Service Modeling in the Digital Age: CMDB, or CMS, and/or DDM?

Dennis Drogseth

Just like in the world at large, there's no absence of political opinions in high tech, only where we live and work those opinions are, appropriately, often directed at technology trends. But as terms and ideas sometimes become first sanctified and then damned, just as they so often do in national media, what usually gets lost in the shuffle are the real nuances of what's happening and why.

One provocative example, the configuration management database (CMDB), along with its more federated companion, the configuration management system (CMS), has been bathed in a deluge of negative opinions from all fronts — industry experts, vendors, and IT professionals. But from what recent EMA research on analytics, ITSM performance and other areas is indicating, those negative views seem to be missing out on a real undercurrent of truth — that CMDB/CMS alignments, whatever their defects, strongly skew to success in terms of overall IT progressiveness and effectiveness. This can be especially true when discovery and dependency mapping (DDM) solutions are included as part of the mix, often for more effective real-time currency. And ideally the hand-off there is bi-directional, as the CMDB/CMS can add valuable context to DDM insights.

With that in mind, and with a history in CMDB consulting to build on, EMA is embarking on research currently titled Service Modeling in the Age of Cloud and Containers. The goal is to set the record straight — maybe not once and for all — but at least in terms of what to expect in 2020. To do this we are looking at a global respondent base of 400 including North America, Europe and Asia. (Data collection is just underway as I'm writing this.)

Who's on First?

One of the goals will be to granularly evaluate the prevalence of CMDB, CMS and DDM deployments across a wide variety of contexts:

Which are the most prevalent?
Which are most in demand?
How many IT organizations see their CMDB as evolving into a CMS?
What are the realized synergies between DDM and CMDB/CMS deployments, and how do these translate into overall success and effectiveness (or for that matter ineffectiveness) across a wide range of use cases?

What's the Use?

We're being quite serious about that "wide range of use cases" for trending and comparisons. The overall list includes:

Asset management
Financial optimization
Performance and availability management
Behavioral analysis
Forecasting prediction
Application lifecycle management
Event troubleshooting and remediation
Change management
Capacity optimization
Cloud migration/optimization
DevOps/release management
DevOps/preproduction provisioning
Security/fraud/risk
Security/compliance
SecOps (integrated security and operations)
IoT-related use cases
IT governance for OpEx efficiencies
Routine service desk requirements (onboarding, etc.)
IT transformation
Digital transformation

We're also bringing AIOps into the picture, to evaluate how AIOps integrations are being impacted by these adoptions. In fact, the overall relevance of service modeling to IT analytics investments, and vice versa, is a core focus of the research.

Cloudy Weather

As suggested in the title, the move to support the dynamic requirements of cloud is another chief area of interest. For instance, we'll be looking at:

Public cloud: VM-specific data
Public cloud: containers
Public cloud: microservices
Private cloud: VM-specific data
Private cloud: containers
Private cloud: microservices
Infrastructure as code (IaC)

Each of these environments will be addressed, along with a wide range of other data sources — from inventory and discovery tools, to asset data, to spreadsheet data, to IoT, just to name a few — all with an eye to what's most pervasive, what's most needed and what is most likely to require real-time currency.

Success - What's Needed and What's Lacking

Along with dimensions such as administrative overhead, time spent keeping data current, and roles associated with "owning" deployments, we'll also be trending on success rates from various perspectives. And we'll be examining what's lacking most in current technologies, brands and products for service modeling, as well as what IT organizations see as the most sought-after functional priorities in CMDB, CMS and DDM adoptions.

Finally, we'll be getting advice on going forward — what's top of mind for forging ahead with more complete service modeling investments?

EMA will be hosting a webinar on all this and a lot more on October 8. With that in mind, I'll be writing another blog well in advance to share some of the data highlights, and let you know how to sign up. But if you have any questions in the meantime, please feel free contact me at drogseth@emausa.com. I welcome your questions, thoughts and views.

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Service Modeling in the Digital Age: CMDB, or CMS, and/or DDM?

Dennis Drogseth

Just like in the world at large, there's no absence of political opinions in high tech, only where we live and work those opinions are, appropriately, often directed at technology trends. But as terms and ideas sometimes become first sanctified and then damned, just as they so often do in national media, what usually gets lost in the shuffle are the real nuances of what's happening and why.

One provocative example, the configuration management database (CMDB), along with its more federated companion, the configuration management system (CMS), has been bathed in a deluge of negative opinions from all fronts — industry experts, vendors, and IT professionals. But from what recent EMA research on analytics, ITSM performance and other areas is indicating, those negative views seem to be missing out on a real undercurrent of truth — that CMDB/CMS alignments, whatever their defects, strongly skew to success in terms of overall IT progressiveness and effectiveness. This can be especially true when discovery and dependency mapping (DDM) solutions are included as part of the mix, often for more effective real-time currency. And ideally the hand-off there is bi-directional, as the CMDB/CMS can add valuable context to DDM insights.

With that in mind, and with a history in CMDB consulting to build on, EMA is embarking on research currently titled Service Modeling in the Age of Cloud and Containers. The goal is to set the record straight — maybe not once and for all — but at least in terms of what to expect in 2020. To do this we are looking at a global respondent base of 400 including North America, Europe and Asia. (Data collection is just underway as I'm writing this.)

Who's on First?

One of the goals will be to granularly evaluate the prevalence of CMDB, CMS and DDM deployments across a wide variety of contexts:

Which are the most prevalent?
Which are most in demand?
How many IT organizations see their CMDB as evolving into a CMS?
What are the realized synergies between DDM and CMDB/CMS deployments, and how do these translate into overall success and effectiveness (or for that matter ineffectiveness) across a wide range of use cases?

What's the Use?

We're being quite serious about that "wide range of use cases" for trending and comparisons. The overall list includes:

Asset management
Financial optimization
Performance and availability management
Behavioral analysis
Forecasting prediction
Application lifecycle management
Event troubleshooting and remediation
Change management
Capacity optimization
Cloud migration/optimization
DevOps/release management
DevOps/preproduction provisioning
Security/fraud/risk
Security/compliance
SecOps (integrated security and operations)
IoT-related use cases
IT governance for OpEx efficiencies
Routine service desk requirements (onboarding, etc.)
IT transformation
Digital transformation

We're also bringing AIOps into the picture, to evaluate how AIOps integrations are being impacted by these adoptions. In fact, the overall relevance of service modeling to IT analytics investments, and vice versa, is a core focus of the research.

Cloudy Weather

As suggested in the title, the move to support the dynamic requirements of cloud is another chief area of interest. For instance, we'll be looking at:

Public cloud: VM-specific data
Public cloud: containers
Public cloud: microservices
Private cloud: VM-specific data
Private cloud: containers
Private cloud: microservices
Infrastructure as code (IaC)

Each of these environments will be addressed, along with a wide range of other data sources — from inventory and discovery tools, to asset data, to spreadsheet data, to IoT, just to name a few — all with an eye to what's most pervasive, what's most needed and what is most likely to require real-time currency.

Success - What's Needed and What's Lacking

Along with dimensions such as administrative overhead, time spent keeping data current, and roles associated with "owning" deployments, we'll also be trending on success rates from various perspectives. And we'll be examining what's lacking most in current technologies, brands and products for service modeling, as well as what IT organizations see as the most sought-after functional priorities in CMDB, CMS and DDM adoptions.

Finally, we'll be getting advice on going forward — what's top of mind for forging ahead with more complete service modeling investments?

EMA will be hosting a webinar on all this and a lot more on October 8. With that in mind, I'll be writing another blog well in advance to share some of the data highlights, and let you know how to sign up. But if you have any questions in the meantime, please feel free contact me at drogseth@emausa.com. I welcome your questions, thoughts and views.

Related Links

Hot Topics

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

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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 ...

AI Deepfakes: Rethinking Trust in the Workplace

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 ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

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 ...

Why ITOps Need Right-Sized AI, Not Bigger Models

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 ...