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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.

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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 ...

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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 ...

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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

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 ...