Advanced IT Analytics: Making it Simpler to Optimize What's More Complex

Dennis Drogseth

The growing market for analytics in IT is one of the more exciting areas to watch in the technology industry. Exciting because of the variety and types of vendor innovation in this area. And exciting as well because our research indicates the adoption of advanced IT analytics supports data sharing and joint decision making in a way that's catalytic for both IT and digital transformation.

On the other hand, IT analytics are not necessarily a panacea. Some of the offerings, rich in potential, are also so rich in complexity that actual use case justification and time to value can create more of a wall blocking forward progress than a bridge crossing the IT-to-business divide.

Moreover, many IT organizations are still resolutely in build-your-own mode using backend data lakes and homemade analytic techniques that can create another steep impasse. In fact, EMA's advanced IT analytics research shows that only 37% of IT organizations are seeking primarily a third-party source for advanced IT analytics, with 39% taking a primarily in-house approach and 24% claiming an even mix of both. When asked what the primary obstacle was to going forward with advanced analytics, the top-line answer was the technology products are not yet fully baked.

Part of the problem in the market (using the term loosely because IT analytics really span multiple markets) is understanding use case. Too many analytics initiatives are focused on data collection without a clear sense of priority, relevance or value. By way of analogy, this is something we saw and still see in our consulting in support of CMDB and CMS solutions. The first thing you want to ask yourself, is what do you want to achieve? What are your (and your organizations') priorities?

Are they, for instance…

■ Performance and availability management across the application infrastructure?

■ Support for understanding the impacts of change and optimizing change for performance?

■ Optimizing cloud resources (public and or private) for service delivery, value and cost — including insights into capacity and usage?

■ Optimizing IT for DevOps and agile efficiencies?

■ Integrated security with performance and change management (SecOps)?

■ Or financial optimization of IT across the board in terms of cost and value from an OpEx and CapEx perspective?

And this list is far from complete.

The truth is, in many cases some of the same data can be applied to virtually all of the above use cases. But the truth is also that to maximize the value of those data interdependencies, significant levels of maturity in reporting and analysis, and flexibility in deployment, are 100% required.

Based on our research, the most in-demand set of objectives for IT analytics is something of a mosaic. It combines performance and availability management, with insights into infrastructure utilization, along with support for cloud migration and DevOps. And in this, increasingly, lurk security concerns as well.

For instance, the top five objectives in leveraging IT analytics in support of cloud were:

■ Improved network security

■ Hybrid cloud optimization

■ Compliance

■ Integrated security and performance

■ Real-time service performance

Note how intertwined security and performance are, along with needs for optimization and real-time insights.

When it came to issues surrounding leveraging AIA for optimizing change, cloud migration dominated — with three of the first four areas of concern, including: public cloud efficiencies, internal cloud efficiencies, and hybrid cloud efficiencies — all virtually tied. The first-ranked issue for optimizing change had to do with managing data for consistency, currency and accuracy.

When it came to DevOps and agile, tops on the IT analytics agenda were:

■ Optimizing application performance by providing feedback to development from production.

■ Minimizing the time developers spend troubleshooting production performance issues.

■ Supporting application developers directly (with performance insights).

I'd also like to point out that selecting advanced IT analytics solutions requires planning for you to meet your own unique requirements. These are never generic, and fortunately the emerging choices are far from generic as well. Moreover, effectively deploying a transformative analytic technology requires a willingness across IT to work in new, more collaborative and more efficient ways.

With all this in mind, I'll be looking more closely at how advanced analytics for IT are being designed, deployed and adopted in the coming months. So stay tuned. There's more to come.

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Advanced IT Analytics: Making it Simpler to Optimize What's More Complex

Dennis Drogseth

The growing market for analytics in IT is one of the more exciting areas to watch in the technology industry. Exciting because of the variety and types of vendor innovation in this area. And exciting as well because our research indicates the adoption of advanced IT analytics supports data sharing and joint decision making in a way that's catalytic for both IT and digital transformation.

On the other hand, IT analytics are not necessarily a panacea. Some of the offerings, rich in potential, are also so rich in complexity that actual use case justification and time to value can create more of a wall blocking forward progress than a bridge crossing the IT-to-business divide.

Moreover, many IT organizations are still resolutely in build-your-own mode using backend data lakes and homemade analytic techniques that can create another steep impasse. In fact, EMA's advanced IT analytics research shows that only 37% of IT organizations are seeking primarily a third-party source for advanced IT analytics, with 39% taking a primarily in-house approach and 24% claiming an even mix of both. When asked what the primary obstacle was to going forward with advanced analytics, the top-line answer was the technology products are not yet fully baked.

Part of the problem in the market (using the term loosely because IT analytics really span multiple markets) is understanding use case. Too many analytics initiatives are focused on data collection without a clear sense of priority, relevance or value. By way of analogy, this is something we saw and still see in our consulting in support of CMDB and CMS solutions. The first thing you want to ask yourself, is what do you want to achieve? What are your (and your organizations') priorities?

Are they, for instance…

■ Performance and availability management across the application infrastructure?

■ Support for understanding the impacts of change and optimizing change for performance?

■ Optimizing cloud resources (public and or private) for service delivery, value and cost — including insights into capacity and usage?

■ Optimizing IT for DevOps and agile efficiencies?

■ Integrated security with performance and change management (SecOps)?

■ Or financial optimization of IT across the board in terms of cost and value from an OpEx and CapEx perspective?

And this list is far from complete.

The truth is, in many cases some of the same data can be applied to virtually all of the above use cases. But the truth is also that to maximize the value of those data interdependencies, significant levels of maturity in reporting and analysis, and flexibility in deployment, are 100% required.

Based on our research, the most in-demand set of objectives for IT analytics is something of a mosaic. It combines performance and availability management, with insights into infrastructure utilization, along with support for cloud migration and DevOps. And in this, increasingly, lurk security concerns as well.

For instance, the top five objectives in leveraging IT analytics in support of cloud were:

■ Improved network security

■ Hybrid cloud optimization

■ Compliance

■ Integrated security and performance

■ Real-time service performance

Note how intertwined security and performance are, along with needs for optimization and real-time insights.

When it came to issues surrounding leveraging AIA for optimizing change, cloud migration dominated — with three of the first four areas of concern, including: public cloud efficiencies, internal cloud efficiencies, and hybrid cloud efficiencies — all virtually tied. The first-ranked issue for optimizing change had to do with managing data for consistency, currency and accuracy.

When it came to DevOps and agile, tops on the IT analytics agenda were:

■ Optimizing application performance by providing feedback to development from production.

■ Minimizing the time developers spend troubleshooting production performance issues.

■ Supporting application developers directly (with performance insights).

I'd also like to point out that selecting advanced IT analytics solutions requires planning for you to meet your own unique requirements. These are never generic, and fortunately the emerging choices are far from generic as well. Moreover, effectively deploying a transformative analytic technology requires a willingness across IT to work in new, more collaborative and more efficient ways.

With all this in mind, I'll be looking more closely at how advanced analytics for IT are being designed, deployed and adopted in the coming months. So stay tuned. There's more to come.

Related Links

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Keys to Building a Partner Ecosystem That Scales

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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 ...

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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 ...

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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 ...

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