If You Are Looking to Invest in Advanced Analytics for IT, Exactly What Should You Be Shopping For? Part 2: Environments

Dennis Drogseth

EMA published the AIA buyer's guide — directed at helping IT invest in Advanced IT Analytics(AIA), what the industry more commonly calls "Operational Analytics." We created what we called "Shopping Cart Criteria" based on our prior research on AIA adoptions over the past three years. We divided the sixteen shopping cart criteria into three parts: Cost advantage, Environments and Scenarios.

Start with Part 1: Cost Advantage

In this blog, I will address environments. Environments, as indicated by the list below, indicate "where" the AIA solutions we investigated can be applied. All 13 solutions supported cloud for performance, core infrastructure, and application performance and availability. Mainframe had the support of six of our respondents, and IoT and cloud for change and capacity were not yet prime areas of focus for most of the vendors in our guide.

■ Cloud for Performance Management

■ Cloud for Change/Capacity/Cost Optimization

■ Core Infrastructure (Network/Data Center)

■ Legacy/Mainframe

■ Application Performance and Availability Management

■ Internet of Things (IoT)

Cloud for Performance Management

Here we addressed cloud in all its forms, including public cloud, virtualization, microservices and containers, and hybrid cloud environments — with a focus on performance management of IT services and their associated infrastructure. We saw fairly pervasive support for AWS, Azure, Docker and containers, and microservices. Present but less prevalent was support for Google Cloud, IBM Bluemix, Rackspace and Fujitsu Cloud. We also saw integrations with Pivotal Cloud Foundry in support of DevOps initiatives.

Cloud for Change/Capacity/Cost Optimization

Capacity and cost decisions are often joined at the hip in dealing with planning deployment choices across public and private cloud. These decisions can also significantly impact performance. And in fact, in last year's AIA research, optimizing cloud resources generally ranked higher than pure-play performance management in AIA requirements. This unique but critical area was addressed proactively by several of the vendors in this report, especially those with integrations for capacity analytics, and in two cases even cloud-related pricing models.

Core Infrastructure (Network/Data Center)

All of the solutions represented in this report were directed to some degree at cross-domain operational needs. But the approach they took varied dramatically. In evaluating ratings associated with this criterion, we looked for breadth of coverage, relevant stakeholder support, and breadth of capabilities for assessing issues with infrastructure performance both in itself, and in the context of service delivery. 

Legacy/Mainframe

Mainframes in various form factors have hardly disappeared, and many of the world's most critical IT business services are still dependent on mainframe availability and performance. In evaluating this criterion, we looked at the established architectural support for mainframes, as well as the history of the vendor in mainframe support, and its currency in keeping up with new mainframe design and features. 

Application Performance and Availability Management

If IT is a business, then theoretically it has "products." And unquestionably the single most prominent products of IT are its application business services. In evaluating this criterion, we looked at breadth and depth of support for a wide range of application types, insight into application-to-application and application-to-infrastructure interdependencies, advanced levels of transaction awareness, and handshakes to support DevOps and business impact.

Internet of Things (IoT)

IoT is an emerging area, and one that many solutions in this report are architecturally designed to support — even if, in most cases, IoT has not yet been a priority in deployments. In evaluating this criterion, we looked at three things: architectural feasibility to support IoT data inputs, proof points of IoT use cases from actual deployments, and proactive support for IoT with unique out-of-the box functionality.

In my next blog I address our seven "scenarios" ranging from DevOps and SecOps to business impact and business alignment.

Read Part 3: Scenarios

Related Links

Hot Topics

The Latest

Keys to Building a Partner Ecosystem That Scales

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 ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

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 ...

AI Deepfakes: Rethinking Trust in the Workplace

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 ...

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 ...

5 Takeaways from the Observability Forecast for Media and Entertainment

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 ...

Why Moving Off the Cloud Is the Easy Part and What Comes Next Is Where Things Get Hard

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 ...

If You Are Looking to Invest in Advanced Analytics for IT, Exactly What Should You Be Shopping For? Part 2: Environments

Dennis Drogseth

EMA published the AIA buyer's guide — directed at helping IT invest in Advanced IT Analytics(AIA), what the industry more commonly calls "Operational Analytics." We created what we called "Shopping Cart Criteria" based on our prior research on AIA adoptions over the past three years. We divided the sixteen shopping cart criteria into three parts: Cost advantage, Environments and Scenarios.

Start with Part 1: Cost Advantage

In this blog, I will address environments. Environments, as indicated by the list below, indicate "where" the AIA solutions we investigated can be applied. All 13 solutions supported cloud for performance, core infrastructure, and application performance and availability. Mainframe had the support of six of our respondents, and IoT and cloud for change and capacity were not yet prime areas of focus for most of the vendors in our guide.

■ Cloud for Performance Management

■ Cloud for Change/Capacity/Cost Optimization

■ Core Infrastructure (Network/Data Center)

■ Legacy/Mainframe

■ Application Performance and Availability Management

■ Internet of Things (IoT)

Cloud for Performance Management

Here we addressed cloud in all its forms, including public cloud, virtualization, microservices and containers, and hybrid cloud environments — with a focus on performance management of IT services and their associated infrastructure. We saw fairly pervasive support for AWS, Azure, Docker and containers, and microservices. Present but less prevalent was support for Google Cloud, IBM Bluemix, Rackspace and Fujitsu Cloud. We also saw integrations with Pivotal Cloud Foundry in support of DevOps initiatives.

Cloud for Change/Capacity/Cost Optimization

Capacity and cost decisions are often joined at the hip in dealing with planning deployment choices across public and private cloud. These decisions can also significantly impact performance. And in fact, in last year's AIA research, optimizing cloud resources generally ranked higher than pure-play performance management in AIA requirements. This unique but critical area was addressed proactively by several of the vendors in this report, especially those with integrations for capacity analytics, and in two cases even cloud-related pricing models.

Core Infrastructure (Network/Data Center)

All of the solutions represented in this report were directed to some degree at cross-domain operational needs. But the approach they took varied dramatically. In evaluating ratings associated with this criterion, we looked for breadth of coverage, relevant stakeholder support, and breadth of capabilities for assessing issues with infrastructure performance both in itself, and in the context of service delivery. 

Legacy/Mainframe

Mainframes in various form factors have hardly disappeared, and many of the world's most critical IT business services are still dependent on mainframe availability and performance. In evaluating this criterion, we looked at the established architectural support for mainframes, as well as the history of the vendor in mainframe support, and its currency in keeping up with new mainframe design and features. 

Application Performance and Availability Management

If IT is a business, then theoretically it has "products." And unquestionably the single most prominent products of IT are its application business services. In evaluating this criterion, we looked at breadth and depth of support for a wide range of application types, insight into application-to-application and application-to-infrastructure interdependencies, advanced levels of transaction awareness, and handshakes to support DevOps and business impact.

Internet of Things (IoT)

IoT is an emerging area, and one that many solutions in this report are architecturally designed to support — even if, in most cases, IoT has not yet been a priority in deployments. In evaluating this criterion, we looked at three things: architectural feasibility to support IoT data inputs, proof points of IoT use cases from actual deployments, and proactive support for IoT with unique out-of-the box functionality.

In my next blog I address our seven "scenarios" ranging from DevOps and SecOps to business impact and business alignment.

Read Part 3: Scenarios

Related Links

Hot Topics

The Latest

Keys to Building a Partner Ecosystem That Scales

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 ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

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 ...

AI Deepfakes: Rethinking Trust in the Workplace

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 ...

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 ...

5 Takeaways from the Observability Forecast for Media and Entertainment

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 ...

Why Moving Off the Cloud Is the Easy Part and What Comes Next Is Where Things Get Hard

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 ...