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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.

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

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.

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...