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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

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

Image
Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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Broadcom

From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...

Today, organizations are generating and processing more data than ever before. From training AI models to running complex analytics, massive datasets have become the backbone of innovation. However, as businesses embrace the cloud for its scalability and flexibility, a new challenge arises: managing the soaring costs of storing and processing this data ...