Skip to main content

CMDB Is Alive and on the Rise - Cloud Found Innocent of Its Demise

Valerie O'Connell
EMA

Articles proclaiming the death of CMDB started appearing with regularity as early as 2010. Cloud was named as the likely killer. The problem with this bit of folk wisdom is that it isn't true.

Enterprise Management Associates (EMA) experience and field research consistently find that CMDB use not only continues but is on the rise. In a 2022 EMA initiative on the rise of ServiceOps, 400 global IT leaders stated that CMDB use was central to major functions. For many of those respondents, CMDB use was viewed as increasing in importance for automation of complex processes.

Where is the disconnect?

A search for in-depth research on the topic came up pretty much empty. Apparently, shinier topics and trends easily overshadow CMDB. As a result, CMDB's reputation and perceived value are largely a matter of myth and anecdotal guidance. EMA set out to right that wrong with independent research focused squarely on CMDB as it is used today and planned for the near future.

The Bottom Line from a Global Panel of IT Professionals

The CMDB remains fundamental to IT service quality — in many cases, critical — and its use is on the rise.

What best characterizes your view of CMDB in cloud times?

■ 50% CMDB is increasing: it is critical in multi-cloud and hybrid environments

■ 48% CMDB remains a fundamental contributor to IT service quality

■ 2% CMDB is declining in importance

If CMDB has the reputation of being the bad boy of IT, disappointing true believers with anemic returns on expectations, how can its use be on the rise globally?

The key is in the word "expectations."

1. Today, CMDB delivers value that directly maps to top IT initiatives, such as improved service quality and IT personnel productivity, as well as the ongoing drive to decrease unplanned work, outages, and costs.

2. It delivers that value using capabilities that weren't generally available when ITIL v2 birthed the notion of CMDB back in 2001. AI/ML, advanced automation, discovery and dependency mapping (DDM), the ability to handle diverse data sources on a massive scale, and mainstream AIOps all make CMDB objectives workable today in a way that just wasn't realistic 20 years ago. 

Expectations ran high. Results ran low. The mismatch was bad news for the reputation of CMDB.

CMDB 2023 is not the same as CMDB 2001. The high-velocity world it lives in is vastly different, marked by changing combinations of multi-clouds alongside enduring on-premises applications and infrastructure.

The complexity, criticality, and dynamic nature of cloud actually increases the need for CMDB-like functionality. When microservices and container architectures or applications are deployed across multiple clouds in volatile combinations, capturing the configuration items (CIs) and their relationships becomes immensely more difficult and arguably more important than ever. IT needs a centralized way to track the sprawl of components in order to address security, threat assessment, compliance, cost management/cloud billing, and performance management complete with troubleshooting.

In fact, when asked to name the CMDB use that delivers the highest impact, "performance management" was the clear leader, followed by "security" and "compliance and risk." Asked to name the two most valuable of CMDB's many organizational benefits, the global panel reported a virtual tie between "improved service quality and performance" and "increased productivity of IT personnel with less unplanned work."


CMDB use is on the rise because it serves critical functions in a world where IT service crosses clouds, containers, mainframes, and microservices in a complex brew of technologies and change. Getting it right is not a simple or easy proposition, but neither are the challenges it addresses.

EMA discusses highlights from the research in an on-demand free webinar: CMDB today - myths, mistakes, and mastery.

Valerie O'Connell is EMA Research Director of Digital Service Execution

Hot Topics

The Latest

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

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 ...

CMDB Is Alive and on the Rise - Cloud Found Innocent of Its Demise

Valerie O'Connell
EMA

Articles proclaiming the death of CMDB started appearing with regularity as early as 2010. Cloud was named as the likely killer. The problem with this bit of folk wisdom is that it isn't true.

Enterprise Management Associates (EMA) experience and field research consistently find that CMDB use not only continues but is on the rise. In a 2022 EMA initiative on the rise of ServiceOps, 400 global IT leaders stated that CMDB use was central to major functions. For many of those respondents, CMDB use was viewed as increasing in importance for automation of complex processes.

Where is the disconnect?

A search for in-depth research on the topic came up pretty much empty. Apparently, shinier topics and trends easily overshadow CMDB. As a result, CMDB's reputation and perceived value are largely a matter of myth and anecdotal guidance. EMA set out to right that wrong with independent research focused squarely on CMDB as it is used today and planned for the near future.

The Bottom Line from a Global Panel of IT Professionals

The CMDB remains fundamental to IT service quality — in many cases, critical — and its use is on the rise.

What best characterizes your view of CMDB in cloud times?

■ 50% CMDB is increasing: it is critical in multi-cloud and hybrid environments

■ 48% CMDB remains a fundamental contributor to IT service quality

■ 2% CMDB is declining in importance

If CMDB has the reputation of being the bad boy of IT, disappointing true believers with anemic returns on expectations, how can its use be on the rise globally?

The key is in the word "expectations."

1. Today, CMDB delivers value that directly maps to top IT initiatives, such as improved service quality and IT personnel productivity, as well as the ongoing drive to decrease unplanned work, outages, and costs.

2. It delivers that value using capabilities that weren't generally available when ITIL v2 birthed the notion of CMDB back in 2001. AI/ML, advanced automation, discovery and dependency mapping (DDM), the ability to handle diverse data sources on a massive scale, and mainstream AIOps all make CMDB objectives workable today in a way that just wasn't realistic 20 years ago. 

Expectations ran high. Results ran low. The mismatch was bad news for the reputation of CMDB.

CMDB 2023 is not the same as CMDB 2001. The high-velocity world it lives in is vastly different, marked by changing combinations of multi-clouds alongside enduring on-premises applications and infrastructure.

The complexity, criticality, and dynamic nature of cloud actually increases the need for CMDB-like functionality. When microservices and container architectures or applications are deployed across multiple clouds in volatile combinations, capturing the configuration items (CIs) and their relationships becomes immensely more difficult and arguably more important than ever. IT needs a centralized way to track the sprawl of components in order to address security, threat assessment, compliance, cost management/cloud billing, and performance management complete with troubleshooting.

In fact, when asked to name the CMDB use that delivers the highest impact, "performance management" was the clear leader, followed by "security" and "compliance and risk." Asked to name the two most valuable of CMDB's many organizational benefits, the global panel reported a virtual tie between "improved service quality and performance" and "increased productivity of IT personnel with less unplanned work."


CMDB use is on the rise because it serves critical functions in a world where IT service crosses clouds, containers, mainframes, and microservices in a complex brew of technologies and change. Getting it right is not a simple or easy proposition, but neither are the challenges it addresses.

EMA discusses highlights from the research in an on-demand free webinar: CMDB today - myths, mistakes, and mastery.

Valerie O'Connell is EMA Research Director of Digital Service Execution

Hot Topics

The Latest

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

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 ...