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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.

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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.

Hot Topics

The Latest

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 ...

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 ...

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 ...

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 ...

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 ...

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

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 ...

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 ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...