How to Read Gartner’s Magic Quadrant + Critical Capabilities Report

Speaker A: Hello everybody, and welcome to the Dedicated Show. My name is Kate Thrashney. Um, I'm the host of the Dedicated show and founder of Dedicated. Today we have an extra special show where we're actually going to teach you how to read Gartner's Magic Quadrant report in conjunction with Gartner's Critical Capabilities report as it regards to the analytics and business intelligence platforms. The reports recently came out, so hopefully it's top of mind for everyone. And because this is a live show, I just want to remind everybody that you can ask questions, you can leave comments if you have opinions on what we're talking about. Go ahead and drop that in the comment if you have a question you want me to ask or, uh, my guest who I'm going to introduce in a minute to ask. Just go ahead and drop that in the comments and we'll try to get to as many as we can. Now, before we kick things off, I just want to bring up that we do have, uh, the Gartner orca here that I got at, um, in Orlando a couple of months ago at Gartner's Data and Analytics Summit. So here to keep me company and, you know, correct me if I'm wrong and when I'm talking about anything as it relates to Gartner. Uh, but besides that, we have an extra special guest that I would like to bring up in just a minute. Um, her name is Ann Lapkin. She's a former Gartner analyst and senior analyst at the Skills Connection. Now, uh, before I bring her on, I just want to do a quick overview of what we're looking at. Right. So I'll share my screen briefly. Today's topic, how to read the two reports in conjunction. So we've got the Magic Quadrant and Critical Capabilities here. Uh, if you haven't seen the reports before, this is a commonly used graphic that highlights sort of the challengers, leaders, niche players and visionaries in the business intelligence and analytics space. Uh, you'll see the dots of sort of how the companies were ranked for this year and the Critical Capabilities report. Um, just showing you the charts here, really, because the full reports are about 30 to 40 plus pages long. And I highly encourage everybody to read the full report and not just look at the charts. Um, but because we don't have the time to read the full report to you, we'll just show you some of the charts that came out. So these are some of the critical capabilities. I'll just flip through them. Uh, we'll talk through this in more detail. Um, but there are four different capabilities here that we can look at. So without further ado, I'm going to go ahead and bring Ann Lapkin onto our virtual stage here. And welcome to the show.

Speaker B: Hello. How are you?

Speaker A: I am doing fantastic. It's a nice, hot summer day here in New Jersey. How are you?

Speaker B: Uh, it is an even hotter summer day in Texas, I'm assuming. I don't care where you are. Texas is always hotter than every other place.

Speaker A: Okay, how hot is it right now? Go ahead.

Speaker B: Uh, I. You know what? I haven't been outside in the last couple of hours. It's that hot.

Speaker A: Smart, smart. We're probably somewhere in the 80s, so I'm assuming you're at least in the 90s.

Speaker B: Uh, we're close to 100, if not passed.

Speaker A: Wow. So it's hot. But we're here. This is virtual, so we have the AC going. Hopefully so do you. And ready to have a great conversation with you. And I know you're. You're known as the chief mischief m maker, but I'd love for you to take a couple minutes and just tell the audience, what does that mean? And who are you really?

Speaker B: Well, who am I really?

Speaker A: Um.

Speaker B: Uh, I am a person with two horses and four dogs.

Speaker A: Um,

Speaker B: I was a research analyst at Gartner for ten years. Uh, and I now work with companies to help them tell their story in the most effective way possible. I'm part of, uh, a loose collective of former analysts that work with clients to help them, uh, with the effectiveness of their communication.

Speaker A: Okay, you got me into two horses. I just went horseback riding, like, three, four days ago, and.

Speaker B: Cool.

Speaker A: I know, it. It was really cool. Um, I would love.

Speaker B: Was it your first time?

Speaker A: No, but it was my first time in probably 10 years, so it's been a while. I.

Speaker B: Okay.

Speaker A: I felt so comfortable, though. I don't know. It was the horse. I think they gave off a good energy. So.

Speaker B: Yeah, that may very well be. I have two horses, which is ridiculous because it's more than one that I, you know, more than I can ride at any given time. But it is a, uh, uh, it is a problem because it's a potato chip problem, really. You know, you have to open the bag and get the second one and, you know.

Speaker A: Yeah, you have to ride them and walk the dogs. And so between all that, you have time to help people with data.

Speaker B: Yes, exactly.

Speaker A: Well, I'm glad to hear that. So, you know, sometimes we see these reports floating around social media or getting emailed to us, and, hey, the critical capabilities are here. Or the Magic Quadrant. So I think it'll be helpful for the dedicated audience and, you know, anyone else who's watching this to get an understanding of what do those reports mean and how do we actually read them in conjunction? Are they saying the same thing? What do they actually cover?

Speaker B: Well, they're not saying the same thing. They're actually complementary to one another. Uh, the Magic Quadrant has been around for a very, very long time. I think, uh, the first one started to come out in the 1990s. So it's a well, well established, ah, tradition at this point. And what Magic Quadrants do is they evaluate the fitness of, of a company to serve a particular market. Uh, and they do that on two axes. One axis is um, uh, the, the execution axis and the second axis is the vision axis. Uh, the execution axis is the vertical one and the vision axis is the horizontal one. Yeah, yeah. What, what, what the Magic quadrant is trying to do is tell you whether a company has the, to support you in a given market. So that's not just about how good the product is, but it's also about how effective they are at selling, uh, how well they treat you after they take your money, uh, a whole bunch of other things, how, how good they are at anticipating what the market is going to need in the future, and so on and so forth. So you basically have four quadrants. Um, the upper right quadrant is leaders. Those are people who have both the ability to execute and a good vision. Uh, challengers are very good at execution, they're very good at selling their existing product, surfacing their existing customers. Uh, but they may not have the vision, uh, for what the market is going to need in the future. Um, the lower right hand is the visionary quadrant, which is where you find companies that don't have the resources for a, uh, very high level of execution. Maybe they're smaller companies with, with smaller budgets, they have fewer customers and so on, but they excel in vision and in anticipating what the market is going to need. Um, and then you have the niche which, you know, everybody treats like purgatory, but it's really not. Because what that means is that you may be particularly well suited to one particular slice of the market, but you don't necessarily, uh, support uh, the broader market overall. So Magic Quadrants are really about is this a company that has the resources and the vision to support me today and to support me in the future? Um, and the product itself, or the service, whatever that is, is only part of that equation.

Speaker A: Mhm.

Speaker B: The critical capabilities focuses exclusively on the product and what they do is they put together a number of common use cases. You have to remember that every Gartner analyst talks to between 700 and 1000 end user customers a year.

Speaker A: Wow. Okay.

Speaker B: So people are calling them up and saying, hey, you know, I'm having a problem with X, or I'm trying to decide what to do about why. They have a pretty good idea of what the common use cases are. And so what they do is they say, okay, now what are the capabilities that this product needs to have and how important is, is each one of those capabilities? In each one of the use cases in this particular magic quadrant, there are four, uh, this critical. Critical capabilities, there are four use cases. Um, and then they basically say, okay, how good is the company at this capability, that capability, and so on, on a scale of one to five, uh, where five is fantastic, one is awful and three is meh, you know, and this is good. Yeah, that's basically how we do it. And uh, and then they do a weighted average to come up with a score for each critical capability. So when you're looking magic quadrant, you're looking at whether the company can support you. When you're looking at the critical capabilities, you're looking at whether a particular tool is suited or not suited for your particular use case.

Speaker A: Okay.

Speaker B: Because I mean you can buy the best tool in the market, but if you want it to do X and it's really good at doing Y, then you'll have wasted a lot of money. So, so is, is there an order

Speaker A: in which you would recommend that people read the reports? Um, or should they be reading them at the same time?

Speaker B: Um, it's, it's actually very instructive to read them at the same time because, um, vendors who score particularly well, who might be in the leaders quadrant, don't necessarily have the best product. And so trying to, to figure out that balance between whether the company has the resources to support you in your analytics journey versus whether the product is actually going to meet your needs means you really, you can't read one without the other. And you really should read them together because they cover completely different aspects of the problem.

Speaker A: Right, so in this case you said we have four use cases, right? So we've got one for analytics developer use case. We have a business analyst use case, augmented consumer use case, and a data scientist use case. And do those use cases change every year with a new report or shift a little bit?

Speaker B: It depends on the market. Um, so, so in this particular market, this market's been around for a long time. And it's gone through several evolutions. Mhm. Uh, and when it goes through major evolutions, that's because the use cases change. When this, this magic quad went quadrant was originally, um, conceived. It was really about reporting tools.

Speaker A: Okay. Uh-huh.

Speaker B: It was about business objects and you know, it was about, it was about tools that people use to, to do what we call today, um, descriptive analytics. Tell me what happened. Right, okay.

Speaker A: Mhm.

Speaker B: Um, and then along came a revolution and companies like Tableau and Click came into the market and they essentially democratized, uh, uh, the, the uh, the access to analytics so that people, uh, who were, were not technology professionals, um, would uh, would be able to actually use these tools and get value out of data. And so the magic quadrant underwent a pretty significant change. Um, most of the players in the quadrant change, the old line reporting tools were all kicked out because the definition of the market had changed and became all about self service and so on and so forth. It's going through another evolution now and actually swinging back, um, to uh, to encompass, you know, new developments in the market like uh, artificial intelligence and generative AI in particular, uh, which you know, continues to democratize access to the masses. But also people are, are moving on to more, um, sophisticated types of analytics. They don't necessarily just want to know what happened, they want to know why it happened. They uh, want to know what might happen next and they also want to know what they ought to do about it. M so uh, the tools are getting more sophisticated for the highly technical users and simpler to use for the less technical users all at the same time.

Speaker A: Yeah, And I can see it changing even more next year as we have more of that AI play.

Speaker B: Right, well, more of the AI play and as data science begins to converge more strongly into analytics and you begin to develop this mythical feedback loop that everybody has always talked about about, you know, being able to determine why something is happening, making course corrections on the operational side and then checking to see if that actually worked and if it didn't, doing something else.

Speaker A: Yeah.

Speaker B: So analytics and operations become one continuum. M Essentially.

Speaker A: So before we get too far into the future and what we think will happen there, I want to talk about some of the biggest changes, the biggest shifts that you saw in this. Um, let's start with the magic quadrant first. Um, there was, there was a reshuffle of uh, some of the dots.

Speaker B: As there was a reshuffle, all of a sudden there are a whole bunch of people in leaders. Uh, okay, um, last year I think there were three.

Speaker A: So, so a Lot, lots of, lots of upward movement.

Speaker B: Lots of upward movement. Um Oracle in particular made uh, a strong jump up in, in the space. Um, the uh, uh, Thoughtspot made a smaller jump. But um, I think part of that is, is because the, the market itself is getting larger and as the tools get easier to use more and more people are using them. So, so you're going to have a, a jump in execution simply by virtue of the fact that you know a rising tide lifts all boats,

Speaker A: right? And um, any, any other shifts that you, you notice. So Oracle,

Speaker B: Oracle and you know, so you've got, you've got a bunch of people in the leaders quadrant now. You actually have a much more established presence by um, you know, by the hyperscalers, uh, Alibaba, Amazon Web Services, ah and Whatnot. Google uh, are all moving um, you know, establishing dominating presence. But I think that the two big gains, uh, at least on the vision axis were made by MicroStrategy and Pyramid. Uh you know MicroStrategy was uh, in challengers. They have moved almost to the leaders line not because they're executing better but because they've demonstrated with the uh, the introduction of their AI tools they've demonstrated a, a uh, strengthening of their vision uh, and Pyramid of course for the same reason because of their innovative use of uh, of generative AI, their innovative use of, of AI in general and also the fact that they are uh, they are showing a, a strong presence in uh, in this convergence of data science and, and uh, and analytics.

Speaker A: So I want to talk about that, that convergence. Um, why do you think that's becoming more important, let's say this year?

Speaker B: Well because first of all the, the, the, the loop, the analysis execution loop is, is getting more converged. Okay. People are now taking analytics. They're able to ask their tools, okay, what does this data actually mean? What is it telling me? Um, and they're able to think about corrective steps that they might take. Increasingly those corrective steps are coming from AI, uh and machine learning. So they're building and training AI models which is what data scientists do essentially. And so to the extent that you can actually incorporate all of those capabilities in a single platform, you actually have a really good shot at creating a truly data driven enterprise which is one where we understand what the data is telling us, we know what to do in order to achieve the results that we want. Okay, did that work? If it didn't work, what should we try next? And so on and so forth.

Speaker A: I think it helps that you can keep things in one when you can keep that whole process within one system where you don't have to collect data.

Speaker B: Uh, you can keep it within one platform. It's never actually going to truly be one system, but to the extent that you can have all of the components talking to all of the other components without having to perform unnatural integration acts, uh, then you're doing well. It's much easier to do when the things talk to each other. And they tend to do that, though not always. Uh, they tend to do that when they are part of the same platform.

Speaker A: Yeah, it just seems logically that it makes more sense. Right. If it's all in one. Um, those are some of the biggest shifts for the Magic Quadrant. I want to also look at the critical capabilities report and maybe we just go one at a time here. Um, I'll put it up on screen for us. So four use cases. This one was the analytics developer use cases. We see Pyramid, uh, domo, Salesforce and MicroStrategy sort of towards the top. Would you say that was expected for, for this use case?

Speaker B: Um, well, historically Pyramid has always performed well in critical capabilities because they have a, a very high quality tool platform, whatever it is you want to call it. Uh, this is the first year, I think, in the history of this report that one vendor has dominated the number one position in all four use cases. Uh, if you look at this, ThinkPyramid is number one in analytics developer, but it's also number one in the other three.

Speaker A: Yeah, analysts. It is, it is very. I actually noticed that they're number one across all of them.

Speaker B: They're number one across all of them and by a pretty significant margin in most cases. Um, but so you know, when you look at that and you look at uh, the capabilities of the tools and you know, is this going to solve my particular problem for what I'm trying to do right now? Um, you know, then that's kind of interesting. But what's interesting also is the fact that uh, you know, the other vendor, MicroStrategy, who made a significant jump in vision with their implementation of AI, the tool itself hasn't completely caught up in all the use cases. In other words, they're demonstrating a very strong vision, but it's not actualizing yet in what people are actually able to, to do.

Speaker A: Okay, so there's uh, a bit of a lag then. Right, so most of the, I guess, capabilities were ranked and researched and looked at last year. And if we were to run, I

Speaker B: mean the capabilities changed a little bit from last year. They, they tend to change from Year to year.

Speaker A: Right.

Speaker B: Uh, uh, in, in this year uh, a couple were added. Composability, uh, was a new capability that was added. A couple of the capabilities were merged together and you know composability is really about uh, the ability of um, of non technical users to put the tools together in different ways to get the answers that they need.

Speaker A: Right. I just want to quickly look into some uh, of the comments. I just see that Robert is just saying hello. Uh, Daniel's letting us know that High Point University students have academic access to Gartner's research. So it's helpful for his students. Yes Daniel, please go ahead and share the link with your students. This is being recorded so you can always watch it later. Christina often references the magic quadrant in her university lectures as well. I think it's very helpful for students who are just trying to maybe become a data analyst, data scientist to get a pulse on what sort of platforms and tools are out there for them to maybe study up. And on that note I wanted to ask, I know it's a shift from where we're going in terms of reading through the reports, but since two people mentioned lectures and universities and I wanted to know, do you think students can use this report to sort of guide them, um, like which skills they should be learning? Since Pyramid's at the top of all the critical capabilities, should students learn how to use Pyramid as one of their main skills?

Speaker B: Um, no, I would have to say not. A good tool is one that does not require, require you to learn a ah, completely new language and a completely new set of things. And most of these tools are fairly intuitive today. Remember a lot of the people that are using them are, are not people who are uh, who are highly technical. You know most business people who are using these things are, are not technical at all. Uh, so I think that it's good to get a foundation in, in data. It's good to get a foundation in what constitutes high quality data because wars have been started over low quality data. You know, you get bad data, it gives you bad answers and, and you know, then bad things happen. Uh, but um, but to the extent that you have to focus on a particular tool, I would say that that's probably not a ah, good strategy. I think most of these tools have um, uh, have hooks into the various universities and students have a wide variety of tools that they can, that they can use.

Speaker A: Mhm.

Speaker B: Uh, it's really a question of being able to ask intelligent, intelligent questions of the data and interpret the answers.

Speaker A: Yeah, I think generally these days if you know how to use one of these tools well, or have an understanding of how it's supposed to work, you can usually transfer those skills over to the next tool. Because you're right, it's not a new coding language. You're dragging and dropping.

Speaker B: That's right. And, you know, even coding languages are pretty easy to learn when you think about it, so it's really kind of counterproductive. Um, I would advise students to get exposed to as many tools as possible because there are going to be different tools that are good for different jobs.

Speaker A: Yeah. There are free trials available for most of these, um, software. And I agree, the more tools you can say you've used, the easier your interview will go.

Speaker B: Yeah.

Speaker A: And these days you can actually talk to your data. Right. I've seen some demos of platforms where you can literally just say, I want to see sales by region, and poof,

Speaker B: I, I want to see sales by region as something that everybody is doing. Um, and, and it's, it's really interesting because, you know, we're getting into. AI has been around. AI and machine learning have been around in this space for a very, very long time. Okay. And they've been used for a wide variety of things. What you're seeing now is all of the hype that's associated with generative AI. Uh, and as a matter of fact, in this market, they've even coined a term for it. It's called genbi. Okay. Generative Business Intelligence. Um, but it's not only. It's. It's actually pretty easy to implement something where, uh, you ask the, the, the tool to build your report. What's more interesting is when you ask it to help you figure out which analytics you should use, you ask it to figure out, you know, what your dimensions should be. When you ask it to help you figure out, um, what, um, uh, you know, what, what algorithms you should be using. Um, I personally do a lot of work with a friend on analyzing local election data, and one day, the two of us just got tired. We've got 10 years worth of elections data from the county about who voted and which primaries they voted in and whether they vote in November and whether they vote in local election and this, that, and the other. Um, and we kept having to go back and crack the sequel books. M. Until one day we looked at each other and one of us said, I don't remember which one of us said this to the other. I said, why don't we just ask ChatGPT to write the sequel for us?

Speaker A: I mean, why not?

Speaker B: If it's gonna do well and actually it worked a treat. So uh, we're, we're actually able to produce analyses much, much faster now than, than we ever were before. Uh, what's really funny if you Pyramid for example. Pyramid's done some very innovative things. So you can ask uh, the GenBI capability in pyramid to explain your data to you and you can ask it to do that, you know, in a number of different voices. One of the voices is sarcastic and it's actually hysterical.

Speaker A: Really? I haven't heard that.

Speaker B: Oh yeah, I mean you'll get a thing that says well you know the, the, the, the, the, the big uh, uh, influence on, on this particular result was X in parentheses. What a surprise.

Speaker A: You know, I think most people would like that. Just some, some levity at work, some fun, um, shows personality doesn't hurt.

Speaker B: But, but it's really, it's, it's, there's a lot of with, with Gen AI, not the least of which is that it tends to make stuff up hallucinations and when it does it makes stuff up in a very authoritative voice.

Speaker A: It's very confident. I agree.

Speaker B: Very confident. I, I, I did some uh, some work with a colleague of mine, um, on, on this a while ago because we were trying to figure out what impact it was going to have on the world of research. And so we ran a number of experiments. Um, uh, and in one experiment, uh, it came, I forget which one of them came back with a whole list of citations and each one of them was in a link. It had just made them up. And so I remember saying to my colleague, I said, you know what, this thing is like a puppy. It just wants to please. Well, it's going to give you the answer that you want to hear. But that doesn't necessarily have anything to do with the fact.

Speaker A: I, I've also heard it uh, being called a very confident recent MBA grad intern where it's very smart and it wants to make you happy and you know, make you look like they know everything.

Speaker B: Um, right when it actually, when it actually knows very little. Um, but, but that creates a problem for companies that are trying to use this stuff. Uh so first of all, do I really want to send my confidential data to a third party? And number two, maybe that third party is not training its models on data that is particularly relevant to me.

Speaker A: Hm.

Speaker B: So uh, you know, a company like, like Pyramid for example, who has, is uh, pursuing a, a multi AI, an open, an open gen AI strategy, an open LLM strategy where you can, you can use a model that they trained, you can use a model that you've trained, you can use anyone or all of the commercial models and you could specify for each data set which model you're going to use. That's one of the ways that this, uh, that, that this market is going to evolve.

Speaker A: Yeah. I was going to ask, um, with some of these potential issues that we're facing when we're using Gen AI and GenBI, how do we actually build trust with the business users that say, oh, well, I don't know this, I want to see an Excel spreadsheet. I, I don't want to talk to AI. How do we bridge that gap and get people more comfortable with using.

Speaker B: Well, I, I don't think, I actually don't think that there, it's going to be that difficult to bridge that gap. I think they'd much prefer to be talking to the tool. Um, then, uh, the, I, I think the big concern is that they, they will too easily believe the answer.

Speaker A: Okay, so it's the flip side.

Speaker B: So, so your issues are, number one, confidentiality of data. If I'm sending, if I'm sending my data out to OpenAI and saying, Please interpret this for me, then that can potentially be an issue. Um, so it, the, the people who are pursuing this and the markets who are pursuing this and actually it's very interesting. Gartner wrote a report, report very recently by Chris Long, I think, on the topic of, of the promise of AI, that I would encourage every one of your listeners with, um, um, with access to a Gartner subscription to read. Um, uh, if we have some time before the end of this, after we're done talking, I'll look it up and you can possibly put it in, in, uh, the comments for your readers.

Speaker A: Yeah, absolutely.

Speaker B: It's a very, very interesting document that talks about what the pitfalls are and what kinds of things companies are going to have to do. Chief security officers, chief data officers and so on are going to have to do to make sure that they have the confidence, uh, that their readers are not going to be led, you know, their, their users are not going to be led down the garden path.

Speaker A: Absolutely. I can definitely, if you send me a link, I'll put it into the LinkedIn and YouTube chat. So anyone who's joined will get a notification of a new comment and they'll

Speaker B: be able to read that. I'll send it to you after the fact then.

Speaker A: Yeah, perfect.

Speaker B: But that was an uh, interesting note. I think it was published the 24th of last month. So it's hot off the presses.

Speaker A: Okay. Yeah. I think the more we can learn about how to mitigate maybe customer expectations when it comes to gen AI, how we can avoid pitfalls, anything else we can learn, it's always good, I think.

Speaker B: Absolutely.

Speaker A: Um, okay, so few, few questions left for you here. And one of the questions I had is, you know, people listening to us right now. Let's say they're on the market for a business intelligence analytics platform, tool, software, whatever we're calling this thing. Should they use the Gartner Magic Quadrant as a, as a way to select that software, or should they go above and beyond and maybe get a demo?

Speaker B: I, I think the current research says that Gartner Magic Quadrants are consulted in between 80 and 85% of purchase decisions. So no matter what you or I say, people are going to look at them ever anyway. Yeah. But I can tell you what the biggest beef that Gartner analysts have with use of a Magic Quadrant is that people tend to look at it for the wrong things. You know, so a company will say, okay, we, we need to go out and find a, uh, data and analytics tool. Let's rfi and let's uh, invite all the leaders in the Magic Quadrant to participate in our rfi and let's ignore everybody else, even though one of the other vendors in the quadrant might actually be a better fit for what that company is trying to do.

Speaker A: Yeah.

Speaker B: So people are going to use Magic Quadrants. They should use Magic Quadrants, they should use the peer review sites. There is no better way of figuring out, you know, what it's actually like to be a customer of Vendor X. Mhm. Than to see what their actual customers are saying about them on Trust radius or on G2 or on Gartner Peer Insights. That should factor into the decision. Um, the other thing that should factor into the decision is, is what is my use case? What is it that I'm trying to do? I mean, analytics and bi is this big ugly yeti of a thing, uh, you know, that has 95 different arms and legs. So figure out what you're trying to do. And then one of the things that Gartner has done, which is actually very interesting, is, um, uh, is the interactivity of Magic Quadrants and critical capabilities reports. So if you're a Gartner subscriber, you can actually go in and you can set your own weight.

Speaker A: Oh.

Speaker B: Or, um, you know, for each critical capability and come up with the weighted average suitability of any vendor or all of the vendors for your Particular use case.

Speaker A: So you can pick what's important and, and weigh that. Great. Oh, interesting. I didn't know that. Okay.

Speaker B: Oh, yeah, yeah.

Speaker A: By the way, you just, it reminded me, I recently saw a video where Cindy Halson from, from Thoughtspot was saying that she, she wishes that Gartner wouldn't post the image that actual quadrant and just share the report for the first two weeks without the quadrant picture.

Speaker B: And, and you know what, that, that goes back to Cindy House and wasn't, what is it? Thought spot. But she was an analyst at Gartner and she was a key player in this magic quadrant for many, many years. So, uh, so what she's talking about is actually the misuse.

Speaker A: Mhm. Yeah. That's what you highlighted.

Speaker B: And they don't bother to read the words.

Speaker A: Yeah. And then, uh, Scott, Scott here is asking, you know, where can we find out more options that are not even on the MQ? Because those 20 companies are, are not the, the full world either, right?

Speaker B: No, they're not. Uh, the, the. So first of all, at least in the Gartner magic quadrant, there will be honorable, uh, mentions, companies that did not get into the magic. But you have to understand also the methodology allows people or, or allows analysts to evaluate up to a certain number of vendors. I think it's 20 or 25.

Speaker A: Mhm.

Speaker B: There are obviously more players than that, particularly in a market as crowded as this one. So one of the things that they have to do is set a particular inclusion bar and there may be companies which have very promising products that fall below that inclusion bar. And Gartner, uh, at least will mention those in a section that they call honorable mentions of the report.

Speaker A: Mhm.

Speaker B: But the other thing to do is look at the peer review sites, because on the peer review sites you will see there there is no barrier to inclusion there. So, uh, any company that's got customers who are going to post reviews will allow you to look at a much larger field and there might be somebody who is very, very specialized in what you're trying to do who's a better fit for you.

Speaker A: Yeah. And people tend to leave reviews when they're either very, very happy or very, very angry. Right. So you'll find the.

Speaker B: Yeah. You know, it's interesting in, in most cases we find that at least this is not true of Yelp, but it appears to be true of, of the technology review sites. Most people tend to leave a review when they have something to say and when they're really happy. You don't necessarily find people, uh, you know, Posting reviews the way they would post about a bad meal that they had at a restaurant. Yeah, I do remember one client of mine a number of years ago who had a review posted and the title of the review was the only thing modern about this platform is the logo. Yeah, but, but those, those are actually not as, as, not as common to think. Yeah, mostly you can actually get a pretty good view because most of the vendors are actually asking customers to post honest reviews because they want people to understand, they know that people are looking at this stuff and they want to understand, they want people to understand what it's like to be a customer of Company X. Mhm.

Speaker A: And that, that makes sense. Um, so just quick comment. Ah, here from Jennifer saying that she appreciates the insights and it's good to know about the changes in the magic quadrant which has been the de facto product credibility standard for years. Yes, Jennifer, we are glad to bring you the, the content and as we get close to wrapping up and I just wanted you to not necessarily get your crystal ball out, but just wanted you to share your thoughts on um, what do you think is going to happen in the next year or so? Are we going to continue trucking along or do you foresee major changes?

Speaker B: Um, I think that the generative AI stuff in particular is going to bring changes and set new bars, which we aren't even thinking about yet.

Speaker A: Mhm.

Speaker B: And we're not even contemplating yet. Um, I think that uh, that's definitely something to watch out for and that's definitely something to approach with a great deal of caution. Um, the, the other thing that I see is a continued convergence of data side data science and analytics. Um, and particularly I'm beginning to see much, much more interest in digital twinning, which uh, you know, which is going to require a lot of analytics horsepower in order to be able to do scenario planning and things of that nature. So you're going to see a, uh, continued convergence of analytics and operational systems or a much tighter feedback loop.

Speaker A: Well, thank you for sharing that. I know, I know it's not easy to predict the future, but I love that you shared your thoughts, so thank you for that. Um, last question I always ask my guests is if people want to learn more, have a conversation with you, follow up, where is the best place to find you?

Speaker B: Um, well, they can find me on LinkedIn, certainly. Uh, my name is Ann Lapkin and you can post the uh, LinkedIn, uh, connection in your comments as well. Uh, the other thing is to go to the skillsconnection.com and uh, look at what we do. Uh, I am joined there by a number of colleagues, and, uh, and we might be able to help you. We do some interesting stuff.

Speaker A: Awesome. Well, Ann, I want to thank you so much for your time. I. I learned a great deal here about how to read both of those reports. The Gardener Magic quadrant, the critical capabilities report was fun to talk about. Some of the key movers and players and how things are shaking out. I look forward to this report every year just to see, like, hey, who moved where? And I know Gartner says we shouldn't focus so much on the movements, but look at each report as more of a standalone. Right?

Speaker B: Yeah, yeah, yeah. They're always going to say that because then they don't have to explain why, you know, somebody moved from here to there.

Speaker A: It's human nature. Point a move from here to there. I want to know why, what happened.

Speaker B: And the first thing I do every year is I take last year's. Last year's graphic and this year's graphic, I superimposed one on the other to see who's gone, who's gone where, and what's happened.

Speaker A: Yes. If it gets a tracing paper. Right. And just start seeing what happened. So. All right, well, Anne, thank you so much for the conversation, and thank you guys for joining us live. And hopefully you've learned something as well. Stay dedicated, and we'll see you online. Thank you.

Speaker B: Thank you, guys.