Évaluation comparative de l'utilisation de vos licences d'ingénierie : Ce qui est normal et ce qui ne l'est pas

[0:00] Dorcas: Good morning, good afternoon, and good evening everyone. Welcome to Open iT’s webinar titled Benchmark Your Engineering License Usage. My name is Dorcas and I’ll be your host for today.

[0:19] In the next 20 minutes, our speaker will show you how to benchmark your license usage against industry norms using Open iT Analytics. You’ll learn what healthy utilization really looks like, how to spot anomalies in your environment, and how to build a strong data back case for optimization or justified expansion.

[0:40] You are welcome to submit your questions through the Q&A panel at the top of your screen. We’ll address them after the main session, and if we run out of time, we’ll be sure to follow up via email or LinkedIn.

[0:57] Now a quick intro to our speaker. As a solutions architect, Sagi focuses on delivering value-driven results. He brings strong expertise in customer relationship management, helping clients across industries maximize their software investment. Let’s give a warm welcome to Sagi.

[1:20] Sagi: Thank you very much, Dorcas. Hey guys, welcome to the webinar. Today we will be talking about benchmarking your license usage. Basically comparing it to standards that we’ve seen across our customers.

[1:36] At Open iT, we have over 25 years of experience and many, many satisfied customers who are able to save significant amounts of money, cutting a significant chunk out of their licensing portfolio cost thanks to the Open iT product and various extensions.

[2:01] We operate in various verticals such as oil and gas, engineering, manufacturing, aviation, automotive, and many more.

[2:12] Over the years, we’ve noticed the various trends that are emerging in license usage across our customers, allowing us to create a rough benchmark for you to be able to compare your license usage to what we usually see in the market.

[2:32] We’ll go over the benchmark, but also various outliers that we have seen play out in some of our customers portfolios. Meaning what we usually see might what you might want to avoid and what you might find that our customers are doing similarly and also what our customers are usually aiming for.

[3:00] We will go over various graphs and charts which can be found in the LicenseAnalyzer™ level one product indicating the application utilization and efficiency of your license inventory.

[3:15] The data which I will be showing you in the various charts builds on top of each other. It makes a coherent efficiency estimation based on license usage patterns made by your engineers on the day-to-day.

[3:34] First, we’ll start with analyzing the possible consumption patterns that we find with our customers. And from there, we’ll be building up the trends.

[3:48] Now to reach the desired cost savings and license efficiency, we take into account various factors. We’ll want to match our users demands and provide them with the best availability possible without having to overspend on licensing.

[4:11] As you probably know, this is a really thin tight rope. All license administrators and procurement specialists have to walk on as the application life cycle and users usage patterns have to be considered in order to make the right choices when purchasing licenses. Purchasing just the right amount of licenses in order to have the engineers work go smoothly but also keep expenditures to a minimum.

[4:47] So let’s take a look at three different scenarios of license consumption.

[4:53] First, we have an over-licensed licensing pool, meaning that you have way too many licenses and might be overspending. Your users usage patterns might not seem to align with what you have purchased. We find the amount of licenses here represented as the black line and the concurrent license usage represented as the gray bars.

[5:21] In practice, they show that all engineers can always get a license when they need it, providing 100% availability at all times to all users without having any denials. But this comes at a cost. As you might know, purchasing and renewing so many licenses might be considered as wasteful and just increase your expenditure, reducing your overall budget in case you need to reallocate funds or allocate to new projects.

[5:57] The other pattern that we find is under-licensed, meaning that your pool has too few licenses purchased, causing low availability to engineers and severely interfering with their work, basically making them sit around and wait for a license to be freed up, and they do it for large stretches of time.

[6:21] We see that in the red bars representing the denials which are in this case surpassing 10%, causing many denial events which translates to many frustrated users.

[6:36] Now this might mean a better bottom line as far as license procurement goes. But if the users are not able to conduct their work in an efficient manner, is it really beneficial? You might be saving on licenses on the one hand, but paying way more on loss of productivity on the other hand.

[7:00] And actually thinking about it in a more behavioral lens, this case creates a strange trend with the end users where they prefer to simply grab a license and sit on it for long stretches of time even if they are not using it just in case, causing even greater consumption on licenses and further exacerbating the scenario.

[7:30] Where over time users will have less and less availability due to their co-workers fear of not getting it themselves, creating an ever-increasing dilemma for the end users and a headache for the license administrators.

[7:50] Now this can of course be mediated by the LicenseAnalyzer™ level three component which harvests any inactive licenses back in the pool for other engineers to consume.

[8:07] So what should be the right sweet spot? Well, it really depends on your business requirements. Does your organization require engineers to have full availability at all times and budgeting is not an issue or is your organization more budget conscience and prefer to run an efficient licensing pool?

[8:37] In that case, a well-balanced 0 to 5% denial rate is usually what you might want to be striving for as it means high availability for your engineers causing them to be denied only around one time out of 100.

[9:00] While the other 99 times they get the licenses once they need. Basically translating to getting denied around once a month for a few minutes or so, which is very tolerable.

[9:13] And for the procurement team and license administrator, it means that they are running an efficient licensing portfolio with as little waste as possible, all while allowing end users a high license availability rate.

[9:32] Now let’s visualize these patterns in reports found under the LicenseAnalyzer™ level one interface.

[9:43] Let’s first take a look at the over-licensed scenario. The SSRS report license usage summary shows us that where we have 32 licenses in this case at max we concurrently used only 24 licenses with a wide range for optimization here.

[10:06] But how long did we actually hit 24 licenses for a full day? Maybe just 1 minute. The license efficiency chart found in the SSRS reporting system can tell us exactly that. Here we can see the same product but now presented as the amount of time spent on each concurrent level.

[10:32] Meaning getting two people to work at the same time on this application. Well, it happens a lot. But getting those 24 users to work at the same time on this application, well if you see that it barely happens.

[10:48] Also getting up to 23, 22, or 21 licenses used concurrently is also a rare occasion, showing a distinct pattern. We see a long and thin efficiency tail which indicates we are way over-licensed.

[11:09] And here on the top we can see that to reach 99% efficiency we just need 16 licenses as the tail really begins forming after 16 licenses.

[11:24] And this is how the efficiency percentages are calculated. You can see here that out of the whole year we’ve reached 24 licenses just for 10 minutes. 23 licenses were reached just for half an hour out of the whole year. Both are under 0.01% of the whole year making the licenses very negligible.

[11:53] Now these percentages get around to 1% only when we get to 16 licenses used as under this amount the utilization percentage starts to drop from 99 to 95 which we see with 13 licenses and 90% efficiency with 11 licenses and so on.

[12:20] Now let’s take a look at the exact opposite. Under-used, under-licensed cases where we have 40 licenses for this product and we consistently reach 40 licenses every day, every working day of the week.

[12:39] And we do it for long stretches of time each day with many denial events happening constantly, represented of course in the gray bars that you see here, effectively depleting our licensing pool for almost the entire duration of the working day.

[13:00] So how will it look like on the efficiency chart? First of all, we can see that the chart now has a wide tail indicating long usage duration even when having 40 licenses used. Our 99% efficiency sits on 40, the same as the amount of licenses we own, dropping just slightly to 36 when referring to 95% efficiency.

[13:33] Looking at the data, we see that our pool remained depleted for well over 8 hours. Plenty of time to allow many users to get denied over and over again, resulting in tremendous loss of productivity and causing the runaway effect we mentioned earlier where users will start competing for licenses causing each user to sit on the license as much as possible because this licensing portfolio basically translates to the user getting the license he needs only on rare occasions, prompting him to keep the licenses close, like they’re made of gold.

[14:20] Well, then what should you probably be aiming for? What should our efficiency patterns show as optimal?

[14:34] Well, it depends on the organization, but a good scenario will be a chart that looks like this. In this case, we have 39 licenses, but most of the time, the license consumption hovers below the purchased amount with some accepted instances of maxing out our licensing pool.

[14:57] But here again, we have been maxing out the licenses. But was it for a whole day, for an hour, maybe just for a few minutes? The efficiency chart shows us a healthy license usage pattern where the efficiency tail is not too thick, not too thin, where we do reach usage of 39, but only for a very short period of time.

[15:28] Indicating that in order to reach 99% efficiency, we might want to reduce around 10% of our licenses to 36 licenses instead of 39.

[15:42] For the end user, it means that they will get the licenses that they need over 99% of the times and in rare cases will they get denied. Then they will just have to wait a few minutes then try again.

[15:59] And this is corroborated by the numbers where out of the whole year we only reached 39 licenses for about 11 hours which is less than half a percent of the entire time span. Leaving just the right amount of licenses as to keep our licensing portfolio trim and well budgeted all the while enabling a high license availability for the engineers. Not too many, not too little. Exactly right.

[16:32] But what do we usually see with customers which have yet to optimize their system? Meaning right before implementing the insights gained from Open iT.

[16:45] Let’s start with the user’s usage patterns. We usually see long usage durations with most users averaging their session duration between 4 to 6 hours of usage every day.

[17:02] This usually translates to charts which resemble the overused scenario where customers constantly find the potential for cost reduction anywhere between 15 to 30%.

[17:23] As they are many times over-licensed and underused as most of the times they have simply accumulated too many licenses in their portfolio.

[17:37] Looking at it at a bird’s eye view, we find usage heat map similar to this one where users use their application mainly within working days, within working hours with a few licenses always being utilized also across the weekend and beyond working hours, indicating that some users repeatedly forget to check back the license after finishing their working day.

[18:06] But we don’t see that in a high degree. Usually only about 1 to 3% of users actually do that.

[18:16] We find that customers usual efficiency charts in many times they look like this with a long thin tail showing a lot of room for optimization. Basically purchasing and renewing licenses that they don’t really need.

[18:35] Giving a distinct efficiency pattern that shows licenses are underused and over-licensed with many of the top levels barely being reached if ever. Meaning that the engineers are happy and have license availability at a high degree. Basically getting the license whenever they need, but it comes at a heavy cost to the organization’s licensing budget for no real reason.

[19:07] So, let’s take a look at a few testimonials created by some of our customers which show exactly that, how much they’re able to save with Open iT and how well they actually fit the benchmark that we averaged out.

[19:30] We have Devon Energy. They had difficulty with monitoring IT spending and consolidating accurate historical technical application usage. So they implemented level one’s utilization and efficiency reports which we’ve seen in this webinar. And as a result they were able to reduce 20% from their licensing cost.

[19:59] ConocoPhillips were manually coordinating their licensing environment and had a severe lack of visibility into their licensing system. They implemented Open iT solution and it greatly reduced the administrative manhours required for analyzing detailed resource usage resulting in an overall 30% cost reduction by bringing down their license consumption and making huge steps forward for getting effective utilization with their resources.

[20:35] AIO Arrow needed a software management solution capable of powerful analysis and found it with the LicenseAnalyzer™ level one and level three resulting in 40% cost reduction during the first phase when analyzing their license consumption and usage efficiency, but then an additional 14% cost reduction during the second phase where they implemented level three and harvested any licenses which were unused or inactive.

[21:12] The last testimonial for today was from a well-known Fortune 500 company which needed enterprise visibility on usage and optimizing their product mapping. As a result of implementing LicenseAnalyzer™ level one, they yielded $1.1 million from optimization of software licenses, an additional $873,000 cost reduction on maintenance cost and even further a $91,000 as overall future cost avoidance, contributing to a drastic optimization effort made in the organization.

[22:02] And that’s it. Now we see how Open iT improves your business decision making using detailed reporting and helping you optimize application licensing with minimal effort, corroborated by Gartner which detailed up to 30% cost reduction matching precisely with our own industry benchmarks collected over the years.

[22:28] So how does your organization stack up?

[22:38] Dorcas: So now we would like to hear from you. You can use the Q&A panel to ask any question that you like.

[22:49] Thank you, Sagi, for your insightful presentation. And we’ve got some great questions lined up. Let’s get right into the Q&A.

[22:57] Dorcas: How fast can a company usually see ROI after optimization?

[23:05] Sagi: So the most optimization that you’ll see and the quickest will be in the first year of implementing this solution because then you untangle all the mess that you have in your system. You’ll find licenses which are not used, servers which are not used. You’ll find places where you can optimize your license usage, meaning purchase less licenses next renewal, and even where you can mix and match different licensing types giving the right user the right type of license and many other options. So I would say about a year.

[23:49] Dorcas: How often should we review or adjust our license pool?

[23:59] Sagi: So this really depends on the vendor and the agreement that you have with the different vendors. Usually it’s done either on a yearly or a bi-yearly basis. Sometimes we are referring to a pay-per-use agreement or other agreements which can be changed more on the fly. So it really depends.

[24:25] Dorcas: How do weekends and off hours usage usually affect efficiency?

[24:35] Sagi: That’s a good one. Because as you can understand after working hours doesn’t really matter if the user got the license, right? Well, not necessarily. Let’s say we’ve got a few users who constantly keep licenses on their workstation and they keep it over the weekend or after working hours, but they might arrive to work late or they might leave a bit early or maybe they have a day or two that they won’t come to the office. So that means the license is still stuck on their machine affecting the overall efficiency.

[25:20] Beyond that, it’s the behavioral aspect of it as it shows that these users might be sitting on licenses basically hugging them for long stretches of time, not only in the weekend.

[25:40] Dorcas: I think question number four is related to that question. The last question that was asked, it says if engineers hold on to licenses for too long how can those licenses be handled?

[25:55] Sagi: So in that case we have level two and level three. So level two will show the administrators where you have those users that are idling with the license. Let’s say they open up the application and just keep it open. Don’t actually use it. Just keep it open. Maybe you don’t need it later. So, level two will give you visibility into that active versus inactive time with the license itself. And level three will automatically harvest that license. Basically, suspend the application, freeze it, and send the license back in the pool for other people to use. So not really giving any users a chance to be inactive because after let’s say 1 hour or half an hour whatever you define then the license goes back in the pool automatically.

[26:54] Dorcas: I see and I think it works also if the user like forget the license over the weekend or during the off days.

[27:03] Sagi: Correct.

[27:04] Dorcas: Helpful. And then I think this is the last question that I see. What do you think or what’s the biggest mistake organizations make with license management?

[27:17] Sagi: So the biggest mistake is not having any visibility into the licensing because it’s such a convoluted space. So many different license managers, so many different vendors, so many different versions of the same application. And most need to just first get a sense of what they have and where and what’s used and what’s not. And the biggest mistake is to just give in to users or administrators that always say, “Oh, we need more licenses. We need more licenses. I always have people getting denied. What’s going on?” But you don’t have real visibility into their behavioral patterns or usage patterns or even overall efficiency.

[28:06] Dorcas: No. Okay. And you cannot optimize what you don’t measure. So I think also visibility is very important.

[28:15] Sagi: Exactly. They’re keeping themselves in the dark basically. That’s the biggest mistake.

[28:23] Dorcas: And that’s a wrap for our Q&A session and thank you for those thoughtful responses. Thanks again Sagi and thank you to everyone who joined us today. This webinar was recorded and the link will be sent to your email shortly.

[28:42] You’ll also find it soon on our webinars on demand page at openit.com. There you can also find our last couple of webinars and you can scan the QR codes on your screen to visit resources and webinars on the website to watch the replay.

[29:02] And we’re also offering a free 30-minute consultation with Open iT business solutions consultant. If you’re ready to explore license optimization in your environment, get in touch with us. The contact details are on the screen.

[29:22] And follow us at Open iT, Inc. on social media to stay updated. Once again, I’m Dorcas, your host for today. Thank you and stay safe.