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Are Your Software Dollars Gathering Dust? It's Time to Eliminate the Shelfware and Cloud Waste!

Rex McMillan

Software spend in 2021 and beyond will be a hot button as organizations redirect priorities and spending as a result of the pandemic. Spend that can be linked to clear results — more productivity, more ROI, and better integration of the remote workforce — will be looked upon as worthy. However, wasted spend — particularly software assets that have devolved into "shelfware" or cloud waste — will be a ripe opportunity for CIOs and IT management to direct a laser beam on optimizing software asset usage and its potential drain on budget.

With IT teams now supporting workers who are predominantly in remote environments and the attendant security challenges, a fair question is, "Should worrying about shelfware and uncontrolled cloud usage be added to the list of top concerns?" According to Gartner, "At any point in time IT operations may be running with 25% plus of software going unused." A benchmark study a few years back estimated U.S. wasted software spend to be $30 billion, or an average $259 per desktop. If your organization has 20,000 desktops, for example, that equals $5.2 million in investment bringing in zero return.

So, the answer is yes, tightening control over software asset and cloud spend and use should be on the radar. Inevitably, the C-suite, looking to 2021, will be asking tough questions about any new requested spending. And importantly, IT will be expected to deliver a thorough, cogent report on "spend intelligence" related to software use and whether these assets are contributing to desired business outcomes or are simply a money drain.

Spend intelligence is, among other attributes, a means of getting control of shelfware and cloud consumption. It captures data on all software asset spend and cloud application usage and assesses actual use. It then gives rise to the ability to better manage and retire software and cloud assets, or repurpose them, throughout their usable lifecycle. It is a noble goal. However, gathering data on all software and cloud applications has become far more difficult as IT teams now must look at the universe of those assets residing on-prem, in the cloud, or at the edge where remote workers are using devices and applications to enter the network.

The solution is to incorporate automation, machine learning and data analytics into software spend inquiries. This will accelerate insights into how well an organization is using its current software asset environment, and to put a laser light on all assets that have become shelfware. A few practices to consider include the following:

Eliminate Time-Wasting Tasks

A survey of IT professionals revealed 45% use inventory tools as one of their resources for asset tracking, 43% are still using spreadsheets and 50% are using an endpoint management solution. Introducing automated processes into spend intelligence gathering will eliminate time-consuming manual tasks. Data can be collected and maintained in a single, easily navigated repository, reducing the risk of error.

Automate Data Intelligence

Capturing software asset data across on-prem, cloud and edge environments requires tools that can employ automation to collect data from these diverse environments, then automatically analyze and organize the data into relevant categories like licenses or subscriptions.

By moving this data to a central repository, IT teams can quickly find information they need on a particular license, for example, by just using a search mechanism on the dedicated dashboard.

Speed Up Visibility

Stopping the shelfware and cloud waste budget drain involves not only knowing what unused software assets already exist but also preventing more of those assets from becoming dormant and unused. That takes constant diligence in tracking usage, license types, purchases, subscriptions, renewals and instances, contract expirations and ongoing spend.

Automated processes give IT clear insights into precisely where software spend waste is occurring.

IT also provides an up-to-the-minute picture of which applications are consistently being used, detail that will eventually need to be factored into budget strategy reports to the C-suite.

Dust Off the Shelf

If a software asset is not being used in a reasonable timeframe, it needs to be eliminated, or redeployed where the license cost is valued. That usually means making changes to subscriptions, licenses and contracts, notably those with built-in renewal clauses.

Ivanti's survey of IT professionals found 28% devote hours each week supporting out-of-warranty/out-of-support policy assets, and 20% of them indicate they don't have insights into which assets are out of date. This combination of unused software and those licenses past their expiration date is a weak link in IT's and CIO's charters to spend carefully and strategically post-pandemic. Integrating automated tools that can deliver the needed due diligence in managing vendor relationships has to be a top priority.

Reclaim Dollars

The payoff for incorporating automation into software asset management is clearer insights into asset spend, usage and contractual agreements — both on-prem and in the cloud. IT teams now can reclaim dollars that no longer need to be spent on software assets that have become shelfware or cloud waste, are underutilized or are now out-of-date.

Going into 2021, software asset spending will be a source of more scrutiny as IT executives and CIOs fine tune spending to further stabilize productivity in the remote environment. Spending will occur, but IT departments who have excessive amounts of shelfware, or cloud application licenses that have long stopped contributing to ROI, will be in a weakened position to make a case for new investments. By incorporating automation into data collection and software asset management due diligence, IT can gain the power of knowledge to make a case for new strategic investments, all with an eye to better business outcomes.

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Are Your Software Dollars Gathering Dust? It's Time to Eliminate the Shelfware and Cloud Waste!

Rex McMillan

Software spend in 2021 and beyond will be a hot button as organizations redirect priorities and spending as a result of the pandemic. Spend that can be linked to clear results — more productivity, more ROI, and better integration of the remote workforce — will be looked upon as worthy. However, wasted spend — particularly software assets that have devolved into "shelfware" or cloud waste — will be a ripe opportunity for CIOs and IT management to direct a laser beam on optimizing software asset usage and its potential drain on budget.

With IT teams now supporting workers who are predominantly in remote environments and the attendant security challenges, a fair question is, "Should worrying about shelfware and uncontrolled cloud usage be added to the list of top concerns?" According to Gartner, "At any point in time IT operations may be running with 25% plus of software going unused." A benchmark study a few years back estimated U.S. wasted software spend to be $30 billion, or an average $259 per desktop. If your organization has 20,000 desktops, for example, that equals $5.2 million in investment bringing in zero return.

So, the answer is yes, tightening control over software asset and cloud spend and use should be on the radar. Inevitably, the C-suite, looking to 2021, will be asking tough questions about any new requested spending. And importantly, IT will be expected to deliver a thorough, cogent report on "spend intelligence" related to software use and whether these assets are contributing to desired business outcomes or are simply a money drain.

Spend intelligence is, among other attributes, a means of getting control of shelfware and cloud consumption. It captures data on all software asset spend and cloud application usage and assesses actual use. It then gives rise to the ability to better manage and retire software and cloud assets, or repurpose them, throughout their usable lifecycle. It is a noble goal. However, gathering data on all software and cloud applications has become far more difficult as IT teams now must look at the universe of those assets residing on-prem, in the cloud, or at the edge where remote workers are using devices and applications to enter the network.

The solution is to incorporate automation, machine learning and data analytics into software spend inquiries. This will accelerate insights into how well an organization is using its current software asset environment, and to put a laser light on all assets that have become shelfware. A few practices to consider include the following:

Eliminate Time-Wasting Tasks

A survey of IT professionals revealed 45% use inventory tools as one of their resources for asset tracking, 43% are still using spreadsheets and 50% are using an endpoint management solution. Introducing automated processes into spend intelligence gathering will eliminate time-consuming manual tasks. Data can be collected and maintained in a single, easily navigated repository, reducing the risk of error.

Automate Data Intelligence

Capturing software asset data across on-prem, cloud and edge environments requires tools that can employ automation to collect data from these diverse environments, then automatically analyze and organize the data into relevant categories like licenses or subscriptions.

By moving this data to a central repository, IT teams can quickly find information they need on a particular license, for example, by just using a search mechanism on the dedicated dashboard.

Speed Up Visibility

Stopping the shelfware and cloud waste budget drain involves not only knowing what unused software assets already exist but also preventing more of those assets from becoming dormant and unused. That takes constant diligence in tracking usage, license types, purchases, subscriptions, renewals and instances, contract expirations and ongoing spend.

Automated processes give IT clear insights into precisely where software spend waste is occurring.

IT also provides an up-to-the-minute picture of which applications are consistently being used, detail that will eventually need to be factored into budget strategy reports to the C-suite.

Dust Off the Shelf

If a software asset is not being used in a reasonable timeframe, it needs to be eliminated, or redeployed where the license cost is valued. That usually means making changes to subscriptions, licenses and contracts, notably those with built-in renewal clauses.

Ivanti's survey of IT professionals found 28% devote hours each week supporting out-of-warranty/out-of-support policy assets, and 20% of them indicate they don't have insights into which assets are out of date. This combination of unused software and those licenses past their expiration date is a weak link in IT's and CIO's charters to spend carefully and strategically post-pandemic. Integrating automated tools that can deliver the needed due diligence in managing vendor relationships has to be a top priority.

Reclaim Dollars

The payoff for incorporating automation into software asset management is clearer insights into asset spend, usage and contractual agreements — both on-prem and in the cloud. IT teams now can reclaim dollars that no longer need to be spent on software assets that have become shelfware or cloud waste, are underutilized or are now out-of-date.

Going into 2021, software asset spending will be a source of more scrutiny as IT executives and CIOs fine tune spending to further stabilize productivity in the remote environment. Spending will occur, but IT departments who have excessive amounts of shelfware, or cloud application licenses that have long stopped contributing to ROI, will be in a weakened position to make a case for new investments. By incorporating automation into data collection and software asset management due diligence, IT can gain the power of knowledge to make a case for new strategic investments, all with an eye to better business outcomes.

Hot Topics

The Latest

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 ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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

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 ...