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Gartner: Major Organizations Need to Grow Performance Management Skills

Agile infrastructures demand that infrastructure and operations (I&O) managers acquire the performance management skills present only in Web-scale IT, according to Gartner, Inc.

While major organizations will need to maintain and sustain their conventional capacity-planning skills and tools, they will also need to regularly re-evaluate the tools available, and make a special effort to acquire and grow the capacity and performance management skills that are rarely evident outside the Web-scale IT community.

"By 2016, the availability of capacity and performance management skills for horizontally scaled architectures will be a major constraint or risk to growth for 80 percent of major businesses," says Ian Head, research director at Gartner. "To take advantage of Web-scale IT approaches to capacity and performance management, IT architects need to fully embrace stateless application architectures and horizontally scaling infrastructure architectures."

Adding additional central processing units (CPUs), memory and storage to a monolithic server has been the traditional, vertical way of scaling up applications while capacity planning has traditionally been developed with the goal of forecasting the requirements for this vertical scaling approach. However, vertical architectures and approaches have limited scalability, making vertical architectures unsuitable for hyperscaling. For service capacity to expand seamlessly to extremely large scales, different approaches are required.

"Organizations managing such services need the ability to rapidly assign and de-assign resources to each service, as well as the ability to scale linearly and continuously as more resources are added or removed," says Head. "They also need high levels of resiliency that will allow sections of the infrastructure to fail without bringing the services down. Designing the underlying infrastructure for horizontal scalability, high degrees of fault-tolerance and rapid, incremental change is a key prerequisite for effective Web-scale operations."

To achieve the overall goal of an infrastructure in which services can consume capacity on an as-needed basis, the overall Web-scale IT capacity planning function may be divided among two teams. The application or product team develops the applications, monitors the consumption of their services in various locations and user subsets, and requests and allocates infrastructure resources based on policy-driven utilization triggers. The infrastructure team ensures that the overall shared physical limits do not constrain the performance of the individual services being continuously developed by the product teams.

Services constructed in this way are better equipped to scale geographically, and share multiple data centers with limited impact on user performance. Although extending service availability to new locations will carry additional burdens and requirements, horizontally scaling application and infrastructure architectures will generally be better equipped to scale geographically, because additional capacity may be used by the different applications, as required and as load patterns shift.

Demand Shaping

Where there are potentially large-scale, limited forecasts or demand history, IT leaders need to develop demand-shaping techniques to provide acceptable performance. Demand shaping enables finite infrastructure resources to maintain their vital always-on character with acceptable, if not consistent, performance experiences across the entire user base. Gartner predicts that through 2017, 25 percent of enterprises will use demand shaping for capacity planning and management, a significant increase from less than one percent in 2014.

When deploying large-scale services, infrastructure and operations leaders need to become proficient in operational analytics tools and big data capabilities, rather than traditional capacity planning tools.

"Traditional capacity-planning tools enable I&O organizations to gather data from various sources, including monitoring tools and, possibly, business demand forecasts, then produce trending, utilization and investment forecast information, taking into account several different scenarios," says Head. "The different architectures and the huge scale of the Web-scale IT organizations make traditional, highly focused tools of limited utility. Demand shaping requires more and different functionality than current off-the-shelf tools provide."

Although different Web-scale organizations grow and adapt their techniques to their specific requirements, a common theme is the extensive use of large volumes of operational data. Even though the infrastructures are large, the horizontal design enables clear visualization and understanding of constraints and dependencies, such that these may be managed as the environment, loads and demands change.

In general, in-memory computing and deep analytics tools are used to extract the required information from a combination of the infrastructure monitoring tools and the instrumentation built into the applications. The resulting analytical information is used to facilitate proactive, real-time and near-real-time actions to allocate resources and manage potential bottlenecks. Similar functionality is also used to model the impact of moving workloads and to simulate the effects of potential infrastructure and application changes.

The outcome is that Web-scale enterprises have developed a set of tools, practices and capabilities that enable real-time demand shaping. These operational skills and tools are unique to each Web-scale organization and so are not yet available in most end-user enterprises.

"Much of the art of achieving always-on, scalable, rapidly changing, high-performance services is a consequence of the advanced use of homegrown and customized analytics and tools by the application and infrastructure teams," Head concludes. "These are used to shape demand in real time and to produce forward-looking capacity and investment plans to an acceptable degree of accuracy."

Related Links:

Gartner analysts will take a deeper look at the outlook for IT operations trends at the Gartner IT Infrastructure & Operations Management Summit taking place June 2-3 in Berlin and June 9-11 in Orlando FL.

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Gartner: Major Organizations Need to Grow Performance Management Skills

Agile infrastructures demand that infrastructure and operations (I&O) managers acquire the performance management skills present only in Web-scale IT, according to Gartner, Inc.

While major organizations will need to maintain and sustain their conventional capacity-planning skills and tools, they will also need to regularly re-evaluate the tools available, and make a special effort to acquire and grow the capacity and performance management skills that are rarely evident outside the Web-scale IT community.

"By 2016, the availability of capacity and performance management skills for horizontally scaled architectures will be a major constraint or risk to growth for 80 percent of major businesses," says Ian Head, research director at Gartner. "To take advantage of Web-scale IT approaches to capacity and performance management, IT architects need to fully embrace stateless application architectures and horizontally scaling infrastructure architectures."

Adding additional central processing units (CPUs), memory and storage to a monolithic server has been the traditional, vertical way of scaling up applications while capacity planning has traditionally been developed with the goal of forecasting the requirements for this vertical scaling approach. However, vertical architectures and approaches have limited scalability, making vertical architectures unsuitable for hyperscaling. For service capacity to expand seamlessly to extremely large scales, different approaches are required.

"Organizations managing such services need the ability to rapidly assign and de-assign resources to each service, as well as the ability to scale linearly and continuously as more resources are added or removed," says Head. "They also need high levels of resiliency that will allow sections of the infrastructure to fail without bringing the services down. Designing the underlying infrastructure for horizontal scalability, high degrees of fault-tolerance and rapid, incremental change is a key prerequisite for effective Web-scale operations."

To achieve the overall goal of an infrastructure in which services can consume capacity on an as-needed basis, the overall Web-scale IT capacity planning function may be divided among two teams. The application or product team develops the applications, monitors the consumption of their services in various locations and user subsets, and requests and allocates infrastructure resources based on policy-driven utilization triggers. The infrastructure team ensures that the overall shared physical limits do not constrain the performance of the individual services being continuously developed by the product teams.

Services constructed in this way are better equipped to scale geographically, and share multiple data centers with limited impact on user performance. Although extending service availability to new locations will carry additional burdens and requirements, horizontally scaling application and infrastructure architectures will generally be better equipped to scale geographically, because additional capacity may be used by the different applications, as required and as load patterns shift.

Demand Shaping

Where there are potentially large-scale, limited forecasts or demand history, IT leaders need to develop demand-shaping techniques to provide acceptable performance. Demand shaping enables finite infrastructure resources to maintain their vital always-on character with acceptable, if not consistent, performance experiences across the entire user base. Gartner predicts that through 2017, 25 percent of enterprises will use demand shaping for capacity planning and management, a significant increase from less than one percent in 2014.

When deploying large-scale services, infrastructure and operations leaders need to become proficient in operational analytics tools and big data capabilities, rather than traditional capacity planning tools.

"Traditional capacity-planning tools enable I&O organizations to gather data from various sources, including monitoring tools and, possibly, business demand forecasts, then produce trending, utilization and investment forecast information, taking into account several different scenarios," says Head. "The different architectures and the huge scale of the Web-scale IT organizations make traditional, highly focused tools of limited utility. Demand shaping requires more and different functionality than current off-the-shelf tools provide."

Although different Web-scale organizations grow and adapt their techniques to their specific requirements, a common theme is the extensive use of large volumes of operational data. Even though the infrastructures are large, the horizontal design enables clear visualization and understanding of constraints and dependencies, such that these may be managed as the environment, loads and demands change.

In general, in-memory computing and deep analytics tools are used to extract the required information from a combination of the infrastructure monitoring tools and the instrumentation built into the applications. The resulting analytical information is used to facilitate proactive, real-time and near-real-time actions to allocate resources and manage potential bottlenecks. Similar functionality is also used to model the impact of moving workloads and to simulate the effects of potential infrastructure and application changes.

The outcome is that Web-scale enterprises have developed a set of tools, practices and capabilities that enable real-time demand shaping. These operational skills and tools are unique to each Web-scale organization and so are not yet available in most end-user enterprises.

"Much of the art of achieving always-on, scalable, rapidly changing, high-performance services is a consequence of the advanced use of homegrown and customized analytics and tools by the application and infrastructure teams," Head concludes. "These are used to shape demand in real time and to produce forward-looking capacity and investment plans to an acceptable degree of accuracy."

Related Links:

Gartner analysts will take a deeper look at the outlook for IT operations trends at the Gartner IT Infrastructure & Operations Management Summit taking place June 2-3 in Berlin and June 9-11 in Orlando FL.

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.