Advanced IT Analytics, AIOps and Big Data - 7 Key Takeaways - Part 2

Dennis Drogseth

The goal of EMA's latest research was to look at how advanced IT analytics (AIA) — EMA's term for primarily what today is best known as "AIOps" — is being deployed. Here are the remaining four of my seven personal takeaways.

Start with Advanced IT Analytics, AIOps and Big Data - 7 Key Takeaways - Part 1

For a much more focused exploration of the "IT analytic universe," one that's all digestible within 45 minutes (including Q&A), register for the upcoming EMA webinar on October 10.

4. Capturing interdependencies and CMDB/CMS

Capturing interdependencies and CMDB/CMS both stood out in importance. As an average, respondents sought to capture nearly five interdependencies across the application/infrastructure, while 54% of respondents viewed the CMDB as "extremely important" to their analytics strategy, a surprising valuation that correlated strongly with success.

The implications of this may seem to contradict notions that the CMDB is "old hat" technology. Rather, what's indicated in these findings is that CMDB/CMS and application/infrastructure dependency mapping are technology areas that are both being reinvigorated by AIA/AIOps investments, while also providing valuable contexts for leveraging and optimizing analytic insights for a variety of use cases.

5. Security on the rise

Security is on the rise. Priorities in cloud, vendor selection, heuristics, and best practices all indicate that security is a leading and largely integrated concern in advanced IT analytics, and AIOps in particular. This was not altogether a surprise given similar findings inEMA's 2016 research (EMA Research: Advanced IT Analytics: A Look at Real-World Adoptions in the Real World, March 2016).

Moreover, it is both a welcome and a much-needed advance, as the trend toward a true SecOps (security + operations) integration across IT organizations is becoming ever more critical given rising vulnerabilities, as well as the growing demand for OpEx efficiencies across IT.

6. Top-down is winning strategy

Top-down for everything is the winning strategy. It is also the most pervasive. The executive suite (VP and above) was more likely to be successful, and more likely to drive AIA strategies, deployment and purchasing decisions.

The reasons for this make sense once AIA/AIOps is understood as a unifying technology that can help to bring IT silos closer together with shared data and common insights. But to realize this advantage fully, process issues, organizational barriers and even habits of mind can be transformed through improved dialog and leadership.

7. Advanced analytics show strong evolutionary values

Advanced analytics are showing strong evolutionary values compared to prior years. EMA research from early 2016 and 2014 (EMA Research: The Many Faces of Advanced Operations Analytics, September 2014) indicate strong growth in heuristics, data sources, integrations, stakeholder roles, and overall versatility in terms of function and purpose.

The implications are that AIA/AIOps solutions are evolving dramatically in terms of functionality, use-case and breadth.

Here, the progress wasn't surprising, but the degree of progress in terms of hard numbers actually was.

… and there's a lot more.

There was no "spoiler's alert" at the beginning because the real proof of the pudding is the hard data and the many other insights that I plan on sharing during the webinar in October.

But hopefully you'll find some of the discoveries mentioned here intriguing, and as always, I welcome your thoughts and comments at drogseth@emausa.com.

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Advanced IT Analytics, AIOps and Big Data - 7 Key Takeaways - Part 2

Dennis Drogseth

The goal of EMA's latest research was to look at how advanced IT analytics (AIA) — EMA's term for primarily what today is best known as "AIOps" — is being deployed. Here are the remaining four of my seven personal takeaways.

Start with Advanced IT Analytics, AIOps and Big Data - 7 Key Takeaways - Part 1

For a much more focused exploration of the "IT analytic universe," one that's all digestible within 45 minutes (including Q&A), register for the upcoming EMA webinar on October 10.

4. Capturing interdependencies and CMDB/CMS

Capturing interdependencies and CMDB/CMS both stood out in importance. As an average, respondents sought to capture nearly five interdependencies across the application/infrastructure, while 54% of respondents viewed the CMDB as "extremely important" to their analytics strategy, a surprising valuation that correlated strongly with success.

The implications of this may seem to contradict notions that the CMDB is "old hat" technology. Rather, what's indicated in these findings is that CMDB/CMS and application/infrastructure dependency mapping are technology areas that are both being reinvigorated by AIA/AIOps investments, while also providing valuable contexts for leveraging and optimizing analytic insights for a variety of use cases.

5. Security on the rise

Security is on the rise. Priorities in cloud, vendor selection, heuristics, and best practices all indicate that security is a leading and largely integrated concern in advanced IT analytics, and AIOps in particular. This was not altogether a surprise given similar findings inEMA's 2016 research (EMA Research: Advanced IT Analytics: A Look at Real-World Adoptions in the Real World, March 2016).

Moreover, it is both a welcome and a much-needed advance, as the trend toward a true SecOps (security + operations) integration across IT organizations is becoming ever more critical given rising vulnerabilities, as well as the growing demand for OpEx efficiencies across IT.

6. Top-down is winning strategy

Top-down for everything is the winning strategy. It is also the most pervasive. The executive suite (VP and above) was more likely to be successful, and more likely to drive AIA strategies, deployment and purchasing decisions.

The reasons for this make sense once AIA/AIOps is understood as a unifying technology that can help to bring IT silos closer together with shared data and common insights. But to realize this advantage fully, process issues, organizational barriers and even habits of mind can be transformed through improved dialog and leadership.

7. Advanced analytics show strong evolutionary values

Advanced analytics are showing strong evolutionary values compared to prior years. EMA research from early 2016 and 2014 (EMA Research: The Many Faces of Advanced Operations Analytics, September 2014) indicate strong growth in heuristics, data sources, integrations, stakeholder roles, and overall versatility in terms of function and purpose.

The implications are that AIA/AIOps solutions are evolving dramatically in terms of functionality, use-case and breadth.

Here, the progress wasn't surprising, but the degree of progress in terms of hard numbers actually was.

… and there's a lot more.

There was no "spoiler's alert" at the beginning because the real proof of the pudding is the hard data and the many other insights that I plan on sharing during the webinar in October.

But hopefully you'll find some of the discoveries mentioned here intriguing, and as always, I welcome your thoughts and comments at drogseth@emausa.com.

Related Links

Hot Topics

The Latest

Manufacturing Organizations Doubled AI Investment Yet Only 37% Fully Prepared to Operationalize AI

While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed ...

Optimizing Decisions with Edge-First Cognitive Intelligence

Many organizations rely on cloud-first architectures to aggregate, analyze, and act on their operational data ... However, not all environments are conducive to cloud-first architectures ... There are limitations to cloud-first architectures that render them ineffective in mission-critical situations where responsiveness, cost control, and data sovereignty are non-negotiable; these limitations include ...

The New Perimeter Is the User: Why Identity Is the Real Network Edge

For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...

When AI Infrastructure Stops Behaving Like Infrastructure

For years, infrastructure teams have treated compute as a relatively stable input. Capacity was provisioned, costs were forecasted, and performance expectations were set based on the assumption that identical resources behaved identically. That mental model is starting to break down. AI infrastructure is no longer behaving like static cloud capacity. It is increasingly behaving like a market ...

Enterprise Resilience: Understanding the Shift from Static to Dynamic

Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...

Mobile Users Expect Perfection in 2026

Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...

AI Confidence Is High. Readiness Is Not. What the Data Tells Us - and Why It Matters

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI, Realistic Expectations and the Future of IT Operations in 2026

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

Trust, But Verify: Building Confidence in AI Through Human Oversight

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

Data Centers Plan to Reduce Reliance on Grid

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