At the Crossroads of Digital Transformation: The Future of the Advanced IT Analytics "Market"

Dennis Drogseth

One of the things that became quickly apparent in researching the thirteen vendors in EMA's Advanced IT Analytics Buyer's Guide was that the market, if you can call it a market at all, is rapidly changing. This is true across the board — in functionality, scope, and use case. Splunk's recent acquisition of Rocana (announced three days before writing this blog) only accentuates the dynamism underway.

It's also important to keep the diversity of the Advanced IT Analytics (AIA) landscape in mind as you plan for your investments. AIA is still not a market in the traditional sense, since market definitions typically require narrow technology parameters for creating discrete buckets for market sizing and contrast. My vision of AIA is rather an arena of fast-growing exploration and invention, in which in-house development is beginning to cede to third-party solutions that can accelerate time to value.

The comments presented below reflect an admittedly optimistic worldview on this topic. But the optimism is founded in dialog and research. What I'm sharing here is both what I believe to be true, and what I hope to be true. Given history of course, there is often something of an abyss between potential and reality. An abyss that is usually emerges from the politics and comfort levels in positions of leadership, as well as boxed-in views of reality defined, alas, too often by the likes of me.

But for the fun of it — let's cross that abyss now.

Architected for growth

After reviewing the ratings for each vendor on a scale of "outstanding" to "not present" for a wide range of capabilities just described in my three prior blogs on shopping cart criteria — I expect to see significant progress as soon as 12 months out. I expect that more than a few ratings of "present" or "in process" will become "strong" or conceivably even "outstanding." This is good news for both the vendors included in this report and for IT adopters seeking to invest in growth opportunities.

All 13 AIA solutions, as different as they are from each other, are architected for growth and versatility. Look especially for innovations in cognitive computing, more prescriptive analytics, and more evolved capabilities to support if/then analytics for change and capacity planning.

Look as well for a growth in business impact values and outreach into emerging areas such as IoT and integrated security. And for those AIA vendors with a strong APM focus, look for continued growth in the DevOpsarena.

Beyond Big Data

AIA is also not strictly about big data in the classic sense. While large volumes of disparate data are central to the AIA story, dynamic currency and relevance is paramount. The cutting edge for AIA is analytics, use case, time to value, and focused realization. To confuse any of the solutions reviewed with simply putting a lot of data into Cassandra or Hadoop and then relying on Qlik or Elasticsearch is to do all 13 AIA vendor-innovators a great disservice.

Dependency mapping

Another trend that surfaced in researching this Buyer's Guide was the growing importance of service-dependency mapping and real-time or continuous discovery — two capabilities that have been at odds in the past but which are beginning to converge within the AIA landscape.

AIA isn't just about data. It's about relevance and action.

Once again, AIA isn't just about data. It's about relevance and action. And insights into real-world and real-time interdependencies across the application/infrastructure, no matter how they are achieved and visualized, can provide a context for action, both in terms of technical relevance and in terms of the politics of siloed IT ownership.

The Best of Both Worlds

What's implied in this still very new arena for IT investment is that IT leadership wants the best of two worlds — diversity, eclectic use case, and broad stakeholder support, on the one hand, with tools that are easy to deploy and administer on the other hand.

Although there is still plenty of room for progress, we're already witnessing surprising advances in each of these areas. Perhaps what's most striking in our assessments, including the vetted comments from actual deployment interviews, is how much progress really has been made already—and how fast these solutions are evolving to address the total AIA wish list.

At the Crossroads of IT and Digital Transformation

Finally, the data from our prior research, confirmed largely by deployment interviews here, indicates that to succeed AIA needs to be viewed as a strategic, not a tactical, investment. AIA and its associated investments in visualization and automation sit at the very crossroads of IT and digital transformation.

In many IT organizations, AIA is best driven with senior executive support. This is because reaching meaningful benefits isn't just about deploying the most game-changing technology. It requires leadership to encourage new ways of working across IT and between IT and business stakeholders. As such, this transformation will eventually mean new kinds of dialogs and new ways of thinking.

As AIA matures, perhaps the very heart of the matter will move beyond technology breaking points and begin to center on more role awareness, dialog, and integrated IT and business transformation — where the analytic magic is so consumable that you don't really have to know it's there. 

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At the Crossroads of Digital Transformation: The Future of the Advanced IT Analytics "Market"

Dennis Drogseth

One of the things that became quickly apparent in researching the thirteen vendors in EMA's Advanced IT Analytics Buyer's Guide was that the market, if you can call it a market at all, is rapidly changing. This is true across the board — in functionality, scope, and use case. Splunk's recent acquisition of Rocana (announced three days before writing this blog) only accentuates the dynamism underway.

It's also important to keep the diversity of the Advanced IT Analytics (AIA) landscape in mind as you plan for your investments. AIA is still not a market in the traditional sense, since market definitions typically require narrow technology parameters for creating discrete buckets for market sizing and contrast. My vision of AIA is rather an arena of fast-growing exploration and invention, in which in-house development is beginning to cede to third-party solutions that can accelerate time to value.

The comments presented below reflect an admittedly optimistic worldview on this topic. But the optimism is founded in dialog and research. What I'm sharing here is both what I believe to be true, and what I hope to be true. Given history of course, there is often something of an abyss between potential and reality. An abyss that is usually emerges from the politics and comfort levels in positions of leadership, as well as boxed-in views of reality defined, alas, too often by the likes of me.

But for the fun of it — let's cross that abyss now.

Architected for growth

After reviewing the ratings for each vendor on a scale of "outstanding" to "not present" for a wide range of capabilities just described in my three prior blogs on shopping cart criteria — I expect to see significant progress as soon as 12 months out. I expect that more than a few ratings of "present" or "in process" will become "strong" or conceivably even "outstanding." This is good news for both the vendors included in this report and for IT adopters seeking to invest in growth opportunities.

All 13 AIA solutions, as different as they are from each other, are architected for growth and versatility. Look especially for innovations in cognitive computing, more prescriptive analytics, and more evolved capabilities to support if/then analytics for change and capacity planning.

Look as well for a growth in business impact values and outreach into emerging areas such as IoT and integrated security. And for those AIA vendors with a strong APM focus, look for continued growth in the DevOpsarena.

Beyond Big Data

AIA is also not strictly about big data in the classic sense. While large volumes of disparate data are central to the AIA story, dynamic currency and relevance is paramount. The cutting edge for AIA is analytics, use case, time to value, and focused realization. To confuse any of the solutions reviewed with simply putting a lot of data into Cassandra or Hadoop and then relying on Qlik or Elasticsearch is to do all 13 AIA vendor-innovators a great disservice.

Dependency mapping

Another trend that surfaced in researching this Buyer's Guide was the growing importance of service-dependency mapping and real-time or continuous discovery — two capabilities that have been at odds in the past but which are beginning to converge within the AIA landscape.

AIA isn't just about data. It's about relevance and action.

Once again, AIA isn't just about data. It's about relevance and action. And insights into real-world and real-time interdependencies across the application/infrastructure, no matter how they are achieved and visualized, can provide a context for action, both in terms of technical relevance and in terms of the politics of siloed IT ownership.

The Best of Both Worlds

What's implied in this still very new arena for IT investment is that IT leadership wants the best of two worlds — diversity, eclectic use case, and broad stakeholder support, on the one hand, with tools that are easy to deploy and administer on the other hand.

Although there is still plenty of room for progress, we're already witnessing surprising advances in each of these areas. Perhaps what's most striking in our assessments, including the vetted comments from actual deployment interviews, is how much progress really has been made already—and how fast these solutions are evolving to address the total AIA wish list.

At the Crossroads of IT and Digital Transformation

Finally, the data from our prior research, confirmed largely by deployment interviews here, indicates that to succeed AIA needs to be viewed as a strategic, not a tactical, investment. AIA and its associated investments in visualization and automation sit at the very crossroads of IT and digital transformation.

In many IT organizations, AIA is best driven with senior executive support. This is because reaching meaningful benefits isn't just about deploying the most game-changing technology. It requires leadership to encourage new ways of working across IT and between IT and business stakeholders. As such, this transformation will eventually mean new kinds of dialogs and new ways of thinking.

As AIA matures, perhaps the very heart of the matter will move beyond technology breaking points and begin to center on more role awareness, dialog, and integrated IT and business transformation — where the analytic magic is so consumable that you don't really have to know it's there. 

Related Links

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AI is Moving Fast, and Enterprise File Data Is Not Ready

Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale. But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within ...

Move Fast Without the Usual Quality Penalty

Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...

When the Observability Bill Arrives for Agentic AI

I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...

AI is Breaking Enterprise Log Management

The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools ...

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For years, secure connectivity has relied on a familiar pattern: route traffic back to centralized gateways, inspect it, and then allow access. This model worked when applications lived in a handful of data centers and users were largely confined to offices. That model is now under strain. Applications are distributed across clouds, users connect from everywhere, and real-time workloads demand performance that centralized inspection points struggle to deliver. As traffic volumes grow and latency expectations shrink, routing everything through a small number of control points has become both a performance bottleneck and a resilience risk. The future of secure connectivity requires a different approach ...

Production Inference Shifts Decisively to Private Cloud

The AI experimentation phase is over, and the private cloud is where enterprise AI workloads are being deployed for security and scale, according to Private Cloud Outlook 2026, a new report from Broadcom ... 2026 marks an acceleration into a full AI tipping point. The shift is being shaped by three forces — costs, complexity, and control — that public cloud environments are increasingly failing to address for production AI at scale. Key findings from the report include ...

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44% of organizations have reported an outage in the past year tied to suppressed or ignored alerts, and 78% had at least one incident where no alert was fired at all ... Engineers learned about failures from customers. That gap between what our tools report and what our customers experience is the problem DevOps teams have been quietly solving with GenAI tooling, even as most enterprises continue to run their NOCs on manual alert triage ...

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Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

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77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

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