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CMDB Systems: Some Key (and Surprising) Findings from Deployments

Dennis Drogseth

In writing CMDB Systems: Making Change Work in the Age of Cloud and Agile, we gained a lot by talking to deployments. In the spirit of our own recommendations for how to manage and optimize a CMDB-related initiative, our goal was to learn from reality more than just preach a series of best practices. What I’ve chosen to write about here are just a few highlights, following four key areas of interest:

■ Dialog, Communication and Stakeholder Planning

■ Architecture

■ Cloud

■ Agile

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

Dialog, Communication and Stakeholder Planning

Taking the time to engage stakeholders effectively is one of the bigger challenges in any strategic IT initiative, and one of the biggest single catalysts for CMDB success. One of the problems, of course, is time and energy to engage in something new, no matter how valuable, when most IT professionals are already overworked and trapped within their own treadmills.

The Hamster Scenario
“Cultural challenges were a big factor — in getting the silos to think about new ways of working together. Something like what I call the “Hamster Scenario” running around its wheel. You’ve got some processes in place and you’re used to those processes. When do you step back and change those processes? In other words when does the hamster get off its wheel and think it might move in other directions?” (a transportation/travel company from Chapter Eight)

Next is a comment in both the challenge and resolution category about dealing with a very different concern — too much enthusiasm, and with it, too many expectations:

Challenge/resolution: Trying to please everyone
“When we discussed the CMDB with different groups in our organization, each team got very excited about what they wanted to get out of our CMDB. But very quickly we could see that many of their priorities were at least a couple of years away from implementation. So everybody’s understanding of the scope was different. For instance, we had desktop people saying 'can I inventory my mouse? Can I inventory my keyboard and my monitor?' They all wanted to see those as CIs.

“But managing this wasn’t too difficult. I asked everyone two questions; first, ‘What is this equipment that you’re prepared to manage from day one through its entire lifecycle as a CI?’ And second, ‘Do you have the resources to manage these items as CIs once they get into our CMDB?’ If there were no costs associated with CI inclusion, then everyone would want everything included in the CMDB right at the start. But as soon as they begin to understand the costs, including update and data access costs, they viewed it differently.” (a manufacturer from Chapter Four)

Architecture

Image removed.We spend a lot of time in the book looking at architectural challenges, including a very progressive technology landscape as it’s evolving to support more dynamic and complex service interdependencies. Below are just two perspectives, the first on service modeling, and the second on assimilating multiple data sets.

Service models vs. data models
“Our Service Model is based on ITIL’s definition, and it’s all about the processes, functions, services and technologies that we deliver to our customers. So therefore it’s a separate idea that can be applied to the data model. Our Global Finance team defines our business strategies company-wide. And these are of course not about a data model or technology. But that’s where we started, so we can trace everything we do back to those business services.” (manufacturer from Chapter Four)

Assimilating and reconciling many multiple sources
“Each discovery tool has its own idiosyncracies in terms of what it captures and how it works. This impacts both our ability to manage it and our ability to optimize our hardware and software asset investments in terms of utilization and licensing. With our integrated data analytics, we’re leveraging 35 different discovery tools in order to get a more cohesive 'golden record' for the CMS ... This includes desktop security, network management and administration, application dependency mapping, systems management and administration, asset management solutions, and BSM performance management, just to mention a few categories.” (a financial services company Chapter Nine)

Cloud and Agile

Since our book does its best to focus on the very current landscape of cloud and agile, I thought it fitting to end with two excerpts — one on each point. I think both are a bit surprising as well as insightful, but I’ll let you decide which might raise more eyebrows.

Cloud
“... the general idea is to leverage ADDM visibility for consistency across the cloud and hybrid cloud environments — so that you can identify servers that are not registered in your configuration management tools. Another example of the value of this type of visibility is keeping track of the Amazon Machine Images (AMIs), which can potentially lead to malware and other problems. There are over 30,000 community AMIs and not all of them are patched correctly, so if a developer decides to use one that’s not authorized it may cause problems. When that happens, ADDM can help to identify and remediate the problem.” (a software manufacturer from Chapter Fifteen)

Agile/ DevOps
“Before investing in the CMDB we were very fragmented in how we saved information. Some of it was in Visio, for instance. Some were in OneNote or in Excel. We also had a lot of Word documents — so it was difficult-to-impossible to get insight into impacts when we were about to make a change …

“At that time, development was a little bit ahead of operations in terms of maturity. Our operations organization was fairly siloed and hadn’t yet invested in best practices. We often had no clear idea what would happen if, for instance, we unplugged a server. Most of our change records there resided inside someone’s head … If somebody was sick at the wrong time, it could be very disruptive …

“But we did have some [scrum] best practices that had evolved in development for source control. So when we saw what a CMDB could do, we felt we had a chance to transform our way of working when it came to managing change.” (a mid-tier financial planning company from Chapter Fifteen)

All right, I’ll be candid. Probably the biggest single “revelation from the trenches” for me was this last — a development team purchasing a CMDB to support an agile environment using scrum and pushing it into operations.

The point being in these and other insights is that each deployment shares common challenges with other deployments, on the one hand; but is also distinctive on the other hand. The goal is, invariably, to find the best path for you — while learning from the many other voices of wisdom and experience available. And this was, perhaps, the single most pervasive "guiding light" in seeking out optimal content for our book.

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 ...

CMDB Systems: Some Key (and Surprising) Findings from Deployments

Dennis Drogseth

In writing CMDB Systems: Making Change Work in the Age of Cloud and Agile, we gained a lot by talking to deployments. In the spirit of our own recommendations for how to manage and optimize a CMDB-related initiative, our goal was to learn from reality more than just preach a series of best practices. What I’ve chosen to write about here are just a few highlights, following four key areas of interest:

■ Dialog, Communication and Stakeholder Planning

■ Architecture

■ Cloud

■ Agile

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

Dialog, Communication and Stakeholder Planning

Taking the time to engage stakeholders effectively is one of the bigger challenges in any strategic IT initiative, and one of the biggest single catalysts for CMDB success. One of the problems, of course, is time and energy to engage in something new, no matter how valuable, when most IT professionals are already overworked and trapped within their own treadmills.

The Hamster Scenario
“Cultural challenges were a big factor — in getting the silos to think about new ways of working together. Something like what I call the “Hamster Scenario” running around its wheel. You’ve got some processes in place and you’re used to those processes. When do you step back and change those processes? In other words when does the hamster get off its wheel and think it might move in other directions?” (a transportation/travel company from Chapter Eight)

Next is a comment in both the challenge and resolution category about dealing with a very different concern — too much enthusiasm, and with it, too many expectations:

Challenge/resolution: Trying to please everyone
“When we discussed the CMDB with different groups in our organization, each team got very excited about what they wanted to get out of our CMDB. But very quickly we could see that many of their priorities were at least a couple of years away from implementation. So everybody’s understanding of the scope was different. For instance, we had desktop people saying 'can I inventory my mouse? Can I inventory my keyboard and my monitor?' They all wanted to see those as CIs.

“But managing this wasn’t too difficult. I asked everyone two questions; first, ‘What is this equipment that you’re prepared to manage from day one through its entire lifecycle as a CI?’ And second, ‘Do you have the resources to manage these items as CIs once they get into our CMDB?’ If there were no costs associated with CI inclusion, then everyone would want everything included in the CMDB right at the start. But as soon as they begin to understand the costs, including update and data access costs, they viewed it differently.” (a manufacturer from Chapter Four)

Architecture

Image removed.We spend a lot of time in the book looking at architectural challenges, including a very progressive technology landscape as it’s evolving to support more dynamic and complex service interdependencies. Below are just two perspectives, the first on service modeling, and the second on assimilating multiple data sets.

Service models vs. data models
“Our Service Model is based on ITIL’s definition, and it’s all about the processes, functions, services and technologies that we deliver to our customers. So therefore it’s a separate idea that can be applied to the data model. Our Global Finance team defines our business strategies company-wide. And these are of course not about a data model or technology. But that’s where we started, so we can trace everything we do back to those business services.” (manufacturer from Chapter Four)

Assimilating and reconciling many multiple sources
“Each discovery tool has its own idiosyncracies in terms of what it captures and how it works. This impacts both our ability to manage it and our ability to optimize our hardware and software asset investments in terms of utilization and licensing. With our integrated data analytics, we’re leveraging 35 different discovery tools in order to get a more cohesive 'golden record' for the CMS ... This includes desktop security, network management and administration, application dependency mapping, systems management and administration, asset management solutions, and BSM performance management, just to mention a few categories.” (a financial services company Chapter Nine)

Cloud and Agile

Since our book does its best to focus on the very current landscape of cloud and agile, I thought it fitting to end with two excerpts — one on each point. I think both are a bit surprising as well as insightful, but I’ll let you decide which might raise more eyebrows.

Cloud
“... the general idea is to leverage ADDM visibility for consistency across the cloud and hybrid cloud environments — so that you can identify servers that are not registered in your configuration management tools. Another example of the value of this type of visibility is keeping track of the Amazon Machine Images (AMIs), which can potentially lead to malware and other problems. There are over 30,000 community AMIs and not all of them are patched correctly, so if a developer decides to use one that’s not authorized it may cause problems. When that happens, ADDM can help to identify and remediate the problem.” (a software manufacturer from Chapter Fifteen)

Agile/ DevOps
“Before investing in the CMDB we were very fragmented in how we saved information. Some of it was in Visio, for instance. Some were in OneNote or in Excel. We also had a lot of Word documents — so it was difficult-to-impossible to get insight into impacts when we were about to make a change …

“At that time, development was a little bit ahead of operations in terms of maturity. Our operations organization was fairly siloed and hadn’t yet invested in best practices. We often had no clear idea what would happen if, for instance, we unplugged a server. Most of our change records there resided inside someone’s head … If somebody was sick at the wrong time, it could be very disruptive …

“But we did have some [scrum] best practices that had evolved in development for source control. So when we saw what a CMDB could do, we felt we had a chance to transform our way of working when it came to managing change.” (a mid-tier financial planning company from Chapter Fifteen)

All right, I’ll be candid. Probably the biggest single “revelation from the trenches” for me was this last — a development team purchasing a CMDB to support an agile environment using scrum and pushing it into operations.

The point being in these and other insights is that each deployment shares common challenges with other deployments, on the one hand; but is also distinctive on the other hand. The goal is, invariably, to find the best path for you — while learning from the many other voices of wisdom and experience available. And this was, perhaps, the single most pervasive "guiding light" in seeking out optimal content for our book.

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 ...