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Making Digital Transformation Work for You – Part 2

Bridging the Performance Gap
Joshua Dobies

Start with Making Digital Transformation Work for You – Part 1

Part 1 of this three-part series examined how the digital transformation wave that has swept through enterprise IT is finally reaching the network. Organizations leverage public and private clouds to enable users to connect 24/7 to applications and information stores via a wide array of devices. This places an ever-increasing strain on the networks, and the professionals who build and manage them.

As a result, application performance levels too often fail to meet the needs of the business. This creates what I call a "performance gap" – a widening gulf between the needs of business and what IT is able to provide (or not) to meet those needs. The business impacts include more unhappy customers, contract delays, missed deadlines and lost revenue. So in Part 2 of this series, let's examine the four key elements any organization can address today to bridge this gap.

First, it's important to understand the solution is not to try to limit the number of applications you provide to users. That's like trying to push back the incoming high tide. Consider these stats:

According to Gartner, worldwide spending on enterprise application software will grow from $149.9 billion in 2015 to more than $201 billion by 2019, driven primarily by modernization, functional expansion and digital transformation projects.1

■ IDC predicts that by 2018, businesses will more than double software development capabilities; two-thirds of their coders will focus on strategic digital transformation apps and services.

IDC predicts that by 2018, there will be 22 billion Internet of Things devices installed, driving the development of more than 200,000 new apps and services.2

You have our global economy based on services to thank. The world has been heading toward a services-based economy for some time, leaving behind an economy dominated by manufacturing. In the 1980s, services accounted for about half of world GDP; by the mid-1990s it was up to two-thirds. The trend is even stronger in post-industrial economies: Services now make up 80 percent of the British and 84 percent of the US economy. Even in countries that are transitioning from agriculture to industry, the services sector is growing faster than the rest of the economy.

Services themselves are evolving rapidly. The old services economy was based on the model of someone doing something for you in the physical world — someone cooks dinner for you in a restaurant, someone fixes your car, someone does your taxes.

The new services economy, in contrast, is dominated by made-to-order digital services. They're differentiated by the quality of the experience for which intuitive ease, convenience, and richness of choice are key criteria. Thus, we are moving from a world dominated by mass-manufactured, mass-marketed products to an immersive market of custom services and digital experiences.

Digital services may seem like magic to users, who now expect – even demand – anytime, anywhere access to them on their desktops and mobile devices. But underneath the magic of the simple UX lies the difficulty of moving apps over long-distance high-speed networks.

Digital services are enabled by a chain of IT interactions that link device, application, data, network, and infrastructure components. This complex chain of interactions is only as strong as its weakest link. All the parts of an application are links in the chain, and these links must mesh seamlessly across a complex, hybrid IT environment which is partly in the cloud, partly on-premises, with connectivity provided by a mix of private and public networks, in order to give users a good experience and drive maximum business productivity. Any grain of sand in the gears, any tiny flaw in the infrastructure—from server failure, to issues within the software code, to a problematic database, to network latency, to user device compatibility—can slow the application down or cause it to fail completely.

And yet, in our globally distributed, hybrid application environment, there is so much complexity, so many moving parts and operational dependencies, that the weak links in the chain are bound to get stressed to the breaking point. This creates the performance gap.

Bridging the Performance Gap

You must get a handle on four elements that comprise the fundamental links to make an app work: data, software, people, and networks. That requires knowing the answers to four key questions (hint – there's really just one answer):

Q: Where are your apps?
A: Everywhere.

Q: Where is your data?
A: Everywhere.

Q: Where are your users?
A: Everywhere.

Q: How is it all connected?
A: Everywhere.

Your apps are everywhere. Your data is everywhere. Your users are everywhere, and it's all connected via multiple types of networks that are … yes … everywhere.

In today's complex hybrid IT environments where data, applications, people, and networks are everywhere, point solutions cannot provide a total solution. The infrastructure challenges that impact application performance are ubiquitous, so only a holistic approach that brings visibility, performance, agility, and security to every aspect and stage of application delivery can provide an enterprise-grade solution for the age of hybrid IT. Just as digital transformation is an enterprise business strategy, enterprises need an architectural strategy to make the underpinning technology work the way it needs to.

The foundation of that architectural strategy is to stop using the traditional tools: routers and switches. In Part 3 of this series, I'll explain why those tools are quickly growing obsolete, and why SD-WAN is emerging as the technology that enables you to create a scalable network architecture that supports, enables and drives digital transformation with new levels of visibility, performance, security and agility.

Joshua Dobies is VP of Product Marketing, Riverbed Technology.

The Latest

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.

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 ...

Making Digital Transformation Work for You – Part 2

Bridging the Performance Gap
Joshua Dobies

Start with Making Digital Transformation Work for You – Part 1

Part 1 of this three-part series examined how the digital transformation wave that has swept through enterprise IT is finally reaching the network. Organizations leverage public and private clouds to enable users to connect 24/7 to applications and information stores via a wide array of devices. This places an ever-increasing strain on the networks, and the professionals who build and manage them.

As a result, application performance levels too often fail to meet the needs of the business. This creates what I call a "performance gap" – a widening gulf between the needs of business and what IT is able to provide (or not) to meet those needs. The business impacts include more unhappy customers, contract delays, missed deadlines and lost revenue. So in Part 2 of this series, let's examine the four key elements any organization can address today to bridge this gap.

First, it's important to understand the solution is not to try to limit the number of applications you provide to users. That's like trying to push back the incoming high tide. Consider these stats:

According to Gartner, worldwide spending on enterprise application software will grow from $149.9 billion in 2015 to more than $201 billion by 2019, driven primarily by modernization, functional expansion and digital transformation projects.1

■ IDC predicts that by 2018, businesses will more than double software development capabilities; two-thirds of their coders will focus on strategic digital transformation apps and services.

IDC predicts that by 2018, there will be 22 billion Internet of Things devices installed, driving the development of more than 200,000 new apps and services.2

You have our global economy based on services to thank. The world has been heading toward a services-based economy for some time, leaving behind an economy dominated by manufacturing. In the 1980s, services accounted for about half of world GDP; by the mid-1990s it was up to two-thirds. The trend is even stronger in post-industrial economies: Services now make up 80 percent of the British and 84 percent of the US economy. Even in countries that are transitioning from agriculture to industry, the services sector is growing faster than the rest of the economy.

Services themselves are evolving rapidly. The old services economy was based on the model of someone doing something for you in the physical world — someone cooks dinner for you in a restaurant, someone fixes your car, someone does your taxes.

The new services economy, in contrast, is dominated by made-to-order digital services. They're differentiated by the quality of the experience for which intuitive ease, convenience, and richness of choice are key criteria. Thus, we are moving from a world dominated by mass-manufactured, mass-marketed products to an immersive market of custom services and digital experiences.

Digital services may seem like magic to users, who now expect – even demand – anytime, anywhere access to them on their desktops and mobile devices. But underneath the magic of the simple UX lies the difficulty of moving apps over long-distance high-speed networks.

Digital services are enabled by a chain of IT interactions that link device, application, data, network, and infrastructure components. This complex chain of interactions is only as strong as its weakest link. All the parts of an application are links in the chain, and these links must mesh seamlessly across a complex, hybrid IT environment which is partly in the cloud, partly on-premises, with connectivity provided by a mix of private and public networks, in order to give users a good experience and drive maximum business productivity. Any grain of sand in the gears, any tiny flaw in the infrastructure—from server failure, to issues within the software code, to a problematic database, to network latency, to user device compatibility—can slow the application down or cause it to fail completely.

And yet, in our globally distributed, hybrid application environment, there is so much complexity, so many moving parts and operational dependencies, that the weak links in the chain are bound to get stressed to the breaking point. This creates the performance gap.

Bridging the Performance Gap

You must get a handle on four elements that comprise the fundamental links to make an app work: data, software, people, and networks. That requires knowing the answers to four key questions (hint – there's really just one answer):

Q: Where are your apps?
A: Everywhere.

Q: Where is your data?
A: Everywhere.

Q: Where are your users?
A: Everywhere.

Q: How is it all connected?
A: Everywhere.

Your apps are everywhere. Your data is everywhere. Your users are everywhere, and it's all connected via multiple types of networks that are … yes … everywhere.

In today's complex hybrid IT environments where data, applications, people, and networks are everywhere, point solutions cannot provide a total solution. The infrastructure challenges that impact application performance are ubiquitous, so only a holistic approach that brings visibility, performance, agility, and security to every aspect and stage of application delivery can provide an enterprise-grade solution for the age of hybrid IT. Just as digital transformation is an enterprise business strategy, enterprises need an architectural strategy to make the underpinning technology work the way it needs to.

The foundation of that architectural strategy is to stop using the traditional tools: routers and switches. In Part 3 of this series, I'll explain why those tools are quickly growing obsolete, and why SD-WAN is emerging as the technology that enables you to create a scalable network architecture that supports, enables and drives digital transformation with new levels of visibility, performance, security and agility.

Joshua Dobies is VP of Product Marketing, Riverbed Technology.

The Latest

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.

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 ...