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Challenges Facing Today's Network Engineers

Jason Baudreau

In today's everchanging IT industry, network engineers face a slew of challenges when it comes to network management. As networks continue to grow and become more complex, many IT professionals struggle to get a grasp on key workflows in which network engineers still rely on manual processes, including network documentation, troubleshooting, change management and cybersecurity.

In April 2017, NetBrain Technologies conducted a survey of more than 200 network engineers, network architects, and IT managers to explore specific challenges facing today's network teams. The findings of the survey were recently released in a report entitled: 2017 State of the Network Engineer: Toward an Automated Future.

Here are some highlights from the survey findings:

Growing Network Size and Complexity is Driving the Need for Automation

Networks are growing, and they are growing fast. Organizations are seeing a significant increase their network devices and applications within the networks. In fact, 83 percent of survey participants indicated that the size of their networks has increased within the past year. Networks are also becoming increasingly more complex. 49 percent of enterprises with over 1,000 employees have more than 1,000 network devices, while 21 percent have more than 10,000 network devices.

With this rapid network evolution, compounded with complex IT initiatives like network security, private/public cloud computing, and software-defined networking, network engineers are forced to consistently adapt accordingly and bring new skills to the table. For instance, 53 percent of network engineers stated they are required to know programming (beyond just scripting) for their jobs, while 30 percent said that they will invest in network automation capabilities in the next 12 to 24 months.

Accurate Network Documentation Remains Elusive

Today, 87 percent of respondents primarily rely on manual processes to create their network diagrams. Documentation is one of the network engineer's most important workflows, and taking on this critical task manually, simply doesn't stack up. For instance, 49 percent of respondents cited the length of time it takes to create network diagrams as a primary challenge, while 33 percent said it would take more than one month to document their entire network manually.

In addition to the amount of time spent on documentation, obsolescence was also cited as a major obstacle. 58 percent of network engineers said that network diagrams become outdated as soon as the network changes. In other words, by the time the network is fully mapped out, the network has already changed and therefore the diagram is essentially useless. This is particularly problematic when it comes to areas like compliance reporting or having full network visibility when diagnosing an outage.


Manual Troubleshooting is Contributing to Longer Network Downtimes

As networks continue to grow, manual methods will continue to challenge engineers when it comes to troubleshooting. For instance, 33 percent of organizations said that they experience multiple network degradations every day, with 10 percent indicating that they experience multiple issues every single hour. Whether it's a slow application or jittery VoIP connection, 43 percent of network engineers said that using command-line interface (CLI) simply takes too long, while 40 percent also indicated it would take more than four hours to resolve a typical network problem. The need to keep network availability high and reduce mean time to repair is business critical, and the longer it takes to isolate and diagnose a network problem, the costlier the impact of that degradation to the enterprise.


Continuously Securing the Network is a Top Priority

Another challenge associated with the growth of networks is the increased vulnerability to cyberattacks. Often, engineers may not have full visibility into what's going on in their networks, which results in the lack of necessary knowledge to effectively mitigate security risks.

Survey data showed network security was the number one project for 64 percent of respondents, who said they plan to invest in security within the next 12 to 24 months. Nearly 50 percent of respondents cited the inability to continuously monitor and mitigate attacks — without human intervention — as a significant issue and 57 percent of respondents cited an inability to isolate the area of the network where an attack is happening. Clearly, networks have become far too vast for network engineers to be able to manually mitigate risk.

Knowledge and Collaboration Gaps Continue to be Barriers in Most Enterprises

Many organizations rely on "tribal" knowledge to manage network problems. This could mean relying on a network engineer's mental picture to create a network diagram or going to the IT expert to troubleshoot advanced configurations. This leaves the ability to solve issues with just a select number of individuals and in turn, slows down processes. 33 percent of survey respondents stated this overreliance as a key obstacle.

Additionally, 45 percent of network engineers surveyed cited a lack of collaboration as the number one challenge for more effective troubleshooting, particularly when tasks involve multiple engineers in the network operations center or multiple IT groups (e.g., network, security, and application teams). Also, 36 percent of respondents felt that the lack of coordination was creating systematic issues for them when attempting to design and execute network changes.

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Challenges Facing Today's Network Engineers

Jason Baudreau

In today's everchanging IT industry, network engineers face a slew of challenges when it comes to network management. As networks continue to grow and become more complex, many IT professionals struggle to get a grasp on key workflows in which network engineers still rely on manual processes, including network documentation, troubleshooting, change management and cybersecurity.

In April 2017, NetBrain Technologies conducted a survey of more than 200 network engineers, network architects, and IT managers to explore specific challenges facing today's network teams. The findings of the survey were recently released in a report entitled: 2017 State of the Network Engineer: Toward an Automated Future.

Here are some highlights from the survey findings:

Growing Network Size and Complexity is Driving the Need for Automation

Networks are growing, and they are growing fast. Organizations are seeing a significant increase their network devices and applications within the networks. In fact, 83 percent of survey participants indicated that the size of their networks has increased within the past year. Networks are also becoming increasingly more complex. 49 percent of enterprises with over 1,000 employees have more than 1,000 network devices, while 21 percent have more than 10,000 network devices.

With this rapid network evolution, compounded with complex IT initiatives like network security, private/public cloud computing, and software-defined networking, network engineers are forced to consistently adapt accordingly and bring new skills to the table. For instance, 53 percent of network engineers stated they are required to know programming (beyond just scripting) for their jobs, while 30 percent said that they will invest in network automation capabilities in the next 12 to 24 months.

Accurate Network Documentation Remains Elusive

Today, 87 percent of respondents primarily rely on manual processes to create their network diagrams. Documentation is one of the network engineer's most important workflows, and taking on this critical task manually, simply doesn't stack up. For instance, 49 percent of respondents cited the length of time it takes to create network diagrams as a primary challenge, while 33 percent said it would take more than one month to document their entire network manually.

In addition to the amount of time spent on documentation, obsolescence was also cited as a major obstacle. 58 percent of network engineers said that network diagrams become outdated as soon as the network changes. In other words, by the time the network is fully mapped out, the network has already changed and therefore the diagram is essentially useless. This is particularly problematic when it comes to areas like compliance reporting or having full network visibility when diagnosing an outage.


Manual Troubleshooting is Contributing to Longer Network Downtimes

As networks continue to grow, manual methods will continue to challenge engineers when it comes to troubleshooting. For instance, 33 percent of organizations said that they experience multiple network degradations every day, with 10 percent indicating that they experience multiple issues every single hour. Whether it's a slow application or jittery VoIP connection, 43 percent of network engineers said that using command-line interface (CLI) simply takes too long, while 40 percent also indicated it would take more than four hours to resolve a typical network problem. The need to keep network availability high and reduce mean time to repair is business critical, and the longer it takes to isolate and diagnose a network problem, the costlier the impact of that degradation to the enterprise.


Continuously Securing the Network is a Top Priority

Another challenge associated with the growth of networks is the increased vulnerability to cyberattacks. Often, engineers may not have full visibility into what's going on in their networks, which results in the lack of necessary knowledge to effectively mitigate security risks.

Survey data showed network security was the number one project for 64 percent of respondents, who said they plan to invest in security within the next 12 to 24 months. Nearly 50 percent of respondents cited the inability to continuously monitor and mitigate attacks — without human intervention — as a significant issue and 57 percent of respondents cited an inability to isolate the area of the network where an attack is happening. Clearly, networks have become far too vast for network engineers to be able to manually mitigate risk.

Knowledge and Collaboration Gaps Continue to be Barriers in Most Enterprises

Many organizations rely on "tribal" knowledge to manage network problems. This could mean relying on a network engineer's mental picture to create a network diagram or going to the IT expert to troubleshoot advanced configurations. This leaves the ability to solve issues with just a select number of individuals and in turn, slows down processes. 33 percent of survey respondents stated this overreliance as a key obstacle.

Additionally, 45 percent of network engineers surveyed cited a lack of collaboration as the number one challenge for more effective troubleshooting, particularly when tasks involve multiple engineers in the network operations center or multiple IT groups (e.g., network, security, and application teams). Also, 36 percent of respondents felt that the lack of coordination was creating systematic issues for them when attempting to design and execute network changes.

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

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 ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

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.