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A Fresh Look at Advanced IT Analytics - Why the Industry Continues to Get it Wrong

Dennis Drogseth

Buzzwords in tech (like politics) do a lot to call attention to themselves, but they don't always do a very good job of calling attention to the truth. Reality, after all, is often mystifyingly multi-dimensional, while "what's hot" tends to become linear and often cartoonish.

Over the last few years I've tried to represent a clear and growing trend that I've come to call "Advanced IT Analytics" or AIA, in contrast with other industry terms such as "IT Operations Analytics" and "Big Data". My issue with the former is that AIA isn't restricted to operations, but can reach out across all of IT, including executives, service desk and ITSM teams, development and even non-IT business stakeholders. It is multi-use case and multi-stakeholder in value, as the same data mosaic may serve performance, security, change management, and DevOps requirements, while also supporting business stakeholders in areas such as customer experience and market planning.

My issue with "big data" is that when it comes to AIA, just taking big data by itself misses the point. While AIA often thrives on significant volumes of data across multiple domains, what's key to the more progressive AIA solutions are its powers to interrelate and analyze data with a clear eye to meaningful outcomes. Genetically (taking the term metaphorically) I would argue that AIA is not primarily an outgrowth of business intelligence and big data pots, including NoSQL options like Hadoop and Cassandra. Rather, AIA grew out of advanced self-learning tools targeting far more finite data sources, such as time-series data directed at service performance outcomes, or even advanced event correlation.

What made AIA distinctive early on was its ability to assimilate data from many different toolsets and create a common fabric of intelligence that crossed domain silos. These tools often had surprising options for predicting future outcomes and discovering patterns that were not looked for or sought after. They also had political and social challenges from IT siloed communities refusing to give up their own siloed toolset preeminence or even share their data with others in IT. These benefits (and these political issues) continue even as AIA continues to evolve to include many new options, including big data pots in some cases.

What we're witnessing now is, I believe, a great deal of industry confusion about how to go about bringing advanced analytics to the IT community — aggravated inevitably by both marketing hype, and, sadly, boxed-in categories from the analyst community wedded far too much to technology and far too little to use case. AIA is, in fact, especially a challenge because it tends to support a diversity of use cases, making it less like a traditional market and more like an architectural revolution (or evolution) in next-generation business service management. Or given current buzzword pre-eminence, let's make that digital service management. At least here the buzzword really does have some genuine meaning and value.

So I'd like to go back to what I believe are AIA's roots. These include tiered or blended capabilities to assimilate data from many different sources — either from many different toolset investments (in recent research our respondents indicated 10-20 toolsets either directly or via an aggregated data store); and/or from a wide variety of sources ranging from transactional data including user and customer behaviors, to log files, to packets and wire data, to events, to Excel spreadsheets, and unstructured data as in text and social media.

What also distinguishes AIA is a unique ability to link critical IT business service interdependencies for both change and performance in context with event, time series, transaction and other data. While many of our research respondents sought out interdependency mapping within the analytics solution itself, probably the most frequent linkage in real adoptions comes from the application discovery and dependency (ADDM) arena, as well as newer, more dynamic instances of CMDBs and federated configuration management systems (CMSs).

The net values of good AIA solutions include much faster time to value and far less administrative overhead than massive data lakes that are created virtually as an end in themselves. The ability to assimilate many multiple "trusted sources" and discover new and unexpected values needn't be an investment in an army of white coats. It can be — in some cases at least — surprisingly dynamic and self-administrating.

This AIA tidal wave is still new. Still a relatively small and distant rise in the information technology ocean. Yet there are already a growing number of AIA innovators with different directions and focus — from cloud, to integrated DevOps and change management, to user and customer and digital experience optimization.

I will be presenting a webinar on November 10 — with a better chance to explain the values of tiered or blended AIA. And I'll be following up with some new research to be completed in Q1 of next year: "Advanced IT Analytics Part II: Deployment Priorities and Lessons Learned." Hopefully the data will reinforce what I believe should be AIA progress toward more effective advanced analytics for IT, and not a sudden dip into white-coated chaos. But then you never know — that's part of the appeal of doing research. Invariably, if it's any good, it will always teach you something new.

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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 ...

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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 ...

A Fresh Look at Advanced IT Analytics - Why the Industry Continues to Get it Wrong

Dennis Drogseth

Buzzwords in tech (like politics) do a lot to call attention to themselves, but they don't always do a very good job of calling attention to the truth. Reality, after all, is often mystifyingly multi-dimensional, while "what's hot" tends to become linear and often cartoonish.

Over the last few years I've tried to represent a clear and growing trend that I've come to call "Advanced IT Analytics" or AIA, in contrast with other industry terms such as "IT Operations Analytics" and "Big Data". My issue with the former is that AIA isn't restricted to operations, but can reach out across all of IT, including executives, service desk and ITSM teams, development and even non-IT business stakeholders. It is multi-use case and multi-stakeholder in value, as the same data mosaic may serve performance, security, change management, and DevOps requirements, while also supporting business stakeholders in areas such as customer experience and market planning.

My issue with "big data" is that when it comes to AIA, just taking big data by itself misses the point. While AIA often thrives on significant volumes of data across multiple domains, what's key to the more progressive AIA solutions are its powers to interrelate and analyze data with a clear eye to meaningful outcomes. Genetically (taking the term metaphorically) I would argue that AIA is not primarily an outgrowth of business intelligence and big data pots, including NoSQL options like Hadoop and Cassandra. Rather, AIA grew out of advanced self-learning tools targeting far more finite data sources, such as time-series data directed at service performance outcomes, or even advanced event correlation.

What made AIA distinctive early on was its ability to assimilate data from many different toolsets and create a common fabric of intelligence that crossed domain silos. These tools often had surprising options for predicting future outcomes and discovering patterns that were not looked for or sought after. They also had political and social challenges from IT siloed communities refusing to give up their own siloed toolset preeminence or even share their data with others in IT. These benefits (and these political issues) continue even as AIA continues to evolve to include many new options, including big data pots in some cases.

What we're witnessing now is, I believe, a great deal of industry confusion about how to go about bringing advanced analytics to the IT community — aggravated inevitably by both marketing hype, and, sadly, boxed-in categories from the analyst community wedded far too much to technology and far too little to use case. AIA is, in fact, especially a challenge because it tends to support a diversity of use cases, making it less like a traditional market and more like an architectural revolution (or evolution) in next-generation business service management. Or given current buzzword pre-eminence, let's make that digital service management. At least here the buzzword really does have some genuine meaning and value.

So I'd like to go back to what I believe are AIA's roots. These include tiered or blended capabilities to assimilate data from many different sources — either from many different toolset investments (in recent research our respondents indicated 10-20 toolsets either directly or via an aggregated data store); and/or from a wide variety of sources ranging from transactional data including user and customer behaviors, to log files, to packets and wire data, to events, to Excel spreadsheets, and unstructured data as in text and social media.

What also distinguishes AIA is a unique ability to link critical IT business service interdependencies for both change and performance in context with event, time series, transaction and other data. While many of our research respondents sought out interdependency mapping within the analytics solution itself, probably the most frequent linkage in real adoptions comes from the application discovery and dependency (ADDM) arena, as well as newer, more dynamic instances of CMDBs and federated configuration management systems (CMSs).

The net values of good AIA solutions include much faster time to value and far less administrative overhead than massive data lakes that are created virtually as an end in themselves. The ability to assimilate many multiple "trusted sources" and discover new and unexpected values needn't be an investment in an army of white coats. It can be — in some cases at least — surprisingly dynamic and self-administrating.

This AIA tidal wave is still new. Still a relatively small and distant rise in the information technology ocean. Yet there are already a growing number of AIA innovators with different directions and focus — from cloud, to integrated DevOps and change management, to user and customer and digital experience optimization.

I will be presenting a webinar on November 10 — with a better chance to explain the values of tiered or blended AIA. And I'll be following up with some new research to be completed in Q1 of next year: "Advanced IT Analytics Part II: Deployment Priorities and Lessons Learned." Hopefully the data will reinforce what I believe should be AIA progress toward more effective advanced analytics for IT, and not a sudden dip into white-coated chaos. But then you never know — that's part of the appeal of doing research. Invariably, if it's any good, it will always teach you something new.

Hot Topics

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 ...