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CMDB Systems in the Age of Cloud and Agile - Why We Wrote the Book

Dennis Drogseth

Everyone presumably loves a good mystery. And in fact the questions “What is a CMDB?” and “What is its relevance in the age of (cloud) (agile) (fill-in-the-blank)?” often provoke such conflicted industry responses that they do suggest the presence of some mystery underfoot.

But we didn’t set out to write a mystery novel per se. Instead, CMDB Systems: Making Change Work in the Age of Cloud and Agile is designed to serve as both a guide and a chronicle of real-world experiences — honoring the mystery by sharing different points of view, while trying to help our readers optimize their CMDB planning for what is increasingly becoming a positive and successful initiative.

What Are We Really Talking About When We Say “CMDB Systems?”

In the words of one of my industry colleagues, the elusive “CMDB unicorn” can possess magic charms, but remains too often a mythic beast. In the popular imagination, the CMDB, or configuration management database, is too often a dumping ground for data that will presumably find value once it’s aggregated — which is something like buying a car without knowing how to drive and expecting to go on a road trip. The results have often met with failure, and indeed, “failure” is the first word of the book.

The name itself doesn’t really help the case. Taken from the IT Infrastructure Library (ITIL) the configuration management database isn’t about configuration management in the common vernacular, and it is really much more than a database. The term works well within ITIL’s specific guidelines, but unfortunately it’s too often a misnomer once it’s taken out into Main Street.

CMDB Systems — as we have come to understand them — require a way of reconciling multiple sources into a modeled view of physical and logical interdependencies. This is becoming an increasingly automated and dynamic capability in many deployments. Moreover as a federated set of resources, CMDB Systems may in some cases involve mashups as much as data stores for certain use cases.

From a process perspective, CMDBs require a more collective, service-aware way of working that should help to prioritize changes, enable more effective capacity planning, and help to accelerate IT’s ability to address critical business and IT initiatives. These may range from the move to cloud, to data center consolidation, to improved business service performance, to name a few.

But successful deployments don’t try to do everything at once. They build from use case to use case in a manner suited to their own, unique environmental requirements — which underscores our reasons for making “CMDB Systems” plural. It also lies behind our preference for not calling them “single sources of truth” but use-case-driven “systems of relevance”.

Like IT organizations, CMDB Systems are not generic, but vary based on use case, scope, resources and even culture. While the term and concept for CMDB is a creation of ITIL, one of the people interviewed in our book led a development team and purchased a CMDB to force operations to become more agile — using scrum!

Why Did We Feel That WE Should Take All This On?

Image removed.We had more than a mystery of definitions and intentions on our hands when we began writing the book. Enterprise Management Associations has spent more than a decade researching CMDB-related adoptions and providing consulting services to IT organizations with CMDB initiatives. We built on that experience in order to develop our book, and continued to enhance our learning curve with fresh interviews and dialogs with both vendors and IT. In the spirit of the elusive unicorn, our own vision of the CMDB continued to evolve with these new insights, giving the work a journalistic feeling for discovery rather than making it a cut-and-dried academic tutorial. Our goal was as much to paint a landscape as it was to provide a checklist — although we tried to be conscientious about doing both.

Is CMDB/CMS More Relevant Than Ever?

Do we truly believe that in 2015 the CMDB/CMS is more relevant than ever in the real world, or is it still a magic unicorn, more myth than beast?

In answer to this I’ll provide just a few data points. In Ecosystem Cloud: Managing and Optimizing IT Services Across the Full Cloud Mosaic (EMA 2013) data showed that those very successful with their cloud deployments were nearly twice as likely to have CMDBs and/or ADDM capabilities deployed as those who were less successful.

Data just in from EMA research (and therefore not available to us when we wrote the book), shows the following:

■ 80% owned or were about to purchase a CMDB/CMS-related technology. Two years ago it was only 52%.

■ Performance-related service impact edged out asset management and change management as the prime CMDB-related use case for the first time ever in our research.

■ 79% have deployed, or had plans to deploy ADDM.

■ Those who were extremely successful were twice as likely to have a CMDB-related technology deployed as those who were not successful.

■ Those who were only somewhat or not successful were 20 times less likely to have ADDM deployed or in plan.

Has the elusive unicorn come out of the forest yet? And is it ready to do more than merely inspire a hopeful myth? Our data, our conversations, and our consulting experience over the years would suggest, strongly, that the answer is indeed: yes. We hope our book will serve as both a guide, and as a stimulant to industry dialog — to help IT emerge into this new era of mobile, cloud and agile far more proactively, while standing on much more solid ground.

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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 ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

CMDB Systems in the Age of Cloud and Agile - Why We Wrote the Book

Dennis Drogseth

Everyone presumably loves a good mystery. And in fact the questions “What is a CMDB?” and “What is its relevance in the age of (cloud) (agile) (fill-in-the-blank)?” often provoke such conflicted industry responses that they do suggest the presence of some mystery underfoot.

But we didn’t set out to write a mystery novel per se. Instead, CMDB Systems: Making Change Work in the Age of Cloud and Agile is designed to serve as both a guide and a chronicle of real-world experiences — honoring the mystery by sharing different points of view, while trying to help our readers optimize their CMDB planning for what is increasingly becoming a positive and successful initiative.

What Are We Really Talking About When We Say “CMDB Systems?”

In the words of one of my industry colleagues, the elusive “CMDB unicorn” can possess magic charms, but remains too often a mythic beast. In the popular imagination, the CMDB, or configuration management database, is too often a dumping ground for data that will presumably find value once it’s aggregated — which is something like buying a car without knowing how to drive and expecting to go on a road trip. The results have often met with failure, and indeed, “failure” is the first word of the book.

The name itself doesn’t really help the case. Taken from the IT Infrastructure Library (ITIL) the configuration management database isn’t about configuration management in the common vernacular, and it is really much more than a database. The term works well within ITIL’s specific guidelines, but unfortunately it’s too often a misnomer once it’s taken out into Main Street.

CMDB Systems — as we have come to understand them — require a way of reconciling multiple sources into a modeled view of physical and logical interdependencies. This is becoming an increasingly automated and dynamic capability in many deployments. Moreover as a federated set of resources, CMDB Systems may in some cases involve mashups as much as data stores for certain use cases.

From a process perspective, CMDBs require a more collective, service-aware way of working that should help to prioritize changes, enable more effective capacity planning, and help to accelerate IT’s ability to address critical business and IT initiatives. These may range from the move to cloud, to data center consolidation, to improved business service performance, to name a few.

But successful deployments don’t try to do everything at once. They build from use case to use case in a manner suited to their own, unique environmental requirements — which underscores our reasons for making “CMDB Systems” plural. It also lies behind our preference for not calling them “single sources of truth” but use-case-driven “systems of relevance”.

Like IT organizations, CMDB Systems are not generic, but vary based on use case, scope, resources and even culture. While the term and concept for CMDB is a creation of ITIL, one of the people interviewed in our book led a development team and purchased a CMDB to force operations to become more agile — using scrum!

Why Did We Feel That WE Should Take All This On?

Image removed.We had more than a mystery of definitions and intentions on our hands when we began writing the book. Enterprise Management Associations has spent more than a decade researching CMDB-related adoptions and providing consulting services to IT organizations with CMDB initiatives. We built on that experience in order to develop our book, and continued to enhance our learning curve with fresh interviews and dialogs with both vendors and IT. In the spirit of the elusive unicorn, our own vision of the CMDB continued to evolve with these new insights, giving the work a journalistic feeling for discovery rather than making it a cut-and-dried academic tutorial. Our goal was as much to paint a landscape as it was to provide a checklist — although we tried to be conscientious about doing both.

Is CMDB/CMS More Relevant Than Ever?

Do we truly believe that in 2015 the CMDB/CMS is more relevant than ever in the real world, or is it still a magic unicorn, more myth than beast?

In answer to this I’ll provide just a few data points. In Ecosystem Cloud: Managing and Optimizing IT Services Across the Full Cloud Mosaic (EMA 2013) data showed that those very successful with their cloud deployments were nearly twice as likely to have CMDBs and/or ADDM capabilities deployed as those who were less successful.

Data just in from EMA research (and therefore not available to us when we wrote the book), shows the following:

■ 80% owned or were about to purchase a CMDB/CMS-related technology. Two years ago it was only 52%.

■ Performance-related service impact edged out asset management and change management as the prime CMDB-related use case for the first time ever in our research.

■ 79% have deployed, or had plans to deploy ADDM.

■ Those who were extremely successful were twice as likely to have a CMDB-related technology deployed as those who were not successful.

■ Those who were only somewhat or not successful were 20 times less likely to have ADDM deployed or in plan.

Has the elusive unicorn come out of the forest yet? And is it ready to do more than merely inspire a hopeful myth? Our data, our conversations, and our consulting experience over the years would suggest, strongly, that the answer is indeed: yes. We hope our book will serve as both a guide, and as a stimulant to industry dialog — to help IT emerge into this new era of mobile, cloud and agile far more proactively, while standing on much more solid ground.

Hot Topics

The Latest

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 ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...