Are You Thinking of Investing in Advanced IT Analytics?

(Hint - it's probably a good idea)
Dennis Drogseth

There may be no more critical emerging technology for IT organizations in the digital age than advanced IT analytics (AIA) — most commonly called “operational analytics.” EMA prefers the term “advanced IT analytics” because these investments, while often centered in operations, can go far beyond classic IT operations to support IT service management (ITSM) teams, development, and the IT executive suite, as well as a growing range of business stakeholders.

AIA is also an area of incredible industry innovation. So far, at least, the leading AIA vendors have not been constrained by rigid technology-driven market definitions of the kind that, for instance, nearly doomed the evolution of configuration management databases (CMDBs).

Instead, AIA solutions are evolving in multiple flavors with a growing range of benefits — most often centered in performance and availability management for IT, but also, and increasingly, addressing change impact awareness, integrated support for change management, and even integrated capabilities for capacity planning and analytics.

It is with this in mind that EMA is launching what we believe is the first ever buyer's guide for AIA adoption: Leaders in Advanced IT Analytics: A Buyer's Guide for Investing in Innovation. To do this, EMA has invited 13 vendors — each with a distinctive footprint — which have met the following set of requirements that made them candidates for this guide.

■ Support for performance, availability and change impact awarenesswith both real-time and historical insights. We also looked for corollaries in change management, capacity planning and capacity optimization when appropriate.

■ Assimilation of data from cross-domain sources in high data volumesfor cross-domain insights, as well as insights into application/infrastructure interdependencies. These interdependency insights can be purely analytic, or affiliated with topology and/or modeling.

■ The ability to access multiple data types, e.g. events, KPIs, logs, flow, configuration data, etc.

■ Capabilities for self-learning, to deliver predictive, and/or prescriptive, and/or if/then actionable insights.

■ Support for a wide range of advanced heuristics such as multivariate analysis, machine learning, streaming data, tiered analytics, cognitive analytics, etc.

■ Use as strategic overlays that may assimilate or consolidate multiple monitoring investments.

■ Support for private cloud, public cloud, as well as hybrid/legacy environments.

Moreover, all 13 vendors have been carefully assessed and vetted in working with EMA, including validation through dialogs with customer deployments.

Who's Not Included?

This buyer's guide is directed at what EMA believes is the AIA heartland, but it is also a first step in charting the broader AIA landscape.

Saved for future evaluations are:

■ AIA solutions that do not support real-time as well as predictive performance-related insights.

■ Cross-domain AIA focused on single targeted data collection — most notably wire, packet or flow data.

■ Monitoring suites with growing investments in analytics, but which don't yet meet all the criteria listed above.

■ Domain-specific AIA — targeted at specific use cases in systems-only, or network-only arenas.

How and Where to Learn More

EMA will be launching the Buyer's Guide with a webinar on September 21, and will do our best to make it a resource for anyone in IT seriously interested in IT analytic adoption.

Our buyer's guide is not about winners or losers — but rather a detailed evaluation of each vendor's design point, attributes, capabilities, market history and unique strengths. These assessments have been supplemented with interviews with actual deployments to further inform each assessment.

Coming AIA Blogs

Looking ahead, I'll be doing follow-up blogs on the following topics:

■ Shopping Cart Criteria — a more detailed look at how we did our assessments

■ Winning strategies for AIA adoption— based on this research, as well as prior research done over the period of the last three years — including roadblocks and organizational as well as technology concerns

■ AIA benefits— what to look for in getting AIA successfully on board, based once again on this and three years of past research

■ Looking Forward and Looking Back— a broader assessment of what we learned and what we expect to see as AIA evolves

In the meantime, I do welcome your questions and comments regarding your own AIA experiences and needs. You can reach me at drogseth@emausa.com

Read the second blog in the series about AIA: Why Advanced IT Analytics Deployments Show Benefits That Are Too Good To Miss

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Are You Thinking of Investing in Advanced IT Analytics?

(Hint - it's probably a good idea)
Dennis Drogseth

There may be no more critical emerging technology for IT organizations in the digital age than advanced IT analytics (AIA) — most commonly called “operational analytics.” EMA prefers the term “advanced IT analytics” because these investments, while often centered in operations, can go far beyond classic IT operations to support IT service management (ITSM) teams, development, and the IT executive suite, as well as a growing range of business stakeholders.

AIA is also an area of incredible industry innovation. So far, at least, the leading AIA vendors have not been constrained by rigid technology-driven market definitions of the kind that, for instance, nearly doomed the evolution of configuration management databases (CMDBs).

Instead, AIA solutions are evolving in multiple flavors with a growing range of benefits — most often centered in performance and availability management for IT, but also, and increasingly, addressing change impact awareness, integrated support for change management, and even integrated capabilities for capacity planning and analytics.

It is with this in mind that EMA is launching what we believe is the first ever buyer's guide for AIA adoption: Leaders in Advanced IT Analytics: A Buyer's Guide for Investing in Innovation. To do this, EMA has invited 13 vendors — each with a distinctive footprint — which have met the following set of requirements that made them candidates for this guide.

■ Support for performance, availability and change impact awarenesswith both real-time and historical insights. We also looked for corollaries in change management, capacity planning and capacity optimization when appropriate.

■ Assimilation of data from cross-domain sources in high data volumesfor cross-domain insights, as well as insights into application/infrastructure interdependencies. These interdependency insights can be purely analytic, or affiliated with topology and/or modeling.

■ The ability to access multiple data types, e.g. events, KPIs, logs, flow, configuration data, etc.

■ Capabilities for self-learning, to deliver predictive, and/or prescriptive, and/or if/then actionable insights.

■ Support for a wide range of advanced heuristics such as multivariate analysis, machine learning, streaming data, tiered analytics, cognitive analytics, etc.

■ Use as strategic overlays that may assimilate or consolidate multiple monitoring investments.

■ Support for private cloud, public cloud, as well as hybrid/legacy environments.

Moreover, all 13 vendors have been carefully assessed and vetted in working with EMA, including validation through dialogs with customer deployments.

Who's Not Included?

This buyer's guide is directed at what EMA believes is the AIA heartland, but it is also a first step in charting the broader AIA landscape.

Saved for future evaluations are:

■ AIA solutions that do not support real-time as well as predictive performance-related insights.

■ Cross-domain AIA focused on single targeted data collection — most notably wire, packet or flow data.

■ Monitoring suites with growing investments in analytics, but which don't yet meet all the criteria listed above.

■ Domain-specific AIA — targeted at specific use cases in systems-only, or network-only arenas.

How and Where to Learn More

EMA will be launching the Buyer's Guide with a webinar on September 21, and will do our best to make it a resource for anyone in IT seriously interested in IT analytic adoption.

Our buyer's guide is not about winners or losers — but rather a detailed evaluation of each vendor's design point, attributes, capabilities, market history and unique strengths. These assessments have been supplemented with interviews with actual deployments to further inform each assessment.

Coming AIA Blogs

Looking ahead, I'll be doing follow-up blogs on the following topics:

■ Shopping Cart Criteria — a more detailed look at how we did our assessments

■ Winning strategies for AIA adoption— based on this research, as well as prior research done over the period of the last three years — including roadblocks and organizational as well as technology concerns

■ AIA benefits— what to look for in getting AIA successfully on board, based once again on this and three years of past research

■ Looking Forward and Looking Back— a broader assessment of what we learned and what we expect to see as AIA evolves

In the meantime, I do welcome your questions and comments regarding your own AIA experiences and needs. You can reach me at drogseth@emausa.com

Read the second blog in the series about AIA: Why Advanced IT Analytics Deployments Show Benefits That Are Too Good To Miss

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 ...

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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 ...

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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 ... 

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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 ...