Advanced IT Analytics, AIOps, Big Data - What's Really Going On?

Dennis Drogseth

This question is really two questions.

The first would be: What's really going on in terms of a confusion of terms? — as we wrestle with AIOps, IT Operational Analytics, big data, AI bots, machine learning, and more generically stated "AI platforms" (… and the list is far from complete).

The second might be phrased as: What's really going on in terms of real-world advanced IT analytics deployments — where are they succeeding, and where are they not?

This blog will look at both questions as a way of introducing EMA's newest research with data just coming in from North America and Europe (UK, Germany and France). Like this blog, our research will also examine both questions, with the weight on examining real-world deployments. We hope to have at least a few real answers for you by September, with fresh data and timely analysis.

A Term by Any Other Name …

I'm borrowing, admittedly, from Shakespeare, to suggest that buzzwords in tech often get in the way of understanding real value, even as they seek to clarify it. In the case of what EMA prefers to call "advanced IT analytics" the fugal use of AI, machine learning, and big data, among other terms, often confuses what's really afoot. The real value is almost always in the mixture of science and artistry with which the analytics are applied to various use cases, not a purely academic discussion about what heuristics lie underneath the hood.

But EMA believes there is nevertheless a commonality across all true AIA solutions.

Last summer, EMA embarked on research that strongly indicates that there are common benefits, requirements and challenge surrounding an investment in AIA. Some of the more dramatic benefits typically included values in unifying IT across silos, toolset consolidation, dramatic reductions in mean-time-to-repair and mean time between failures, as well as other use cases that typically ranged from performance and availability management, to change management and capacity optimization, to support for DevOps and SecOps, to optimizing migrations to public cloud. As such we view AIA as a potentially transformative arena for both IT and the business it serves.

In our current research, we will be asking some simple questions regarding terminology and attributes to test the waters, especially in the now prevalent area of AIOps. But we'll also be able to track deployments centering on big data, security-related analytics, capacity-specific analytics and end-user or customer experience analytics, to see what patterns emerge and how they actually differ.

How Do You Make it All Real?

What's currently afoot in operationalizing advanced analytics for IT?

This is the main focus for our research, and it will also help to inform on the first question — what people are actually doing when they champion AIOps, or big data, etc.

Some areas of focus include:

■ Use cases: Here we are expanding on capacity, security and end-user experience to include cross-domain application/infrastructure availability and performance, DevOps/agile, cost management (including hybrid and multi-cloud), change management, and IoT.

■ Leadership: Who's leading in investments in advanced IT analytics, and who's leading in overseeing and actually delivering on deployments? What are their objectives, and how are they going about it?

■ Best practices: Are there any consistent best practices that emerge from the usual laundry list when advanced analytics are being deployed and used? If so, what are they? And how effective are they?

■ Integrations: How much are investments in advanced analytics being used to assimilate and optimize other toolsets?

■ Automation: What are the current priorities for integrated automation, where AI and machine learning can help to intelligently and adaptively drive more automated outcomes?

■ AI bots: Along with general automation priorities, we are looking at AI bot strategies to see how they converge (or don't) with AIOps and other analytics investments.

■ Technology and data sources: What data sets are IT organizations most hungry for when it comes to advanced analytics? What heuristics do they feel are most critical now, and in the future? How is service modeling and dependency mapping playing in the advanced IT analytics arena?

■ Roadblocks and benefits: What are the major obstacles remaining in 2018 to effective advanced IT analytics deployments? And what are the more prevalent benefits achieved?

Summing Up

These are admittedly a lot of areas for examination, and once again, the list is not complete. Moreover, we plan to investigate the answers we receive for all these questions from various perspectives, including company size, vertical, geography, roles (what do IT executives think versus more hands-on stakeholders?), success rates and other factors.

Finally, we'll be looking for trends based on the research done in two prior reports: Advanced IT Analytics: A Look at Real-World Adoptions in the Real World March 2016, and The Many Faces of Advanced Operations Analytics September 2014.

What I'm hoping we'll see in September is continued growth toward a more mature, more business-aligned, and more IT-unifying approach to advanced analytics deployments, with a growing number of stakeholders and benefits. I'm also hoping for a more definitive set of AIA profiles, as operations analytics continues to redefine itself away from just "big data," and as the need for more evolved, holistic and dynamic multi-use-case AIA platforms becomes more pronounced.

But it's too soon to tell. The data is still coming in. Nevertheless, I should know soon. In a follow-up blog in the first-half of September I'll be able to present some real news.

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Advanced IT Analytics, AIOps, Big Data - What's Really Going On?

Dennis Drogseth

This question is really two questions.

The first would be: What's really going on in terms of a confusion of terms? — as we wrestle with AIOps, IT Operational Analytics, big data, AI bots, machine learning, and more generically stated "AI platforms" (… and the list is far from complete).

The second might be phrased as: What's really going on in terms of real-world advanced IT analytics deployments — where are they succeeding, and where are they not?

This blog will look at both questions as a way of introducing EMA's newest research with data just coming in from North America and Europe (UK, Germany and France). Like this blog, our research will also examine both questions, with the weight on examining real-world deployments. We hope to have at least a few real answers for you by September, with fresh data and timely analysis.

A Term by Any Other Name …

I'm borrowing, admittedly, from Shakespeare, to suggest that buzzwords in tech often get in the way of understanding real value, even as they seek to clarify it. In the case of what EMA prefers to call "advanced IT analytics" the fugal use of AI, machine learning, and big data, among other terms, often confuses what's really afoot. The real value is almost always in the mixture of science and artistry with which the analytics are applied to various use cases, not a purely academic discussion about what heuristics lie underneath the hood.

But EMA believes there is nevertheless a commonality across all true AIA solutions.

Last summer, EMA embarked on research that strongly indicates that there are common benefits, requirements and challenge surrounding an investment in AIA. Some of the more dramatic benefits typically included values in unifying IT across silos, toolset consolidation, dramatic reductions in mean-time-to-repair and mean time between failures, as well as other use cases that typically ranged from performance and availability management, to change management and capacity optimization, to support for DevOps and SecOps, to optimizing migrations to public cloud. As such we view AIA as a potentially transformative arena for both IT and the business it serves.

In our current research, we will be asking some simple questions regarding terminology and attributes to test the waters, especially in the now prevalent area of AIOps. But we'll also be able to track deployments centering on big data, security-related analytics, capacity-specific analytics and end-user or customer experience analytics, to see what patterns emerge and how they actually differ.

How Do You Make it All Real?

What's currently afoot in operationalizing advanced analytics for IT?

This is the main focus for our research, and it will also help to inform on the first question — what people are actually doing when they champion AIOps, or big data, etc.

Some areas of focus include:

■ Use cases: Here we are expanding on capacity, security and end-user experience to include cross-domain application/infrastructure availability and performance, DevOps/agile, cost management (including hybrid and multi-cloud), change management, and IoT.

■ Leadership: Who's leading in investments in advanced IT analytics, and who's leading in overseeing and actually delivering on deployments? What are their objectives, and how are they going about it?

■ Best practices: Are there any consistent best practices that emerge from the usual laundry list when advanced analytics are being deployed and used? If so, what are they? And how effective are they?

■ Integrations: How much are investments in advanced analytics being used to assimilate and optimize other toolsets?

■ Automation: What are the current priorities for integrated automation, where AI and machine learning can help to intelligently and adaptively drive more automated outcomes?

■ AI bots: Along with general automation priorities, we are looking at AI bot strategies to see how they converge (or don't) with AIOps and other analytics investments.

■ Technology and data sources: What data sets are IT organizations most hungry for when it comes to advanced analytics? What heuristics do they feel are most critical now, and in the future? How is service modeling and dependency mapping playing in the advanced IT analytics arena?

■ Roadblocks and benefits: What are the major obstacles remaining in 2018 to effective advanced IT analytics deployments? And what are the more prevalent benefits achieved?

Summing Up

These are admittedly a lot of areas for examination, and once again, the list is not complete. Moreover, we plan to investigate the answers we receive for all these questions from various perspectives, including company size, vertical, geography, roles (what do IT executives think versus more hands-on stakeholders?), success rates and other factors.

Finally, we'll be looking for trends based on the research done in two prior reports: Advanced IT Analytics: A Look at Real-World Adoptions in the Real World March 2016, and The Many Faces of Advanced Operations Analytics September 2014.

What I'm hoping we'll see in September is continued growth toward a more mature, more business-aligned, and more IT-unifying approach to advanced analytics deployments, with a growing number of stakeholders and benefits. I'm also hoping for a more definitive set of AIA profiles, as operations analytics continues to redefine itself away from just "big data," and as the need for more evolved, holistic and dynamic multi-use-case AIA platforms becomes more pronounced.

But it's too soon to tell. The data is still coming in. Nevertheless, I should know soon. In a follow-up blog in the first-half of September I'll be able to present some real news.

Related Links

Hot Topics

The Latest

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 ...

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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 ...

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 ...

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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 ...

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