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If You Are Looking to Invest in Advanced Analytics for IT, Exactly What Should You Be Shopping For? Part 3: Scenarios

Dennis Drogseth

This is the sixth in my series of blogs inspired by EMA's AIA buyer's guide — directed at helping IT invest in Advanced IT Analytics (AIA), what the industry more commonly calls "Operational Analytics." The goal was to create a "Consumer's Report" approach. And to do that we took it one step further. We created what we called "Shopping Cart Criteria" based on our prior research on AIA adoptions over the past three years.

My last two blogs looked at Cost Advantage parameters and Environments.

Start with Part 1: Cost Advantage

Start with Part 2: Environments

Cost Advantage included:

■ Time to Value

■ Administration and Support

■ Toolset Consolidation

Environments included:

■ 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)

In this blog, I examine scenario-related shopping cart objectives for AIA.

At EMA, we evaluated seven unique scenarios relevant to AIA adoptions. Our scenarios included agile/DevOps, Integrated security, change impact awareness, capacity optimization, business impact, business alignment and unifying IT.

Agile/DevOps

DevOps is a key area of opportunity.

We found that some vendors had made DevOps a clear and proven focus, whereas for others it was more a direction of future interest. But DevOps is a key area of opportunity. In prior research we saw that 69 percent of our respondents were looking to link their AIA investments to DevOps requirements.

In evaluating this scenario, we looked at discreet requirements in terms of agile/DevOps needs including support for both development professionals and quality assurance and testing (QA Test). To do this we considered overall APM strengths, application change impact awareness, and proof points in terms of actual deployment scenarios. We also targeted analytic insight into digital experience management across the full application lifecycle.

Integrated Security

Integrated security was another scenario where almost all the vendors provided basic functionality, but only a few had made it a primary focus. However, based on recent EMA research in both analytics and SecOps, integrated security is a very high-growth opportunity, with surprisingly strong priorities among both operations and security stakeholders for shared data, shared analytics and shared insights.

In evaluating this criterion, we looked for bidirectional security-related toolset integrations for analysis and visualization relevant to SecOps requirements. We also considered appropriate stakeholder support, and proof points in terms of actual deployments.

Change Impact Awareness

It is well known that performance management and change impact awareness go hand in hand. To be "outstanding" in this area, however, requires many fundamentals. Among them are:

■ analytic awareness of changes in performance-related metrics

■ insight into dependencies to see how and where abnormalities are most likely to impact a critical business service

■ insights into change management procedures and histories so that timely correlations can be proactively understood between change histories and performance and availability metrics

In determining a rating for change impact awareness, we also considered integrations with IT service management (ITSM) sources, CMDBs, CMSs, and ADDM capabilities.

Capacity Optimization

We reserved this scenario for those vendors that went a step beyond change impact awareness. In other words, no vendor could excel here without at least being "strong" in change impact awareness. Capacity Optimization featured those vendors with significant integrations with capacity analytics and automation to make all the requisite connections between performance, change, capacity, and, ideally, cost. In multiple research initiatives, we've seen capacity and even cost analytics stand out as a leading priority for AIA — especially when it comes to optimizing the move to cloud.

Business Impact

In the age of digital transformation, little could be more important than energizing the handshake between IT service delivery and business outcomes. In evaluating this criterion, we considered basic strengths in transactional performance and support for business stakeholders. The highest ratings required data and analytics integrating business and IT sources, as well as common dashboard visualizations of business outcomes such as revenue, business process optimization and conversions from competitive websites.

Business Alignment

Business impact factors into business alignment, but data sharing for optimal business alignment also requires reports and visualization that promote IT-to-business dialog along multiple fronts in a current and dynamic way. In evaluating this scenario, we looked at well-defined stakeholder support for business as well as IT stakeholders, well-evolved dashboarding and workflows, and at least some strengths in unifying IT.

Unifying IT

Unifying IT, much like toolset consolidation, is something of a Holy Grail in value when it comes to investing in AIA. Advanced IT analytics can enable a common layer of efficiency that helps to promote better processes, dialog, data sharing, and automation across virtually all of IT — not just operations. Integrations and stakeholder support were paramount for this scenario, as was social IT and mobile support. For proof points, we looked for real-world examples where a wide range of IT stakeholders were in fact beginning to work differently and more effectively together.

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Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...

If You Are Looking to Invest in Advanced Analytics for IT, Exactly What Should You Be Shopping For? Part 3: Scenarios

Dennis Drogseth

This is the sixth in my series of blogs inspired by EMA's AIA buyer's guide — directed at helping IT invest in Advanced IT Analytics (AIA), what the industry more commonly calls "Operational Analytics." The goal was to create a "Consumer's Report" approach. And to do that we took it one step further. We created what we called "Shopping Cart Criteria" based on our prior research on AIA adoptions over the past three years.

My last two blogs looked at Cost Advantage parameters and Environments.

Start with Part 1: Cost Advantage

Start with Part 2: Environments

Cost Advantage included:

■ Time to Value

■ Administration and Support

■ Toolset Consolidation

Environments included:

■ 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)

In this blog, I examine scenario-related shopping cart objectives for AIA.

At EMA, we evaluated seven unique scenarios relevant to AIA adoptions. Our scenarios included agile/DevOps, Integrated security, change impact awareness, capacity optimization, business impact, business alignment and unifying IT.

Agile/DevOps

DevOps is a key area of opportunity.

We found that some vendors had made DevOps a clear and proven focus, whereas for others it was more a direction of future interest. But DevOps is a key area of opportunity. In prior research we saw that 69 percent of our respondents were looking to link their AIA investments to DevOps requirements.

In evaluating this scenario, we looked at discreet requirements in terms of agile/DevOps needs including support for both development professionals and quality assurance and testing (QA Test). To do this we considered overall APM strengths, application change impact awareness, and proof points in terms of actual deployment scenarios. We also targeted analytic insight into digital experience management across the full application lifecycle.

Integrated Security

Integrated security was another scenario where almost all the vendors provided basic functionality, but only a few had made it a primary focus. However, based on recent EMA research in both analytics and SecOps, integrated security is a very high-growth opportunity, with surprisingly strong priorities among both operations and security stakeholders for shared data, shared analytics and shared insights.

In evaluating this criterion, we looked for bidirectional security-related toolset integrations for analysis and visualization relevant to SecOps requirements. We also considered appropriate stakeholder support, and proof points in terms of actual deployments.

Change Impact Awareness

It is well known that performance management and change impact awareness go hand in hand. To be "outstanding" in this area, however, requires many fundamentals. Among them are:

■ analytic awareness of changes in performance-related metrics

■ insight into dependencies to see how and where abnormalities are most likely to impact a critical business service

■ insights into change management procedures and histories so that timely correlations can be proactively understood between change histories and performance and availability metrics

In determining a rating for change impact awareness, we also considered integrations with IT service management (ITSM) sources, CMDBs, CMSs, and ADDM capabilities.

Capacity Optimization

We reserved this scenario for those vendors that went a step beyond change impact awareness. In other words, no vendor could excel here without at least being "strong" in change impact awareness. Capacity Optimization featured those vendors with significant integrations with capacity analytics and automation to make all the requisite connections between performance, change, capacity, and, ideally, cost. In multiple research initiatives, we've seen capacity and even cost analytics stand out as a leading priority for AIA — especially when it comes to optimizing the move to cloud.

Business Impact

In the age of digital transformation, little could be more important than energizing the handshake between IT service delivery and business outcomes. In evaluating this criterion, we considered basic strengths in transactional performance and support for business stakeholders. The highest ratings required data and analytics integrating business and IT sources, as well as common dashboard visualizations of business outcomes such as revenue, business process optimization and conversions from competitive websites.

Business Alignment

Business impact factors into business alignment, but data sharing for optimal business alignment also requires reports and visualization that promote IT-to-business dialog along multiple fronts in a current and dynamic way. In evaluating this scenario, we looked at well-defined stakeholder support for business as well as IT stakeholders, well-evolved dashboarding and workflows, and at least some strengths in unifying IT.

Unifying IT

Unifying IT, much like toolset consolidation, is something of a Holy Grail in value when it comes to investing in AIA. Advanced IT analytics can enable a common layer of efficiency that helps to promote better processes, dialog, data sharing, and automation across virtually all of IT — not just operations. Integrations and stakeholder support were paramount for this scenario, as was social IT and mobile support. For proof points, we looked for real-world examples where a wide range of IT stakeholders were in fact beginning to work differently and more effectively together.

Hot Topics

The Latest

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...