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Enterprise Management Associates Announces New Research on Software Defined Data Centers

Enterprise Management Associates (EMA) released its latest research report entitled, Obstacles and Priorities on the Journey to the Software-Defined Data Center.

Based on research criteria defined by EMA Research Director, Systems Management, Torsten Volk, and EMA VP of Research, Network Management, Jim Frey, this major research study examines the expertise and opinions of early adopters and visionaries to determine obstacles and priorities on the way to managing the data center and external resources—IaaS, PaaS, SaaS and, ultimately, BPaaS—in a performance-, resilience-, security- and SLA-driven manner

At the core of the SDDC is the belief that in order to better serve the business, IT infrastructure—internal and external—must be controlled centrally and become radically aligned along application and service requirements. Deploying, operating, managing and updating applications in the most cost-effective, secure, agile and policy-compliant manner is the key goal of the SDDC. Business units are exerting a tremendous amount of pressure on the IT department to accelerate this process, requiring IT to obtain new skills, such as “programming,” and to focus on developing cross-domain expertise.

Today there are no central management technologies that are able to control and unify the entire data center and the public cloud. However, this EMA research illustrates that successfully implementing the SDDC starts with an IT operations mindset that focuses on reinventing the infrastructure provisioning and management process in a much more policy-driven manner.

“Study respondents acknowledged the fact that the SDDC cannot be implemented in the form of a technology project, but rather constitutes a concept that describes guidelines that follow the multi-year vision of entirely closing the traditional gap between enterprise IT and the business,” said Volk.

This in-depth research explores:

- The key components and business drivers of the SDDC

- The SDDC technologies and services IT professionals will invest in and which ones promise the highest ROI

- The key considerations when optimally placing new applications and the core risks

- The key challenges when creating new application environments

- How the Software Defined Storage (SDS) can enhance the data center

- The ‘net’ impact of Software Defined Networking (SDN) and network virtualization

- How the SDDC concept increase the ROI of private and public cloud

- The role of OpenStack within the SDDC and the key reasons for adopting OpenStack

- The role security plays within the SDDC

“Our study focused on organizations that are committed to the SDDC path and reveal a range of best practices experiences that span compute, storage, and networking across internal and external cloud environments, while highlighting the influence of standards, legacy infrastructure, and more,” said Frey.

Related Links:

EMA Report: Obstacles and Priorities on the Journey to the Software-Defined Data Center

EMA Webinar Feb 18: Journey to the Software Defined Data Center - EMA Research Results Revealed

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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 ...

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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 ...

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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 ...

Enterprise Management Associates Announces New Research on Software Defined Data Centers

Enterprise Management Associates (EMA) released its latest research report entitled, Obstacles and Priorities on the Journey to the Software-Defined Data Center.

Based on research criteria defined by EMA Research Director, Systems Management, Torsten Volk, and EMA VP of Research, Network Management, Jim Frey, this major research study examines the expertise and opinions of early adopters and visionaries to determine obstacles and priorities on the way to managing the data center and external resources—IaaS, PaaS, SaaS and, ultimately, BPaaS—in a performance-, resilience-, security- and SLA-driven manner

At the core of the SDDC is the belief that in order to better serve the business, IT infrastructure—internal and external—must be controlled centrally and become radically aligned along application and service requirements. Deploying, operating, managing and updating applications in the most cost-effective, secure, agile and policy-compliant manner is the key goal of the SDDC. Business units are exerting a tremendous amount of pressure on the IT department to accelerate this process, requiring IT to obtain new skills, such as “programming,” and to focus on developing cross-domain expertise.

Today there are no central management technologies that are able to control and unify the entire data center and the public cloud. However, this EMA research illustrates that successfully implementing the SDDC starts with an IT operations mindset that focuses on reinventing the infrastructure provisioning and management process in a much more policy-driven manner.

“Study respondents acknowledged the fact that the SDDC cannot be implemented in the form of a technology project, but rather constitutes a concept that describes guidelines that follow the multi-year vision of entirely closing the traditional gap between enterprise IT and the business,” said Volk.

This in-depth research explores:

- The key components and business drivers of the SDDC

- The SDDC technologies and services IT professionals will invest in and which ones promise the highest ROI

- The key considerations when optimally placing new applications and the core risks

- The key challenges when creating new application environments

- How the Software Defined Storage (SDS) can enhance the data center

- The ‘net’ impact of Software Defined Networking (SDN) and network virtualization

- How the SDDC concept increase the ROI of private and public cloud

- The role of OpenStack within the SDDC and the key reasons for adopting OpenStack

- The role security plays within the SDDC

“Our study focused on organizations that are committed to the SDDC path and reveal a range of best practices experiences that span compute, storage, and networking across internal and external cloud environments, while highlighting the influence of standards, legacy infrastructure, and more,” said Frey.

Related Links:

EMA Report: Obstacles and Priorities on the Journey to the Software-Defined Data Center

EMA Webinar Feb 18: Journey to the Software Defined Data Center - EMA Research Results Revealed

Hot Topic

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 ...