Next Steps for ITOA - Part 1

Managing application performance today requires analytics. IT Operations Analytics (ITOA) is often used to augment or built into Application Performance Management solutions to process the massive amounts of metrics coming out of today's IT environment. Because of the relationship between APM and analytics, APMdigest has covered ITOA and related topics for many years. But today ITOA stands at a crossroads as revolutionary technologies and capabilities are emerging to push it into new realms.

So where is ITOA going next? With this question in mind, 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. This is a rare opportunity to gain a glimpse of what many of the world's leading experts see as the future of ITOA.

This list of "Next Steps for ITOA" will be posted in 5 parts over the next 2 weeks. Part 1 covers some of the most revolutionary changes facing ITOA today.

MACHINE LEARNING

To handle the increased volume, velocity and variety of operations big data, businesses will need a new class of analytics solution. By embracing open architectures, correlating across apps, infrastructure and networks, and apply machine-learning in context of deep domain expertise, these solutions will help businesses gain the insights needed to accelerate digital success and build lasting relationships with customers.
Kieran Taylor
Senior Director, Agile Operations, CA Technologies

The evolution of ITOA will be the convergence of machine learning and advanced analytics into a performance management platform. Within two years, it will be table stakes for vendors to be able to integrate different forms and sources of operational data to provide intelligence that drives stellar user experience, application performance and business outcomes.
Gabe Lowy
Technology Analyst and Founder of TechTonics Advisors

One of the major issues emerging in IT operations analytics in relation to performance management is that topological approaches to monitoring performance of the stack are weakening in importance, as Gartner analyst Will Cappelli points out in IT Operations Analytics Must Be Placed Within an AIOps Context. This is due to the increasing volume of unstructured data (e.g. datasets from social media) that needs to be parsed to diagnose performance issues and spot opportunities for optimization, as well as the fact that correlations must be identified across diverse datasets. Increasingly, as Colin Fletcher and Jonah Kowall point out in Apply IT Operations Analytics to Broader Datasets for Greater Business Insight, IT teams are analyzing non-IT data sets alongside IT datasets, which demands the use of more comprehensive approaches based in machine learning rather than topological analysis. Because of these shifts, ITOA will increasingly evolve from local, application-focused performance monitoring into a discipline resembling data science, in which machine learning is used to ingest and combine log files with business datasets to identify correlations that point the way to optimization.
Daniel Harris
Market Researcher, Software Advice (a Gartner Company)

Algorithmic IT Operations (AIOps)

While we only introduced the AIOps term/concept a little over a year ago, we believe the need for and recent emergence of capabilities that reach well beyond that originally described as ITOA had long reached its boiling point. The disruptive impacts of digital business, DevOps, the Internet of Things, and the recent machine learning renaissance are just a few indicators of a larger, generational, transformative shift for IT operations towards a future where the lines between IT and other business functions, operations and development, internal and external customer, even infrastructure and applications will only get blurrier. This shift necessitates the reorientation of a typically inward-looking, reactive "IT Operations Analytics" strategy towards a logical platform capable of continuously delivering proactive insights to any number of internal and external customers, a concept we call AIOps.
Colin Fletcher
Research Director, IT Operations, Gartner

Read Q&A: Gartner Talks About AIOps

ITOA represents using more basic analytics (query/response) on various data sources. With the advent of machine learning, inexpensive storage and compute resources (cloud) new machine learning algorithms and complex modeling allow for new solutions to solve today's increasingly complex applications and infrastructures. These new approaches have been coined AIOps. Hence, ITOA is yesterday's news, and AIOps attempts to solve the problems which ITOA could not.
Jonah Kowall
VP of Market Development and Insights, AppDynamics

LOGGING REVOLUTION

A significant trend in performance monitoring for cloud native environments (cloud/DevOps/micrsoservices/containers) is the revolution of logging applied to performance metrics at massive scale with millions of data points, and distributed tracing: these are essential tools for diagnosing and solving deep cloud native issues.
Michael Azoff
Principal Analyst, Ovum

ELIMINATION OF DOWNTIME

We are seeing an acceleration of cloud native applications replacing the traditional monolithic application. Applications that are allowed to "go down" for a maintenance window or be measured on "mean time to repair" are disappearing as applications that are designed to expect failures and be resilient take over the landscape. Teams are being measured on uptime with expectations of only a few minutes of downtime being allowable per month. Thus the progression for performance management tools will focus on analytics to proactively alert operations to problems at the earliest stages before impacting performance and availability of the application.
Mike Mallo
Offering Manager and Program Director, IBM Application Insights

FOCUS ON VALUE

The Next Step in ITOA is to understand ITOA, or advanced IT analytics as EMA calls it, not just in terms of technology, but in terms of a shopping cart of values. These could range from use cases like availability and performance management, to features like integrated security, or unifying values in enabling IT to work more effectively across silos, or business impact, or change awareness … just to name a few. Buyers and vendors need to respect technology foundations (and there are many multiple approaches) but also relate these to demonstrable and proven benefits along a reasonable set of shopping criteria for executive and technical IT buyers. This should help ITOA to evolve more quickly, while also benefiting IT organizations seeking unique benefits in the near term.
Dennis Drogseth
VP of Research, Enterprise Management Associates (EMA)

Read Dennis Drogseth's blog: Advanced IT Analytics: Making it Simpler to Optimize What's More Complex

Read Next Steps for ITOA - Part 2, covering visibility and data.

Related Links

Hot Topics

The Latest

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

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 ...

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Why FinOps Rewrote Its Mission and What It Signals for Technology Management

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 ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

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 ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

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 ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

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 ...

Explainability Is the New Battleground in AI-Powered Observability

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 ...

Empowering the Human Side of the Autonomous IT Age

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 Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

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.

No Escalations ≠ No Work: Why Visibility in DevOps Matters More Now That AI Is Accelerating Everything

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 1

Managing application performance today requires analytics. IT Operations Analytics (ITOA) is often used to augment or built into Application Performance Management solutions to process the massive amounts of metrics coming out of today's IT environment. Because of the relationship between APM and analytics, APMdigest has covered ITOA and related topics for many years. But today ITOA stands at a crossroads as revolutionary technologies and capabilities are emerging to push it into new realms.

So where is ITOA going next? With this question in mind, 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. This is a rare opportunity to gain a glimpse of what many of the world's leading experts see as the future of ITOA.

This list of "Next Steps for ITOA" will be posted in 5 parts over the next 2 weeks. Part 1 covers some of the most revolutionary changes facing ITOA today.

MACHINE LEARNING

To handle the increased volume, velocity and variety of operations big data, businesses will need a new class of analytics solution. By embracing open architectures, correlating across apps, infrastructure and networks, and apply machine-learning in context of deep domain expertise, these solutions will help businesses gain the insights needed to accelerate digital success and build lasting relationships with customers.
Kieran Taylor
Senior Director, Agile Operations, CA Technologies

The evolution of ITOA will be the convergence of machine learning and advanced analytics into a performance management platform. Within two years, it will be table stakes for vendors to be able to integrate different forms and sources of operational data to provide intelligence that drives stellar user experience, application performance and business outcomes.
Gabe Lowy
Technology Analyst and Founder of TechTonics Advisors

One of the major issues emerging in IT operations analytics in relation to performance management is that topological approaches to monitoring performance of the stack are weakening in importance, as Gartner analyst Will Cappelli points out in IT Operations Analytics Must Be Placed Within an AIOps Context. This is due to the increasing volume of unstructured data (e.g. datasets from social media) that needs to be parsed to diagnose performance issues and spot opportunities for optimization, as well as the fact that correlations must be identified across diverse datasets. Increasingly, as Colin Fletcher and Jonah Kowall point out in Apply IT Operations Analytics to Broader Datasets for Greater Business Insight, IT teams are analyzing non-IT data sets alongside IT datasets, which demands the use of more comprehensive approaches based in machine learning rather than topological analysis. Because of these shifts, ITOA will increasingly evolve from local, application-focused performance monitoring into a discipline resembling data science, in which machine learning is used to ingest and combine log files with business datasets to identify correlations that point the way to optimization.
Daniel Harris
Market Researcher, Software Advice (a Gartner Company)

Algorithmic IT Operations (AIOps)

While we only introduced the AIOps term/concept a little over a year ago, we believe the need for and recent emergence of capabilities that reach well beyond that originally described as ITOA had long reached its boiling point. The disruptive impacts of digital business, DevOps, the Internet of Things, and the recent machine learning renaissance are just a few indicators of a larger, generational, transformative shift for IT operations towards a future where the lines between IT and other business functions, operations and development, internal and external customer, even infrastructure and applications will only get blurrier. This shift necessitates the reorientation of a typically inward-looking, reactive "IT Operations Analytics" strategy towards a logical platform capable of continuously delivering proactive insights to any number of internal and external customers, a concept we call AIOps.
Colin Fletcher
Research Director, IT Operations, Gartner

Read Q&A: Gartner Talks About AIOps

ITOA represents using more basic analytics (query/response) on various data sources. With the advent of machine learning, inexpensive storage and compute resources (cloud) new machine learning algorithms and complex modeling allow for new solutions to solve today's increasingly complex applications and infrastructures. These new approaches have been coined AIOps. Hence, ITOA is yesterday's news, and AIOps attempts to solve the problems which ITOA could not.
Jonah Kowall
VP of Market Development and Insights, AppDynamics

LOGGING REVOLUTION

A significant trend in performance monitoring for cloud native environments (cloud/DevOps/micrsoservices/containers) is the revolution of logging applied to performance metrics at massive scale with millions of data points, and distributed tracing: these are essential tools for diagnosing and solving deep cloud native issues.
Michael Azoff
Principal Analyst, Ovum

ELIMINATION OF DOWNTIME

We are seeing an acceleration of cloud native applications replacing the traditional monolithic application. Applications that are allowed to "go down" for a maintenance window or be measured on "mean time to repair" are disappearing as applications that are designed to expect failures and be resilient take over the landscape. Teams are being measured on uptime with expectations of only a few minutes of downtime being allowable per month. Thus the progression for performance management tools will focus on analytics to proactively alert operations to problems at the earliest stages before impacting performance and availability of the application.
Mike Mallo
Offering Manager and Program Director, IBM Application Insights

FOCUS ON VALUE

The Next Step in ITOA is to understand ITOA, or advanced IT analytics as EMA calls it, not just in terms of technology, but in terms of a shopping cart of values. These could range from use cases like availability and performance management, to features like integrated security, or unifying values in enabling IT to work more effectively across silos, or business impact, or change awareness … just to name a few. Buyers and vendors need to respect technology foundations (and there are many multiple approaches) but also relate these to demonstrable and proven benefits along a reasonable set of shopping criteria for executive and technical IT buyers. This should help ITOA to evolve more quickly, while also benefiting IT organizations seeking unique benefits in the near term.
Dennis Drogseth
VP of Research, Enterprise Management Associates (EMA)

Read Dennis Drogseth's blog: Advanced IT Analytics: Making it Simpler to Optimize What's More Complex

Read Next Steps for ITOA - Part 2, covering visibility and data.

Related Links

Hot Topics

The Latest

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

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 ...

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Why FinOps Rewrote Its Mission and What It Signals for Technology Management

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 ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

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 ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

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 ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

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 ...

Explainability Is the New Battleground in AI-Powered Observability

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 ...

Empowering the Human Side of the Autonomous IT Age

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 Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

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.

No Escalations ≠ No Work: Why Visibility in DevOps Matters More Now That AI Is Accelerating Everything

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 ...