>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> UiPath has had a long, strange trip to IPO. How so you ask? Well, the company was started in 2005. But it's culture, is akin to a frenetic startup. The firm shunned conventions and instead of focusing on a narrow geographic area to prove its product market fit before it started to grow, it aggressively launched international operations prior to reaching unicorn status. Well prior, when it had very little revenue, around a million dollars. Today, more than 60% of UiPath business is outside of the United States. Despite its headquarters being in New York city. There's more, according to recent SEC filings, UiPath total revenue grew 81% last year. But it's free cash flow, is actually positive, modestly. Wait, there's more. The company raised $750 million in a Series F in early February, at a whopping $35 billion valuation. Yet, the implied back of napkin valuation, based on the number of shares outstanding after the offering multiplied by the proposed maximum offering price per share yields evaluation of just under 26 billion. (Dave chuckling) And there's even more to this crazy story. Hello everyone, and welcome to this week's Wikibon CUBE Insights, Powered by ETR. In this Breaking Analysis we'll share our learnings, from sifting through hundreds of pages (paper rustling) of UiPath's red herring. So you didn't have to, we'll share our thoughts on its market, its competitive position and its outlook. Let's start with a question. Mark Roberge, is a venture capitalist. He's a managing director at Stage 2 Capital and he's also a teacher, a professor at the B-School in Harvard. One of his favorite questions that he asks his students and others, is what's the best way to grow a company? And he uses this chart to answer that question. On the vertical axis is customer retention and the horizontal axis is growth to growth rate and you can see he's got modest and awesome and so forth. Now, so I want to let you look at it for a second. What's the best path to growth? Of course you want to be in that green circle. Awesome retention of more than 90% and awesome growth but what's the best way to get there? Should you blitz scale and go for the double double, triple, triple blow it out and grow your go to market team on the horizontal axis or should be more careful and focus on nailing retention and then, and only then go for growth? What do you think? What do you think most VCs would say? What would you say? When you want to maybe run the table, capture the flag before your competitors could get there or would you want to take a more conservative approach? What would Daniel Dines say the CEO of UiPath? Again, I'll let you think about that for a second. Let's talk about UiPath. What did they do? Well, I shared at the top that the company shunned conventions and expanded internationally, very rapidly. Well before it hit escape velocity and they grew like crazy and it got out of control and he had to reign it in, plug some holes, but the growth didn't stop, go. So very clearly based on it's performance and reading through the S1, the company has great retention. It uses a metric called gross retention rate which is at 96 or 97%, very high. Says customers are sticking with it. So maybe that's the right formula go for growth and grow like crazy. Let chaos reign, then reign in the chaos as Andy Grove would say. Go fast horizontally, and you can go vertically. Let me tell you what I think Mark Roberge would say, he told me you can do that. But churn is the silent killer of SaaS companies and perhaps the better path is to nail product market fit. And then your retention metrics, before you go into hyperbolic growth mode. There's all science behind this, which may be antithetical to the way many investors want to roll the dice and go for super growth, like go fast or die. Well, it worked for UiPath you might say, right. Well, no. And this is where the story gets even more interesting and long and strange for UiPath. As we shared earlier, UiPath was founded in 2005 out of Bucharest Romania. The company actually started as a software outsourcing startup. It called the company, DeskOver and it built automation libraries and SDKs for companies like Microsoft, IBM and Google and others. It also built automation scripts and developed importantly computer vision technology which became part of its secret sauce. In December 2015, DeskOver changed its name to UiPath and became a Delaware Corp and moved its headquarters to New York City a couple of years later. So our belief is that UiPath actually took the preferred path of Mark Roberge, five ticks North, then five more East. They slow-cooked for the better part of 10 years trying to figure out what market to serve. And they spent that decade figuring out their product market fit. And then they threw gas in the fire. Pretty crazy. All right, let's take a peak (chuckling) at the takeaways from the UiPath S1 the numbers are impressive. 580 million ARR with 65% growth. That asterisk is there because like you, we thought ARR stood for annual recurring revenue. It really stands for annualized renewal run rate. annualized renewal run rate is a metric that is one of UiPath's internal KPIs and are likely communicate that publicly over time. We'll explain that further in a moment. UiPath has a very solid customer base. Nearly 8,000, I've interviewed many of them. They're extremely happy. They have very high retention. They get great penetration into the fortune 500, around 63% of the fortune 500 has UiPath. Most of UiPath business around 70% comes from existing customers. I always say you're going to get more money out of existing customers than new customers but everybody's trying to go out and get new customers. But UiPath I think is taking a really interesting approach. It's their land and expand and they didn't invent that term but I'll come back to that. It kind of reminds me of the early days of Tableau. Actually I think Tableau is an interesting example. Like UiPath, Tableau started out as pretty much a point tool and it had, but it had very passionate customers. It was solving problems. It was simplifying things. And it would have bid into a company and grow and grow. Now the market fundamentals for UiPath are very good. Automation is super hot right now. And the pandemic has created an automation mandate to date and I'll share some data there as well. UiPath is a leader. I'm going to show you the Gartner Magic Quadrant for RPA. That's kind of a good little snapshot. UiPath pegs it's TAM at 60 billion dollars based on some bottoms up calculations and some data from Bain. Pre-pandemic, we pegged it at over 30 billion and we felt that was conservative. Post-pandemic, we think the TAM is definitely higher because of that automation mandate, it's been accelerated. Now, according to the S1, UiPath is going to raise around 1.2 billion. And as we said, if that's an implied valuation that is lower than the Series F, so we suspect the Series F investors have some kind of ratchet in there. UiPath needed the cash from its Series F investors. So it took in 750 million in February and its balance sheet in the S1 shows about 474 million in cash and equivalent. So as I say, it needed that cash. UiPath has had significant expense reductions that we'll show you in some detail. And it's brought in some fresh talent to provide some adult supervision around 70% of its executive leadership team and outside directors came to the company after 2019 and the company's S1, it disclosed that it's independent accounting firm identified last year what it called the "material weakness in our internal controls over financial report relating to revenue recognition for the fiscal year ending 2018, caused by a lack of oversight and technical competence within the finance department". Now the company outlined the steps it took to remediate the problem, including hiring new talent. However, we said that last year, we felt UiPath wasn't quite ready to go public. So it really had to get its act together. It was not as we said at the time, the well-oiled machine, that we said was Snowflake under Mike Scarpelli's firm operating guidance. The guy's the operational guru, but we suspect the company wants to take advantage of this mock market. It's a good time to go public. It needs the cash to bolster its balance sheet. And the public offering is going to give it cache in a stronger competitive posture relative to its main new competitor, autumn newbie competitor Automation Anywhere and the big whales like Microsoft and others that aspire and are watching what UiPath is doing and saying, hey we want a piece of that action. Now, one other note, UiPath's CEO Daniel Dines owns 100% of the class B shares of the company and has a 35 to one voting power. So he controls the company, subject of course to his fiduciary responsibilities but if UiPath, let's say it gets in trouble financially, he has more latitude to do secondary offerings. And at the same time, it's insulated from activist shareholders taking over his company. So lots of detail in the S1 and we just wanted to give you some of those highlights. Here are the pretty graphs. If whoever wrote this F1 was a genius. It's just beautiful. As we said, ARR, annualized renewal run rate all it does is it annualizes the invoice amount from subscriptions in the maintenance portion of the revenue. In other words, the parts that are recurring revenue, it excludes revenue from support and perpetual license. Like one-time licenses and services is just kind of the UiPath's and maybe that's some sort of legacy there. It's future is that recurring revenue. So it's pretty similar to what we think of as ARR, but it's not exact. Lots of customers with a growing number of six and seven figure accounts and a dollar-based net retention of 145%. This figure represents the rate of net expansion of the UiPath ARR, from existing listing customers over a 12 month period. Translation. This says UiPath's existing customers are spending more with the company, land and expand and we'll share some data from ETR on that. And as you can see, the growth of 86% CAGR over the past nine quarters, very impressive. Let's talk about some of the fundamentals of UiPath's business. Here's some data from the Brookings Institute and the OECD that shows productivity statistics for the US. The smaller charts in the right are for Germany and Japan. And I've shared some similar data before the US showed in the middle there. Showed productivity improvements with the personal productivity boom in the mid to late 90s. And it spilled into the early 2000s. But since then you can see it's dropped off quite significantly. Germany and Japan are also under pressure as are most developed countries. China's labor productivity might show declines but it's level, is at level significantly higher than these countries, April 16th headline of the Wall Street Journal says that China's GDP grew 18% this quarter. So, we've talked about the snapback in post-COVID and the post-isolation economy, but these are kind of one time bounces. But anyway, the point is we're reaching the limits of what humans can do alone to solve some of the world's most pressing challenges. And automation is one key to shifting labor away from these more mundane tasks toward more productive and more important activities that can deliver lasting benefits. This according to UiPath, is its stated purpose to accelerate human achievement, big. And the market is ready to be automated, for the most part. Now the post-isolation economy is increasingly going to focus on automation to drive toward activity as we've discussed extensively, I got to share the RPA Magic Quadrant where nearly everyone's a winner, many people are of course happy. Many companies are happy, just to get into the Magic Quadrant. You can't just, you have to have certain criteria. So that's good. That's what I mean by everybody wins. We've reported extensively on UiPath and Automation Anywhere. Yeah, we think we might shuffle the deck a little bit on this picture. Maybe creating more separation between UiPath and Automation Anywhere and the rest. And from our advantage point, UiPath's IPO is going to either force Automation Anywhere to respond. And I don't know what its numbers are. I don't know if it's ready. I suspect it's not, we'd see that already but I bet you it's trying to get there. Or if they don't, UiPath is going to extend its lead even further, that would be our prediction. Now personally, I would have Pegasystems higher on the vertical. Of course they're not an IPO, RPA specialist, so I kind of get what Gartner is doing there but I think they're executing well. And I'd probably, in a broader context I'd probably maybe drop blue prism down a little bit, even though last year was a pretty good year for the company. And I would definitely have Microsoft looming larger up in the upper left as a challenger more than a visionary in my opinion, but look, Gartner does good work and its analysts are very deep into this stuff, deeper than I am. So I don't want to discount that. It's just how I see it. Let's bring in the ETR data and show some of the backup here. This is a candlestick chart that shows the components of net score, which is spending momentum, however, ETR goes out every quarter. Says you're spending more, you're spending less. They subtract the lesses from the mores and that's net score. It's more complicated than that, but that's that blue line that you see in the top and yes it's trending downward but it's still highly elevated. We'll talk about that. The market share is in the yellow line at the bottom there. That green represents the percentage of customers that are spending more and the reds are spending less or replacing. That gray is flat. And again, even though UiPath's net score is declining, it's that 61%, that's a very elevated score. Anything over 40% in our view is impressive. So it's, UiPath's been holding in the 60s and 70s percents over the past several years. That's very good. Now that yellow line market share, yes it dips a bit, but again it's nuanced. And this is because Microsoft is so pervasive in the data stat. It's got so many mentions that it tends to somewhat overwhelm and skew these curves. So let's break down net score a little bit. Here's another way to look at this data. This is a wheel chart we show this often it shows the components of net score and what's happening here is that bright red is defection. So look at it, it's very small that wouldn't be churn. It's tiny. Remember that it's churn is the killer for software companies. And so that forest green is existing customers spending more at 49%, that's big. That lime green is new customers. So again, it's from the S1, 70% of UiPath's revenue comes from existing customers. And this really kind of underscores that. Now here's more evidence in the ETR data in terms of land and expand. This is a snapshot from the January survey and it lines up UiPath next to its competitors. And it cuts the data just on those companies that are increasing spending. It's so that forest green that we saw earlier. So what we saw in Q1 was the pace of new customer acquisition for UiPath was decelerating from previous highs. But UiPath, it shows here is outpacing its competition in terms of increasing spend from existing customers. So we think that's really important. UiPath gets very high scores in terms of customer satisfaction. There's, I've talked to many in theCUBE. There's places on the web where we have customer ratings. And so you want to check that out, but it'll confirm that the churn is low, satisfaction is high. Yeah, they get dinged sometimes on pricing. They get dinged sometimes, lately on service cause they're growing so fast. So, maybe they've taken the eye off the ball in a couple of counts, but generally speaking clients are leaning in, they're investing heavily. They're creating centers of excellence around RPA and automation, and UiPath is very focused on that. Again, land and expand. Now here's further evidence that UiPath has a strong account presence, even in accounts where its competitors are presence. In the 149 shared accounts from the Q1 survey where UiPath, Automation Anywhere and Microsoft have a presence, UiPath's net score or spending velocity is not only highly elevated, it's relative momentum, is accelerating compared to last year. So there's some really good news in the numbers but some other things stood out in the S1 that are concerning or at least worth paying attention to. So we want to talk about that. Here is the income statement and look at the growth. The company was doing like 1 million dollars in 2015 like I said before. And when it started to expand internationally it surpassed 600 million last year. It's insane growth. And look at the gross profit. Gross margin is almost 90% because revenue grew so rapidly. And last year, its cost went down in some areas like its services, less travel was part of that. Now jump down to the net loss line. And normally you would expect a company growing at this rate to show a loss. The street wants growth and UiPath is losing money, but it's net loss went from 519 million, half a billion down to only 92 million. And that's because the operating expenses went way down. Now, again, typically a company growing at this rate would show corresponding increases in sales and marketing expense, R&D and even G&A but all three declined in the past 12 months. Now reading the notes, there was definitely some meaningful savings from no travel and canceled events. UiPath has great events around the world. In fact theCUBE, Knock Wood is going to be at its event in October, in Las Vegas at the Bellagio . So we're stoked for that. But, to drop expenses that precipitously with such high growth, is kind of strange. Go look at Snowflake's income statement. They're in hyper-growth as well. We like to compare it to Snowflake is a very well-run company and it's in hyper-growth mode, but it's sales and marketing and R&D and G&A expense lines. They're all growing along with that revenue. Now, perhaps they're growing at a slower rate. Perhaps the percent of revenue is declining as it should as they achieve operating leverage but they're not shrinking in absolute dollar terms as shown in the UiPath S1. So either UiPath has applied some magic automation mojo to it's business (chuckling). Like magic beans or magic grits with my cousin Vinny. Maybe it has found the Holy grail of operating leverage. It's a company that's all about automation or the company was running way too hot on the expense side and had a cut and clean up its income statement for the IPO and conserve some cash. Our guess is the latter but maybe there's a combination there. We'll give him the benefit of the doubt. And just to add a bit more to this long, strange trip. When have you seen an explosive growth company just about to go public, show positive cashflow? Maybe it's happened, but it's rare in the tech and software business these days. Again, go look at companies like Snowflake. They're not showing positive cashflow, not yet anyway. They're growing and trying to run the table. So you have to ask why is UiPath operating this way? And we think it's because they were so hot and burning cash that they had to reel things in a little bit and get ready to IPO. It's going to be really interesting to see how this stock reacts when it does IPO. So here's some things that we want you to pay attention to. We have to ask. Is this IPO, is it window dressing? Or did UiPath again uncover some new productivity and operating leverage model. I doubt there's anything radically new here. This company doesn't want to miss the window. So I think it said, okay, let's do this. Let's get ready for IPO. We got to cut expenses. It had a lot of good advisors. It surrounded itself with a new board. Extended that board, new management, and really want to take advantage of this because it needs the cash. In addition, it really does want to maintain its lead. It's got Automation Anywhere competing with it. It's got Microsoft looming large. And so it wants to continue to lead. It's made some really interesting acquisitions. It's got very strong vision as you saw in the Gartner Magic Quadrant and obviously it's executing well but it's really had to tighten things up. So we think it's used the IPO as a fortune forcing function to really get its house in order. Now, will the automation mandate sustain? We think it will. The forced match to digital worked, it was effective. It wasn't pleasant, but even in a downturn we think it will confer advantage to automation players and particularly companies like UiPath that have simplified automation in a big way and have done a great job of putting in training, great freemium model and has a culture that is really committed to the future of humankind. It sounds ambitious and crazy but talk to these people, you'll see it's true. Pricing, UiPath had to dramatically expand or did dramatically expand its portfolio and had to reprice everything. And I'm not so worried about that. I think it'll figure that pricing out for that portfolio expansion. My bigger concern is for SaaS companies in general. I don't like SaaS pricing that has been popularized by Workday and ServiceNow, and Salesforce and DocuSign and all these companies that essentially lock you in for a year or two and basically charge you upfront. It's really is a one-way street. You can't dial down. You can only dial up. It's not true Cloud pricing. You look at companies like Stripe and Datadog and Snowflake. It is true Cloud pricing. It's consumption pricing. I think the traditional SaaS pricing model is flawed. It's very unfairly weighted toward the vendors and I think it's going to change. Now, the reason we put cloud on the chart is because we think Cloud pricing is the right way to price. Let people dial up and dial down, let them cancel anytime and compete on the basis of your product excellence. And yeah, give them a price concession if they do lock in. But the starting point we think should be that flexibility, pay by the drink. Cancel anytime. I mentioned some companies that are doing that as well. If you look at the modern SaaS startups and the forward-thinking VCs they're really pushing their startups to this model. So we think over time that the term lock-in model is going to give way to true consumption-based pricing and at the clients option, allow them to lock-in for a better price, way better model. And UiPath's Cloud revenue today is minimal but over time, we think it's going to continue to grow that cloud. And we think it will force a rethink in pricing and in revenue recognition. So watch for that. How is the street going to react to Daniel Dines having basically full control of the company? Generally, we feel that that solid execution if UiPath can execute is going to outweigh those concerns. In fact, I'm very confident that it will. We'll see, I kind of like what the CEO says has enough mojo to say (chuckling) you know what, I'm not going to let what happened to for instance, EMC happen to me. You saw Michael Dell do that. You saw just this week they're spinning out VMware, he's maintaining his control. VMware Dell shareholders get get 40.44 shares for every Dell share they're holding. And who's the biggest shareholder? Michael Dell. So he's, you got two companies, one chairman. He's controlling the table. Michael Dell beat the great Icahn. Who beats Carl Icahn? Well, Michael Dell beats Carl Icahn. So Daniel Dines has looked at that and says, you know what? I'm not just going to give up my company. And the reason I like that with an if, is that we think will allow the company to focus more on the long-term. The if is, it's got to execute otherwise it's so much pressure and look, the bottom line is that UiPath has really favorable market momentum and fundamentals. But it is signing up for the 90 day short clock. The fact that the CEO has control again means they can look more long term and invest accordingly. Oftentimes that's easier said than done. It does come down to execution. So it is going to be fun to watch (chuckling). That's it for now, thanks to the community for your comments and insights and really always appreciate your feedback. Remember, I publish each week on Wikibon.com and siliconangle.com and these episodes are all available as podcasts. All you got to do is search for the Breaking Analysis podcast. You can always connect with me on Twitter @dvellante or email me at david.vellante@siliconangle.com or comment on my LinkedIn posts. And we'll see you in clubhouse. Follow me and get notified when we start a room, which we've been doing with John Furrier and Sarbjeet Johal and others. And we love to riff on these topics and don't forget, please check out etr.plus for all the survey action. This is Dave Vellante, for theCUBE Insights Powered by ETR. Be well everybody. And we'll see you next time. (gentle upbeat music)

from the cube studios in palo alto in boston bringing you data-driven insights from the cube and etr this is breaking analysis with dave vellante snowflake's eye-popping ipo this week has the industry buzzing we have had dozens and dozens of inbound pr from firms trying to hook us offering perspectives on the snowflake ipo so they can pitch us on their latest and greatest product people are pumped and why not an event like this doesn't happen very often hello everyone and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we'll give you our take on the snowflake ipo and address the many questions that we've been getting on the topic i'm also going to discuss at the end of this segment an angle for getting in on the ground floor and investments which is not for the faint of heart but it's something that i believe is worth talking about now let's first talk about the hottest ipo in software industry history first i want to say congratulations to the many people at snowflake you know the big hitters yeah they're all the news slootman mooglia spicer buffett benioff even scarpelli interestingly you know you don't hear much about the founders they're quite humble and we're going to talk about that in some future episodes but they created snowflake they had the vision and the smarts to bring in operators that could get the company to this point so awesome for them but you know i'm especially happy for the rank and file and the many snowflake people where an event like this it really can be life-changing versus the billionaires on the leaderboard so fantastic for you okay but let's get into the madness as you know by now snowflake ipod at a price of 120. now unless you knew a guy he paid around 245 at the open that's if you got in otherwise you bought at a higher price so you kind of just held your nose and made the trade i guess you know but snowflakes value it went from 33 billion to more than 80 billion in a matter of minutes now there's a lot of finger pointing going on this is this issue that people are claiming that it was underpriced and snowflake left four billion dollars on the table please stop that's just crazy to me snowflakes balance sheet is in great shape thanks to this offering and you know i'm not sure jamming later stage investors even more would have been the right thing to do this was a small float i think it was around 10 percent of the company so you would expect a sharp uptick on day one i had predicted a doubling to a 66 billion dollar valuation and it ended up around 70. now the big question that we now get is is this a fair valuation and can snowflake grow into its value we'll address this in more detail but the short answer is snowflake is overvalued in my opinion right now but it can grow into its valuation and of course as always they're going to be challenges now the other comment we get is yeah but the company is losing tons of money and i say no kidding that's why they're so valuable we've been saying for years that the street right now is rewarding growth because they understand that to compete in software you need to have massive scale so i'm not worried in the least about snowflakes bottom line not yet eventually i'm going to pay much closer attention to operating cash flow but right now i want to see growth i want to see them grow into their valuation now the other common question we get is should i buy when should i buy what are the risks and can snowflake compete with the biggest cloud vendors i'll say this before we get into it and i've said before look it's it's very rare that you're not going to get better buying opportunities than day one of an ipo and i think in this case you will i remember back in 2015 it was i think it was the first calendar for quarter and servicenow missed its earnings and the stock got hit and we had the opportunity to interview frank slootman then ceo of servicenow right after that and i think it's instructive to hear what he said let's listen roll the clip well yeah i think that a lot of the high-flying cloud companies and obviously we're one of them you know we're we're priced to perfection right um and that's that's not an easy place to be for uh for for anybody and you know we're not really focused on that it's it's this is a marathon you know every quarter is one mile marker you can't get too excited about you know one versus the other we're really pacing ourselves we're building you know an enterprise that's going to be here for for a long time you know and after that we saw the stock drop as low as 50 today servicenow is a 450 stock so my point is that snowflake like servicenow is going to be priced to perfection and there will be bumps in the road possibly macro factors or other and if you're a believer you'll have opportunities to get in so be patient now finally i'm going to make some comments later but i'll give you the bumper sticker right now i mean i calculated the weighted average price that the insiders paid on the the s1 that they paid for snowflake and it came out to around six dollars a share and i heard somebody say on tv it was five dollars but my weighted average math got me to six dollars regardless on day one of the ipo the insiders made a 50x return on their investment if you bought on day one you're probably losing some money or maybe about even and there are some ground floor opportunities that exist that are complicated and may be risky but if you're young and motivated or older and have some time to research i think you'll be interested in what i have to say later on all right let's compare snowflake to some other companies on a valuation basis this ought to be interesting so this chart shows some high flyers as compared to snowflake we show the company the trailing 12-month revenue the market cap at the close of the 16th which is the day that snowflake ipod and then we calculate and sort the data on the revenue multiple of the trailing 12 months and the last column is the year-on-year growth rate of the last quarter and i used trailing 12 months because it's simple and it's easy to understand and it makes the revenue multiple bigger so it's more dramatic and many prefer to use a forward revenue uh but that's why i put the growth rate there you can pick your own projected revenue growth and and do the math yourself so let's start with snowflake 400 million dollars in revenue and that's based on a newish pricing model of consumption not a sas subscription that locks you in for a year or two years or three years i love this model because it's true cloud and i've talked about it a while so for a while so i'm not going to dwell on it today but you can see the trailing 12-month revenue multiple is massive and the growth rate is 120 which is very very impressive for a company this size zoom we put zoom in the chart just because why not and and the growth grade is sick so so who knows how that correlates to the revenue multiple but as you can see snowflake actually tops the zoom frothiness on that metric now maybe zoom is undervalued i should take that back let's see i think crowdstrike is really interesting here and as a company that we've been following and talking about quite a bit in my last security breaking analysis they were at a 65 x trailing 12-month revenue multiple and you see how that's jumped since they reported and they beat expectations but they're similar in size to snowflake with a slower growth rate in a lower revenue multiple so there's some correlation between that growth rate and the revenue multiple sort of now snowflake pulled back on day two it was down early uh this morning as you would expect with both the market being off and maybe some profit taking you know if you got in an allocation at 120 why not take some profits and play with house money so snowflake's value is hovering today it actually bounced back is hovering today you're just under 70 billion and that that brings the revenue multiple down a bit but it's still very elevated now if you project 2x growth let's say 100 for next year and the stock stays in some kind of range which i think it likely will you could see snowflake coming down to crowdstrike revenue multiples in 12 months it'll depend of course on snowflakes earnings reports which i'm sure are going to beat estimates for the next several quarters and if if it's growing faster than these others at that time it should command a premium you know wherever the market prices market's going to go up it's going to go down but we'll look at all these companies i think on a relative basis snowflakes still should command a premium at higher growth rates so you can see also in this chart you've got shopify awesome mongodb twilio servicenow and their respective growth rates shopify incredibly impressive [ __ ] and twilio as well servicenow is like the old dog in this mix so that's kind of interesting now the other big question we get is can snowflake grow in to its valuation this is a chart we shared with you a bit ago and it talks to snowflake's total available market and its expansion opportunity there tam expansion this is something we saw slootman execute at servicenow when everybody underestimated that company's value and i'll briefly explain here look snowflake is disrupting the traditional data warehouse and data lake markets data lake spending is relatively small it's under 2 billion but data lakes they're inexpensive and that's what made them attractive the edw market however the enterprise data warehouse market is it's much much larger now traditional edws they're they're big they're slow they're cumbersome they're expensive and they're complicated but they've been operationalized and are critical for companies reporting and basic analytics but they've failed to live up to their promise of the 360 degree view of the customer and real-time analytics you know i had a customer tell me a while ago that my data warehouse it's like a snake swallowing a basketball he gave me example where a change in a regulation this was a financial company it would occur and it would force a change in the data model in their data warehouse and they'd have to ingest all this new data and the data warehouse choked and every time intel came out with a new processor they'd rush out they'd throw more compute at the problem he called this chasing the chips now what snowflake did was to envision a cloud native world where you could bring compute to massive data volumes on an elastic basis and only pay for what you use sounds so simple but technically snowflakes founders and those innovations of that innovation of separating compute from storage to leverage the flexibility of the cloud it really was profound and clearly based on this week's performance was the right call now i'll come back to this in a bit now where we think snowflake is going is to build a data cloud and and you can see this in the chart where your data can be ingested and accessed to perform near real-time analytics with machine learning and ai and snowflake's advantage as we've discussed in the past is that it runs on any cloud and it can ingest data from a variety of sources now there are some challenges here we're not saying that snowflake is going to participate in all these use cases that we show however with its resources now we expect snowflake to create new capabilities organically and then do tuck-in acquisitions that will allow it to attack many more more use cases in adjacent markets and so you look at this chart and the third layer if that's 60 billion it means snowflake needs to extend into the fourth layer because its valuation is already over 60 billion it's not going to get 100 market share so we call this next layer automated decision making this is where real time analytics and systems are making decisions for humans and acting in real time now clearly data is going to be a pretty critical part of this equation now at this point it's unclear that snowflake has the capability to go after this space as much of the data in this area is probably going to live at the edge but snowflake is betting on becoming a data data layer across clouds and presumably at the edge and as you can see this market is enormous so there's no lack of tam in our view for snowflakes that brings us to the other big question around competition everybody's talking about this look a lot of the investment thesis behind snowflakes snowflake is that slootman and his army including cfo mike scarpelli and what they did at servicenow will be repeated scarpelli is this operational guru he keeps the engine running you know with very very tight controls and you know what it's a pretty good bet snoopman and scarpelli and their team i'm not denying that but i will tell you that snowflake's competition is much more capable than what servicenow faced in its early days now here's a picture of some of the key competitors this is one of our favorites the xy graph and on the vertical axis is net score or spending momentum that is etr's version of velocity based on their quarterly surveys now i'm showing july survey october is in the works it's in the field as i speak on the horizontal axis is market share or pervasiveness in the data set so it's a proxy for market share it's it's based on mentions not dollars and and that's why microsoft is so far to the right because they're huge and they're everywhere and they get a lot of mentions the more relevant data to us is the position of snowflake it remains one of the highest net scores in the entire etr survey based not just the database sector aw aws is its biggest competitor because most of snowflake's business runs on aws but google bigquery you can see there is is technically the most capable relative to snowflake because it's a true cloud native database built from the ground up whereas aws took a database that was built for on-prem par excel and brilliantly really made it work in the cloud by re-architecting many of the pieces but it still has legacy parts to it now here's oracle oracle's huge it's slow growth overall but it's making investments in r d we've talked about that a lot and that's going to allow it to hold on to its customers huge base and you can see teradata and cloud era cloudera is a proxy for data lakes which are low cost as i said and cloudera which acquired hortonworks is credited with the commercialization of that whole big datum and hadoop movement and then teradata is in there as well which of course they've been around forever now there are a zillion other database players we've heard a lot of them from a lot of them this week is on that inbound pr that i talked about but these are the ones that we wanted to focus on today the bottom line is we expect snowflakes vertical axis spending momentum to remain elevated and we think it will continue to steadily move to the right now let's drill into this data a bit more here we break down the components of etr's net score and this is specifically for snowflake over time now remember lime green is new adoptions the forest green is spending more relative to last year than more five percent more uh than last year or or greater gray is flat spending the pink is less spending and the bright red is we're leaving the platform the line up top that's netscore which subtracts the red from the green is an indicator of spending velocity the yellow line at the bottom is market market share or pervasiveness in the survey based on mentions now note the the blue text there that's etr's number one takeaway on snowflake two h-20 spending intentions on snowflake continue to trend robustly mostly characterized by high customer acquisition and expansion rates new adoptions market share among all customers is simultaneously growing impressive let's now look at snowflake against the competition in fortune 500 customers now here we show net score or again spending momentum over time for some of the key competitors and you can see snowflakes net score has actually increased since the april survey again this is the july survey this was taken the april survey was taken at the height of the us lockdown so snowflake's net score is actually higher in the fortune 500 than it was overall which is a good proxy for spend because fortune 500 spends more google mongodb and microsoft also also show meaningful momentum growth since the april survey you know notably aws has come off its elevated levels from last october and april it's still strong but that's something that we're going to continue to watch finally let's look at snowflakes market share or pervasiveness within the big three cloud vendors again this is a cut on the fortune 500 and you can see there are 125 respondents within the big three cloud and the fortune 500 and 21 snowflake respondents within that base of 125 and you can see the steady and consistent growth of share not huge ends but enough to give some confidence in the data now again note the etr callout but this trend is occurring despite the fact that each of the big three cloud vendors has its own competitive offering okay but i want to stress this is not a layup for snowflake as i've said this is not servicenow part two it's a different situation so let's talk about that look the competition here is not bmc which was servicenow's target as much as i love the folks at bmc we're talking here about aws microsoft and google amazon with redshift is dialed into this i've said often that they have copycatted snowflake in many cases and last fall at re invent we heard andy jassy make a big deal about separating compute from storage and he took a kind of a swipe at snowflake without mentioning them by name but let's listen to what andy jassy had had to say and then we'll come back and talk about it play the clip then what we did is because we have nitro like i was talking about earlier we built unique instances that have very fast bandwidth so that if you actually need some of those data from s3 for a query it moves much faster than if you just had to leave it there with without that high speed bandwidth instance and so with ra3s you get to separate your storage from your compute if it turns out by the way on your local ssds that you're not using all the ssd on that local ssd you only pay for what you use so a pretty significant enhancement for customers using redshift at the same time if you think about the prevailing way that people are thinking about separating storage from compute letting people scale separately that way as well as how you're going to do this large-scale compute where you move the storage to the a bunch of awaiting compute nodes there are some issues with this that you got to think about the first is think about how much data you're going to have at the scale that we're at but then just fast forward a few years think about how much data you're going to actually have to move over the network to get to the compute and we so look first of all jassy is awesome he stands up at these events for like reinvent for two hours and it connects trends and business to technology he's got a very deep understanding of the tech he's amazing however what aws has done in separating compute and storage is good but it's not as elegant architecturally as snowflake aws essentially has tiered the storage off the cluster to lower the overall costs but you really you can't turn off the compute completely with snowflake they've truly separated compute and storage and the reason is that redshift is great but it's built on an on-prem architecture that was originally an on-prem architecture that they had to redo so when jassy talks about moving the data to compute what he's really saying is our architecture is such that we had to do this workaround which is actually quite clever but this whole narrative about the prevailing ways to separate compute from storage that's snowflake and moving the data's use the word data's plural to the compute it really doesn't apply to snowflake because they'll just move the compute to the data thank you hadoop for that profound concept now does this mean snowflake is going to cakewalk over redshift not at all aws is going to continue to innovate so snowflake had better keep moving fast multi-cloud new workloads adjacent markets tam expansion etc etc etc microsoft they're huge but as usual there's not a lot to say you know about them they're everywhere they put out 1.0 products they eventually get them right because with their heft they get mulligans that they turn into pars or birdies but i think snowflake is going to bring some innovations to azure and that they're going to get good traction there in my opinion now google bigquery is interesting by all accounts it gets very high technical marks google's playing the long game and i would expect that snowflake is going to have a harder time competing in google cloud than it does within aws and what i'm predicting for azure but we'll see the last point here is that many are talking about the convergence of analytic and operational and transaction databases and the thinking is this doesn't necessarily bode well for specialists like snowflake and i would say a couple of things here first is that while it's definitely true you're not seeing snowflake positioning today as responding at the point of transaction to say for instance influence and order in real time and this may have implications at the edge it's going to have a lot of real-time inferencing but we've learned there are a lot of ways to skin a cat and we see integration layers and innovative approaches emerging in the cloud that could address this gap and present opportunities for snowflake now the other thing i'd say is you know maybe that thinking misses something altogether with the idea of snowflake in that third data layer that we showed you in our tam chart that data as a service layer or data cloud which is maybe a giant opportunity that they are uniquely positioned to address because they're cloud agnostic they've got the vision and they've got the architecture to allow them to very simply ingest data and then serve it up to businesses nonetheless we're going to see this battle continue between what i've often talked about these integrated suites and converged databases in the case of oracle converged pipelines in the case of the cloud guys versus the best of breed players like snowflake we talk about this all the time and there really isn't one single answer it's really horses for courses and customer preferences okay well you know i know you've been waiting for for me to tell you about the angles on ground floor investing and you probably think this is going to be crazy but bear with me and i got to caution you this is a bit tongue-in-cheek and it's one big buyer beware but as i said the insiders on snowflake had a 50x return on day one you probably didn't so i want to talk about the confluence of software engineering crypto cryptography and game theory powered by the underlying value of blockchain and we're talking here about innovations around a new internet in a distributed web or d-web where many distributed computers come together to form one computer that guarantees trust between two or more users for a variety of use cases not just financial store like bitcoin but that too and the motivation behind this is the fact that a small number of companies say five or six today control the internet and have essentially co-opted the major protocols like tcp http smtp pop3 etc etc and these people that we're showing here on this chart they're working on these new innovations there are many of them but i just name a few here olaf carlson we he started poly chain capital to invest in core infrastructure around these new computing paradigms this gentleman mark nadal is someone who's working on new d apps tim berners-lee who invented the internet he's got a project called solid at mit and it emphasizes data ownership and privacy and of course satoshi got it all started when she invented bitcoin and created the notion of fractional shares and by the way the folks at andreessen horowitz are actively making bets in this space so you know maybe this is not so crazy but here's the premise if you're a little guy and you wanted to invest in snowflake you couldn't until late in the game if you wanted to invest in the lamp stack directly in the late 90s there was no way to do that you had to wait for red hat to go public or to get a piece of the linux action but in this world that we're talking about here there are opportunities that are not mainstream and often they're based yes on cryptocurrencies again it's dangerous there are scams and and losers but if you do your homework there are actually vehicles for you to get in on the ground floor and you know some of these innovations are going to take off you could get a 50x or 100 bagger but you have to do your research and there's no guarantee that these innovations are going to be able to take on the big internet giants but there are people really smart technologists and software engineers that are young they're mission driven and they're forming a collective voice against a dystopian future because they want to level the playing field on the internet and this may be the disruptive force that challenges today's giants and if your game i would take a look at the space and see if it's worth throwing a few dollars at okay a little tangent from snowflake but i wanted to put that out there snowflake wow closes its first trading week as a company worth 66 billion dollars roughly the same as goldman sachs worth more than vmware and the list goes on i mean what's what's more is there to say other than remember these episodes are all available as podcasts so please subscribe i publish weekly on wikibon.com and siliconangle.com so please check that out and please comment on my linkedin post or feel free to email me at david.velante at siliconangle.com this is dave vellante for the cube insights powered by etr thanks for watching everyone we'll see you next time you

>> From theCUBE Studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR. This is breaking analysis with Dave Vellante. >> In June of this year, Snowflake filed a confidential document suggesting that it would do an IPO. Now of course, everybody knows about it, found out about it and it had a $20 billion valuation. So, many in the community and the investment community and so forth are excited about this IPO. It could be the hottest one of the year, and we're getting a number of questions from investors and practitioners and the entire Wiki bond, ETR and CUBE community. So, welcome everybody. This is Dave Vellante. This is "CUBE Insights" powered by ETR. In this breaking analysis, we're going to unpack five critical questions around Snowflake's IPO or pending IPO. And with me to discuss that is Erik Bradley. He's the Chief Engagement Strategists at ETR and he's also the Managing Director of VENN. Erik, thanks for coming on and great to see you as always. >> Great to see you too. Always enjoy being on the show. Thank you. >> Now for those of you don't know Erik, VENN is a roundtable that he hosts and he brings in CIOs, IT practitioners, CSOs, data experts and they have an open and frank conversation, but it's private to ETR clients. But they know who the individual is, what their role is, what their title is, et cetera and it's a kind of an ask me anything. And I participated in one of them this past week. Outstanding. And we're going to share with you some of that. But let's bring up the agenda slide if we can here. And these are really some of the questions that we're getting from investors and others in the community. There's really five areas that we want to address. The first is what's happening in this enterprise data warehouse marketplace? The second thing is kind of a one area. What about the legacy EDW players like Oracle and Teradata and Netezza? The third question we get a lot is can Snowflake compete with the big cloud players? Amazon, Google, Microsoft. I mean they're right there in the heart, in the thick of things there. And then what about that multi-cloud strategy? Is that viable? How much of a differentiator is that? And then we get a lot of questions on the TAM. Meaning the total available market. How big is that market? Does it justify the valuation for Snowflake? Now, Erik, you've been doing this now. You've run a couple VENNs, you've been following this, you've done some other work that you've done with Eagle Alpha. What's your, just your initial sort of takeaway from all this work that you've been doing. >> Yeah, sure. So my first take on Snowflake was about two and a half years ago. I actually hosted them for one of my VENN interviews and my initial thought was impressed. So impressed. They were talking at the time about their ability to kind of make ease of use of a multi-cloud strategy. At the time although I was impressed, I did not expect the growth and the hyper growth that we have seen now. But, looking at the company in its current iteration, I understand where the hype is coming from. I mean, it's 12 and a half billion private valuation in the last round. The least confidential IPO (laughs) anyone's ever seen (Dave laughs) with a 15 to $20 billion valuation coming out, which is more than Teradata, Margo and Cloudera combined. It's a great question. So obviously the success to this point is warranted, but we need to see what they're going to be able to do next. So I think the agenda you laid out is a great one and I'm looking forward to getting into some of those details. >> So let's start with what's happening in the marketplace and let's pull up a slide that I very much love to use. It's the classic X-Y. On the vertical axis here we show net score. And remember folks, net score is an indicator of spending momentum. ETR every quarter does like a clockwork survey where they're asking people, "Essentially are you spending more or less?" They subtract the less from the more and comes up with a net score. It's more complicated than, but like NPS, it's a very simple and reliable methodology. That's the vertical axis. And the horizontal axis is what's called market share. Market share is the pervasiveness within the data set. So it's calculated by the number of mentions of the vendor divided by the number of mentions within that sector. And what we're showing here is the EDW sector. And we've pulled out a few companies that I want to talk about. So the big three, obviously Microsoft, AWS and Google. And you can see Microsoft has a huge presence far to the right. AWS, very, very strong. A lot of Redshift in there. And then they're pretty high on the vertical axis. And then Google, not as much share, but very solid in that. Close to 60% net score. And then you can see above all of them from a vertical standpoint is Snowflake with a 77.5% net score. You can see them in the upper right there in the green. One of the highest Erik in the entire data set. So, let's start with some sort of initial comments on the big guys and Snowflakes. Your thoughts? >> Sure. Just first of all to comment on the data, what we're showing there is just the data warehousing sector, but Snowflake's actual net score is that high amongst the entire universe that we follow. Their data strength is unprecedented and we have forward-looking spending intention. So this bodes very well for them. Now, what you did say very accurately is there's a difference between their spending intentions on a net revenue level compared to AWS, Microsoft. There no one's saying that this is an apples-to-apples comparison when it comes to actual revenue. So we have to be very cognizant of that. There is domination (laughs) quite frankly from AWS and from Azure. And Snowflake is a necessary component for them not only to help facilitate a multi-cloud, but look what's happening right now in the US Congress, right? We have these tech leaders being grilled on their actual dominance. And one of the main concerns they have is the amount of data that they're collecting. So I think the environment is right to have another player like this. I think Snowflake really has a lot of longevity and our data is supporting that. And the commentary that we hear from our end users, the people that take the survey are supporting that as well. >> Okay, and then let's stay on this X-Y slide for a moment. I want to just pull out a couple of other comments here, because one of the questions we're asking is Whither, the legacy EDW players. So we've got in here, IBM, Oracle, you can see Teradata and then Hortonworks and MapR. We're going to talk a little bit about Hortonworks 'cause it's now Cloudera. We're going to talk a little bit about Hadoop and some of the data lakes. So you can see there they don't have nearly the net score momentum. Oracle obviously has a huge install base and is investing quite frankly in R&D and do an Exadata and it has its own cloud. So, it's got a lock on it's customers and if it keeps investing and adding value, it's not going away. IBM with Netezza, there's really been some questions around their commitment to that base. And I know that a lot of the folks in the VENNs that we've talked to Erik have said, "Well, we're replacing Netezza." Frank Slootman has been very vocal about going after Teradata. And then we're going to talk a little bit about the Hadoop space. But, can you summarize for us your thoughts in your research and the commentary from your community, what's going on with the legacy guys? Are these guys cooked? Can they hang on? What's your take? >> Sure. We focus on this quite a bit actually. So, I'm going to talk about it from the data perspective first, and then we'll go into some of the commentary and the panel. You even joined one yesterday. You know that it was touched upon. But, first on the data side, what we're noticing and capturing is a widening bifurcation between these cloud native and the legacy on-prem. It is undeniable. There is nothing that you can really refute. The data is concrete and it is getting worse. That gap is getting wider and wider and wider. Now, the one thing I will say is, nobody's going to rip out their legacy applications tomorrow. It takes years and years. So when you look at Teradata, right? Their market cap's only 2 billion, 2.3 billion. How much revenue growth do they need to stay where they are? Not much, right? No one's expecting them to grow 20%, which is what you're seeing on the left side of that screen. So when you look at the legacy versus the cloud native, there is very clear direction of what's happening. The one thing I would note from the data perspective is if you switched from net score or adoptions and you went to flat spending, you suddenly see Oracle and Teradata move over to that left a little bit, because again what I'm trying to say is I don't think they're going to catch up. No, but also don't think they're going away tomorrow. That these have large install bases, they have relationships. Now to kind of get into what you were saying about each particular one, IBM, they shut down Netezza. They shut it down and then they brought it back to life. How does that make you feel if you're the head of data architecture or you're DevOps and you're trying to build an application for a large company? I'm not going back to that. There's absolutely no way. Teradata on the other hand is known to be incredibly stable. They are known to just not fail. If you need to kind of re-architect or you do a migration, they work. Teradata also has a lot of compliance built in. So if you're a financials, if you have a regulated business or industry, there's still some data sets that you're not going to move up to the cloud. Whether it's a PII compliance or financial reasons, some of that stuff is still going to live on-prem. So Teradata is still has a very good niche. And from what we're hearing from our panels, then this is a direct quote if you don't mind me looking off screen for one second. But this is a great one. Basically said, "Teradata is the only one from the legacy camp who is putting up a fight and not giving up." Basically from a CIO perspective, the rest of them aren't an option anymore. But Teradata is still fighting and that's great to hear. They have their own data as a service offering and listen, they're a small market cap compared to these other companies we're talking about. But, to summarize, the data is very clear. There is a widening bifurcation between the two camps. I do not think legacy will catch up. I think all net new workloads are moving to data as a service, moving to cloud native, moving to hosted, but there are still going to be some existing legacy on-prem applications that will be supported with these older databases. And of those, Oracle and Teradata are still viable options. >> I totally agree with you and my colleague David Floyd is actually quite high on Teradata Vantage because he really does believe that a key component, we're going to talk about the TAM in a minute, but a key component of the TAM he believes must include the on-premises workloads. And Frank Slootman has been very clear, "We're not doing on-prem, we're not doing this halfway house." And so that's an opportunity for companies like Teradata, certainly Oracle I would put it in that camp is putting up a fight. Vertica is another one. They're very small, but another one that's sort of battling it out from the old NPP world. But that's great. Let's go into some of the specifics. Let's bring up here some of the specific commentary that we've curated here from the roundtables. I'm going to go through these and then ask you to comment. The first one is just, I mean, people are obviously very excited about Snowflake. It's easy to use, the whole thing zero to Snowflake in 90 minutes, but Snowflake is synonymous with cloud-native data warehousing. There are no equals. We heard that a lot from your VENN panelist. >> We certainly did. There was even more euphoria around Snowflake than I expected when we started hosting these series of data warehousing panels. And this particular gentleman that said that happens to be the global head of data architecture for a fortune 100 financials company. And you mentioned earlier that we did a report alongside Eagle Alpha. And we noticed that among fortune 100 companies that are also using the big three public cloud companies, Snowflake is growing market share faster than anyone else. They are positioned in a way where even if you're aligned with Azure, even if you're aligned with AWS, if you're a large company, they are gaining share right now. So that particular gentleman's comments was very interesting. He also made a comment that said, "Snowflake is the person who championed the idea that data warehousing is not dead yet. Use that old monthly Python line and you're not dead yet." And back in the day where the Hadoop came along and the data lakes turned into a data swamp and everyone said, "We don't need warehousing anymore." Well, that turned out to be a head fake, right? Hadoop was an interesting technology, but it's a complex technology. And it ended up not really working the way people want it. I think Snowflake came in at that point at an opportune time and said, "No, data warehousing isn't dead. We just have to separate the compute from the storage layer and look at what I can do. That increases flexibility, security. It gives you that ability to run across multi-cloud." So honestly the commentary has been nothing but positive. We can get into some of the commentary about people thinking that there's competition catching up to what they do, but there is no doubt that right now Snowflake is the name when it comes to data as a service. >> The other thing we heard a lot was ETL is going to get completely disrupted, you sort of embedded ETL. You heard one panelist say, "Well, it's interesting to see that guys like Informatica are talking about how fast they can run inside a Snowflake." But Snowflake is making that easy. That data prep is sort of part of the package. And so that does not bode well for ETL vendors. >> It does not, right? So ETL is a legacy of on-prem databases and even when Hadoop came along, it still needed that extra layer to kind of work with the data. But this is really, really disrupting them. Now the Snowflake's credit, they partner well. All the ETL players are partnered with Snowflake, they're trying to play nice with them, but the writings on the wall as more and more of this application and workloads move to the cloud, you don't need the ETL layer. Now, obviously that's going to affect their talent and Informatica the most. We had a recent comment that said, this was a CIO who basically said, "The most telling thing about the ETL players right now is every time you speak to them, all they talk about is how they work in a Snowflake architecture." That's their only metric that they talk about right now. And he said, "That's very telling." That he basically used it as it's their existential identity to be part of Snowflake. If they're not, they don't exist anymore. So it was interesting to have sort of a philosophical comment brought up in one of my roundtables. But that's how important playing nice and finding a niche within this new data as a service is for ETL, but to be quite honest, they might be going the same way of, "Okay, let's figure out our niche on these still the on-prem workloads that are still there." I think over time we might see them maybe as an M&A possibility, whether it's Snowflake or one of these new up and comers, kind of bring them in and sort of take some of the technology that's useful and layer it in. But as a large market cap, solo existing niche, I just don't know how long ETL is for this world. >> Now, yeah. I mean, you're right that if it wasn't for the marketing, they're not fighting fashion. But >> No. >> really there're some challenges there. Now, there were some contrarians in the panel and they signaled some potential icebergs ahead. And I guarantee you're going to see this in Snowflake's Red Herring when we actually get it. Like we're going to see all the risks. One of the comments, I'll mention the two and then we can talk about it. "Their engineering advantage will fade over time." Essentially we're saying that people are going to copycat and we've seen that. And the other point is, "Hey, we might see some similar things that happened to Hadoop." The public cloud players giving away these offerings at zero cost. Essentially marginal cost of adding another service is near zero. So the cloud players will use their heft to compete. Your thoughts? >> Yeah, first of all one of the reasons I love doing panels, right? Because we had three gentlemen on this panel that all had nothing but wonderful things to say. But you always get one. And this particular person is a CTO of a well known online public travel agency. We'll put it that way. And he said, "I'm going to be the contrarian here. I have seven different technologies from private companies that do the same thing that I'm evaluating." So that's the pressure from behind, right? The technology, they're going to catch up. Right now Snowflake has the best engineering which interestingly enough they took a lot of that engineering from IBM and Teradata if you actually go back and look at it, which was brought up in our panel as well. He said, "However, the engineering will catch up. They always do." Now from the other side they're getting squeezed because the big cloud players just say, "Hey, we can do this too. I can bundle it with all the other services I'm giving you and I can squeeze your pay. Pretty much give it a waive at the cost." So I do think that there is a very valid concern. When you come out with a $20 billion IPO evaluation, you need to warrant that. And when you see competitive pressures from both sides, from private emerging technologies and from the more dominant public cloud players, you're going to get squeezed there a little bit. And if pricing gets squeezed, it's going to be very, very important for Snowflake to continue to innovate. That comment you brought up about possibly being the next Cloudera was certainly the best sound bite that I got. And I'm going to use it as Clickbait in future articles, because I think everyone who starts looking to buy a Snowflake stock and they see that, they're going to need to take a look. But I would take that with a grain of salt. I don't think that's happening anytime soon, but what that particular CTO was referring to was if you don't innovate, the technology itself will become commoditized. And he believes that this technology will become commoditized. So therefore Snowflake has to continue to innovate. They have to find other layers to bring in. Whether that's through their massive war chest of cash they're about to have and M&A, whether that's them buying analytics company, whether that's them buying an ETL layer, finding a way to provide more value as they move forward is going to be very important for them to justify this valuation going forward. >> And I want to comment on that. The Cloudera, Hortonworks, MapRs, Hadoop, et cetera. I mean, there are dramatic differences obviously. I mean, that whole space was so hard, very difficult to stand up. You needed science project guys and lab coats to do it. It was very services intensive. As well companies like Cloudera had to fund all these open source projects and it really squeezed their R&D. I think Snowflake is much more focused and you mentioned some of the background of their engineers, of course Oracle guys as well. However, you will see Amazon's going to trot out a ton of customers using their RA3 managed storage and their flash. I think it's the DC two piece. They have a ton of action in the marketplace because it's just so easy. It's interesting one of the comments, you asked this yesterday, was with regard to separating compute from storage, which of course it's Snowflakes they basically invented it, it was one of their climbs to fame. The comment was what AWS has done to separate compute from storage for Redshift is largely a bolt on. Which I thought that was an interesting comment. I've had some other comments. My friend George Gilbert said, "Hey, despite claims to the contrary, AWS still hasn't separated storage from compute. What they have is really primitive." We got to dig into that some more, but you're seeing some data points that suggest there's copycatting going on. May not be as functional, but at the same time, Erik, like I was saying good enough is maybe good enough in this space. >> Yeah, and especially with the enterprise, right? You see what Microsoft has done. Their technology is not as good as all the niche players, but it's good enough and I already have a Microsoft license. So, (laughs) you know why am I going to move off of it. But I want to get back to the comment you mentioned too about that particular gentleman who made that comment about RedShift, their separation is really more of a bolt on than a true offering. It's interesting because I know who these people are behind the scenes and he has a very strong relationship with AWS. So it was interesting to me that in the panel yesterday he said he switched from Redshift to Snowflake because of that and some other functionality issues. So there is no doubt from the end users that are buying this. And he's again a fortune 100 financial organization. Not the same one we mentioned. That's a different one. But again, a fortune 100 well known financials organization. He switched from AWS to Snowflake. So there is no doubt that right now they have the technological lead. And when you look at our ETR data platform, we have that adoption reasoning slide that you show. When you look at the number one reason that people are adopting Snowflake is their feature set of technological lead. They have that lead now. They have to maintain it. Now, another thing to bring up on this to think about is when you have large data sets like this, and as we're moving forward, you need to have machine learning capabilities layered into it, right? So they need to make sure that they're playing nicely with that. And now you could go open source with the Apache suite, but Google is doing so well with BigQuery and so well with their machine learning aspects. And although they don't speak enterprise well, they don't sell to the enterprise well, that's changing. I think they're somebody to really keep an eye on because their machine learning capabilities that are layered into the BigQuery are impressive. Now, of course, Microsoft Azure has Databricks. They're layering that in, but this is an area where I think you're going to see maybe what's next. You have to have machine learning capabilities out of the box if you're going to do data as a service. Right now Snowflake doesn't really have that. Some of the other ones do. So I had one of my guest panelist basically say to me, because of that, they ended up going with Google BigQuery because he was able to run a machine learning algorithm within hours of getting set up. Within hours. And he said that that kind of capability out of the box is what people are going to have to use going forward. So that's another thing we should dive into a little bit more. >> Let's get into that right now. Let's bring up the next slide which shows net score. Remember this is spending momentum across the major cloud players and plus Snowflake. So you've got Snowflake on the left, Google, AWS and Microsoft. And it's showing three survey timeframes last October, April 20, which is right in the middle of the pandemic. And then the most recent survey which has just taken place this month in July. And you can see Snowflake very, very high scores. Actually improving from the last October survey. Google, lower net scores, but still very strong. Want to come back to that and pick up on your comments. AWS dipping a little bit. I think what's happening here, we saw this yesterday with AWS's results. 30% growth. Awesome. Slight miss on the revenue side for AWS, but look, I mean massive. And they're so exposed to so many industries. So some of their industries have been pretty hard hit. Microsoft pretty interesting. A little softness there. But one of the things I wanted to pick up on Erik, when you're talking about Google and BigQuery and it's ML out of the box was what we heard from a lot of the VENN participants. There's no question about it that Google technically I would say is one of Snowflake's biggest competitors because it's cloud native. Remember >> Yep. >> AWS did a license one time. License deal with PowerShell and had a sort of refactor the thing to be cloud native. And of course we know what's happening with Microsoft. They basically were on-prem and then they put stuff in the cloud and then all the updates happen in the cloud. And then they pushed to on-prem. But they have that what Frank Slootman calls that halfway house, but BigQuery no question technically is very, very solid. But again, you see Snowflake right now anyway outpacing these guys in terms of momentum. >> Snowflake is out outpacing everyone (laughs) across our entire survey universe. It really is impressive to see. And one of the things that they have going for them is they can connect all three. It's that multi-cloud ability, right? That portability that they bring to you is such an important piece for today's modern CIO as data architects. They don't want vendor lock-in. They are afraid of vendor lock-in. And this ability to make their data portable and to do that with ease and the flexibility that they offer is a huge advantage right now. However, I think you're a hundred percent right. Google has been so focused on the engineering side and never really focusing on the enterprise sales side. That is why they're playing catch up. I think they can catch up. They're bringing in some really important enterprise salespeople with experience. They're starting to learn how to talk to enterprise, how to sell, how to support. And nobody can really doubt their engineering. How many open sources have they given us, right? They invented Kubernetes and the entire container space. No one's really going to compete with them on that side if they learn how to sell it and support it. Yeah, right now they're behind. They're a distant third. Don't get me wrong. From a pure hosted ability, AWS is number one. Microsoft is yours. Sometimes it looks like it's number one, but you have to recognize that a lot of that is because of simply they're hosted 365. It's a SAS app. It's not a true cloud type of infrastructure as a service. But Google is a distant third, but their technology is really, really great. And their ability to catch up is there. And like you said, in the panels we were hearing a lot about their machine learning capability is right out of the box. And that's where this is going. What's the point of having this huge data if you're not going to be supporting it on new application architecture. And all of those applications require machine learning. >> Awesome. So we're. And I totally agree with what you're saying about Google. They just don't have it figured out how to sell the enterprise yet. And a hundred percent AWS has the best cloud. I mean, hands down. But a very, very competitive market as we heard yesterday in front of Congress. Now we're on the point about, can Snowflake compete with the big cloud players? I want to show one more data point. So let's bring up, this is the same chart as we showed before, but it's new adoptions. And this is really telling. >> Yeah. >> You can see Snowflake with 34% in the yellow, new adoptions, down yes from previous surveys, but still significantly higher than the other players. Interesting to see Google showing momentum on new adoptions, AWS down on new adoptions. And again, exposed to a lot of industries that have been hard hit. And Microsoft actually quite low on new adoption. So this is very impressive for Snowflake. And I want to talk about the multi-cloud strategy now Erik. This came up a lot. The VENN participants who are sort of fans of Snowflake said three things: It was really the flexibility, the security which is really interesting to me. And a lot of that had to do with the flexibility. The ability to easily set up roles and not have to waste a lot of time wrangling. And then the third was multi-cloud. And that was really something that came through heavily in the VENN. Didn't it? >> It really did. And again, I think it just comes down to, I don't think you can ever overstate how afraid these guys are of vendor lock-in. They can't have it. They don't want it. And it's best practice to make sure your sensitive information is being kind of spread out a little bit. We all know that people don't trust Bezos. So if you're in certain industries, you're not going to use AWS at all, right? So yeah, this ability to have your data portability through multi-cloud is the number one reason I think people start looking at Snowflake. And to go to your point about the adoptions, it's very telling and it bodes well for them going forward. Most of the things that we're seeing right now are net new workloads. So let's go again back to the legacy side that we were talking about, the Teradatas, IBMs, Oracles. They still have the monolithic applications and the data that needs to support that, right? Like an old ERP type of thing. But anyone who's now building a new application, bringing something new to market, it's all net new workloads. There is no net new workload that is going to go to SAP or IBM. It's not going to happen. The net new workloads are going to the cloud. And that's why when you switch from net score to adoption, you see Snowflake really stand out because this is about new adoption for net new workloads. And that's really where they're driving everything. So I would just say that as this continues, as data as a service continues, I think Snowflake's only going to gain more and more share for all the reasons you stated. Now get back to your comment about security. I was shocked by that. I really was. I did not expect these guys to say, "Oh, no. Snowflake enterprise security not a concern." So two panels ago, a gentleman from a fortune 100 financials said, "Listen, it's very difficult to get us to sign off on something for security. Snowflake is past it, it is enterprise ready, and we are going full steam ahead." Once they got that go ahead, there was no turning back. We gave it to our DevOps guys, we gave it to everyone and said, "Run with it." So, when a company that's big, I believe their fortune rank is 28. (laughs) So when a company that big says, "Yeah, you've got the green light. That we were okay with the internal compliance aspect, we're okay with the security aspect, this gives us multi-cloud portability, this gives us flexibility, ease of use." Honestly there's a really long runway ahead for Snowflake. >> Yeah, so the big question I have around the multi-cloud piece and I totally and I've been on record saying, "Look, if you're going looking for an agnostic multi-cloud, you're probably not going to go with the cloud vendor." (laughs) But I've also said that I think multi-cloud to date anyway has largely been a symptom as opposed to a strategy, but that's changing. But to your point about lock-in and also I think people are maybe looking at doing things across clouds, but I think that certainly it expands Snowflake's TAM and we're going to talk about that because they support multiple clouds and they're going to be the best at that. That's a mandate for them. The question I have is how much of complex joining are you going to be doing across clouds? And is that something that is just going to be too latency intensive? Is that really Snowflake's expertise? You're really trying to build that data layer. You're probably going to maybe use some kind of Postgres database for that. >> Right. >> I don't know. I need to dig into that, but that would be an opportunity from a TAM standpoint. I just don't know how real that is. >> Yeah, unfortunately I'm going to just be honest with this one. I don't think I have great expertise there and I wouldn't want to lead anyone a wrong direction. But from what I've heard from some of my VENN interview subjects, this is happening. So the data portability needs to be agnostic to the cloud. I do think that when you're saying, are there going to be real complex kind of workloads and applications? Yes, the answer is yes. And I think a lot of that has to do with some of the container architecture as well, right? If I can just pull data from one spot, spin it up for as long as I need and then just get rid of that container, that ethereal layer of compute. It doesn't matter where the cloud lies. It really doesn't. I do think that multi-cloud is the way of the future. I know that the container workloads right now in the enterprise are still very small. I've heard people say like, "Yeah, I'm kicking the tires. We got 5%." That's going to grow. And if Snowflake can make themselves an integral part of that, then yes. I think that's one of those things where, I remember the guy said, "Snowflake has to continue to innovate. They have to find a way to grow this TAM." This is an area where they can do so. I think you're right about that, but as far as my expertise, on this one I'm going to be honest with you and say, I don't want to answer incorrectly. So you and I need to dig in a little bit on this one. >> Yeah, as it relates to question four, what's the viability of Snowflake's multi-cloud strategy? I'll say unquestionably supporting multiple clouds, very viable. Whether or not portability across clouds, multi-cloud joins, et cetera, TBD. So we'll keep digging into that. The last thing I want to focus on here is the last question, does Snowflake's TAM justify its $20 billion valuation? And you think about the data pipeline. You go from data acquisition to data prep. I mean, that really is where Snowflake shines. And then of course there's analysis. You've got to bring in EMI or AI and ML tools. That's not Snowflake's strength. And then you're obviously preparing that, serving that up to the business, visualization. So there's potential adjacencies that they could get into that they may or may not decide to. But so we put together this next chart which is kind of the TAM expansion opportunity. And I just want to briefly go through it. We published this stuff so you can go and look at all the fine print, but it's kind of starts with the data lake disruption. You called it data swamp before. The Hadoop no schema on, right? Basically the ROI of Hadoop became reduction of investment as my friend Abby Meadow would say. But so they're kind of disrupting that data lake which really was a failure. And then really going after that enterprise data warehouse which is kind of I have it here as a 10 billion. It's actually bigger than that. It's probably more like a $20 billion market. I'll update this slide. And then really what Snowflake is trying to do is be data as a service. A data layer across data stores, across clouds, really make it easy to ingest and prepare data and then serve the business with insights. And then ultimately this huge TAM around automated decision making, real-time analytics, automated business processes. I mean, that is potentially an enormous market. We got a couple of hundred billion. I mean, just huge. Your thoughts on their TAM? >> I agree. I'm not worried about their TAM and one of the reasons why as I mentioned before, they are coming out with a whole lot of cash. (laughs) This is going to be a red hot IPO. They are going to have a lot of money to spend. And look at their management team. Who is leading the way? A very successful, wise, intelligent, acquisitive type of CEO. I think there is going to be M&A activity, and I believe that M&A activity is going to be 100% for the mindset of growing their TAM. The entire world is moving to data as a service. So let's take as a backdrop. I'm going to go back to the panel we did yesterday. The first question we asked was, there was an understanding or a theory that when the virus pandemic hit, people wouldn't be taking on any sort of net new architecture. They're like, "Okay, I have Teradata, I have IBM. Let's just make sure the lights are on. Let's stick with it." Every single person I've asked, they're just now eight different experts, said to us, "Oh, no. Oh, no, no." There is the virus pandemic, the shift from work from home. Everything we're seeing right now has only accelerated and advanced our data as a service strategy in the cloud. We are building for scale, adopting cloud for data initiatives. So, across the board they have a great backdrop. So that's going to only continue, right? This is very new. We're in the early innings of this. So for their TAM, that's great because that's the core of what they do. Now on top of it you mentioned the type of things about, yeah, right now they don't have great machine learning. That could easily be acquired and built in. Right now they don't have an analytics layer. I for one would love to see these guys talk to Alteryx. Alteryx is red hot. We're seeing great data and great feedback on them. If they could do that business intelligence, that analytics layer on top of it, the entire suite as a service, I mean, come on. (laughs) Their TAM is expanding in my opinion. >> Yeah, your point about their leadership is right on. And I interviewed Frank Slootman right in the heart of the pandemic >> So impressed. >> and he said, "I'm investing in engineering almost sight unseen. More circumspect around sales." But I will caution people. That a lot of people I think see what Slootman did with ServiceNow. And he came into ServiceNow. I have to tell you. It was they didn't have their unit economics right, they didn't have their sales model and marketing model. He cleaned that up. Took it from 120 million to 1.2 billion and really did an amazing job. People are looking for a repeat here. This is a totally different situation. ServiceNow drove a truck through BMCs install base and with IT help desk and then created this brilliant TAM expansion. Let's learn and expand model. This is much different here. And Slootman also told me that he's a situational CEO. He doesn't have a playbook. And so that's what is most impressive and interesting about this. He's now up against the biggest competitors in the world: AWS, Google and Microsoft and dozens of other smaller startups that have raised a lot of money. Look at the company like Yellowbrick. They've raised I don't know $180 million. They've got a great team. Google, IBM, et cetera. So it's going to be really, really fun to watch. I'm super excited, Erik, but I'll tell you the data right now suggest they've got a great tailwind and if they can continue to execute, this is going to be really fun to watch. >> Yeah, certainly. I mean, when you come out and you are as impressive as Snowflake is, you get a target on your back. There's no doubt about it, right? So we said that they basically created the data as a service. That's going to invite competition. There's no doubt about it. And Yellowbrick is one that came up in the panel yesterday about one of our CIOs were doing a proof of concept with them. We had about seven others mentioned as well that are startups that are in this space. However, none of them despite their great valuation and their great funding are going to have the kind of money and the market lead that Slootman is going to have which Snowflake has as this comes out. And what we're seeing in Congress right now with some antitrust scrutiny around the large data that's being collected by AWS as your Google, I'm not going to bet against this guy either. Right now I think he's got a lot of opportunity, there's a lot of additional layers and because he can basically develop this as a suite service, I think there's a lot of great opportunity ahead for this company. >> Yeah, and I guarantee that he understands well that customer acquisition cost and the lifetime value of the customer, the retention rates. Those are all things that he and Mike Scarpelli, his CFO learned at ServiceNow. Not learned, perfected. (Erik laughs) Well Erik, really great conversation, awesome data. It's always a pleasure having you on. Thank you so much, my friend. I really appreciate it. >> I appreciate talking to you too. We'll do it again soon. And stay safe everyone out there. >> All right, and thank you for watching everybody this episode of "CUBE Insights" powered by ETR. This is Dave Vellante, and we'll see you next time. (soft music)

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante at our inaugural super cloud 22 event we further refined the concept of a super cloud iterating on the definition the salient attributes and some examples of what is and what is not a super cloud welcome to this week's wikibon cube insights powered by etr you know snowflake has always been what we feel is one of the strongest examples of a super cloud and in this breaking analysis from our studios in palo alto we unpack our interview with benoit de javille co-founder and president of products at snowflake and we test our super cloud definition on the company's data cloud platform and we're really looking forward to your feedback first let's examine how we defl find super cloudant very importantly one of the goals of super cloud 22 was to get the community's input on the definition and iterate on previous work super cloud is an emerging computing architecture that comprises a set of services which are abstracted from the underlying primitives of hyperscale clouds we're talking about services such as compute storage networking security and other native tooling like machine learning and developer tools to create a global system that spans more than one cloud super cloud as shown on this slide has five essential properties x number of deployment models and y number of service models we're looking for community input on x and y and on the first point as well so please weigh in and contribute now we've identified these five essential elements of a super cloud let's talk about these first the super cloud has to run its services on more than one cloud leveraging the cloud native tools offered by each of the cloud providers the builder of the super cloud platform is responsible for optimizing the underlying primitives of each cloud and optimizing for the specific needs be it cost or performance or latency or governance data sharing security etc but those primitives must be abstracted such that a common experience is delivered across the clouds for both users and developers the super cloud has a metadata intelligence layer that can maximize efficiency for the specific purpose of the super cloud i.e the purpose that the super cloud is intended for and it does so in a federated model and it includes what we call a super pass this is a prerequisite that is a purpose-built component and enables ecosystem partners to customize and monetize incremental services while at the same time ensuring that the common experiences exist across clouds now in terms of deployment models we'd really like to get more feedback on this piece but here's where we are so far based on the feedback we got at super cloud 22. we see three deployment models the first is one where a control plane may run on one cloud but supports data plane interactions with more than one other cloud the second model instantiates the super cloud services on each individual cloud and within regions and can support interactions across more than one cloud with a unified interface connecting those instantiations those instances to create a common experience and the third model superimposes its services as a layer or in the case of snowflake they call it a mesh on top of the cloud on top of the cloud providers region or regions with a single global instantiation a single global instantiation of those services which spans multiple cloud providers this is our understanding from a comfort the conversation with benoit dejaville as to how snowflake approaches its solutions and for now we're going to park the service models we need to more time to flesh that out and we'll propose something shortly for you to comment on now we peppered benoit dejaville at super cloud 22 to test how the snowflake data cloud aligns to our concepts and our definition let me also say that snowflake doesn't use the term data cloud they really want to respect and they want to denigrate the importance of their hyperscale partners nor do we but we do think the hyperscalers today anyway are building or not building what we call super clouds but they are but but people who bar are building super clouds are building on top of hyperscale clouds that is a prerequisite so here are the questions that we tested with snowflake first question how does snowflake architect its data cloud and what is its deployment model listen to deja ville talk about how snowflake has architected a single system play the clip there are several ways to do this you know uh super cloud as as you name them the way we we we picked is is to create you know one single system and that's very important right the the the um [Music] there are several ways right you can instantiate you know your solution uh in every region of a cloud and and you know potentially that region could be a ws that region could be gcp so you are indeed a multi-cloud solution but snowflake we did it differently we are really creating cloud regions which are superposed on top of the cloud provider you know region infrastructure region so we are building our regions but but where where it's very different is that each region of snowflake is not one in instantiation of our service our service is global by nature we can move data from one region to the other when you land in snowflake you land into one region but but you can grow from there and you can you know exist in multiple clouds at the same time and that's very important right it's not one single i mean different instantiation of a system is one single instantiation which covers many cloud regions and many cloud providers snowflake chose the most advanced level of our three deployment models dodgeville talked about too presumably so it could maintain maximum control and ensure that common experience like the iphone model next we probed about the technical enablers of the data cloud listen to deja ville talk about snow grid he uses the term mesh and then this can get confusing with the jamaicani's data mesh concept but listen to benoit's explanation well as i said you know first we start by building you know snowflake regions we have today furry region that spawn you know the world so it's a worldwide worldwide system with many regions but all these regions are connected together they are you know meshed together with our technology we name it snow grid and that makes it hard because you know regions you know azure region can talk to a ws region or gcp regions and and as a as a user of our cloud you you don't see really these regional differences that you know regions are in different you know potentially clown when you use snowflake you can exist your your presence as an organization can be in several regions several clouds if you want geographic and and and both geographic and cloud provider so i can share data irrespective of the the cloud and i'm in the snowflake data cloud is that correct i can do that today exactly and and that's very critical right what we wanted is to remove data silos and and when you instantiate a system in one single region and that system is locked in that region you cannot communicate with other parts of the world you are locking the data in one region right and we didn't want to do that we wanted you know data to be distributed the way customer wants it to be distributed across the world and potentially sharing data at world scale now maybe there are many ways to skin the other cat meaning perhaps if a platform does instantiate in multiple places there are ways to share data but this is how snowflake chose to approach the problem next question how do you deal with latency in this big global system this is really important to us because while snowflake has some really smart people working as engineers and and the like we don't think they've solved for the speed of light problem the best people working on it as we often joke listen to benoit deja ville's comments on this topic so yes and no the the way we do it it's very expensive to do that because generally if you want to join you know data which is in which are in different regions and different cloud it's going to be very expensive because you need to move you know data every time you join it so the way we do it is that you replicate the subset of data that you want to access from one region from other regions so you can create this data mesh but data is replicated to make it very cheap and very performant too and is the snow grid does that have the metadata intelligence yes to actually can you describe that a little bit yeah snow grid is both uh a way to to exchange you know metadata about so each region of snowflake knows about all the other regions of snowflake every time we create a new region diary you know the metadata is distributed over our data cloud not only you know region knows all the regions but knows you know every organization that exists in our clouds where this organization is where data can be replicated by this organization and then of course it's it's also used as a way to uh uh exchange data right so you can exchange you know beta by scale of data size and we just had i was just receiving an email from one of our customers who moved more than four petabytes of data cross-region cross you know cloud providers in you know few days and you know it's a lot of data so it takes you know some time to move but they were able to do that online completely online and and switch over you know to the diff to the other region which is failover is very important also so yes and no probably means typically no he says yes and no probably means no so it sounds like snowflake is selectively pulling small amounts of data and replicating it where necessary but you also heard him talk about the metadata layer which is one of the essential aspects of super cloud okay next we dug into security it's one of the most important issues and we think one of the hardest parts related to deploying super cloud so we've talked about how the cloud has become the first line of defense for the cso but now with multi-cloud you have multiple first lines of defense and that means multiple shared responsibility models and multiple tool sets from different cloud providers and an expanded threat surface so listen to benoit's explanation here please play the clip this is a great question uh security has always been the most important aspect of snowflake since day one right this is the question that every customer of ours has you know how you can you guarantee the security of my data and so we secure data really tightly in region we have several layers of security it starts by by encrypting it every data at rest and that's very important a lot of customers are not doing that right you hear these attacks for example on on cloud you know where someone left you know their buckets uh uh open and then you know you can access the data because it's a non-encrypted uh so we are encrypting everything at rest we are encrypting everything in transit so a region is very secure now you know you never from one region you never access data from another region in snowflake that's why also we replicate data now the replication of that data across region or the metadata for that matter is is really highly secure so snow grits ensure that everything is encrypted everything is you know we have multiple you know encryption keys and it's you know stored in hardware you know secure modules so we we we built you know snow grids such that it's secure and it allows very secure movement of data so when we heard this explanation we immediately went to the lowest common denominator question meaning when you think about how aws for instance deals with data in motion or data and rest it might be different from how another cloud provider deals with it so how does aws uh uh uh differences for example in the aws maturity model for various you know cloud capabilities you know let's say they've got a faster nitro or graviton does it do do you have to how does snowflake deal with that do they have to slow everything else down like imagine a caravan cruising you know across the desert so you know every truck can keep up let's listen it's a great question i mean of course our software is abstracting you know all the cloud providers you know infrastructure so that when you run in one region let's say aws or azure it doesn't make any difference as far as the applications are concerned and and this abstraction of course is a lot of work i mean really really a lot of work because it needs to be secure it needs to be performance and you know every cloud and it has you know to expose apis which are uniform and and you know cloud providers even though they have potentially the same concept let's say blob storage apis are completely different the way you know these systems are secure it's completely different the errors that you can get and and the retry you know mechanism is very different from one cloud to the other performance is also different we discovered that when we were starting to port our software and and and you know we had to completely rethink how to leverage blob storage in that cloud versus that cloud because just of performance too so we had you know for example to you know stripe data so all this work is work that's you know you don't need as an application because our vision really is that applications which are running in our data cloud can you know be abstracted of all this difference and and we provide all the services all the workload that this application need whether it's transactional access to data analytical access to data you know managing you know logs managing you know metrics all of these is abstracted too such that they are not you know tied to one you know particular service of one cloud and and distributing this application across you know many regions many cloud is very seamless so from that answer we know that snowflake takes care of everything but we really don't understand the performance implications in you know in that specific case but we feel pretty certain that the promises that snowflake makes around governance and security within their data sharing construct construct will be kept now another criterion that we've proposed for super cloud is a super pass layer to create a common developer experience and an enabler for ecosystem partners to monetize please play the clip let's listen we build it you know a custom build because because as you said you know what exists in one cloud might not exist in another cloud provider right so so we have to build you know on this all these this components that modern application mode and that application need and and and and that you know goes to machine learning as i say transactional uh analytical system and the entire thing so such that they can run in isolation basically and the objective is the developer experience will be identical across those clouds yes right the developers doesn't need to worry about cloud provider and actually our system we have we didn't talk about it but the marketplace that we have which allows actually to deliver we're getting there yeah okay now we're not going to go deep into ecosystem today we've talked about snowflakes strengths in this regard but snowflake they pretty much ticked all the boxes on our super cloud attributes and definition we asked benoit dejaville to confirm that this is all shipping and available today and he also gave us a glimpse of the future play the clip and we are still developing it you know the transactional you know unistore as we call it was announced in last summit so so they are still you know working properly but but but that's the vision right and and and that's important because we talk about the infrastructure right you mentioned a lot about storage and compute but it's not only that right when you think about application they need to use the transactional database they need to use an analytical system they need to use you know machine learning so you need to provide also all these services which are consistent across all the cloud providers so you can hear deja ville talking about expanding beyond taking advantage of the core infrastructure storage and networking et cetera and bringing intelligence to the data through machine learning and ai so of course there's more to come and there better be at this company's valuation despite the recent sharp pullback in a tightening fed environment okay so i know it's cliche but everyone's comparing snowflakes and data bricks databricks has been pretty vocal about its open source posture compared to snowflakes and it just so happens that we had aligotsy on at super cloud 22 as well he wasn't in studio he had to do remote because i guess he's presenting at an investor conference this week so we had to bring him in remotely now i didn't get to do this interview john furrier did but i listened to it and captured this clip about how data bricks sees super cloud and the importance of open source take a listen to goatzee yeah i mean let me start by saying we just we're big fans of open source we think that open source is a force in software that's going to continue for you know decades hundreds of years and it's going to slowly replace all proprietary code in its way we saw that you know it could do that with the most advanced technology windows you know proprietary operating system very complicated got replaced with linux so open source can pretty much do anything and what we're seeing with the data lake house is that slowly the open source community is building a replacement for the proprietary data warehouse you know data lake machine learning real-time stack in open source and we're excited to be part of it for us delta lake is a very important project that really helps you standardize how you lay out your data in the cloud and with it comes a really important protocol called delta sharing that enables you in an open way actually for the first time ever share large data sets between organizations but it uses an open protocol so the great thing about that is you don't need to be a database customer you don't even like databricks you just need to use this open source project and you can now securely share data sets between organizations across clouds and it actually does so really efficiently just one copy of the data so you don't have to copy it if you're within the same cloud so the implication of ellie gotzi's comments is that databricks with delta sharing as john implied is playing a long game now i don't know if enough about the databricks architecture to comment in detail i got to do more research there so i reached out to my two analyst friends tony bear and sanji mohan to see what they thought because they cover these companies pretty closely here's what tony bear said quote i've viewed the divergent lake house strategies of data bricks and snowflake in the context of their roots prior to delta lake databrick's prime focus was the compute not the storage layer and more specifically they were a compute engine not a database snowflake approached from the opposite end of the pool as they originally fit the mold of the classic database company rather than a specific compute engine per se the lake house pushes both companies outside of their original comfort zones data bricks to storage snowflake to compute engine so it makes perfect sense for databricks to embrace the open source narrative at the storage layer and for snowflake to continue its walled garden approach but in the long run their strategies are already overlapping databricks is not a 100 open source company its practitioner experience has always been proprietary and now so is its sql query engine likewise snowflake has had to open up with the support of iceberg for open data lake format the question really becomes how serious snowflake will be in making iceberg a first-class citizen in its environment that is not necessarily officially branding a lake house but effectively is and likewise can databricks deliver the service levels associated with walled gardens through a more brute force approach that relies heavily on the query engine at the end of the day those are the key requirements that will matter to data bricks and snowflake customers end quote that was some deep thought by by tony thank you for that sanjay mohan added the following quote open source is a slippery slope people buy mobile phones based on open source android but it's not fully open similarly databricks delta lake was not originally fully open source and even today its photon execution engine is not we are always going to live in a hybrid world snowflake and databricks will support whatever model works best for them and their customers the big question is do customers care as deeply about which vendor has a higher degree of openness as we technology people do i believe customers evaluation criteria is far more nuanced than just to decipher each vendor's open source claims end quote okay so i had to ask dodgeville about their so-called wall garden approach and what their strategy is with apache iceberg here's what he said iceberg is is very important so just to to give some context iceberg is an open you know table format right which was you know first you know developed by netflix and netflix you know put it open source in the apache community so we embrace that's that open source standard because because it's widely used by by many um many you know companies and also many companies have you know really invested a lot of effort in building you know big data hadoop solution or data like solution and they want to use snowflake and they couldn't really use snowflake because all their data were in open you know formats so we are embracing icebergs to help these companies move through the cloud but why we have been relentless with direct access to data direct access to data is a little bit of a problem for us and and the reason is when you direct access to data now you have direct access to storage now you have to understand for example the specificity of one cloud versus the other so as soon as you start to have direct access to data you lose your you know your cloud diagnostic layer you don't access data with api when you have direct access to data it's very hard to secure data because you need to grant access direct access to tools which are not you know protected and you see a lot of you know hacking of of data you know because of that so so that was not you know direct access to data is not serving well our customers and that's why we have been relented to do that because it's it's cr it's it's not cloud diagnostic it's it's you you have to code that you have to you you you need a lot of intelligence while apis access so we want open apis that's that's i guess the way we embrace you know openness is is by open api versus you know you access directly data here's my take snowflake is hedging its bets because enough people care about open source that they have to have some open data format options and it's good optics and you heard benoit deja ville talk about the risks of directly accessing the data and the complexities it brings now is that maybe a little fud against databricks maybe but same can be said for ollie's comments maybe flooding the proprietaryness of snowflake but as both analysts pointed out open is a spectrum hey i remember unix used to equal open systems okay let's end with some etr spending data and why not compare snowflake and data bricks spending profiles this is an xy graph with net score or spending momentum on the y-axis and pervasiveness or overlap in the data set on the x-axis this is data from the january survey when snowflake was holding above 80 percent net score off the charts databricks was also very strong in the upper 60s now let's fast forward to this next chart and show you the july etr survey data and you can see snowflake has come back down to earth now remember anything above 40 net score is highly elevated so both companies are doing well but snowflake is well off its highs and data bricks has come down somewhat as well databricks is inching to the right snowflake rocketed to the right post its ipo and as we know databricks wasn't able to get to ipo during the covet bubble ali gotzi is at the morgan stanley ceo conference this week they got plenty of cash to withstand a long-term recession i'm told and they've started the message that they're a billion dollars in annualized revenue i'm not sure exactly what that means i've seen some numbers on their gross margins i'm not sure what that means i've seen some numbers on their net retention revenue or net revenue retention again i'll reserve judgment until we see an s1 but it's clear both of these companies have momentum and they're out competing in the market well as always be the ultimate arbiter different philosophies perhaps is it like democrats and republicans well it could be but they're both going after a solving data problem both companies are trying to help customers get more value out of their data and both companies are highly valued so they have to perform for their investors to paraphrase ralph nader the similarities may be greater than the differences okay that's it for today thanks to the team from palo alto for this awesome super cloud studio build alex myerson and ken shiffman are on production in the palo alto studios today kristin martin and sheryl knight get the word out to our community rob hoff is our editor-in-chief over at siliconangle thanks to all please check out etr.ai for all the survey data remember these episodes are all available as podcasts wherever you listen just search breaking analysis podcasts i publish each week on wikibon.com and siliconangle.com and you can email me at david.vellante at siliconangle.com or dm me at devellante or comment on my linkedin posts and please as i say etr has got some of the best survey data in the business we track it every quarter and really excited to be partners with them this is dave vellante for the cube insights powered by etr thanks for watching and we'll see you next time on breaking analysis [Music] you

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante earnings season has shown a conflicting mix of signals for software companies well virtually all firms are expressing caution over so-called macro headwinds we're talking about ukraine inflation interest rates europe fx headwinds supply chain just overall i.t spend mongodb along with a few other names appeared more sanguine thanks to a beat in the recent quarter and a cautious but upbeat outlook for the near term hello and welcome to this week's wikibon cube insights powered by etr in this breaking analysis ahead of mongodb world 2022 we drill into mongo's business and what etr survey data tells us in the context of overall demand and the patterns that we're seeing from other software companies and we're seeing some distinctly different results from major firms these days we'll talk more about [ __ ] in this session which beat eps by 30 cents in revenue by more than 18 million dollars salesforce had a great quarter and its diversified portfolio is paying off as seen by the stocks noticeable uptick post earnings uipath which had been really beaten down prior to this quarter it's brought in a new co-ceo and it's business is showing a nice rebound with a small three cent eps beat and a nearly 20 million dollar top line beat crowdstrike is showing strength as well meanwhile managements at microsoft workday and snowflake expressed greater caution about the macroeconomic climate and especially on investors minds his concern about consumption pricing models snowflake in particular which had a small top-line beat cited softness and effects from reduced consumption especially from certain consumer-facing customers which has analysts digging more deeply into the predictability of their models in fact barclays analyst ramo lenchow published an especially thoughtful piece on this topic concluding that [ __ ] was less susceptible to consumption headwinds than for example snowflake essentially for a few reasons one because atlas mongo's cloud managed service which is the consumption model comprises only about 60 percent of mongo's revenue second is the premise that [ __ ] is supporting core operational applications that can't be easily dialed down or turned off and three that snowflake customers it sounds like has a more concentrated customer base and due to that fact there's a preponderance of its revenue is consumption driven and would be more sensitive to swings in these consumption patterns now i'll say this first consumption pricing models are here to stay and the much preferred model for customers is consumption the appeal of consumption is i can actually dial down turn off if i need to and stop spending for a while which happened or at least happened to a certain extent this quarter for certain companies but to the point about [ __ ] supporting core applications i do believe that over time you're going to see the increased emergence of data products that will become core monetization drivers in snowflake along with other data platforms is going to feed those data products and services and become over time maybe less susceptible and less sensitive to these consumption patterns it'll always be there but i think increasingly it's going to be tied to operational revenue last two points here in this slide software evaluations have reverted to their historical mean which is a good thing in our view we've taken some air out of the bubble and returned to more normalized valuations was really predicted and looked forward to look we're still in a lousy market for stocks it's really a bear market for tech the market tends to be at least six months ahead of the economy and often not always but often is a good predictor we've had some tough compares relative to the pandemic days in tech and we'll be watching next quarter very closely because the macro headwinds have now been firmly inserted into the guidance of software companies okay let's have a look at how certain names have performed relative to a software index benchmark so far this year here's a year-to-date chart comparing microsoft salesforce [ __ ] and snowflake to the igv software heavy etf which is shown in the darker blue line which by the way it does not own the ctf does not own snowflake or [ __ ] you can see that these big super caps have fared pretty well whereas [ __ ] and especially snowflake those higher growth companies have been much more negatively impacted year to date from a stock price standpoint now let's move on let's take a financial snapshot of [ __ ] and put it next to snowflake so we can compare these two higher growth names what we've done here in this chart has taken the most recent quarters revenue and multiplied it by 4x to get a revenue run rate and we've parenthetically added a projection for the full year revenue [ __ ] as you see will do north of a billion dollars in revenue while snowflake will begin to approach three billion dollars 2.7 and run right through that that four quarter run rate that they just had last quarter and you can see snowflake is growing faster than [ __ ] at 85 percent this past quarter and we took now these most of these profit of these next profitability ratios off the current quarter with one exception both companies have high gross margins of course you'd expect that but as we've discussed not as high as some traditional software companies in part because of their cloud costs but also you know their maturity or lack thereof both [ __ ] and snowflake because they are in high growth mode have thin operating margins they spend nearly half or more than half of their revenue on growth that's the sg a line mostly the s the sales and marketing is really where they're spending money uh and and they're specialists so they spend a fair amount of their revenue on r d but maybe not as high as you might think but a pretty hefty percentage the free cash flow as a percentage of revenue line we calculated off the full year projections because there was a kind of an anomaly this quarter in the in the snowflake numbers and you can see snowflakes free cash flow uh which again was abnormally high this quarter is going to settle in around 16 this year versus mongo's six percent so strong focus by snowflake on free cash flow and its management snowflake is about four billion dollars in cash and marketable securities on its balance sheet with little or no debt whereas [ __ ] has about two billion dollars on its balance sheet with a little bit of longer term debt and you can see snowflakes market cap is about double that of mongos so you're paying for higher growth with snowflake you're paying for the slootman scarpelli execution engine the expectation there a stronger balance sheet etc but snowflake is well off its roughly 100 billion evaluation which it touched during the peak days of tech during the pandemic and just that as an aside [ __ ] has around 33 000 customers about five times the number of customers snowflake has so a bit of a different customer mix and concentration but both companies in our view have no lack of market in terms of tam okay now let's dig a little deeper into mongo's business and bring in some etr data this colorful chart shows the breakdown of mongo's net score net score is etr's proprietary methodology that measures the percent of customers in the etr survey that are adding the platform new that's the lime green at nine percent existing customers that are spending six percent or more on the platform that's the forest green at 37 spending flat that's the gray at 46 percent decreasing spend that's the pinkish at around 5 and churning that's only 3 that's the bright red for [ __ ] subtract the red from the greens and you net out to a 38 which is a very solid net score figure note this is a survey of 1500 or so organizations and it includes 150 mongodb customers which includes by the way 68 global 2000 customers and they show a spending velocity or a net score of 44 so notably higher among the larger clients and while it's a smaller sample only 27 emea's net score for [ __ ] is 33 now that's down from 60 last quarter note that [ __ ] cited softness in its european business on its earning calls so that aligns to the gtr data okay now let's plot [ __ ] relative to some other data platforms these don't all necessarily compete head to head with [ __ ] but they are in data and database platforms in the etr data set and that's what this chart shows it's an xy graph with net score or as we say spending momentum on the vertical axis and overlap or presence or pervasiveness in the data set on the horizontal axis see that red dotted line there at 40 that indicates an elevated level of spending anything above that is highly elevated we've highlighted [ __ ] in that red box which is very close to that 40 percent line it has a pretty strong presence on the x-axis right there with gcp snowflake as we've reported has come down to earth but still well elevated again that aligns with the earnings releases uh aws and microsoft they have many data platforms especially aws so their plot position reflects their broad portfolio massive size on the x-axis um that's the presence and and very impressive on the vertical axis so despite that size they have strong spending momentum and you can see the pack of others including cockroach small on the verdict on the horizontal but elevated on the vertical couch base is creeping up since its ipo redis maria db which was launched the day that oracle bought sun and and got my sequel and some legacy platforms including the leader in database oracle as well as ibm and teradata's both cloud and on-prem platforms now one interesting side note here is on mongo's earning call it clearly cited the advantages of its increasingly all-in-one approach relative to others that offer a portfolio of bespoke or what we some sometimes call horses for courses databases [ __ ] cited the advantages of its simplicity and lower costs as it adds more and more functionality this is an argument often made by oracle and they often target aws as the company with too many databases and of course [ __ ] makes that argument uh as well but they also make the argument that oracle they don't necessarily call them out but they talk about traditional relational databases of course they're talking about oracle and others they say that's more complex less flexible and less appealing to developers than is [ __ ] now oracle of course would retur we retort saying hey we now support a mongodb api so why go anywhere else we're the most robust and the best for mission critical but this gives credence to the fact that if oracle is trying to capture business by offering a [ __ ] api for example that [ __ ] must be doing something right okay let's look at why they buy [ __ ] here's an etr chart that addresses that question it's it's mongo's feature breadth is the number one reason lower cost or better roi is number two integrations and stack alignment is third and mongo's technology lead is fourth those four kind of stand out with notice on the right hand side security and vision much lower there in the right that doesn't necessarily mean that [ __ ] doesn't have good security and and good vision although it has been cited uh security concerns um and and so we keep an eye on that but look [ __ ] has a document database it's become a viable alternative to traditional relational databases meaning you have much more flexibility over your schema um and in fact you know it's kind of schema-less you can pretty much put anything into a document database uh developers seem to love it generally it's fair to say mongo's architecture would favor consistency over availability because it uses a single master architecture as a primary and you can create secondary nodes in the event of a primary failure but you got to think about that and how to architect availability into the platform and got to consider recovery more carefully now now no schema means it's not a tables and rows structure and you can again shove anything you want into the database but you got to think about how to optimize performance um on queries now [ __ ] has been hard at work evolving the platform from the early days when you go back and look at its roadmap it's been you know started as a document database purely it added graph processing time series it's made search you know much much easier and more fundamental it's added atlas that fully managed cloud database uh service which we said now comprises 60 of its revenue it's you know kubernetes integrations and kind of the modern microservices stack and dozens and dozens and dozens of other features mongo's done a really fine job we think of creating a leading database platform today that is loved by customers loved by developers and is highly functional and next week the cube will be at mongodb world and we'll be looking for some of these items that we're showing here and this this chart this always going to be main focus on developers [ __ ] prides itself on being a developer friendly platform we're going to look for new features especially around security and governance and simplification of configurations and cluster management [ __ ] is likely going to continue to advance its all-in-one appeal and add more capabilities that reduce the need to to spin up bespoke platforms and we would expect enhance enhancements to atlas further enhancements there is atlas really is the future you know maybe adding you know more cloud native features and integrations and perhaps simplified ways to migrate to the cloud to atlas and improve access to data sources generally making the lives of developers and data analysts easier that's going to be we think a big theme at the event so these are the main things that we'll be scoping out at the event so please stop by if you're in new york city new york city at mongodb world or tune in to thecube.net okay that's it for today thanks to my colleagues stephanie chan who helps research breaking analysis from time to time alex meyerson is on production as today is as is andrew frick sarah kenney steve conte conte anderson hill and the entire team in palo alto thank you kristen martin and cheryl knight helped get the word out and rob hof is our editor-in-chief over there at siliconangle remember all these episodes are available as podcasts wherever you listen just search breaking analysis podcast we do publish each week on wikibon.com and siliconangle.com want to reach me email me david.velante siliconangle.com or dm me at divalante or a comment on my linkedin post and please do check out etr.ai for the best survey data in the enterprise tech business this is dave vellante for the cube insights powered by etr thanks for watching see you next time [Music] you

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante snowflake they love the stock at 400 and hated at 165 that's the nature of the business i guess especially in this crazy cycle over the last two years of lockdowns free money exploding demand and now rising inflation and rates but with the fed providing some clarity on its actions the time has come to really dig into the fundamentals of companies and there's no tech company that's more fun to analyze than snowflake hello and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we look at the action of snowflake stock since its ipo why it's behaved the way it has how some sharp traders are looking at the stock and most importantly what customer demand looks like the stock has really provided some great theater since its ipo i know people who got in at 120 before the open and i know lots of people who kind of held their noses and bought the stock on day one at over 300 a day when it closed at around 240 that first day of trading snowflake hit 164 this week it's all-time low as a public company as my college roommate chip simonton a long time trader told me when great companies trade at all times time lows because of panic it's worth taking a shot he did now of course the stock could go lower there's geopolitical risk and the stock with a 64 billion market cap is expensive for a company that's forecast to do around 2 billion in product revenue this year and remember i don't recommend stocks you shouldn't take my advice and my comments you got to do your own research but i have lots of data and i have opinions and i'm willing to share that with you stocks like snowflake crowdstrike z-scaler octa and companies like this are highly volatile when markets are moving up they're going to move up faster than the mean when they're declining they're going to drop more severely and that's clearly what's happened to snowflake so with a company like this you when you see panic selling you'll also see panic buying sometimes like we we've seen with this name it went from 220 to 320 in a very short period earlier snowflake put in a short-term bottom this week and many traders feel the issue was oversold so they bought okay but not everyone felt this way and you can see this in the headlines snowflake hits low but cloud stocks rise and we're going to come back to that is it a buy don't buy the dip buy the dip and what snowflake investors can learn from microsoft and from the street.com snow stock is sliding on the back of ill-conceived guidance and to that i would say that conservative guidance these days is anything but ill-conceived now let's unpack all this a bit and to do so i reached out to ivana delevska who has been on this program before she's with spear invest a female-led etf that goes deep into understanding supply chains she came on breaking analysis and laid out her thesis to buy the dip on snowflake this is a while ago she told me currently spear still likes snowflake and has doubled its position let me share her analysis she called out two drivers for the downside interest rates you know rising of course in snowflakes guidance which my own publication called weak in that previous chart that i just showed you so let's dig into that a bit snowflake guided for product revenues of 67 year on year which was below buy side expectations but i believe within sell side consensus regardless the guide was nuanced and driven by snowflake's decision to pass along price efficiencies to customers from optimizing processor price performance predominantly from aws's graviton too this is going to hit snowflakes revenue a net of about a hundred million dollars this year but the timing's not precise because it's going to hit 165 million but they're going to make up 65 million in increased demand frank slootman on the earnings call made this very clear he said quote this is not philanthropy this stimulates demand classic slootman the point is spear and other bulls believe that this will result in a gain for snowflake over the medium term and we would agree price goes down roi gets better you throw more projects at snowflakes customers going to buy more snowflake and when that happens and it gives the company an advantage as they continue to build their moat it's a longer term bet on cloud and data which are good bets now some of this could also be competitive pressures there have been you know studies that are out there from competitors attacking snowflakes pricing and price performance and they make comparisons oracle's been pretty aggressive as have others but so far the company's customers continue to consume now at a very fast rate now on on this front what can we learn from microsoft that applies to snowflake that's the headline here from benzinga so the article quoted a wealth manager named josh brown talking about what happened to microsoft after the dot-com bubble burst and how they quadrupled earnings over the next decade and the stock went sideways suggesting the same thing could happen to snowflake now i'd like to make a couple of comments here first at the time microsoft was a 23 billion dollar company and it had a monopoly and was already highly profitable steve ballmer became the ceo of microsoft right after the dot-com bubble burst and he hugged onto windows for dear life and lived off of microsoft's pc software monopoly microsoft became an extremely profitable and remarkably uninteresting caretaker of a pc in on-prem software estate during balmer's tenure so i just don't see the comparison as relevant snowflake you know they're going to make struggle for other reasons but that one didn't really resonate with me what's interesting is this chart it poses the question do cloud and data markets behave differently it's a chart that shows aws growth rates over time and superimposes the revenue in the red in q1 2018 aws generated 5.4 billion dollars in revenue and that was growing at the time at nearly a 50 rate now that rate as you can see decelerated quite significantly as aws grew to a 50 billion dollar run rate company that down below where you see it bottoms now it makes sense right law of large numbers you can't keep growing that fast when you get that big well oops look what happened in 2021 aws's growth rate bottoms in the high 20s and then rockets back up to 40 this past quarter as aws surpasses a 70 billion dollar run rate so you have to ask is cloud different is data different is cloud data different or data cloud different let's put it in the snowflake parlance can cloud because of its consumption model and the speed of innovation and ecosystem depth and breadth enable snowflake to exhibit lots of variability in its growth rates versus a say progressive and somewhat linear decline as the company grows revenue which is what you would expect historically and part of the answer relates to its market size here's a chart we've shared before with some additions it's our version of snowflake's total available market they're tam which snowflake's version that that blue data cloud thing superimposed on the right it shows the various layers of market opportunity that we came up with that that snowflake and others we think have in front of them emerging from the disruption of legacy data lakes and data warehouses to what snowflake refers to as its data cloud we think about the data mesh concept and decentralized data architectures with domain ownership and data product and service builders as consistent with snowflake's data cloud vision where snowflake data stores are nodes they're just simply discoverable nodes on the mesh you could have you know data bricks data lakes you know s3 buckets on that mesh it doesn't matter they can be discovered they can be shared and of course they're governed in a federated model now in snowflake's model it's all inside the snowflake data cloud that's fine then you'll go to the out years it gets a little fuzzy you know from edge locations and ai inference it becomes massive and decision making occurs in real time where machines and machine data take over the world instead of you know clicks and keystrokes sounds out there but it's real and how exactly snowflake plays there at this point is unclear but one thing's for sure there'll be a lot of data and it's going to find its way into snowflake you know snowflake's not a real-time engine it's an analytical system it's moving into the realm of data science and you know we've talked about the need for you know semantic layer between those those two worlds of analytics and data science but expanding the scope further out we think that snowflake is a big role to play in this future and the future is massive okay check you got the big tam now as someone that looks at companies through a fundamentals prism you've got to look obviously at the markets in the tan which we just did but you also want to understand customers and it's not hard to find snowflake customers capital one disney micron alliance sainsbury sonos and hundreds of other companies i've talked to snowflake customers who have also been customers of oracle teradata ibm neteza vertica serious database practitioners and they tell me it's consistent soulflake is different they say it's simpler it's more agile it's less complicated to secure and it's disruptive to their traditional ways of doing data management now of course there are naysayers i've spoken to a number of analysts that feel snowflake is deficient in areas like workload management and course complex joins and it's too specialized in a world where we're seeing the convergence of analytics and transactional workloads our own david floyer believes that what oracle is doing with mysql heatwave is radically disruptive to many of the database architectures and blows away anything out there and he believes that snowflake and the likes of aws are going to have to respond now this the other criticism here is that snowflake is not architected for real-time inference where a lot of that edge activity is is going to happen it's a multi-hundred billion dollar market and so look snowflake has a ton of competition that's the other thing all the major cloud players have very capable and competitive database platforms even though they all partner with snowflake except oracle of course but companies like databricks and have garnered tons of vc other vc funded companies have raised billions of dollars to do this kind of elastic consumption based separate compute from storage stuff so you have to always keep an open mind and be aware of potential blind spots for these companies but to the criticisms i would say look snowflake they got there first and watch their ecosystem it's a real key to its continued success snowflake's not going to go it alone and it's going to use its ecosystem partners to expand its reach and accelerate the network effects and fill those gaps and it will acquire its stock is valuable so it should be doing that just as it did with streamlit a zero revenue company that it bought for 800 million dollars in stock and cash just recently streamlit is an open source python library that gets snowflake further deeper into that data science space that data brick space and look watch what snowflake is doing with snowpark it's an api library for processing data and building data intensive applications we've talked about snowflake essentially being becoming the super cloud and building this sort of path-like layer across clouds rather than trying to do it all themselves it seems snowflake is really staring at the api economy and building its ecosystem to plug those holes so let's come back to the customers here's a chart that shows snowflakes customer spending momentum or net score on the the top line that's the vertical axis and pervasiveness in the data or market share and that bottom brown line snowflake has unprecedented net scores and held them up for many many quarters as you can see here going back you know a couple years all leading to its expanded market penetration and measured as pervasiveness of so-called market share within the etr survey it's not like idc market share it's pervasiveness in the data set now i'll say this i don't see how this is sustainable i've been waiting for this to moderate i wouldn't be surprised to see snowflake come back to earth a little bit i think they'll clearly still be highly elevated based on the data that i've seen but but i could see in in one or more of the etr surveys this year this starting to moderate as they get they get big it's just it has to happen um but i would again expect them to have a high spending velocity score but i think we're going to see snowflake you know maybe porpoise a bit here meaning you know it moderates it comes back up it's just really hard to sustain this piece of momentum and higher train retain and scale without absorbing some some friction and some head woods that's going to slow you down but back to the aws growth example it's entirely possible that we could see a similar dynamic with snowflake that you saw with aws and you kind of see it with salesforce and servicenow very successful large entrenched entrenched companies and it's very possible that snowflake could pull back moderate and then accelerate that growth even though people are concerned about the moderated guidance of 80 percent growth yeah that's that's the new definition of tepid i guess i look i like to look at other some other metrics the one that really called you know my my my attention was the remaining performance obligations this last quarter rpo snowflakes is up to something like 2.6 billion and that is a forward-looking indicator of of future revenues so i want to i'd like to see that growing and it's growing at a fast pace so you're going to see some ups and downs with snowflake i have no doubt but i think things are still looking pretty solid for the company growth companies like snowflake and octa and z scalar those other ones that i mentioned earlier have probably been repriced and refactored by investors while there's always going to be market and of course geopolitical risk especially in these times fundamentals matter you've got huge market well capitalized you got a leadership position great products and strong customer adoption you also have a great team team is something else that we look for we haven't touched on that but i'll leave you with this thought everyone knows about frank slootman mike scarpelli and what they've accomplished in their years of working together that's why the stock you know in ipo was was so overvalued they had seen these guys do it before slootman just documented in all this in his book amp it up which gives great insight into the history of of that though you know that pair and and the teams that they've built the companies that they've built how he thinks about building companies and markets and and how you know total available markets super important but the whole philosophy and culture that that he's building in his management style but you got to wonder right how long is this guy going to keep going what keeps him motivated you know i asked him that one time here's what he said why i mean are you in this for the sport what's the story here uh actually that that's not a bad way of characterizing it i think i am in it uh you know for the sport uh you know the only way to become the best version of yourself is to be uh to be under the gun and uh you know every single day and that's that's certainly uh what we are it sort of has its own rewards building great products building great companies uh you know regardless of you know uh what the spoils may be uh it has its own rewards and i i it's hard for people like us to get off the field and uh you know hang it up so here we are so there you have it he's in it for the sport how great is that he loves building companies and that my opinion that's how frank slootman thinks about success it's not about money money's the byproduct of success as earl nightingale would say success is the progressive realization of a worthy ideal i love that quote building great companies building products that change the world changing people's lives with data and insights creating jobs creating life-altering wealth opportunities not for himself but for thousands of employees and partners i'd say that's a pretty worthy ideal and i hope frank slootman sticks with it for a while okay that's it for today thanks to stephanie chan for the background research she does for breaking analysis alex meyerson on production kristen martin and cheryl knight on social with rob hoff on siliconangle and thanks to ivana delevska of spear invest and my friend chip symington for the angles from the money side of things remember all these episodes are available as podcasts just search breaking analysis podcast i publish weekly on wikibon.com and siliconangle.com and don't forget to check out etr.plus for all the survey data you can reach me at devolante or david.velante siliconangle.com and this is dave vellante for cube insights powered by etrbsafe stay well and we'll see you next time [Music] you

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante predictions are all the rage at this time of year now on december 29th 2020 in collaboration with eric porter bradley of enterprise technology research etr we put forth our predictions for 2021 and the focus of our prognostications included tech spending remote work productivity apps cyber security ipos specs m a data architecture cloud hybrid cloud multi-cloud ai containers automation and semiconductors we covered a lot of ground now over the past several weeks we've been inundated with literally thousands of inbound emails pitching us on various predictions and trends in these and other areas here's my predictions folder and this is only a portion of the documents that i've received by email obviously printed them out killed a few trees sorry hello and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we're going to review briefly each of our predictions for this past year 2021 and suggest a grade as to how we did we're going to do this as a little warm up for our 2022 predictions which we'll be doing in the next over the next couple of weeks now before we dig in i want to make an observation many of the predictions that we received they were observations of trends and sometimes not really predictions or you know or not surprising we got a lot of self-serving marketing statements you know predictions in our view they should be measurable so you can look back and say okay did they get it right now granted there are gray areas so that's why we'll use a grading system today now there are also many really well done and thought-provoking predictions and there's an example of one that we received that is strong it's from equinix cio milan waglay who said within the decade data centers will be powered by a hundred percent renewable energy okay so you know that's clear and we can measure that but anyway thanks to all the pr folks who sent along like i said literally thousands of predictions we tried to read them all but the volume over the past week or so was just so overwhelming and we'll try to scan them before we do our 2022 predictions but today we want to do that warm up by evaluating how we did in 2021 so let's get started our first prediction was that tech spending would increase by four percent this year coming off of what we had thought was a contraction in 2020 and depending on which data you look at you know best case maybe was flat we definitely correctly called the continuation into 2022 of the remote work trend and the positive impact it would have on pcs and the like but we underestimated the shape of that rebound that that spend back curve idc has tech spending wrote this year at five and a half percent so we feel like while we called the bounce back it was more pronounced than we had thought in fact you know we think that idc number is probably going to go up even higher and we'll address that in our 2022 predictions so so we'll give ourselves a b minus here okay next prediction was remote worker trends become fossilized settling in at an average of 34 percent by year end 2021. so on average 34 of the workers would be remote by the end of this year now you know we made the call but we missed delta no we missed omacrom we said 34 remote which would be 2x the historical norms now the etr data suggests it was 52 in september and it's probably going to be somewhere in the 40 to 45 range by by the end of this month into december and the thing is 75 of the workforce is probably still working either fully remote or in a hybrid model and hybrid work is probably going to be the dominant trend and we're going to have to revisit that framework or how we think about this whole structure and we'll do that again in our 2022 predictions so we'll give ourselves a c on that one we'll take some credit for the permanence of the trend but the percentage was well off the mark you know thanks to the variance as well as some cultural shifts that whole hybrid notion okay so hey not really a great start for eric and me but we rebound with the next one the productivity increases we said seen in 2020 will lead organizations to double down on the successes and certain productivity apps will benefit so to measure this we said let's take a look at the most recent quarterly earnings and gauge the revenue growth year on year as an indicator docusign was up 42 smartsheet who we also called up was up 46 in revenue twilio up 65 zoom growth was 35 down from 325 confirming our layup call the zoom growth would moderate it had nowhere to go but down and microsoft teams has never been more ubiquitous has never seen greater adoption with hundreds of companies having a hundred thousand or more users and thousands of companies with ten thousand users or more so we really feel like we nailed this one so we're gonna give us give ourselves an a plus okay so now on to cyber it's an area that we've been making calls in for a couple of years now and we're really pleased looking back here we said permanent shifts in cso strategies are going to lead to share shifts in network security now we said to give you more detail maybe that sounds like an easy one but we said specifically identity cloud security and endpoint security would continue to benefit and we specifically named crowdstrike octa zscaler and a few others that are targeting their growth rates now gartner has the security market growing at 11 percent octa and zscaler revenues last quarter grew at 62 percent year over year crowdstrike 63 illumia we also called out they raised 225 million dollars on a 2.75 billion valuation on the strength of its growth that was in september now akamai acquired guardiocor for 600 million dollars another company we called out that they would do it they did that as a ransomware protection play and they paid a huge revenue multiple for the company and it seems the guys listed on the last line are all talking about subscriptions sas arr remaining performance obligations or rpo so we feel very good about this look back we'll take an a on this one no it's not an a plus because we're too conservative on the growth of octa crowdstrike and zscaler topping at 50 they they blew that away by another 10 points or so 10 to 15. but look pretty good call nonetheless okay again the next one you might feel like is a layup but not really so we said the increased tech spend would drive even more ipos spax and m a according to spac analytics ipos were up 109 this year the spac attack continued up 109 percent in 2021 on top of a record 2020 and according to kpmg m a dollar volume was up 19 okay you might say uh that was easy call but there was much more underneath this prediction we called out uipass ipo which was a lock but also said automation anywhere would go public uipath did aa didn't we did correctly call the hashicorp ipo we said they'd either get go ipo or get acquired and cloud flare grew revenue 219 percent last quarter but akamai was not acquired so the degree of difficulty on the overall prediction wasn't high but the automation anywhere in akamai events we made those calls that didn't happen and those were you know obviously tougher calls so we think this still deserves a b grade all right as you know data is one of our favorite subjects and we've reported extensively in the successes and failures of so-called big data we said next in the next prediction that in the 2020s 75 percent of large organizations will re-architect their big data platforms and we said this would occur you know in earnest over the next four to five years now again you may say duh dave but you have to evaluate the prediction based on the underlying comments here the jury is still out on things like snowflakes data cloud but we absolutely believe that it's the right direction but then you have then you have data bricks coming in taking a different approach they're coming at the problem from a data science angle trying to take on traditional bi and then you get snowflake coming from the analytics space and moving into ai and data science and you know we asked at aws aws re invent we asked benoit dejaville on the cube if there needs to be a semantic layer to bring these two worlds together and he said yes and that's what he claims snowflake is building meanwhile you got the big whales like oracle they continue to invest in their capabilities to try to eliminate data movement and then there's aws taking a totally different approach to data where it gives customers maximum optionality of offerings and database and other services and you can't forget microsoft and google so many customers might not take the steps that we predicted because they're comfortable where they are specifically we're talking about here a shift toward domain ownership and data product thinking and the reorganization of hyper-specialized technical teams many of the principles put forth by data mesh and we've said this change is going to take a number of years to play out four to five years so we start noticing in 2021 that that's clearly been the case as we reported on parts of jpmorgan chase uh rethinking its data architecture hellofresh and many others so this is still an incomplete the professor we'll give ourselves an incomplete on this one but we think it's trending in the right direction okay the next one is always fun discussion that's the battle to define hybrid and multi-cloud we said that's going to escalate in 2021 and we'll create bifurcated cio strategies now here we go aws sees the world as bringing its apis and primitives and model to the edge and the data center to aws is just another edge node and the company says that in still believes in the fullness of time that all data will be in the cloud however that's defined and aws awareness would say all this talk about hybrid of connecting on-prem to a cloud they would flat out say adam silipsky told us this that's not cloud is what he said then on the other side of the table you have the likes of cisco dell hpe etc saying hold on cloud is an operating model it's not a place and aws might say yeah and aws along with its customers is defining that operating model and these other guys would say no actually you're not we are with our customers and this battle 100 percent escalated in 2021 with the launch of apex by dell hp e double down on green lake cisco's as the service models and then of course oracle which actually announced a true same same public to on-prem hybrid capability two years before aws announced outpost and of course oracle's executing on that strategy in earnest in 2021 and the other nuance here is a concept that we introduced called super cloud which refers to the notion that look something like for example multi-cloud is not about running within a respective cloud it's not about cloud compatibility rather it's about abstracting the complexity of the underlying cloud primitives and building value on top of those cloud services on top of the investments in capex that the hyperscalers have made now some people didn't like the term super cloud maybe uber cloud would be a better term we're going to continue to use it to describe this capability we think it has meaning and we're seeing new examples like goldman sachs's financial cloud running on top of aws so a super cloud is not as an application or a suite of applications running on a single cloud now if those applications span multiple clouds like like snowflake is trying to do okay that's a service that could span multiple clouds or in the case of goldman sachs it's a portfolio of data tools and software that's made accessible as a service that floats on top of a single or even multiple clouds regardless we feel that this was a correct call given the evidence and we'll give ourselves an a minus taking points off for the somewhat anecdotal and observational measurement system that we apply to look back at this prediction okay the next prediction was we made was cloud containers ai and ml automation uh are gonna power that those big four are gonna power 2021 spending here's a graphic we use to predict that it plots survey data for the various technologies within the etr taxonomy net score or spending momentum on the vertical axis and market share or presence in the data set it's a pervasive measurement on the horizontal axis the one that matters here is the vertical that dotted line of 40 percent anything above that is considered highly elevated and these four areas have held served this year based on recent etr survey data that we're not showing here we'll we'll bring that into our 2022 prediction so this prediction came in correctly for the most recent survey data and that's our measurement system on this one so we're going to take an a for this one too now on the penelope ultimate prediction here we came back to automation saying that the automation mandate accelerates in 2021 uipath and automation anywhere we said would go public but microsoft remains a threat to these pure play rpa vendors well we gave ourselves a b on this one doubling down on automation anywhere going public you know that was wrong but we definitely saw this year companies leaning hard into automation and microsoft despite the fact that it doesn't have as feature rich a product and offering as uipath and automation anywhere microsoft remains a very large presence you know we spoke to a lot of customers at the uipath forward four event in october in las vegas physical event and they confirmed you know this is true but at the same time so they're using power automate from microsoft but also using in this case uipath so they've kind of confirmed that yeah it's not the same we use that for some of our productivity we're an azure customer it's easy for us but they're still leaning heavily and investing heavily into uipath and i think the same can be said for automation anywhere but autom but power automate shows up as a big time leader in the magic gartner magic quadrant so it can't be ignored but clearly the two leaders in rpa have a sizable product advantage relative to the legacy software players now if you look at the comment on pega systems they cooled off a bit as measured by their stock price their revenue grew 13 percent last quarter on a year-on-year basis but perhaps we overestimated the tailwind effect and the company's momentum so we'll take a b on this prediction correct call on the automation trend and the big software vendors piling in ibm et cetera but the chance we took on automation anywhere again was a miss so we'll dig ourselves on that and our last prediction for 2021 was 5g rollouts push new edge iot workloads and necessitate new system architectures now much of this prediction you can see in the underlying bullets here really related to the observation that arm was dominating at the edge it would find its way into the mainstream enterprise workloads and we've been asking a lot of the mainstream you know companies the oems you know what do you what do you see with with arm in the enterprise and they say yeah we don't see it yet but very clearly this came into focus in 2021 is aws announced graviton 3 now and new inference and new training silicon these are different types of workloads that are emerging in the enterprise these are all based on arm microsoft google alibaba oracle and others are now shipping or readying arm-based systems for the enterprise when you look at new storage network and security appliances and other systems they're very offering and often including arm-based processors to assist with the offloads and look intel is definitely under product under pressure as we've predicted many times not just in our predictions post even pat gelsinger has admitted this is a turnaround it's going to take at least five years that's kind of new and recent data that he's made public so we're going to take an a minus on this one we're going to take off some points for the fact that you know 5g rollouts in edge are evolving and this is a longer term trend but the underlying points that we made on this slide are still pretty solid now if we use the following scale where a plus is a hundred out of a hundred a minus is a 90 a b is an 85 a b minus is an 80 and a c is a 75 out of 100 and we exclude that incomplete prediction on data architectures we average out to an 87.8 so that's a solid b plus and so the professor in us said hey little yellow sticky good effort as most of the predictions could be quantified and or you know we tried to object objectively score them there were some layups in there so yeah maybe we'll try to take more risks uh you know or not you know we we we'll see we like winning and so you know you always have to couch some of these things with some obvious ones but but really try to give some detail underneath that's maybe non-obvious um and we'll try to keep it down in the legs we did this year to one or two multi-year predictions so what's next well eric bradley and i were working on our 2022 predictions we're going to release those in the next couple of weeks so stay tuned for that you know what do you think how did we do you know we're grading ourselves here love to know you know for we're off base on base we're too hard on ourselves too easy give us your feedback don't forget these episodes are all available as podcasts wherever you listen all you do is search breaking analysis podcast check out etr's website at etr dot plus remember we also publish a full report every week on wikibon.com and siliconangle.com you can always get in touch with email david.velante at siliconangle.com you can dm me at divalante or comment on our linkedin posts this is dave vellante for the cube insights powered by etr have a great week everybody stay safe be well we'll see you next time [Music] you

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante last week's aws re invent brought into focus the degree to which cloud computing generally and aws specifically have impacted the technology landscape from making infrastructure orders of magnitude simpler to deploy to accelerating the pace of innovation to the formation of the world's most active and vibrant infrastructure ecosystem it's clear that aws has been the number one force for change in the technology industry in the last decade now going forward we see three high-level contributors from aws that will drive the next 10 years of innovation including one the degree to which data will play a defining role in determining winners and losers two the knowledge assimilation effect of aws's cultural processes such as two pizza teams customer obsession and working backwards and three the rise of super clouds that is clouds that run on top of hyperscale infrastructure that focus not only on i.t transformation but deeper business integration and digital transformation of entire industries hello everyone and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we'll review some of the takeaways from the 10th annual aws re invent conference and focus on how we see the rise of super clouds impacting the future of virtually all industries one of the most poignant moments for me was a conversation with steve mullaney at aw aws re invent he's the ceo of networking company aviatrix now just before we went on the cube nick sterile one of aviatrix's vcs looked up at steve and said it's happening now before i explain what that means this was the most important hybrid event of the year you know no one really knew what the crowd would be like but well over twenty 000 people came to reinvent and i'd say at least 25 to 26 000 people attended the expo and probably another 10 000 or more came without badges to have meetings and side meetings and do networking off the expo floor so let's call it somewhere between thirty to forty thousand people physically attended the reinvent and another two hundred thousand or more online so huge event now what nick sterile meant by its happening was the next era of cloud innovation is upon us and it's happening in earnest the cloud is expanding out to the edge aws is bringing its operating model its apis its primitives and services to more and more locations yes data and machine learning are critical we talk about that all the time but the ecosystem flywheel was so evident at this year's re invent more so than any other re invent partners were charged up you know there wasn't nearly as much chatter about aws competing with them rather there was much more excitement around the value that partners are creating on top of aws's massive platform now despite aggressive marketing from competitive hyperscalers other cloud providers and as a service or on-prem slash hybrid offerings aws lead appears to be accelerating a notable example is aws's efforts around custom silicon far more companies especially isvs are tapping into aws's silicon advancements we saw the announcement of graviton 3 and new chips for training and inference and as we've reported extensively aws is now on a curve a silicon curve that will outpace x86 vis-a-vis performance price performance cost power consumption and speed of innovation and its nitro platform is giving aws and its partners the greatest degree of optionality in the industry from cpus gpus intel amd and nvidia and very importantly arm-based custom silicon springing from aws's acquisition of annapurna aws started its custom silicon journey in 2008 and is and it has invested massive resources into this effort other hyperscalers notably microsoft google and alibaba which have the scale economics to justify such custom silicon efforts are just recently announcing initiatives in this regard others who don't have the scale will be relying on third-party silicon providers a perfectly reasonable strategy but because aws has control of the entire stack we believe it has a strategic advantage in this respect silicon especially is a domain where to quote andy jassy there is no compression algorithm for experience b on the curve matters a lot and the biggest story in my view this past week was the rise of the super clouds in his 2020 book with steve hamm frank slootman laid out the case for the rise of data cloud a title which i've conveniently stolen for this breaking analysis rise of the super cloud thank you frank in his book slootman made a case for companies to put data at the center of their organizations rather than organizing just around people for example the idea is to create data networks while people of course are critical organizing around data and enabling people to access and share data will lead to the democracy democratization of data and network effects will kick in this was essentially metcalfe's law for data bob metcalf was the inventor of ethernet ethernet he put forth that premise when we we both worked or the premise when we both worked for pat mcgovern at idg that the value of a network is proportional to the square of the number of its users or nodes on the network thought of another way the first connection isn't so valuable but the billionth connection is really valuable slootman's law if i may says the more people that have access to the data governed of course and the more data connections that can be shared or create sharing the more value will be realized from that data exponential value in fact okay but what is a super cloud super cloud is an architecture that taps the underlying services and primitives of hyperscale clouds to deliver incremental value above and beyond what's available from the public cloud provider a super cloud delivers capabilities through software consumed as services and can run on a single hyperscale cloud or span multiple clouds in fact to the degree that a super cloud can span multiple clouds and even on-premises workloads and hide the underlying complexity of the infrastructure supporting this work the more adoption and the more value will be realized now we've listed some examples of what we consider to be super clouds in the making snowflake is an example we use frequently frequently building a data cloud that spans multiple clouds and supports distributed data but governs that data centrally somewhat consistent with the data mesh approach that we've been talking about for quite some time goldman sachs announced at re invent this year a new data management cloud the goldman sachs financial cloud for data with amazon web services we're going to come back to that later nasdaq ceo adina friedman spoke at the day one keynote with adam silipsky of course the new ceo of aws and talked about the super cloud they're building they didn't use that term that's our term dish networks is building a super cloud to power 5g wireless networks united airlines is really in my view they're porting applications to aws as part of its digital transformation but eventually it will start building out a super cloud travel platform what was most significant about the united effort is the best practices they're borrowing from aws like small teams and moving fast but many others that we've listed here are on a super cloud journey just some of the folks we talked to at reinvent that are building clouds on top of clouds that are shown here cohesity building out a data management cloud focused on data protection and governance hashicorp announced its ipo at a 13 billion valuation building an it automation super cloud data bricks chaos search z-scaler z-scaler is building a security super cloud and many others that we spoke with at the event now we want to take a moment to talk about castles in the cloud it's a premise put forth by jerry chen and the team at greylock it's a really important piece of work that is building out a data set and categorizing the various cloud services to better understand where the cloud giants are investing where startups can participate and how companies can play in the castles that are being built that have been built by the hyperscalers and how they can cross the moats that have been dug and where innovation opportunities exist for other companies now frequently i'm challenged about our statements that there really are only four hyperscalers that exist in the world today aws microsoft google and alibaba while we recognize that companies like oracle have done a really excellent job of improving their clouds we don't consider companies like oracle ibm and other managed service providers as hyperscalers and one of the main data points that we use to defend our thinking is capex investment this was a point that was made in castles in the cloud there are many others that we look at elder kpi size of ecosystem partner acceleration enablement for partners feature sets etc but capex is a big one here's a chart from platform nomics a firm that is obsessed with cl with capex showing annual capex spend for five cloud companies amazon google microsoft ibm and oracle this data goes through 2019 it's annual spend and we've superimposed the direction for each of these companies amazon spent more than 40 billion dollars on capex in 2020 and will spend more than 50 billion this year sure there are some warehouses for the amazon retail business in there and there's other capital expenses in these numbers but the vast majority spent on building out its cloud infrastructure same with google and microsoft now oracle is at least increasing its cap x it's going to spend about 4 billion but it's de minimis compared to the cloud giants and ibm is headed in the other direction it's choosing to invest for instance 34 billion dollars in acquiring red hat instead of putting its capital into a cloud infrastructure look that's a very reasonable strategy but it underscores the gap okay another metric we look at is i as revenue here's an updated chart that we showed last month in our cloud update which at the time excluded alibaba's most recent quarter results so we've updated that very slight change it wasn't really material so you see the four hyperscalers and by the way they invested more than a hundred billion dollars in capex last year it's gonna be larger this year they'll collectively generate more than 120 billion dollars in revenue this year and they're growing at 41 collectively that is remarkable for such a large base of revenue and for aws the rate of revenue growth is accelerating it's the only hyperscaler that can say that that's unreal at their size i mean they're going to do more than 60 billion dollars in revenue this year okay so that's why we say there are only four hyperscalers but so what there are so many opportunities to build on top of the infrastructure that the three u.s giants especially are building as folks are really cautious about china at the moment so let's take a look at what some of the companies that we've been following are doing in the super cloud arena if you will this chart shows some etr data plotting net score or spending momentum on the vertical axis and market share or presence in the etr data set on the horizontal axis most every name on the chart is building some type of super cloud but let me start as we often do calling out aws and azure i guess they're already super clouds but they're not building necessarily on top of of of other people's clouds and there are a little bit you know microsoft does some of that certainly google's doing some of that amazon really bringing its cloud to the edge at this point it's not participating in multi-cloud actively anyway aws and azure they stand alone as the cloud leaders and you can debate what's included in azure in our previous chart on revenue attempts to strip out the microsoft sas business but this is a customer view they see microsoft as a cloud leader which it is so that's why its presence on the horizontal axis and its momentum is is you know very large and very strong stronger than even in aws in this view even though it's is revenue that we showed earlier microsoft is significantly smaller but they both have strong momentum on the vertical axis as shown by that red horizontal line anything above that remember is considered considered elevated that 40 percent or above now google cloud it's well behind these two to we kind of put a red dotted line around it but look at snowflake that blue circle i mean i realize we repeat ourselves often but snowflake continues to hold a net score in the mid to high 70s it held 80 percent for a long time it's getting much much bigger it's so hard to hold that and in 165 mentions in the survey which you can see in the inserted table it continues to expand its market's presence on the horizontal axis now all the technology companies that we track of all of them we feel snowflake's vision and execution on its data cloud and that strategy is most is the most prominent example of a super cloud truly every tech company every company should be paying attention to snowflakes moves and carving out unique value propositions for their customers by standing on the shoulders of cloud giants as ceo ed walsh likes to say now on the left hand side of the chart you can see a number of companies that we spoke with that are in various stages of building out their super clouds data bricks dot spot data robots z z scalar mentioned hashi you see elastic confluent they're all above the forty percent line and somewhat below that line but still respectable we see vmware with tanzu cohesity rubric and veeam and many others that we didn't necessarily speak with directly at reinvent and or they don't show up in the etr dataset now we've also called out cisco dell hpe and ibm we didn't plot them because there's so much other data in there that's not apples to apple but we want to call them up because they all have different points of view and are two varying degrees building super clouds but to be honest these large companies are first protecting their respective on-prem turf you can't blame them those are very large install basis now they're all adding as a service offerings which is cloud-like i mean they're behind way behind trying to figure out you know things like billing and they don't nearly have the ecosystem but they're going to fight rightly they're going to fight hard and compete with their respective portfolios with their channels and their vastly improved simplicity but when you speak to customers at re invent and these are not just startups we're talking to we're talking about customers of these enterprise tech companies these customers want to build on aws they look at aws as cloud and that is the cloud that they want to write to now they want to connect they're on-prem but they're still largely different worlds when you when you talk to these customers now they'll fully admit they can't or won't move everything out of their data centers but the vast vast majority of the customers i spoke with last week at reinvent have much more momentum around moving towards aws they're not repatriating as everybody's talking about or not everybody but many are talking about and yeah there's some recency bias because we just got back but the numbers that we shared earlier don't lie the trend is very clear now these large firms that we mentioned these incumbents in the tech industry these big enterprise tech giants they're starting to move in the super cloud direction and they will have much more credibility around multi-cloud than the hyperscalers but my honest view is that aws's lead is actually accelerating the gap in my opinion is not closing now i want to come back and dig into super cloud a little bit more around 2010 and 2011 we collaborated with two individuals who really shaped our thinking in the big data space peter goldmaker was a cell side analyst at common at the time and abi abhishek meta was with bank of america and b of a was transforming its data operations and avi was was leading that now peter was you know an analyst sharp and less at the time he said you know it's going to be the buyers of big data technology and those that apply big data to their operations who would create the most value he used an example of sap he said look you you couldn't have chosen that sap was going to lead an erp but if you could have figured out who which companies were going to apply erp to their business you would have made a lot of money investing so that was kind of one of his investment theses now he posited that the companies that would apply the big data technology the buyers if you will would create far more value than the cloud errors or the hortonworks or a collection of other number of big data players and clearly he was right in that regard now abi mehta was an example of that and he posited that ecosystems would evolve within vertical industries around data kind of going back to frank slootman's premise that in putting data at the core and that would power the next generation of value creation via data machine learning and business transformation and he was right and that's what we're seeing with the rise of super cloud now after the after the first reinvent we published a post seen on the right hand side of this chart on wikibon about the making of a new gorilla aws and we said the way to compete would be to take an industry focus or one way to compete with take an industry focus and become best to breed within that industry and we aligned really with abbey meta's point of view that industry ecosystems would evolve around data and offer opportunities for non-hyperscalers to compete now what we didn't predict at the time but are now seeing clearly emerge is that these super clouds are going to be built on top of aws and other hyperscale clouds makes sense goldman's financial cloud for data is taking a page out of aws it's pointing its proprietary data algorithms tools and processes at its clients just like amazon did with its technology and it's making these assets available as a service on top of the aws cloud a super cloud for financial services if you will they are relying on aws for infrastructure compute storage networking security and other services like sagemaker to power that super cloud but they're bringing their own ip to the table nasdaq and dish similarly bringing forth their unique value and as i said as i said earlier united airlines will in our view eventually evolve from migrating its apps portfolio to the cloud to building out a super cloud for travel what about your logo what's your super cloud strategy i'm sure you've been thinking about it or perhaps you're already well down the road i'd love to hear how you're doing it and if you see the trends the same or differently as we do okay that's it for now don't forget these episodes are all available as podcasts wherever you listen all you do is search breaking analysis podcast you definitely want to check out etr's website at etr.plus for all the survey data remember we publish a full report every week on wikibon.com and siliconangle.com you can email me if you want to get in touch with david.velante at siliconangle.com you can dm me at devolante on twitter you can comment on our linkedin posts this is dave vellante for the cube insights powered by etr have a great week stay safe be well and we'll see you next time [Music] you

>> Welcome back to Los Angeles. The cube is live. It feels so good to say that. I'm going to say that again. The cube is alive in Los Angeles. We are a coop con cloud native con 21. Lisa Martin with Dave Nicholson. We're talking to storm forge next. Cool name, right? We're going to get to the bottom of that. Please welcome Matt Provo, the founder and CEO of storm forge and Tom Ellery, the SVP of revenue storm forge, guys, welcome to the program. Thanks for having us. So storm forge, you have to say it like that. Like I feel like do you guys wear Storm trooper outfits on Halloween. >> Sometimes Storm trooper? The colors are black. You know, we hit anvils from time to time. >> I thought I, I thought they, that I saw >> Or may not be a heavy metal band that might be infringing on our name. It's all good. That's where we come from. >> I see. So you, so you started the company in 2015. Talk to me about the Genesis of the company. What were some of the gaps in the market that you saw that said we got to come in here and solve this? >> Yeah, so I was fortunate to always know. I think when you start a company, sometimes you, you know exactly the set of problems that you want to go after and potentially why you might be uniquely set up to solve it. What we knew at the beginning was we had a number of really talented data scientists. I was frustrated by the buzzwords around AI and machine learning when under the hood, this really a lot of vaporware. And so at the outset, really the, the point was build something real at the core, connect that to a set of problems that could drive value. And when we looked at really the beginnings of Kubernetes and containerization five, six years ago at its Genesis, we saw just a bunch of opportunity for machine learning, to play the right kind of role if we could build it correctly. And so at the outset it was what's going on. Why are people are people moving content workloads over to containers in the first place? And, you know, because of the flexibility and the portability around Kubernetes, we then ran into quickly its complexity. And within that complexity was really the foundation to set up the company and the solution for prob a set of problems uniquely and most beneficially solved by using machine learning. And so when we sort of brought that together and designed out some ideas, we, we did what any, any founder with a product background would do. We went and talked to a bunch of potential users and kind of tried to validate the problems themselves and, and got a really positive response. So. >> So Tom, from a business perspective, what, what attracted you to this? >> Well, initially I wasn't attracted just, I'll say that just from a startup standpoint. So I've been in the industry for 30 years, I've done six or seven pre IPO companies. I was exiting a private company. I did not want to go do another startup company, but being in the largest enterprise companies for the last 20 years, you see Kubernetes like wildfire in these places. And you knew there was huge amount of complexity and sophistication when they deployed it. So I started talking to Matt early on. He explained what they were doing and how unique the offer was around machine learning. I already knew the problems that customers had at scale with Kubernetes. So it was for me, I said, all right, I'm going to take one more run at this with Matt. I think we're, we're in a great position to differentiate ourselves. So that was really the launch pad for me, was really the technology and the market space. Those, those two things in combination are very exciting for us as a business. >> And, you know, a couple of bottles of amazing wine and a number of dinners that. >> Helps as well. >> That definitely helped twist his arm? >> Now tell us, just really kind of get into the technology. What does it do? How does it help facilitate the Kubernetes environment? >> Yeah, absolutely. So when organizations start moving workloads over to Kubernetes and get their applications up and running, there's a number of amazing organizations, whether it's through cloud providers or otherwise that that sort of solved that day one problem, those challenges. And as I was mentioning, you know, they moved because of flexibility and so developers love it and it starts to create a great experience, but there's these set of expectations. >> Where, where typically are these moving from? What you, what, what are the, what are the top three environments these are, that these are moving out of? >> Yeah. I mean, of course, non containerized environments, more generally. They could be coming from, you know, bare metal environment and it could be coming from kind of a VM driven environment. >> Okay. >> So when you look back at kind of the, the growth and Genesis and of VMs, you see a lot of parallels to what we're seeing now with, with containerization. And so as you move, it's, it's exciting. And then you get smacked in the face with the complexity, for all of the knobs that are able to be turned within a Kubernetes environment. It gives developers a lot of flexibility. These knobs, as you turn them, you have no visibility into how into the impact on the application itself. And so often organizations are become, you know, becoming more agile shipping, you know, shipping code more quickly, but then all of a sudden the, the cloud bill comes and they've, over-provisioned by 80, 90%, the, they didn't need nearly as many resources. And so what we do is we help understand the unique goals and requirements for each of the applications that are running in Kubernetes. And we have machine learning capabilities that can predict very accurately what organizations will need from a resource standpoint, in order to meet their goals, not just from a cost standpoint, but also from a performance standpoint. And so we allow organizations to typically save usually between 40 and 60% off their cloud bill and usually increased performance between 30 and 50%. Historically developers had to choose between cost and performance and their worldview on the application environment was very limited to a small set of what we would call parameters or metrics that they could choose from. And machine learning allows that world to just be blown open and not many humans are, are sophisticated in the way we think about multidimensional math to be able to make those kinds of predictions. You're talking about billions and billions of combinations, not just in a static environment, but an ongoing basis. So our technology sits in the middle of all that chaos and, and allows it to allows organizations just to re reap a whole lot of benefits that they otherwise may not ever find. >> Those numbers that you mentioned were, were big from a cost savings perspective than a performance increased perspective, which is so critical these days is in the last 18 months, we've seen so much change. We've seen massive pivots from companies in every industry to survive first of all, and then to be able to thrive and be able to iterate quickly enough to develop new products and services and get them to market to be competitive. >> Yeah. >> Yeah. Sorry. I mean, the thing that's interesting, there was an article by Andreessen Horowitz. I don't know if you've taken to the cloud paradox. So we actually, if you start looking at that great example would be some of these cloud companies that are growing like astronomical rates, snowflakes, like phenomenal what they're doing, but go look at their cogs and what it's doing. Also, it's growing almost proportionately as the revenues growing. So you need to be able to solve that problem in a way that is sophisticated enough with machine learning algorithms, that people don't have to be in the loop to do it. And that the math can prove out the solution as you go out and scale your environments. And a lot of companies now are all transitioning over SAS based platforms, and they're going to start running into these problems that they go as they go to scale. And those are the areas that we're really focused and concentrating on as an organization. >> As the leader of sales, talk to me about the voice of the customer. What are some- you've been there six months or so we heard, we heard about the wine and the dinners is obvious. >> We haven't done a lot of that over the last 18 months. >> You'll have to make for lost time then >> As soon as he closes more business. >> Oh, oh there we go, we got that on camera! >> There's, there's been three, a market spaces that we've had some really good success in that. So we talked about a SAS marketplace. So there's a company that does Drupal and Matt knows very well up in Boston, Aquia. And they have every customer is a unique snowflake customer. So they need to optimize each of their customers in order to ensure the cost as well as performance for that customer on their site works appropriately. So that's one example of a SAS based company that where we can go in and help them optimize without humans doing the optimization and the math and the machine learning from storm forge doing that. So that's an area, the other area that we've seen some really good traction Cantonese with GSI. So part of our go to market model is with GSI. So if you think about what a GSI does, a lot of times customers are struggling either initially deploying Kubernetes or putting it in for 12, 18 months and realizing we're starting to scale, we got all kinds of performance issues. How do I solve it? A lot of these people go to the Accentures, the cognizance and other ones, and start flying their ninjas into kind of solve the problem. So we're getting a lot of traction with them because they're using our tool as a way to help solve the customer's problems. And they're in the largest enterprise customers as possible. >> So if I'm hearing what you're saying correctly, you're saying that when I deploy server less applications, I may in fact, get a bill for servers that are being used? Is it, is that what you're telling us? >> They're there in fact may be a bill for what was coined as server less. That is very difficult to understand, by the way, >> That's crazy talk, Matt. >> And connect back. >> Yeah. But absolutely we deal with that all the time. It's a, it's a painful process from time to time. >> Have you, have you, have you seen the statistics that's going on with how people, I mean, there was huge inertia from every CIO that you had have a cloud strategy in place. Everyone ran out and had a cloud strategy in place. And then they started deploying on Kubernetes. Now they're realizing, oh wow, we can run it, but it's costing us more than it ever costs us on prem and the operational complexity associated with that. So there's not enough people in the industry to help solve that problem, especially at the grass roots, that's where you need sophisticated solutions like storm forge and machine learning to help solve this at scale problem in a way that humans could never solve. >> And I would, I would just add to that, that the, the same humans managing the Kubernetes application environments today are likely the same humans that we're managing it in a, in a BM world. So there's a huge skills gap. I love what Castin announced at KU KU con this year around their learning environment where it's free. Come learn Kubernetes and this, and we need more of that. There's an enormous skills gap and, and the problems are complex enough in and of themselves. But when we have, when you add that to the skills gap, it it's, it presents a lot of challenges for organizations. >> What are some the ways in which you think that gap can start to be made smaller. >> Yeah. I mean, I think as more workloads get moved over, over, you know, over time, you see, you see more and more people becoming comfortable in an environment where scale is a part of what they have to manage and take care of. I love what the Linux foundation and the CNCF are doing around Kubernetes certifications, you know, more and more training. I think you're going to see training, you know, availability for more and more developers and practitioners be adopted more widely. You know, and I think that, you know, as the tool chain itself hardens within a CCD world in a containerized world, as that hardens, you're going to, you're going to start seeing more and more individuals who are comfortable across all these different tools. If you look at the CNCF landscape, I mean, today compared to four or five years ago, it's growing like crazy. And so, but, but there's also consolidation taking place within the tools. And people have an opportunity to, to learn and gain expertise within us. Which is very marketable by the way, >> Absolutely >> My employees often show me their LinkedIn profiles and remind me of how , how much they're getting recruited, but they've been loyal. So it's been a fantastic. >> Are there are so many parallels when you look at a VM in virtualization and what's happening with covers, obviously all the abstractions and stuff, but there was this whole concept of VM sprawl, you know, maybe 10 years in, if you think about the Kubernetes environment, that is exponentially bigger problem because of how many they're spitting up versus how, how many you spun up in VM. So those things ultimately need to be solved. It's not just going to be solved with people. It needs to be solved with sophisticated software. That's the only way you're going to solve a problem at scale like that. No matter how many people you have in the industry, it's just never going to solve the problem. >> So when you're in customer conversations, Tom, what are you say are like the top three differentiators that really set storm forage apart? >> Well, so the first one is we're very focused on Kubernetes only. So that's all we do is just Kubernetes environment. So we understand not just the applications that run in Kubernetes, but we understand the underlying architectures and techniques, which we think is really important. From a solution standpoint, >> So you're specialists? >> We are absolutely specialists. The other areas obviously are machine learning and the sophistication of our machine learning. And Matt said this really well, early on, I mean, the buzzwords are all out there. You can read them all up, all over the place for the last five to seven year AI and ML. And a lot of them are very hollow, but our whole foundation was based on machine learning and PhDs from Harvard. That's where we came out of from a technology background. So we were solving more, we weren't just solving the Kubernetes problems. We were solving machine learning problems. And so that's another really big area of differential for us. And I think the ability to actually scale and not just deal with small problems, but very large problems, because our focus is the fortune 2000 companies. And most of them have been deploying like financial services and stuff, Kubernetes for three, four or five years. And so they have had scale challenges that they're trying to solve. >> Yeah. It's Lisa and I talk about this concept of machine learning and looking under the covers and trying to find out is the machine really learning? Is it really learning or is it people are telling the machine, you need to do this. If you see that Where's the machine actually making those correlations and doing something intelligently. So can you give us an example of something that is actually happening that's intelligent? >> Well, so the, the, if this, then that problem is actually a huge source of my original frustration for starting the company, because you, you, you tag AI as a buzzword onto a lot of stuff. And we see that growing like crazy. And so I literally at the beginning said, if we can't actually build something real, that solves problems, like we're going to hang it up. And, you know, as Tom said, we came out of Harvard and, you know, there was a challenge initially of, are we just going to build like a really amazing algorithm? That's so heavy, it can never be productized or commercialized and it really should have just stayed in academia. And, you know, I the I, I will say a couple of things. One is I do not believe that that black box AI is a thing. We believe in what we would call human, augmented AI. So we want to empower practitioners and developers into the process instead of automate them out. We just want to give them the information and we want to save time for them and make their lives easier. But there's a kill switch on the technology. They can intervene at any point in time. They can direct the technology as they see fit. And what's really, really interesting is because their worldview of this application environment gets opened up by all the predictions and all of the learning that actually is taking place and, you know, give it because that worldview is open, they then get into a kind of a tinkering or experimental mindset with the technology. And they start thinking about all these other scenarios that they never were able to explore previously with the application. And, and so the machine learning itself is on an ongoing basis. Understanding changes in traffic, understanding and changes, changes in workloads for the application or demand. If you thought about like surge pricing for Uber, you know, because of a, a big game that took place. And you know, that, that change in peaks and valleys in demand, our, our technology not only understands those reactively, but it starts to build models and predict proactively in advance of the events that are going to take place on, on what ne- what kind of resources need to be allocated. And why that's the other piece around it is often solutions are giving you a little bit of a what, but they certainly are not giving you any explanation of the why. So the holy grail really like in our world is kind of truly explainable AI, which we're not there yet. Nobody's there yet. But human augmented AI with, with actual intelligence that's taking place that also is relevant to business outcomes is, is pretty exciting. So that's why where try to operate. >> Very exciting guys. Thanks for joining us, talking to us about storm forage, to feel like we need some store in forge. T-shirts what do you think? >> (unintelligible) >> See, I'm not even asking for the bottle of wine. I liked that idea. I thank Matt and Tom, thank you so much for joining us exciting company. Congratulations on your success. And we look forward to seeing what great things are to come from storm forage. >> Thanks so much for the time. >> Our pleasure. For Dave Nicholson. I'm Lisa Martin. We are alive in Los Angeles, the cube covering Kube con and cloud native con 21 stick around. Dave and I will be right back with our next guest.

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante by the dip has been been an effective strategy since the market bottomed in early march last year the approach has been especially successful in tech and even more so for those tech names that one were well positioned for the forced march to digital i sometimes call it i.e remote work online commerce data centric platforms and certain cyber security plays and two already had the cloud figured out the question on investors minds is where to go from here should you avoid some of the high flyers that are richly valued with eye-popping multiples or should you continue to buy the dip and if so which companies that capitalized on the trends from last year will see permanent shifts in spending patterns that make them a solid long-term play hello and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we shine the spotlight on three companies that may be candidates for a buy the dip strategy and it's our pleasure to welcome in ivana delevco who's the chief investment officer and founder of spear alpha a new research-centric etf focused on industrial technology ivana is a long-time equity analyst with a background in both long and short investing ivana welcome to the program thanks so much for coming on thanks for having me david yeah it's really our pleasure i i want to start with your etf and give the folks a bit more background about you first you know we gotta let people know i'm not an investment pro i'm not an advisor i don't make stock recommendations i don't sell investments so you got to do your own research i have a lot of data so happy to share it but you got to understand your own risks you of course yvonne on the other hand you do offer investment services and so people before investing got to carefully review all the available available investment docs understand what you're getting into before you invest now with that out of the way ivana i have some stats up here on this slide your spear you're a newly launched female lead firm that does deep research into the supply chain we're going to talk about that you try to uncover as i understand it under-appreciated industrial tech firms and some really pretty cool areas that we list here but tell us a little bit more about your background and your etf so thanks for having me david my background is in industrial research and industrial technology investments i've spent the past 15 years covering this space and what we've seen over the past five years is technology changes that are really driving fundamental shifts in industrial manufacturing processes so whether this is 5g connectivity innovation in the software stack increasing compute speeds all of these are major technological advancements that are impacting uh traditional manufacturers so what we try to do is assess speak to these firms and assess who is at the leading and who is at the lagging end of this digital transformation and we're trying to assess what vendors they're using what processes they're implementing and that is how we generate most of our investment ideas okay great and and we show on the bottom of of this sort of intro slide if you will uh so one of the processes that you use and one of the things that that is notable a lot of people compare you uh to kathy woods are investments when you came out uh i think you use a different process i mean maybe there are some similarities in terms of disruption but at the bottom of this slide it shows a mckinsey sort of graphic that that i think informs people as to how you really dig into the supply chain from a research standpoint is that right absolutely so for us it's all about understanding the supply chain going deep in the supply chain and gather data points from primary sources that we can then translate into investment opportunities so if you look at this mckinsey graph uh you will see that there is a lot of opportunity to for these companies to transform themselves both on the front end which means better revenue better products and on their operation side which means lower cost whether it's through better operations or through better processes on the the back end so what we do is we will speak to a traditional manufacturing company and ask them okay well what do you use for better product development and they will give us the name of the firms and give us an assessment of what's the differences between the competitors why they like one versus the other so then we're gonna take the data and we will put it into our financial model and we'll understand the broader market for it um the addressable market the market share that the company has and will project the growth so for these higher growth stocks that that you cover the main alpha generation uh potential here is to understand what the amount of growth these companies will generate over the next 10 to 20 years so it's really all about projecting growth in the next three years in the next five years and where will growth ultimately settle in in the next 10 to 20 years love it we're gonna have a fun conversation because today we're going to get into your thesis for cooper snowflake and z scalar we're going to bring in some of our own data some of our data from etr and and why you think these companies may be candidates for long-term growth and and be buy the dip stock so to do that i hacked up this little comparison slide we're showing here i do this for context our audience knows i'm not a cfa or a valuation expert but we like to do simple comparisons just to give people context and a sense of relative size growth and valuation and so this chart attempts to do that so what i did is i took the most recent quarterly revenue for cooper snowflake and z scalar multiplied it by four to get a run rate we included servicenow in the table just for baseline reference because bill mcdermott as we've reported aspires to make service now the next great enterprise software company alongside with salesforce and oracle and some of the others and and all these companies that we list here that through the three here they aspire to do so in their own domain so we're displaying the market cap from friday morning september 10th we calculated a revenue run rate multiple and we show the quarterly revenue growth and what this data does is gives you a sense of the three companies they're well on their way to a billion dollars in revenue it underscores the relationship between revenue growth and valuation snowflake being the poster child for that dynamic savannah i know you do much more detailed financial analysis but let's talk about these companies in order maybe start with koopa they just crushed their quarter i mean they blew away consensus on the top line what else about the company do you like and why is it on your by the dip list so just to back up david on valuation these companies investors either directly or indirectly value on a dcf basis and what happened at the beginning of the year as interest rates started increasing people started freaking out and once you plug in 100 basis points higher interest rate in your dcf model you get significant price downside so that really drove a lot of the pullback at the beginning of the year right now where we stand today interest rates haven't really moved all that significantly off the bot of the bottom they're still around the same levels maybe a little bit higher but those are not the types of moves that are going to drive significant downside in this stock so as things have stabilized here a lot of these opportunities look pretty attractive on that basis so koopa specifically came out of our um if you go back to that uh the chart of like where the opportunities lie in um in across the manufacturing uh um enterprise koopa is really focused on business pen management so they're really trying to help companies reduce their cost uh and they're a leader in the space uh they're unique uh unique in that they're cloud-based so the feedback we've been hearing from from our companies that use it jetblue uses it train technologies uses it the feedback we've been hearing is that they love the ease of implementation so it's very easy to implement and it drives real savings um savings for these companies so we see in our dcf model we see multiple years of this 30 40 percent growth and that's really driving our price target yeah and we can i can confirm that i mean i mean just anecdotally you know you know we serve a lot of the technology community and many of our clients are saying hey okay you know when you go to do invoicing or whatever you work with procurement it's koopa you know this is some ariba that's kind of the legacy which is sap we'll talk about that a little later but let's talk about snowflake um you know snowflake we've been tracking them very closely we know the management there we've watched them through their last two companies now here and have been following that company early on since since really 2015. tell us why you like snowflake um and and maybe why you think it can continue its rapid growth thanks david so first of all i need to compliment you on your research on the company on the technology side so where we come in is more from understanding where our companies can use soft snowflake and where snowflake can add value so what we've been hearing from our companies is the challenge that they're facing is that everybody's moving to the cloud but it's not as simple as just send your data to the cloud and call aws and they're gonna generate more revenue for your solve your cost problem so what we've been hearing is that companies need to find tools that are easy to use where they can use their own domain expertise and just plug and play so um ansys is one of the companies we covered the dust simulation they've found snowflake to be an extremely useful tool in sales lead generation and within sales crm systems have been around for a while and they're they've really been implemented but analyzing sales numbers is something that is new to this company some some of our companies don't even know what their sales are even when they look back after the quarter is closed so tools like this help um companies do easy analytics and therefore drive revenue and cost savings growth so we see really big runway for for this company and i think the most misunderstood part about it is that people view it as a warehousing data warehousing play while this is all about compute and the company does a good job separating the two and what our their customers like or like the companies that we cover like about it is that it can lower their compute costs um and make it much easier much more easily manageable for them great and we're going to talk about more about each of these companies but let's talk about z-scaler a bit i mean z-scaler is a company we've been very excited about and identified them kind of early on they've definitely benefited from the move to cloud generally and specifically the remote work uh situation with the cyber threats etc but tell us why you like z-scaler so interestingly z-scaler um we like the broader security space um the broader cyber security space and interestingly our companies are not yet spending to the level that is commensurate with the increase in attack rate so we think this is a trend that is really going to accelerate as we go forward um my own board 20 of the time on the last board meeting was spent on cyber security what we're doing and this is a pretty simple operation that that we're running here so you can imagine for a large enterprise with thousands of people all around the world um needing to be on a single simple system z-scaler really fits well here very easy to implement several of our industrial companies use it siemens uses it ge uses it and they've had great great experience with it excellent i just want to take a quick look at how some of these names have performed over the last year and and what if anything this data tells us this is a chart comparing the past 12 months performance of of those four companies uh that we just talked about and we added in you know servicenow z scalar as you can see has outperformed the other despite your commentary on discounted cash flow snowflake is underperformed really precisely for the reasons that you mentioned not to mention the fact that it was pretty highly valued and you can see relative to the nas but it's creeping back lately after very strong earnings even though the stock dropped after it beat earnings because the street wants the cfo to say to guide even higher than maybe as mike scarpelli feels is prudent and you can see cooper has also underperformed relatively speaking i mean it absolutely destroyed consensus this week the stock went up but it's been off with the the weaker market this week i know you like to take a longer term view but but anything you would add here yeah so interestingly both z-scaler and koopa were in the camp of as we went into earnings expectations were already pretty high because few of their competitors reported very strong results so this scalar yesterday their revenue growth was was pretty strong the stock is down today uh and the reason is because people were kind of caught up a little bit in the noise of this quarter growth is 57 last quarter it was 60 like is this a deceleration we don't see it as that at all and the company brought up one point that i thought was extremely interesting which is as their deal sizes are getting larger it takes a little longer time for them to see the revenue come through so it takes a little bit of time to for you to see it into from billings into into revenue same thing with cooper very strong earnings report but i think expectations were already pretty high going into it uh given the service now and um and anna plan as well reported strong results so i think it's all about positioning so we love these setups where you can buy the deep in on this opportunity where like people get caught up in um short-term noise and and it creates good entry points excellent i i want to bring in some data from our partner etr and see if you have any comments ivana so what we're showing here is a two-dimensional chart we like to show this uh very frequently it's based on a survey of between a thousand and fifteen hundred chief information officers and technology buyers every quarter this is from their most recent july survey the vertical axis shows net score which is a measure of spending momentum i mean this it measures the net percentage of customers in the survey that are spending more on a particular product or platform in other words it essentially subtracts the percentage of customers spending less from those spending more which yields a net score it's more granular than that but basically that's what it does the horizontal axis is market share or pervasiveness in the data set it's not revenue market share like you get from idc it's it's a mention market share and now that red dotted line at the 40 percent mark on the vertical represents an elevated level in other words anything above 40 percent we consider notable and we've plotted our three by the dip companies and included some of their competitors for context and you can see we added salesforce servicenow and oracle and that orange ellipse because they're some of the bigger names in the software business so let's take these in alphabetical order ivana starting with koopa in the blue you can see we plotted them next to sap's ariba and you can see cooper has stronger spending momentum but not as much presence in the market so to me my influence is oh that's an opportunity for them to steal share more modern technology you know more facile and of course oracle has products in this space but the oracle dot includes all oracle products not just the procurement stuff but uh maybe your thoughts on this absolutely i love this chart i think that's your spot on this would be the same way i would interpret the chart where um increased spending momentum is is a sign of the company providing products that people like and we we expect to see cooper's share grow market share grow over time as well so let's come back to the chart and i want to i want to really point out the green ellipse this is the data zone if you will uh and we're like a broken record on this program with snowflake has performed unbelievably well in net score and spending momentum every quarter the dtr has captured enough end sample in its survey holding near or above 80 percent its net score consistently is has been up there and we've plotted data bricks in that zone it's been expected right that data bricks is going to do an ipo this year late last month company raised 1.6 billion in a private round so i guess that was either a strategy to delay the ipo or raise a bunch more cash and give late investors a low risk bite at the apple you know pre-ipo as we saw with snowflake last year what we didn't plot here are some of snowflake's biggest competitors ivana who also happen to be their partners most notably the big cloud players all who have their own database offerings aws microsoft and google now you've said snowflake is much more than a database company i wonder if you could add some color here yeah that's a very good point david uh basically the the driver of the thesis in snowflake is all about acceleration and spending and what we are seeing is the customers that are signed up on their platform today they're not even spending they're probably spending less than five percent of what they can ultimately spend on this product and the reason is because they don't yet know what the ultimate applications are for this right so you're gonna start with putting the data in a format you can use and you need to come up with use cases or how are you actually going to use this data so back to the example that i gave with answers the first use case that they found was trying to optimize leads there could be like 100 other use cases and they're coming up with with those on a daily basis so i would expect um this score to keep keep uh keep up pretty high or or go even higher as we as people figure out how they can use this product you know the buy-the-dip thesis on snowflake was great last quarter because the stock pulled back after they announced earnings and when we reported we said you know mike the the company see well cleveland research came out remember they got the dip on that and we looked at the data and we said mike scarpelli said that you know we're going to probably as a percentage of overall customers decelerate the net net new logos but we're going deeper into the customer base and that's exactly what's happening with with snowflake but okay let's bring up the slide again last but not least the z scaler we love z scalar we named z scaler in 2019 as an emerging four-star security company along with crowdstrike and octa and we said these three should be on your radar and as you see we've plotted z scalar with octa who with its it's its recent move into to converging identity and governance uh it gets kind of interesting uh we plotted them with palo alto as well another cyber security player that we've covered extensively we love octa in addition to z-scaler we great respect for palo alto and you'll note all of them are over that 40 percent line these are disruptors they're benefiting well not so much palo alto they're more legacy but the the other two are benefiting from that shift to work from home cloud security modern tech stack uh the acquisition that octa-made of of of auth0 and again z scalar cloud security getting rid of a lot of hardware uh really has a huge tailwind at its back if on a zscaler you know they've benefited from the huge my cloud migration trend what are your thoughts on the company so i actually love all three companies that are there right and the point is people are just going to spend more money whether you are on the cloud of the cloud the data centers need more security as well so i think there is a strong case to be made for all three with this scaler the upside is that it's just very easy to use very easy to implement and if you're somebody that is just setting up infrastructure on the cloud there is no reason for you to call any other competitor right with palo alto the case there is that if you have an established um security platfor if you're on their security platform the databa on the data center side uh they they did introduce through several acquisitions a pretty attractive cloud offering as well so they've been gaining share as well in the space and and the company does look pretty attractive on valiation basis so for us cyber security is really all about rising tide lifts all boats here right so you can have a pure play like this scaler uh that benefits from the cloud but even somebody like palo alto is pretty well positioned um to benefit yeah we think so too over a year ago we reported on the valuation divergence between palo alto and fortinet fortinet was doing a better job moving to the cloud and obviously serves more of a mid-market space palo alto had some go-to-market execution challenges we said at the time they're going to get through those and when we talk to chief information security officers palo alto is like the gold standard they're the thought leader they want to work with them but at the same time they also want to participate in some of these you know modern cloud stacks so i we agree there's plenty of room for all three um just to add a bit more color and drill into the spending data a little bit more this slide here takes that net score and shows the progression since january 2019 and you can see a snowflake just incredible in terms of its ability to maintain that elevated net score as we talked about and the table on the insert it shows you the number of responses and all three of these companies have been getting more mentions over time but snowflake and z scale are now both well over 100 n in the survey each quarter and the other notable piece here and this is really important you can see all three are coming out of the isolation economy with the spending uptick nice upticks shown in the most recent survey so that's again another positive but i want to close ivana with kind of making the bull and bear case and have you address really the risks to the buy the dip scenario so look there are a lot of reasons to like these companies we talked about them cooper they've got earnings momentum you know management on the call side had very strong end market demand this the stock you know has underperformed the nasdaq you know this year snowflake and zscaler they also have momentum snowflake get this enormous tam uh although they were punished for not putting a hard number on it which is ridiculous in my opinion i mean the thing is it's huge um the investors were just kind of you know wanting a little binky baby blanket but they all have modern tech in the cloud and really importantly this shows in the etr surveys you know the momentum that they have so very high retention is the other point i wanted to make the very very low churn of these companies however cooper's management despite the blowout quarter they gave kind of underwhelming guidance they've cited headwinds uh they've with the the the lamisoft uh migration to their cloud platform snowflake is kind of like price to perfection so maybe that's an advantage because every every little negative news is going to going to cause the company to dip but it's you know it's pretty high value because salutman and scarpelli everybody expects them to surpass what happened at servicenow which was a rocket ship and it could be all argued that all three are richly priced and overvalued so but ivana you're looking out as you said a couple of years three years maybe even five years how do you think about the potential downside risks in in your by the dip scenario you buy every dip you looking for bigger dips or what's your framework there so what we try to do is really look every quarter the company reports is there something that's driving fundamental change to the story or is it a one-off situation where people are just misunderstanding what the company is reporting so in the case we kind of addressed some of the earnings that that were reported but with koopa we think the man that management is guiding conservatively as they should so we're not very concerned about their ability to execute on on the guidance and and to exceed the guidance with snowflake price to perfection that's never a good idea to avoid a stock uh because it just shows that there is the company is doing a great job executing right so um we are looking for reports like the cleveland report where they would be like negative on the stock and that would be an entry point uh for us so broadly we apply by the deep philosophy but not not if something fundamentally changes in the story and none of these three are showing any signs of fundamental change okay we're going to leave it right there thanks to my guest today ivana tremendous having you would love to have you back great to see you thank you david and def you definitely want to check out sprx and the spear etf now remember i publish each week on wikibon.com and siliconangle.com these episodes they're all available as podcasts all you do is search breaking analysis podcasts you can always connect with me on twitter i'm at d vallante or email me at david.vellante at siliconangle.com love the comments on linkedin don't forget to check out etr.plus for all the survey action this is dave vellante for the cube insights powered by etr be well and we'll see you next time [Music] you

(gentle upbeat music) >> Everyone welcome to the AWS Startup Showcase presented by theCUBE. I'm John Furrier, host of theCUBE. We are here talking about the next big thing in cloud featuring Explorium. For the AI track, we've got AI cybersecurity and life sciences. Obviously AI is hot, machine learning powering that. Today we're joined by Zach Booth, director of global partnerships and channels like Explorium. Zach, thank you for joining me today remotely. Soon we'll be in person, but thanks for coming on. We're going to talk about rethinking external data. Thanks for coming on theCUBE. >> Absolutely, thanks so much for having us, John. >> So you guys are a hot startup. Congratulations, we just wrote about on SiliconANGLE, you're a new $75 million of fresh funding. So you're part of the Amazon partner network and growing like crazy. You guys have a unique value proposition looking at external data and that having a platform for advanced analytics and machine learning. Can you take a minute to explain what you guys do? What is this platform? What's the value proposition and why do you exist? >> Bottom line, we're bringing context to decision-making. The premise of Explorium and kind of this is consistent with the framework of advanced analytics is we're helping customers to reach better, more relevant, external data to feed into their predictive and analytical models. It's quite a challenge to actually integrate and effectively leverage data that's coming from beyond your organization's walls. It's manual, it's tedious, it's extremely time consuming and that's a problem. It's really a problem that Explorium was built to solve. And our philosophy is it shouldn't take so long. It shouldn't be such an arduous process, but it is. So we built a company, a technology that's capable for any given analytical process of connecting a customer to relevant sources that are kind of beyond their organization's walls. And this really impacts decision-making by bringing variety and context into their analytical processes. >> You know, one of the things I see a lot in my interviews with theCUBE and talking to people in the industry is that everyone talks a big game about having some machine learning and AI, they're like, "Okay, I got all this cool stuff". But at the end of the day, people are still using spreadsheets. They're wrangling data. And a lot of it's dominated by these still fenced-off data warehousing and you start to see the emergence of really companies built on the cloud. I saw the snowflake IPO, you're seeing a whole new shift of new brands emerging that are doing things differently, right? And because there's such a need for just move out of the archaic spreadsheet and data presentation layers, it's a slower antiquated, outdated. How do you guys solve that problem? You guys are on the other side of that equation, you're on the new wave of analytics. What are you guys solving? How do you make that work? How do you get on that way? >> So basically the way Explorium sees the world, and I think that most analytical practitioners these days see it in a similar way, but the key to any analytical problem is having the right data. And the challenge that we've talked about and that we're really focused on is helping companies reach that right data. Our focus is on the data part of data science. The science part is the algorithmic side. It's interesting. It was kind of the first frontier of machine learning as practitioners and experts were focused on it and cloud and compute really enabled that. The challenge today isn't so much "What's the right model for my problem?" But it's "What's the right data?" And that's the premise of what we do. Your model's only as strong as the data that it trains on. And going back to that concept of just bringing context to decision-making. Within that framework that we talked about, the key is bringing comprehensive, accurate and highly varied data into my model. But if my model is only being informed with internal data which is wonderful data, but only internal, then it's missing context. And we're helping companies to reach that external variety through a pretty elegant platform that can connect the right data for my analytical process. And this really has implications across several different industries and a multitude of use cases. We're working with companies across consumer packaged goods, insurance, financial services, retail, e-commerce, even software as a service. And the use cases can range between fraud and risk to marketing and lifetime value. Now, why is this such a challenge today with maybe some antiquated or analog means? With a spreadsheet or with a rule-based approach where we're pretty limited, it was an effective means of decision-making to generate and create actions, but it's highly limited in its ability to change, to be dynamic, to be flexible. And with modeling and using data, it's really a huge arsenal that we have at our fingertips. The trick is extracting value from within it. There's obviously latent value from within our org but every day there's more and more data that's being created outside of our org. And that is a challenge to go out and get to effectively filter and navigate and connect to. So we've basically built that tech to help us navigate and query for any given analytical question. Find me the right data rather than starting with what's the problem I'm looking for, now let me think about the right data. Which is kind of akin to going into a library and searching for a specific book. You know which book you're looking for. Instead of saying, there's a world, a universe of data outside there. I want to access it. I want to tap into what's right. Can I use a tool that can effectively query all that data, find what's relevant for me, connect it and match it with my own and distill signals or features from that data to provide more variety into my modeling efforts yielding a robust decision as an output. >> I love that paradigm of just having that searchable kind of paradigm. I got to ask you one of the big things that I've heard people talk about. I want to get your thoughts on this, is that how do I know if I even have the right data? Is the data addressable? Can I find it? Is it even, can I even be queried? How do you solve that problem for customers when they say, "I really want the best analytics but do I even have the data or is it the right data?" How do you guys look at that? >> So the way our technology was built is that it's quite relevant for a few different profile types of customers. Some of these customers, really the genesis of the company started with those cloud-based, model-driven since day one organizations, and they're working with machine learning and they have models in production. They're quite mature in fact. And the problem that they've been facing is, again, our models are only as strong as the data that they're training on. The only data that they're training on is internal data. And we're seeing diminishing returns from those decisions. So now suddenly we're looking for outside data and we're finding that to effectively use outside data, we have to spend a lot of time. 60% of our time spent thinking of data, going out and getting it, cleaning it, validating it, and only then can we actually train a model and assess if there's an ROI. That takes months. And if it doesn't push the needle from an ROI standpoint, then it's an enormous opportunity cost, which is very, very painful, which goes back to their decision-making. Is it even worth it if it doesn't push the needle? That's why there had to be a better way. And what we built is relevant for that audience as well as companies that are in the midst of their digital transformation. We're data rich, but data science poor. We have lots of data. A latent value to extract from within our own data and at the same time tons of valuable data outside of our org. Instead of waiting 18, 36 months to transform ourselves, get our infrastructure in place, our data collection in place, and really start having models in production based on our own data. You can now do this in tandem. And that's what we're seeing with a lot of our enterprise customers. By using their analysts, their data engineers, some of them in their innovation or kind of center of excellences have a data science group as well. And they're using the platform to inform a lot of their different models across lines of businesses. >> I love that expression, "data-rich". A lot of people becoming full of data too. They have a data problem. They have a lot of it. I think I want to get your thoughts but I think that connects to my next question which is as people look at the cloud, for instance, and again, all these old methods were internal, internal to the company, but now that you have this idea of cloud, more integration's happening. More people are connecting with APIs. There's more access to potentially more signals, more data. How does a company go to that next level to connect in and acquire the data and make it faster? Because I can almost imagine that the signals that come from that context of merging external data and that's the topic of this theme, re-imagining external data is extremely valuable signaling capability. And so it sounds like you guys make it go faster. So how does it work? Is it the cloud? Take us through that value proposition. >> Well, it's a real, it's amazing how fast the rate of change organizations have been moving onto the cloud over the past year during COVID and the fact that alternative or external data, depending on how you refer to it, has really, really blown up. And it's really exciting. This is coming in the form of data providers and data marketplaces, and everybody is kind of, more and more organizations are moving from rule-based decision-making to predictive decision making, and that's exciting. Now what's interesting about this company, Explorium, we're working with a lot of different types of customers but our long game has a real high upside. There's more and more companies that are starting to use data and are transformed or already are in the midst of their transformation. So they need outside data. And that challenge that I described is exists for all of them. So how does it really work? Today, if I don't have data outside, I have to think. It's based on hypothesis and it all starts with that hypothesis which is already prone to error from the get-go. You and I might be domain experts for a given use case. Let's say we're focusing on fraud. We might think about a dozen different types of data sources, but going out and getting it like I said, it takes a lot of time harmonizing it, cleaning it, and being able to use it takes even more time. And that's just for each one. So if we have to do that across dozens of data sources it's going to take far too much time and the juice isn't worth the squeeze. And so I'm going to forego using that. And a metaphor that I like to use when I try to describe what Explorium does to my mom. I basically use this connection to buying your first home. It's a very, very important financial decision. You would, when you're buying this home, you're thinking about all the different inputs in your decision-making. It's not just about the blueprint of the house and how many rooms and the criteria you're looking for. You're also thinking external variables. You're thinking about the school zone, the construction, the property value, alternative or similar neighborhoods. That's probably your most important financial decision or one of the largest at least. A machine learning model in production is an extremely important and expensive investment for an organization. Now, the problem is as a consumer buying a home, we have all this data at our fingertips to find out all of those external-based inputs. Organizations don't, which is kind of crazy when I first kind of got into this world. And so, they're making decisions with their first party data only. First party data's wonderful data. It's the best, it's representative, it's high quality, it's high value for their specific decision-making and use cases but it lacks context. And there's so much context in the form of location-based data and business information that can inform decision-making that isn't being used. It translates to sub-optimal decision-making, let's say. >> Yeah, and I think one of the insights around looking at signal data in context is if by merging it with the first party, it creates a huge value window, it gives you observational data, maybe potentially insights into customer behavior. So totally agree, I think that's a huge observation. You guys are definitely on the right side of history here. I want to get into how it plays out for the customer. You mentioned the different industries, obviously data's in every vertical. And vertical specialization with the data it has to be, is very metadata driven. I mean, metadata and oil and gas is different than fintech. I mean, some overlap, but for the most part you got to have that context, acute context, each one. How are you guys working? Take us through an example of someone getting it right, getting that right set up, taking us through the use case of how someone on boards Explorium, how they put it to use, and what are some of the benefits? >> So let's break it down into kind of a three-step phase. And let's use that example of fraud earlier. An organization would have basically past historical data of how many customers were actually fraudulent in the end of the day. So this use case, and it's a core business problem, is with an intention to reduce that fraud. So they would basically provide, going with your description earlier, something similar to an Excel file. This can be pulled from any database out there, we're working with loads of them, and they would provide this what's called training data. This training data is their historical data and would have as an output, the outcome, the conclusion, was this business fraudulent or not? Yes or no. Binary. The platform would understand that data itself to train a model with external context in the form of enrichments. These data enrichments at the end of the day are important, they're relevant, but their purpose is to generate signals. So to your point, signals is the bottom line what everyone's trying to achieve and identify and discover, and even engineer by using data that they have and data that they yet to integrate with. So the platform would connect to your data, infer and understand the meaning of that data. And based on this matching of internal plus external context, the platform automates the process of distilling signals. Or in machine learning this is called, referred to as features. And these features are really the bread and butter of your modeling efforts. If you can leverage features that are coming from data that's outside of your org, and they're quantifiably valuable which the platform measures, then you're putting yourself in a position to generate an edge in your modeling efforts. Meaning now, you might reduce your fraud rate. So your customers get a much better, more compelling offer or service or price point. It impacts your business in a lot of ways. What Explorium is bringing to the table in terms of value is a single access point to a huge universe of external data. It expedites your time to value. So rather than data analysts, data engineers, data scientists, spending a significant amount of time on data preparation, they can now spend most of their time on feature or signal engineering. That's the more fun and interesting part, less so the boring part. But they can scale their modeling efforts. So time to value, access to a huge universe of external context, and scale. >> So I see two things here. Just make sure I get this right 'cause it sounds awesome. So one, the core assets of the engineering side of it, whether it's the platform engineer or data engineering, they're more optimized for getting more signaling which is more impactful for the context acquisition, looking at contexts that might have a business outcome, versus wrangling and doing mundane, heavy lifting. >> Yeah so with it, sorry, go ahead. >> And the second one is you create a democratization for analysts or business people who just are used to dealing with spreadsheets who just want to kind of play and play with data and get a feel for it, or experiment, do querying, try to match planning with policy - >> Yeah, so the way I like to kind of communicate this is Explorium's this one, two punch. It's got this technology layer that provides entity resolution, so matching with external data, which otherwise is a manual endeavor. Explorium's automated that piece. The second is a huge universe of outside data. So this circumvents procurement. You don't have to go out and spend all of these one-off efforts on time finding data, organizing it, cleaning it, etc. You can use Explorium as your single access point to and gateway to external data and match it, so this will accelerate your time to value and ultimately the amount of valuable signals that you can discover and leverage through the platform and feed this into your own pipelines or whatever system or analytical need you have. >> Zach, great stuff. I love talking with you and I love the hot startup action here. Cause you're again, you're on the net new wave here. Like anything new, I was just talking to a colleague here. (indistinct) When you have something new, it's like driving a car for the first time. You need someone to give you some driving lessons or figure out how to operationalize it or take advantage of the one, two, punch as you pointed out. How do you guys get someone up and running? 'Cause let's just say, I'm like, okay, I'm bought into this. So no brainer, you got my attention. I still don't understand. Do you provide a marketplace of data? Do I need to get my own data? Do I bring my own data to the party? Do you guys provide relationships with other data providers? How do I get going? How do I drive this car? How do you answer that? >> So first, explorium.ai is a free trial and we're a product-focused company. So a practitioner, maybe a data analyst, a data engineer, or data scientist would use this platform to enrich their analytical, so BI decision-making or any models that they're working on either in production or being trained. Now oftentimes models that are being trained don't actually make it to production because they don't meet a minimum threshold. Meaning they're not going to have a positive business outcome if they're deployed. With Explorium you can now bring variety into that and increase your chances that your model that's being trained will actually be deployed because it's being fed with the right data. The data that you need that's not just the data that you have. So how a business would start working with us would typically be with a use case that has a high business value. Maybe this could be a fraud use case or a risk use case and B2B, or even B2SMB context. This might be a marketing use case. We're talking about LTV modeling, lookalike modeling, lead acquisition and generation for our CPGs and field sales optimization. Explore and understand your data. It would enrich that data automatically, it would generate and discover new signals from external data plus from your own and feed this into either a model that you have in-house or end to end in the platform itself. We provide customer success to generate, kind of help you build out your first model perhaps, and hold your hands through that process. But typically most of our customers are after a few months time having run in building models, multiple models in production on their own. And that's really exciting because we're helping organizations move from a more kind of rule-based decision making and being their bridge to data science. >> Awesome. I noticed that in your title you handle global partnerships and channels which I'm assuming is you guys have a network and ecosystem you're working with. What are some of the partnerships and channel relationships that you have that you bring to bear in the marketplace? >> So data and analytics, this space is very much an ecosystem. Our customers are working across different clouds, working with all sorts of vendors, technologies. Basically they have a pretty big stack. We're a part of that stack and we want to symbiotically play within our customer stack so that we can contribute value whether they sit here, there, or in another place. Our partners range from consulting and system integration firms, those that perhaps are building out the blueprint for a digital transformation or actually implementing that digital transformation. And we contribute value in both of these cases as a technology innovation layer in our product. And a customer would then consume Explorium afterwards, after that transformation is complete as a part of their stack. We're also working with a lot of the different cloud vendors. Our customers are all cloud-based and data enrichment is becoming more and more relevant with some wonderful machine-learning tools. Be they AutoML, or even some data marketplaces are popping up and very exciting. What we're bringing to the table as an edge is accelerating the connection between the data that I think I want as a company and how to actually extract value from that data. Being part of this ecosystem means that we can be working with and should be working with a lot of different partners to contribute incremental value to our end customers. >> Final question I want to ask you is if I'm in a conference room with my team and someone says, "Hey, we should be rethinking our external data." What would I say? How would I pound my fist on the table or raise my hand in saying, "Hey, I have an idea, we should be thinking this way." What would be my argument to the team, to re-imagine how we deal with external data? >> So it might be a scenario that rather than banging your hands on the table, you might be banging your heads on the table because it's such a challenging endeavor today. Companies have to think about, What's the right data for my specific use cases? I need to validate that data. Is it relevant? Is it real? Is it representative? Does it have good coverage, good depth and good quality? Then I need to procure that data. And this is about getting a license from it. I need to integrate that data with my own. That means I need to have some in-house expertise to do so. And then of course, I need to monitor and maintain that data on an ongoing basis. All of this is a pretty big thing to undertake and undergo and having a partner to facilitate that external data integration and ongoing refresh and monitoring, and being able to trust that this is all harmonized, high quality, and I can find the valuable ones without having to manually pick and choose and try to discover it myself is a huge value add, particularly the larger the organization or partner. Because there's so much data out there. And there's a lot of noise out there too. And so if I can through a single partner or access point, tap into that data and quantify what's relevant for my specific problem, then I'm putting myself in a really good position and optimizing the allocation of my very expensive and valuable data analysts and engineering resources. >> Yeah, I think one of the things you mentioned earlier I thought was a huge point was good call out was it goes beyond the first party data because and even just first party if you just in an internal view, some of the best, most successful innovators that we've been covering with cloud scale is they're extending their first party data to external providers. So they're in the value chains of solutions that share their first party data with other suppliers. And so that's just, again, more of an extension of the first party data. You're kind of taking it to a whole 'nother level of there's another external, external set of data beyond it that's even more important. I think this is a fascinating growth area and I think you guys are onto it. Great stuff. >> Thank you so much, John. >> Well, I really appreciate you coming on Zach. Final word, give a quick plug for the company. What are you up to, and what's going on? >> What's going on with Explorium? We are growing very fast. We're a very exciting company. I've been here since the very early days and I can tell you that we have a stellar working environment, a very, very, strong down to earth, high work ethic culture. We're growing in the sense of our office in San Mateo, New York, and Tel Aviv are growing rapidly. As you mentioned earlier, we raised our series C so that totals Explorium to raising I think 127 million over the past two years and some change. And whether you want to partner with Explorium, work with us as a customer, or join us as an employee, we welcome that. And I encourage everybody to go to explorium.ai. Check us out, read some of the interesting content there around data science, around the processes, around the business outcomes that a lot of our customers are seeing, as well as joining a free trial. So you can check out the platform and everything that has to offer from machine learning engine to a signal studio, as well as what type of information might be relevant for your specific use case. >> All right Zach, thanks for coming on. Zach Booth, director of global partnerships and channels that explorium.ai. The next big thing in cloud featuring Explorium and a part of our AI track, I'm John Furrier, host of theCUBE. Thanks for watching.

from the cube studios in palo alto in boston bringing you data-driven insights from the cube and etr this is breaking analysis with dave vellante as you well know by now the cloud is about shifting i.t labor to more strategic initiatives or as andy jassy laid out years ago removing the undifferentiated heavy lifting associated with deploying and managing i.t infrastructure cloud is also about changing the operating model and rapidly scaling a business operation or a company often overlooked with cloud however is the innovation piece of the puzzle a main source of that innovation is venture funded startup companies that have brilliant technologists who are mission driven and have a vision to solve really hard problems and enter a large market at scale to disrupt it hello everyone and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we're pleased to welcome a special guest and author of the elite 80. a report that details the hottest privately held cyber security and i.t infrastructure companies in the world eric suppenger is that author and joins us today from jmp securities eric welcome to the cube thanks for being here thank you very much dave i'm uh i'm looking forward to uh to having a discussion here with you yeah me too this is going to be great so let's dive right into the elite 80. first if you could tell us about jmp securities and fill us in on the report its history your approach to picking the 80 companies out of thousands of choices sure so jmp is a middle markets investment bank we're a full full-service investment bank based in san francisco we were founded in 2000 and we focus on technology health care financial services and real estate i've been with jmp since 2011. um i've uh i've i cover uh cyber security companies public companies uh i cover uh it infrastructure companies uh more broadly and um we have having been based here in san francisco i've long kept uh a good dialogue with uh private companies uh that that compete with the public companies that i cover and so um about seven years ago i i started uh developing this uh this report which is really designed to highlight uh emerging uh private companies that uh that i think are are well positioned to be leaders in their respective markets and uh and over time we've um we've built the list up to about 80 companies and uh and we publish this report every year uh it's designed to uh to keep tabs on on the companies that are doing well and uh and we rotate about uh about 15 to 20 to 25 percent of the companies uh out of the report every year either as they get acquired or they do an ipo or um uh they uh they if we think that they are slowing and others are getting a little bit more uh more exciting and you talk directly to the companies that's part of your methodology as well you do a lot of background research digging into funding but you also talk to the executives at these companies correct yes for the most part we uh we try to talk to the ceos at least the cfos the object here is to build a a relationship with these companies so that we have some good insights into uh into how they're doing and and how the market trends are evolving as they relate to those companies in particular some of the some of the dynamics that go into us selecting companies is one we do have to talk to the management teams uh two we uh we we base our decisions on who we include on how the companies are performing on how their competitors are uh are are discussing those companies their performance uh how other industry contacts talk about those companies and then we we track their hiring and uh and and how they've uh you know other metrics that we can uh we can gauge them by got it okay so i dug into the report a little bit and tried to summarize a few key takeaways so let's take a look at those and if if you allow me just set up the points and then and ask you to add some color so the first two things that really you know jumped out i want to comment on are the perspectives of the technology companies and then of course the other side is the buyers so it seems that the pandemic really got startups to sharpen their focus i remember talking to a number of vcs early on in the shutdown and they were all over their portfolio companies to reset their icp their ideal customer profile and sharpen their uvp their unique value proposition and they wanted them to do that specifically in the context of the pandemic and the new reality and then on the buy side let's face it if you weren't a digital business you were out of business so picking up on those two thoughts eric what can you share with us in terms of the findings that you have well that's that's very uh consistent with what we had found uh basically um when the pandemic first when the lockdown came uh in march we reached out to quite a few companies and industry contacts at that time feedback was uh you know it was uh it was a period of great uncertainty and a lot of a lot of budgets were were tightened pretty quickly but it didn't take very long and a lot of these companies uh you know having been uh innovation engines and and emerging players what they found was that uh the broader market quickly adopted uh digital transformation in response to the pandemic basically that was how they they uh facilitated uh keeping their their doors open so to speak and so um the ones that were able to uh to leverage uh need for emerging technologies because of an acceleration in digital transformation uh they they really stepped up and and quite a few of these companies they kept hiring they kept uh their sales uh did very well and uh and ultimately um a lot of the vcs that had been uh putting on the brakes uh they actually stepped up and uh and and continued funding uh pretty generously yeah we've got some data on that that we wanna look into so thank you for that now let's take a look at some of the the specific date of the study just break that down the elite 80 raised more than three billion dollars last year eclipsing the previous highs in your studies of 2019 and then a big portion of that capital went to pretty small number only 10 of the 80 firms and and most of that went to cyber security plays so what do you make of these numbers especially you know given your history with with this group of elite companies and the high concentration this year this past year so one of the trends that we've seen in the public in the public market or the ipo market is um companies are are waiting until they're a little bit more mature than they used to be so what we've seen is um the the funding for companies uh the the larger rounds are far larger than they used to be these companies typically are waiting until they're of size you know maybe now they're waiting to be uh 200 million uh in annual salary in annual revenues versus a 100 million before and so they are consuming quite a bit the larger rounds are are much bigger than they used to be um in the in the most recent uh report that we published we had uh one round that was over half a billion and another one that was over 400 million and if you go back just a couple of few years ago a large round was over 100 million and you didn't get too many that were over 200 million so that's that's been a distinct change and and i think that's not necessarily just a function of the pandemic but i think the pandemic caused caused some companies to kind of step up the size of their rounds uh and so there were a handful of uh very large rounds uh certainly bigger than what we've ever seen before yeah those are great observations i mean you're right it was 100 million used to be the magic number to go public and now you get so much late money coming in locking in maybe smaller gains but giving that company you know a little more time to get their act together pre-ipo let's take a look at where the money went you know talk about follow the money and eric you and your team you segmented that three billion dollars into a number of different categories as i said most of it go into cyber security uh categories like application security is assessment and risk there's endpoint endpoint boomed during the pandemic same with identity and this chart really shows those categories that you created to better understand these dynamics and sort of figure out where the money went how did you come up with these these categories and what does this data tell you so these categories were basically uh homegrown these are how i um i think of these companies um it's a little bit of uh pulling some information out of uh the likes of gartner but uh for the most part this was how i how i conceptualize the landscape uh in my mind um the interesting thing to me is you know so a lot of that data is skewed by a few large transactions so um you know if you if you think about the the the allocation of those uh those different categories and and the uh investments in those categories it's it's skewed by large transactions and what was most interesting to me was one the application security space is a space that had quite a few additional smaller rounds and i think that's one that's pretty interesting going forward and then the one that was a surprise to me more than that was the data management um outside of cyber security uh data management's a space that's getting uh a lot more attention and uh and it's getting um uh some pretty good uh growth so that's a space that we're uh we're paying some good good attention to as well yeah that's interesting i mean of course data management means a lot of different things to a lot of different people and vc's throwing money at it maybe trying to define it and then and then the the the ai ops and and the that data management piece you know took a took a portion of it but wow the the cyber guys really are are killing it and now as we mentioned ten companies sucked up the lion's share of of the funding and this next chart shows that concentration of those 10 investments so eric some big numbers here one trust secured more than a half a billion dollars four others nabbed more than a quarter billion in funding give us your thoughts on this what do you make of that high concentration well um i i think this is a function of companies that are waiting uh longer than they used to um they're these these companies are getting to be of considerable scale i mean titanium would be a good example that's a company that could have gone public years ago and uh and i don't think they're particularly eager to get out the door uh they provide liquidity to their previous investors by raising money and uh and and buying those shares back um and so they uh they basically uh just continue to uh to grow uh without the uh the burden or or the um uh the demands that being a public company create um so there's this that's that's really a function of of companies just waiting longer before they get out the door got it now here's another view of that that data the so the left side of this chart uh that we we want to show you next um gives you a sense of the size of the companies the revenue in the elite 80 and you know most of these companies have broken through the 100 million dollar revenue mark as you say uh and they're they're still private and so you can see the breakdown and then the right-hand side of the chart shows the most active investors we just pulled out those with three or more transactions and it's it's interesting to see the players there and of course you've got some strategics you got city in there you've got cisco along with a little bit of p and e private equity action maybe your thoughts on on on this data so so to give you a little flavor around the uh the size of these companies when we first started publishing this report a little bit of the goal was to try to keep those categories relatively equal and as you can see they've skewed uh far to the left uh towards the uh to the larger revenue stream you know size so that's that just goes to the point that um uh the the companies that uh you know that are getting that a lot of these private companies uh they're they're of saw considerable size before they uh they really go out the door and and i think that's a reflection of um of the caliber of uh of or the quality of investments that uh that are out there today these are companies that have built very mature businesses and they're not going into the market until um until they can demonstrate uh high confidence and uh and consistency in their performance yeah i mean you i remember when when cloudera took that massive i think it was the 750 billion a million dollar investment from uh from intel you know way back when they that bridged them to ipo and that was sort of if i recall started that that trend and then now you get a ipo last year like snowflake which is price to perfection and you got guys that really know how to do this they've done it a number of times and so it really is somewhat changed that that dynamic uh for ipos which of course came booming back it was so quiet there for so many years but let's look into these markets a bit um i want to talk about the security space and the i.t infrastructure space and here's a chart from optiv which is one of the elite 80 ironically and we've shared this with with our audience before and the point of this is that the cyber security spaces it's highly fragmented we've reported on this a lot it's got hundreds and hundreds of companies in there it's just mosaic of solutions so very complicated and bespoke sets of tooling and combine that with a lack of skilled expertise you know csos tell us the lack of talent is their biggest challenge makes it a really dynamic market and eric this is part of the reason why vcs they want in so the takeaway i get from that chart is we have a lot of um we still have a great need for best of breed um digital transformation uh cloud mobile all these trends are creating such a disruption that there's still a great opportunity for somebody that can deliver a uh you know a real best of the best of breed uh solution uh in spite of uh all the challenges that uh id it departments are having with trying to uh to meet you know security requirements and things like that uh the the world has embraced uh you know digital delivery and uh you are your success is oftentimes dependent on your your digital differentiation and if that's the case then there's always going to be opportunity for a better technology out there so that's that in the end is uh is why uh optiv has a uh a line card that's uh as as long as you can read it i'm glad you brought the point about best of breeze it's an age-old debate in the industry it's do we go best of breed or do we go you know integrated suites you know you look at a company like microsoft obviously that that works very well for them uh companies like cisco but so this next uh set of data we're gonna bring in some etr customer spending data and see where the momentum is and i think it'll really underscore the points that you're making there in terms of best of breed this chart shares a popular view that we like to to share with our community on the vertical axis is net score or that's spending velocity and the horizontal axis shows market share or pervasiveness in the data as we've said before anything above 40 percent that red line on the vertical axis is considered elevated and you can see a lot of companies in cyber security are above that mark now a couple points i want to make here before we bring eric back in first is the market it's fragmented but it's pretty large at over 100 billion dollars depending on which research firm you look at it's growing at you know the low double digits so so nice growth is putting on 10 billion dollars a year into that number and there are some big pure plays like palo alto networks and fortinet but the market includes some other large whales like cisco uh they've built up a sizeable security business microsoft microsoft's in most markets and serves its you know software customers so but you can see how crowded this market is now we've superimposed in the red recent valuations for some of the companies and and the other point we want to make is there's some big numbers here and some divergence between us eric was saying the the best of breed and the integrated suites and the pandemic as we've talked about a lot is fueled a shift in cyber strategies toward endpoint identity and cloud and you can see that in crowdstrike's 50 billion plus valuation octa another best of breed 34 billion dollars in identity they just bought off zero and paid four and a half billion dollars for auth0 to get access to the developer community z scaler at 28 billion proof point is going private at a 12 billion dollar number so you can see why vcs are pouring money into this market some really attractive valuations eric what are your thoughts on this data so my interpretation is that's that's just further validation that uh that these security markets are uh are getting disrupted and uh and the truth of the matter is there's only one um really well positioned uh platform player in there uh uh palo alto the rest of them are are platforms within their respective uh security technology space but uh you know there's there's not very many um you know broad security solution providers today and the reason for that is because we've got such a uh transformation going on uh across uh technology that the need for best of breed is uh is is getting recognized uh day in day out yeah you're right palo alto they're they csos love to work with palo alto they're kind of the high-end gold standard but and we reported last year on the divergence in valuations between fortinet and palo alto networks fortinet was doing a better job you know pivoting to the cloud we said palo alto will get its act together it did but then you see these pure play best of breeds really you know doing well so now let's take a look at the it infrastructure space and it's it's quite different in terms of the dynamics of the market so here's that same view of the etr data and we've cut it by uh three categories we cut on networking servers and storage and this is a very large market it's it's it's over 200 billion dollars but it's much more of an oligopoly in that you've got great concentration at the top you've got some really big companies like cisco and dell which is spinning out vmware so we're going to unlock you know more value of the core dell company dell's valuation is 79 billion and that includes its 80 ownership in vmware so you do the math and figure out what core dell is worth hpe is much smaller it's notable that its valuation is comparable to netapp netapp's around you know one-fifth the size of revenue-wise uh hpe now eric arista they stand out as the lone player that's having some success clearly against cisco what are your thoughts on on the infrastructure space so so a couple things i'll take away from that now first off uh you mentioned arista arista is a bit of an anomaly um a switching company you know a networking company that is in that upper echelon like you've pointed out above 40 percent it is it is unique and and basically they kind of cracked the code they figured out how to beat cisco at cisco's core competency which is traditionally switching switching and routing and they they did that by delivering a very differentiated uh uh hardware product um that that they were able to tap into some markets that uh that even cisco hasn't been able to open up and and those would be the hyperscale uh hyperscale you know hosting vendors like uh google and facebook and microsoft but i would i would put i would put arista kind of in a in a unique situation the other thing that i'll just point out that i think is an interesting takeaway from the um from the the the slide that you showed is there are some uh infrastructure or what i would consider is bordering on data management type companies i mean you look at uh rubric you look at cohesity and nutanix veeam they're they're all kind of bubbling up there and pure storage and i think that comes back to what i was mentioning earlier where there is some pretty interesting innovation going on in data management which has traditionally not had a lot of innovation so i would bet you those names would have bubbled up just in the last uh year or two where that's been a market that hasn't had a lot of innovation and and now there's some interesting things coming down the pipe you know that's interesting comments that you make in there because if you think back to sort of last decade arista obviously broke out the only two other companies in the in the core infrastructure space and this was a hardware game historically but it's obviously becoming a software game but take a look at pure storage and nutanix you can see their valuations at five billion and seven point four billion dollars respectively uh and then to your point cohesity you got them at 3.7 billion just did a recent you know round rubric 3.3 billion that's from 2019 and so you know presumably that's a higher valuation now veeam got taken out last january at five billion by uh inside capital uh and so i think they're doing very well and they're probably uh up from that and susa is going public at uh at a reported seven billion dollar valuation so quite a bit different dynamic in the infrastructure space so eric i want to bring it back to the elite 80 in in in in startups in general my first question to you is is what do you look for from successful startups to make this elite 80 list so a few factors first off uh their performance is uh is is one of the primary uh situations if it's a company that's not growing we'll we'll probably pull it from the list um i would say it is also very much a function of my perception of the quality of management uh we we do meet with all these management teams um if we feel like uh they're they're they're putting together a uh you know a um a leadership team that's gonna be around for a long time and they've got a product position that's uh pretty attractive uh those would certainly be two key aspects of what i look for beyond that uh certainly feedback that we get from competitors uh feedback that we get from industry contacts like resellers and then then i'd also just say my enthusiasm for their respective market that they're in if it's a a market that i think is is going to be difficult or flat or not very interesting then then that would certainly be a a reason to to not include them uh conversely even if it's a small company if it's if it's a sector that i think is going to be uh around for quite a while and it's very differentiated uh then we'll include um a lot of the smaller companies too well a good example that's like a weka i mean i don't want to i don't want to go into these companies but two because we believe we 80 companies are going to leave somebody else but that that's a good example of a smaller company that looks to be disruptive um how should enterprise customers the buyers do you think evaluate and filter startups you have any sense of that well um a couple things that i struggle with that that would be uh you know something that's a lot more readily available to them is uh is just the quality of the product i mean that's obviously uh why why they're looking at it but uh if it's a uh if it's a company that's got a a unique product that uh is is built uh you know that that can that can that works that would be the starting point then then beyond that it's also is it a management team is is the behavior of the company something that uh reflects a management team that's uh that's that's you know a high quality management team if they if they you know are responsive if they're following up if they're not trying to pull in business uh quickly if they're priced appropriately uh metrics like that would certainly be um key aspects that would be readily available to uh to the you know to the the buyers of technology beyond that um you know i think the viability of that market is going to be uh a key aspect as to whether or not that company is going to be around even if it's a good company if uh if it's a highly competitive uh market that's going to have some big big players that can kind of integrate it and to make it a feature across other other product lines then that's going to make it a a tough a tough road to to go for a start-up these days you know the other thing i wanted to to talk about was the risks and the rewards of working with with startup companies and i've had i've had cios and and enterprise architects tell me that they'll when when they have to do an rfp they'll pull out the gartner magic quadrant they'll always you know pick a couple in the top right just to cover their butts but they many say you know what we also pick some of those those in the challenger space because because that are that are really interesting to us and and we run them through the paces and we manage those risks we don't we don't run the company on them but it helps us find these diamonds in the rough i mean think about you know in the in this in the second part of last decade if you picked a snowflake you might have been able to get ahead of some of your competition things like data sharing or or maybe you found that that well you know what octa is going to help me with my identity in in a new way and you're going to be better prepared to be a digital business but do you have any thoughts on how uh people should manage those risks and and how they should think about the upside i don't i don't think today um a a you know a company can work today using legacy technologies i i think the risk the greater risk is falling behind from a a digital transformation perspective this this era i think the pandemic is probably the best proof point of this um you can't you can't go with just a uh a traditional legacy architecture in a in in a key aspect of your business and so the startups um i i think you've got to take the quote-unquote risk of working with a startup that's uh you know that's got a viability concern or sustainability concern uh the risk of of having a um uh an i.t infrastructure that's inadequate is uh is a far greater risk from my perspective so i think that the startups right now are are are in a very strong position and they're well funded that's the last question i wanted to talk about is how will startups kind of penetrate the enterprise in this modern era i mean you know this is really a software world and software is this sort of capital efficient business but yet you're seeing companies raise hundreds of millions of dollars i mean that's not even absurd these days you see companies go to ipo that have raised over a billion dollars and much of that if not most of it goes to promotion and go to market uh so so how maybe you could give us your perspectives on how you see startups getting into the enterprise in these sectors so i one of the really interesting things that we've seen in the last couple years is a lot of changes to sales models and and if you look at the mid market the ability to leverage viral sales models uh has been wildly successful for some companies um it's been um you know a great strategy uh there's a public company ubiquity that did a uh has built a multi-billion dollar uh you know business on on without without a sales organization so there's some pretty interesting um directions that i think sales and go to market is going to uh incur over the over the coming years uh traditional enterprise sales i think are still uh pretty standard today but i i think that the efficiency of um of you know social networking and and uh and and what would the the delivery of uh of products on on a digital for in a digital format is going to change the way that we do sales so i think i think there's a lot of efficiencies that are going to come in uh in sales over the coming years that's interesting because then you'll you know i i think you're right and and and instead of just just pouring money at promotion maybe get more efficient there and pour money in into engineering because that really is the long-term sustainable value that these companies are going to create right yeah i i would absolutely agree with that and um again if you look at the you know if you look at the charts of the well-established players that that you had mentioned those companies are where they are that the ones at the top are where they are because of their technology i mean it's it's not because of uh their go to market it's it's it's because they have something that other people can't uh can't replicate right well eric hey it's been great having you on today thanks so much for joining us really appreciate your time well dave i greatly appreciate it uh it's been a lot of fun so uh so thank you all right hey go get the elite 80 report all you got to do is search jmp elite 80 and it'll it'll come up there's a there's a lot of data out there so it's really a worthwhile reference tool and uh so thank you everybody for watching remember these episodes are all available as podcasts wherever you listen you can check out etr's website at etr dot plus and we also publish weekly a full report on wikibon.com and siliconangle.com you can email me david.velante at siliconangle.com or dm me on twitter at divalante hit up hit our linkedin post and really appreciate those comments this is dave vellante for the cube insights powered by etr have a great week everybody stay safe and we'll see you next time you

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante recent survey data from etr shows that enterprise tech spending is tracking with projected u.s gdp growth at six to seven percent this year many markers continue to point the way to a strong recovery including hiring trends and the loosening of frozen it project budgets however skills shortages are blocking progress at some companies which bodes well for an increased reliance on external i.t services moreover while there's much to talk about well there's much talk about the rotation out of work from home plays and stocks such as video conferencing vdi and other remote worker tech we see organizations still trying to figure out the ideal balance between funding headquarter investments that have been neglected and getting hybrid work right in particular the talent gap combined with a digital mandate means companies face some tough decisions as to how to fund the future while serving existing customers and transforming culturally hello everyone and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we welcome back eric porter bradley of etr who will share fresh data perspectives and insights from the latest survey data eric great to see you welcome thank you very much dave always good to see you and happy to be on the show again okay we're going to share some macro data and then we're going to dig into some highlights from etr's most recent march covid survey and also the latest april data so eric the first chart that we want to show it shows cio and it buyer responses to expected i.t spend for each quarter of 2021 versus 2020. and you can see here a steady quarterly improvement eric what are the key takeaways from your perspective sure well first of all for everyone out there this particular survey had a record-setting number of uh participation we had uh 1 500 i.t decision makers participate and we had over half of the fortune 500 and over a fifth of the global 1000. so it was a really good survey this is the seventh iteration of the covet impact survey specifically and this is going to transition to an over large macro survey going forward so we could continue it and you're 100 right what we've been tracking here since uh march of last year was how is spending being impacted because of covid where is it shifting and what we're seeing now finally is that there is a real re-acceleration in spend i know we've been a little bit more cautious than some of the other peers out there that just early on slapped an eight or a nine percent number but what we're seeing is right now it's at a midpoint of over six uh about six point seven percent and that is accelerating so uh we are still hopeful that that will continue uh really that spending is going to be in the second half of the year as you can see on the left part of this chart that we're looking at uh it was about 1.7 versus 3 for q1 spending year over year so that is starting to accelerate through the back half you know i think it's prudent to be be cautious relative because normally you'd say okay tech is going to grow a couple of points higher than gdp but it's it's really so hard to predict this year okay the next chart is here that we want to show you is we ask respondents to indicate what strategies they're employing in the short term as a result of coronavirus and you can see a few things that i'll call out and then i'll ask eric to chime in first there's been no meaningful change of course no surprise in tactics like remote work and halting travel however we're seeing very positive trends in other areas trending downward like hiring freezes and freezing i.t deployments downward trend in layoffs and we also see an increase in the acceleration of new i.t deployments and in hiring eric what are your key takeaways well first of all i think it's important to point out here that uh we're also capturing that people believe remote work productivity is still increasing now the trajectory might be coming down a little bit but that is really key i think to the backdrop of what's happening here so people have a perception that productivity of remote work is better than hybrid work and that's from the i.t decision makers themselves um but what we're seeing here is that uh most importantly these organizations are citing plans to increase hiring and that's something that i think is really important to point out it's showing a real thawing and to your point in right in the beginning of the intro uh we are seeing deployments stabilize versus prior survey levels which means early on they had no plans to launch new tech deployments then they said nope we're going to start and now that's stalling and i think it's exactly right what you said is there's an i.t skills shortage so people want to continue to do i.t deployments because they have to support work from home and a hybrid back return to the office but they just don't have the skills to do so and i think that's really probably the most important takeaway from this chart um is that stalling and to really ask why it's stalling yeah so we're going to get into that for sure and and i think that's a really key point is that that that accelerating it deployments is some it looks like it's hit a wall in the survey and so but before before we get deep into the skills let's let's take a look at this next chart and we're asking people here how a return to the new normal if you will and back to offices is going to change spending with on-prem architectures and applications and so the first two bars they're cloud-friendly if you add them up at 63 percent of the respondents say that either they'll stay in the cloud for the most part or they're going to lower the on-prem spend when they go back to the office the next three bars are on-prem friendly if you add those up as 29 percent of the respondents say their on-prem spend is going to bounce back to pre-covert levels or actually increase and of course 12 percent of that number by the way say they they've never altered their on-prem spend so eric no surprise but this bodes well for cloud but but it it isn't it also a positive for on-prem this we've had this dual funding premise meaning cloud continues to grow but neglected data center spend also gets a boost what's your thoughts you know really it's interesting it's people are spending on all fronts you and i were talking in a prep it's like you know we're we're in battle and i've got naval i've got you know air i've got land uh i've got to spend on cloud and digital transformation but i also have to spend for on-prem uh the hybrid work is here and it needs to be supported so this spending is going to increase you know when you look at this chart you're going to see though that roughly 36 percent of all respondents say that their spending is going to remain mostly on cloud so this you know that is still the clear direction uh digital transformation is still happening covid accelerated it greatly um you know you and i as journalists and researchers already know this is where the puck is going uh but spend has always lagged a little bit behind because it just takes some time to get there you know inversely 27 said that their on-prem spending will decrease so when you look at those two i still think that the trend is the friend for cloud spending uh even though yes they do have to continue spending on hybrid some of it's been neglected there are refresh cycles coming up so overall it just points to more and more spending right now it really does seem to be a very strong backdrop for it growth so i want to talk a little bit about the etr taxonomy before we bring up the next chart we get a lot of questions about this and of course when you do a massive survey like you're doing you have to have consistency for time series so you have to really think through what that what the buckets look like if you will so this next chart takes a look at the etr taxonomy and it breaks it down into simple to understand terms so the green is the portion of spending on a vendor's tech within a category that is accelerating and the red is the portion that is decelerating so eric what are the key messages in this data well first of all dave thank you so much for pointing that out we used to do uh just what we call a next a net score it's a proprietary formula that we use to determine the overall velocity of spending some people found it confusing um our data scientists decided to break this sector breakdown into what you said which is really more of a mode analysis in that sector how many of the vendors are increasing versus decreasing so again i just appreciate you bringing that up and allowing us to explain the the the reasoning behind our analysis there but what we're seeing here uh goes back to something you and i did last year when we did our predictions and that was that it services and consulting was going to have a true rebound in 2021 and that's what this is showing right here so in this chart you're going to see that consulting and services are really continuing their recovery uh 2020 had a lot of declines and they have the biggest sector over year-over-year acceleration sector-wise the other thing to point out in this which we'll get to again later is that the inverse analysis is true for video conferencing uh we will get to that so i'm going to leave a little bit of ammunition behind for that one but what we're seeing here is it consulting services being the real favorable and video conferencing uh having a little bit more trouble great okay and then let's let's take a look at that services piece and this next chart really is a drill down into that space and emphasizes eric what you were just talking about and we saw this in ibm's earnings where still more than 60 percent of ibm's business comes from services and the company beat earnings you know in part due to services outperforming expectations i think it had a somewhat easier compare and some of this pen-up demand that we've been talking about bodes well for ibm and in other services companies it's not just ibm right eric no it's not but again i'm going to point out that you and i did point out ibm in our in our predictions one we did in late december so it is nice to see one of the reasons we don't have a more favorable rating on ibm at the moment is because they are in the the process of spinning out uh this large unit and so there's a little bit of you know corporate action there that keeps us off on the sideline but i would also want to point out here uh tata infosys and cognizant because they're seeing year-over-year acceleration in both it consulting and outsourced i t services so we break those down separately and those are the three names that are seeing acceleration in both of those so again a tata emphasis and cognizant are all looking pretty well positioned as well so we've been talking a little bit about this skill shortage and this is what's i think so hard for for forecasters um is that you know on the one hand there's a lot of pent up demand you know it's like scott gottlieb said it's like woodstock coming out of the covid uh but on the other hand if you have a talent gap you've got to rely on external services so there's a learning curve there's a ramp up it's an external company and so it takes time to put those together so so this data that we're going to show you next uh is is really important in my view and ties what we're saying we're saying at the top it asks respondents to comment on their staffing plans the light blue is we're increasing staff the gray is no change in the magenta or whatever whatever color that is that sort of purplish color anyway that color is is decreasing and the picture is very positive across the board full-time staff offshoring contract employees outsourced professional services all up trending upwards and this eric is more evidence of the services bounce back yeah it certainly is david and what happened is when we caught this trend we decided to go one level deeper and say all right we're seeing this but we need to know why and that's what we always try to do here data will tell you what's happening it doesn't always tell you why and that's one of the things that etr really tries to dig in with through the insights interviews panels and also going direct with these more custom survey questions uh so in this instance i think the real takeaway is that 30 of the respondents said that their outsourced and managed services are going to increase over the next three months that's really powerful that's a large portion of organizations in a very short time period so we're capturing that this acceleration is happening right now and it will be happening in real time and i don't see it slowing down you and i are speaking about we have to you know increase cloud spend we have to increase hybrid spend there are refresh cycles coming up and there's just a real skill shortage so this is a long-term setup that bodes very well for it services and consulting you know eric when i came out of college i somebody told me read read read read as much as you can and and so i would and they said read the wall street journal every day and i so i did it and i would read the tech magazines and back then it was all paper and what happens is you begin to connect the dots and so the reason i bring that up is because i've now been had taken a bath in the etr data for the better part of two years and i'm beginning to be able to connect the dots you know the data is not always predictive but many many times it is and so this next data gets into the fun stuff where we name names a lot of times people don't like it because the marketing people and organizations say well the data's wrong of course that's the first thing they do is attack the data but you and i know we've made some really great calls work from home for sure you're talking about the services bounce back uh we certainly saw the rise of crowdstrike octa zscaler well before people were talking about that same thing with video conferencing and so so anyway this is the fun stuff and it looks at positive versus negative sentiment on on companies so first how does etr derive this data and how should we interpret it and what are some of your takeaways [Music] sure first of all how we derive the data or systematic um survey responses that we do on a quarterly basis and we standardize those responses to allow for time series analysis so we can do trend analysis as well we do find that our data because it's talking about forward-looking spending intentions is really more predictive because we're talking about things that might be happening six months three months in the future not things that a lot of other competitors and research peers are looking at things that already happened uh they're looking in the past etr really likes to look into the future and our surveys are set up to do so so thank you for that question it's an enjoyable lead-in but to get to the fun stuff like you said uh what we do here is we put ratings on the data sets i do want to put the caveat out there that our spending intentions really only captures top-line revenue it is not indicative of profit margin or any other line items so this is only going to be viewed as what we are rating the data set itself not the company um you know that's not what we're in the game of doing so i think that's very important for the marketing and the vendors out there themselves when they when they take a look at this we're just talking about what we can control which is our data we're going to talk about a few of the names here on this highlighted vendors list one we're going to go back to that you and i spoke about i guess about six months ago or maybe even earlier which was the observability space um you and i were noticing that it was getting very crowded a lot of new entrants um there was a lot of acquisition from more of the legacy or standard entrance players in the space and that is continuing so i think in a minute we're going to move into that observability space but what we're seeing there is that it's becoming incredibly crowded and we're possibly seeing signs of them cannibalizing each other uh we're also going to move on a little bit into video conferencing where we're capturing some spend deceleration and then ultimately we're going to get into a little bit of a storage refresh cycle and talk about that but yeah these are the highlighted vendors for april um we usually do this once a quarter and they do change based on the data but they're not usually whipsawed around the data doesn't move that quickly yeah so you can see the some of the big names on the left-hand side some of the sas companies that have momentum obviously servicenow has been doing very very well we've talked a lot about snowflake octa crowdstrike z scalar in all very positive as well as you know several others i i guess i'd add some some things i mean i think if thinking about the next decade it's it's cloud which is not going to be like the same cloud as last decade a lot of machine learning and deep learning and ai and the cloud is extending to the edge in the data center data obviously very important data is decentralized and distributed so data architectures are changing a lot of opportunities to connect across clouds and actually create abstraction layers and then something that we've been covering a lot is processor performance is actually accelerating relative to moore's law it's probably instead of doubling every two years it's quadrupling every two years and so that is a huge factor especially as it relates to powering ai and ai inferencing at the edge this is a whole new territory custom silicon is is really becoming in vogue uh and so we're something that we're watching very very closely yeah i completely completely agree on that and i do think that the the next version of cloud will be very different another thing to point out on that too is you can't do anything that you're talking about without collecting the data and and organizations are extremely serious about that now it seems it doesn't matter what industry they're in every company is a data company and that also bodes well for the storage call we do believe that there is going to just be a huge increase in the need for storage um and yes hopefully that'll become portable across multi-cloud and hybrid as well now as eric said the the etr data's it's it's really focused on that top line spend so if you look at the uh on on the right side of that chart you saw you know netapp was kind of negative was very negative right but there's a company that's in in transformation now they've lowered expectations and they've recently beat expectations that's why the stock has been doing better but but at the macro from a spending standpoint it's still challenged so you have big footprint companies like netapp and oracle is another one oracle's stock is at an all-time high but the spending relative to sort of previous cycles or relative to you know like for instance snowflake much much smaller not as high growth but they're managing expectations they're managing their transition they're managing profitability zoom is another one zoom looking looking negative but you know zoom's got to use its market cap now to to transform and increase its tam uh and then splunk is another one we're going to talk about splunk is in transition it acquired signal fx it just brought on this week teresa carlson who was the head of aws public sector she's the president and head of sales so they've got a go to market challenge and they brought in teresa carlson to really solve that but but splunk has been trending downward we called that you know several quarters ago eric and so i want to bring up the data on splunk and this is splunk eric in analytics and it's not trending in the right direction the green is accelerating span the red is and the bars is decelerating spend the top blue line is spending velocity or net score and the yellow line is market share or pervasiveness in the data set your thoughts yeah first i want to go back is a great point dave about our data versus a disconnect from an equity analysis perspective i used to be an equity analyst that is not what we do here and you you may the main word you said is expectations right stocks will trade on how they do compared to the expectations that are set uh whether that's buy side expectations sell side expectations or management's guidance themselves we have no business in tracking any of that what we are talking about is top line acceleration or deceleration so uh that was a great point to make and i do think it's an important one for all of our listeners out there now uh to move to splunk yes i've been capturing a lot of negative commentary on splunk even before the data turned so this has been about a year-long uh you know our analysis and review on this name and i'm dating myself here but i know you and i are both rock and roll fans so i'm gonna point out a led zeppelin song and movie and say that the song remains the same for splunk we are just seeing uh you know recent spending intentions are taking yet another step down both from prior survey levels from year ago levels uh this we're looking at in the analytics sector and spending intentions are decelerating across every single customer group if we went to one of our other slide analysis um on the etr plus platform and you do by customer sub sample in analytics it's dropping in every single vertical it doesn't matter which one uh it's really not looking good unfortunately and you had mentioned this as an analytics and i do believe the next slide is an information security yeah let's bring that up and it's unfortunately it's not doing much better so this is specifically fortune 500 accounts and information security uh you know there's deep pockets in the fortune 500 but from what we're hearing in all the insights and interviews and panels that i personally moderate for etr people are upset they didn't like the the strong tactics that splunk has used on them in the past they didn't like the ingestion model pricing the inflexibility and when alternatives came along people are willing to look at the alternatives and that's what we're seeing in both analytics and big data and also for their sim in security yeah so i think again i i point to teresa carlson she's got a big job but she's very capable she's gonna she's gonna meet with a lot of customers she's a go to market pro she's gonna have to listen hard and i think you're gonna you're gonna see some changes there um okay so there's more sorry there's more bad news on splunk so bring this up is is is net score for splunk in elastic accounts uh this is for analytics so there's 106 elastic accounts that uh in the data set that also have splunk and it's trending downward for splunk that's why it's green for elastic and eric the important call out from etr here is how splunk's performance in elastic accounts compares with its performance overall the elk stack which obviously elastic is a big part of that is causing pain for splunk as is data dog and you mentioned the pricing issue uh is it is it just well is it pricing in your assessment or is it more fundamental you know it's multi-level based on the commentary we get from our itdms that take the survey so yes you did a great job with this analysis what we're looking at is uh the spending within shared accounts so if i have splunk already how am i spending i'm sorry if i have elastic already how is my spending on splunk and what you're seeing here is it's down to about a 12 net score whereas splunk overall has a 32 net score among all of its customers so what you're seeing there is there is definitely a drain that's happening where elastic is draining spend from splunk and usage from them uh the reason we used elastic here is because all observabilities the whole sector seems to be decelerating splunk is decelerating the most but elastic is the only one that's actually showing resiliency so that's why we decided to choose these two but you pointed out yes it's also datadog datadog is cloud native uh they're more devops oriented they tend to be viewed as having technological lead as compared to splunk so that's a really good point a dynatrace also is expanding their abilities and splunk has been making a lot of acquisitions to push their cloud services they are also changing their pricing model right they're they're trying to make things a little bit more flexible moving off ingestion um and moving towards uh you know consumption so they are trying and the new hires you know i'm not gonna bet against them because the one thing that splunk has going for them is their market share in our survey they're still very well entrenched so they do have a lot of accounts they have their foothold so if they can find a way to make these changes then they you know will be able to change themselves but the one thing i got to say across the whole sector is competition is increasing and it does appear based on commentary and data that they're starting to cannibalize themselves it really seems pretty hard to get away from that and you know there are startups in the observability space too that are going to be you know even more disruptive i think i think i want to key on the pricing for a moment and i've been pretty vocal about this i think the the old sas pricing model where essentially you essentially lock in for a year or two years or three years pay up front or maybe pay quarterly if you're lucky that's a one-way street and i think it's it's a flawed model i like what snowflake's doing i like what datadog's doing look at what stripe is doing look what twilio is doing these are cons you mentioned it because it's consumption based pricing and if you've got a great product put it out there and you know damn the torpedoes and i think that is a game changer i i look at for instance hpe with green lake i look at dell with apex they're trying to mimic that model you know they're there and apply it to to infrastructure it's much harder with infrastructure because you got to deploy physical infrastructure but but that is a model that i think is going to change and i think all of the traditional sas pricing is going to is going to come under disruption over the next you know better part of the decades but anyway uh let's move on we've we've been covering the the apm space uh pretty extensively application performance management and this chart lines up some of the big players here comparing net score or spending momentum from the april 20th survey the gray is is um is sorry the the the gray is the april 20th survey the blue is jan 21 and the yellow is april 21. and not only are elastic and data dog doing well relative to splunk eric but everything is down from last year so this space as you point out is undergoing a transformation yeah the pressures are real and it's you know it's sort of that perfect storm where it's not only the data that's telling us that but also the direct feedback we get from the community uh pretty much all the interviews i do i've done a few panels specifically on this topic for anyone who wants to you know dive a little bit deeper we've had some experts talk about this space and there really is no denying that there is a deceleration in spend and it's happening because that spend is getting spread out among different vendors people are using you know a data dog for certain aspects they're using elastic where they can because it's cheaper they're using splunk because they have to but because it's so expensive they're cutting some of the things that they're putting into splunk which is dangerous particularly on the security side if i have to decide what to put in and whatnot that's not really the right way to have security hygiene um so you know this space is just getting crowded there's disruptive vendors coming from the emerging space as well and what you're seeing here is the only bit of positivity is elastic on a survey over survey basis with a slight slight uptick everywhere else year over year and survey over survey it's showing declines it's just hard to ignore and then you've got dynatrace who based on the the interviews you do in the venn you're you know one on one or one on five you know the private interviews that i've been invited to dynatrace gets very high scores uh for their road map you've got new relic which has been struggling you know financially but they've got a purpose built they've got a really good product and a purpose-built database just for this apm space and then of course you've got cisco with appd which is a strong business for them and then as you mentioned you've got startups coming in you've got chaos search which ed walsh is now running you know leave the data in place in aws and really interesting model honeycomb it's going to be really disruptive jeremy burton's company observed so this space is it's becoming jump ball yeah there's a great line that came out of one of them and that was that the lines are blurring it used to be that you knew exactly that app dynamics what they were doing it was apm only or it was logging and monitoring only and a lot of what i'm hearing from the itdm experts is that the lines are blurring amongst all of these names they all have functionality that kind of crosses over each other and the other interesting thing is it used to be application versus infrastructure monitoring but as you know infrastructure is becoming code more and more and more and as infrastructure becomes code there's really no difference between application and infrastructure monitoring so we're seeing a convergence and a blurring of the lines in this space which really doesn't bode well and a great point about new relic their tech gets good remarks uh i just don't know if their enterprise level service and sales is up to snuff right now um as one of my experts said a cto of a very large public online hospitality company essentially said that he would be shocked that within 18 months if all of these players are still uh standalone that there needs to be some m a or convergence in this space okay now we're going to call out some of the data that that really has jumped out to etr in the latest survey and some of the names that are getting the most queries from etr clients which are many of which are investor clients so let's start by having a look at one of the most important and prominent work from home names zoom uh let's let's look at this eric is the ride over for zoom oh i've been saying it for a little bit of a time now actually i do believe it is um i will get into it but again pointing out great dave uh the reason we're presenting today splunk elastic and zoom are they are the most viewed on the etr plus platform uh trailing behind that only slightly is f5 i decided not to bring f5 to the table today because we don't have a rating on the data set um so then i went one deep one below that and it's pure so the reason we're presenting these to you today is that these are the ones that our clients and our community are most interested in which is hopefully going to gain interest to your viewers as well so to get to zoom um yeah i call zoom the pandec pandemic bull market baby uh this was really just one that had a meteoric ride you look back january in 2020 the stock was at 60 and 10 months later it was like like 580. that's in 10 months um that's cooled down a little bit uh into the mid 300s and i believe that cooling down should continue and the reason why is because we are seeing a huge deceleration in our spending intentions uh they're hitting all-time lows it's really just a very ugly data set um more importantly than the spending intentions for the first time we're seeing customer growth in our survey flattened in the past we could we knew that the the deceleration and spend was happening but meanwhile their new customer growth was accelerating so it was kind of hard to really make any call based on that this is the first time we're seeing flattening customer growth trajectory and that uh in tandem with just dominance from microsoft in every sector they're involved in i don't care if it's ip telephony productivity apps or the core video conferencing microsoft is just dominating so there's really just no way to ignore this anymore the data and the commentary state that zoom is facing some headwinds well plus you've pointed out to me that a lot of your private conversations with buyers says that hey we're we're using the freebie version of zoom you know we're not paying them and so in that combined with teams i mean it's it's uh it's i think you know look zoom has to figure it out they they've got to they've got to figure out how to use their elevated market cap to transform and expand their tan um but let's let's move on here's the data on pure storage and we've highlighted a number of times this company is showing elevated spending intentions um pure announces earnings in in may ibm uh just announced storage what uh it was way down actually so sort of still pure more positive but i'll comment on a moment but what does this data tell you eric yeah you know right now we started seeing this data last survey in january and that was the first time we really went positive on the data set itself and it's just really uh continuing so we're seeing the strongest year-over-year acceleration in the entire survey um which is a really good spot to be pure is also a leading position in among its sector peers and the other thing that was pretty interesting from the data set is among all storage players pure has the highest positive public cloud correlation so what we can do is we can see which respondents are accelerating their public cloud spend and then cross-reference that with their storage spend and pure is best positioned so as you and i both know uh you know digital transformation cloud spending is increasing you need to be aligned with that and among all storage uh sector peers uh pure is best positioned in all of those in spending intentions and uh adoptions and also public cloud correlation so yet again just another really strong data set and i have an anecdote about why this might be happening because when i saw the date i started asking in my interviews what's going on here and there was one particular person he was a director of cloud operations for a very large public tech company now they have hybrid um but their data center is in colo so they don't own and build their own physical building he pointed out that doran kovid his company wanted to increase storage but he couldn't get into his colo center due to covert restrictions they weren't allowed you had so 250 000 square feet right but you're only allowed to have six people in there so it's pretty hard to get to your rack and get work done he said he would buy storage but then the cola would say hey you got to get it out of here it's not even allowed to sit here we don't want it in our facility so he has all this pent up demand in tandem with pent up demand we have a refresh cycle the ssd you know depreciation uh you know cycle is ending uh you know ssds are moving on and we're starting to see uh new technology in that space nvme sorry for technology increasing in that space so we have pent up demand and we have new technology and that's really leading to a refresh cycle and this particular itdm that i spoke to and many of his peers think this has a long tailwind that uh storage could be a good sector for some time to come that's really interesting thank you for that that extra metadata and i want to do a little deeper dive on on storage so here's a look at storage in the the industry in context and some of the competitive i mean it's been a tough market for the reasons that we've highlighted cloud has been eating away that flash headroom it used to be you'd buy storage to get you know more spindles and more performance and you were sort of forced to buy more flash gave more headroom but it's interesting what you're saying about the depreciation cycle so that's good news so etr combines just for people's benefit here combines primary and secondary storage into a single category so you have companies like pure and netapp which are really pure play you know primary storage companies largely in the sector along with veeam cohesity and rubric which are kind of secondary data or data protection so my my quick thoughts here are that pure is elevated and remains what i call the one-eyed man in the land of the blind but that's positive tailwinds there so that's good news rubric is very elevated but down it's a big it's big competitor cohesity is way off its highs and i have to say to me veeam is like the steady eddy consistent player here they just really continue to do well in the data protection business and and the highs are steady the lows are steady dell is also notable they've been struggling in storage their isg business which comprises service and storage it's been soft during covid and and during even you know this new product rollout so it's notable with this new mid-range they have in particular the uptick in dell this survey because dell so large a small uptick can be very good for dell hpe has a big announcement next month in storage so that might improve based on a product cycle of course the nimble brand continues to do well ibm as i said just announced a very soft quarter you know down double digits again uh and there in a product cycle shift and netapp is that looks bad in the etr data from a spending momentum standpoint but their management team is transforming the company into a cloud play which eric is why it was interesting that pure has the greatest momentum in in cloud accounts so that is sort of striking to me i would have thought it would be netapp so that's something that we want to pay attention to but i do like a lot of what netapp is doing uh and other than pure they're the only big kind of pure play in primary storage so long winded uh uh intro there eric but anything you'd add no actually i appreciate it was long winded i i'm going to be honest with you storage is not my uh my best sector as far as a researcher and analyst goes uh but i actually think a lot of what you said is spot on um you know we do capture a lot of large organizations spend uh we don't capture much mid and small so i think when you're talking about these large large players like netapp and um you know not looking so good all i would state is that we are capturing really big organizations spending attention so these are names that should be doing better to be quite honest uh in those accounts and you know at least according to our data we're not seeing it and it's long-term depression as you can see uh you know netapp now has a negative spending velocity in this analysis so you know i can go dig around a little bit more but right now the names that i'm hearing are pure cohesity uh um i'm hearing a little bit about hitachi trying to reinvent themselves in the space but you know i'll take a wait-and-see approach on that one but uh pure and cohesity are the ones i'm hearing a lot from our community so storage is transforming to cloud as a service you're seeing things like apex and in green lake from dell and hpe and container storage little so not really a lot of people paying attention to it but pure about a company called portworx which really specializes in container storage and there's many startups there they're trying to really change the way david flynn has a startup in that space he's the guy who started fusion i o so a lot a lot of transformations happening here okay i know it's been a long segment we have to summarize and then let me go through a summary and then i'll give you the last word eric so tech spending appears to be tracking us gdp at six to seven percent this talent shortage could be a blocker to accelerating i.t deployments and that's kind of good news actually for for services companies digital transformation you know it's it remains a priority and that bodes well not only for services but automation uipath went public this week we we profiled that you know extensively that went public last wednesday um organizations they've i said at the top face some tough decisions on how to allocate resources you know running the business growing the business transforming the business and we're seeing a bifurcation of spending and some residual effects on vendors and that remains a theme that we're watching eric your final thoughts yeah i'm going to go back quickly to just the overall macro spending because there's one thing i think is interesting to point out and we're seeing a real acceleration among mid and small so it seems like early on in the covid recovery or kovitz spending it was the deep pockets that moved first right fortune 500 knew they had to support remote work they started spending first round that in the fortune 500 we're only seeing about five percent spent but when you get into mid and small organizations that's creeping up to eight nine so i just think it's important to point out that they're playing catch-up right now uh also would point out that this is heavily skewed to north america spending we're seeing laggards in emea they just don't seem to be spending as much they're in a very different place in their recovery and uh you know i do think that it's important to point that out um lastly i also want to mention i know you do such a great job on following a lot of the disruptive vendors that you just pointed out pure doing container storage we also have another bi-annual survey that we do called emerging technology and that's for the private names that's going to be launching in may for everyone out there who's interested in not only the disruptive vendors but also private equity players uh keep an eye out for that we do that twice a year and that's growing in its respondents as well and then lastly one comment because you mentioned the uipath ipo it was really hard for us to sit on the sidelines and not put some sort of rating on their data set but ultimately um the data was muted unfortunately and when you're seeing this kind of hype into an ipo like we saw with snowflake the data was resoundingly strong we had no choice but to listen to what the data said for snowflake despite the hype um we didn't see that for uipath and we wanted to and i'm not making a large call there but i do think it's interesting to juxtapose the two that when snowflake was heading to its ipo the data was resoundingly positive and for uipath we just didn't see that thank you for that and eric thanks for coming on today it's really a pleasure to have you and uh so really appreciate the the uh collaboration and look forward to doing more of these we enjoy the partnership greatly dave we're very very happy to have you in the etr family and looking forward to doing a lot lot more with you in the future ditto okay that's it for today remember these episodes are all available as podcasts wherever you listen all you got to do is search breaking analysis podcast and please subscribe to the series check out etr's website it's etr dot plus we also publish a full report every week on wikibon.com at siliconangle.com you can email me david.velante at siliconangle.com you can dm me on twitter at dvalante or comment on our linkedin post i could see you in clubhouse this is dave vellante for eric porter bradley for the cube insights powered by etr have a great week stay safe be well and we'll see you next time