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EMA Announces New Research on Managing Networks in the Age of Cloud, SDN, and Big Data

Enterprise Management Associates (EMA) released its latest research report entitled Managing Networks in the Age of Cloud, SDN, and Big Data: Network Management Megatrends 2014.

Based on research criteria defined by EMA Vice President of Research, Network Management, Jim Frey, and EMA Principal Research Analyst, Tracy Corbo, this new research study looks at the current state of networks and network management, and examines five major areas of change and evolution affecting network management. These areas are cloud and virtualization, Software Defined Networking (SDN), big data, the rise of log data and APIs as management data sources, and the ongoing convergence of network operations teams and tools.

The study also examines the context and influence that broader IT and organizational priorities and projects are having on network management priorities, as well as resulting requirements for network management products and solutions. The findings reveal the experiences and objectives that a broad range of organizations have had regarding network management, and thus should serve as a source of requirements and input for strategic network engineering and operations planning.

Some of the key findings in this study include:

- 80% of network engineers and managers are finding it necessary to learn new programming/scripting skills to keep up with increasing numbers of device and system APIs

- 80% of organizations are willing to consider consuming network management in a SaaS model

- 60%+ of organizations rely on log data analysis to support network monitoring, planning, and troubleshooting – more than any other data source.

- While the average organization uses 4-5 tools for network monitoring and troubleshooting, 1/3 of large organizations are using 11 or more tools, and some are using more than 25!

“The world of networks and network management are being rocked by disruptive trends such as cloud, SDN, and big data,” said Frey. “While network management is considered by many to be a mature discipline, our results indicate there is a lot of work remaining to accommodate these new initiatives and bring the network management into the cloud and big data era.”

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EMA Announces New Research on Managing Networks in the Age of Cloud, SDN, and Big Data

Enterprise Management Associates (EMA) released its latest research report entitled Managing Networks in the Age of Cloud, SDN, and Big Data: Network Management Megatrends 2014.

Based on research criteria defined by EMA Vice President of Research, Network Management, Jim Frey, and EMA Principal Research Analyst, Tracy Corbo, this new research study looks at the current state of networks and network management, and examines five major areas of change and evolution affecting network management. These areas are cloud and virtualization, Software Defined Networking (SDN), big data, the rise of log data and APIs as management data sources, and the ongoing convergence of network operations teams and tools.

The study also examines the context and influence that broader IT and organizational priorities and projects are having on network management priorities, as well as resulting requirements for network management products and solutions. The findings reveal the experiences and objectives that a broad range of organizations have had regarding network management, and thus should serve as a source of requirements and input for strategic network engineering and operations planning.

Some of the key findings in this study include:

- 80% of network engineers and managers are finding it necessary to learn new programming/scripting skills to keep up with increasing numbers of device and system APIs

- 80% of organizations are willing to consider consuming network management in a SaaS model

- 60%+ of organizations rely on log data analysis to support network monitoring, planning, and troubleshooting – more than any other data source.

- While the average organization uses 4-5 tools for network monitoring and troubleshooting, 1/3 of large organizations are using 11 or more tools, and some are using more than 25!

“The world of networks and network management are being rocked by disruptive trends such as cloud, SDN, and big data,” said Frey. “While network management is considered by many to be a mature discipline, our results indicate there is a lot of work remaining to accommodate these new initiatives and bring the network management into the cloud and big data era.”

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AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

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Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...