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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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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

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 ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...