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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

Outages aren't new. What's new is how quickly they spread across systems, vendors, regions and customer workflows. The moment that performance degrades, expectations escalate fast. In today's always-on environment, an outage isn't just a technical event. It's a trust event ...

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...