Skip to main content

Organizations Struggle to Expand IT Support to Remote Locations

Alison Hubbard

Cloud computing and mobile computing enable enterprises to deploy more of their employees and resources to remote and branch offices (ROBOs). That's contributing to a new set of challenges for their IT organizations that simply did not exist when the central office was home base for the data center and majority of users. Today, users at ROBOs must have quick and easy access to systems and applications on a multitude of devices, and cannot tolerate performance slowdowns or outages. After all, they are on the frontlines of where business happens. IT professionals are finding that supporting remote users' demands for anytime, anywhere support is growing too costly, demands resources they cannot spare, and increases the security risk of critical company data.

Those are the key findings of the 2016 Riverbed Remote Office/Branch Office IT Survey. Riverbed commissioned a survey of 183 attendees on the show floor during EMC World 2016 in Las Vegas. They represented SMBs and large organizations. Most (82 percent) worked in IT, while 9 percent worked in development.

For most respondents, the data center remains their primary focus, with supporting ROBOs a close second. Yet, organizations are rarely able to staff ROBOs with trained IT personnel, forcing IT to remotely perform monitoring, maintenance, troubleshooting, and other operations intended to accelerate business, often one location at a time. This makes deploying and maintaining systems and applications for each ROBO complex, expensive, and time-consuming, particularly with today's hybrid IT architectures.

A top challenge is managing the ever-growing volumes of data ROBO workers generate and need instant access to in order to get their work done on a daily basis. Where organizations store ROBO data is crucial to achieving operational efficiencies and high availability. Three-quarters (75 percent) of the respondents say that consolidating ROBO data back to the data center, or in the cloud, was somewhat to extremely desirable.

Other top challenges include:

Disaster recovery: 54 percent cited delays when recovering from ROBO outages as their top issue. These delays hurt the business' ability to generate revenue, exposes the ROBO to risk from data loss and can tarnish the business' reputation.

Staffing: 46 percent struggle to supply adequate IT staff at ROBOs. In fact, they often have no IT staff onsite. This makes it especially difficult to supervise and ensure backups.

Provisioning delays: 45 percent reported the time it takes them to provision ROBO infrastructure, applications and services hurt their organizations' ability to generate revenue.

Software-Defining the Edge

IT can reduce the costs and complexities of managing a highly distributed environment without increasing security risks by implementing a "Software-defined Edge" model to centralize all systems, operations and services. IT manages everything inside a secure, centralized datacenter and delivers applications and data to users at ROBOs.

Benefits include:

Hardened security posture: 100 percent of data is secured in the data center, not sitting on a piece of hardware in a far-away ROBO location, out of your control; and without compromise to remote user productivity. All data is encrypted at-rest and in-motion for true end-to-end encryption.

Improved user productivity: Generate up to a 100x increase in branch application performance. Users will encounter far fewer instances of downtime due to system outages or poor performance. Ensuring information and system availability enables users to get their work done using any device they choose.

Ensure business continuity: 100x faster recovery times (RTO) minimizes the damage done by outages, with RPO time practically eliminated. Perform backup and recovery operations in mere seconds instead of days or weeks.

Improved operational agility: IT can deploy branch services and sites in under 15 minutes, and manage everything via the central dashboard. All heavy ROBO IT operations, such as provisioning new services and sites, and recovery of sites in the case of outages, take seconds instead of days. Remote backup headaches are completely eliminated. The result is a more agile IT team that is better able to support the needs of the business.

Consolidating infrastructure at the edge is just the first step. Cobbling together disparate pieces of hardware into one appliance will not solve short- or long-term performance, data security and management issues. An effective Software-defined Edge model requires making the edges "stateless."

Storage professionals realize that the word "state" refers to facing daily operational challenges to manage and protect data at the ROBO that's vulnerable to loss and theft. A lost storage piece at the ROBO will require hours, days, (or in some cases longer) of effort to bring it back online. And there's no guarantee of success, particularly when resorting to older backups. Moving data storage away from the edges to the central data center or to the cloud creates stateless data stores without compromising user experience.

Combining storage delivery, server virtualization and hybrid WAN optimization technologies will enable IT organizations to eliminate the need for physical servers, storage and backup infrastructure at ROBO locations. Realizing this vision, and the resulting reduction in risk and cost savings – both dollars and manpower – requires full visibility and complete control over the entire network. The key is to software-define the edge so IT can make better-informed decisions about which applications and services to provide to workers at various ROBOs worldwide.

Alison Hubbard is Senior Director of Product Marketing, SteelFusion, at Riverbed.

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.

Organizations Struggle to Expand IT Support to Remote Locations

Alison Hubbard

Cloud computing and mobile computing enable enterprises to deploy more of their employees and resources to remote and branch offices (ROBOs). That's contributing to a new set of challenges for their IT organizations that simply did not exist when the central office was home base for the data center and majority of users. Today, users at ROBOs must have quick and easy access to systems and applications on a multitude of devices, and cannot tolerate performance slowdowns or outages. After all, they are on the frontlines of where business happens. IT professionals are finding that supporting remote users' demands for anytime, anywhere support is growing too costly, demands resources they cannot spare, and increases the security risk of critical company data.

Those are the key findings of the 2016 Riverbed Remote Office/Branch Office IT Survey. Riverbed commissioned a survey of 183 attendees on the show floor during EMC World 2016 in Las Vegas. They represented SMBs and large organizations. Most (82 percent) worked in IT, while 9 percent worked in development.

For most respondents, the data center remains their primary focus, with supporting ROBOs a close second. Yet, organizations are rarely able to staff ROBOs with trained IT personnel, forcing IT to remotely perform monitoring, maintenance, troubleshooting, and other operations intended to accelerate business, often one location at a time. This makes deploying and maintaining systems and applications for each ROBO complex, expensive, and time-consuming, particularly with today's hybrid IT architectures.

A top challenge is managing the ever-growing volumes of data ROBO workers generate and need instant access to in order to get their work done on a daily basis. Where organizations store ROBO data is crucial to achieving operational efficiencies and high availability. Three-quarters (75 percent) of the respondents say that consolidating ROBO data back to the data center, or in the cloud, was somewhat to extremely desirable.

Other top challenges include:

Disaster recovery: 54 percent cited delays when recovering from ROBO outages as their top issue. These delays hurt the business' ability to generate revenue, exposes the ROBO to risk from data loss and can tarnish the business' reputation.

Staffing: 46 percent struggle to supply adequate IT staff at ROBOs. In fact, they often have no IT staff onsite. This makes it especially difficult to supervise and ensure backups.

Provisioning delays: 45 percent reported the time it takes them to provision ROBO infrastructure, applications and services hurt their organizations' ability to generate revenue.

Software-Defining the Edge

IT can reduce the costs and complexities of managing a highly distributed environment without increasing security risks by implementing a "Software-defined Edge" model to centralize all systems, operations and services. IT manages everything inside a secure, centralized datacenter and delivers applications and data to users at ROBOs.

Benefits include:

Hardened security posture: 100 percent of data is secured in the data center, not sitting on a piece of hardware in a far-away ROBO location, out of your control; and without compromise to remote user productivity. All data is encrypted at-rest and in-motion for true end-to-end encryption.

Improved user productivity: Generate up to a 100x increase in branch application performance. Users will encounter far fewer instances of downtime due to system outages or poor performance. Ensuring information and system availability enables users to get their work done using any device they choose.

Ensure business continuity: 100x faster recovery times (RTO) minimizes the damage done by outages, with RPO time practically eliminated. Perform backup and recovery operations in mere seconds instead of days or weeks.

Improved operational agility: IT can deploy branch services and sites in under 15 minutes, and manage everything via the central dashboard. All heavy ROBO IT operations, such as provisioning new services and sites, and recovery of sites in the case of outages, take seconds instead of days. Remote backup headaches are completely eliminated. The result is a more agile IT team that is better able to support the needs of the business.

Consolidating infrastructure at the edge is just the first step. Cobbling together disparate pieces of hardware into one appliance will not solve short- or long-term performance, data security and management issues. An effective Software-defined Edge model requires making the edges "stateless."

Storage professionals realize that the word "state" refers to facing daily operational challenges to manage and protect data at the ROBO that's vulnerable to loss and theft. A lost storage piece at the ROBO will require hours, days, (or in some cases longer) of effort to bring it back online. And there's no guarantee of success, particularly when resorting to older backups. Moving data storage away from the edges to the central data center or to the cloud creates stateless data stores without compromising user experience.

Combining storage delivery, server virtualization and hybrid WAN optimization technologies will enable IT organizations to eliminate the need for physical servers, storage and backup infrastructure at ROBO locations. Realizing this vision, and the resulting reduction in risk and cost savings – both dollars and manpower – requires full visibility and complete control over the entire network. The key is to software-define the edge so IT can make better-informed decisions about which applications and services to provide to workers at various ROBOs worldwide.

Alison Hubbard is Senior Director of Product Marketing, SteelFusion, at Riverbed.

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.