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Do More with Your Data Center

Ofer Laksman

Today, most IT environments are constantly shifting, and the escalating costs might affect a data center's performance. CIOs, CTOs, DCOs and even CEOs must adapt to the needs and requirements, but without an effective planning process, any changes in demand could easily affect data systems which could result in decreased revenue, productivity, sales, and customer service.

When organizations lack the forecasting ability to predict resource needs, IT managers are left to make certain that capacities and resources remain supportive enough to meet the demands. All of this starts with accurate management and optimization of infrastructure, applications, and those business drivers that equate to revenue.

How to be More Proactive?

The trend continues for IT managers to respond to the multiple needs and requests (reactive mode), only to be overwhelmed by service demand peaks and valleys. Forecasting and reports need to be updated weekly and daily to give IT managers the real-time vision to react proactively so that data center capacity can remain ahead of service.

The goal then is to best determine how a data center's overall business drivers are utilizing its resources, and how the industry markets, legal, compliance, or initiatives can alter and have impacts on the outcome.

This is where having powerful analytical tools becomes important and a necessary business element, giving IT managers a more accurate view on industry trends, baseline shifts, or anomalies that could correlate costs and grouping reports.

Analytic tools can help provide a vertical and horizontal view and make you better prepared as demand spikes present themselves. Further, it can provide better control over purchases whereas each could be tied back to a real business demand.

Is Traditional Planning Leaving You Vulnerable?

When committing to a data center's resources without the right planning or predictive analysis tools, you might be left vulnerable. To ensure a data centers' resources can keep up with your market or demand needs, IT managers must automate their forecasting and the data metrics should be monitored and analyzed.

Further, IT managers must be able to run test scenarios to provide insight on the requirements their data center needs to reduce overall costs and risks. It remains important that IT managers understand not only the silos of data migrating, but the horizontal view across all business channels through their software and hardware. Such tools that can provide a full-scale horizontal view for planning and resource allocation becomes that key piece of information that is needed to best assist CIOs, CTOs, DCOs and even CEOs with metrics that the organization can use to become business drivers versus business liabilities.

Identify Efficient Assets

Does it come as a surprise that today's data centers are transforming into profit centers and business assets as they embrace cloud solutions, compliance, automation, machine learning, AI, and new, emerging technologies?

To fully understand these forward-facing technologies, the entire C-suite will need to rely on the most powerful software and tools within the market. These revolutionary tools will be required to monitor the future data center environments that are built around the lack of human intervention.

Equipped with the right tools, data center managers can improve their data center's cost efficiency by embracing the vertical and horizontal analytics that provide a full view of their data center's usage. The goal is not to reduce spending, but to have the ability to obtain more performance from what is being budgeted for and spent, to then improve service, overall.

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.

Do More with Your Data Center

Ofer Laksman

Today, most IT environments are constantly shifting, and the escalating costs might affect a data center's performance. CIOs, CTOs, DCOs and even CEOs must adapt to the needs and requirements, but without an effective planning process, any changes in demand could easily affect data systems which could result in decreased revenue, productivity, sales, and customer service.

When organizations lack the forecasting ability to predict resource needs, IT managers are left to make certain that capacities and resources remain supportive enough to meet the demands. All of this starts with accurate management and optimization of infrastructure, applications, and those business drivers that equate to revenue.

How to be More Proactive?

The trend continues for IT managers to respond to the multiple needs and requests (reactive mode), only to be overwhelmed by service demand peaks and valleys. Forecasting and reports need to be updated weekly and daily to give IT managers the real-time vision to react proactively so that data center capacity can remain ahead of service.

The goal then is to best determine how a data center's overall business drivers are utilizing its resources, and how the industry markets, legal, compliance, or initiatives can alter and have impacts on the outcome.

This is where having powerful analytical tools becomes important and a necessary business element, giving IT managers a more accurate view on industry trends, baseline shifts, or anomalies that could correlate costs and grouping reports.

Analytic tools can help provide a vertical and horizontal view and make you better prepared as demand spikes present themselves. Further, it can provide better control over purchases whereas each could be tied back to a real business demand.

Is Traditional Planning Leaving You Vulnerable?

When committing to a data center's resources without the right planning or predictive analysis tools, you might be left vulnerable. To ensure a data centers' resources can keep up with your market or demand needs, IT managers must automate their forecasting and the data metrics should be monitored and analyzed.

Further, IT managers must be able to run test scenarios to provide insight on the requirements their data center needs to reduce overall costs and risks. It remains important that IT managers understand not only the silos of data migrating, but the horizontal view across all business channels through their software and hardware. Such tools that can provide a full-scale horizontal view for planning and resource allocation becomes that key piece of information that is needed to best assist CIOs, CTOs, DCOs and even CEOs with metrics that the organization can use to become business drivers versus business liabilities.

Identify Efficient Assets

Does it come as a surprise that today's data centers are transforming into profit centers and business assets as they embrace cloud solutions, compliance, automation, machine learning, AI, and new, emerging technologies?

To fully understand these forward-facing technologies, the entire C-suite will need to rely on the most powerful software and tools within the market. These revolutionary tools will be required to monitor the future data center environments that are built around the lack of human intervention.

Equipped with the right tools, data center managers can improve their data center's cost efficiency by embracing the vertical and horizontal analytics that provide a full view of their data center's usage. The goal is not to reduce spending, but to have the ability to obtain more performance from what is being budgeted for and spent, to then improve service, overall.

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.