Skip to main content

Words from the CMDB System Trenches

Dennis Drogseth

While it’s become fashionable to look at CMDBs as unwieldy and generally Quixotiesque endeavors—the truth is that solid technologies for effective CMDB and CMS—(if you believe as I do that the move towards a federated system is the future) – are just now really emerging. When you consider how revolutionary the idea is of reconciling everything from discovery and monitoring tools to asset databases in different formats from different companies into a single cohesive system—then I think you would have to agree that it’s only fair that this should take time.

EMA (i.e. me in this case) just completed a radar analysis of eleven vendors and talked to more than 20 CMDB/CMS deployments in North America and Europe. The research confirmed my views that CMDB-related technologies are evolving to become more dynamic, more real-time, more deployable, more use-case directed, and more varied in design. They are also becoming more essential than ever—spurred in part, ironically – by cloud computing which is pressuring companies to move towards a more cross-domain, and ultimately more service-centric model for management. (As I’ve pointed out in the past, companies with CMDB and application dependency tools show a 1.4 to 1.5 times advantage over those without in making cloud investments more effective.)

For this radar I excluded platform or framework vendors from the eleven vendors I worked with, in part because of the nature of EMA’s radar technology to create “framework ghettos” richer in function and higher in price.(I’ll be doing a platform-centric radar in Q 3-4 and include a few of the vendors here for comparison sake.) But I also wanted to focus on the differences in design to show, or confirm—what I believed to be true – that innovation in CMDB/CMS design is accelerating in multiple different directions.

The eleven vendors are:

- AccelOps
- ASG Software
- Axios Systems
- FireScope
- iET Solutions
- Interlink Software
- LANDesk
- n(i)2 Network Infrastructure Inventory
- Numara Software
- Service-now.com
- SunView Software

They were assessed along three use cases: Asset Management and Cost Optimization; Change Management and Change Impact Analysis; and Service Impact Management.

To give you an idea of some of the perspectives, I thought I’d wrap up with a few quotes from vendors and customer deployments—that highlight how CMDB-related technologies are changing, or not- -and how they are working in the real world. One thing, BTW, that hasn’t changed is the fact that just as CMDB/CMS is a revolutionary technology—it has parallel revolutionary implications for organizations, cultures, and processes as a few quotes below indicate.

Vendor: A couple of customer opportunities presented themselves—but the potential buyers said, “When you send in the P.O., make sure it doesn’t have the letters CMDB anywhere.” So we sometimes sell CMDB solutions by avoiding the letters C-M-D-B in our proposal.

Customer: We want to minimize the human interaction to get the CMDB up to date. The more manual the effort, the lower the accuracy of the CMDB.

Customer: In order to manage vMotion we needed a solution that was dynamically current and automatic in modeling infrastructure and application interdependencies.

Customer: We weren’t prepared for the political discussions going into this.

Customer: A large part of what we do is get people to work with changes—people are always resistant to change, we always get resistance.

Customer: Originally there was resistance. But two years later there are no complaints.

Customer: We have about 300 different types of stakeholders defined including business executives, applications management, desktops, servers, mainframes, merger recovery services, facilities planning, and ITIL process owners—just to name a few.

Customer: Our CMDB is doing a good job of supporting our migration towards a more fully virtualized infrastructure. It’s also helping us to cut down on the number of management tools we need.

Customer: We’re using the CMDB as the system of record for all of our PCs and their owners and where they are located and what applications run on them, in addition to our data center assets. It has become our authorized system of record.

These are just a few of many quotes used in the report—as added insight to the vendor assessments. In this unique radar, each Use Case has its own Bubble Chart – so that CMDB designs can be viewed in context with value instead of obscuring their positions by forcing everyone into a single, one-size-fits-all mold. The Webinar will spotlight these differences, while also providing a short-hand RFP for you to use in evaluating CMDB/CMS solutions – platform or not.

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 ...

Words from the CMDB System Trenches

Dennis Drogseth

While it’s become fashionable to look at CMDBs as unwieldy and generally Quixotiesque endeavors—the truth is that solid technologies for effective CMDB and CMS—(if you believe as I do that the move towards a federated system is the future) – are just now really emerging. When you consider how revolutionary the idea is of reconciling everything from discovery and monitoring tools to asset databases in different formats from different companies into a single cohesive system—then I think you would have to agree that it’s only fair that this should take time.

EMA (i.e. me in this case) just completed a radar analysis of eleven vendors and talked to more than 20 CMDB/CMS deployments in North America and Europe. The research confirmed my views that CMDB-related technologies are evolving to become more dynamic, more real-time, more deployable, more use-case directed, and more varied in design. They are also becoming more essential than ever—spurred in part, ironically – by cloud computing which is pressuring companies to move towards a more cross-domain, and ultimately more service-centric model for management. (As I’ve pointed out in the past, companies with CMDB and application dependency tools show a 1.4 to 1.5 times advantage over those without in making cloud investments more effective.)

For this radar I excluded platform or framework vendors from the eleven vendors I worked with, in part because of the nature of EMA’s radar technology to create “framework ghettos” richer in function and higher in price.(I’ll be doing a platform-centric radar in Q 3-4 and include a few of the vendors here for comparison sake.) But I also wanted to focus on the differences in design to show, or confirm—what I believed to be true – that innovation in CMDB/CMS design is accelerating in multiple different directions.

The eleven vendors are:

- AccelOps
- ASG Software
- Axios Systems
- FireScope
- iET Solutions
- Interlink Software
- LANDesk
- n(i)2 Network Infrastructure Inventory
- Numara Software
- Service-now.com
- SunView Software

They were assessed along three use cases: Asset Management and Cost Optimization; Change Management and Change Impact Analysis; and Service Impact Management.

To give you an idea of some of the perspectives, I thought I’d wrap up with a few quotes from vendors and customer deployments—that highlight how CMDB-related technologies are changing, or not- -and how they are working in the real world. One thing, BTW, that hasn’t changed is the fact that just as CMDB/CMS is a revolutionary technology—it has parallel revolutionary implications for organizations, cultures, and processes as a few quotes below indicate.

Vendor: A couple of customer opportunities presented themselves—but the potential buyers said, “When you send in the P.O., make sure it doesn’t have the letters CMDB anywhere.” So we sometimes sell CMDB solutions by avoiding the letters C-M-D-B in our proposal.

Customer: We want to minimize the human interaction to get the CMDB up to date. The more manual the effort, the lower the accuracy of the CMDB.

Customer: In order to manage vMotion we needed a solution that was dynamically current and automatic in modeling infrastructure and application interdependencies.

Customer: We weren’t prepared for the political discussions going into this.

Customer: A large part of what we do is get people to work with changes—people are always resistant to change, we always get resistance.

Customer: Originally there was resistance. But two years later there are no complaints.

Customer: We have about 300 different types of stakeholders defined including business executives, applications management, desktops, servers, mainframes, merger recovery services, facilities planning, and ITIL process owners—just to name a few.

Customer: Our CMDB is doing a good job of supporting our migration towards a more fully virtualized infrastructure. It’s also helping us to cut down on the number of management tools we need.

Customer: We’re using the CMDB as the system of record for all of our PCs and their owners and where they are located and what applications run on them, in addition to our data center assets. It has become our authorized system of record.

These are just a few of many quotes used in the report—as added insight to the vendor assessments. In this unique radar, each Use Case has its own Bubble Chart – so that CMDB designs can be viewed in context with value instead of obscuring their positions by forcing everyone into a single, one-size-fits-all mold. The Webinar will spotlight these differences, while also providing a short-hand RFP for you to use in evaluating CMDB/CMS solutions – platform or not.

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 ...