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IoT Driving Network Management Evolution

Shamus McGillicuddy

Network managers will need to upgrade, expand, and adapt their network monitoring and management tools and practices if they are going to support the Internet of Things (IoT), according to new research by Enterprise Management Associates (EMA).

EMA recently surveyed 100 IT professionals who are (a) directly involved in enterprise networks and (b) supporting their organizations’ IoT initiatives. We published the results in our research report, The Internet of Things and Enterprise Networks: Planning, Engineering and Operational Strategies.

The research found that network monitoring tools and practices are challenged by IoT.

First of all, 52 percent reported that IoT had introduced or worsened blindspots in their network monitoring and service assurance architecture.

52 percent reported that IoT had introduced or worsened blindspots in their network monitoring and service assurance architecture

Additionally, EMA asked research participants to identify their top IoT network monitoring challenges. Scalability (26 percent ) was the most cited problem. IoT simply adds too many devices to the network. Rogue device detection (23 percent ) is also a struggle for these organizations. Many are also struggling with insufficient monitoring granularity (22 percent ) and high rates of change (21 percent).

So how do network teams adapt their monitoring tools to address IoT? The four most common actions the network teams take, according to our research:

■ Upgrade the data processing capacity of network monitoring tools (45 percent). This addresses the scalability issue.

■ Upgrade monitoring tool licenses to account for more monitored devices and objects (33 percent)

■ Install network visibility controllers (AKA network packet brokers) to aggregate monitoring data (29 percent)

■ Increase monitoring granularity (e.g. shorter polling intervals) 28 percent)

68 percent of network managers are extending their tools to monitor and manage IoT devices

IoT devices present another challenge to network operations, because network teams often take ownership of certain elements of the IoT device lifecycle. More than half (51 percent) of network professionals take a leading role in IoT device deployment, and 64 percent lead the implementation of IoT device security policy and access controls. Furthermore, 57 percent play a supporting role in troubleshooting IoT devices. For this reason, network teams need to evolve their tools.

68 percent of network managers are extending their tools to monitor and manage IoT devices.

Many network managers will find that their tools do not natively support IoT devices. They will have to modify the tools themselves or ask their vendors to customize the tools. If your enterprise is launching one or more IoT initiatives, it’s time to evaluate how your current tools and practices will support IoT.

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IoT Driving Network Management Evolution

Shamus McGillicuddy

Network managers will need to upgrade, expand, and adapt their network monitoring and management tools and practices if they are going to support the Internet of Things (IoT), according to new research by Enterprise Management Associates (EMA).

EMA recently surveyed 100 IT professionals who are (a) directly involved in enterprise networks and (b) supporting their organizations’ IoT initiatives. We published the results in our research report, The Internet of Things and Enterprise Networks: Planning, Engineering and Operational Strategies.

The research found that network monitoring tools and practices are challenged by IoT.

First of all, 52 percent reported that IoT had introduced or worsened blindspots in their network monitoring and service assurance architecture.

52 percent reported that IoT had introduced or worsened blindspots in their network monitoring and service assurance architecture

Additionally, EMA asked research participants to identify their top IoT network monitoring challenges. Scalability (26 percent ) was the most cited problem. IoT simply adds too many devices to the network. Rogue device detection (23 percent ) is also a struggle for these organizations. Many are also struggling with insufficient monitoring granularity (22 percent ) and high rates of change (21 percent).

So how do network teams adapt their monitoring tools to address IoT? The four most common actions the network teams take, according to our research:

■ Upgrade the data processing capacity of network monitoring tools (45 percent). This addresses the scalability issue.

■ Upgrade monitoring tool licenses to account for more monitored devices and objects (33 percent)

■ Install network visibility controllers (AKA network packet brokers) to aggregate monitoring data (29 percent)

■ Increase monitoring granularity (e.g. shorter polling intervals) 28 percent)

68 percent of network managers are extending their tools to monitor and manage IoT devices

IoT devices present another challenge to network operations, because network teams often take ownership of certain elements of the IoT device lifecycle. More than half (51 percent) of network professionals take a leading role in IoT device deployment, and 64 percent lead the implementation of IoT device security policy and access controls. Furthermore, 57 percent play a supporting role in troubleshooting IoT devices. For this reason, network teams need to evolve their tools.

68 percent of network managers are extending their tools to monitor and manage IoT devices.

Many network managers will find that their tools do not natively support IoT devices. They will have to modify the tools themselves or ask their vendors to customize the tools. If your enterprise is launching one or more IoT initiatives, it’s time to evaluate how your current tools and practices will support IoT.

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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 ...

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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 ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...

If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...