How Do You Prove Cloud ROI? IT Performance Systems Help

Michael Procopio

I saw a survey in an interesting article the other day by ZDNet blogger Joe McKendrick. McKendrick cites a new cloud survey for The Open Group. The survey indicated that many believe cloud will bring favorable ROI to IT shops, but they lack a mechanism to track results.

ROI is a purely financial calculation which is usually expressed as dollar amount or a percentage. The catch is many IT shops don’t know what a project or a service costs. If you don’t know how much you will invest to get to cloud and how much cost you will take out by doing it (return), ROI will stay illusive.

Cloud computing is predicted by many to be a fundamental shift in the way IT works. As more public cloud applications become available, which are nicely prices on a per user per month basis – IT management is going to need to have the same information and more.

Good or bad, the per user per month metric is not the only consideration your company has of you if it matches most of those surveyed. Agility, innovation, business alignment and more are being asked of IT management. All these are hard to measure now. Cloud adds more moving parts which rarely makes things easier.

What to do? Start now creating your own IT Performance system. How, starting thinking about all the KPIs (key performance indicators) you wish you had that can be derived from data that exists somewhere in the organization.
Here are some examples:

*  % of Service Performance Not Met
*  MTBF (mean time between failure)
*  MTTR (mean time to repair)
*  Time-To-Market of New IT Products or Services (from the time there is agreement we will do project zebra to the time IT says the zebra is ready for production)

The way you will prove ROI is seeing improvement in these (and other) measures after moving to cloud.

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How Do You Prove Cloud ROI? IT Performance Systems Help

Michael Procopio

I saw a survey in an interesting article the other day by ZDNet blogger Joe McKendrick. McKendrick cites a new cloud survey for The Open Group. The survey indicated that many believe cloud will bring favorable ROI to IT shops, but they lack a mechanism to track results.

ROI is a purely financial calculation which is usually expressed as dollar amount or a percentage. The catch is many IT shops don’t know what a project or a service costs. If you don’t know how much you will invest to get to cloud and how much cost you will take out by doing it (return), ROI will stay illusive.

Cloud computing is predicted by many to be a fundamental shift in the way IT works. As more public cloud applications become available, which are nicely prices on a per user per month basis – IT management is going to need to have the same information and more.

Good or bad, the per user per month metric is not the only consideration your company has of you if it matches most of those surveyed. Agility, innovation, business alignment and more are being asked of IT management. All these are hard to measure now. Cloud adds more moving parts which rarely makes things easier.

What to do? Start now creating your own IT Performance system. How, starting thinking about all the KPIs (key performance indicators) you wish you had that can be derived from data that exists somewhere in the organization.
Here are some examples:

*  % of Service Performance Not Met
*  MTBF (mean time between failure)
*  MTTR (mean time to repair)
*  Time-To-Market of New IT Products or Services (from the time there is agreement we will do project zebra to the time IT says the zebra is ready for production)

The way you will prove ROI is seeing improvement in these (and other) measures after moving to cloud.

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In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

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In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

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Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

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Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

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For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

Most Enterprises Think They Can Switch AI Vendors in a Month ... Most Who've Tried Couldn't

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Your Observability Stack Has a Telemetry Pipeline Problem

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

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