Advanced IT Analytics, AIOps and Big Data - 7 Key Takeaways - Part 1

Dennis Drogseth

OK, the data is in! Three hundred respondents and analysis that took me multiple weeks and resulted in a summary deck of nearly 200 slides. And that's just the summary deck.

But I promise a much more focused exploration of the "IT analytic universe," one that's all digestible within 45 minutes (including Q&A), with the upcoming EMA webinar on October 10.

The goal of the research was to look at how advanced IT analytics (AIA) — or EMA's term for primarily what today is best known as "AIOps" — is being deployed, as mentioned in a prior APMdigest blog.

We asked what contributes to its success in terms of technology, process and best practices, organizational ownership, and functional priorities.

We also wanted to map how AIOps, or IT operations analytics, was being deployed in the context with other analytic technologies, such as big data, as well as more niche areas such as security-specific analytics, end-user-experience analytics, change management analytics, and capacity analytics.

We asked these questions to a respondent base that was about 2/3 North America, 1/3 Europe (England, Germany and France), across a wide range of roles. We got a solid IT executive presence, along with technical stakeholders such as data scientists, security-related stakeholders, and operational and IT service management (ITSM) stakeholders.

So what did we find?

Without giving away the heart and soul of the webinar, which will give you data to draw your own conclusions, here are seven of my own personal takeaways, some of which frankly surprised me.

1. AIOps is winning strategy

AIOps was the overall the winning strategy. While AIOps was not the most pervasive technology associated with advanced IT analytics in our research (big data led as the most prevalent before quotas), it was the most effective and pervasively advanced.

Indeed, AIOps showed the highest success rates, the greatest likelihood of supporting DevOps, IoT and AI bots, and led in use case capabilities as well.

2. AIA are eclectic in use case

Advanced IT analytics are eclectic in use case and becoming more so. Overall support for DevOps, IoT, AI bots, and multiple use cases including end-user experience, security, capacity analytics, cost-related optimization, show increasing diversity in need and value.

The implications of this are significant. AIOps and AIA more broadly are evolving as platform investments rather than niche solutions. This means that the data consumed and applied can be leveraged in multiple ways, bringing added benefits to the investment, while also helping to more effectively unify various roles, organizations and stakeholders across IT.

3. AI bots and automation

AI bots and automation are not a separate world from AIOps and AIA. The strong and perhaps surprising correlation between AI bots in use, AI bots as a sign of overall analytics success, and AI bot integrations into broader analytic directions all indicate that the AIOps "market" and the AI bots "market" should not be viewed in isolation.

This also helps to reinforce the critical handshake between automation and AI which was also reinforced by the research findings indicating that, on average, respondents targeted more than five automation integrations.

Read Advanced IT Analytics, AIOps and Big Data - 7 Key Takeaways - Part 2, covering 4 more key takeaways from EMA's research.

Related Links

Hot Topics

The Latest

Reliability Is the New Bottleneck of Innovation

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

5 Takeaways from the Observability Forecast for Retail and eCommerce

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Maximizing Impact Amid Constraints: The Role of Automation and Orchestration in Federal IT Modernization

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

The SRE Report 2026: Reliability Is Being Redefined

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

The Hidden Data Engineering Work Required to Make Enterprise AI Viable

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

Why AI Is the Differentiator for Operationally Resilient Organizations

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

Escaping Pilot Purgatory: How AI Becomes an Operational Advantage

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 ...

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 ...

Advanced IT Analytics, AIOps and Big Data - 7 Key Takeaways - Part 1

Dennis Drogseth

OK, the data is in! Three hundred respondents and analysis that took me multiple weeks and resulted in a summary deck of nearly 200 slides. And that's just the summary deck.

But I promise a much more focused exploration of the "IT analytic universe," one that's all digestible within 45 minutes (including Q&A), with the upcoming EMA webinar on October 10.

The goal of the research was to look at how advanced IT analytics (AIA) — or EMA's term for primarily what today is best known as "AIOps" — is being deployed, as mentioned in a prior APMdigest blog.

We asked what contributes to its success in terms of technology, process and best practices, organizational ownership, and functional priorities.

We also wanted to map how AIOps, or IT operations analytics, was being deployed in the context with other analytic technologies, such as big data, as well as more niche areas such as security-specific analytics, end-user-experience analytics, change management analytics, and capacity analytics.

We asked these questions to a respondent base that was about 2/3 North America, 1/3 Europe (England, Germany and France), across a wide range of roles. We got a solid IT executive presence, along with technical stakeholders such as data scientists, security-related stakeholders, and operational and IT service management (ITSM) stakeholders.

So what did we find?

Without giving away the heart and soul of the webinar, which will give you data to draw your own conclusions, here are seven of my own personal takeaways, some of which frankly surprised me.

1. AIOps is winning strategy

AIOps was the overall the winning strategy. While AIOps was not the most pervasive technology associated with advanced IT analytics in our research (big data led as the most prevalent before quotas), it was the most effective and pervasively advanced.

Indeed, AIOps showed the highest success rates, the greatest likelihood of supporting DevOps, IoT and AI bots, and led in use case capabilities as well.

2. AIA are eclectic in use case

Advanced IT analytics are eclectic in use case and becoming more so. Overall support for DevOps, IoT, AI bots, and multiple use cases including end-user experience, security, capacity analytics, cost-related optimization, show increasing diversity in need and value.

The implications of this are significant. AIOps and AIA more broadly are evolving as platform investments rather than niche solutions. This means that the data consumed and applied can be leveraged in multiple ways, bringing added benefits to the investment, while also helping to more effectively unify various roles, organizations and stakeholders across IT.

3. AI bots and automation

AI bots and automation are not a separate world from AIOps and AIA. The strong and perhaps surprising correlation between AI bots in use, AI bots as a sign of overall analytics success, and AI bot integrations into broader analytic directions all indicate that the AIOps "market" and the AI bots "market" should not be viewed in isolation.

This also helps to reinforce the critical handshake between automation and AI which was also reinforced by the research findings indicating that, on average, respondents targeted more than five automation integrations.

Read Advanced IT Analytics, AIOps and Big Data - 7 Key Takeaways - Part 2, covering 4 more key takeaways from EMA's research.

Related Links

Hot Topics

The Latest

Reliability Is the New Bottleneck of Innovation

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

5 Takeaways from the Observability Forecast for Retail and eCommerce

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Maximizing Impact Amid Constraints: The Role of Automation and Orchestration in Federal IT Modernization

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

The SRE Report 2026: Reliability Is Being Redefined

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

The Hidden Data Engineering Work Required to Make Enterprise AI Viable

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

Why AI Is the Differentiator for Operationally Resilient Organizations

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

Escaping Pilot Purgatory: How AI Becomes an Operational Advantage

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 ...

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 ...