 From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. The reverse momentum in tech stocks caused by rising interest rates, less attractive discounted cash flow models, and more tepid forward guidance, can be easily measured by public market valuations. And while there's lots of discussion about the impact on private companies and cash runway and 409a valuations, measuring the performance of non-public companies isn't as easy. IPOs have dried up and public statements by private companies, of course, they accentuate the good and they kind of hide the bad. Real data, unless you're an insider? Insider is hard to find. Hello and welcome to this week's Wikibon Cube Insights, powered by ETR. In this Breaking Analysis, we unlock some of the secrets that non-public emerging tech companies may and may not be sharing. And we do this by introducing you to a capability from ETR that we've not exposed you to over the past couple of years. It's called the Emerging Technologies Survey and it is packed with sentiment data and performance data based on surveys of more than 1,000 CIOs and IT buyers covering more than 400 companies. And we've invited back our colleague, Eric Bradley of ETR, to help explain the survey and the data that we're going to cover today. Eric, this survey is something that I've not personally spent much time on, but I'm blown away at the data. It's really unique and detailed. First of all, welcome, good to see you again. Great to see you too, Dave. And I'm really happy to be talking about the ETS or the Emerging Technology Survey. Even our own clients of constituents probably don't spend as much time in here as they should. Yeah, because there's so much in the mainstream, but let's pull up a slide to bring out the survey composition. Tell us about the study. How often do you run it? What's the background and the methodology? Yeah, you were just spot on the way you were talking about the private tech companies out there. So what we did is we decided to take all the vendors that we tracked that are not yet public and move them over to the ETS. And there isn't a lot of information out there. If you're not in Silicon Island, you're not going to get this stuff. So Pitchbook and Tech Crunch are two out there that give some data on these guys. But what we really wanted to do was go out to our community. We have 6,000 ITDMs in our community. We wanted to ask them, are you aware of these companies? And if so, are you allocating any resources to them? Are you planning to evaluate them? And really just kind of figure out what we can do. So this particular survey, as you can see, 1,000 plus responses, over 450 vendors that we track. And essentially what we're trying to do here is talk about your evaluation and awareness of these companies and also your utilization. And also if you're not utilizing them, then we can also figure out your sales conversion or churn. So this is interesting not only for the ITDMs themselves to figure out what their peers are evaluating and what they should put in POCs against the big guys when contracts come up, but it's also really interesting for the tech vendors themselves to see how they're performing. Yeah, and you can see two thirds of the respondents are director level or above. You got 28% of C-suite. There is of course a North America bias, 70, 75% is North America, but these smaller companies, they start doing, that's what they start doing business. So, okay, we're going to do a couple of things here today. First, we're going to give you the big picture across the sectors that ETR covers within the ETS survey. And then we're going to look at the high and low sentiment for the larger private companies, and then we're going to do the same for the smaller private companies, the ones that don't have as much mind share. And then we're going to put those two groups together and we're going to look at two dimensions, actually three dimensions. Which companies are being evaluated the most? Second, companies are getting the most usage and adoption of their offerings. And then third, which companies are seeing the highest churn rates, which of course is the silent killer of companies. And then finally, we're going to look at the sentiment and mind share for two key areas that we like to cover often here on breaking analysis security and data. And data comprises database, including data warehousing, and then big data analytics is the second part of data. And then machine learning and AI is the third section within data that we're going to look at. Now, one other thing before we get into it, ETR very often will include open source offerings in the mix, even though they're not companies, like TensorFlow or Kubernetes for example. And we'll call that out during this discussion. The reason this is done is for context because everyone is using open source, it is the heart of innovation. And many business models are super glued to an open source offering, like take MariaDB for example. There's the foundation, and then there's the, with the open source code, and then of course the company that sells services around the offering. Okay, so let's first look at the highest and lowest sentiment among these private firms, the ones that have the highest mind share. So they're naturally going to be somewhat larger. And we do this on two dimensions, sentiment on the vertical axis and mind share on the horizontal axis. And note the open source to see Kubernetes, Postgres, Kafka, TensorFlow, Jenkins, Grafana, et cetera. So Eric, please explain what we're looking at here, how it's derived, and what the data tells us. Certainly, so there is a lot here. So we're going to break it down first of all by explaining just what mind share and net sentiment is. You explain the axis. We have so many evaluation metrics, but we need to aggregate them into one. So that way we can rank against each other. Net sentiment is really the aggregation of all the positive and subtracting out the negative. So the net sentiment is a very quick way of looking at where these companies stand versus their peers in their sectors and sub-sectors. Mind share is basically the awareness of them, which is good for very early stage companies. And you'll see some names on here that are obviously been around for a very long time. And they'll really be the bigger on the axis on the outside. Kubernetes, for instance, as you mentioned, open source. This fact is standard for all container orchestration. And it should be that far up to the right because that's what everyone's using. In fact, the open source leaders are so prevalent in the emerging technology survey that we break them out later in our analysis because it's really not fair to include them and compare them to the actual companies that are providing the support and the security around that open source technology. But no survey, no analysis, no research would be complete without including these open source tech. So what we're looking at here, if I can just get away from the open source names, we see other things like Databricks and OneTrust. They're repeating as top net sentiment performers here. And then also the design vendors, people don't spend a lot of time on them, but Miro and Figma, this is their third survey in a row where they're just dominating that sentiment overall. And Adobe should probably take note of that because they're really coming out from. But Databricks, we all know probably would have been a public company by now if the market hadn't turned, but you can see just how dominant they are in a survey of nothing but private companies. And we'll see that again when we talk about the database later. Yeah, and I'll just add, so you see Automation Anywhere on there, the big UI path competitor, an company that was not able to get to the public markets, they've been trying. Sneak, Peter McKay's company, they've raised a bunch of money, big security player, they're doing some really interesting things in developer security, helping developers secure the data flow. H2O.ai, DataIQ, AI company, we saw them at the Snowflake Summit, Redis Labs, Netscope and Security. So a lot of really names that we know that ultimately we think are probably going to be hitting the public market. Okay, here's the same view for private companies with less mind share, Eric. Take us through this one. Yeah, on the previous slide too, real quickly, I wanted to pull out security scorecard and we'll get back into it, but this is a newcomer that I just think is, I couldn't believe how strong their data was, but we'll bring that up in a second. Now, when we go to the ones of lower mind share, you know, it's interesting to talk about open source, right? Kubernetes was all the way on the top right. Everyone uses containers. Here we see Istio up there. Not everyone is using service mesh as much. And that's why Istio was in the small amount of breakout. But still, when you talk about Netsentimate, it's about the leader. It's the highest one there is. So really interesting to point out, then we see other names like Kaliber in the data side, really performing well. And again, as always, security very well represented here, right? We have Aqua, Wiz, Armus, which is a standout in this survey this time around. They do IoT security. I hadn't even heard of them until I started digging into the data here. And I couldn't believe how well they were doing. And then of course you have any scale, which is doing a second best in this. And the best name in the survey, Huggingface, which is a machine learning AI tool, also doing really well on a Netsentimate. But they're not as far along on that axis of mind share just yet. So these are again, emerging companies that might not be as well represented in the enterprise as they will be in a couple of years. Yeah, Huggingface sounds like something to do with your two year old. Like you said, you see high performers, any scale is doing machine learning. You mentioned them, they came out of Berkeley, Kaliber, governance, influx data is on there. Influx DB is a time series database. And of course, Alex, if you bring that back up, you get a big group of red dots, right? That's the bad zone, I guess, which, you know, Sysense does Viz, Yellowbrick data is a MPP database. How should we interpret the red dots, Eric? Is that, I mean, is it necessarily a bad thing? Is it, could it be misinterpreted? What's your take on that? Sure, well, let me just explain the definition of it first from a data science perspective, right? We're a data company first. So the gray dots that you're seeing that aren't named, that's the mean, that's the average. So in order for you to be on this chart, you have to be at least one standard deviation above or below that average. So that gray is where we're saying, hey, this is where the lump of average comes in. This is, you know, where everyone normally stands. So you either have to be an out performer or an under performer to even show up in this analysis. So by definition, yes, the red dots are bad. You're at least one standard deviation below the average of your peers. It's not where you want to be. And if you're on the lower left, not only are you not performing well from a utilization or an actual usage rate, but people don't even know who you are. So that's a problem, obviously. The VC is the P's out there that are backing these companies, you know, they're the ones who mostly are interested in this data. Yeah, oh, that's great explanation. Thank you for that. No, nice benchmarking there. And yeah, you don't want to be in the red. All right, let's get into the next segment. Here we're going to look at evaluation rates, adoption and the all important churn. First, new evaluations. Let's bring up that slide. And Eric, take us through this. Yeah, so essentially, I just want to explain what evaluation means is that people will cite that they either plan to evaluate the company or they're currently evaluating. So that means we're aware of them and we are choosing to do a POC of them. And then we'll see later how that turns into utilization, which is what a company wants to see. Awareness, evaluation, and then actually utilizing them. That's sort of the life cycle for these emerging companies. So what we're seeing here, again, with very high evaluation rates, H2O, we mentioned, security scorecard jumped up again. Chargebee, Sneak, Salt Security, Armist. A lot of security names are up here. Aqua, NetScope, which God has been around forever. I still can't believe it's in an emerging technology survey, but so many of these names fall in data and security again, which is why we decided to pick those out, Dave. But, and on the lower side, Veena Acton, those unfortunately took the dubious award of the lowest evaluations in our survey. But I prefer to, you know, focus on the positive. So security scorecard, again, real standout in this one. They're in a security assessment space, basically. They'll come in and assess for you how your security hygiene is. And it's an area of real interest right now amongst our ITDM community. Yeah, I mean, I think those Arctic Wolf is up there too. They're doing managed services, you mentioned NetScope. Yeah, so okay. All right, let's look at now adoption. These are the companies whose offerings are being used the most and, you know, are above that standard deviation in the green. Take us through this, Eric. Sure, yet again, what we're looking at is okay, we went from awareness. We went to evaluation. Now it's about utilization, which means a company, a survey respondent's going to state, yes, we evaluated and we plan to utilize it, or it's already in our enterprise and we're actually allocating further resources to it. Not surprising again, a lot of open source. The reason why it's free. So it's really easy to grow your utilization on something that's free. But as you and I both know, as Red Hat proved, there's a lot of money to be made once the open source is adopted, right? You need the governance, you need the security, you need the support wrapped around it. So here we're seeing Kubernetes, Postgres, Apache Kafka, Jenkins, Grafana. These are all open source based names. But if we're looking at names that are non-open source, we're going to see Databricks, Automation Anywhere, Rubrik, all have the highest mind share. So these are the names, not surprisingly, all names that probably should have been public by now. Everyone's expecting an IPO imminently. You know, these are the names that have the highest mind share. If we talk about the highest utilization rates, again, Miro and Figma pop up, and I know they're not household names, but they are just dominant in this survey. These are applications that are meant for design software. And again, they're going after like an auto desk or a CAD or Adobe type of thing. It is just dominant how high the utilization rates are here, which again, it's something Adobe should be paying attention to. And then you'll see a little bit lower, but also interesting, we see Kaliber again. We see Hugging Face again. And these are names that are obviously in the data governance MLAI side. So we're seeing a ton of data, a ton of security. And Rubrik was interesting in this one, too. High utilization and high mind share. We know how pervasive they are in the enterprise already. Yeah, Eric, Alex, keep that up for a second, if you would. So yeah, you mentioned Rubrik, Cohesity's not on there. They're like sort of the big one. I'm going to talk about them in a moment. Puppet is interesting to me because maybe the early days of that sort of space, you had Puppet and Shaft, Shaft and they had Ansible, Red Hat bought Ansible and Ansible really took off. So it's interesting to see Puppet on there as well. Okay, so now let's look at the churn because this one is where you don't want to be. It's of course all red because churn is bad. Take us through this, Eric. Yeah, definitely don't want to be here. And I don't want to dwell on the negatives. We won't spend as much time, but to your point, there's one thing I want to point out that I think it's important. So you see Rubrik in the same spot, but Rubrik has so many citations in our survey that it actually would make sense that they're both being high utilization and churn just because they're so well represented. They have such a high overall representation in our survey. And the reason I call that out is Cohesity. Cohesity has an extremely high churn rate here about 17% and unlike Rubrik, they were not on the utilization side. So Rubrik is seeing both, Cohesity is not. It's not being utilized, but it's seeing a high churn. So that's the way you can kind of look at this data and say, hmm, same thing with puppet. You notice that it was on the other slide. It's also on this one. So basically what it means is a lot of people are giving puppet a shot, but it's starting to churn, which means it's not as sticky as we would like. One that was surprising on here for me was Taneum. It's kind of jumbled in there. It's hard to see in the middle, but Taneum, I was very surprised to see as high of a churn because what I do hear from our end user community is that people that use it like it, it really kind of spreads into not only vulnerability management, but also that endpoint detection response side. So I was surprised by that one mostly to see Taneum in here. Mural, again, was another one of those application design softwares that's seeing a very high churn as well. So you're saying if you're in both, like Alex, bring that back up if you would. So if you're in both, like MariaDB is, for example, I think, yeah, they're in both. They're both green and the previous one in red here. That's not as bad. You mentioned Rubrik is going to be in both. Cohesity is a bit of a concern. Cohesity just brought on Sanjay Poonin. So this could be a go-to-market issue, right? I mean, because Cohesity's got a great product and they got really happy customers. So they just maybe having to figure out, okay, what's the right ideal customer profile and Sanjay Poonin, I guarantee, is going to have that company cranking. I mean, they had been doing very well in the surveys and had fallen off of it. The other interesting things, when they were in the previous survey, I saw C-Vent, which is an event platform. My only reason I pay attention to this, because we actually have an event platform. We don't sell it separately. We bundle it as part of our offerings. And you see Hoppin on here. Hoppin raised a billion dollars during the pandemic and we were like, wow, that's going to blow up. And so you see Hoppin on the churn. You didn't see him in the previous chart, but that's sort of interesting. Like you said, let's not kind of dwell on the negative, but you really don't, churn is a real big concern. Okay, now we're going to drill down into two sectors, security and data, where data comprises three areas, database and data warehousing, machine learning and AI and big data analytics. So first let's take a look at the security sector. Now this is interesting because not only is it a sector drill down, but it also gives an indicator of how much money the firm has raised, which is the size of that bubble. And tell us if a company is punching above its weight and efficiently using its venture capital. Eric, take us through this slide. What are the, you know, explain the dots, the size of the dots, you know, set this up please. Yeah, so again, the access is still the same. That's sentiment in mind share, but what we've done this time is we've taken publicly available information on how much capital a company has raised. And that'll be the size of the circle you see around the name. And then whether it's green or red is basically saying, relative to the amount of money they've raised, how are they doing in our data? So when you see a net scope, which has been around forever, raised a lot of money, that's why you're going to see that more leading towards red, because it's just been around forever and kind of it would expect it versus a name like security scorecard, which has only raised a little bit of money and it's actually performing just as well if not better than a name like a net scope. One trust doing absolutely incredible right now beyond trust, we've seen the issues with Okta, right? So those are two names that play in that space that obviously are probably getting some looks about what's going on right now. Wiz, we've all heard about, right? So it's raised a ton of money. It's doing well on that sentiment, but the mind share isn't as well as you'd want, which is why you're going to see a little bit of that red versus a name like Aqua, which is doing container and application security. It hasn't raised as much money, but it's really neck and neck with a name like Wiz. So that is why on a relative basis, you'll see that more green. As we all know, information security is never going away, but as we'll get to later in the program, Dave, I'm not sure in this current market environment if people are as willing to do POCs and switch away from their security provider, right? There's a little bit of tepidness out there, a little trepidation. So right now we're seeing overall a slight pause, a slight pooling and overall evaluations on the security side versus historical levels a year ago. Now, let's keep, let's stay on here for a second. So a couple of things I want to point out. So it's interesting. Now, Sneak has raised over, I think, $800 million. Now you can see them, they're high on the vertical and the horizontal, but now compare that to lacework. It's hard to see, but they're kind of buried in the middle there. That's the biggest dot in this whole thing. I think I'm interpreting this correctly. They've raised over a billion dollars. So Mike Spicer company, he's the one who funded, he was the founding investor in Snowflake. So, you know, people watch that very closely, but that's an example of where they're not punching above their weight. You know, they recently had a layoff and they got to fine-tune things. I'm still confident that they're going to do well because they're approaching security as a data problem, which is probably people having trouble getting their arms around that. And then again, I see Arctic Wolf. They're not red, they're not green, but they've raised a fair amount of money, but it's showing up to the right and decent level there. And a couple of the other ones that you mentioned, Netscope, yeah, they've raised a lot of money, but they're actually performing where you want. What you don't want is kind of where lacework is, right? They've got some work to do to really take advantage of the money that they raised last November and prior to that, so. Yeah, if you're seeing that more neutral color like you're calling out with an Arctic Wolf, like that means relative to their peers, this is where they should be, right? It's when you're seeing that red like on a lacework where we all know, wow, you raised a ton of money and your mind share isn't where it should be. Your net sentiment is not where it should be comparatively. And then you see these great, you know, standouts like salt security and security scorecard and abnormal, you know, they haven't raised that much money yet, but their net sentiment's high and their mind share's doing well. So those basically in a nutshell of you're a PE or a VC and you see a small green circle, then you're doing well. That means you made a good investment. Yeah, some of these guys I don't know, but you see these small green circles, those are the ones you want to start digging into and maybe help them catch a wave. Okay, let's get into the data discussion. And again, three areas, database slash data warehousing, big data analytics and MLAI. First we're going to look at the database sector. So Alex, thank you for bringing that up. All right, take us through this, Eric. Actually, let me just say, PostgreSQL, I got to ask you about this. It shows some funding, but that actually could be a mix of EDB, the company that commercializes Postgres and Postgres, the open source database, which is a transaction system, kind of an open source oracle. You see MariaDB as a database, but open source database with the companies, they've raised over 200 million and they filed an S4. So Eric, it looks like this might be a little bit of mashup of companies and open source products. Help us understand this. Yeah, it's tough when you start dealing with the open source side, right? And I just, I'll be honest with you, there is a little bit of a mashup here, right? There are certain names here that are 100% for profit companies, and then there are others that are obviously open source based. Like Redis is open source, but Redis Labs is the one trying to monetize the support around it. So you're 100% accurate on this slide. I think one of the things here that's important to note though is just how important open source is to data, right? If you're going to be going to any of these areas, it's going to be open source based to begin with. And Neo4j is one I want to call out here. It's not one everyone's familiar with, but it's basically geographical charting database, which is a name that we're seeing on a net sentiment side, actually really, really high. When you think about it, it's the third overall net sentiment for a niche database play. It's not as big on the mind share because it's use cases aren't as often, but third biggest play on net sentiment I found really interesting on this slide. Yeah, and again, I want to just, so MariaDB, as I said, they file an S4, I think $50 million in revenue. It might even be ARR, so they're not huge, but they're getting there. And by the way, MariaDB, if you don't know, was the company that was formed like the day that Oracle bought Sun, in which they got MySQL, and MariaDB has done a really good job of replacing a lot of MySQL instances. Oracle has responded with MySQL Heatwave, which was kind of the Oracle version of MySQL, so there's some interesting battles going on there. If you think about the Lampstack, the M in the Lampstack was MySQL, and so now it's all MariaDB replacing that MySQL for a large part. And then you see, again, the red. You got to have some concerns about there. Are there spikes been around for a long time? Single store, you know, changed their name, I think a couple of years ago last year, Yellowbrick data, Firebolts was kind of going after Snowflake for a while, but yeah, you want to get out of that red zone, so they got some work to do. And Dave, real quick, for the people that aren't aware, I just want to let them know that we can cut this data with the public company data as well. Right. So we can cross over this with that, because some of these names are competing with the larger public company names as well. So we can go ahead and cross reference like a MariaDB, you know, with like a Mongo, for instance, or of something of that nature. So it's not in this slide, but at another point, we can certainly explain on a relative basis how these private names are doing compared to the other ones as well. All right, let's take a quick look at analytics. Alex, bring that up if you would. Go ahead, Eric. Yeah, I mean, essentially here, I can't see it on my screen, my apologies. I just kind of went blank on that. So give me one second to catch up on this. Yeah, so I could set it up while you're doing that. You got Grafana up until the right. I mean, this is huge, right? Thank you, you lost my screen there for a second. Yep, again, open source name Grafana, absolutely up into the right. But, you know, as we know, Grafana Labs is actually picking up a lot of speed based on Grafana, of course. And I think we might actually hear some noise from them coming this year. The names that are actually a little bit more disappointing that I want to call out are names like ThoughtSpot. It's been around forever. Their mind share, of course, is second best here, but based on the amount of time they've been around and the amount of money they've raised, it's not actually outperforming the way it should be. We're seeing Moogsoft obviously make some waves that's very high net sentiment for that company. It's, you know what, third, fourth position overall in this entire area. Another name like FiveTran, Matillion is doing well. FiveTran, even though it's got a high net sentiment, again, it's raised so much money that we would have expected a little bit more at this point. I know you know the space extremely well, but you know, basically what we're looking at here, and to the bottom left, you're going to see some names with a lot of red, large circles that really just aren't performing that well. Influx data, however, second highest net sentiment, and it's really pretty early on in this stage. And the feedback we're getting on this name is the use cases are great, the efficacy is great, and I think it's one to watch out for. Yeah, Influx data, time series, database. The other interesting things I just noticed here, you got Tamer on here, which is that little small green, those are the ones we were saying before, look for those guys, they might be some of the interesting companies out there, and then Observe, Jeremy Burton's company, they do observability on top of Snowflake, not green, but kind of in that gray, so that's kind of cool. Monte Carlo is another one, they're sort of slightly green. They are doing some really interesting things in data and data mesh. So yeah, okay, so I can spend all day on this stuff, Eric, phenomenal data. I got to get back and really dig in. Let's end with machine learning and AI. Now this chart, it's similar in its dimensions of course, except for the money raised. We're not showing that size of the bubble, but AI is so hot, we wanted to cover that here. Eric, explain this please, why TensorFlow is highlighted and walk us through this chart. Yeah, it's funny, yet again, right? Another open source name, TensorFlow, being up there. And I just want to explain, we do break out machine learning AI as its own sector. A lot of this of course really is intertwined with the data side, but it is on its own area. And one of the things I think that's most important here to break out is that Databricks, we started to cover Databricks in machine learning AI. That company has grown, it's a much, much more than that. So I do want to state to you, Dave, and also the audience out there that moving forward, we're going to be moving Databricks out of only the ML AI into other sectors, so we can kind of value them against their peers a little bit better. But in this instance, you could just see how dominant they are in this area. And one thing that's not here, but I do want to point out is that we have the ability to break this down by industry vertical, organization size. And when I break this down at the Fortune 500 and Fortune 1000, both Databricks and TensorFlow are even better than you see here. So it's quite interesting to see that the names that are succeeding are also succeeding with the largest organizations in the world. And as we know, large organizations means large budgets. So this is one area that I just thought was really interesting to point out that as we break it down the data by vertical, these two names still are the outstanding players. Yeah, I just also want to call it H20 AI. They're getting a lot of buzz in the marketplace and I'm seeing them a lot more, Anaconda, another one, DataIcu, consistently popping up. DataRobot is also interesting because all the kerfuffle that's going on there, the Cube guy, Cube alum, Chris Lynch, stepped down as executive chairman. All this stuff came out about how the executives were taking money off the table and didn't allow the employees to participate in that money-raising deal. So that's pissed a lot of people off and so they're now going through some uncomfortable things which is unfortunate because DataRobot, we haven't covered them that much in breaking analysis but I've noticed them often times, Eric, in the surveys, doing really well. So you would think that company has a lot of potential but yeah, there's an important space that we're going to continue to watch. Let me ask you, Eric, can you contextualize this from a time series standpoint? But how has this changed over time? Yeah, again, not shown here but in the data, I'm sorry, Jordan. No, I'm sorry, I should have interjected. In other words, you would think in a downturn that these emerging companies would be less interesting to buyers because they're more risky. What have you seen? Yeah, and it was interesting before we went live, you and I were having this conversation about is the downturn stopping people from evaluating these private companies or not, right? In a larger sense, that's really what we're doing here. How are these private companies doing when it comes down to the actual practitioners, the people with the budget, the people with the decision-making? And so what I did is we have historical data, as you know. I went back to the emerging technology survey we did in November of 21, right at the crest, right before the market started to really fall and everything kind of started to fall apart there. And what I noticed is on the security side, very much so we're seeing less evaluations than we were in November 21. So I broke it down on cloud security, net sentiment went from 21% to 16% from November 21. That's a pretty big drop. And again, net sentiment is our one aggregate metric for overall positivity, meaning utilization and actual evaluation of a name. Again, in database, we saw it drop a little bit from 19% to 13%. However, in analytics, we actually saw it stay steady. So it's pretty interesting that, yes, cloud security and security in general is always gonna be important, but right now we're seeing less overall net sentiment in that space, but within analytics, we're seeing steady with growing mind share. And also to your point earlier in machine learning AI, we're seeing steady net sentiment and mind share has grown a whopping 25% to 30%. So despite the downturn, we're seeing more awareness of these companies in analytics and machine learning and a steady actual utilization of them. I can't say the same in security and database. They're actually shrinking a little bit since the end of last year. You know, it's interesting. We were on a round table. Eric does these round tables with CISOs and CIOs. And I remember one time you had asked the question, do you, you know, how do you think about some of these emerging tech companies? And one of the executives said, I always include somebody in the bottom left of the Magic Quadrant, Gartner Magic Quadrant in my RFPs. I think he said, that's how I found, I don't know, Zscaler or something like that, years before anybody ever knew of them, because they're gonna help me get to the next level. So it's interesting to see Eric in these sectors, how they're holding up in many cases. Yeah, it's a very important part for the actual IT practitioners themselves. You know, there's always contracts coming up and you always have to worry about your next round of negotiations. And that's one of the roles these guys play. You have to do a POC when contracts come up, but it's also their job to stay on top of the new technology. You can't fall behind. And like, everyone's a software company now. Everyone's a tech company, no matter what you're doing. So these guys have to stay on top of it. And that's what this ETS can do. You can go in here and look and say, all right, I'm gonna evaluate the technology. And it could be twofold. It might be that you're ready to upgrade your technology and they're actually pushing the envelope or it simply might be using them as a negotiation ploy. So when I go back to the big guy who I have full intentions of writing that contract to, at least I have some negotiation leverage. Eric, we got to leave it there. I mean, I could spend all day. I'm gonna definitely dig into this on my own time. Thank you for introducing this. Really appreciate your time today. I'll always enjoy it, David. And I hope everyone out there has a great holiday weekend. Enjoy the rest of the summer and I love to talk data. So anytime you want, just point the camera on me and I'll start talking data. You got it. I also want to thank the team at ETR, not only Eric, but Darren Braiman, who's a data scientist, really helped prepare this data. The entire team over at ETR, I cannot tell you how much additional data there is. We are just scratching the surface in this breaking analysis. So great job, guys. I want to thank Alex Meyerson who's on production. He manages the podcast. Ken Schiffman as well, who's just coming back from VMware Explorer. Kristen Martin and Cheryl Knight helped get the word out on social media. And in our newsletters, and Rob Hoef is our editor-in-chief over at Silicon Angle. Does some great editing for us. Thank you, all of you guys. Remember these episodes, they're all available as podcasts. Wherever you listen, all you gotta do is just search, breaking analysis podcast. I publish each week on wikibon.com and siliconangle.com. Or you can email me to get in touch, david.volante at siliconangle.com. You can DM me at davilante. Or comment on my LinkedIn posts and please do check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for Eric Bradley and theCUBE Insights powered by ETR. Thanks for watching. Be well and we'll see you next time on breaking analysis.