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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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For fifteen years, observability lived downstream of everything else. Code shipped, something broke, an engineer went to the dashboards. The job was forensic. The pillars we built, such as logs, metrics, and traces, were designed for that role: tell a human what just happened, fast enough that they can make it stop. That role has quietly ended ...

Hybrid IT has become the standard operating model for enterprises — but that companies are still looking for the right hybrid IT mix, according to the 2026 State of the Data Center Report from CoreSite. After years of cloud migration and hybrid adoption, organizations are shifting their focus from deciding whether to use cloud, colocation or on-premises infrastructure to determining which workloads belong in each environment ...

Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale. But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within ...

Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...

I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...

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For years, secure connectivity has relied on a familiar pattern: route traffic back to centralized gateways, inspect it, and then allow access. This model worked when applications lived in a handful of data centers and users were largely confined to offices. That model is now under strain. Applications are distributed across clouds, users connect from everywhere, and real-time workloads demand performance that centralized inspection points struggle to deliver. As traffic volumes grow and latency expectations shrink, routing everything through a small number of control points has become both a performance bottleneck and a resilience risk. The future of secure connectivity requires a different approach ...

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44% of organizations have reported an outage in the past year tied to suppressed or ignored alerts, and 78% had at least one incident where no alert was fired at all ... Engineers learned about failures from customers. That gap between what our tools report and what our customers experience is the problem DevOps teams have been quietly solving with GenAI tooling, even as most enterprises continue to run their NOCs on manual alert triage ...

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

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

For fifteen years, observability lived downstream of everything else. Code shipped, something broke, an engineer went to the dashboards. The job was forensic. The pillars we built, such as logs, metrics, and traces, were designed for that role: tell a human what just happened, fast enough that they can make it stop. That role has quietly ended ...

Hybrid IT has become the standard operating model for enterprises — but that companies are still looking for the right hybrid IT mix, according to the 2026 State of the Data Center Report from CoreSite. After years of cloud migration and hybrid adoption, organizations are shifting their focus from deciding whether to use cloud, colocation or on-premises infrastructure to determining which workloads belong in each environment ...

Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale. But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within ...

Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...

I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...

The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools ...

For years, secure connectivity has relied on a familiar pattern: route traffic back to centralized gateways, inspect it, and then allow access. This model worked when applications lived in a handful of data centers and users were largely confined to offices. That model is now under strain. Applications are distributed across clouds, users connect from everywhere, and real-time workloads demand performance that centralized inspection points struggle to deliver. As traffic volumes grow and latency expectations shrink, routing everything through a small number of control points has become both a performance bottleneck and a resilience risk. The future of secure connectivity requires a different approach ...

The AI experimentation phase is over, and the private cloud is where enterprise AI workloads are being deployed for security and scale, according to Private Cloud Outlook 2026, a new report from Broadcom ... 2026 marks an acceleration into a full AI tipping point. The shift is being shaped by three forces — costs, complexity, and control — that public cloud environments are increasingly failing to address for production AI at scale. Key findings from the report include ...

44% of organizations have reported an outage in the past year tied to suppressed or ignored alerts, and 78% had at least one incident where no alert was fired at all ... Engineers learned about failures from customers. That gap between what our tools report and what our customers experience is the problem DevOps teams have been quietly solving with GenAI tooling, even as most enterprises continue to run their NOCs on manual alert triage ...

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...