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BSM and the Art of Discovery

Monitoring, Management and BSM: Discovery and Automation

Successfully implementing BSM in the daily operations of a data center brings into play a multitude of skills, tools, processes and personalities. Boundaries, responsibilities, authorities and accountabilities must be identified, negotiated and exercised in a complex, dynamic series of interactions. Properly done, the result will be a smooth, consistent and near problem-free operations environment where the goals of both IT and business partners are achieved with a minimum of effort.

OOPS! Someone has lost touch with the real world while chasing unicorns. The reality is that a successful implementation is merely the first step. Today’s data center and business service operations and delivery environments are, and will continue to be, highly dynamic. They require active, intelligent and increasingly automated oversight and control of available assets (including infrastructure, processes, etc.) for reliable service delivery.

Doing this requires active monitoring and management of the infrastructure, assets and processes involved in and impacting the delivery of a service. To assure reliable delivery at the level that meets business needs, services must be monitored and data made available to various business and operations staff.

The needed data includes:

- Performance and availability of the service

- The assets used by a service and their impact on service delivery

- Other assets in the environment – in-use/idle/available configurations and capabilities (bandwidth, size, speeds, etc.)

- Who/which service is using these assets

- What services assets can support

- Interdependencies between services

- The impact of re-assignment or change (priority to business, value received, cost of change, etc.)

Information based on this data must be available in a quickly comprehensible format to a wide list of consumers. This list will include operations staff, administration staff, service managers, line-of-business managers, as well as executives who want assurance that service SLAs are being met.

Getting accurate and timely data about the infrastructure and processes involved in service delivery doesn’t happen automatically. You need to assure that your monitoring solution will discover and report on process and infrastructure changes that will impact specific services. Typically, this involves building a model of the assets, infrastructure and processes used in service delivery and definition of dependencies.

Business requires change and adaptation to attract, service, and maintain customer (or user) satisfaction. New services must be developed and delivered. Infrastructure (both virtual and physical) will be altered, reassigned, and reconfigured as it adapts to evolving environmental, operational and business needs. Today’s highly dynamic data center operations will rapidly make a static model outdated and useless.

What is required is a monitoring solution with automated capabilities to build on and update the basic modeling engine. It must automatically discover and be able to integrate changes in these processes and infrastructure. It must be able to inform when such changes will affect service delivery.

Typically, infrastructure elements are part of the delivery of multiple, different services. Such elements can be manipulated by staff that are unaware of or don’t care about the impact on other services when changes are made. For example, one can imagine an infrastructure change that optimizes a delivery path for Service A under stress conditions. Service B shares infrastructure elements with Service A. The changes don’t normally affect B’s delivery, but will disrupt Service B when invoked. It is also possible to make changes that will benefit Service B (i.e. provide a redundant service path), but mask a failure in Service B primary resources. Only intelligent monitoring with an ability to create correlated service-oriented infrastructure views will provide the holistic visibility into infrastructure utilization needed to avoid such potential issues.

BSM requires dynamic, intelligent asset management able to provide a range of IT and business views that accurately represent service delivery and the operational environment (virtual and physical) that enables delivery. A critical element supporting that management is a monitoring solution that can automatically discover and dynamically adjust to changes in assets and infrastructure. And, that can detect and report on changes in shared assets that may affect service delivery.

About Rich Ptak

Rich Ptak, Managing Partner at Ptak, Noel & Associates LLC. has over 30 years experience in systems product management, working closely with Fortune 50 companies in developing product direction and strategies at a global level. Previously Ptak held positions as Senior Vice President at Hurwitz Group and D.H. Brown Associates. Earlier in his career he held engineering and marketing management positions with Western Electric’s Electronic Switch Manufacturing Division and Digital Equipment Corporation. He is frequently quoted in major business and trade press. Ptak holds a master’s in business administration from the University of Chicago and a master of science in engineering from Kansas State University.

Related Links:

www.ptaknoel.com

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Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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 ...

BSM and the Art of Discovery

Monitoring, Management and BSM: Discovery and Automation

Successfully implementing BSM in the daily operations of a data center brings into play a multitude of skills, tools, processes and personalities. Boundaries, responsibilities, authorities and accountabilities must be identified, negotiated and exercised in a complex, dynamic series of interactions. Properly done, the result will be a smooth, consistent and near problem-free operations environment where the goals of both IT and business partners are achieved with a minimum of effort.

OOPS! Someone has lost touch with the real world while chasing unicorns. The reality is that a successful implementation is merely the first step. Today’s data center and business service operations and delivery environments are, and will continue to be, highly dynamic. They require active, intelligent and increasingly automated oversight and control of available assets (including infrastructure, processes, etc.) for reliable service delivery.

Doing this requires active monitoring and management of the infrastructure, assets and processes involved in and impacting the delivery of a service. To assure reliable delivery at the level that meets business needs, services must be monitored and data made available to various business and operations staff.

The needed data includes:

- Performance and availability of the service

- The assets used by a service and their impact on service delivery

- Other assets in the environment – in-use/idle/available configurations and capabilities (bandwidth, size, speeds, etc.)

- Who/which service is using these assets

- What services assets can support

- Interdependencies between services

- The impact of re-assignment or change (priority to business, value received, cost of change, etc.)

Information based on this data must be available in a quickly comprehensible format to a wide list of consumers. This list will include operations staff, administration staff, service managers, line-of-business managers, as well as executives who want assurance that service SLAs are being met.

Getting accurate and timely data about the infrastructure and processes involved in service delivery doesn’t happen automatically. You need to assure that your monitoring solution will discover and report on process and infrastructure changes that will impact specific services. Typically, this involves building a model of the assets, infrastructure and processes used in service delivery and definition of dependencies.

Business requires change and adaptation to attract, service, and maintain customer (or user) satisfaction. New services must be developed and delivered. Infrastructure (both virtual and physical) will be altered, reassigned, and reconfigured as it adapts to evolving environmental, operational and business needs. Today’s highly dynamic data center operations will rapidly make a static model outdated and useless.

What is required is a monitoring solution with automated capabilities to build on and update the basic modeling engine. It must automatically discover and be able to integrate changes in these processes and infrastructure. It must be able to inform when such changes will affect service delivery.

Typically, infrastructure elements are part of the delivery of multiple, different services. Such elements can be manipulated by staff that are unaware of or don’t care about the impact on other services when changes are made. For example, one can imagine an infrastructure change that optimizes a delivery path for Service A under stress conditions. Service B shares infrastructure elements with Service A. The changes don’t normally affect B’s delivery, but will disrupt Service B when invoked. It is also possible to make changes that will benefit Service B (i.e. provide a redundant service path), but mask a failure in Service B primary resources. Only intelligent monitoring with an ability to create correlated service-oriented infrastructure views will provide the holistic visibility into infrastructure utilization needed to avoid such potential issues.

BSM requires dynamic, intelligent asset management able to provide a range of IT and business views that accurately represent service delivery and the operational environment (virtual and physical) that enables delivery. A critical element supporting that management is a monitoring solution that can automatically discover and dynamically adjust to changes in assets and infrastructure. And, that can detect and report on changes in shared assets that may affect service delivery.

About Rich Ptak

Rich Ptak, Managing Partner at Ptak, Noel & Associates LLC. has over 30 years experience in systems product management, working closely with Fortune 50 companies in developing product direction and strategies at a global level. Previously Ptak held positions as Senior Vice President at Hurwitz Group and D.H. Brown Associates. Earlier in his career he held engineering and marketing management positions with Western Electric’s Electronic Switch Manufacturing Division and Digital Equipment Corporation. He is frequently quoted in major business and trade press. Ptak holds a master’s in business administration from the University of Chicago and a master of science in engineering from Kansas State University.

Related Links:

www.ptaknoel.com

Hot Topics

The Latest

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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 ...