CMDB/CMS Use Cases: Service Impact Management

Although CMDBs grew up with a focus more on process control than on performance management and real-time actions, design advances and new trends such as cloud computing are changing that dramatically. Operational professionals with concerns such as Mean-Time-to-Repair (MTTR) and Mean-Time-Between-Failure (MTBF) can benefit greatly from a “reconciled view of truth” including the impacts of change on performance, both of which are ultimately dependent on a CMDB/CMS foundation.

Some of the more dramatic use cases here include:

A Reconciled View of Truth Across Many Multiple Sources: One CMDB/CMS initiative reduced Mean-Time-to-Repair (MTTR) 70%, when downtime costs were estimated at $1 million a minute, by providing a more cohesive way of leveraging its many monitoring tools, and consolidating down to a single service desk.

Reflexive Insights into Change and Configuration for Diagnostics: Automating insights between configuration and change issues and performance issues to support real-time or proactive diagnostics is a core value of a service impact CMDB/CMS.

Validation that a Newly Provisioned Service is Performing Effectively (or not): Once a service has been deployed, what for instance is the impact on end-user experience?

Incident and Problem Management Automation and Governance: When CMDB/CMS is combined with strong support ITIL or other workflows and process definitions, it can accelerate the time to resolve problems and harden desired processes so that they are more consistently followed.

Finding the Owner: Automating and securing the process of finding individuals who “own” a problem or CI, though seemingly trivial, can nevertheless bring significant benefits –- up to $100,000 a year in just opex-related phone time between the service desk and operations in the case of one EMA client.

Business Process and Service-Specific Benefits: Having a cohesive vision of “truth” can positively impact business processes as far ranging as loan processing, to hospital management and admissions, to manufacturing line efficiencies, as just a few examples.


Figure 1: Service Impact Management prioritizes more real-time, operational insights and is in many respects a complementary technology to more process-centric, traditional CMDBs. However, both are anchored core insights into application-to-infrastructure and infrastructure-to-infrastructure interdependencies.

From a Service Impact perspective, EMA identified the following four Value Leaders:

AccelOps

AccelOps was third from the highest score in overall rankings and first in Deployment and Cost Efficiency as well as Functionality. AccelOps is often purchased as a service management system with strong capabilities in discovery, security, application dependency and CMDB modeling. Its deployments are consistently efficient and quickly lead to value, and so AccelOps is well optimized to the requirements of virtualized, cloud and hybrid environments.

ASG

ASG came in first in its overall vendor score. ASG has unique capabilities for assimilating third-party information, including that from performance management solutions to complement or supplement its own. It also has a healthy ability to federate and adapt to real-time insights. Combine this with strong application discovery and dependency mapping, solid dashboards, and a suite of its own monitoring tools –- and its position in the sun here is clear. As you may remember, ASG was also a value leader in Asset Management and Financial Optimization.

FireScope

FireScope was at the top for Deployment and Cost Efficiency for Service Impact Management, within the top three for Architecture and Integration and within the top third for Functionality. In addition to FireScope’s strong capabilities for Service Impact Management via its own product -- Unify, Orchestrate also profited from attractive dashboards -- including mashups, and a flexible way of assimilating third-party solutions that includes putting a “freshness stamp” on incoming data. FireScope won an award for “Most easily administered CMDB.”

Interlink

Interlink excelled in all functional and deployment areas. Interlink’s Service Configuration Manager is optimized to work with its Business Enterprise Server (BES) where it provides a complete system for Service Impact Management. It leverages insights into the impact of change, and has a far-reaching capability to assimilate interdependencies across the full service infrastructure from third-party sources. Interlink is a small vendor based in Edinburgh, Scotland, but it does have customers in the U.S. Its customers give it very high praise for its support.

Click here to read the CMDB/CMS use cases for Change Management and Change Impact Analysis

Click here to read the CMDB/CMS use cases for Asset Management and Financial Optimization

Hot Topics

The Latest

Keys to Building a Partner Ecosystem That Scales

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 ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

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 ...

The End of Reactive DevOps: AI-Driven Observability for Zero-Defect Digital Systems

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 ...

5 Takeaways from the Observability Forecast for Media and Entertainment

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 ...

Why Moving Off the Cloud Is the Easy Part and What Comes Next Is Where Things Get Hard

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 ...

MEAN TIME TO INSIGHT Podcast - Episode 24: Network Observability Tool Sprawl

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

Capacity Isn't a Guess: Observability-Driven Sizing for On-Prem Databases

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 ...

CMDB/CMS Use Cases: Service Impact Management

Although CMDBs grew up with a focus more on process control than on performance management and real-time actions, design advances and new trends such as cloud computing are changing that dramatically. Operational professionals with concerns such as Mean-Time-to-Repair (MTTR) and Mean-Time-Between-Failure (MTBF) can benefit greatly from a “reconciled view of truth” including the impacts of change on performance, both of which are ultimately dependent on a CMDB/CMS foundation.

Some of the more dramatic use cases here include:

A Reconciled View of Truth Across Many Multiple Sources: One CMDB/CMS initiative reduced Mean-Time-to-Repair (MTTR) 70%, when downtime costs were estimated at $1 million a minute, by providing a more cohesive way of leveraging its many monitoring tools, and consolidating down to a single service desk.

Reflexive Insights into Change and Configuration for Diagnostics: Automating insights between configuration and change issues and performance issues to support real-time or proactive diagnostics is a core value of a service impact CMDB/CMS.

Validation that a Newly Provisioned Service is Performing Effectively (or not): Once a service has been deployed, what for instance is the impact on end-user experience?

Incident and Problem Management Automation and Governance: When CMDB/CMS is combined with strong support ITIL or other workflows and process definitions, it can accelerate the time to resolve problems and harden desired processes so that they are more consistently followed.

Finding the Owner: Automating and securing the process of finding individuals who “own” a problem or CI, though seemingly trivial, can nevertheless bring significant benefits –- up to $100,000 a year in just opex-related phone time between the service desk and operations in the case of one EMA client.

Business Process and Service-Specific Benefits: Having a cohesive vision of “truth” can positively impact business processes as far ranging as loan processing, to hospital management and admissions, to manufacturing line efficiencies, as just a few examples.


Figure 1: Service Impact Management prioritizes more real-time, operational insights and is in many respects a complementary technology to more process-centric, traditional CMDBs. However, both are anchored core insights into application-to-infrastructure and infrastructure-to-infrastructure interdependencies.

From a Service Impact perspective, EMA identified the following four Value Leaders:

AccelOps

AccelOps was third from the highest score in overall rankings and first in Deployment and Cost Efficiency as well as Functionality. AccelOps is often purchased as a service management system with strong capabilities in discovery, security, application dependency and CMDB modeling. Its deployments are consistently efficient and quickly lead to value, and so AccelOps is well optimized to the requirements of virtualized, cloud and hybrid environments.

ASG

ASG came in first in its overall vendor score. ASG has unique capabilities for assimilating third-party information, including that from performance management solutions to complement or supplement its own. It also has a healthy ability to federate and adapt to real-time insights. Combine this with strong application discovery and dependency mapping, solid dashboards, and a suite of its own monitoring tools –- and its position in the sun here is clear. As you may remember, ASG was also a value leader in Asset Management and Financial Optimization.

FireScope

FireScope was at the top for Deployment and Cost Efficiency for Service Impact Management, within the top three for Architecture and Integration and within the top third for Functionality. In addition to FireScope’s strong capabilities for Service Impact Management via its own product -- Unify, Orchestrate also profited from attractive dashboards -- including mashups, and a flexible way of assimilating third-party solutions that includes putting a “freshness stamp” on incoming data. FireScope won an award for “Most easily administered CMDB.”

Interlink

Interlink excelled in all functional and deployment areas. Interlink’s Service Configuration Manager is optimized to work with its Business Enterprise Server (BES) where it provides a complete system for Service Impact Management. It leverages insights into the impact of change, and has a far-reaching capability to assimilate interdependencies across the full service infrastructure from third-party sources. Interlink is a small vendor based in Edinburgh, Scotland, but it does have customers in the U.S. Its customers give it very high praise for its support.

Click here to read the CMDB/CMS use cases for Change Management and Change Impact Analysis

Click here to read the CMDB/CMS use cases for Asset Management and Financial Optimization

Hot Topics

The Latest

Keys to Building a Partner Ecosystem That Scales

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 ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

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 ...

The End of Reactive DevOps: AI-Driven Observability for Zero-Defect Digital Systems

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 ...

5 Takeaways from the Observability Forecast for Media and Entertainment

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 ...

Why Moving Off the Cloud Is the Easy Part and What Comes Next Is Where Things Get Hard

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 ...

MEAN TIME TO INSIGHT Podcast - Episode 24: Network Observability Tool Sprawl

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

Capacity Isn't a Guess: Observability-Driven Sizing for On-Prem Databases

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 ...