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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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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

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 ...