ライセンス・ホーダーがチームにいることを示す5つの兆候

[0:02] Good morning, good afternoon, or good evening, wherever you’re joining us from. Welcome. This is Open iT’s webinar, five signs you have a license hoarder on your team. I’m Nix, your host for today, and we’re taking a practical look at one of the most common sources of hidden license waste, hoarding. Over the next 20 minutes, our speaker will walk you through key usage patterns that lead to license waste, how to detect them early, and how teams can shift towards smarter, more efficient practices without slowing down productivity.

[0:33] Feel free to send your questions through the Q&A panel at the top of your screen. We’ll go through as many as we can live, and if we miss any, we’ll follow up with you directly after the session.

[0:44] Now, let’s meet today’s speaker. Sagi is a solutions architect who’s helped global organizations make better data-driven decisions about software usage. He’s passionate about helping teams work smarter by unlocking the full potential of the tools they already have without overspending. Let’s welcome Sagi to the session.

[1:05] Sagi: Thank you very much Nix. So today we will be talking about user usage patterns. Specifically we’ll try to find hoarders in your team.

[1:16] So what is a license hoarder? That is basically someone who uses licenses beyond his need causing a false sense of low license availability and a very high license utilization. Taking up more than his fair share of licenses and making it seem like there’s always a shortage of licenses due to their usage patterns, basically leading the license administrator to think that the organization needs more licenses than they actually need.

[1:58] Today we’ll go over five signs of a license hoarder, how to use analytics and spot them, and what can be done to rectify the situation to provide higher license availability for other users.

[2:17] Now, keep in mind that there are many valid reasons for hoarding a license, such as users that need to be using the license for their project. And this needs to be thoroughly investigated beyond the analytics in order to figure out if there is a real need for these users to actually hoard licenses.

[2:46] So we first need to leverage our analytical power and find these users and we need to find them out of the whole user base and isolate. We need to show the criteria that actually indicate that the user might be hoarding a license beyond the normal capabilities. And then it’s up to the administrator to decide if a further investigation is required or maybe just having a friendly conversation with the user to better understand his work habits with the engineering applications.

[3:25] Some hoarding behaviors might be justified such as users who have a heavy workload with several projects at once, basically requires them to run several processes in parallel in order to meet their assigned goals. But some might not be justified, requiring some adjustments to be made either via general memo or a specific personal approach.

[3:56] So how do we find these users? We’ll start with the obvious metric, finding out who is using licenses for a long time in a sustained manner. That means users who checked out a license and are keeping it for long stretches of time until they actually check it back in.

[4:21] We have to remember that one sign alone is not a definer of a hoarder, as a user grabbing a license for the whole day is of course a normal occurrence and it is in fact okay. But combining this metric with other signs which we will present later on might indicate a license hoarder.

[4:49] In order to find the first sign, we will generate a report showing us yearly sum of all users and their elapsed time. Basically laying out in front of us all the time spent using engineering applications. Some users might have a low value, some medium, but some will have an extremely high elapsed time, making them suspected for long session durations and making us drill down further into their license usage patterns.

[5:29] Here we find Aaron, Alice, and Amanda who have the highest elapsed time out of the whole user base in our company. So, let’s take a look at their daily routine. First, looking at an average workday, we sum up the usage hours of the day and find out their overall elapsed time. Now we can clearly see that all of them are using the application for the whole day from start to finish.

[6:06] But we see other users doing the same thing. So how can we isolate them? Well, we will do a monthly average of each hour of each day taking into account even multiple months combined and seeing each user’s average. So right away we can see that this pattern repeats itself on a daily basis as even with the overall average which we see in the chart below. We find their average usage time indicating that each day they are checking out licenses for the entire day. While for other users we see that it’s a more rare occurrence. Their averages do not show them using all hours of the day each day, but in a much lower capacity than the suspected hoarder team.

[7:07] Now we drill up further. We can create a report showing a full year along with their max hourly usage, average, and minimum. And we see that Amanda, Alice, and Aaron are indeed exhibiting high average hourly usage times along with very high minimum hourly usage times, indicating that this pattern of long sessions is repeating throughout the whole year, increasing our confidence that in fact this is their general usage pattern and not a rare occurrence.

[7:51] Now, these findings might cause the administrator to raise an eyebrow at first, justifying any further investigation into other signs of hoarding or even just having a conversation with the users to better understand how they utilize engineering applications on the day-to-day.

[8:16] The second sign of a license hoarder is checking out multiple licenses at the same time. Basically meaning that the user might be opening up the application many times in parallel on his workstation or even multiple instances on various workstations all at the same time.

[8:41] Now these users might have a solid reason behind these usage patterns. Some applications will require the user to leave the app to run some simulation or calculation or any other long running process, basically requiring the user to open another instance of the application for his operational work while the other one is left to do the processing. But sometimes it’s not the case and there might not be such a clear justification for having too many application instances open in parallel.

[9:27] Here we will apply the same strategy but now with a different measure, the max in use, showing us each user and the amount of licenses that each one checked out in parallel. We might find that most users have the max in use measure set to just one, but some values might be higher, leading us to suspect that they could be hoarding licenses. But this is not certain. We need to drill down further in order to figure out if this is indeed true or not. Maybe it’s a rare occurrence. After all, we are looking at the maximum. So maybe it happened only once or maybe it’s a regular occurrence that actually happens every day.

[10:23] We found a few suspected hoarders in this list. So let’s drill down and see how they stack up. We’ll create a heat map showing us their daily max in use values across the month and we’ll pick this month to sample and see if our suspicion is actually confirmed. Here we have a heat map showing us each user the max in use for each day of the month.

[10:55] Well, right away we found that six out of our suspected group are not actually using licenses in parallel on a daily basis, but showing this to be really a rare occurrence and not their typical usage patterns. But we also found three users who indeed are doing this almost every day out of the month, hinting that this is their general way of consuming licenses.

[11:29] This can of course be the result of them taking a license in order to run a simulation at the same time as they might be working on several projects simultaneously. But it can also just be that they leave the application instances open on various workstations or even simply opening a new instance and forgetting to close the old one.

[11:59] So we’ll drill up into the yearly view. We can see if this is happening across the year or if it’s just that specific month that we chose to sample. Well, in our case, we see that these three users are indeed operating in the same manner throughout the whole year. As this might just be their work pattern, grabbing many licenses at the same time, every day, every month out of the year.

[12:31] This is another sign which should make the administrator wonder about how these users are utilizing licenses, how their workday actually looks like and what can be changed in their mode of operation. As you can understand, these licenses might be very useful for other users in the organization, but they are taken by these three users causing a lack of availability for others.

[13:08] Another sign of a license hoarder is using licenses outside working hours and working days, indicating that users might forget to close the application once they’re done using them or even just keeping them indefinitely on the workstation as to not actually have to check out a license the next day, ensuring themselves to have the license that they need when they need it. Basically taking a shared license and turning it into a private named license severely hindering the utilization efficiency of this license.

[13:51] Now here as well there might be a good reason for leaving a license checked out after working hours. For example, the engineer runs a processing job like a simulation or intense calculation to run overnight while he’s away, which is perfectly fine. But it still requires to check the analytics in order to figure out if this is some rare occurrence or just forgetfulness or a more persistent premeditated pattern.

[14:28] So in order to achieve that we will first filter our report to show only non-working hours and only weekends. Once we do that any users that come up in the reports are sure to have been leaving their applications open beyond the assigned working hours. Here we have found a few suspected users with some having low usage time values but some having very high usage time values hinting at the fact that this might be a repeating pattern.

[15:04] So again we’ll generate a report showing us each hour of the day for each user focusing only on the non-working hours and seeing who has high elapsed time values. In our case we can identify four users who frequently leave their applications open outside working hours.

[15:26] Now running a report summing up Sundays and Saturdays, we find them again with the highest usage time values indicating that this is their general usage pattern. It might be a good enough justification for the administrator to investigate further with these users in order to fully understand why they are leaving their applications open under non-working hours.

[15:58] There might be of course a very good reason to do so. But in order to keep an efficient licensing environment, it would be better for the administrator to understand the exact reason as it can greatly help with the license efficiency.

[16:18] Now, how can it help with efficiency if it’s outside working hours or weekends? Well, for example, let’s say a user left his application open and went home and the next day he did not show up to work. Maybe he got sick. Maybe he had a day off or even just left for a week-long vacation. So now this license will basically stay there until he gets back to work, preventing anyone else from using that license and hindering the utilization efficiency as a result.

[17:01] Another strong sign is high inactivity rates. This utilizes the LicenseAnalyzer™ level two capabilities allowing the administrator to get analytics relating to usage patterns with the licenses after they were checked out. Meaning after the license was checked out, was it actually used or was it just sitting idle on the user’s workstation most of the time?

[17:34] A user might have checked out a license for let’s say 8 hours, but maybe he only used it for 3 hours and it was just sitting there for 5 hours. During this time, the license could have gone to someone else, but instead it just remained there sitting waiting for the user to come back. This directly hurts license availability causing a false sense of low availability and always needing to buy more licenses as the organization never seems to have enough.

[18:11] So we will generate a report which shows us the inactivity ratio of each user. We have here the active time versus the idle time and calculated next to it is the overall ratio of inactivity. The higher it goes, the more inactive the user is. And we find in our case five suspected hoarders based on their overall usage.

[18:40] But let’s drill down into their daily average ratio spanning a whole year to understand what are their actual usage patterns? We have here a heat map showing each of our users with each day of the month and what their average inactivity ratio for each day. This way we can see if inactivity is a repetitive occasion or if it’s just a rare occasion.

[19:14] For example, we find that for three of our suspected users, the inactivity ratio is actually not a repeating pattern, but just an outlier of their overall way of using applications. While Emily and Ernest are repeatedly inactive with the licenses that they checked out, showing a clear usage pattern that should be handled either by the administrator initiating a discussion with the user about their usage patterns or simply using the LicenseAnalyzer™ level three which automatically freezes their inactive application and sends the license back to the pool for other people to use.

[20:05] Once they’re back, they can resume the application, allowing them to unfreeze the application and go back to work.

[20:15] And the last sign is using many different applications at the same time. This means a user checked out a license of various applications and might not be using all of them but just keeping them on hand just in case. Now there is also a justified reason for having multiple applications open at the same time as some might be running simulations on other applications while the user uses different applications in the meanwhile. In any case, this sign added to other signs might indicate how the user works and operates on the day-to-day.

[21:01] For this report, we will have to run on a specific hour-by-hour basis looking at a specific hour across the day and drilling up to the month and years. What we are looking for is usage time generated by the same user on various applications at the same time. As we see here, three users have multiple applications opened on the same hour, summing up to a long session duration across the days and months.

[21:37] So now we found various signs that help us identify a license hoarder. Now, keep in mind that having just one of the signs apparent in a user might not mean he’s hoarding, but having multiple signs point to the same users should require further investigation into their work routine by the license administrator as modifying their pattern might mean higher license availability and better overall license efficiency.

[22:14] And that’s it. These are the five signs of finding a license hoarder on your team. Long sessions, parallel license usage, usage outside working hours, high inactivity rates, and multiple applications used at the same time. Open iT can help you easily identify all these signs using our robust and coherent reporting capabilities, allowing you to pinpoint license hoarding and greatly increase license efficiency.

[22:55] Nix: Thank you, Sagi. Insightful and helpful as always. We have here some questions. So, let’s jump right into Q&A.

[23:00] Nix: How can admins balance license efficiency with making sure users aren’t slowed down at work?

[23:14] Sagi: All right, so there are several ways to do so. First of all, a tight ship, getting the licenses used well, preventing users from just sitting on licenses, camping on licenses, hoarding licenses, making sure there’s high license availability without having to buy more licenses. Now, this can be done either with the analytics that we’ve seen today, but this can also be done using the LicenseAnalyzer™ level three, which basically suspends any inactive application and sends the license back in the pool, allowing for great availability.

[24:04] Nix: Thank you for the clear explanation, Sagi. Next, we have here another question.

[24:10] Nix: Besides reports and analytics, what policies, education or automations can help stop license hoarding early?

[24:17] Sagi: So, it all starts with educating the users. Basically, making them understand that the license they take is affecting other users. This can be done using training, a memo, recording a video, sending it to the users or having a yearly or bimonthly short sessions with each. It really depends on the organization and how they are structured. The best way to go about this is to educate them before they start working so they know that the license they take are basically taken from others that can use it as well.

[25:08] Nix: Thank you for breaking it down, Sagi. Next, we have here another question.

[25:10] Nix: Can you share a case where finding license hoarders saved a company from buying extra licenses and helped their budget?

[25:21] Sagi: Yes, we had one not long ago. A company in the aviation and defense industry. They had two pools of licenses. One of them was a floating pool, one named pool. And something that they found out in the floating pool is that people always got denied and were complaining about it. They were about to buy new licenses, but then they checked with level two, basically active versus inactive time. And they saw that most of the users are camping on the licenses, leaving them for long stretches beyond working days, over weekends, just holding on to them like they are a named license. So, a reshuffling and getting those users to the named pool made it so everybody had a license when they need it and they didn’t have to camp on it, but they didn’t need to buy more licenses.

[26:26] Nix: Thank you for sharing that, Sagi. And we also have here another question.

[26:35] Nix: What is your AI journey moving forward? It would be great to have agents doing most of the workload.

[26:40] Sagi: Yeah, that is a very good point. At the moment we are working on a component. It’s called the license predictor. At the moment it’s still under development and it will help predict future analytics based on past reporting patterns.

[27:05] Nix: Thank you, Sagi. And that would be the end of our Q&A session. Thank you for those thoughtful responses. Before we close out today’s session, a quick reminder. This webinar was recorded and we’ll send the replay link to your email shortly. You can also find it on our webinars on demand page at openit.com. If you’d like to catch up on our previous session in the series, we’ve covered preparing for cloud-based and SaaS licensing models. Next week, we also have another webinar upcoming. It’s about CLIMS, centralized license integration management system, one portal, total license control. Just scan the QR code on your screen or visit resources webinars on our website. If you’re ready to dig into your own usage data and address license hoarding or any other optimization challenge, we’re offering a free 30-minute consultation with one of our business solutions consultants. Use the contact details on screen to get in touch and follow us at Open iT, Inc. on social media for more updates. Once again, I’m Nix. Thank you for spending your time with us and we hope to see you again.