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Next Steps for ITOA - Part 5

APMdigest asked experts across the industry — including analysts, consultants and vendors — for their opinions on the next steps for ITOA. These next steps include where the experts believe ITOA is headed, as well as where they think it should be headed. Part 5 offers some interesting final thoughts.

Start with Next Steps for ITOA - Part 1

Start with Next Steps for ITOA - Part 2

Start with Next Steps for ITOA - Part 3

Start with Next Steps for ITOA - Part 4

REACTIVE TO PROACTIVE

ITOA will help evolve tomorrow's IT organization from a reactive speeds and feeds provider focused on capacity availability into a proactive data-driven fulfillment engine delivering stability, agility and innovation ahead of business needs.
Trace3 Research 360 View Trend Report: IT Operations Monitoring & Analytics (ITOMA)

HOLISTIC APPROACH

The next step in the evolution of IT Operations Analytics is establishing a more holistic approach that considers the performance of people AND machines. Metrics tied to machines and tools are now table stakes for ITOA. However, in the future, organizations will need to look at the system as a whole, which includes the humans involved. In order to have a complete understanding of ITOps health, IT organizations must have a comprehensive view of how their people are interacting with machines, data and other people, and establish metrics accordingly to this whole rather than just the parts.
Eric Sigler
Head of DevOps, PagerDuty

OPEN SOURCE

Organizations are collecting massive amounts of live data streams, which on its own can feel like a major accomplishment. But the key question is: so what? If they have no way to analyze billions of data points from servers, machines, containers and applications in millisecond response time, none of that work matters. By adopting newer and more flexible open source products with machine learning capabilities tailored to time series use cases, organizations will be better equipped to use all of their data to help them operate better, detect infrastructure problems, cybersecurity, or fraud, and solve critical business issues.
Jeff Yoshimura
VP Worldwide Marketing, Elastic

MULTI-VENDOR COLLABORATION

The next natural step for ITOA is for the machines to leverage the analytics to make reasoned decisions and take actions based on the information collected. Analytics leads to heuristics – when machine intelligence is able to interpret the data based on business defined policies and standards. Once the machine can make recommendations, the next evolutionary step is for the machine to act on those recommendations. The orchestration and automation of IT environments is evolving. Tools and standards such as Openstack are being developed to enable the automated management and orchestration of IT architectures. Expect more multi-vendor collaboration to build architectures that can be integrated into a single management and orchestration environment over the next couple years, but do not expect full integration and a mature, automated, self-analyzing, and self-healing network ecosystem for years to come.
Frank Yue
Director of Application Delivery Solutions, Radware

SECURITY

As threats continue to increase in frequency and sophistication, enterprises will need to look to IT Operations Analytics as a tactic to identify and proactively address anomalies before security threats fully materialize. With the rise of connected devices and the Internet of Things and emerging technologies like Artificial Intelligence, organizations are increasingly moving toward analytics and automation as a tactic to supercharge cybersecurity.
Ananda Rajagopal
VP, Product Management, Gigamon

COST OPTIMIZATION

Performance management focused on the speed and reliability of user interactions will always be very important. But performance management must also focus on efficiency of code execution, with an eye towards cost optimization for underlying CPU resources. As the mainframe continues to be the platform of choice for mission-critical transactional applications, slight code tweaks can yield performance boosts for thousands of users. However, with mainframe licensing costs (MLCs) comprising approximately 30 percent of mainframe budgets – and withh these costs continuing to rise – it is equally critical to be more pro-active about service level management of the workload so R4HA peaks can be minimized, keeping costs in check and wasted expenses down. We expect IT Operations Analytics - particularly for mainframe user organizations - to expand in focus, optimizing not just the user experience but costs as well.
Spencer Hallman
Product Manager, Compuware

ANALYTICS AVAILABLE TO ALL

Predictive analytics in application performance management offers a powerful way to improve customer experience. By deploying correlation and mathematical modeling techniques, it analyzes relationships between multiple data points to accurately predict future application behavioral trends, and data anomalies which would affect end-users. Presently predictive analytics is available and affordable for large business with money and resources, however that is going to change in the near future. With emerging technologies and new and easy ways of presenting information to end-users, vendors will differentiate themselves by offering simpler and more affordable ways to deploy predictive analytics in their APM solutions, making it available to all.
Pritika Ramani
Product Analyst, ManageEngine

Hot Topics

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 ...

Next Steps for ITOA - Part 5

APMdigest asked experts across the industry — including analysts, consultants and vendors — for their opinions on the next steps for ITOA. These next steps include where the experts believe ITOA is headed, as well as where they think it should be headed. Part 5 offers some interesting final thoughts.

Start with Next Steps for ITOA - Part 1

Start with Next Steps for ITOA - Part 2

Start with Next Steps for ITOA - Part 3

Start with Next Steps for ITOA - Part 4

REACTIVE TO PROACTIVE

ITOA will help evolve tomorrow's IT organization from a reactive speeds and feeds provider focused on capacity availability into a proactive data-driven fulfillment engine delivering stability, agility and innovation ahead of business needs.
Trace3 Research 360 View Trend Report: IT Operations Monitoring & Analytics (ITOMA)

HOLISTIC APPROACH

The next step in the evolution of IT Operations Analytics is establishing a more holistic approach that considers the performance of people AND machines. Metrics tied to machines and tools are now table stakes for ITOA. However, in the future, organizations will need to look at the system as a whole, which includes the humans involved. In order to have a complete understanding of ITOps health, IT organizations must have a comprehensive view of how their people are interacting with machines, data and other people, and establish metrics accordingly to this whole rather than just the parts.
Eric Sigler
Head of DevOps, PagerDuty

OPEN SOURCE

Organizations are collecting massive amounts of live data streams, which on its own can feel like a major accomplishment. But the key question is: so what? If they have no way to analyze billions of data points from servers, machines, containers and applications in millisecond response time, none of that work matters. By adopting newer and more flexible open source products with machine learning capabilities tailored to time series use cases, organizations will be better equipped to use all of their data to help them operate better, detect infrastructure problems, cybersecurity, or fraud, and solve critical business issues.
Jeff Yoshimura
VP Worldwide Marketing, Elastic

MULTI-VENDOR COLLABORATION

The next natural step for ITOA is for the machines to leverage the analytics to make reasoned decisions and take actions based on the information collected. Analytics leads to heuristics – when machine intelligence is able to interpret the data based on business defined policies and standards. Once the machine can make recommendations, the next evolutionary step is for the machine to act on those recommendations. The orchestration and automation of IT environments is evolving. Tools and standards such as Openstack are being developed to enable the automated management and orchestration of IT architectures. Expect more multi-vendor collaboration to build architectures that can be integrated into a single management and orchestration environment over the next couple years, but do not expect full integration and a mature, automated, self-analyzing, and self-healing network ecosystem for years to come.
Frank Yue
Director of Application Delivery Solutions, Radware

SECURITY

As threats continue to increase in frequency and sophistication, enterprises will need to look to IT Operations Analytics as a tactic to identify and proactively address anomalies before security threats fully materialize. With the rise of connected devices and the Internet of Things and emerging technologies like Artificial Intelligence, organizations are increasingly moving toward analytics and automation as a tactic to supercharge cybersecurity.
Ananda Rajagopal
VP, Product Management, Gigamon

COST OPTIMIZATION

Performance management focused on the speed and reliability of user interactions will always be very important. But performance management must also focus on efficiency of code execution, with an eye towards cost optimization for underlying CPU resources. As the mainframe continues to be the platform of choice for mission-critical transactional applications, slight code tweaks can yield performance boosts for thousands of users. However, with mainframe licensing costs (MLCs) comprising approximately 30 percent of mainframe budgets – and withh these costs continuing to rise – it is equally critical to be more pro-active about service level management of the workload so R4HA peaks can be minimized, keeping costs in check and wasted expenses down. We expect IT Operations Analytics - particularly for mainframe user organizations - to expand in focus, optimizing not just the user experience but costs as well.
Spencer Hallman
Product Manager, Compuware

ANALYTICS AVAILABLE TO ALL

Predictive analytics in application performance management offers a powerful way to improve customer experience. By deploying correlation and mathematical modeling techniques, it analyzes relationships between multiple data points to accurately predict future application behavioral trends, and data anomalies which would affect end-users. Presently predictive analytics is available and affordable for large business with money and resources, however that is going to change in the near future. With emerging technologies and new and easy ways of presenting information to end-users, vendors will differentiate themselves by offering simpler and more affordable ways to deploy predictive analytics in their APM solutions, making it available to all.
Pritika Ramani
Product Analyst, ManageEngine

Hot Topics

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 ...