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Making the Right Application Discovery and Dependency Mapping (ADDM) Investment

Dennis Drogseth

This is the second in a series taken from Chapters Three, Twelve, and Appendix B in CMDB Systems: Making Change Work in the Age of Cloud and Agile. It is not meant as a substitute in any way for the book, but should provide you with a good beginning point for thinking about the technology selection process. Our first blog was on core CMDB selection.

The Application Discovery and Dependency Mapping (ADDM) market is evolving rapidly, and in multiple directions at once. While this can be confusing, it is overall a good thing. Through this diversity, vendors delivering ADDM capabilities are, as an aggregate, seeking to be more responsive to a yet broader set of constituents, use cases, and roles than ever before. This includes requirements emerging from internal and external (public) cloud, the extended enterprise across ecosystems, agile application development, and a dramatic upswing in currency, ease of deployment and modularity.

In some cases you will want to be sure to select an ADDM package that integrates with your core CMDB at initial deployment. In other cases it may come at a later time as a separate investment. On the other hand, depending on use case and overall readiness, an ADDM package may be the right starting point for growing your CMDB System in Phase One even without a core CMDB.

Multi-Use Case versus Performance-Optimized

Image removed.Probably the first place to start in evaluating the ADDM opportunity is to group vendor solutions into two general categories: multi-use-case and performance-optimized. While there has been some blending, each group is optimized for distinct values.

Multi-Use Case: ADDM first became an area of intense innovation roughly 10 years ago with the initial tidal wave of interest in CMDB deployments and the need to capture service-related interdependencies more effectively. Subsequently, that first crop of companies was largely acquired by leading platform solutions with native CMDB integrations. As a group, these ADDM pioneers were and still are focused on capturing configuration-related changes as well as application-to-infrastructure residency, with use cases targeted at asset and change management.

Performance-optimized ADDM: About five years ago, the industry began to see a new crop of ADDM solutions more focused on performance interdependencies, transactional awareness, and more real-time dynamic currency. Many of these also supported CMDB integrations; all were highly automated and, to some degree, were complementary to ADDM-related investments from the first wave. Vendors in this category are raising the bar on in-depth transactional awareness; dynamic, operational insights into application-to-application and application-to-infrastructure interdependencies; and higher levels of automation in terms of discovery and currency.

As the ADDM market progresses, both groups are beginning to harvest strengths from each other, and in this respect, they are becoming more alike. On the other hand, at least for the foreseeable future, there will be numerous situations where a complementary relationship between two separate ADDM packages may well be the right choice.

Read: 6 Key Points of ADDM Evaluation

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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 ...

Making the Right Application Discovery and Dependency Mapping (ADDM) Investment

Dennis Drogseth

This is the second in a series taken from Chapters Three, Twelve, and Appendix B in CMDB Systems: Making Change Work in the Age of Cloud and Agile. It is not meant as a substitute in any way for the book, but should provide you with a good beginning point for thinking about the technology selection process. Our first blog was on core CMDB selection.

The Application Discovery and Dependency Mapping (ADDM) market is evolving rapidly, and in multiple directions at once. While this can be confusing, it is overall a good thing. Through this diversity, vendors delivering ADDM capabilities are, as an aggregate, seeking to be more responsive to a yet broader set of constituents, use cases, and roles than ever before. This includes requirements emerging from internal and external (public) cloud, the extended enterprise across ecosystems, agile application development, and a dramatic upswing in currency, ease of deployment and modularity.

In some cases you will want to be sure to select an ADDM package that integrates with your core CMDB at initial deployment. In other cases it may come at a later time as a separate investment. On the other hand, depending on use case and overall readiness, an ADDM package may be the right starting point for growing your CMDB System in Phase One even without a core CMDB.

Multi-Use Case versus Performance-Optimized

Image removed.Probably the first place to start in evaluating the ADDM opportunity is to group vendor solutions into two general categories: multi-use-case and performance-optimized. While there has been some blending, each group is optimized for distinct values.

Multi-Use Case: ADDM first became an area of intense innovation roughly 10 years ago with the initial tidal wave of interest in CMDB deployments and the need to capture service-related interdependencies more effectively. Subsequently, that first crop of companies was largely acquired by leading platform solutions with native CMDB integrations. As a group, these ADDM pioneers were and still are focused on capturing configuration-related changes as well as application-to-infrastructure residency, with use cases targeted at asset and change management.

Performance-optimized ADDM: About five years ago, the industry began to see a new crop of ADDM solutions more focused on performance interdependencies, transactional awareness, and more real-time dynamic currency. Many of these also supported CMDB integrations; all were highly automated and, to some degree, were complementary to ADDM-related investments from the first wave. Vendors in this category are raising the bar on in-depth transactional awareness; dynamic, operational insights into application-to-application and application-to-infrastructure interdependencies; and higher levels of automation in terms of discovery and currency.

As the ADDM market progresses, both groups are beginning to harvest strengths from each other, and in this respect, they are becoming more alike. On the other hand, at least for the foreseeable future, there will be numerous situations where a complementary relationship between two separate ADDM packages may well be the right choice.

Read: 6 Key Points of ADDM Evaluation

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 ...