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Infrastructure Management - Do You Still Care?

A few good reasons why you should ...

Infrastructure Management is so 1990s. Everyone has it. Could there be anything interesting left to say? Yes -- that it is more critical than ever. It may still be just the plumbing but the demands on it are only growing and yesterday’s point solutions won't do the job.

In a June 2011 article in CIOInsight, interestingly titled, “IT Investment Trends: Infrastructure Back in the Mix” they note, “CIO Insight’s latest IT Investment Trends study shows renewed interest in the fundamentals of the IT infrastructure. This is refreshing amid today’s ethereal talk about clouds and virtual machines.”

It is, in fact, those ethereal technologies that are driving Infrastructure Management solutions to grow up and meet the demands placed on them by combining integration, automation and intelligent analytics. These new, unified infrastructure management solutions can step up to the requirements for agility and the ability to handle the complexity that new technologies and higher expectations inflict on all organizations - enterprises, government agencies and service providers alike.

Here are four reasons why - in 2012 - you should still care:

Too big to fail infrastructures are the backbone of an organization’s bottom line. While the “infrastructure” was once a code word for “the network” and the applications running over it only impacted employees, infrastructures are now a growing conglomeration of the network, systems, databases and applications that affect customers and employees alike, with direct impact on your bottom line.

Managing the infrastructure as a whole is imperative. Attempting to manage with a mix of point products becomes part of the problem rather than the solution. Effort by IT staff or service network operators to manually correlate events from silo-focused management products and track impact to individuals and services is a practice that simply doesn’t scale. Integrated and unified infrastructure management solutions automatically correlate events between silos, identify root cause and track impact to individuals and business services, letting IT staff and service network operators practice rapid remediation. Performance and trend analysis even lets them get ahead of developing issues to avoid some problems altogether.

Voice and video are rapidly gaining acceptance into the infrastructure “club.” Once outside the data network, they now sit well within an organization’s infrastructure, even while requiring essentially different service qualities than standard IP data traffic. Isn’t that why we have Quality of Service (QoS) capabilities? However, manually creating QoS service classes and hoping they have the desired effect is risky without a clear view into traffic flows and performance. With good visibility, QoS classes can be fine tuned to support both business critical applications and the delay-sensitive voice and video services.

Virtualization is the perfect technology to take an infrastructure running close to the edge from bad to worse. If your IT staff is struggling to manually track systems capacity and utilization, adding virtualization will create the tipping point. With automated tools for infrastructure management they can keep up with the volume of workload and application movement while holding down capital costs, data center expansion and staff stress.

Private clouds bring many technologies together (server, network and storage virtualization, high availability, automation and self-service) to optimize an organization’s assets and provide a highly responsive infrastructure service environment, much like a data center on steroids. The private cloud is expected to scale to absorb utilization peaks and protect against outages and degradation, thereby making it a prime consumer of unified infrastructure management. Without the ability to manage the scope and complexity of the private cloud, it can become a single point of multiple, potential failures.

With these technologies resetting the bar for organizational competence and competitiveness, it is a good time to review your infrastructure management solution. Is it unified, managing performance and available for physical and virtual systems, the network, applications and databases and giving you a single view? Can it provide visibility into traffic flows so voice and video can safely co-exist in your infrastructure? Can it scale as your organization grows? Time to reconsider.

ABOUT Pam Snaith

Pam Snaith joined CA Technologies in 2005 as part of the Concord Communications acquisition. In her role in Product Marketing she is focused on solutions that drive business Service Assurance. Snaith has broad experience in the networking industry, from software engineering to product management and marketing for voice and data products at institutions and companies including the Federal Reserve Bank, Digital Equipment Corporation, Xyplex Networks, Lucent Technologies and Avaya. She has published magazine articles and numerous white papers. Snaith earned a B.A. from New York University and completed additional coursework at Cornell Medical College.

Related Links:

www.ca.com

Hot Topics

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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.

Infrastructure Management - Do You Still Care?

A few good reasons why you should ...

Infrastructure Management is so 1990s. Everyone has it. Could there be anything interesting left to say? Yes -- that it is more critical than ever. It may still be just the plumbing but the demands on it are only growing and yesterday’s point solutions won't do the job.

In a June 2011 article in CIOInsight, interestingly titled, “IT Investment Trends: Infrastructure Back in the Mix” they note, “CIO Insight’s latest IT Investment Trends study shows renewed interest in the fundamentals of the IT infrastructure. This is refreshing amid today’s ethereal talk about clouds and virtual machines.”

It is, in fact, those ethereal technologies that are driving Infrastructure Management solutions to grow up and meet the demands placed on them by combining integration, automation and intelligent analytics. These new, unified infrastructure management solutions can step up to the requirements for agility and the ability to handle the complexity that new technologies and higher expectations inflict on all organizations - enterprises, government agencies and service providers alike.

Here are four reasons why - in 2012 - you should still care:

Too big to fail infrastructures are the backbone of an organization’s bottom line. While the “infrastructure” was once a code word for “the network” and the applications running over it only impacted employees, infrastructures are now a growing conglomeration of the network, systems, databases and applications that affect customers and employees alike, with direct impact on your bottom line.

Managing the infrastructure as a whole is imperative. Attempting to manage with a mix of point products becomes part of the problem rather than the solution. Effort by IT staff or service network operators to manually correlate events from silo-focused management products and track impact to individuals and services is a practice that simply doesn’t scale. Integrated and unified infrastructure management solutions automatically correlate events between silos, identify root cause and track impact to individuals and business services, letting IT staff and service network operators practice rapid remediation. Performance and trend analysis even lets them get ahead of developing issues to avoid some problems altogether.

Voice and video are rapidly gaining acceptance into the infrastructure “club.” Once outside the data network, they now sit well within an organization’s infrastructure, even while requiring essentially different service qualities than standard IP data traffic. Isn’t that why we have Quality of Service (QoS) capabilities? However, manually creating QoS service classes and hoping they have the desired effect is risky without a clear view into traffic flows and performance. With good visibility, QoS classes can be fine tuned to support both business critical applications and the delay-sensitive voice and video services.

Virtualization is the perfect technology to take an infrastructure running close to the edge from bad to worse. If your IT staff is struggling to manually track systems capacity and utilization, adding virtualization will create the tipping point. With automated tools for infrastructure management they can keep up with the volume of workload and application movement while holding down capital costs, data center expansion and staff stress.

Private clouds bring many technologies together (server, network and storage virtualization, high availability, automation and self-service) to optimize an organization’s assets and provide a highly responsive infrastructure service environment, much like a data center on steroids. The private cloud is expected to scale to absorb utilization peaks and protect against outages and degradation, thereby making it a prime consumer of unified infrastructure management. Without the ability to manage the scope and complexity of the private cloud, it can become a single point of multiple, potential failures.

With these technologies resetting the bar for organizational competence and competitiveness, it is a good time to review your infrastructure management solution. Is it unified, managing performance and available for physical and virtual systems, the network, applications and databases and giving you a single view? Can it provide visibility into traffic flows so voice and video can safely co-exist in your infrastructure? Can it scale as your organization grows? Time to reconsider.

ABOUT Pam Snaith

Pam Snaith joined CA Technologies in 2005 as part of the Concord Communications acquisition. In her role in Product Marketing she is focused on solutions that drive business Service Assurance. Snaith has broad experience in the networking industry, from software engineering to product management and marketing for voice and data products at institutions and companies including the Federal Reserve Bank, Digital Equipment Corporation, Xyplex Networks, Lucent Technologies and Avaya. She has published magazine articles and numerous white papers. Snaith earned a B.A. from New York University and completed additional coursework at Cornell Medical College.

Related Links:

www.ca.com

Hot Topics

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.