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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.

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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.

Hot Topics

The Latest

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...