Today's Top WAN Issues and How to Solve Them - Part 2

The top SD-WAN implantation challenges IT professionals experience today
Jay Botelho
Learn more about BlueCat Networks

In Part 1 of this series, we explored the top pain points associated with managing Internet-based WANs today. This second installment will focus on today's most prevalent SD-WAN deployment challenges specifically and what you can do to better manage modern WANs overall.

Start with Today's Top WAN Issues and How to Solve Them - Part 1

Many organizations flock to SD-WAN to realize potential network performance, security and cost reduction benefits. But according to recent research from EMA, it's not all sunshine and rainbows. Here are several of the top SD-WAN implantation challenges IT professionals experience today:

1. Implementation Complexity

More than 40% of organizations identify implementation complexity as a top hurdle to SD-WAN success. Most organizations introduce public Internet options into their SD-WAN, and these increase complexity for many of the reasons highlighted in Part 1 of this series, but also because they require additional security technologies that IT teams aren't as accustomed to managing.

Additionally, you need to integrate SD-WANs with existing network elements, which can account for additional complexity and the need for in-depth programming and scripting expertise. SD-WAN success demands a detailed set of expectations for what the solutions should achieve concerning performance, security and cost, as well as a clear accounting of all the existing elements in your network.

Assembling this information and establishing an exhaustive integration plan is the only way to manage the inherent complexity of a new SD-WAN deployment (and avoid cost overruns and frustration).

2. Integration with Existing Network Technology

SD-WANs are essentially just an overlay on top of your existing network, which many take to mean they're simple to deploy. But, nearly 40% of IT professionals cited integration with current network technology as a significant SD-WAN roadblock. As the name implies, "soft-defined" means this software must communicate with all your existing hardware and software network components — something far easier said than done.

Are you doing the integration or is the SD-WAN vendor?

What existing network elements will be the most challenging?

What skills are required?

SD-WAN is a relatively new technology, so if you have some older components in your network, compatibility with this new SD-WAN technology could be an issue or drive up the solution's cost.

For example, say you have your entire SD-WAN project scoped out, including integration costs, and you're ready to go. Then you realize you have some fairly old switches in your stack that you didn't realize don't integrate properly with your chosen SD-WAN solution. Without the proper visibility, tools and planning, it's easy to miss certain points of integration and run into time-consuming obstacles and budget overages.

3. Network Team Skills Gaps

Roughly 22% of organizations believe skills deficits within their network team are impeding progress on SD-WAN deployment projects. These issues can quickly become apparent when organizations decide to forgo the help of an SD-WAN vendor and perform the integration for a new rollout internally in the interest of saving money.

As teams begin digging into these projects, they often realize SD-WAN integrations are not as "plug-and-play" as vendors typically advertise. SD-WAN deployments require skillsets that might be in short supply within most NetOps teams. Whether it's a lack of familiarity with security solutions and procedures, software development and scripting expertise, or experience troubleshooting issues at ISPs, you're sure to experience schedule delays and cost increases as the team learns on the job or brings in a third party to help.

The Power of End-to-End Network Visibility

When asked to identify the top root causes of WAN issues today, 30% of organizations listed application errors and performance, while 30% cited ISP or MPLS providers, and 28% listed end-user error or client device failure. Establishing comprehensive network visibility is the key to addressing these issues, and managing and optimizing your modern WAN.

Distributed organizations such as retailer chains and healthcare branches need end-to-end network visibility to identify application performance issues such as intermittent asymmetric VoIP routing issues, poor traffic flows from branches to the data center, and WAN application traffic steering problems.

Flow-based network analysis can help perform real-time network topology mapping for devices, interfaces, applications, VPNs and users. It can also help establish critical baselines for SD-WAN deployments, such as site-to-site traffic types and paths, application behaviors and consumption patterns, and more.

These are just a few examples that illustrate why your team must establish end-to-end network visibility in order to address today's hybrid WAN challenges and their root causes. This means leveraging modern network monitoring solutions to collect and analyze disparate data sources, including network flow data, packet data, device metrics, active monitoring data, endpoint data, and cloud provider flow data. Hybrid WANs are here to stay, and the common issues associated with them will be too unless you're equipped to visualize and manage every domain and element of your network.

Related Links

Hot Topics

Related Vendors

The Latest

Escaping Pilot Purgatory: How AI Becomes an Operational Advantage

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 ...

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

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 ...

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

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

Why FinOps Rewrote Its Mission and What It Signals for Technology Management

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 ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

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 ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

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 ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

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 ...

Explainability Is the New Battleground in AI-Powered Observability

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 ...

Empowering the Human Side of the Autonomous IT Age

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 Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

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.

Today's Top WAN Issues and How to Solve Them - Part 2

The top SD-WAN implantation challenges IT professionals experience today
Jay Botelho
Learn more about BlueCat Networks

In Part 1 of this series, we explored the top pain points associated with managing Internet-based WANs today. This second installment will focus on today's most prevalent SD-WAN deployment challenges specifically and what you can do to better manage modern WANs overall.

Start with Today's Top WAN Issues and How to Solve Them - Part 1

Many organizations flock to SD-WAN to realize potential network performance, security and cost reduction benefits. But according to recent research from EMA, it's not all sunshine and rainbows. Here are several of the top SD-WAN implantation challenges IT professionals experience today:

1. Implementation Complexity

More than 40% of organizations identify implementation complexity as a top hurdle to SD-WAN success. Most organizations introduce public Internet options into their SD-WAN, and these increase complexity for many of the reasons highlighted in Part 1 of this series, but also because they require additional security technologies that IT teams aren't as accustomed to managing.

Additionally, you need to integrate SD-WANs with existing network elements, which can account for additional complexity and the need for in-depth programming and scripting expertise. SD-WAN success demands a detailed set of expectations for what the solutions should achieve concerning performance, security and cost, as well as a clear accounting of all the existing elements in your network.

Assembling this information and establishing an exhaustive integration plan is the only way to manage the inherent complexity of a new SD-WAN deployment (and avoid cost overruns and frustration).

2. Integration with Existing Network Technology

SD-WANs are essentially just an overlay on top of your existing network, which many take to mean they're simple to deploy. But, nearly 40% of IT professionals cited integration with current network technology as a significant SD-WAN roadblock. As the name implies, "soft-defined" means this software must communicate with all your existing hardware and software network components — something far easier said than done.

Are you doing the integration or is the SD-WAN vendor?

What existing network elements will be the most challenging?

What skills are required?

SD-WAN is a relatively new technology, so if you have some older components in your network, compatibility with this new SD-WAN technology could be an issue or drive up the solution's cost.

For example, say you have your entire SD-WAN project scoped out, including integration costs, and you're ready to go. Then you realize you have some fairly old switches in your stack that you didn't realize don't integrate properly with your chosen SD-WAN solution. Without the proper visibility, tools and planning, it's easy to miss certain points of integration and run into time-consuming obstacles and budget overages.

3. Network Team Skills Gaps

Roughly 22% of organizations believe skills deficits within their network team are impeding progress on SD-WAN deployment projects. These issues can quickly become apparent when organizations decide to forgo the help of an SD-WAN vendor and perform the integration for a new rollout internally in the interest of saving money.

As teams begin digging into these projects, they often realize SD-WAN integrations are not as "plug-and-play" as vendors typically advertise. SD-WAN deployments require skillsets that might be in short supply within most NetOps teams. Whether it's a lack of familiarity with security solutions and procedures, software development and scripting expertise, or experience troubleshooting issues at ISPs, you're sure to experience schedule delays and cost increases as the team learns on the job or brings in a third party to help.

The Power of End-to-End Network Visibility

When asked to identify the top root causes of WAN issues today, 30% of organizations listed application errors and performance, while 30% cited ISP or MPLS providers, and 28% listed end-user error or client device failure. Establishing comprehensive network visibility is the key to addressing these issues, and managing and optimizing your modern WAN.

Distributed organizations such as retailer chains and healthcare branches need end-to-end network visibility to identify application performance issues such as intermittent asymmetric VoIP routing issues, poor traffic flows from branches to the data center, and WAN application traffic steering problems.

Flow-based network analysis can help perform real-time network topology mapping for devices, interfaces, applications, VPNs and users. It can also help establish critical baselines for SD-WAN deployments, such as site-to-site traffic types and paths, application behaviors and consumption patterns, and more.

These are just a few examples that illustrate why your team must establish end-to-end network visibility in order to address today's hybrid WAN challenges and their root causes. This means leveraging modern network monitoring solutions to collect and analyze disparate data sources, including network flow data, packet data, device metrics, active monitoring data, endpoint data, and cloud provider flow data. Hybrid WANs are here to stay, and the common issues associated with them will be too unless you're equipped to visualize and manage every domain and element of your network.

Related Links

Hot Topics

Related Vendors

The Latest

Escaping Pilot Purgatory: How AI Becomes an Operational Advantage

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 ...

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

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 ...

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

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

Why FinOps Rewrote Its Mission and What It Signals for Technology Management

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 ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

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 ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

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 ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

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 ...

Explainability Is the New Battleground in AI-Powered Observability

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 ...

Empowering the Human Side of the Autonomous IT Age

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 Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

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.