Transforming Operations, and IT as a Whole, with the Right Technology Investments

Dennis Drogseth

In my last blog, I expressed my opinion that IT operations teams may be about to enjoy a renaissance rather than dismally fading away — but only if they adopt new ways of working, measuring themselves and interacting with business stakeholders.

Start with Is IT Operations Going Away or Is It Enjoying a Renaissance?

In this blog, I'd like to discuss how technology investments can help smooth the way toward operational transformation with a few examples from recent interviews. More specifically, I'd like to focus on three key areas of innovation, all in some way related to Advanced IT Analytics (what some in the industry call IT Operations Analytics or ITOA):

1. How Advanced IT Analytics (AIA) can pave the way

2. How next-generation ITSM promotes AIA-relevant process improvements

3. How AIA and service modeling can become a magic combination

These three areas of innovation are, admittedly, far from a complete list. But hopefully they will offer you a provocative place to start for seeking out transformative IT technologies.

How Advanced IT Analytics Can Pave the Way

Probably one of the hottest, most diverse and most misunderstood areas of technological innovation is AIA. There are a lot of reasons for this, but the most significant reason is that AIA is not a single market but a diverse landscape of options ranging from those bordering on traditional "big data" to those that are focused on real-time predictive insights with tiered approaches to data collection that typically include leveraging third-party tools. The variances here also embrace multiple use cases ranging from performance management to change and capacity optimization to financial optimization to integrated security concerns.

Here are just a few highlights from EMA's recent AIA research focusing on performance, change and capacity management:

■ AIA analytic heuristics range from anomaly detection to predictive trending to machine learning, to rule-based analytics to data mining — as just some examples. On average, our respondents wanted at least four different heuristics.

■ On average, respondents wanted their AIA investments to support 11 different roles (and many more stakeholders), including four domain-specific roles, four cross-domain roles (including executive IT), and three non-IT business roles.

■ The top three achieved benefits were with faster time to repair problems, faster time to deliver new services and more efficient use of cloud resources.

Quoting from a conversation earlier this year:

QUOTE #1: "The move [to advanced analytics] allowed us to unify our operations team with a single-pane-of-glass view and drill down so that we could share information more effectively. In the past, we caught only 3% of our problems proactively. Now that percentage went up to 88% … In effect, we are able to see everything we need to see to focus and resolve issues far more cohesively and dynamically than before."

How Next-Generation ITSM Promotes AIA-Relevant Process Improvements

Next-generation ITSM is in my view pivotal for both IT and IT operational transformation. So what are they? Next-generation ITSM teams are more progressively integrated with operations teams, more proactive, more likely to leverage and share analytics, more likely to provide workflows and automation that unify IT as a whole (as well as support enterprise business process needs), and more likely to provide meaningful metrics for IT efficiencies and governance than the more reactive ITSM teams of the past.

Following the AIA data path, we saw that 82% of our respondents indicated strong levels of ITSM/operations integration for shared advanced analytics!

Quoting from two other conversations earlier this year highlighting next-generation ITSM values:

QUOTE #2: "First and foremost we've been able to consolidate our processes for change, incident, and problem management across our entire operation by leveraging one single platform …"

QUOTE #3: "We've also enjoyed improved visibility into the impacts of changes on service performance and availability, so we can more quickly get to the root cause of many of the issues caused by changes and begin to automate fixes more consistently."

How AIA + Service Modeling Can Result in (at least a little) Magic

The AIA research showed that 96% of respondents wanted at least some level of modeled insight on interdependencies across the application infrastructure. Among the top three priorities were application-to-infrastructure, infrastructure-to-infrastructure, and application-to-application (application ecosystem) dependency insights.

Combining analytics with service modeling is a growth area in AIA, as analytics providers are becoming more effective in not only leveraging existing application dependency mapping solutions and even CMDB data, but also in creating their own unique approach to dependency modeling.

In another very recent dialog, I found some rather striking benefits when service modeling and analytics are combined:

QUOTE #4: "We estimate that we will be saving about $500,000 in the area of toolset consolidation … We were averaging 2.5 hours for MTTR … now it's about 38 minutes … You might say we never had eyes before. Now we have eyes."

In Summary

This is, admittedly, only a taste of what I've learned from research and conversations with IT about how technology can help to transform IT operations for the "brave new world" we live in. My focus on AIA, next-generation ITSM and service modeling was deliberate, as I see these as lying at the heart of the "operations renaissance." Also important are requirements for more advanced levels of automation and integrated support for security and operations. Superior digital experience management or end user experience management is also key.

The challenge in the market today is that while all these technologies are interdependent and mutually reinforcing, the innovations are coming from many different vendor sources. And true to form, many of these vendors are seeking to redefine the world around themselves in their marketing and messaging. Sorting through the pieces, and understanding where real value lies, takes time and patience and more than a little sober skepticism. But the innovations are real. And hopefully this blog can give you at least a hint — by category — of where to begin to look for them.

I deliberately kept the quotes anonymous on all fronts. If you'd like to see more information, then please check out our EMA library:

■ quote 1

■ quotes 2 and 3

■ quote 4

Image removed.

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Transforming Operations, and IT as a Whole, with the Right Technology Investments

Dennis Drogseth

In my last blog, I expressed my opinion that IT operations teams may be about to enjoy a renaissance rather than dismally fading away — but only if they adopt new ways of working, measuring themselves and interacting with business stakeholders.

Start with Is IT Operations Going Away or Is It Enjoying a Renaissance?

In this blog, I'd like to discuss how technology investments can help smooth the way toward operational transformation with a few examples from recent interviews. More specifically, I'd like to focus on three key areas of innovation, all in some way related to Advanced IT Analytics (what some in the industry call IT Operations Analytics or ITOA):

1. How Advanced IT Analytics (AIA) can pave the way

2. How next-generation ITSM promotes AIA-relevant process improvements

3. How AIA and service modeling can become a magic combination

These three areas of innovation are, admittedly, far from a complete list. But hopefully they will offer you a provocative place to start for seeking out transformative IT technologies.

How Advanced IT Analytics Can Pave the Way

Probably one of the hottest, most diverse and most misunderstood areas of technological innovation is AIA. There are a lot of reasons for this, but the most significant reason is that AIA is not a single market but a diverse landscape of options ranging from those bordering on traditional "big data" to those that are focused on real-time predictive insights with tiered approaches to data collection that typically include leveraging third-party tools. The variances here also embrace multiple use cases ranging from performance management to change and capacity optimization to financial optimization to integrated security concerns.

Here are just a few highlights from EMA's recent AIA research focusing on performance, change and capacity management:

■ AIA analytic heuristics range from anomaly detection to predictive trending to machine learning, to rule-based analytics to data mining — as just some examples. On average, our respondents wanted at least four different heuristics.

■ On average, respondents wanted their AIA investments to support 11 different roles (and many more stakeholders), including four domain-specific roles, four cross-domain roles (including executive IT), and three non-IT business roles.

■ The top three achieved benefits were with faster time to repair problems, faster time to deliver new services and more efficient use of cloud resources.

Quoting from a conversation earlier this year:

QUOTE #1: "The move [to advanced analytics] allowed us to unify our operations team with a single-pane-of-glass view and drill down so that we could share information more effectively. In the past, we caught only 3% of our problems proactively. Now that percentage went up to 88% … In effect, we are able to see everything we need to see to focus and resolve issues far more cohesively and dynamically than before."

How Next-Generation ITSM Promotes AIA-Relevant Process Improvements

Next-generation ITSM is in my view pivotal for both IT and IT operational transformation. So what are they? Next-generation ITSM teams are more progressively integrated with operations teams, more proactive, more likely to leverage and share analytics, more likely to provide workflows and automation that unify IT as a whole (as well as support enterprise business process needs), and more likely to provide meaningful metrics for IT efficiencies and governance than the more reactive ITSM teams of the past.

Following the AIA data path, we saw that 82% of our respondents indicated strong levels of ITSM/operations integration for shared advanced analytics!

Quoting from two other conversations earlier this year highlighting next-generation ITSM values:

QUOTE #2: "First and foremost we've been able to consolidate our processes for change, incident, and problem management across our entire operation by leveraging one single platform …"

QUOTE #3: "We've also enjoyed improved visibility into the impacts of changes on service performance and availability, so we can more quickly get to the root cause of many of the issues caused by changes and begin to automate fixes more consistently."

How AIA + Service Modeling Can Result in (at least a little) Magic

The AIA research showed that 96% of respondents wanted at least some level of modeled insight on interdependencies across the application infrastructure. Among the top three priorities were application-to-infrastructure, infrastructure-to-infrastructure, and application-to-application (application ecosystem) dependency insights.

Combining analytics with service modeling is a growth area in AIA, as analytics providers are becoming more effective in not only leveraging existing application dependency mapping solutions and even CMDB data, but also in creating their own unique approach to dependency modeling.

In another very recent dialog, I found some rather striking benefits when service modeling and analytics are combined:

QUOTE #4: "We estimate that we will be saving about $500,000 in the area of toolset consolidation … We were averaging 2.5 hours for MTTR … now it's about 38 minutes … You might say we never had eyes before. Now we have eyes."

In Summary

This is, admittedly, only a taste of what I've learned from research and conversations with IT about how technology can help to transform IT operations for the "brave new world" we live in. My focus on AIA, next-generation ITSM and service modeling was deliberate, as I see these as lying at the heart of the "operations renaissance." Also important are requirements for more advanced levels of automation and integrated support for security and operations. Superior digital experience management or end user experience management is also key.

The challenge in the market today is that while all these technologies are interdependent and mutually reinforcing, the innovations are coming from many different vendor sources. And true to form, many of these vendors are seeking to redefine the world around themselves in their marketing and messaging. Sorting through the pieces, and understanding where real value lies, takes time and patience and more than a little sober skepticism. But the innovations are real. And hopefully this blog can give you at least a hint — by category — of where to begin to look for them.

I deliberately kept the quotes anonymous on all fronts. If you'd like to see more information, then please check out our EMA library:

■ quote 1

■ quotes 2 and 3

■ quote 4

Image removed.

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 ...