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CMDB/CMS Use Cases: Change Management and Change Impact Analysis

Change Management and Change Impact Analysis are at the very heart of the CMDB/CMS value set. This includes change management for impact analysis of changes to Configuration Items (CIs) and their associated services, as well as change automation for activating changes (release) management. Many vendors favored one over the other, although Value Leaders did well in both. This category in the radar also included capacity planning and infrastructure optimization in support of service delivery -- an area often requested but until recently not largely supported through CMDB/CMS initiatives.

Other use cases for change management include:

Governance and Compliance: Managing change to support security-related, industry-related and other compliance-driven audits can be even more costly and labor intensive than asset-specific audits. One of EMA’s consulting clients estimates that a CMDB in hindsight could have saved them nearly $5 million in consulting costs.

Service Availability and Performance: EMA estimates that roughly 60% of IT service disruptions come from the impacts of planned or unplanned changes across the application infrastructure.

Data Center Consolidation: With the rise of virtualization in the data center, planning new options for data center consolidation is definitely on the uptake. Mergers and acquisitions can also factor in as a driver for data center consolidation.

Disaster Recovery: Automating change in case of disaster is one of the more pervasive drivers for CMDB/CMS initiatives.

Facilities management and Green IT: Extending the role of IT governance to areas external to traditional IT boundaries (facilities, power, etc.) requires core capabilities for capturing CMS-related interdependencies and exploiting the "logical and physical" extensions of CMS modeling.

Support for Provisioning New Application Services: This can include cloud-related service provisioning potentially via service catalogs and blueprints, as well as more traditionally developed (in-house developed) applications. The CMDB/CMS role in supporting Dev Ops is just now beginning to take on new life -- although it’s not brand new. I’ve seen examples of this use case going back as many as five years.


Figure 3: Change Management and Change Impact Analysis pose a greater foundational set of insights into interdependencies, and so application dependency mapping, configuration, asset management and capacity management are all in black. In many respects, it’s the heart of what CMDB/CMS capabilities are all about.

The following vendors were Value Leaders in EMA’s CMDB/CMS radar:

Axios

Axios received the highest overall score in the radar for Change Management. Among the things that stood out most about assyst were its strong support for third-party integration in discovery combined with a staging area; and its proven integrations with third-party application dependency mapping solutions for service impact analysis -- including those from VMware, BMC, and IBM. Axios also won an award as “Most Balanced CMDB/CMS functionality” across all three use cases.

N(i)2

N(i)2 lead dramatically in Architecture and Integration and Functionality, and was also significantly ahead of average in Deployment and Administration. One way to get a feel for N(i)2’s unique depth are the change management histories it keeps beyond traditional IT technology boundaries—such as: slot occupancy in chassis; rack mounting position, power management, WAN circuit, etc. Although it is highly applicable to enterprises, N(i)2 remains best understood to date by service providers. N(i)2 also won the award for “Most Innovative CMDB/CMS Deployment.”

Numara

Numara FootPrints scored well above the average in Change Management, leading in Cost Advantage, Administration and Deployment, with scores just slightly under in Functionality and Architecture and Integration. According to Numara, Change Management is its fastest ROI and this was borne out by customer interviews as well. However, it should be pointed out that across the broader FootPrints family, change management and lifecycle asset management are so closely intertwined.

ServiceNow

ServiceNow significantly outperformed the vendor average in all areas. It received best overall score in Deployment and Administration, in the top third for Cost Advantage. It was also well in the top half for Architecture and Integration, and in the top third for functionality. This shouldn’t be surprising given its rich automation capabilities (IT Process Automation or runbook plus workflow) combined with its Service-now Discovery and Dependency Mapping. As you may have noticed, this is the second time ServiceNow appears as a value leader.

SunView Software

SunView Software’s ChangeGear scored meaningfully above average in Deployment and Administration, Functionality, and Architecture and Integration. SunView was dramatically ahead in Change Management in Cost Advantage. And indeed, no vendor in EMA’s radar was more optimally tuned to address Change Management in mid-tier and mid-tier enterprise environments where administration and cost are strong counterweights to functionality and architecture.

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

Click here to read the CMDB/CMS use cases for Service Impact Management

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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

CMDB/CMS Use Cases: Change Management and Change Impact Analysis

Change Management and Change Impact Analysis are at the very heart of the CMDB/CMS value set. This includes change management for impact analysis of changes to Configuration Items (CIs) and their associated services, as well as change automation for activating changes (release) management. Many vendors favored one over the other, although Value Leaders did well in both. This category in the radar also included capacity planning and infrastructure optimization in support of service delivery -- an area often requested but until recently not largely supported through CMDB/CMS initiatives.

Other use cases for change management include:

Governance and Compliance: Managing change to support security-related, industry-related and other compliance-driven audits can be even more costly and labor intensive than asset-specific audits. One of EMA’s consulting clients estimates that a CMDB in hindsight could have saved them nearly $5 million in consulting costs.

Service Availability and Performance: EMA estimates that roughly 60% of IT service disruptions come from the impacts of planned or unplanned changes across the application infrastructure.

Data Center Consolidation: With the rise of virtualization in the data center, planning new options for data center consolidation is definitely on the uptake. Mergers and acquisitions can also factor in as a driver for data center consolidation.

Disaster Recovery: Automating change in case of disaster is one of the more pervasive drivers for CMDB/CMS initiatives.

Facilities management and Green IT: Extending the role of IT governance to areas external to traditional IT boundaries (facilities, power, etc.) requires core capabilities for capturing CMS-related interdependencies and exploiting the "logical and physical" extensions of CMS modeling.

Support for Provisioning New Application Services: This can include cloud-related service provisioning potentially via service catalogs and blueprints, as well as more traditionally developed (in-house developed) applications. The CMDB/CMS role in supporting Dev Ops is just now beginning to take on new life -- although it’s not brand new. I’ve seen examples of this use case going back as many as five years.


Figure 3: Change Management and Change Impact Analysis pose a greater foundational set of insights into interdependencies, and so application dependency mapping, configuration, asset management and capacity management are all in black. In many respects, it’s the heart of what CMDB/CMS capabilities are all about.

The following vendors were Value Leaders in EMA’s CMDB/CMS radar:

Axios

Axios received the highest overall score in the radar for Change Management. Among the things that stood out most about assyst were its strong support for third-party integration in discovery combined with a staging area; and its proven integrations with third-party application dependency mapping solutions for service impact analysis -- including those from VMware, BMC, and IBM. Axios also won an award as “Most Balanced CMDB/CMS functionality” across all three use cases.

N(i)2

N(i)2 lead dramatically in Architecture and Integration and Functionality, and was also significantly ahead of average in Deployment and Administration. One way to get a feel for N(i)2’s unique depth are the change management histories it keeps beyond traditional IT technology boundaries—such as: slot occupancy in chassis; rack mounting position, power management, WAN circuit, etc. Although it is highly applicable to enterprises, N(i)2 remains best understood to date by service providers. N(i)2 also won the award for “Most Innovative CMDB/CMS Deployment.”

Numara

Numara FootPrints scored well above the average in Change Management, leading in Cost Advantage, Administration and Deployment, with scores just slightly under in Functionality and Architecture and Integration. According to Numara, Change Management is its fastest ROI and this was borne out by customer interviews as well. However, it should be pointed out that across the broader FootPrints family, change management and lifecycle asset management are so closely intertwined.

ServiceNow

ServiceNow significantly outperformed the vendor average in all areas. It received best overall score in Deployment and Administration, in the top third for Cost Advantage. It was also well in the top half for Architecture and Integration, and in the top third for functionality. This shouldn’t be surprising given its rich automation capabilities (IT Process Automation or runbook plus workflow) combined with its Service-now Discovery and Dependency Mapping. As you may have noticed, this is the second time ServiceNow appears as a value leader.

SunView Software

SunView Software’s ChangeGear scored meaningfully above average in Deployment and Administration, Functionality, and Architecture and Integration. SunView was dramatically ahead in Change Management in Cost Advantage. And indeed, no vendor in EMA’s radar was more optimally tuned to address Change Management in mid-tier and mid-tier enterprise environments where administration and cost are strong counterweights to functionality and architecture.

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

Click here to read the CMDB/CMS use cases for Service Impact Management

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.