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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.

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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

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 ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...