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Advanced Operations Analytics - Use Cases and Perspectives

Dennis Drogseth

Last month I introduced some recent — and I believe highly significant — research into the area of what EMA calls "Advanced Operations Analytics" or AOA. In that blog, I defined AOA as "big data for IT" and went on to explain how it transcends operations to include all of IT as well as some business constituents. In parallel, AOA also supports many multiple use cases.

Now I’d like to share some of the "use case" insights and perspectives we learned from the research.

Probably the first thing to say is that people don’t deploy AOA to do just one thing. That’s something for a monitoring tool or some other more traditional investment to do.

Our use-case picture for AOA has multiple dimensions. For instance when asked about benefits such as ...

■ Faster time to repair problems

■ Better optimization of IT assets

■ Gaining real-time and historical trends on IT services

■ Faster time to deliver IT services

■ Faster identification of advanced threats and internal security threats

■ More efficient use of infrastructure capacity

■ Superior financial planning for IT as a business

... the average respondent checked five. In other words, the average respondent wanted five benefits of the kinds indicated above from his or her AOA investment. And in fact some respondents checked as many as ten and even thirteen.

We also looked at DevOps and cloud. Nearly two-thirds (65%) of respondents, planned to support DevOps requirements through AOA. This was predominantly through either direct support for the application development process, or for minimizing the time developers would have to spend troubleshooting production problems. Other areas of interest included providing feedback to development from production issues, and supporting more effective hand-offs between development and operations.

When it came to cloud — the data showed that those who viewed themselves as "extremely successful" in AOA were 20 times more likely to be "very successful" in their hybrid cloud adoptions — than those who were only moderately or less successful in AOA. That’s not a typo — it’s twenty times! This is data speaking, not a holy writ, so it should be taken, like all data, with an appropriate grain of salt. But there are lots of good reasons — just given some of the use cases examined above — why this might be so.

Cloud priorities included security and real-time service performance as the not surprising leads, but coordinated business impact, financial optimization and capacity optimization were also strong.

Another way to look at use case is to ask WHO should be supported by the AOA investment.

The top five domain roles our respondents wanted to support with AOA were:

1. Security

2. Network

3. Database

4. Application development (two percentage points ahead of application management)

5. Storage

The top cross-domain roles for AOA were:

1. Service delivery (application services across the infrastructure)

2. Capacity planning

3. Infrastructure planning

4. Configuration management

5. Change management

And the top non-IT roles for AOA were:

1. Business development and planning

2. Line of business

3. On-line operations

4. User experience management

5. Supply chain management

When we did the numbers, we saw that the average respondent had indicated 4 domain-specific roles, 3 cross-domain roles and 2 non-IT roles. That’s a total of nine roles for an AOA investment — once again proof of what we might call "use case diversity."

In yet another look at the many faces of AOA deployments, we channeled the data to see how one skill set might differ from another in priorities. The results below are focused on just that — how each group contrasts with the other groups, as opposed to a total assessment of each group in and of itself.

■ Security: Consistently prioritizes security values, favors predictive trending, events and time series data, values real-time insights and historical trends on IT services.

■ ITSM/service desk: Favors reports on asset and financial optimization, and better alignment of IT service and business performance.

■ Software development: Prioritizes systems availability and performance, if/then change impact, log files, integrations with DevOps tools and application dependency mapping for performance.

■ Application management: Prioritizes application optimization, problem isolation in systems, business events and time series data, app performance, event management and operational dashboard integrations, and ADDM for performance.

■ Change and configuration management: Prioritizes configuration and change management effectiveness, isolating problems in provisioning applications, if/then change impact analytics, capacity planning analytics, and CMDB/CMS /ADDM linkages.

■ IT asset and financial optimization: Prioritizes business activity metrics, CapEx savings, OpEx savings, application usage for cost, business process impacts and supply-chain related outcomes, and CMDB/CMS and financial planning integrations.

Hopefully this gives you some sense of "AOA diversity" or as we like to say: The Many Faces of Advanced Operations Analytics. Speaking of the latter — the full report with far more context, insight and data can be found on the EMA website.

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Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

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In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Advanced Operations Analytics - Use Cases and Perspectives

Dennis Drogseth

Last month I introduced some recent — and I believe highly significant — research into the area of what EMA calls "Advanced Operations Analytics" or AOA. In that blog, I defined AOA as "big data for IT" and went on to explain how it transcends operations to include all of IT as well as some business constituents. In parallel, AOA also supports many multiple use cases.

Now I’d like to share some of the "use case" insights and perspectives we learned from the research.

Probably the first thing to say is that people don’t deploy AOA to do just one thing. That’s something for a monitoring tool or some other more traditional investment to do.

Our use-case picture for AOA has multiple dimensions. For instance when asked about benefits such as ...

■ Faster time to repair problems

■ Better optimization of IT assets

■ Gaining real-time and historical trends on IT services

■ Faster time to deliver IT services

■ Faster identification of advanced threats and internal security threats

■ More efficient use of infrastructure capacity

■ Superior financial planning for IT as a business

... the average respondent checked five. In other words, the average respondent wanted five benefits of the kinds indicated above from his or her AOA investment. And in fact some respondents checked as many as ten and even thirteen.

We also looked at DevOps and cloud. Nearly two-thirds (65%) of respondents, planned to support DevOps requirements through AOA. This was predominantly through either direct support for the application development process, or for minimizing the time developers would have to spend troubleshooting production problems. Other areas of interest included providing feedback to development from production issues, and supporting more effective hand-offs between development and operations.

When it came to cloud — the data showed that those who viewed themselves as "extremely successful" in AOA were 20 times more likely to be "very successful" in their hybrid cloud adoptions — than those who were only moderately or less successful in AOA. That’s not a typo — it’s twenty times! This is data speaking, not a holy writ, so it should be taken, like all data, with an appropriate grain of salt. But there are lots of good reasons — just given some of the use cases examined above — why this might be so.

Cloud priorities included security and real-time service performance as the not surprising leads, but coordinated business impact, financial optimization and capacity optimization were also strong.

Another way to look at use case is to ask WHO should be supported by the AOA investment.

The top five domain roles our respondents wanted to support with AOA were:

1. Security

2. Network

3. Database

4. Application development (two percentage points ahead of application management)

5. Storage

The top cross-domain roles for AOA were:

1. Service delivery (application services across the infrastructure)

2. Capacity planning

3. Infrastructure planning

4. Configuration management

5. Change management

And the top non-IT roles for AOA were:

1. Business development and planning

2. Line of business

3. On-line operations

4. User experience management

5. Supply chain management

When we did the numbers, we saw that the average respondent had indicated 4 domain-specific roles, 3 cross-domain roles and 2 non-IT roles. That’s a total of nine roles for an AOA investment — once again proof of what we might call "use case diversity."

In yet another look at the many faces of AOA deployments, we channeled the data to see how one skill set might differ from another in priorities. The results below are focused on just that — how each group contrasts with the other groups, as opposed to a total assessment of each group in and of itself.

■ Security: Consistently prioritizes security values, favors predictive trending, events and time series data, values real-time insights and historical trends on IT services.

■ ITSM/service desk: Favors reports on asset and financial optimization, and better alignment of IT service and business performance.

■ Software development: Prioritizes systems availability and performance, if/then change impact, log files, integrations with DevOps tools and application dependency mapping for performance.

■ Application management: Prioritizes application optimization, problem isolation in systems, business events and time series data, app performance, event management and operational dashboard integrations, and ADDM for performance.

■ Change and configuration management: Prioritizes configuration and change management effectiveness, isolating problems in provisioning applications, if/then change impact analytics, capacity planning analytics, and CMDB/CMS /ADDM linkages.

■ IT asset and financial optimization: Prioritizes business activity metrics, CapEx savings, OpEx savings, application usage for cost, business process impacts and supply-chain related outcomes, and CMDB/CMS and financial planning integrations.

Hopefully this gives you some sense of "AOA diversity" or as we like to say: The Many Faces of Advanced Operations Analytics. Speaking of the latter — the full report with far more context, insight and data can be found on the EMA website.

Hot Topics

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...