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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.

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AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

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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 ...

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 ...

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 ...

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 ...

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

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

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 ...

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 ...

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 ...

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 ...