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The CMDB of the Future - Part 1

The Challenge of the Conventional CMDB

The complex software applications that run modern businesses are often referred to as "mission-critical" and must be kept running 24x7. Unfortunately, the complexity of these applications is often so great that keeping them in a healthy state can be challenging, to say the least.

The Configuration Management Database, or CMDB, was conceived a few years back as a way to discover and maintain a repository of all components on which an application is dependent, along with information about their relationships. Apart from its use in asset management, the thought was that combining the CMDB with real-time monitoring metrics obtained from the underlying components could provide insight into the health state of complex applications, with early warning of incipient problems, and guidance to root cause when incidents do occur.

This is a powerful vision with potentially far-reaching benefits. It is a bit like the internal monitoring system in a modern automobile which relies on a complete and well-defined database of all the components on which the vehicle's operation is dependent and how a failure of any one component might affect the mission-critical operation of the vehicle. A soft female voice might warn you, for example, that your tire pressure is low — and she didn't have to "learn" that low tire pressure can cause a blowout by having one first.

Similarly, today's large, complex, mission-critical business applications can have a huge number of moving parts and underlying software components — with lots of things that can go wrong.

Is it possible to identify and maintain a database of all the internal dependencies of a complex application and create a warning system like that in a modern automobile that is highly deterministic and reliable and can prevent incidents from ever happening in the first place?

Sounds like a really great idea. Why then, has the CMDB seen only limited adoption and little commercial success?

Weaknesses of Conventional Configuration Management Tools

We have seen many products in recent years designed to create and maintain such a Configuration Management Database. However, practical challenges have prevented this vision from becoming reality, and the CMDB seems to have lost favor as a realistic contributor to a monitoring solution.

While successful to some extent, the general consensus seems to be that these products have been simply too limited in functionality or too difficult to use for maintaining reliable content. For some detailed criticism, see "IT Skeptic" Rob England's blog: CMDB: What Does It Really Mean?.

Monitoring a complex multi-tiered application involves the collection of data from many different sources, including infrastructure data (host cpu and memory), middleware service data (message flows, session counts), and application data like log file content or data exposed through JMX. Typically, this can include a dozen or more specific types of data for any platform you build.

Using a traditional CMDB or service model a user would either:

1. manually define the dependency relationships between these components and each application that uses them, or

2. use a tool to auto-discover the relationships using some form of heuristic algorithm.

Both of these methods have serious drawbacks. It is impractical to manually maintain a service model when components are continually being added or the system is modified. The heuristic method seems promising but the drawbacks are just as severe although more subtle; minor flaws and inaccuracies constantly plague the system and can cause mysterious errors that go undetected for a long time.

Automobile manufacturers gradually figured out how to manage the information needed to effectively maintain the health and safety of a moving vehicle. In a similar way, developers of complex applications are slowly discovering ways to make monitoring these systems more automatic and reliable. Fundamental changes in the IT landscape are helping as well.

The CMDB of the Future - Part 2

ABOUT Tom Lubinski

Tom Lubinski is President and CEO, and Board Chairman, of SL Corporation, which he founded in 1983.

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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 ...

The CMDB of the Future - Part 1

The Challenge of the Conventional CMDB

The complex software applications that run modern businesses are often referred to as "mission-critical" and must be kept running 24x7. Unfortunately, the complexity of these applications is often so great that keeping them in a healthy state can be challenging, to say the least.

The Configuration Management Database, or CMDB, was conceived a few years back as a way to discover and maintain a repository of all components on which an application is dependent, along with information about their relationships. Apart from its use in asset management, the thought was that combining the CMDB with real-time monitoring metrics obtained from the underlying components could provide insight into the health state of complex applications, with early warning of incipient problems, and guidance to root cause when incidents do occur.

This is a powerful vision with potentially far-reaching benefits. It is a bit like the internal monitoring system in a modern automobile which relies on a complete and well-defined database of all the components on which the vehicle's operation is dependent and how a failure of any one component might affect the mission-critical operation of the vehicle. A soft female voice might warn you, for example, that your tire pressure is low — and she didn't have to "learn" that low tire pressure can cause a blowout by having one first.

Similarly, today's large, complex, mission-critical business applications can have a huge number of moving parts and underlying software components — with lots of things that can go wrong.

Is it possible to identify and maintain a database of all the internal dependencies of a complex application and create a warning system like that in a modern automobile that is highly deterministic and reliable and can prevent incidents from ever happening in the first place?

Sounds like a really great idea. Why then, has the CMDB seen only limited adoption and little commercial success?

Weaknesses of Conventional Configuration Management Tools

We have seen many products in recent years designed to create and maintain such a Configuration Management Database. However, practical challenges have prevented this vision from becoming reality, and the CMDB seems to have lost favor as a realistic contributor to a monitoring solution.

While successful to some extent, the general consensus seems to be that these products have been simply too limited in functionality or too difficult to use for maintaining reliable content. For some detailed criticism, see "IT Skeptic" Rob England's blog: CMDB: What Does It Really Mean?.

Monitoring a complex multi-tiered application involves the collection of data from many different sources, including infrastructure data (host cpu and memory), middleware service data (message flows, session counts), and application data like log file content or data exposed through JMX. Typically, this can include a dozen or more specific types of data for any platform you build.

Using a traditional CMDB or service model a user would either:

1. manually define the dependency relationships between these components and each application that uses them, or

2. use a tool to auto-discover the relationships using some form of heuristic algorithm.

Both of these methods have serious drawbacks. It is impractical to manually maintain a service model when components are continually being added or the system is modified. The heuristic method seems promising but the drawbacks are just as severe although more subtle; minor flaws and inaccuracies constantly plague the system and can cause mysterious errors that go undetected for a long time.

Automobile manufacturers gradually figured out how to manage the information needed to effectively maintain the health and safety of a moving vehicle. In a similar way, developers of complex applications are slowly discovering ways to make monitoring these systems more automatic and reliable. Fundamental changes in the IT landscape are helping as well.

The CMDB of the Future - Part 2

ABOUT Tom Lubinski

Tom Lubinski is President and CEO, and Board Chairman, of SL Corporation, which he founded in 1983.

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 ...