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New Research from EMA Provides Insight into APM for Production-Ready Cloud Ecosystems

Enterprise Management Associates (EMA) released its latest research report entitled, Public Cloud Comes of Age: Application Performance Management (APM) Strategies & Products for Production-Ready Cloud Ecosystems.

Based on research criteria defined by EMA research director, application management, Julie Craig, this new research focuses on the enterprise management implications of the wave of innovation engulfing today’s companies.

During the economic disaster that defined the years between 2008 and 2012, time stood still for many businesses. Licking the wounds inflicted by business reversals and decreases in revenues and stock prices, they survived by reducing costs, laying off staff, and drawing on existing cash.

Surprisingly, these same economic conditions proved to be a catalyst for technology vendors. During that time frame, many expressed optimism for the future and a driving need to prepare for the time when the economy would improve. As a result, companies in almost every vertical are now finding themselves riding the biggest wave of innovation the technology industry has ever produced.

IT organizations are seeking new ways to manage the fallout of this wave, which has introduced rapid and often unplanned changes into IT ecosystems. At the same time, modern applications are increasingly part of “Extended Enterprises” in which execution can traverse multiple locations and spheres of control, and integrate data from widely dispersed sources. Such applications are already spanning on-premise and cloud, mobile, Internet of Things (IoT), and social media platforms, often connected by a web of tools and custom code dubbed the “API Economy.”

“This research examines the implications of rapid technology evolution in IT environments already saturated with multiple dimensions of application complexity,” says Craig. "It assesses the tool sets, technology usage, and challenges of today’s real-world IT organizations as they prepare to support the Extended Enterprises of the future.”

Key findings include:

- “Incorporating new technologies into the existing ecosystem” is the top challenge cited by IT professionals as a whole, followed by skills gaps and the high cost of IT administration/support. ”Shadow IT,” despite being at the high end of the hype cycle, is near the bottom of IT’s list of concerns.

- Significant challenges persist in moving applications to the cloud – Both data and application migration are key issues, although lack of skills and “keeping track of what is hosted where” scored high on the list as well.

- The top challenges cited by development professionals are “software deployment” and “production support,” both of which require operational skills and are often considered to be operations tasks. It is becoming increasingly clear that development is spending too much time doing non-core work. This, in turn, can impact the speed with which development organizations can deliver business-differentiating new features and functions. As industry leading companies become increasingly adept at delivering on the Extended Enterprise, re-focusing development on value-add tasks may well prove to be the deciding factor between companies which can and cannot compete.

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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.

New Research from EMA Provides Insight into APM for Production-Ready Cloud Ecosystems

Enterprise Management Associates (EMA) released its latest research report entitled, Public Cloud Comes of Age: Application Performance Management (APM) Strategies & Products for Production-Ready Cloud Ecosystems.

Based on research criteria defined by EMA research director, application management, Julie Craig, this new research focuses on the enterprise management implications of the wave of innovation engulfing today’s companies.

During the economic disaster that defined the years between 2008 and 2012, time stood still for many businesses. Licking the wounds inflicted by business reversals and decreases in revenues and stock prices, they survived by reducing costs, laying off staff, and drawing on existing cash.

Surprisingly, these same economic conditions proved to be a catalyst for technology vendors. During that time frame, many expressed optimism for the future and a driving need to prepare for the time when the economy would improve. As a result, companies in almost every vertical are now finding themselves riding the biggest wave of innovation the technology industry has ever produced.

IT organizations are seeking new ways to manage the fallout of this wave, which has introduced rapid and often unplanned changes into IT ecosystems. At the same time, modern applications are increasingly part of “Extended Enterprises” in which execution can traverse multiple locations and spheres of control, and integrate data from widely dispersed sources. Such applications are already spanning on-premise and cloud, mobile, Internet of Things (IoT), and social media platforms, often connected by a web of tools and custom code dubbed the “API Economy.”

“This research examines the implications of rapid technology evolution in IT environments already saturated with multiple dimensions of application complexity,” says Craig. "It assesses the tool sets, technology usage, and challenges of today’s real-world IT organizations as they prepare to support the Extended Enterprises of the future.”

Key findings include:

- “Incorporating new technologies into the existing ecosystem” is the top challenge cited by IT professionals as a whole, followed by skills gaps and the high cost of IT administration/support. ”Shadow IT,” despite being at the high end of the hype cycle, is near the bottom of IT’s list of concerns.

- Significant challenges persist in moving applications to the cloud – Both data and application migration are key issues, although lack of skills and “keeping track of what is hosted where” scored high on the list as well.

- The top challenges cited by development professionals are “software deployment” and “production support,” both of which require operational skills and are often considered to be operations tasks. It is becoming increasingly clear that development is spending too much time doing non-core work. This, in turn, can impact the speed with which development organizations can deliver business-differentiating new features and functions. As industry leading companies become increasingly adept at delivering on the Extended Enterprise, re-focusing development on value-add tasks may well prove to be the deciding factor between companies which can and cannot compete.

Hot Topic

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.