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.

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AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

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Why ITOps Need Right-Sized AI, Not Bigger Models

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

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For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

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Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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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

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Why ITOps Need Right-Sized AI, Not Bigger Models

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

The End of Reactive DevOps: AI-Driven Observability for Zero-Defect Digital Systems

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

5 Takeaways from the Observability Forecast for Media and Entertainment

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Why Moving Off the Cloud Is the Easy Part and What Comes Next Is Where Things Get Hard

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

MEAN TIME TO INSIGHT Podcast - Episode 24: Network Observability Tool Sprawl

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

Capacity Isn't a Guess: Observability-Driven Sizing for On-Prem Databases

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

5 Security Principles Every Entrepreneur Should Apply to Leadership

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...