Discovery and Dependency Mapping—CMDB/CMS: The Synergies Are There!

by Dennis Drogseth, VP of Research, IT Megatrends, Analytics, and CMDB Systems

Okay, I admit it. “Service modeling” is an awkward term, especially when you’re trying to frame three rather controversial acronyms in the same overall place:

  • Configuration management database (CMDB)
  • Configuration management system (CMS) – a more federated CMDB
  • Discovery and dependency mapping (DDM)

Nevertheless, that’s exactly what we did in EMA’s most recent research, “Service Modeling in the Age of Cloud and Containers.” We also put a strong focus on how AIOps and IT analytics more broadly intersect with service modeling for good or for ill. As the data in our webinar later this month will show, the goal was to establish a more holistic context for looking at the synergies and differences across all these areas.

Methodology and collection
The data was collected in August of this year across a global population in North America, Europe, and Asia, with 398 respondents. As you will see from some of the highlights here, one of the goals was to examine the data from multiple points of view, including role-related perceptions and success rates, along with more standard contexts for analysis, such as company size, geography, and verticals.

A firm thumbs up
Admittedly, we didn’t set a quota for naysayers, and maybe we should have, as many of our respondents strongly indicated that the valued service modeling was in one or multiple of the relevant form factors (CMDB, CMS, or DDM). Their top reasons for valuation were:

  • Application performance management
  • Infrastructure optimization
  • Cloud migration and digital transformation tied for third place
  • Asset management and financial optimization also loomed large throughout the research, especially in terms of stakeholder support

What’s really being deployed?

For our research, we required some form of modeling to be in play so participants could answer our questions credibly. Given that criterion, when we asked, “What was deployed in your organization?” we got this spread from our respondents:

  • Configuration management system (CMS) – a more federated CMDB
  • CMDB + DDM – 43%
  • CMS (standalone) – 21%
  • CMDB (standalone) – 15%
  • DDM (standalone) – 12%
  • CMS + DDM – 10%

Interestingly, those 65% of respondents indicating that DDM was in play also showed us that on average, more than two DDM solutions were deployed in their IT organization, with 20% claiming four or more. The reason for this was use case-driven. For instance, having both a real-time performance-aware DDM solution, as well as DDM capabilities more expressly directed at service-aware asset management or cloud migration was beneficial.

What’s optimal in deployment?

How does a CMDB/DDM integration work? The optimal answer there is bidirectionally, meaning that that more real-time DDM tools can update the CMDB or CMS, while the DDM solution can gain added contextual insights from configuration item (CI-related) data and attributes. With this in mind, 45% of respondents indicated some level of bidirectional DDM-CMDB/CMS integration, which strongly correlated with success in achieving their strategic goals.

The service modeling/AIOps handshake

Our research also focused on the growing role of AIOps, sometimes known as “IT operations analytics” (ITOA). In fact, 80% of our respondents indicated that AIOps was either fully active or in deployment, which correlated with more progressive CMDB, CMS, and DDM adoptions, as well as strategic success rates overall. The data underscored the fact that artificial intelligence and machine learning are continuing to become less of a science project and more of a platform-driven resource that can help bring value in both unifying and transforming IT.

The human factor

Not surprisingly, what you learn about CMDB/CMS and DDM deployments depends to a large degree on who ask. This is partly because of service modeling’s wide-ranging strategic value, which often touches many varied roles and stakeholders differently. For instance, our data here showed that:

  • Role-based perceptions (asset management, operations, ITSM, etc.) indicate telling and often predictable differences in use cases, buying priorities, and other areas.
  • Type of involvement differences underscored the fact that executive or managerial oversight had the greatest breadth of vision in terms of what was deployed, while hands-on technical support came second, and stakeholders were least aware.
  • The executive suite was 2x more likely to see CIOs as driving service modeling strategies and buying decisions as all other groups.

As EMA pointed out in its book CMDB Systems: Making Change Work in the Age of Cloud and Agile,1 service modeling touches on so many roles and stakeholders that perceptions are bound to vary, much like the story of the blind men and the elephant.

Discover and dependency mapping

Success and EMA’s “More Syndrome”

As with other EMA research, respondents who indicated that they were extremely successful in achieving their strategic goals followed what I now call the “More Syndrome.” This included factors such as more use cases, more stakeholder roles, more analytics and automation integrations, more asset data, more best practices in play, etc.

Indeed, both this research and EMA’s consulting show that successful IT organizations evolve across multiple technologies and profit from their synergies. Service modeling capabilities are especially central to this equation, given their rich variety of use cases and the context they can provide for analytics, automation, monitoring, discovery, service catalogs, and other IT-related technology investments.

In the webinar on October 29th, I’ll be able to share far more insights surrounding these and other top findings and seek to place them in context to help you navigate your current choice of options and priorities for technology, process, and approach in your service modeling investments.



1CMDB Systems: Making Change Work in the Age of Cloud and Agile, Dennis Drogseth, Rick Sturm, Dan Twing. Elsevier, 2015.