ...

Cyber threats are growing more sophisticated every day, and at their forefront are zero-day vulnerabilities. These elusive security gaps are exploited before a fix becomes available, making them among the most dangerous threats in today's digital landscape ... This guide will explore what these vulnerabilities are, how they work, why they pose such a significant threat, and how modern organizations can stay protected ...

The prevention of data center outages continues to be a strategic priority for data center owners and operators. Infrastructure equipment has improved, but the complexity of modern architectures and evolving external threats presents new risks that operators must actively manage, according to the Data Center Outage Analysis 2025 from Uptime Institute ...

As observability engineers, we navigate a sea of telemetry daily. We instrument our applications, configure collectors, and build dashboards, all in pursuit of understanding our complex distributed systems. Yet, amidst this flood of data, a critical question often remains unspoken, or at best, answered by gut feeling: "Is our telemetry actually good?" ... We're inviting you to participate in shaping a foundational element for better observability: the Instrumentation Score ...

We're inching ever closer toward a long-held goal: technology infrastructure that is so automated that it can protect itself. But as IT leaders aggressively employ automation across our enterprises, we need to continuously reassess what AI is ready to manage autonomously and what can not yet be trusted to algorithms ...

Much like a traditional factory turns raw materials into finished products, the AI factory turns vast datasets into actionable business outcomes through advanced models, inferences, and automation. From the earliest data inputs to the final token output, this process must be reliable, repeatable, and scalable. That requires industrializing the way AI is developed, deployed, and managed ...