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How Understanding "Usage" Can Transform IT

Dennis Drogseth

I recently participated in a webinar, still available on replay, called "Optimizing IT Costs and Value Through Usage-Driven Insights," that explored a personal focus of mine, in research and other areas, for more than five years. The most striking reason is that "usage" data is itself multifaceted, with many diverse benefits. Another reason is that harvesting usage-driven insights effectively requires both good foundational technology and a nimbleness of mind to unify insights across IT's many silos of domains and disciplines. Because of this, leveraging usage-driven insights can in itself become a catalyst for helping IT as a whole transform toward improved efficiencies and enhanced levels of business alignment.

OK, So What Am I Really Talking About When I Say "Usage" and What's Required to Get There?

Not only are the benefits of usage data broad, but usage data, in itself, has many interrelated dimensions. Sorting through these and leveraging these coherently is critical to empowering IT to truly run itself as a business. The many dimensions of usage from recent EMA research includes the following:

■ Knowing where SW and HW come together through superior inventory and discovery

■ Knowing the impact of usage on SW license requirements

■ Knowing the impact of usage on HW lifecycle management

■ Knowing how much applications are being used, and by whom, resident on end-user computers and mobile

■ Knowing how much data-center-delivered applications are being used, and by whom, across all devices

■ Knowing how much, and by whom, cloud-resident (SaaS) applications are being used

■ Analyzing the impact of integrated application usage for portfolio planning

■ Analyzing the impact of usage data overall for IT-to-business alignment

By the way, this isn't meant to be a complete list. Usage can translate into other areas, where telecommunications costs come into play, for example. But this list at minimum shows how basic awareness of SW and HW inventories and interdependencies can lead to more application-centric awareness, which in turn can lead to portfolio planning and optimization, and finally superior IT-to-business alignment overall.

Note, for instance, the need to integrate both cloud-delivered third-party-hosted applications with data-center hosted, as well as those resident on endpoints. Once these are mapped to consumer behavior, IT's ability to navigate its own ship in the face of business demands and shifting consumer winds becomes much stronger.

Another perspective on this diversity is to ask, "what are the technology sources for all this data? What tools should I invest in to be complete?"

Based on a cross section of EMA research, a good starter list is as follows:

■ Inventory/ discovery across the application/infrastructure

■ Dependency mapping across the application/infrastructure

■ Insights into public (and private) cloud interdependencies

■ Endpoint discovery and inventory

■ Endpoint "ownership" groupings

■ SW inventory and identification

■ SW license T's and C's

■ User activity data

■ Corresponding cost-related information across all of the above (not just SW licenses, but also HW costs, infrastructure end of life, etc.)

■ Corresponding performance-related information across all of the above (to map usage and costs to the actual performance of IT services and their supporting infrastructure, including endpoints)

Once again, this list is not meant to be 100% complete, but it does provide a useful panorama of what ideally should come together, and what far too often doesn't. What's important to note is that current industry convention has grouped most of these areas into separate "markets" which reflect separate value statements, separate stakeholders, and siloed approaches. However there are solutions (as explored in the webinar) that can help to unify this information.

What Kind of Benefits/Results Can I Expect?

In the webinar we look at benefits from multiple perspectives. But the one of the more complete was when we asked users in our research what they're top financial optimization priorities were — all of which depend on usage.

Here are the top seven:

1. Optimizing IT process efficiencies (IT becomes more effective once it knows what's there, how it's used and how it may be aging)

2. Improving overall IT to business alignment from a cost/value perspective

3. Becoming more proactive in dealing with audits (software, GRC. Etc.). There are implications here for security and compliance, as well.

4. Managing and optimizing endpoint/mobile assets as integrated resources

5. Lifecycle planning of IT application services from a cost/value perspective

6. Managing and optimizing IT HW and SW assets across their full lifecycles

7. Managing partners/suppliers as integrated resources

And the list goes on, but hopefully you get the picture. If we were to be complete, the value points become even more diverse than the sources, emphasizing the advantages to be had in bringing the data together in creative and meaningful ways.

What Are the Challenges in Going Forward?

There is a lot to talk about here, so once again the webinar is your best source. But for now, keep in mind that one "obstacle" is the other side of the coin for "opportunity." Let's look at a few key points.

Data managementin all its aspects: getting accurate and timely data, bringing it together and analyzing it effectively is one of the core obstacles that stands out.

Organizational leadership, ideally top down, helps to facilitate the need for IT silos to work together better and share data in new ways.

Siloed organizationsversus having a common organization across IT seeking to understand usage and costs across IT silos and from multiple dimensions.

Communication issues, though rarely at the top of the "requirements" chart, always appear on our lists. Socializing what you're doing can sometimes be just as important as actually doing it.

These are just a few highlights from the webinar we gave on October 10. It will be available throughout this year and more, so I welcome you to join us and listen in. I also welcome your comments and thoughts.

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

How Understanding "Usage" Can Transform IT

Dennis Drogseth

I recently participated in a webinar, still available on replay, called "Optimizing IT Costs and Value Through Usage-Driven Insights," that explored a personal focus of mine, in research and other areas, for more than five years. The most striking reason is that "usage" data is itself multifaceted, with many diverse benefits. Another reason is that harvesting usage-driven insights effectively requires both good foundational technology and a nimbleness of mind to unify insights across IT's many silos of domains and disciplines. Because of this, leveraging usage-driven insights can in itself become a catalyst for helping IT as a whole transform toward improved efficiencies and enhanced levels of business alignment.

OK, So What Am I Really Talking About When I Say "Usage" and What's Required to Get There?

Not only are the benefits of usage data broad, but usage data, in itself, has many interrelated dimensions. Sorting through these and leveraging these coherently is critical to empowering IT to truly run itself as a business. The many dimensions of usage from recent EMA research includes the following:

■ Knowing where SW and HW come together through superior inventory and discovery

■ Knowing the impact of usage on SW license requirements

■ Knowing the impact of usage on HW lifecycle management

■ Knowing how much applications are being used, and by whom, resident on end-user computers and mobile

■ Knowing how much data-center-delivered applications are being used, and by whom, across all devices

■ Knowing how much, and by whom, cloud-resident (SaaS) applications are being used

■ Analyzing the impact of integrated application usage for portfolio planning

■ Analyzing the impact of usage data overall for IT-to-business alignment

By the way, this isn't meant to be a complete list. Usage can translate into other areas, where telecommunications costs come into play, for example. But this list at minimum shows how basic awareness of SW and HW inventories and interdependencies can lead to more application-centric awareness, which in turn can lead to portfolio planning and optimization, and finally superior IT-to-business alignment overall.

Note, for instance, the need to integrate both cloud-delivered third-party-hosted applications with data-center hosted, as well as those resident on endpoints. Once these are mapped to consumer behavior, IT's ability to navigate its own ship in the face of business demands and shifting consumer winds becomes much stronger.

Another perspective on this diversity is to ask, "what are the technology sources for all this data? What tools should I invest in to be complete?"

Based on a cross section of EMA research, a good starter list is as follows:

■ Inventory/ discovery across the application/infrastructure

■ Dependency mapping across the application/infrastructure

■ Insights into public (and private) cloud interdependencies

■ Endpoint discovery and inventory

■ Endpoint "ownership" groupings

■ SW inventory and identification

■ SW license T's and C's

■ User activity data

■ Corresponding cost-related information across all of the above (not just SW licenses, but also HW costs, infrastructure end of life, etc.)

■ Corresponding performance-related information across all of the above (to map usage and costs to the actual performance of IT services and their supporting infrastructure, including endpoints)

Once again, this list is not meant to be 100% complete, but it does provide a useful panorama of what ideally should come together, and what far too often doesn't. What's important to note is that current industry convention has grouped most of these areas into separate "markets" which reflect separate value statements, separate stakeholders, and siloed approaches. However there are solutions (as explored in the webinar) that can help to unify this information.

What Kind of Benefits/Results Can I Expect?

In the webinar we look at benefits from multiple perspectives. But the one of the more complete was when we asked users in our research what they're top financial optimization priorities were — all of which depend on usage.

Here are the top seven:

1. Optimizing IT process efficiencies (IT becomes more effective once it knows what's there, how it's used and how it may be aging)

2. Improving overall IT to business alignment from a cost/value perspective

3. Becoming more proactive in dealing with audits (software, GRC. Etc.). There are implications here for security and compliance, as well.

4. Managing and optimizing endpoint/mobile assets as integrated resources

5. Lifecycle planning of IT application services from a cost/value perspective

6. Managing and optimizing IT HW and SW assets across their full lifecycles

7. Managing partners/suppliers as integrated resources

And the list goes on, but hopefully you get the picture. If we were to be complete, the value points become even more diverse than the sources, emphasizing the advantages to be had in bringing the data together in creative and meaningful ways.

What Are the Challenges in Going Forward?

There is a lot to talk about here, so once again the webinar is your best source. But for now, keep in mind that one "obstacle" is the other side of the coin for "opportunity." Let's look at a few key points.

Data managementin all its aspects: getting accurate and timely data, bringing it together and analyzing it effectively is one of the core obstacles that stands out.

Organizational leadership, ideally top down, helps to facilitate the need for IT silos to work together better and share data in new ways.

Siloed organizationsversus having a common organization across IT seeking to understand usage and costs across IT silos and from multiple dimensions.

Communication issues, though rarely at the top of the "requirements" chart, always appear on our lists. Socializing what you're doing can sometimes be just as important as actually doing it.

These are just a few highlights from the webinar we gave on October 10. It will be available throughout this year and more, so I welcome you to join us and listen in. I also welcome your comments and thoughts.

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...