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CMDB/CMS Use Cases: Asset Management and Financial Optimization

Asset Management is often the fastest use case to deploy for an initial phase CMDB/CMS because, as one vendor put it, “It’s the most linear use case” and generally speaking the least time sensitive. As a result it’s probably the most popular initial use case for CMDB/CMS initiatives -- which doesn’t mean it’s necessarily the right first phase deployment for you. You should start where your needs, and your readiness, combine to give you greatest value –- which could be any one of the three here.

Some specific use cases for asset management include:

Asset and Inventory Analysis: This most often comes up in broadly based asset management initiatives, but it is also relevant to the other use cases – and ALWAYS a challenge.

Asset Lifecycle Management: This includes SLAs, maintenance windows, and service contracts, and is central to managing assets effectively across their lifecycles. Strong support for SW license management is also important. This is often achieved through separate but well integrated solutions –- including third-party solutions.

Compliance Audits: These can now be far more effectively automated and structured based on consistent policies once assets are mapped into a CMDB/CMS system.

Financial Optimization: This is essentially optimizing your IT mission from a dollars-and-cents perspective. CMDB/CMS-driven asset and service management initiatives lay the core foundation for capturing and analyzing cost and value metrics in a substantive and contextually consistent way.


Figure 2: The darker the color, the more central the capability is to the use case. As you can see, the core CMDB, asset management and configuration management are all in black. Next in line are application dependency mapping, capacity, usage and optimization, and application development (as new application services can also be viewed as assets and relevant to asset planning and financial optimization).

EMA had four categories for assessing vendors in its radar: Value Leader, Strong Value, Specific Value, and Limited Value –- from highest to lowest. The value leaders from the radar in Asset Management and Financial Optimization were:

ASG

ASG is arguably the most platform-like of all the vendors in the radar in terms of breadth of capability and function, ASG was one of four Value Leaders in Asset Management where it received the highest overall score of any vendor. Relevant to this, ASG-Trackbird for asset management also received a Value Leader ranking in EMA’s May, 2011 radar on Software Asset Management. ASG offers a CMDB design that’s matured to focus on real needs and use cases -- with an exceptional ability to take in third-party information from a wide variety of sources.

iET Solutions

iET Solutions received especially high ratings in Cost Advantage but clear dominance in all function-related and cost-related vectors. The company’s strengths come in part from well thought out process flows to support asset lifecycle management along with its solid integration capabilities for third-party sources. It was really a balanced solution -- and offered the only deployment where the CMDB was working across a fully third-party management landscape.

LANDesk

LANDesk Service Desk Suite excelled across all areas for Asset Management and scored a first place total score (closely followed by ASG) across all eleven vendors for Asset Management and Financial Optimization. LANDesk’s Service Desk Suite is enhanced by integrations with its own Asset Lifecycle Manager which was one of the Value Leaders in EMA’s Software Asset Management Radar from May, 2011.

ServiceNow

ServiceNow's CMDB outperformed the vendor average in all areas -- with especially strong support for Integration and Interoperability and top-ranked scores for automation in Functionality. ServiceNow’s capabilities to assimilate relevant third-party sources, map them via strong discovery and inventory, and set up process automation for lifecycle control are all impressive. As you may know, ServiceNow also shook the industry by successfully establishing the SaaS model in this space –- and it continues to grow at a dramatic pace.

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 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: Asset Management and Financial Optimization

Asset Management is often the fastest use case to deploy for an initial phase CMDB/CMS because, as one vendor put it, “It’s the most linear use case” and generally speaking the least time sensitive. As a result it’s probably the most popular initial use case for CMDB/CMS initiatives -- which doesn’t mean it’s necessarily the right first phase deployment for you. You should start where your needs, and your readiness, combine to give you greatest value –- which could be any one of the three here.

Some specific use cases for asset management include:

Asset and Inventory Analysis: This most often comes up in broadly based asset management initiatives, but it is also relevant to the other use cases – and ALWAYS a challenge.

Asset Lifecycle Management: This includes SLAs, maintenance windows, and service contracts, and is central to managing assets effectively across their lifecycles. Strong support for SW license management is also important. This is often achieved through separate but well integrated solutions –- including third-party solutions.

Compliance Audits: These can now be far more effectively automated and structured based on consistent policies once assets are mapped into a CMDB/CMS system.

Financial Optimization: This is essentially optimizing your IT mission from a dollars-and-cents perspective. CMDB/CMS-driven asset and service management initiatives lay the core foundation for capturing and analyzing cost and value metrics in a substantive and contextually consistent way.


Figure 2: The darker the color, the more central the capability is to the use case. As you can see, the core CMDB, asset management and configuration management are all in black. Next in line are application dependency mapping, capacity, usage and optimization, and application development (as new application services can also be viewed as assets and relevant to asset planning and financial optimization).

EMA had four categories for assessing vendors in its radar: Value Leader, Strong Value, Specific Value, and Limited Value –- from highest to lowest. The value leaders from the radar in Asset Management and Financial Optimization were:

ASG

ASG is arguably the most platform-like of all the vendors in the radar in terms of breadth of capability and function, ASG was one of four Value Leaders in Asset Management where it received the highest overall score of any vendor. Relevant to this, ASG-Trackbird for asset management also received a Value Leader ranking in EMA’s May, 2011 radar on Software Asset Management. ASG offers a CMDB design that’s matured to focus on real needs and use cases -- with an exceptional ability to take in third-party information from a wide variety of sources.

iET Solutions

iET Solutions received especially high ratings in Cost Advantage but clear dominance in all function-related and cost-related vectors. The company’s strengths come in part from well thought out process flows to support asset lifecycle management along with its solid integration capabilities for third-party sources. It was really a balanced solution -- and offered the only deployment where the CMDB was working across a fully third-party management landscape.

LANDesk

LANDesk Service Desk Suite excelled across all areas for Asset Management and scored a first place total score (closely followed by ASG) across all eleven vendors for Asset Management and Financial Optimization. LANDesk’s Service Desk Suite is enhanced by integrations with its own Asset Lifecycle Manager which was one of the Value Leaders in EMA’s Software Asset Management Radar from May, 2011.

ServiceNow

ServiceNow's CMDB outperformed the vendor average in all areas -- with especially strong support for Integration and Interoperability and top-ranked scores for automation in Functionality. ServiceNow’s capabilities to assimilate relevant third-party sources, map them via strong discovery and inventory, and set up process automation for lifecycle control are all impressive. As you may know, ServiceNow also shook the industry by successfully establishing the SaaS model in this space –- and it continues to grow at a dramatic pace.

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 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.