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EMA Publishes New Radar Report on AIOps

Enterprise Management Associates (EMA) announced the release of its newest EMA Radar Report, titled EMA Radar Report: AIOps – A Guide for Investing in Innovation.

Download a complimentary copy of the report

Created to assist IT professionals in selecting the right solutions for their specific needs, EMA identifies the leading vendors in this space based on key criteria defined by EMA VP of Research, Dennis Drogseth.

“All indications are that this is groundbreaking research,” said Drogseth. “So far, the industry has been struggling to define and understand AIOps, including its benefits, requirements, and challenges. Our extensive data gathering, vendor dialogues, and 31 supplemental deployment interviews have brought AIOps into a new level of clarity—one that underscores both its diversity and its richly beneficial common ground.”

The fact that AIOps is a market showing strong growth in value has been borne out time and again in EMA research over the past decade and remains true in this newest iteration of research on the topic. The message for IT organizations looking to pursue a forward path in AIOps adoption is overall a strongly positive one. The benefits achieved are growing in diversity and value. The obstacles remain similar, as they reflect not only on a technology purchase, but also on processes, organizations, and cultural realities.

To assist IT organizations pursuing this path, EMA evaluated 17 vendors providing AIOps solutions. Any of the 17 vendors represented in the report might be the best choice for an IT organization depending on what tools and solutions they currently have, their level of process and organizational maturity, their goals and priorities, and what advanced technologies they already have deployed.

The following criteria were used for market inclusion:

- Assimilation of data from cross-domain sources in high data volumes for cross-domain insights.

- The ability to access critical data types, e.g., events, KPIs, logs, flow, configuration data, etc.

- Capabilities for self-learning to deliver predictive and/or prescriptive and/or if/then actionable insights.

- Support for a wide range of advanced heuristics, such as multivariate analysis, machine learning, streaming data, tiered analytics, cognitive analytics, etc.

- Potential use as a strategic overlay that may assimilate or consolidate multiple monitoring investments.

- Support for private cloud and public cloud, as well as hybrid/legacy environments.

- The ability to support multiple use cases, including but not limited to application/infrastructure performance and availability.

Two areas of primary interest not on this list, but examined closely in this Radar, were support for automation to accelerate action and how platforms leverage discovery and dependency mapping for improved context.

A detailed, comparative study of solutions from the following vendors is provided in the report:

Aisera
BigPanda
BMC Software
Broadcom
Centerity
CloudFabrix
Digital.ai
Digitate
IBM
Interlink Software
Micro Focus
Moogsoft
Resolve Systems
ScienceLogic
ServiceNow
Splunk
Virtana

The objective of the Radar was not to pick a single winner but, instead, to provide IT organizations with use case descriptions relevant to purchase. The three use cases evaluated were:

Incident, performance, and availability management. This used case focused on optimizing the resiliency of critical application and business services—including microservices, VoIP, and rich media—in cloud (public/private) as well as non-cloud environments with a strong focus on triage, diagnostics, roles supported, self-learning capabilities, and associated automation.

Change impact and capacity optimization. These are admittedly two use cases combined into one but share requirements in terms of understanding interdependencies across the application/service infrastructure as volumes increase, changes are made, configuration issues arise, and automated actions are required.

Business impact and IT-to-business alignment. This use case includes user experience, customer experience, customer management, business process impacts, and other relevant data, with an eye to supporting business initiatives, such as digital transformation through superior IT-to-business alignment.

The Radar also looked at DevOps support, integrated SecOps capabilities, and IoT support, which could variously play to each, or all, of the use cases listed depending on the platform’s design and the vendor’s focus.

AIOps can and should be transformative in enabling more effective decision-making, data sharing, and analytics-driven automation. The recommendation from EMA remains that buyers should consider their own realities, then begin a search for the AIOps platform that most fits their requirements. Which vendor can most effectively address top prioritized long-term goals? Which vendor is a most natural fit for the current technology environment? Which vendor is likely to bring the fastest near-term wins? The answer could be any one of the 17 presented in this Radar, depending on the answers to these and other questions.

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

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 ...

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 ...

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 ...

EMA Publishes New Radar Report on AIOps

Enterprise Management Associates (EMA) announced the release of its newest EMA Radar Report, titled EMA Radar Report: AIOps – A Guide for Investing in Innovation.

Download a complimentary copy of the report

Created to assist IT professionals in selecting the right solutions for their specific needs, EMA identifies the leading vendors in this space based on key criteria defined by EMA VP of Research, Dennis Drogseth.

“All indications are that this is groundbreaking research,” said Drogseth. “So far, the industry has been struggling to define and understand AIOps, including its benefits, requirements, and challenges. Our extensive data gathering, vendor dialogues, and 31 supplemental deployment interviews have brought AIOps into a new level of clarity—one that underscores both its diversity and its richly beneficial common ground.”

The fact that AIOps is a market showing strong growth in value has been borne out time and again in EMA research over the past decade and remains true in this newest iteration of research on the topic. The message for IT organizations looking to pursue a forward path in AIOps adoption is overall a strongly positive one. The benefits achieved are growing in diversity and value. The obstacles remain similar, as they reflect not only on a technology purchase, but also on processes, organizations, and cultural realities.

To assist IT organizations pursuing this path, EMA evaluated 17 vendors providing AIOps solutions. Any of the 17 vendors represented in the report might be the best choice for an IT organization depending on what tools and solutions they currently have, their level of process and organizational maturity, their goals and priorities, and what advanced technologies they already have deployed.

The following criteria were used for market inclusion:

- Assimilation of data from cross-domain sources in high data volumes for cross-domain insights.

- The ability to access critical data types, e.g., events, KPIs, logs, flow, configuration data, etc.

- Capabilities for self-learning to deliver predictive and/or prescriptive and/or if/then actionable insights.

- Support for a wide range of advanced heuristics, such as multivariate analysis, machine learning, streaming data, tiered analytics, cognitive analytics, etc.

- Potential use as a strategic overlay that may assimilate or consolidate multiple monitoring investments.

- Support for private cloud and public cloud, as well as hybrid/legacy environments.

- The ability to support multiple use cases, including but not limited to application/infrastructure performance and availability.

Two areas of primary interest not on this list, but examined closely in this Radar, were support for automation to accelerate action and how platforms leverage discovery and dependency mapping for improved context.

A detailed, comparative study of solutions from the following vendors is provided in the report:

Aisera
BigPanda
BMC Software
Broadcom
Centerity
CloudFabrix
Digital.ai
Digitate
IBM
Interlink Software
Micro Focus
Moogsoft
Resolve Systems
ScienceLogic
ServiceNow
Splunk
Virtana

The objective of the Radar was not to pick a single winner but, instead, to provide IT organizations with use case descriptions relevant to purchase. The three use cases evaluated were:

Incident, performance, and availability management. This used case focused on optimizing the resiliency of critical application and business services—including microservices, VoIP, and rich media—in cloud (public/private) as well as non-cloud environments with a strong focus on triage, diagnostics, roles supported, self-learning capabilities, and associated automation.

Change impact and capacity optimization. These are admittedly two use cases combined into one but share requirements in terms of understanding interdependencies across the application/service infrastructure as volumes increase, changes are made, configuration issues arise, and automated actions are required.

Business impact and IT-to-business alignment. This use case includes user experience, customer experience, customer management, business process impacts, and other relevant data, with an eye to supporting business initiatives, such as digital transformation through superior IT-to-business alignment.

The Radar also looked at DevOps support, integrated SecOps capabilities, and IoT support, which could variously play to each, or all, of the use cases listed depending on the platform’s design and the vendor’s focus.

AIOps can and should be transformative in enabling more effective decision-making, data sharing, and analytics-driven automation. The recommendation from EMA remains that buyers should consider their own realities, then begin a search for the AIOps platform that most fits their requirements. Which vendor can most effectively address top prioritized long-term goals? Which vendor is a most natural fit for the current technology environment? Which vendor is likely to bring the fastest near-term wins? The answer could be any one of the 17 presented in this Radar, depending on the answers to these and other questions.

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

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 ...

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 ...

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 ...