効率化への道をマッピングする：フィーチャー・トゥ・フィーチャー・セット・マッピングがいかに価値を解き放つか

[0:01] Mae: Good morning, good afternoon, and good evening everyone. I am pleased to welcome you to today’s session mapping the way to efficiency. How feature-to-feature set mapping unlocks value. I am Mae and I’ll be your host for this webinar. Today, our speaker will tackle how feature-to-feature set mapping can provide the missing context in license usage reports. At the end of the session, you will understand why mapping is essential for accurate reporting and how comparing feature level and package level data reveals deeper insights into utilization, entitlement alignment, and cost efficiency. If you have any questions during the presentation, please write them down in the Q&A panel so that our speaker can answer them during the Q&A.

[0:44] Our speaker engages audiences with practical insights on software license optimization, helping them tackle real world IT challenges with confidence. With 35 plus years in IT management and leadership roles like general manager, she brings expertise in virtualization, cloud adoption, and IT infrastructure. Having been with Open iT for over 11 years, she has guided clients to achieve substantial cost savings and optimize software licensing through data-driven insights and software usage metering. Ladies and gentlemen, let’s welcome Open iT Senior Solutions Consultant, Linda Cole.

[1:25] Linda: Thank you so much for that introduction, Mae. I appreciate it. And welcome everybody to this week’s webinar where we’re going to tackle what we refer to as feature-to-feature set mapping. And so let’s just kind of start at the basics, right? What are we talking about? Where does this come into play? And what we’re going to focus on today is it’s very difficult for those of us who have to manage these licenses to be able to marry up what we’re buying, you know, what the quotations and the invoices, etc. from the vendor say versus what our different metering tools are showing us. So, you buy at a package or a bundle level, but you don’t really have reporting at that level. If you manage the actual license files themselves then you’re familiar with the kind of cryptic naming that goes on. And so that’s a challenge right because it’s great for maybe the license administrators who are used to that but for vendor management or procurement or department heads etc. they don’t really understand what that is. They haven’t seen it. They can’t relate to it. So when you speak to the vendors, they’re always speaking in their what we call price book naming. So we’ll talk about that and so without having that in context both in the packaging but also in the name normalization, it makes it really difficult for us to determine how many, you know, where is our effective license position. Do I have too many? Do I not have enough? Where do I need to be in my organization?

[3:04] So this is not a simple packaging where okay we have a bundle and it includes these you know three, four, five different components and the next one does where it’s a one to many. What we’re really focused on today is a many to many relationship and so we’ll kind of talk about that. So the solution of course is what we refer to as feature to feature set mapping. Others might, I haven’t seen anyone else doing this. There may be something I’m not aware of, but really today want to talk about the concept of doing this. We certainly, you know, see folks that have written their own tools, etc. But you know so this may be helpful from that perspective but really want to talk about the concept and why it’s so important that you be able to do this type of configuration in your environment and of course as I mentioned we want to look at the normalization of that data as well. That’s a key challenge in anyone’s operation is to understand you know what that means so that anybody consuming the data can understand and relate to that.

[4:20] So I’ll just give you a couple of different examples. I’m going to start with Aspentech, but basically if we look here at the again what I’ve termed feature set, this is the type of information that you would see on a quotation from a vendor, on their website, on your invoice, that type of thing. So these are the various different bundles or packages that are there. If we come over here to the feature name, this is that cryptic name that I mentioned. So this is what you would see for example in your license file. And I know a lot of you probably don’t look at the license files, but again I’m just trying to set the groundwork to understand what the concept is and what we’re talking about.

[5:07] So this first capital cost estimator comprises three different features and then of course you know how many do I own? So here’s just a sample invoice again where they’ve got that normalized price book name if you will and how do we relate that over?

[5:30] So at Open iT we do a sophisticated kind of algorithm to do the what we refer to as the feature to feature set mapping. And so let me talk to you about how this works and why it’s not just simple if you will. So here what we have is I’ve got three different packages I’m going to show you. But this is how many I own. I have five of this particular CFD Pro. And these are the different features that are listed inside that package. So let me just go ahead and build this out. There’s different levels. So we’re going to look at CFD Pro, premium, and enterprise. So what I’ve done to kind of make it a little easier is I’ve highlighted everything that’s in Pro in this lighter green color. And you can see it also appears in premium. It also appears in enterprise. By the same token, premium is the light blue and then this kind of light brownish color is the enterprise.

[6:39] So the way this works and the way that makes it more complex is in real life application what you’re looking at is let’s say you have somebody that wants to use the fluent meshing pro for example. Well, the first five users that open that feature, right, this is going to be mapped to the package A with this cost basis of $5,000. But the sixth person that needs that, it’s going to cascade over into package B, which is significantly more expensive. And then the next 10 follow here, and then it fails over again. So what you’ll see is what makes this so difficult is there’s no unique features. Now certainly if you get to the enterprise level there may be a unique feature in these three that I’m showing you but there’s a huge portfolio from this vendor. And so these individual features may appear in other packages as well. You know a prep post or whatever. So there’s not necessarily a unique feature for you to be able to say, “Okay, I want to do my analysis.”

[7:49] And with ANSYS and the other vendors I’ve seen, because they’re not the only, but with ANSYS specifically, these licenses run off of a license manager, sits on a license server, and ANSYS has an add-on or an enhancement to that license manager, and it has reporting in there. It’s great, but it’s reporting at these individual feature levels. It’s not reporting at this package level. So I’m going to show you some various different reporting and different ways to look at it and then how we analyze that data, but I wanted to kind of set that baseline. Right? So this is why it’s not necessarily simple. There’s, you know, a more complex algorithm in place for you to move through that to understand that.

[8:40] And we talked about the normalization again just briefly want to talk about you know the information coming in is not necessarily in a friendly or a price book type of naming convention. So what we now do is we’re using what we call SSAD or a shared software asset directory where we’re taking this information and converting it. So you know in the past we would get that information from customers but now we’ve implemented and we are continuously adding to it a database so that you can just tap into that database. So here’s an example where we have that cryptic name right the fluent setup post and so once that processes it will convert that to what we again call a price book name, ANSYS fluent setup and post, right. So this is important again you want both components you can do it without the normalization just it makes it more difficult depending upon who’s consuming the data within your organization.

[9:59] So let me show you a couple of different ways that you can look at the data and why it matters. So here we’re looking at that individual feature. So again you can log on to your license server and see this information now at that feature level. You know did I have distinct users? How many were in use? What’s the time frame? Those types of things. How many user days, I mean you can do all that normal type of usage information that you get from your tool today. That’s pretty common across the board regardless of what tool you’re doing. This is the type of information that you should be able to get. But we can contrast that with here. Now what we’re looking at is the actual feature set or that price book name. So now we have these data points to determine what our effective license position could be. Do I need more? Do I need less? What is the situation based upon my actual usage in my organization?

[11:07] Right? So I think you know most people tell me oh we ask our department heads what they’re going to need and then you know they do a user and we do a budget and a forecasting but really what we want to look at is what did they actually use, right, and how does that marry up by group or division. You know is what they’re saying they need what they actually used or is there a mismatch there. So being able to do this type of reporting, you can also validate whatever the workflow is in your organization to do your budgeting and forecasting for next year’s budgets. It also is key in terms of when you have a, let’s say you have a three-year contract, but you have a reallocation mix negotiated in that. So annually you can remix what your licenses are. So, you’re not necessarily saving money, but you’re becoming more productive because you can remix your portfolio based upon your usage. Does that make sense? So a lot of different benefits to being able to look at this type of data.

[12:16] And so those were ANSYS examples. Here’s an Aspentech. Again, we’re just reporting at that more common price book name. And then you still get all the normal information that you want. Again, this is the way it works in our tool. In your other tools, you probably have that more raw name. Even if it’s normalized to a more friendly name, it’s not packaged into those bundles. So now what do we do? We have this information. We can report at the package level. How do I analyze whether it’s a midterm true up, whether it’s a whole new contract I’m going in to? How do I look at that data? So, let me give you a few examples here.

[13:10] This particular portfolio, I’m just going to focus here on prepost. You know what you want is you want a lot of different data points because the more data points you have, the more informed decision making you can make. So, if you were to look at just these two data points, how many do I own? I own seven. How many did I use? I used seven. That doesn’t really tell us the whole picture, right? So, were there people that were denied a license when they needed it? You know, what do I need more? Do I need less? Am I licensed? Do I have the best effective license position? Well, to get that information, again, we need more data points to look at that.

[14:05] So, one way to look at that, and there’s different ways, one way to look at that is to look at what we refer to as a license efficiency chart. And that’s where you can see exactly, well, that seventh license was only used a little over half an hour, right? So that gives us, okay, now I know that I don’t necessarily need more. We could look at denials etc. But then we can calculate 99% of the time or 95% of the time you know how many licenses. So now that we’ve added these additional data points, if it was me doing the analysis, I would feel, number one, absolutely I do not need any additional licenses for this package, but I may even be able to take a reduced license position.

[14:58] Now, in the past, let me just go to this next slide. You’ll see again we’ve expanded out this analysis for you know the whole portfolio for what we refer to as business as usual, but basically zero impact, that’s where you license at the max you ever used, right, so I never used more than this so I might as well do a reduced license position, but then we also have the 99 and 95. So I would tell you historically, you know, our customers would pick and choose. So maybe something that was more expensive, they might take a tighter license position. And what I mean by that is they would not, you know, say, “Oh, I can live with 95 or 99% of the time.” But it would be different for different line items in the portfolio, it wasn’t just a, “Hey, pick a column.”

[15:43] I will tell you recently, you know, with the markets the way they are and the demands of the senior management saying you have to take a reduction. We have got to reduce cost across the board. Software licensing even though that’s you know our bread and butter for our engineering and development. We have got to take a reduced license position. So I do have you know recently a couple of customers. One I was pretty surprised took the 95% across the board. I have another who took a 98% across the board. So you can change that reporting. This is our standard 99 and 95 but you know you can change that or modify that but you need to understand where’s that threshold in your organization and do you take you know a column approach where you want to take the whole thing at a certain percentage, you know, you can live with x amount of denials if you will. I do have one customer that they were like we don’t care what it costs, we never ever ever want a denial, so they don’t have in their particular business, they don’t have that cost constraint that 99% of the rest of us have, right, going on in our organizations.

[17:08] So, let me show you a couple of other examples and let me show you this one first. So, this is that same information that we were looking at, right? Our business as usual versus the 99 and the 95 on a portfolio. But what I wanted to show you is even more additional data points, right? So here we have the CFD enterprise again, we have that same number across here and this particular one 99% and 95, they’re all eight. But we can drill into that and look at again even more data points. So we can see, wow, we have 912 users sharing those eight licenses. We can look at that license efficiency. We can look at how much each of the licenses was used. It’s a little bit different format when you look at it in Excel. We can look at the breakdown of what that usage is by department, but over here we’re bringing in some different information again to give you more data points. So what this refers to, this greater than 12 and less than 12, is what we refer to as long checkouts or camping. So we can see, if we glance, so this is the user and then across here are the days and how much usage they have on that day. But what we want to look at here is we have plenty of users and this is probably an admin or a machine. We have plenty of users, pardon me, that have camped on the license or they have a long checkout for that license. So what this tells me is, okay, maybe because we have a lot of usage, right?

[18:58] But because of this, is there anything I can do to get my users to change their behavior so they’re not camping on the license? And if we couple that with what we refer to as hogging, meaning they have multiple licenses checked out simultaneously, we can also see that there’s many users that have had a situation where this is the days, how many days it happened. But you can see they were using, you know, two, three, four, six, eight licenses, four simultaneously.

[19:34] So what this tells me is we have the ability to help educate. What we find is most of your end users, they don’t have any idea. They don’t know what’s happening. They don’t understand the cost implications, right? So we can utilize alerts, we can do training, we can do all kinds of things to help them do that. And then we can you know have more data points to have a better feel. Okay, I feel comfortable with the eight. And let me just look at another example. So here, you know, 569, we use the max, but 99% of the time, right, it was only 508. Again, you have those same data points that you can go across and look at to make it a lot easier.

[20:22] But I want to show you and contrast that with something you may not be familiar with and that is the ability to do what I’m going to refer to as a generic feature set. So if you remember earlier I showed you the slide where we’re actually the feature set mapping takes into consideration how many you own and the cost, right? So it ranks the costing right for that cascading effect if you will. You also have the ability, and this is very beneficial for some organizations, to do what we refer to as a generic setting. So regardless of how many I own just based on the usage. So we do still have the cost component in the algorithm. But now, instead of having my actual max, I’ve just put in a big number, right, 9999. So, now what it’s doing is it’s calculating if this was the cost distribution, right? The ranking, how many would I use? And so, it’s that same scenario. What’s the max I would use? The 99, the 95. And it’ll give you that data.

[21:43] But now you can go back and say, “Okay, well I only own, well this one you own 1300, but all I need is the 764 and I think I have an example. Let’s see maybe where it’s different.” So this one it worked out the same, right? It was still three. But the point is that you can then go in and figure out across your whole portfolio. I just put a few examples here. And so currently, for example, on premium you only own two, but to optimize it so that they’re taking the less expensive mechanical option, you could use three. And on the mechanical pro, instead of six, you could use 22. So you’ll see that delta, it’s going to come up as a negative because obviously you’re purchasing more licenses. But when you have especially that inner contract remix, you can remix that. So then when you go into your new contract, you can actually take that information to the bottom line if that makes sense.

[22:52] So let me just show you a couple of other examples. I’ve got like four here, but what you’ll see on the left hand side is going to be the actual owned mapping versus the generic or if I opened it up. So again, here’s an example. This is CFD premium solver. I own five, but really from an optimized perspective, seven would make more sense based upon my usage. Same thing here, 7 to 9. This one is 1 to 7. So that’s a big difference. But again, being able to get those data points and simulating, and all of this happens, you know, without you making any changes in your environment. So being able to simulate this, plus all kinds of others, but today we’re talking about feature-to-feature set mapping, is very very beneficial for your organization.

[23:48] So, in conclusion, I know we kind of went through that fast. I was trying to kind of build it. Some people don’t have any exposure necessarily that are on the call today. So, hopefully I was able to build that up to talk about what that issue is and different, you know, ways to resolve that. And of course, the whole topic today is feature-to-feature set mapping to analyze that. But it’s again it’s not quite as simple. It’s a many to many relationship with the components of how many you own and the cost factors that come into that with various different vendors. So I think with that we have a few minutes, I guess I’m on time. So, we have a few minutes for questions. Mae if we have any.

[24:45] Mae: That was an excellent presentation, Linda. Thank you very much. And yes, our audience has got some great questions for you. So, let’s not delay. Our first question is, if we already have LicenseAnalyzer™, where do we configure the feature to feature set mapping?

[25:00] Linda: Oh, that’s a great question. And the answer is you just open a support ticket. So, you know, we do that for you on the back end. We’ll validate what you have. So, you know, there’s obviously some information that we have to exchange, but there’s not a GUI in the product. We do that for you. It’s all just included in your maintenance and support. Super simple. And if you have any questions, you know, even if you don’t know if you need it, you can just open a support ticket and we’ll jump online and go through the details and look at your whole installation and see where it applies and what we need to do to handle the mapping for you. So, we take care of that heavy lifting for you.

[25:51] Mae: Thank you so much, Linda. And we have another question here. It says does Open iT offer assistance in the analysis like if we want to do the generic mapping to see what license position we should have.

[26:05] Linda: So the answer is yes. I think this is probably one of the biggest differentiators for us is we’re not just providing the tool. Whether you’re, you know, an existing customer, a prospector, or just looking, is that yes, we have a whole professional services consulting. We are more than happy to help you with this type of vendor situation or others. We can help you with the analysis and depending upon what you’re trying to analyze, you know, we can help you even without our tool, so we can take data from different tools or license managers etc. So the quick answer is yes, we do have professional and consulting services around the data analytics and you don’t even have to have our tool necessarily. So depending upon what you have obviously affects the analysis, right, depending upon how many data points you have, but the quick answer to that is yes.

[27:13] Mae: And yes we have a third question here. For what Open iT version is this available?

[27:22] Linda: Any version. So we’ve had this capability my entire time here. I will say that we do have some very long-term customers who, I’m working with a customer right now who’s been a customer longer than me and it had not come up previously, you know, in their interactions with support or what have you. So it’s any version going backwards. So if we have any customers on the call and you haven’t done this, you know, reach out to your account manager or open a support ticket. We can certainly help you immediately, but any version.

[28:12] Mae: Thank you, Linda. That concludes our Q&A. Thank you for your answers, but do you also have any parting words for our audience?

[28:21] Linda: So I would just say again the more data points you have the better your analytics, and being able to do that, the vendors are doing, not necessarily around feature set mapping, we’re seeing more vendors come with this type of packaging if you will that requires the feature to feature set mapping. But I’m just going to make a generic statement and that is, you know, data is king. I’m sure I’ve said that in some of my other webinars. I think I even had a slide on it. But the more data you have, the more informed decisions you can make. It is so complex and the vendors are changing so many different things, their entitlements and their packaging and you know on and on and on. So being able to do that and simulate that to, you know, confirm your decisions is really important going forward. And so this little feature to feature set mapping is just one little bitty component, but again, we’re trying to break these down to more bite-sized pieces for our weekly webinars. So if you need any help, let us know.

[29:38] Mae: Thank you. And we have one more question, Linda. We just received this one if that’s still okay. Does the tool read product description from the license file which are commented?

[29:52] Linda: Well, I’m not sure, we definitely read license files. The description that comes generically on the reporting does come from the license file. But again we have the capability to normalize that information as well and of course it depends on where the data is collected from. So we collect data from all different places not just license servers, but I hope that answers it. If not, like can you, if that’s not answering it, you know, give me some more information here in the chat. I think. Oh, we’re we’re out of time. But you can still put it in here and I’ll follow up with you if I didn’t answer that, if I didn’t understand your question correctly.

[30:40] Mae: Right. Excuse me. So, thank you again, Linda. And that ends our webinar today. You will get a link to this recording in your email shortly. Alongside the webinar recordings, you will find a link to our survey. We would appreciate it if you could send us your feedback and the topics you’d like us to cover. You can also visit our webinar on demand page at openit.com to access the recording or scan the code on your screen for the survey. Connect with an Open iT business solutions consultant for a free 30-minute consultation. Contact us using the details displayed on your screen and follow us on social media at Open iT, Inc. to get more insights and updates. Thank you so much once again for joining us today. This has been Mae and I hope you have a great rest of your day.

[31:32] Linda: Thank you everybody.