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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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I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...

The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools ...

For years, secure connectivity has relied on a familiar pattern: route traffic back to centralized gateways, inspect it, and then allow access. This model worked when applications lived in a handful of data centers and users were largely confined to offices. That model is now under strain. Applications are distributed across clouds, users connect from everywhere, and real-time workloads demand performance that centralized inspection points struggle to deliver. As traffic volumes grow and latency expectations shrink, routing everything through a small number of control points has become both a performance bottleneck and a resilience risk. The future of secure connectivity requires a different approach ...

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Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...

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

Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale. But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within ...

Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...

I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...

The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools ...

For years, secure connectivity has relied on a familiar pattern: route traffic back to centralized gateways, inspect it, and then allow access. This model worked when applications lived in a handful of data centers and users were largely confined to offices. That model is now under strain. Applications are distributed across clouds, users connect from everywhere, and real-time workloads demand performance that centralized inspection points struggle to deliver. As traffic volumes grow and latency expectations shrink, routing everything through a small number of control points has become both a performance bottleneck and a resilience risk. The future of secure connectivity requires a different approach ...

The AI experimentation phase is over, and the private cloud is where enterprise AI workloads are being deployed for security and scale, according to Private Cloud Outlook 2026, a new report from Broadcom ... 2026 marks an acceleration into a full AI tipping point. The shift is being shaped by three forces — costs, complexity, and control — that public cloud environments are increasingly failing to address for production AI at scale. Key findings from the report include ...

44% of organizations have reported an outage in the past year tied to suppressed or ignored alerts, and 78% had at least one incident where no alert was fired at all ... Engineers learned about failures from customers. That gap between what our tools report and what our customers experience is the problem DevOps teams have been quietly solving with GenAI tooling, even as most enterprises continue to run their NOCs on manual alert triage ...

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...