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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

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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

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 ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...