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4 Ways Data and Analytics Help Optimize Your Data Center for Today's Apps

Tim Conley

Remember the days when you went to the bookstore, wandered the aisles, paged through a few books and then made your selection? Or when you used to fly cross-country for a meeting with business associates in another state so you could share information face-to-face?

Today, you likely visit fewer bookstores, and you may have curtailed your business travel. If you want something to read, you download it on your eReader. If you need to meet with someone hundreds of miles away, you might use Skype, WebEx or another online meeting application to meet virtually.

The Internet, mobile and today's apps have fundamentally changed the way we live our personal lives and conduct business. Today, people have come to expect instant gratification and an unprecedented level of convenience. They want what they want now, and it should be at their fingertips.

As businesses respond to customers' and internal stakeholders' rising demands, information technology departments are on the forefront, shaping their company's future. Given that 90% of IT decision makers who responded to a survey by Red Hat Mobile expect to increase their mobile app development in 2016, IT organizations are facing tremendous challenges.

How can you be responsive to user demands and support the apps they require?

The first step is to optimize your data center. Data center monitoring that provides analytics about your entire IT infrastructure, whether it's on premise, in the cloud or a hybrid environment, is the foundation for this process.

It's difficult, however, to use such data efficiently if you have a patchwork of monitoring solutions. For instance, looking at servers, storage, SAN and applications separately is not helpful because they are all interdependent. Instead, you need one cloud-based monitoring tool with an enterprise dashboard that gives you an at-a-glance big picture of your entire infrastructure. It should also provide predictive analytics and enable you to drill down to unravel any issues. With these capabilities, you should be able to do the following:

1. Get Utilization "Just Right"

Goldilocks was not happy until the porridge, the chair, and the bed were "just right." Likewise, IT leaders cannot be satisfied until the utilization of their assets is "just right." Under-utilization may feel comfortable because it ensures performance for end users. However, it wastes IT resources. On the other hand, over-utilization puts the user experience at risk due to potential slowdowns and outages. Using data to identify under- and over-utilization issues can help you to address them.

2. Squeeze the Most Out of Tight IT Budgets

If you're like many IT leaders, you're likely dealing with a stagnant or declining budget. Within that, you're expected to achieve more than ever before.

You can use your infrastructure-wide data to review patterns in capacity and performance, gaining insights into how you can best accommodate peak times. Also, it helps with server consolidation projects. Instead of assuming you are maxing out the usage of your current servers, for example, you can know your utilization levels and make intelligent decisions based on facts. Other budget-saving projects that depend on enterprise-wide data include the elimination of unused virtual machines (VMs), improving forecasts for servers and storage, and asset management.

3. Shore up Security

One of today's nightmares is that a security breach could disrupt operations. More and more business units, such as marketing and sales, are using public cloud-based applications to meet their needs. They often do so outside the guidance of IT. If this phenomenon exists within your company, commonly known as "shadow IT," it has the potential to open the door to hackers. Cloud-based data and analytics with encryption and authentication can help you determine applications and data that may be at risk.

4. Ace the Agility Test

With the increasing demand for apps, it's challenging to deliver them on time. But data and analytics can help you to provide storage and server capacity rapidly to support their needs. You may, for example, be able to use under-utilized assets to accommodate new apps and associated data. However, you can only do this if you can identify them easily and quickly.

In the app economy, there's a lot on the line for IT organizations. They must be proactive in optimizing their data centers to meet consumer and internal user demands. To do so, they should take advantage of data center monitoring tools that provide the data and analytics they need to make intelligent, rapid decisions about their IT infrastructure.

Tim Conley is Co-Founder and Principal of Galileo.

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 ...

4 Ways Data and Analytics Help Optimize Your Data Center for Today's Apps

Tim Conley

Remember the days when you went to the bookstore, wandered the aisles, paged through a few books and then made your selection? Or when you used to fly cross-country for a meeting with business associates in another state so you could share information face-to-face?

Today, you likely visit fewer bookstores, and you may have curtailed your business travel. If you want something to read, you download it on your eReader. If you need to meet with someone hundreds of miles away, you might use Skype, WebEx or another online meeting application to meet virtually.

The Internet, mobile and today's apps have fundamentally changed the way we live our personal lives and conduct business. Today, people have come to expect instant gratification and an unprecedented level of convenience. They want what they want now, and it should be at their fingertips.

As businesses respond to customers' and internal stakeholders' rising demands, information technology departments are on the forefront, shaping their company's future. Given that 90% of IT decision makers who responded to a survey by Red Hat Mobile expect to increase their mobile app development in 2016, IT organizations are facing tremendous challenges.

How can you be responsive to user demands and support the apps they require?

The first step is to optimize your data center. Data center monitoring that provides analytics about your entire IT infrastructure, whether it's on premise, in the cloud or a hybrid environment, is the foundation for this process.

It's difficult, however, to use such data efficiently if you have a patchwork of monitoring solutions. For instance, looking at servers, storage, SAN and applications separately is not helpful because they are all interdependent. Instead, you need one cloud-based monitoring tool with an enterprise dashboard that gives you an at-a-glance big picture of your entire infrastructure. It should also provide predictive analytics and enable you to drill down to unravel any issues. With these capabilities, you should be able to do the following:

1. Get Utilization "Just Right"

Goldilocks was not happy until the porridge, the chair, and the bed were "just right." Likewise, IT leaders cannot be satisfied until the utilization of their assets is "just right." Under-utilization may feel comfortable because it ensures performance for end users. However, it wastes IT resources. On the other hand, over-utilization puts the user experience at risk due to potential slowdowns and outages. Using data to identify under- and over-utilization issues can help you to address them.

2. Squeeze the Most Out of Tight IT Budgets

If you're like many IT leaders, you're likely dealing with a stagnant or declining budget. Within that, you're expected to achieve more than ever before.

You can use your infrastructure-wide data to review patterns in capacity and performance, gaining insights into how you can best accommodate peak times. Also, it helps with server consolidation projects. Instead of assuming you are maxing out the usage of your current servers, for example, you can know your utilization levels and make intelligent decisions based on facts. Other budget-saving projects that depend on enterprise-wide data include the elimination of unused virtual machines (VMs), improving forecasts for servers and storage, and asset management.

3. Shore up Security

One of today's nightmares is that a security breach could disrupt operations. More and more business units, such as marketing and sales, are using public cloud-based applications to meet their needs. They often do so outside the guidance of IT. If this phenomenon exists within your company, commonly known as "shadow IT," it has the potential to open the door to hackers. Cloud-based data and analytics with encryption and authentication can help you determine applications and data that may be at risk.

4. Ace the Agility Test

With the increasing demand for apps, it's challenging to deliver them on time. But data and analytics can help you to provide storage and server capacity rapidly to support their needs. You may, for example, be able to use under-utilized assets to accommodate new apps and associated data. However, you can only do this if you can identify them easily and quickly.

In the app economy, there's a lot on the line for IT organizations. They must be proactive in optimizing their data centers to meet consumer and internal user demands. To do so, they should take advantage of data center monitoring tools that provide the data and analytics they need to make intelligent, rapid decisions about their IT infrastructure.

Tim Conley is Co-Founder and Principal of Galileo.

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 ...