(upbeat music) >> From theCube Studios in Palo Alto in Boston, bringing you data driven insights from theCube and ETR. This is Breaking Analysis with Dave Vellante. >> While by no means a safe haven, the cybersecurity sector has outpaced the broader tech market by a meaningful margin, that is up until very recently. Cybersecurity remains the number one technology priority for the C-suite, but as we've previously reported the CISO's budget has constraints just like other technology investments. Recent trends show that economic headwinds have elongated sales cycles, pushed deals into future quarters, and just like other tech initiatives, are pacing cybersecurity investments and breaking them into smaller chunks. Hello and welcome to this week's Wikibon Cube Insights powered by ETR. In this Breaking Analysis we explain how cybersecurity trends are reverting to the mean and tracking more closely with other technology investments. We'll make a couple of valuation comparisons to show the magnitude of the challenge and which cyber firms are feeling the heat, which aren't. There are some exceptions. We'll then show the latest survey data from ETR to quantify the contraction in spending momentum and close with a glimpse of the landscape of emerging cybersecurity companies, the private companies that could be ripe for acquisition, consolidation, or disruptive to the broader market. First, let's take a look at the recent patterns for cyber stocks relative to the broader tech market as a benchmark, as an indicator. Here's a year to date comparison of the bug ETF, which comprises a basket of cyber security names, and we compare that with the tech heavy NASDAQ composite. Notice that on April 13th of this year the cyber ETF was actually in positive territory while the NAS was down nearly 14%. Now by August 16th, the green turned red for cyber stocks but they still meaningfully outpaced the broader tech market by more than 950 basis points as of December 2nd that Delta had contracted. As you can see, the cyber ETF is now down nearly 25%, year to date, while the NASDAQ is down 27% and change. Now take a look at just how far a few of the high profile cybersecurity names have fallen. Here are six security firms that we've been tracking closely since before the pandemic. We've been, you know, tracking dozens but let's just take a look at this data and the subset. We show for comparison the S&P 500 and the NASDAQ, again, just for reference, they're both up since right before the pandemic. They're up relative to right before the pandemic, and then during the pandemic the S&P shot up more than 40%, relative to its pre pandemic level, around February is what we're using for the pre pandemic level, and the NASDAQ peaked at around 65% higher than that February level. They're now down 85% and 71% of their previous. So they're at 85% and 71% respectively from their pandemic highs. You compare that to these six companies, Splunk, which was and still is working through a transition is well below its pre pandemic market value and 44, it's 44% of its pre pandemic high as of last Friday. Palo Alto Networks is the most interesting here, in that it had been facing challenges prior to the pandemic related to a pivot to the Cloud which we reported on at the time. But as we said at that time we believe the company would sort out its Cloud transition, and its go to market challenges, and sales compensation issues, which it did as you can see. And its valuation jumped from 24 billion prior to Covid to 56 billion, and it's holding 93% of its peak value. Its revenue run rate is now over 6 billion with a healthy growth rate of 24% expected for the next quarter. Similarly, Fortinet has done relatively well holding 71% of its peak Covid value, with a healthy 34% revenue guide for the coming quarter. Now, Okta has been the biggest disappointment, a darling of the pandemic Okta's communication snafu, with what was actually a pretty benign hack combined with difficulty absorbing its 7 billion off zero acquisition, knocked the company off track. Its valuation has dropped by 35 billion since its peak during the pandemic, and that's after a nice beat and bounce back quarter just announced by Okta. Now, in our view Okta remains a viable long-term leader in identity. However, its recent fiscal 24 revenue guide was exceedingly conservative at around 16% growth. So either the company is sandbagging, or has such poor visibility that it wants to be like super cautious or maybe it's actually seeing a dramatic slowdown in its business momentum. After all, this is a company that not long ago was putting up 50% plus revenue growth rates. So it's one that bears close watching. CrowdStrike is another big name that we've been talking about on Breaking Analysis for quite some time. It like Okta has led the industry in a key ETR performance indicator that measures customer spending momentum. Just last week, CrowdStrike announced revenue increased more than 50% but new ARR was soft and the company guided conservatively. Not surprisingly, the stock got absolutely crushed as CrowdStrike blamed tepid demand from smaller and midsize firms. Many analysts believe that competition from Microsoft was one factor along with cautious spending amongst those midsize and smaller customers. Notably, large customers remain active. So we'll see if this is a longer term trend or an anomaly. Zscaler is another company in the space that we've reported having great customer spending momentum from the ETR data. But even though the company beat expectations for its recent quarter, like other companies its Outlook was conservative. So other than Palo Alto, and to a lesser extent Fortinet, these companies and others that we're not showing here are feeling the economic pinch and it shows in the compression of value. CrowdStrike, for example, had a 70 billion valuation at one point during the pandemic Zscaler top 50 billion, Okta 45 billion. Now, having said that Palo Alto Networks, Fortinet, CrowdStrike, and Zscaler are all still trading well above their pre pandemic levels that we tracked back in February of 2020. All right, let's go now back to ETR'S January survey and take a look at how much things have changed since the beginning of the year. Remember, this is obviously pre Ukraine, and pre all the concerns about the economic headwinds but here's an X Y graph that shows a net score, or spending momentum on the y-axis, and market presence on the x-axis. The red dotted line at 40% on the vertical indicates a highly elevated net score. Anything above that we think is, you know, super elevated. Now, we filtered the data here to show only those companies with more than 50 responses in the ETR survey. Still really crowded. Note that there were around 20 companies above that red 40% mark, which is a very, you know, high number. It's a, it's a crowded market, but lots of companies with, you know, positive momentum. Now let's jump ahead to the most recent October survey and take a look at what, what's happening. Same graphic plotting, spending momentum, and market presence, and look at the number of companies above that red line and how it's been squashed. It's really compressing, it's still a crowded market, it's still, you know, plenty of green, but the number of companies above 40% that, that key mark has gone from around 20 firms down to about five or six. And it speaks to that compression and IT spending, and of course the elongated sales cycles pushing deals out, taking them in smaller chunks. I can't tell you how many conversations with customers I had, at last week at Reinvent underscoring this exact same trend. The buyers are getting pressure from their CFOs to slow things down, do more with less and, and, and prioritize projects to those that absolutely are critical to driving revenue or cutting costs. And that's rippling through all sectors, including cyber. Now, let's do a bit more playing around with the ETR data and take a look at those companies with more than a hundred citations in the survey this quarter. So N, greater than or equal to a hundred. Now remember the followers of Breaking Analysis know that each quarter we take a look at those, what we call four star security firms. That is, those are the, that are in, that hit the top 10 for both spending momentum, net score, and the N, the mentions in the survey, the presence, the pervasiveness in the survey, and that's what we show here. The left most chart is sorted by spending momentum or net score, and the right hand chart by shared N, or the number of mentions in the survey, that pervasiveness metric. that solid red line denotes the cutoff point at the top 10. And you'll note we've actually cut it off at 11 to account for Auth 0, which is now part of Okta, and is going through a go to market transition, you know, with the company, they're kind of restructuring sales so they can take advantage of that. So starting on the left with spending momentum, again, net score, Microsoft leads all vendors, typical Microsoft, very prominent, although it hadn't always done so, it, for a while, CrowdStrike and Okta were, were taking the top spot, now it's Microsoft. CrowdStrike, still always near the top, but note that CyberArk and Cloudflare have cracked the top five in Okta, which as I just said was consistently at the top, has dropped well off its previous highs. You'll notice that Palo Alto Network Palo Alto Networks with a 38% net score, just below that magic 40% number, is healthy, especially as you look over to the right hand chart. Take a look at Palo Alto with an N of 395. It is the largest of the independent pure play security firms, and has a very healthy net score, although one caution is that net score has dropped considerably since the beginning of the year, which is the case for most of the top 10 names. The only exception is Fortinet, they're the only ones that saw an increase since January in spending momentum as ETR measures it. Now this brings us to the four star security firms, that is those that hit the top 10 in both net score on the left hand side and market presence on the right hand side. So it's Microsoft, Palo Alto, CrowdStrike, Okta, still there even not accounting for a Auth 0, just Okta on its own. If you put in Auth 0, it's, it's even stronger. Adding then in Fortinet and Zscaler. So Microsoft, Palo Alto, CrowdStrike, Okta, Fortinet, and Zscaler. And as we've mentioned since January, only Fortinet has shown an increase in net score since, since that time, again, since the January survey. Now again, this talks to the compression in spending. Now one of the big themes we hear constantly in cybersecurity is the market is overcrowded. Everybody talks about that, me included. The implication there, is there's a lot of room for consolidation and that consolidation can come in the form of M&A, or it can come in the form of people consolidating onto a single platform, and retiring some other vendors, and getting rid of duplicate vendors. We're hearing that as a big theme as well. Now, as we saw in the previous, previous chart, this is a very crowded market and we've seen lots of consolidation in 2022, in the form of M&A. Literally hundreds of M&A deals, with some of the largest companies going private. SailPoint, KnowBe4, Barracuda, Mandiant, Fedora, these are multi billion dollar acquisitions, or at least billion dollars and up, and many of them multi-billion, for these companies, and hundreds more acquisitions in the cyberspace, now less you think the pond is overfished, here's a chart from ETR of emerging tech companies in the cyber security industry. This data comes from ETR's Emerging Technologies Survey, ETS, which is this diamond in a rough that I found a couple quarters ago, and it's ripe with companies that are candidates for M&A. Many would've liked, many of these companies would've liked to, gotten to the public markets during the pandemic, but they, you know, couldn't get there. They weren't ready. So the graph, you know, similar to the previous one, but different, it shows net sentiment on the vertical axis and that's a measurement of, of, of intent to adopt against a mind share on the X axis, which measures, measures the awareness of the vendor in the community. So this is specifically a survey that ETR goes out and, and, and fields only to track those emerging tech companies that are private companies. Now, some of the standouts in Mindshare, are OneTrust, BeyondTrust, Tanium and Endpoint, Net Scope, which we've talked about in previous Breaking Analysis. 1Password, which has been acquisitive on its own. In identity, the managed security service provider, Arctic Wolf Network, a company we've also covered, we've had their CEO on. We've talked about MSSPs as a real trend, particularly in small and medium sized business, we'll come back to that, Sneek, you know, kind of high flyer in both app security and containers, and you can just see the number of companies in the space this huge and it just keeps growing. Now, just to make it a bit easier on the eyes we filtered the data on these companies with with those, and isolated on those with more than a hundred responses only within the survey. And that's what we show here. Some of the names that we just mentioned are a bit easier to see, but these are the ones that really stand out in ERT, ETS, survey of private companies, OneTrust, BeyondTrust, Taniam, Netscope, which is in Cloud, 1Password, Arctic Wolf, Sneek, BitSight, SecurityScorecard, HackerOne, Code42, and Exabeam, and Sim. All of these hit the ETS survey with more than a hundred responses by, by the IT practitioners. Okay, so these firms, you know, maybe they do some M&A on their own. We've seen that with Sneek, as I said, with 1Password has been inquisitive, as have others. Now these companies with the larger footprint, these private companies, will likely be candidate for both buying companies and eventually going public when the markets settle down a bit. So again, no shortage of players to affect consolidation, both buyers and sellers. Okay, so let's finish with some key questions that we're watching. CrowdStrike in particular on its earnings calls cited softness from smaller buyers. Is that because these smaller buyers have stopped adopting? If so, are they more at risk, or are they tactically moving toward the easy button, aka, Microsoft's good enough approach. What does that mean for the market if smaller company cohorts continue to soften? How about MSSPs? Will companies continue to outsource, or pause on on that, as well as try to free up, to try to free up some budget? Adam Celiski at Reinvent last week said, "If you want to save money the Cloud's the best place to do it." Is the cloud the best place to save money in cyber? Well, it would seem that way from the standpoint of controlling budgets with lots of, lots of optionality. You could dial up and dial down services, you know, or does the Cloud add another layer of complexity that has to be understood and managed by Devs, for example? Now, consolidation should favor the likes of Palo Alto and CrowdStrike, cause they're platform players, and some of the larger players as well, like Cisco, how about IBM and of course Microsoft. Will that happen? And how will economic uncertainty impact the risk equation, a particular concern is increase of tax on vulnerable sectors of the population, like the elderly. How will companies and governments protect them from scams? And finally, how many cybersecurity companies can actually remain independent in the slingshot economy? In so many ways the market is still strong, it's just that expectations got ahead of themselves, and now as earnings forecast come, come, come down and come down to earth, it's going to basically come down to who can execute, generate cash, and keep enough runway to get through the knothole. And the one certainty is nobody really knows how tight that knothole really is. All right, let's call it a wrap. Next week we dive deeper into Palo Alto Networks, and take a look at how and why that company has held up so well and what to expect at Ignite, Palo Alto's big user conference coming up later this month in Las Vegas. We'll be there with theCube. Okay, many thanks to Alex Myerson on production and manages the podcast, Ken Schiffman as well, as our newest edition to our Boston studio. Great to have you Ken. Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our EIC over at Silicon Angle. He does some great editing for us. Thank you to all. Remember these episodes are all available as podcasts. Wherever you listen, just search Breaking Analysis podcast. I publish each week on wikibond.com and siliconangle.com, or you can email me directly David.vellante@siliconangle.com or DM me @DVellante, or comment on our LinkedIn posts. Please do checkout etr.ai, they got the best survey data in the enterprise tech business. This is Dave Vellante for theCube Insights powered by ETR. Thanks for watching, and we'll see you next time on Breaking Analysis. (upbeat music)

>>Good afternoon from the Venetian Expo, center, hall, whatever you wanna call it, in Las Vegas. Lisa Martin here. It's day four. I'm not sure what this place is called. Wait, >>What? >>Lisa Martin here with Dave Ante. This is the cube. This is day four of a ton of coverage that we've been delivering to you, which, you know, cause you've been watching since Monday night, Dave, we are almost at the end, we're almost at the show wrap. Excited to bring back, we've been talking about security, a lot about security. Excited to bring back a, an alumni to talk about that. But what's your final thoughts? >>Well, so just in, in, in the context of security, we've had just three in a row talking about cyber, which is like the most important topic. And I, and I love that we're having Palo Alto Networks on Palo Alto Networks is the gold standard in security. Talk to CISOs, they wanna work with them. And, and it was, it's interesting because I've been following them for a little bit now, watch them move to the cloud and a couple of little stumbling points. But I said at the time, they're gonna figure it out and, and come rocking back. And they have, and the company's just performing unbelievably well despite, you know, all the macro headwinds that we love to >>Talk about. So. Right. And we're gonna be unpacking all of that with one of our alumni. As I mentioned, Anker Shaw is with us, the SVP and GM of Palo Alto Networks. Anker, welcome back to the Cub. It's great to see you. It's been a while. >>It's good to be here after a couple years. Yeah, >>Yeah. I think three. >>Yeah, yeah, for sure. Yeah. Yeah. It's a bit of a blur after Covid. >>Everyone's saying that. Yeah. Are you surprised that there are still this many people on the show floor? Cuz I am. >>I am. Yeah. Look, I am not, this is my fourth, last year was probably one third or one fourth of this size. Yeah. But pre covid, this is what dream went looked like. And it's energizing, it's exciting. It's just good to be doing the good old things. So many people and yeah. Amazing technology and innovation. It's been incredible. >>Let's talk about innovation. I know you guys, Palo Alto Networks recently acquired cyber security. Talk to us a little bit about that. How is it gonna compliment Prisma? Give us all the scoop on that. >>Yeah, for sure. Look, some of the recent, the cybersecurity attacks that we have seen are related to supply chain, the colonial pipeline, many, many supply chain. And the reason for that is the modern software supply chain, not the physical supply chain, the one that AWS announced, but this is the software supply chain is really incredibly complicated, complicated developers that are building and shipping code faster than ever before. And the, the site acquisition at the center, the heart of that was securing the entire supply chain. White House came with a new initiative on supply chain security and SBO software bill of material. And we needed a technology, a company, and a set of people who can really deliver to that. And that's why we acquired that for supply chain security, otherwise known as cicd, security, c >>IDC security. Yeah. So how will that complement PRIs McCloud? >>Yeah, so look, if you look at our history lease over the last four years, we have been wanting to, our mission mission has been to build a single code to cloud platform. As you may know, there are over 3000 security vendors in the industry. And we said enough is enough. We need a platform player who can really deliver a unified cohesive platform solution for our customers because they're sick and tired of buying PI point product. So our mission has been to deliver that code to cloud platform supply chain security was a missing piece and we acquired them, it fits right really nicely into our portfolio of products and solution that customers have. And they'll have a single pin of glass with this. >>Yeah. So there's a lot going on. You've got, you've got an adversary that is incredibly capable. Yeah. These days and highly motivated and extremely sophisticated mentioned supply chain. It's caused a shift in, in CSO strategies, talking about the pandemic, of course we know work from home that changed things. You've mentioned public policy. Yeah. And, and so, and as well you have the cloud, cloud, you know, relatively new. I mean, it's not that new, but still. Yeah. But you've got the shared responsibility model and not, not only do you have the shared responsibility model, you have the shared responsibility across clouds and OnPrem. So yes, the cloud helps with security, but that the CISO has to worry about all these other things. The, the app dev team is being asked to shift left, you know, secure and they're not security pros. Yeah. And you know, kind audit is like the last line of defense. So I love this event, I love the cloud, but customers need help in making their lives simpler. Yeah. And the cloud in and of itself, because, you know, shared responsibility doesn't do that. Yeah. That's what Palo Alto and firms like yours come in. >>Absolutely. So look, Jim, this is a unable situation for a lot of the Cisco, simply because there are over 26 million developers, less than 3 million security professional. If you just look at all the announcement the AWS made, I bet you there were like probably over 2000 features. Yeah. I mean, they're shipping faster than ever before. Developers are moving really, really fast and just not enough security people to keep up with the velocity and the innovation. So you are right, while AWS will guarantee securing the infrastructure layer, but everything that is built on top of it, the new machine learning stuff, the new application, the new supply chain applications that are developed, that's the responsibility of the ciso. They stay up at night, they don't know what's going on because developers are bringing new services and new technology. And that's why, you know, we've always taken a platform approach where customers and the systems don't have to worry about it. >>What AWS new service they have, it's covered, it's secured. And that's why the adopters, McCloud and Palo Alto Networks, because regardless what developers bring, security is always there by their side. And so security teams need just a simple one click solution. They don't have to worry about it. They can sleep at night, keep the bad actors away. And, and that's, that's where Palo Alto Networks has been innovating in this area. AWS is one of our biggest partners and you know, we've integrated with, with a lot of their services. We launch about three integrations with their services. And we've been doing this historically for more and >>More. Are you still having conversations with the security folks? Or because security is a board level conversation, are your conversations going up a stack because this is a C-suite problem, this is a board level initiative? >>Absolutely. Look, you know, there was a time about four years ago, like the best we could do is director of security. Now it's just so CEO level conversation, board level conversation to your point, simply because I mean, if, if all your financial stuff is going to public cloud, all your healthcare data, all your supply chain data is going to public cloud, the board is asking very simple question, what are you doing to secure that? And to be honest, the question is simple. The answer's not because all the stuff that we talked about, too many applications, lots and lots of different services, different threat vectors and the bad actors, the bad guys are always a step ahead of the curve. And that's why this has become a board level conversation. They wanna make sure that things are secure from the get go before, you know, the enterprises go too deep into public cloud adoption. >>I mean there, there was shift topics a little bit. There was hope or kinda early this year that that cyber was somewhat insulated from the sort of macro press pressures. Nobody's safe. Even the cloud is sort of, you know, facing those, those headwinds people optimizing costs. But one thing when you talk to customers is, I always like to talk about that, that optiv graph. We've all seen it, right? And it's just this eye test of tools and it's a beautiful taxonomy, but there's just too many tools. So we're seeing a shift from point tools to platforms because obviously a platform play, and that's a way. So what are you seeing in the, in the field with customers trying to optimize their infrastructure costs with regard to consolidating to >>Platforms? Yeah. Look, you rightly pointed out one thing, the cybersecurity industry in general and Palo Alto networks, knock on wood, the stocks doing well. The macro headwinds hasn't impacted the security spend so far, right? Like time will tell, we'll, we'll see how things go. And one of the primary reason is that when you know the economy starts to slow down, the customers again want to invest in platforms. It's simple to deploy, simple to operationalize. They want a security partner of choice that knows that they, it's gonna be by them through the entire journey from code to cloud. And so that's why platform, especially times like these are more important than they've ever been before. You know, customers are investing in the, the, the product I lead at Palo Alto network called Prisma Cloud. It's in the cloud network application protection platform seen app space where once again, customers that investing in platform from quote to cloud and avoiding all the point products for sure. >>Yeah. Yeah. And you've seen it in, in Palo Alto's performance. I mean, not every cyber firm has is, is, >>You know, I know. Ouch. CrowdStrike Yeah. >>Was not. Well you saw that. I mean, and it was, and and you know, the large customers were continuing to spend, it was the small and mid-size businesses Yeah. That were, were were a little bit soft. Yeah. You know, it's a really, it's really, I mean, you see Okta now, you know, after they had some troubles announcing that, you know, their, their, their visibility's a little bit better. So it's, it's very hard to predict right now. And of course if TOMA Brava is buying you, then your stock price has been up and steady. That's, >>Yeah. Look, I think the key is to have a diversified portfolio of products. Four years ago before our CEO cash took over the reins of the company, we were a single product X firewall company. Right. And over time we have added XDR with the first one to introduce that recently launched x Im, you know, to, to make sure we build an NextGen team, cloud security is a completely net new investment, zero trust with access as workers started working remotely and they needed to make sure enterprises needed to make sure that they're accessing the applications securely. So we've added a lot of portfolio products over time. So you have to remain incredibly diversified, stay strong, because there will be stuff like remote work that slowed down. But if you've got other portfolio product like cloud security, while those secular tailwinds continue to grow, I mean, look how fast AWS is growing. 35, 40%, like $80 billion run rate. Crazy at that, that scale. So luckily we've got the portfolio of products to ensure that regardless of what the customer's journey is, macro headwinds are, we've got portfolio of solutions to help our customers. >>Talk a little bit about the AWS partnership. You talked about the run rate and I was reading a few days ago. You're right. It's an 82 billion arr, massive run rate. It's crazy. Well, what are, what is a Palo Alto Networks doing with aws and what's the value in it to help your customers on a secure digital transformation journey? >>Well, absolutely. We have been doing business with aws. We've been one of their security partners of choice for many years now. We have a presence in the marketplace where customers can through one click deploy the, the several Palo Alto Networks security solutions. So that's available. Like I said, we had launch partner to many, many new products and innovation that AWS comes up with. But always the day one partner, Adam was talking about some of those announcements and his keynote security data lake was one of those. And they were like a bunch of others related to compute and others. So we have been a partner for a long time, and look, AWS is an incredibly customer obsessed company. They've got their own security products. But if the customer says like, Hey, like I'd like to pick this from yours, but there's three other things from Palo Alto Networks or S MacCloud or whatever else that may be, they're open to it. And that's the great thing about AWS where it doesn't have to be wall garden open ecosystem, let the customer pick the best. >>And, and that's, I mean, there's, there's examples where AWS is directly competitive. I mean, my favorite example is Redshift and Snowflake. I mean those are directly competitive products, but, but Snowflake is an unbelievably great relationship with aws. They do cyber's, I think different, I mean, yeah, you got guard duty and you got some other stuff there. But generally speaking, the, correct me if I'm wrong, the e the ecosystem has more room to play on AWS than it may on some other clouds. >>A hundred percent. Yeah. Once again, you know, guard duty for examples, we've got a lot of customers who use guard duty and Prisma Cloud and other Palo Alto Networks products. And we also ingest the data from guard duty. So if customers want a single pane of glass, they can use the best of AWS in terms of guard duty threat detection, but leverage other technology suite from, you know, a platform provider like Palo Alto Networks. So you know, that that, you know, look, world is a complicated place. Some like blue, some like red, whatever that may be. But we believe in giving customers that choice, just like AWS customers want that. Not a >>Problem. And at least today they're not like directly, you know, in your space. Yeah. You know, and even if they were, you've got such a much mature stack. Absolutely. And my, my frankly Microsoft's different, right? I mean, you see, I mean even the analysts were saying that some of the CrowdStrike's troubles for, cuz Microsoft's got the good enough, right? So >>Yeah. Endpoint security. Yeah. And >>Yeah, for sure. So >>Do you have a favorite example of a customer where Palo Alto Networks has really helped them come in and, and enable that secure business transformation? Anything come to mind that you think really shines a light on Palo Alto Networks and what it's able to do? >>Yeah, look, we have customers across, and I'm gonna speak to public cloud in general, right? Like Palo Alto has over 60,000 customers. So we've been helping with that business transformation for years now. But because it's reinvented aws, the Prisma cloud product has been helping customers across different industry verticals. Some of the largest credit card processing companies, they can process transactions because we are running security on top of the workloads, the biggest financial services, biggest healthcare customers. They're able to put the patient health records in public cloud because Palo Alto Networks is helping them get there. So we are helping accelerated that digital journey. We've been an enabler. Security is often perceived as a blocker, but we have always treated our role as enabler. How can we get developers and enterprises to move as fast as possible? And like, my favorite thing is that, you know, moving fast and going digital is not a monopoly of just a tech company. Every company is gonna be a tech company Oh absolutely. To public cloud. Yes. And we want to help them get there. Yeah. >>So the other thing too, I mean, I'll just give you some data. I love data. I have a, ETR is our survey partner and I'm looking at Data 395. They do a survey every quarter, 1,250 respondents on this survey. 395 were Palo Alto customers, fortune 500 s and P 500, you know, big global 2000 companies as well. Some small companies. Single digit churn. Yeah. Okay. Yeah. Very, very low replacement >>Rates. Absolutely. >>And still high single digit new adoption. Yeah. Right. So you've got that tailwind going for you. Yeah, >>Right. It's, it's sticky because especially our, our main business firewall, once you deploy the firewall, we are inspecting all the network traffic. It's just so hard to rip and replace. Customers are getting value every second, every minute because we are thwarting attacks from public cloud. And look, we, we, we provide solutions not just product, we just don't leave the product and ask the customers to deploy it. We help them with deployment consumption of the product. And we've been really fortunate with that kind of gross dollar and netten rate for our customers. >>Now, before we wrap, I gotta tease, the cube is gonna be at Palo Alto Ignite. Yeah. In two weeks back here. I think we're at D mgm, right? We >>Were at D MGM December 13th and >>14th. So give us a little, show us a little leg if you would. What could we expect? >>Hey, look, I mean, a lot of exciting new things coming. Obviously I can't talk about it right now. The PR Inc is still not dry yet. But lots of, lots of new innovation across our three main businesses. Network security, public cloud, security, as well as XDR X. Im so stay tuned. You know, you'll, you'll see a lot of new exciting things coming up. >>Looking forward to it. >>We are looking forward to it. Last question on curf. You, if you had a billboard to place in New York Times Square. Yeah. You're gonna take over the the the Times Square Nasdaq. What does the billboard say about why organizations should be working with Palo Alto Networks? Yeah. To really embed security into their dna. Yeah. >>You know when Jim said Palo Alto Networks is the gold standard for security, I thought it was gonna steal it. I think it's pretty good gold standard for security. But I'm gonna go with our mission cyber security partner's choice. We want to be known as that and that's who we are. >>Beautifully said. Walker, thank you so much for joining David in the program. We really appreciate your insights, your time. We look forward to seeing you in a couple weeks back here in Vegas. >>Absolutely. Can't have enough of Vegas. Thank you. Lisa. >>Can't have in Vegas, >>I dunno about that. By this time of the year, I think we can have had enough of Vegas, but we're gonna be able to see you on the cubes coverage, which you could catch up. Palo Alto Networks show Ignite December, I believe 13th and 14th on the cube.net. We want to thank Anker Shaw for joining us. For Dave Ante, this is Lisa Martin. You're watching the Cube, the leader in live enterprise and emerging tech coverage.

>>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 ante. >>When Frank sluman took service, now public many people undervalued the company, positioning it as just a better help desk tool. You know, it turns out that the firm actually had a massive Tam expansion opportunity in it. SM customer service, HR, logistics, security marketing, and service management. Generally now stock price followed over the years, the stellar execution under Slootman and CFO, Mike scar Kelly's leadership. Now, when they took the reins at snowflake expectations were already set that they'd repeat the feet, but this time, if anything, the company was overvalued out of the gate, the thing is people didn't really better understand the market opportunity this time around, other than that, it was a bet on Salman's track record of execution and on data, pretty good bets, but folks really didn't appreciate that snowflake. Wasn't just a better data warehouse that it was building what they call a data cloud, and we've turned a data super cloud. >>Hello and welcome to this. Week's Wikibon cube insights powered by ETR in this breaking analysis, we'll do four things. First. We're gonna review the recent narrative and concerns about snowflake and its value. Second, we're gonna share survey data from ETR that will confirm precisely what the company's CFO has been telling anyone who will listen. And third, we're gonna share our view of what snowflake is building IE, trying to become the defacto standard data platform, and four convey our expectations for the upcoming snowflake summit. Next week at Caesar's palace in Las Vegas, Snowflake's most recent quarterly results they've been well covered and well documented. It basically hit its targets, which for snowflake investors was bad news wall street piled on expressing concerns about Snowflake's consumption, pricing model, slowing growth rates, lack of profitability and valuation. Given the, given the current macro market conditions, the stock dropped below its IPO offering price, which you couldn't touch on day one, by the way, as the stock opened well above that and, and certainly closed well above that price of one 20 and folks express concerns about some pretty massive insider selling throughout 2021 and early 2022, all this caused the stock price to drop quite substantially. >>And today it's down around 63% or more year to date, but the only real substantive change in the company's business is that some of its largest consumer facing companies, while still growing dialed back, their consumption this past quarter, the tone of the call was I wouldn't say contentious the earnings call, but Scarelli, I think was getting somewhat annoyed with the implication from some analyst questions that something is fundamentally wrong with Snowflake's business. So let's unpack this a bit first. I wanna talk about the consumption pricing on the earnings call. One of the analysts asked if snowflake would consider more of a subscription based model so that they could better weather such fluctuations and demand before the analyst could even finish the question, CFO Scarelli emphatically interrupted and said, no, <laugh> the analyst might as well have asked, Hey Mike, have you ever considered changing your pricing model and screwing your customers the same way most legacy SaaS companies lock their customers in? >>So you could squeeze more revenue out of them and make my forecasting life a little bit easier. <laugh> consumption pricing is one of the things that makes a company like snowflake so attractive because customers is especially large customers facing fluctuating demand can dial and their end demand can dial down usage for certain workloads that are maybe not yet revenue producing or critical. Now let's jump to insider trading. There were a lot of insider selling going on last year and into 2022 now, I mean a lot sloop and Scarelli Christine Kleinman. Mike SP several board members. They sold stock worth, you know, many, many hundreds of millions of dollars or, or more at prices in the two hundreds and three hundreds and even four hundreds. You remember the company at one point was valued at a hundred billion dollars, surpassing the value of service now, which is this stupid at this point in the company's tenure and the insider's cost basis was very often in the single digit. >>So on the one hand, I can't blame them. You know what a gift the market gave them last year. Now also famed investor, Peter Linsey famously said, insiders sell for many reasons, but they only buy for one. But I have to say there wasn't a lot of insider buying of the stock when it was in the three hundreds and above. And so yeah, this pattern is something to watch our insiders buying. Now, I'm not sure we'll keep watching snowflake. It's pretty generous with stock based compensation and insiders still own plenty of stock. So, you know, maybe not, but we'll see in future disclosures, but the bottom line is Snowflake's business. Hasn't dramatically changed with the exception of these large consumer facing companies. Now, another analyst pointed out that companies like snap, he pointed to company snap, Peloton, Netflix, and face Facebook have been cutting back. >>And Scarelli said, and what was a bit of a surprise to me? Well, I'm not gonna name the customers, but it's not the ones you mentioned. So I, I thought I would've, you know, if I were the analyst I would've follow up with, how about Walmart target visa, Amex, Expedia price line, or Uber? Any of those Mike? I, I doubt he would've answered me anything. Anyway, the one thing that Scarelli did do is update Snowflake's fiscal year 2029 outlook to emphasize the long term opportunity that the company sees. This chart shows a financial snapshot of Snowflake's current business using a combination of quarterly and full year numbers in a model of what the business will look like. According to Scarelli in Dave ante with a little bit of judgment in 2029. So this is essentially based on the company's framework. Snowflake this year will surpass 2 billion in revenues and targeting 10 billion by 2029. >>Its current growth rate is 84% and its target is 30% in the out years, which is pretty impressive. Gross margins are gonna tick up a bit, but remember Snowflake's cost a good sold they're dominated by its cloud cost. So it's got a governor. There has to pay AWS Azure and Google for its infrastructure. But high seventies is a, is a good target. It's not like the historical Microsoft, you know, 80, 90% gross margin. Not that Microsoft is there anymore, but, but snowflake, you know, was gonna be limited by how far it can, how much it can push gross margin because of that factor. It's got a tiny operating margin today and it's targeting 20% in 2029. So that would be 2 billion. And you would certainly expect it's operating leverage in the out years to enable much, much, much lower SGNA than the current 54%. I'm guessing R and D's gonna stay healthy, you know, coming in at 15% or so. >>But the real interesting number to watch is free cash flow, 16% this year for the full fiscal year growing to 25% by 2029. So 2.5 billion in free cash flow in the out years, which I believe is up from previous Scarelli forecast in that 10, you know, out year view 2029 view and expect the net revenue retention, the NRR, it's gonna moderate. It's gonna come down, but it's still gonna be well over a hundred percent. We pegged it at 130% based on some of Mike's guidance. Now today, snowflake and every other stock is well off this morning. The company had a 40 billion value would drop well below that midday, but let's stick with the 40 billion on this, this sad Friday on the stock market, we'll go to 40 billion and who knows what the stock is gonna be valued in 2029? No idea, but let's say between 40 and 200 billion and look, it could get even ugly in the market as interest rates rise. >>And if inflation stays high, you know, until we get a Paul Voker like action, which is gonna be painful from the fed share, you know, let's hope we don't have a repeat of the long drawn out 1970s stagflation, but that is a concern among investors. We're gonna try to keep it positive here and we'll do a little sensitivity analysis of snowflake based on Scarelli and Ante's 2029 projections. What we've done here is we've calculated in this chart. Today's current valuation at about 40 billion and run a CAGR through 2029 with our estimates of valuation at that time. So if it stays at 40 billion valuation, can you imagine snowflake grow into a 10 billion company with no increase in valuation by the end, by by 2029 fiscal 2029, that would be a major bummer and investors would get a, a 0% return at 50 billion, 4% Kager 60 billion, 7%. >>Kegar now 7% market return is historically not bad relative to say the S and P 500, but with that kind of revenue and profitability growth projected by snowflake combined with inflation, that would again be a, a kind of a buzzkill for investors. The picture at 75 billion valuation, isn't much brighter, but it picks up at, at a hundred billion, even with inflation that should outperform the market. And as you get to 200 billion, which would track by the way, revenue growth, you get a 30% plus return, which would be pretty good. Could snowflake beat these projections. Absolutely. Could the market perform at the optimistic end of the spectrum? Sure. It could. It could outperform these levels. Could it not perform at these levels? You bet, but hopefully this gives a little context and framework to what Scarelli was talking about and his framework, not with notwithstanding the market's unpredictability you're you're on your own. >>There. I can't help snowflake looks like it's going to continue either way in amazing run compared to other software companies historically, and whether that's reflected in the stock price. Again, I, I, I can't predict, okay. Let's look at some ETR survey data, which aligns really well with what snowflake is telling the street. This chart shows the breakdown of Snowflake's net score and net score. Remember is ETS proprietary methodology that measures the percent of customers in their survey that are adding the platform new. That's the lime green at 19% existing snowflake customers that are ex spending 6% or more on the platform relative to last year. That's the forest green that's 55%. That's a big number flat spend. That's the gray at 21% decreasing spending. That's the pinkish at 5% and churning that's the red only 1% or, or moving off the platform, tiny, tiny churn, subtract the red from the greens and you get a net score that, that, that nets out to 68%. >>That's an, a very impressive net score by ETR standards. But it's down from the highs of the seventies and mid eighties, where high seventies and mid eighties, where snowflake has been since January of 2019 note that this survey of 1500 or so organizations includes 155 snowflake customers. What was really interesting is when we cut the data by industry sector, two of Snowflake's most important verticals, our finance and healthcare, both of those sectors are holding a net score in the ETR survey at its historic range. 83%. Hasn't really moved off that, you know, 80% plus number really encouraging, but retail consumer showed a dramatic decline. This past survey from 73% in the previous quarter down to 54%, 54% in just three months time. So this data aligns almost perfectly with what CFO Scarelli has been telling the street. So I give a lot of credibility to that narrative. >>Now here's a time series chart for the net score and the provision in the data set, meaning how penetrated snowflake is in the survey. Again, net score measures, spending velocity and a specific platform and provision measures the presence in the data set. You can see the steep downward trend in net score this past quarter. Now for context note, the red dotted line on the vertical axis at 40%, that's a bit of a magic number. Anything above that is best in class in our view, snowflake still a well, well above that line, but the April survey as we reported on May 7th in quite a bit of detail shows a meaningful break in the snowflake trend as shown by ETRS call out on the bottom line. You can see a steady rise in the survey, which is a proxy for Snowflake's overall market penetration. So steadily moving up and up. >>Here's a bit of a different view on that data bringing in some of Snowflake's peers and other data platforms. This XY graph shows net score on the vertical axis and provision on the horizontal with the red dotted line. At 40%, you can see from the ETR callouts again, that snowflake while declining in net score still holds the highest net score in the survey. So of course the highest data platforms while the spending velocity on AWS and Microsoft, uh, data platforms, outperforms that have, uh, sorry, while they're spending velocity on snowflake outperforms, that of AWS and, and Microsoft data platforms, those two are still well above the 40% line with a stronger market presence in the category. That's impressive because of their size. And you can see Google cloud and Mongo DB right around the 40% line. Now we reported on Mongo last week and discussed the commentary on consumption models. >>And we referenced Ray Lenchos what we thought was, was quite thoughtful research, uh, that rewarded Mongo DB for its forecasting transparency and, and accuracy and, and less likelihood of facing consumption headwinds. And, and I'll reiterate what I said last week, that snowflake, while seeing demand fluctuations this past quarter from those large customers is, is not like a data lake where you're just gonna shove data in and figure it out later, no schema on, right. Just throw it into the pond. That's gonna be more discretionary and you can turn that stuff off. More likely. Now you, you bring data into the snowflake data cloud with the intent of driving insights, which leads to actions, which leads to value creation. And as snowflake adds capabilities and expands its platform features and innovations and its ecosystem more and more data products are gonna be developed in the snowflake data cloud and by data products. >>We mean products and services that are conceived by business users. And that can be directly monetized, not just via analytics, but through governed data sharing and direct monetization. Here's a picture of that opportunity as we see it, this is our spin on our snowflake total available market chart that we've published many, many times. The key point here goes back to our opening statements. The snowflake data cloud is evolving well beyond just being a simpler and easier to use and more elastic cloud database snowflake is building what we often refer to as a super cloud. That is an abstraction layer that companies that, that comprises rich features and leverages the underlying primitives and APIs of the cloud providers, but hides all that complexity and adds new value beyond that infrastructure that value is seen in the left example in terms of compressed cycle time, snowflake often uses the example of pharmaceutical companies compressing time to discover a drug by years. >>Great example, there are many others this, and, and then through organic development and ecosystem expansion, snowflake will accelerate feature delivery. Snowflake's data cloud vision is not about vertically integrating all the functionality into its platform. Rather it's about creating a platform and delivering secure governed and facile and powerful analytics and data sharing capabilities to its customers, partners in a broad ecosystem so they can create additional value. On top of that ecosystem is how snowflake fills the gaps in its platform by building the best cloud data platform in the world, in terms of collaboration, security, governance, developer, friendliness, machine intelligence, etcetera, snowflake believes and plans to create a defacto standard. In our view in data platforms, get your data into the data cloud and all these native capabilities will be available to you. Now, is that a walled garden? Some might say it is. It's an interesting question and <laugh>, it's a moving target. >>It's definitely proprietary in the sense that snowflake is building something that is highly differentiatable and is building a moat around it. But the more open snowflake can make its platform. The more open source it uses, the more developer friendly and the great greater likelihood people will gravitate toward snowflake. Now, my new friend Tani, she's the creator of the data mesh concept. She might bristle at this narrative in favor, a more open source version of what snowflake is trying to build, but practically speaking, I think she'd recognize that we're a long ways off from that. And I also think that the benefits of a platform that despite requiring data to be inside of the data cloud can distribute data globally, enable facile governed, and computational data sharing, and to a large degree be a self-service platform for data, product builders. So this is how we see snow, the snowflake data cloud vision evolving question is edge part of that vision on the right hand side. >>Well, again, we think that is going to be a future challenge where the ecosystem is gonna have to come to play to fill those gaps. If snowflake can tap the edge, it'll bring even more clarity as to how it can expand into what we believe is a massive 200 billion Tam. Okay, let's close on next. Week's snowflake summit in Las Vegas. The cube is very excited to be there. I'll be hosting with Lisa Martin and we'll have Frank son as well as Christian Kleinman and several other snowflake experts. Analysts are gonna be there, uh, customers. And we're gonna have a number of ecosystem partners on as well. Here's what we'll be looking for. At least some of the things, evidence that our view of Snowflake's data cloud is actually taking shape and evolving in the way that we showed on the previous chart, where we also wanna figure out where snowflake is with it. >>Streamlet acquisition. Remember streamlet is a data science play and an expansion into data, bricks, territory, data, bricks, and snowflake have been going at it for a while. Streamlet brings an open source Python library and machine learning and kind of developer friendly data science environment. We also expect to hear some discussion, hopefully a lot of discussion about developers. Snowflake has a dedicated developer conference in November. So we expect to hear more about that and how it's gonna be leveraging further leveraging snow park, which it has previously announced, including a public preview of programming for unstructured data and data monetization along the lines of what we suggested earlier that is building data products that have the bells and whistles of native snowflake and can be directly monetized by Snowflake's customers. Snowflake's already announced a new workload this past week in security, and we'll be watching for others. >>And finally, what's happening in the all important ecosystem. One of the things we noted when we covered service now, cause we use service now as, as an example because Frank Lupin and Mike Scarelli and others, you know, DNA were there and they're improving on that service. Now in his post IPO, early adult years had a very slow pace. In our view was often one of our criticism of ecosystem development, you know, ServiceNow. They had some niche SI uh, like cloud Sherpa, and eventually the big guys came in and, and, and began to really lean in. And you had some other innovators kind of circling the mothership, some smaller companies, but generally we see sluman emphasizing the ecosystem growth much, much more than with this previous company. And that is a fundamental requirement in our view of any cloud or modern cloud company now to paraphrase the crazy man, Steve bomber developers, developers, developers, cause he screamed it and ranted and ran around the stage and was sweating <laugh> ecosystem ecosystem ecosystem equals optionality for developers and that's what they want. >>And that's how we see the current and future state of snowflake. Thanks today. If you're in Vegas next week, please stop by and say hello with the cube. Thanks to my colleagues, Stephanie Chan, who sometimes helps research breaking analysis topics. Alex, my is, and OS Myerson is on production. And today Andrew Frick, Sarah hiney, Steven Conti Anderson hill Chuck all and the entire team in Palo Alto, including Christian. Sorry, didn't mean to forget you Christian writer, of course, Kristin Martin and Cheryl Knight, they helped get the word out. And Rob ho is our E IIC over at Silicon angle. Remember, all these episodes are available as podcast, wherever you listen to search breaking analysis podcast, I publish each week on wikibon.com and Silicon angle.com. You can email me directly anytime David dot Valante Silicon angle.com. If you got something interesting, I'll respond. If not, I won't or DM me@deteorcommentonmylinkedinpostsandpleasedocheckoutetr.ai for the best survey data in the enterprise tech business. This is Dave Valante for the insights powered by ETR. Thanks for watching. And we'll see you next week. I hope if not, we'll see you next time on breaking analysis.

>> From theCUBE studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> The pandemic precipitated what is shaping up to be a permanent shift in cybersecurity spending patterns. As a direct result of hybrid work, CSOs have vested heavily in endpoint security, identity access management, cloud security, and further hardening the network beyond the headquarters. We've reported on this extensively in this Breaking Analysis series. Moreover, the need to build security into applications from the start rather than bolting protection on as an afterthought has led to vastly high heightened awareness around DevSecOps. Finally, attacking security as a data problem with automation and AI is fueling new innovations in cyber products and services and startups. Hello and welcome to this week's Wikibon CUBE Insights powered by ETR. In this Breaking Analysis, we present our quarterly findings in the security industry, and share the latest ETR survey data on the spending momentum and market movers. Let's start with the most recent news in cybersecurity. Nary a week goes by without more concerning news. The latest focus in the headlines is, of course, Russia's relentless cyber attacks on critical infrastructure in the Ukraine, including banking, government websites, weaponizing information. The hacker group, BlackByte, put a double whammy on the San Francisco 49ers, meaning they exfiltrated data and they encrypted the organization's files as part of its ransomware attack. Then there's the best Super Bowl ad last Sunday, the Coinbase floating QR code. Did you catch that? As people rushed to scan the code and participate in the Coinbase Bitcoin giveaway, it highlights yet another exposure, meaning we're always told not to click on links that we don't trust or we've never seen, but so many people activated this random QR code on their smartphones that it crashed Coinbase's website. What does that tell you? In other news, Securonix raised a billion dollars. They did this raise on top of Lacework's massive $1.3 billion raise last November. Both of these companies are attacking security with data automation and APIs that can engage machine intelligence. Securonix, specifically in the announcement, mentioned the uptake from MSSPs, managed security service providers, something we've talked about in this series. And that's a trend that we see as increasingly gaining traction as customers are just drawing in and drowning in security incidents. Peter McKay's company, Snyk, acquired Fugue, a company focused on making sure security policies are consistent throughout the software development life cycle. It's a really an example of a developer-defined security approach where policy can be checked at the dev, deployment, and production phases to ensure the same policies are in place at all stages, including monitoring at runtime. Fugue, according to Crunchbase, had raised $85 million to date. In some other company news, Cisco was rumored to be acquiring Splunk for not much more than Splunk is worth today. And the talks reportedly broke down. This would be a major move in security by Cisco and underscores the pressure to consolidate. Cisco would get an extremely strong customer base and through efficiencies could improve Splunk's profitability, but it seems like the premium Cisco was willing to pay was not enough to entice board to act. Splunk board, that is. Datadog blew away its earnings, and the stock was up 12%. It's pulled back now, thanks to Putin, but it's one of those companies that is disrupting Splunk. Datadog is less than half the size of Splunk, revenue-wise, but its valuation is more than 2 1/2 times greater. Finally, Elastic, another Splunk disruptor, settled its trademark dispute with AWS, and now AWS will now stop using the name Elasticsearch. All right, let's take a high level look at how cyber companies have performed in the stock market over time. Here's a graph of the Cyber ETF, and you can see the March 1st crosshairs of 2020 signifying the start of the lockdown. The trajectory of cybersecurity stocks is shown by the orange and blue lines, and it surely has steepened post March of 2020. And, of course, it's been down with the market lately, but the run up, as you can see, was substantial and eclipsed the trajectory of the previous cycles over the last couple of years, owing much of the momentum to the spending dynamics that we talked about at our open. Let's now drill into some of the names that we've been following over the last few years and take a look at the firm level. This chart shows some data that we've been tracking since before the pandemic. The top rows show the S&P 500 and the NASDAQ prices, and the bottom rows show specific stocks. The first column is the index price or the market cap of the company just before the pandemic, then the same data one year later. Then the next column shows the peak value during the pandemic, and then the current value. Then it shows in the next column where it is today, in percentage terms, i.e., how far has it pulled back from the peak, then the delta from pre-pandemic, in other words, how much did the issue earn or lose during the pandemic for investors? We then compare the pre-pandemic revenue multiple using a trailing 12-month revenue metric. Sorry, that's what we used. It's easy to get. (laughs) And that's the revenue multiple compared to the August in 2020, when multiples were really high, and where they are today, and then a recent quarterly growth rate guide based on the last earnings report. That's the last column. Okay, so I'm throwing a lot of data at you here, but what does it tell us? First, the S&P and the NAS are well up from pre-pandemic levels, yet they're off 9% and 15%, respectively, from their peaks today. That was earlier on Friday morning. Now let's look at the names more closely. Splunk has been struggling. It definitely had a tailwind from the pandemic as all boats seem to rise, but its execution has been lacking. It's now 30% off from its pre-pandemic levels. (groans) And it's multiple is compressing, and perhaps Cisco thought it could pick up the company for a discount. Now let's talk about Palo Alto Networks. We had reported on some of the challenges the company faced moving into a cloud-friendly model. that was before the pandemic. And we talked about the divergence between Palo Alto's stock price and the valuations relative to Fortinet, and we said at the time, we fully expected Palo Alto to rebound, and that's exactly what happened. It rode the tailwinds of the last two years. It's up over 100% from its pre-COVID levels, and its revenue multiple is expanding, owing to the nice growth rates. Now Fortinet had been doing well coming into the pandemic. In fact, we said it was executing on a cloud strategy better than Palo Alto Networks, hence that divergence in valuations at the time. So it didn't get as much of a boost from the pandemic. Didn't get that momentum at first, but the company's been executing very well. And as you can see, with 155% increase in valuation since just before the pandemic, it's going more than okay for Fortinet. Now, Okta is a name that we've really followed closely, the identity access management specialist that rocketed. But since it's Auth0 acquisition, it's pulled back. Investors are concerned about its guidance and its profitability. And several analyst have downgraded their price targets on Okta. We still really like the company. The Auth0 acquisition gives Okta a developer vector, and we think the company is going hard after market presence and is willing to sacrifice short-term profitability. We actually like that posture. It's very Frank Slupin-like. This company spends a lot of money on R&D and go-to-market. The question is, does Okta have inherent profitability? The company, as they say, spends a ton in some really key areas but it looks to us like it's going to establish a footprint. It's guiding revenue CAGR in the mid-30s over the mid to long-term and near term should beat that benchmark handily. But you can see the red highlights on Okta. And even though Okta is up 59% from its pre-pandemic levels, it's far behind its peers shown in the chart, especially CrowdStrike and Zscaler, the latter being somewhat less impacted by the pullback in stocks recently, of course, due to the fears of inflation and interest rates, and, of course, Russian invasion escalation. But these high flyers, they were bound to pull back. The question is can they maintain their category leadership? And for the most part, we think they can. All right, let's get into some of the ETR data. Here's our favorite XY view with net score, or spending momentum on the Y-axis, and market share or pervasiveness in the data center on the horizontal axis. That red 40% line, that indicates a highly elevated spending level. And the chart inserts to the right, that shows how the data is plotted with net score and shared N in each of the columns by each company. Okay, so this is an eye chart, but there really are three main takeaways. One is that it's a crowded market. And this shows only the companies ETR captures in its survey. We filtered on those that had more than 50 mentions. So there's others in the ETR survey that we're not showing here, and there are many more out there which don't get reported in the spending data in the ETR survey. Secondly, there are a lot of companies above the 40% mark, and plenty with respectable net scores just below. Third, check out SentinelOne, Elastic, Tanium, Datadog, Netskope, and Darktrace. Each has under 100 N's but we're watching these companies closely. They're popping up in the survey, and they're catching our attention, especially SentinelOne, post-IPO. So we wanted to pare this back a bit and filter the data some more. So let's look at companies with more than 100 mentions in the same chart. It gets a little cleaner this picture, but it's still crowded. Auth0 leads everyone in net score. Okta is also up there, so that's very positive sign since they had just acquired Auth0. CrowdStrike SalePoint, Cyberark, CloudFlare, and Zscaler are all right up there as well. And then there's the bigger security companies. Palo Alto Network, very impressive because it's well above the 40% mark, and it has a big presence in the survey, and, of course, in the market. And Microsoft as well. They're such a big whale. They skew the data for everybody else to kind of mess up these charts. And the position of Cisco and Splunk make for an interesting combination. They get both decent net scores, not above the 40% line but they got a good presence in the survey as well. Thinking about the acquisition, Al Shugart was the CEO of of Seagate, and founder. Brilliant Silicon valley icon and engineer. Great business person. I was asking him one time, hey, you thinking about buying this company or that company? And of course, he's not going to tell me who he's thinking about buying or acquiring. He said, let me just tell you this. If you want to know what I'm thinking, ask yourself if it were free, would you take it? And he said the answer's not always obviously yes, because acquisitions can be messy and disruptive. In the case of Cisco and Splunk, I think the answer would be a definitive yes It would expand Cisco's portfolio and make it the leader in security, with an opportunity to bring greater operating leverage to Splunk. Cisco's just got to pay more if it wants that asset. It's got to pay more than the supposed $20 billion offer that it made. It's going to have to get kind of probably north of 23 billion. I pinged my ETR colleague, Erik Bradley, on this, and he generally agreed. He's very close to the security space. He said, Splunk isn't growing the customer base but the customers are sticky. I totally agree. Cisco could roll Splunk into its security suite. Splunk is the leader in that space, security information and event management, and Cisco really is missing that piece of the pie. All right, let's filter the data even more and look at some of the companies that have moved in the survey over the past year and a half. We'll go back here to July 2020. Same two-dimensional chart. And we're isolating here Auth0, Okta, SalePoint CrowdStrike, Zscaler, Cyberark, Fortinet, and Cisco. No Microsoft. That cleans up the chart. Okay, why these firms? Because they've made some major moves to the right, and some even up since last July. And that's what this next chart shows. Here's the data from the January 2022 survey. The arrow start points show the position that we just showed you earlier in July 2020, and all these players have made major moves to the right. How come? Well, it's likely a combination of strong execution, and the fact that security is on the radar of every CEO, CIO, of course, CSOs, business heads, boards of directors. Everyone is thinking about security. The market momentum is there, especially for the leaders. And it's quite tremendous. All right, let's now look at what's become a bit of a tradition with Breaking Analysis, and look at the firms that have earned four stars. Four-star firms are leaders in the ETR survey that demonstrate both a large presence, that's that X-axis that we showed you, and elevated spending momentum. Now in this chart, we filter the N's. Has to be greater than 100. And we isolate on those companies. So more than 100 responses in the survey. On the left-hand side of the chart, we sort by net score or spending velocity. On the right-hand side, we sort by shared N's or presence in the dataset. We show the top 20 for each of the categories. And the red line shows the top 10 cutoffs. Companies that show up in the top 10 for both spending momentum and presence in the data set earn four stars. If they show up in one, and make the top 10 in one, and make the top 20 in the other, they get two stars. And we've added a one-star category as honorable mention for those companies that make the top 20 in both categories. Microsoft, Palo Alto Networks, CrowdStrike, and Okta make the four-star grade. Okta makes it even without Auth0, which has the number one net score in this data set with 115 shared N to boot. So you can add that to Okta. The weighted average would pull Okta's net score to just above Cyberark's into fourth place. And its shared N would bump Okta up to third place on the right-hand side of the chart Cisco, Splunk, Proofpoint, KnowBe4, Zscaler, and Cyberark get two stars. And then you can see the honorable mentions with one star. Now thinking about a Cisco, Splunk combination. You'd get an entity with a net score in the mid-20s. Yeah, not too bad, definitely respectable. But they'd be number one on the right-hand side of this chart, with the largest market presence in the survey by far. Okay, let's wrap. The trends around hybrid work, cloud migration and the attacker escalation that continue to drive cybersecurity momentum and they're going to do so indefinitely. And we've got some bullet points here that you're seeing private companies, (laughs) they're picking up gobs of money, which really speaks to the fact that there's no silver bullet in this market. It's complex, chaotic, and cash-rich. This idea of MSSPs on the rise is going to continue, we think. About half the mid-size and large organization in the US don't have a SecOps, a security operation center, and outsourcing to one that can be tapped on a consumption basis, cloud-like, as a service just makes sense to us. We see the momentum that companies that we've highlighted over the many quarters of Breaking Analysis are forming. They're forming a strong base in the market. They're going for market share and footprint, and they're focusing on growth, at bringing in new talent. They have good balance sheets and strong management teams and we think they'll be leading companies in the future, Zscaler, CrowdStrike, Okta, SentinelOne, Cyberark, SalePoint, over time, joining the ranks of billion dollar cyber firms, when I say billion dollar, billion dollar revenue like Palo Alto Networks, Fortinet, and Splunk, if it doesn't get acquired. These independent firms that really focus on security. Which underscores the pressure and consolidation and M&A in the whole space. It's almost assured with the fragmentation of companies and so many new entrants fighting for escape velocity that this market is going to continue with robust M&A and consolidation. Okay, that's it for today. Thanks to my colleague, Stephanie Chan, who helped research this week's topics, and Alex Myerson on the production team. He also manages the Breaking Analysis podcast. Kristen Martin and Cheryl Knight, who get the word out. Thank you to all. Remember 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.ai. We also publish a full report every week on wikibon.com and siliconangle.com. You can email me at david.vellante@siliconangle.com. @dvellante is my DM. Comment on our LinkedIn posts. This is Dave Vellante for theCUBE Insights powered by ETR. Have a great week. Be safe, be well, and we'll see you next time. (upbeat music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from the cube in ETR. This is Breaking Analysis with Dave Vellante. >> On Friday, December 10th, Oracle announced a strong earnings beat and raise, on the strength of its licensed business, and slightly better than expected cloud performance. The stock was up sharply on the day and closed up nearly 16% surpassing 280 billion in market value. Oracle's success is due largely to its execution, of a highly differentiated strategy, that has really evolved over the past decade or more, deeply integrating its hardware and software, heavily investing in next generation cloud, creating a homogeneous experience across its application portfolio, and becoming the number one platform. Number one for the world's most mission critical applications. Now, while investors piled into the stock, skeptics will point to the beat being weighed toward licensed revenue and likely keep one finger on the sell button until they're convinced Oracle's cloud momentum, is more consistent and predictable. Hello and welcome to this week's Wikibond CUBE insights powered by ETR. In this breaking analysis, we'll review Oracle's most recent quarter, and pull in some ETR survey data, to frame the company's cloud business, the momentum of fusion ERP, where the company is winning and some gaps and opportunities that we see. The numbers this quarter was strong, particularly top line growth. Here are a few highlights. Oracle's revenues that grew 6% year on year that's in constant currency, surpassed $10 billion for the quarter. Oracle's non-gap operating margins, were an impressive 47%. Safra Catz has always said cloud is more profitable business and it's really starting to show in the income statement. Operating cash and free cash flow were 10.3 billion and 7.1 billion respectively, for the past four quarters, and would have been higher, if not for charges largely related to litigation expenses tied to the hiring of Mark Hurd, which the company said would not repeat in the future quarters. And you can see in this chart how Oracle breaks down its business, which is kind of a mishmash of items they lump into so-called the cloud. The largest piece of the revenue pie is cloud services, and licensed support, which in reading 10Ks, you'll find statements like the following; licensed support revenues are our largest revenue stream and include product upgrades, and maintenance releases and patches, as well as technical support assistance and statements like the following; cloud and licensed revenue, include the sale of cloud services, cloud licenses and on-premises licenses, which typically represent perpetual software licenses purchased by customers, for use in both cloud, and on-premises, IT environments. And cloud license and on-prem license revenues primarily represent amounts earned from granting customers perpetual licenses to use our database middleware application in industry specific products, which our customers use for cloud-based, on-premise and other IT environments. So you tell me, "is that cloud? I don't know." In the early days of Oracle cloud, the company used to break out, IaaS, PaaS and SaaS revenue separately, but it changed its mind, which really makes it difficult to determine what's happening in true cloud. Look I have no problem including same same hardware software control plane, et cetera. The hybrid if it's on-prem in a true hybrid environment like exadata cloud@customer or AWS outposts. But you have to question what's really cloud in these numbers. And Larry in the earnings call mentioned that Salesforce licenses the Oracle database, to run its cloud and Oracle doesn't count that in its cloud number, rather it counts it in license revenue, but as you can see it varies that into a line item that starts with the word cloud. So I guess I would say that Oracle's reporting is maybe somewhat better than IBM's cloud reporting, which is the worst, but I can't really say what is and isn't cloud, in these numbers. Nonetheless, Oracle is getting it done for investors. Here's a chart comparing the five-year performance of Oracle to some of its legacy peers. We excluded Microsoft because it skews the numbers. Microsoft would really crush all these names including Oracle. But look at Oracle. It's wedged in between the performance of the NASDAQ and the S&P 500, it's up over 160% in that five-year timeframe, well ahead of SAP which is up 59% in that time, and way ahead of the dismal -22% performance of IBM. Well, it's a shame. The tech tide is rising, it's lifting all boats but, IBM has unfortunately not been able to capitalize. That's a story for another day. As a market watcher, you can't help but love Larry Ellison. I only met him once at an IDC conference in Paris where I got to interview Scott McNealy, CEO at the time. Ellison is great for analysts because, he's not afraid to talk about the competition. He'll brag, he'll insult, he'll explain, and he'll pitch his stories. Now on the earnings call last night, he went off. Educating the analyst community, on the upside in the fusion ERP business, making the case that because only a thousand of the 7,500 legacy on-prem ERP customers from Oracle, JD Edwards and PeopleSoft have moved Oracle's fusion cloud ERP, and he predicted that Oracle's cloud ERP business will surpass 20 billion in five years. In fact, he said it's going to bigger than that. He slammed the hybrid cloud washing. You can see one of the quotes here in this chart, that's going on when companies have customers running in the cloud and they claim whatever they have on premise hybrid, he called that ridiculous. I would agree. And then he took an opportunity to slam the hyperscale cloud vendors, citing a telco customer that said Oracle's cloud never goes down, and of course, he chose the same week, that AWS had a major outage. And so to these points, I would say that Oracle really was the first tech company, to announce a true hybrid cloud strategy, where you have an entirely identical experience on prem and in the cloud. This was announced with cloud@customer, two years, before AWS announced outposts. Now it probably took Oracle two years to get it working as advertised, but they were first. And to the second point, this is where Oracle differentiates itself. Oracle is number one for mission critical applications. No other vendor really can come close to Oracle in this regard. And I would say that Oracle is recent quarterly performance to a large extent, is due to this differentiated approach. Over the past 10 years, we've talked to hundreds literally. Hundreds and hundreds of Oracle customers. And while they may not always like the tactics and licensing policies of Oracle in their contracting, they will tell you, that business case for investing and staying with Oracle are very strong. And yes, a big part of that is lock-in but R&D investments innovation and a keen sense of market direction, are just as important to these customers. When you're chairman and founder is a technologist and also the CTO, and has the cash on hand to invest, the results are a highly competitive story. Now that's not to say Oracle is not without its challenges. That's not to say Oracle is without its challenges. Those who follow this program know that when it comes to ETR survey data, the story is not always pretty for Oracle. So let's take a look. This chart shows the breakdown of ETR is net score methodology, Net score measures spending momentum and works ETR. Each quarter asks customers, are you adding in the platform, That's the lime green. Increasing spend by 6% or more, that's the fourth green. Is you're spending E+ or minus 5%, that's the gray. You're spending climbing by 6%, that's the pinkish. Or are you leaving the platform, that's the bright red retiring. You subtract the reds from the greens, and that yields a net score, which an Oracle's overall case, is an uninspiring -4%. This is one of the anomalies in the ETR dataset. The net score doesn't track absolute actual levels, of spending the dollars. Remember, as the leader in mission critical workloads, Oracle commands a premium price. And so what happens here is the gray, is still spending a large amount of money, enough to offset the declines, and the greens are spending more than they would on other platforms because Oracle could command higher prices. And so that's how Oracle is able to grow its overall revenue by 6% for example, whereas the ETR methodology, doesn't capture that trend. So you have to dig into the data a bit deeper. We're not going to go too deep today, but let's take a look at how some of Oracle's businesses are performing relative to its competitors. This is a popular view that we like to share. It shows net score or spending momentum on the vertical axis, and market share. Market share is a measure of pervasiveness in the survey. Think of it as mentioned share. That's on the x-axis. And we've broken down and circled Oracle overall, Oracle on prem, which is declining on the vertical axis, Oracle fusion and NetSuite, which are much higher than Oracle overall. And in the case of fusion, much closer to that 40% magic red horizontal line, remember anything above that line, we consider to be elevated. Now we've added SAP overall which has, momentum comparable to fusion in the survey, using this methodology and IBM, which is in between fusion and Oracle, overall on the y-axis. Oracle as you can see on the horizontal axis, has a larger presence than any of these firms that are below the 40% line. Now, above that 40% line, you see companies with a smaller presence in the survey like Workday, salesforce.com, pretty big presence still, Google cloud also, and Snowflake. Smaller presence but much much higher net score than anybody else on this chart. And AWS and Microsoft overall with both a strong presence, and impressive momentum, especially for their respective sizes. Now that view that we just showed you excluded on purpose Oracle specific cloud offering. So let's now take a look at that relative to other cloud providers. This chart shows the same XY view, but it cuts the data by cloud only. And you can see Oracle while still well below the 40% line, has a net score of +15 compared to a -4 overall that we showed you earlier. So here we see two key points. One, despite the convoluted reporting that we talked about earlier, the ETR data supports that Oracle's cloud business has significantly more momentum than Oracle's overall average momentum. And two, while Oracle is smaller and doesn't have the growth of the hyperscale giants, it's cloud is performing noticeably better than IBM's within the ETR survey data. Now a key point Ellison emphasized on the earnings call, was the importance of ERP, and the work that Oracle has done in this space. It lives by this notion of a cloud first mentality. It builds stuff for the cloud and then, would bring it on-prem. And it's been attracting new customers according to the company. He said Oracle has 8,500 fusion ERP customers, and 28,000 NetSuite customers in the cloud. And unlike Microsoft, it hasn't migrated its on-prem install base, to the cloud yet. Meaning these are largely new customers. Now this chart isolates fusion and NetSuite, within a sector ETR calls GPP. The very giant, public and private companies. And this is a bellwether of spending in the ETR dataset. They've gone back and it correlates to performance. So think large public companies, the biggest ones, and also privates big privates like Mars or Cargo or Fidelity. The chart shows the net score breakdown over time for fusion and NetSuite going back to 2019. And you can see, a big uptick as shown in the blue line from the October, 2020 survey. So Oracle has done a good job building and now marketing its cloud ERP to these important customers. Now, the last thing we want to show you is Oracle's performance within industry sectors. On the earnings call, Oracle said that it had a very strong momentum for fusion in financial services and healthcare. And this chart shows the net score for fusion, across each industry sector that ETR tracks, for three survey points. October, 2020, that's the gray bars, July 21, that's the blue bars and October, 2021, the yellow bars. So look it confirms Oracles assertions across the board that they're seeing fusion perform very well including the two verticals that are called out healthcare and banking slash financial services. Now the big question is where does Oracle go from here? Oracle has had a history of looking like it's going to break out, only to hit some bumps in the road. And so investors are likely going to remain a bit cautious and take profits off the table along the way. But since the Barron's article came out, we reported on that earlier this year in February, declaring Oracle a cloud giant, the stock is up more than 50% of course. 16 of those points were from Friday's move upward, but still, Oracle's highly differentiated strategy of integrating hardware and software together, investing in a modern cloud platform and selectively offering services that cater to the hardcore mission critical buyer, these have served the company, its customers and investors as well. From a cloud standpoint, we'd like to see Oracle be more inclusive, and aggressively expand its marketplace and its ecosystem. This would provide both greater optionality for customers, and further establish Oracle as a major cloud player. Indeed, one of the hallmarks of both AWS and Azure is the momentum being created, by their respective ecosystems. As well, we'd like to see more clear confirmation that Oracle's performance is being driven by its investments in technology IE cloud, same same hybrid, and industry features these modern investments, versus a legacy licensed cycles. We are generally encouraged and are reminded, of years ago when Sam Palmisano, he was retiring and leaving as the CEO of IBM. At the time, HP under the direction ironically of Mark Hurd, was the now company, Palmisano was asked, "do you worry about HP?" And he said in fact, "I don't worry about HP. I worry about Oracle because Oracle invests in R&D." And that statement has proven present. What do you think? Has Oracle hit the next inflection point? Let me know. Don't forget these episodes they're all available as podcasts wherever you listen, all you do is search it. Breaking Analysis podcast, check out ETR website at etr.plus. We also publish a full report every week on wikibon.com and siliconANGLE.com. You can get in touch with me on email David.vellante@siliconangle.com, you can DM me @dvellante on Twitter or, comment on our LinkedIn posts. This is Dave Vellante for theCUBE Insights. Powered by ETR. Have a great week everybody. Stay safe, be well, and we'll see you next time. (upbeat music)

>> From theCUBE studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE in ETR. This is breaking analysis with Dave Vellante. >> ServiceNow is a company that investors love to love, but there's caution in the investor community right now is confusion about transitory inflation and higher interest rates looms. ServiceNow also suffers from a perfection syndrome of sorts. The company has seen that the slightest misstep can cause many freak outs from the investor community. So what it's done is it's architected a financial and communications model that allows it to beat expectations and raise its outlook on a consistent basis. Regardless, ServiceNow appears to be on track to vie for what its CEO Bill McDermott refers to as the next great enterprise software company. Wait, I thought Marc Benioff had his hands on that steering wheel. Hello everyone, and welcome to this week's Wikibon CUBE insights powered by ETR. In this breaking analysis, we'll dig into one of the companies we began following almost 10 years ago and provide some thoughts on ServiceNow's March to 15 billion by 2026, which we think is a highly probable achievement. In 2020, despite the contraction in IT spending, SeviceNow outperformed both the S&P 500 and the NASDAQ, but here's a view of 2021. And you can see while the stock has done well since it saw a softness in May and again in early June, and it bounced off that double bottom, it's performance is well below those other benchmarks. This is not a big surprise given the fact that this is a high growth stock and we all know that those names with high multiples get hurt in an inflationary environment, but still the gaps are notable. This is especially true given the performance of the company. It's not often that you see a company with four to $5 billion in revenue growing at a 30% clip, throwing off billions of dollars in free cash flow and increasing operating margins at 100 basis points a year and promising to do that over the next several years. In fact, I don't think we've ever seen that before. I remember years ago, when the trade press was criticizing SeviceNow for its lofty valuation, despite the fact that it was losing money, then CEO, Frank Slootman said to me, "Dave, we can be highly profitable tomorrow if we want it to be, but this is a marathon and we're planning to go big." So essentially Slootman was telling me that this company was going to be an ATM machine that prints money. And that seems to be how it's shaping up. I happened to be at SeviceNow headquarters in 2017, literally the first day on the job for John Donahoe, the CEO replaced Slootman, and I remember while I was there thinking Donahoe was certainly capable, but why the heck I said, would the board let Frank Slootman get away? You know what? It turned great for Slootman, he's at snowflake. Donahoe, I always felt was a consumer guy anyway, and not long for SeviceNow. And now you have this guy, new CEO, Bill McDermott at the helm. He's not a more qualified CEO for the company in my view. About two months ago, McDermott led a virtual investor day. We've had McDermott on theCUBE a couple of times back when he was CEO of SAP and this individual is very compelling. He's got JFK like looks and charisma, but more than that, he's passionate and convincing. And he obviously knows enterprise software. And with conviction, he laid the groundwork for how SeviceNow will get to $10 billion in revenue by 2024 on its way to 15 billion two years thereafter. And one of the big things McDermott's stressed was they're going to get there without any big M&A moves. And that's important because previously the door was left open for that possibility. And now the company is assuring investors that it can get there organically, even with slower growth. So this chart implies no big M&A, and you can see Slootman handed over the reigns at that year one tick on the horizontal axis. This was not a turnaround story. It was a rocket ship at the time. And look at the logos on this chart. This is a revenue view and SeviceNow is aiming to be the fastest to get to 10 billion in software industry history. SeviceNow is valuation just to sort of shift gears here for a minute blew by workdays years ago. Its sites are now set on SAP which is currently valued at 170 billion. And then there's Oracle and Salesforce. They're at around 250 billion and 225 billion in valuation respectively. And these lines back to revenue show the trajectory that these companies took to get to 10 billion. And you can see how SeviceNow plans to get there with those dotted lines. And this is why I call this a collision course with Salesforce, because I think Marc Benioff might say, "Hey, we are ready." Are the next great enterprise software company. We have no plans to give up that post, that mantle anytime soon. I want to share a clip from four years ago. something we've been saying for a long, long time. Roll the clip. >> As they say their goal now is to be four billion by 2020. It feels like, you know, when we first covered SeviceNow knowledge, we said, wow, this company reminds us a lot of the early days of Salesforce. They've got this platform you can develop on this platform, you know, call it paths or, you know, whatever you want to call it, but we at the time said, they're on a collision course with Salesforce. Now there's plenty of room for both of those companies in the marketplace. Salesforce obviously focused predominantly on Salesforce automation, SeviceNow really on workflow automation, but you can see those sort of two markets coming together. >> Now you may be thinking isn't Salesforce's revenue like 5X that of SeviceNow? And yes it is. But I would say a couple of things. One is that Salesforce has gotten to where it is with a lot of M&A, more than 60 acquisitions. At some high profile wants to like slack and Tableau as well as MuleSoft and Heroku back in the day and many others. So we'll see how far McDermott can get before he reverts to his inquisitive self that we saw at SAP. But the second thing I'll say is serviceNow positions itself as the platform of platforms. And the thing is it runs its own cloud. And when it does acquisitions, it replatforms the acquiree into the now platform so that it can drive integrations more seamlessly. That's fundamentally part of its value proposition, a big part of its value proposition. And that means it's somewhat limited on the acquisitions it can make, it has to be pretty selective. Otherwise it's got to do a heavy lift to get it the now platform. It's the power of the models, especially if customers can get to a single CMDB, that configuration database management system, which by the way, a lot of customers never get to that kind of skirt that, but remember SeviceNow is like the ERP for IT. So the more you can get to a single data model, the more effective you're going to be, especially in this data era where you got to put data at the core of your organization, something we've talked about a lot. And the third thing I'll mention the SeviceNow wants to use this platform to attack what it sees as a very large TAM as shown here. Now, a couple of things I want to point out. One is when SeviceNow IPO in 2012, a lot of the analysts said that they were way overvalued because they were in a market. It was help desk and writing tickets was a $2 billion business that was in decline and BMC remedy. Wasn't really that big of a base to attack. In 2013, the Wikibon team took a stab at sizing the TAM. I dug back into the old Wiki. We had well over 30 billion at the time and we expected the company to move deeper into IT and then beyond IT into lines of business and line of business management. Yeah, we felt we were being conservative. We thought the number could be as big as 100 billion, but we felt like putting that number out there, was too aggressive but, you know, it turns out from SeviceNow standpoint, it sees these new software opportunities coming together. And SeviceNow in a way they can double dip both in and beyond their current markets. What I mean by that is it can partner with, for instance, HCM vendors and then at the same time offer employee workflows. They can partner or even purchase RPA tools from specialists like UI path or automation anywhere. And it can go acquire a company which it did like Intel a bot and integrate what I would consider lighter-weight RPA into its platform. So it can manage workflows for best of breed and pick off functionality throughout the software stack. Now what's interesting in this chart is first, the size of the TAM that SeviceNow sees 175 billion, but also how it's now reorganizing its business around workflows, which you see in the left-hand side. This was done of course, to simplify the many, many, many things that you can buy from SeviceNow. But there's also speculation that SeviceNow is leveraging its orchestration and service catalog capabilities, which are meaningful from a revenue standpoint and using them to power these workflows because the way it was organized was both confusing and not as effective as it could be. Now, it's well known that SeviceNow has ITSM this comprises the biggest piece of its revenue pie, probably a couple billion. And it's adding to that with ITSM pro and ITSM enterprise going deeper, deeper into the ITSM space. And it's ITAM business is also doing well against the likes of Datadog and Elastic and Splunk and others and its acquisition of LightStep. It's going to push it further into this space, which is both crowded is morphing into observability as we've been reporting. What's unclear though is how well, for instance, HR and the CSM businesses are doing as sort of standalone businesses, you might remember they used to be standalone businesses with standalone GMs. They've sort of changed that up a little bit. So this is potentially not only a way to simplify, but also shuffle the deck chairs a bit and maybe prop up the non IT workflows, which then allows SeviceNow to show this chart, which essentially says to the street, see, we have this huge TAM and our TAM expansion strategy is working as the overall business is growing nicely yet the mix is shifting toward customer, employee and creator workflows. See how awesome our business is and see how smart we are. So this is possibly a way to hide some of the warts and accentuate the growth. Look, there's not a lot to criticize SeviceNow about, but they've been pretty good at featuring what some perceive as weaknesses. Like for instance, the way it marketed it's a multi-instance and turned that into an advantage as a better model. Even though the whole cloud world was going multitenant and within a ServiceNow you got to really plan new releases, which they drop every six months, although CJ decide. So he's SeviceNows head of products. He did say at the investor meeting, that event that they held last May, that they do certain releases now bi-monthly and even some bi-weekly. So, yeah, maybe a little bit of nitpicking here, but I always liked to question when such changes are made to the reporting structures to the street. And if workflows are the new black, so to speak, I wonder will SeviceNow start pricing by workflows versus what really has been a legacy of, you know, what's your ticket volume and how many agents need access to the model and we'll charge you accordingly? Now, I'm not a service pricing expert and they don't make it easy to figure out that pricing. So let's dig a little bit more on that and keep an eye on it. Now I want to turn to the customers survey data from ETR on ServiceNow. First, here's the latest update on IT spending from ETR, something that we've been tracking for quite some time. We've been consistently saying to expect this year a seven to 8% growth for 2021 IT spend off of last year's contraction. And the latest ETR survey data puts it right at 8%. So we really liked that number. You know, could even be higher push 10% this year. Now, let's look at the spending profile within the ETR dataset. Of the 1100 plus respondents to this quarter, there were 377 SeviceNow customers, and this chart shows the breakdown of net score or spending velocity among those respondents. Remember, net score is a measure of that spending momentum. What it does is it takes the lime green bar, which is adopting new, that says 11% of that 377 customers are adopting ServiceNow for the first time. It takes that lime green and it adds the forest green bar that's growth in spending of 6% or more this half relative to the first half. That's 43% of the customers that have been surveyed here. And then it subtracts out the reds, which is that pinkish is spending less, that's 3%, small number of spending less. And then the bright red is we're leaving the platform. That's a minuscule 1% of the respondents. And you can see the rest in that gray area is flat spending, which is ignored. And so what this does is it calculates out, you'd take the greens minus the reds. It calculates out to a net score 50% for SeviceNow, which is well above that magic 40% elevated mark that we'd like to see. It's rare for a company of this size, except for the hyperscalers. You see AWS and Microsoft and Google are up that high and oh, there's another great enterprise software company at the 45% net score level. Guess who that is, salesforce.com. But anyway, it's rare to see that large of a company have that much spending momentum in the ETR surveys. Now let's take a look at the time series data for ServiceNow. This chart shows the net score granularity over time. So you see the bars, that time series, the blue line is net score. And you can see that it was dragged down during last year's lockdown. As, even though SeviceNow did pretty well last year and it's now spiking back to pre-COVID levels, which is a very positive sign for the company. That red call-out that ETR makes it shows market share. That's an indicator of pervasiveness in the dataset. I'm not overlyconcern there that downturn. I don't think it's a meaningful indicator because ServiceNow revenue is skewed towards a big spender accounts and this is an account unit indicator, if you will not spending level metric. And okay, and here's another reason and why I'm not concerned about SeviceNow is a so-called market share number in the ETR dataset as ETR defines it. This is an X, Y Z view chart that we'd like to show here. We've got net score on the vertical axis and market share in the horizontal plane. This is focusing on enterprise software. So remember that 40% red line is the magic level, anything above that is really indicative of momentum. Oh look, there's Salesforce and ServiceNow on that little collision course that I talked about. Now, CEO McDermott, I would say as by the way, would his predecessors, look, we're a platform of platforms and we partner with other companies, we'll meet at the customer level and sure we'll integrate functions where we think it can add value to customers. But we also understand we have to work with the vendors that our customers are using. So it's all good, plenty of room for growth for all of us, which by the way is true. But I would say this, anyone who's ever been in the enterprise software industry knows that enterprise software execs and their salespeople believe that every dollar spent on software should go to them. And if it's a good market with momentum and growth, they believe they can either organically write software to deliver customer function and value, or they can acquire to fill gaps. So, well, what McDermott would say is true. The likes of Oracle, Microsoft, SAP, Salesforce, Infor, et cetera, they all want as big of a budget piece as possible in the enterprise software space. That's just the way it is. Now, we're going to close with some anecdotal comments from ETR insights, formerly called VENN, which is a round table discussion with CXOs. You can read the summaries when we post on Wikibon and SiliconANGLE but let me summarize. This first comment comes from an assistant VP in retail who says SeviceNow is a key part of their digital transformation. They moved off of BMC remedy two years ago for the global ticketing system. And this person is saying that while the platform is extremely powerful, you got to buy into specific modules to just get one feature that you want. You may not need a lot of the other features, so it starts to get expensive. The other thing this individual is saying is initially, it's a very services heavy project. And so I'll tell you, when you look at the SeviceNow ecosystem the big SIs, the big names, they have big appetites. They love to eat at the trough as I sometimes say, and they want big clients with big budgets. So if you're not one of those top 500 or 700 customers, the big name SIs, you know, they might not be for you. They're not going to pay attention to you. They're going after the big prizes. So what I would suggest is you call up someone like Jason Wojahn of third era, he's the CEO over there and he's got a lot of experience in this space or some more specialized SeviceNow consultancy like them because you're going to get better value for the money. And you're going to get short-term ROI faster with a long-term sustainable ROI as a measurable objective. And I think this last comment sums it up nice, let me to skip over the second one and go just jump to the third one. This basically says the platform is integrated. It's like a mesh. It's not a bunch of stovepipes and cul-de-sacs. Yes it's expensive, but people love it. And like the iPhone, it just works. And their feature pace is accelerating. So pretty strong testimonials, but I want to keep an eye on price transparency any possible backlash there and how the ecosystem evolves. It's something that we called out early on. It's an indicator and SeviceNow needs to continue to invest in that partner network is especially as it builds out its vertical industry practices and expands internationally. Okay, we'll leave it there for now. Remember I publish each week on wikibon.com and siliconangle.com. These episodes they're all available as podcasts. All you got to do is search for breaking analysis podcast. You can always connect with me on Twitter @DVellante or email me @david.vellantesiliconangle.com. Appreciate the comments on LinkedIn. And don't forget to check out etr.plus for all the survey data. This is Dave Vellante for theCUBE insights powered by ETR. Be well, and we'll see you next time. (upbeat music)

>> From theCUBE studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR, this is Breaking Analysis with Dave Vellante. >> In our 2020 predictions post, we said that organizations would begin to operationalize their digital transformation experiments and POCs. We also said that based on spending data that cybersecurity companies like CrowdStrike and Okta were poised to rise above the rest in 2020, and we even said the S&P 500 would surpass 3,700 this year. Little did we know that we'd have a pandemic that would make these predictions a virtual lock, and, of course, COVID did blow us out of the water in some other areas, like our prediction that IT spending would increase plus 4% in 2020, when in reality, we have a dropping by 4%. We made a number of other calls that did pretty well, but I'll let you review last year's predictions at your leisure to see how we did. Hello, everyone. This is Dave Vellante and welcome to this week's Wikibon CUBE Insights powered by ETR. Erik Bradley of ETR is joining me again for this Breaking Analysis, and we're going to lay out our top picks for 2021. Erik, great to see you. Welcome back. Happy to have you on theCUBE, my friend. >> Always great to see you too, Dave. I'm excited about these picks this year. >> Well, let's get right into it. Let's bring up the first prediction here. Tech spending will rebound in 2021. We expect a 4% midpoint increase next year in spending. Erik, there are a number of factors that really support this prediction, which of course is based on ETR's most recent survey work, and we've listed a number of them here in this slide. I wonder if we can talk about that a little bit, the pace of the vaccine rollout. I've called this a forced march to COVID, but I can see people doubling down on things that are working. Productivity improvements are going to go back into the business. People are going to come back to the headquarters and that maybe is going to spur infrastructure on some pent-up demand, and work from home, we're going to talk about that. What are your thoughts on this prediction? >> Well, first of all, you weren't wrong last year. You were just, (laughs) you were just delayed. Just delayed a little bit, that's all. No, very much so. Early on, just three months ago, we were not seeing this optimism. The most recent survey, however, is capturing 4%. I truly believe that still might be a little bit mild. I think it can go even higher, and that's going to be driven by some of the things you've said about. This is a year where a lot of spending was paused on machine learning, on automation, on some of these projects that had to be stopped because of what we all went through. Right now, that is not a nice to have, it's a must have, and that spending is going quickly. There's a rapid pace on that spending, so I do think that's going to push it and, of course, security. We're going to get to this later on so I don't want to bury the lede, but with what's happening right now, every CISO I speak to is not panicked, but they are concerned and there will definitely be increased security spending that might push this 4% even higher. >> Yeah, and as we've reported as well, the survey data shows that there's less freezing of IT, there are fewer layoffs, there's more hiring, we're accelerating IT deployments, so that, I think, 34% last survey, 34% of organizations are accelerating IT deployments over the next three months, so that's great news. >> And also your point too about hiring. I was remiss in not bringing that up because we had layoffs and we had freezes on hiring. Both of that is stopping. As you know, as more head count comes in, whether that be from home or whether that be in your headquarters, both of those require support and require spending. >> All right, let's bring up the next prediction. Remote worker trends are going to become fossilized, settling in at an average of 34% by year-end 2021. Now, I love this chart, you guys. It's been amazingly consistent to me, Erik. We're showing data here from ETR's latest COVID survey. So it shows that prior to the pandemic, about 15 to 16% of employees on average worked remotely. That jumped to where we are today and well into the 70s, and we're going to stay close to that, according to the ETR data, in the first half of 2021, but by the end of the year, it's going to settle in at around 34%. Erik, that's double the pre-pandemic numbers and that's been consistent in your surveys over the past six month, and even within the sub-samples. >> Yeah, super surprised by the consistency, Dave. You're right about that. We were expecting the most recent data to kind of come down, right? We see the vaccines being rolled out. We kind of thought that that number would shift, but it hasn't, it has been dead consistent, and that's just from the data perspective. What we're hearing from the interviews and the feedback is that's not going to change, it really isn't, and there's a main reason for that. Productivity is up, and we'll talk about that in a second, but if you have productivity up and you have employees happy, they're not commuting, they're working more, they're working effectively, there is no reason to rush. And now imagine if you're a company that's trying to hire the best talent and attract the best talent but you're also the only company telling them where they have to live. I mean, good luck with that, right? So even if a few of them decide to make this permanent, that's something where you're going to really have to follow suit to attract talent. >> Yeah, so let's talk about that. Productivity leads us to our next prediction. We can bring that up. Number three is productivity increases are going to lead organizations to double down on the successes of 2020 and productivity apps are going to benefit. Now, of course, I'm always careful to cautious to interpret when you ask somebody by how much did productivity increase. It's a very hard thing to estimate depending on how you measure it. Is it revenue per employee? Is it profit? But nonetheless, the vast majority of people that we talk to are seeing productivity is going up. The productivity apps are really the winners here. Who do you see, Erik, as really benefiting from this trend? This year we saw Zoom, Teams, even Webex benefit, but how do you see this playing out in 2021? >> Well, first of all, the real beneficiaries are the companies themselves because they are getting more productivity, and our data is not only showing more productivity, but that's continuing to increase over time, so that's number one. But you're 100% right that the reason that's happening is because of the support of the applications and what would have been put in place. Now, what we do expect to see here, early on it was a rising tide lifted all boats, even Citrix got pulled up, but over time you realize Citrix is really just about legacy applications. Maybe that's not really the virtualization platform we need or maybe we just don't want to go that route at all. So the ones that we think are going to win longer term are part of this paradigm shift. The easiest one to put out as example is DocuSign. Nobody is going to travel and sit in an office to sign a paper ever again. It's not happening. I don't care if you go back to the office or you go back to headquarters. This is a paradigm shift that is not temporary. It is permanent. Another one that we're seeing is Smartsheet. Early on it started in. I was a little concerned about it 'cause it was a shadow IT type of a company where it was just spreading and spreading and spreading. It's turned out that this, the data on Smartsheet is continuing to be strong. It's an effective tool for project management when you're remotely working, so that's another one I don't see changing anytime. The other one I would call out would be Twilio. Slightly different, yes. It's more about the customer experience, but when you look at how many brick and mortar or how many in-person transactions have moved online and will stay there, companies like Twilio that support that customer experience, I'll throw out a Qualtrics out there as well, not a name we hear about a lot, but that customer experience software is a name that needs to be watched going forward. >> What do you think's going to happen to Zoom and Teams? Certainly Zoom just escalated this year, a huge ascendancy, and Teams I look at a little differently 'cause it's not just video conferencing, and both have done really, really well. How do you interpret the data that you're seeing there? >> There's no way around it, our data is decelerating quickly, really quickly. We were kind of bullish when Zoom first came out on the IPO prospects. It did very well. Obviously what happened in this remote shift turned them into an absolute overnight huge success. I don't see that continuing going forward, and there's a reason. What we're seeing and hearing from our feedback interviews is that now that people recognize this isn't temporary and they're not scrambling and they need to set up for permanency, they're going to consolidate their spend. They don't need to have Teams and Zoom. It's not necessary. They will consolidate where they can. There's always going to be the players that are going to choose Slack and Zoom 'cause they don't want to be on Microsoft architecture. That's fine, but you and I both know that the majority of large enterprises have Microsoft already. It's bundled in in pricing. I just don't see it happening. There's going to be M&A out there, which we can talk about again soon, so maybe Zoom, just like Slack, gets to a point where somebody thinks it's worthwhile, but there's a lot of other video conferencing out there. They're trying to push their telephony. They're trying to push their mobile solutions. There's a lot of companies out there doing it, so we'll see, but the current market cap does not seem to make sense in a permanent remote work situation. >> I think I'm inferring Teams is a little different because it's Microsoft. They've got this huge software estate they can leverage. They can bundle. Now, it's going to be interesting to see how and if Zoom can then expand its TAM, use its recent largesse to really enter potentially new markets. >> It will be, but listen, just the other day there was another headline that one of Zoom's executives out in China was actually blocking content as per directed by the Chinese government. Those are the kind of headlines that just really just get a little bit difficult when you're running a true enterprise size. Zoom is wonderful in the consumer space, but what I do is I research enterprise technology, and it's going to be really, really difficult to make inroads there with Microsoft. >> Yep. I agree. Okay, let's bring up number four, prediction number four. Permanent shifts in CISO strategies lead to measurable share shifts in network security. So the remote work sort of hyper-pivot, we'll call it, it's definitely exposed us. We've seen recent breaches that underscore the need for change. They've been well-publicized. We've talked a lot about identity access management, cloud security, endpoint security, and so as a result, we've seen the upstarts, and just a couple that we called, CrowdStrike, Okta, Zscaler has really benefited and we expect them to continue to show consistent growth, some well over 50% revenue growth. Erik, you really follow this space closely. You've been focused on microsegmentation and other, some of the big players. What are your thoughts here? >> Yeah, first of all, security, number one in spending overall when we started looking and asking people what their priority is going to be. That's not changing, and that was before the SolarWinds breach. I just had a great interview today with a CISO of a global hospitality enterprise to really talk about the implications of this. It is real. Him and his peers are not panicking but pretty close, is the way he put it, so there is spend happening. So first of all, to your point, continued on Okta, continued on identity access. See no reason why that changes. CrowdStrike, continue. What this is going to do is bring in some new areas, like we just mentioned, in network segmentation. Illumio is a pure play in that name that doesn't have a lot of citations, but I have watched over the last week their net spending score go from about 30 to 60%, so I am watching in real time, as this data comes in in the later part of our survey, that it's really happening Forescout is another one that's in there. We're seeing some of the zero trust names really picking up in the last week. Now, to talk about some of the more established names, yeah, Cisco plays in this space and we can talk about Cisco and what they're doing in security forever. They're really reinventing themselves and doing a great job. Palo Alto was in this space as well, but I do believe that network and microsegmentation is going to be something that's going to continue. The other one I'm going to throw out that I'm hearing a lot about lately is user behavior analytics. People need to be able to watch the trends, compare them to past trends, and catch something sooner. Varonis is a name in that space that we're seeing get a lot of adoptions right now. It's early trend, but based on our data, Varonis is a name to watch in that area as well. >> Yeah, and you mentioned Cisco transitioning, reinventing themselves toward a SaaS player. Their subscription, Cisco's security business is a real bright spot for them. Palo Alto, every time I sit in on a VENN, which is ETR's proprietary roundtable, the CISOs, they love Palo Alto. They want to work, many of them, anyway, want to work with Palo Alto. They see them as a thought leader. They seem to be getting their cloud act together. Fortinet has been doing a pretty good job there and especially for mid-market. So we're going to see this equilibrium, best of breed versus the big portfolio companies, and I think 2021 sets up as a really interesting battle for those guys with momentum and those guys with big portfolios. >> I completely agree and you nailed it again. Palo Alto has this perception that they're really thought leaders in the space and people want to work with them, but let's not rule Cisco out. They have a much, much bigger market cap. They are really good at acquisitions. In the past, they maybe didn't integrate them as well, but it seems like they're getting their act together on that. And they're pushing now what they call SecureX, which is sort of like their own full-on platform in the cloud, and they're starting to market that, I'm starting to hear more about it, and I do think Cisco is really changing people's perception of them. We shall see going forward because in the last year, you're 100% right, Palo Alto definitely got a little bit more of the sentiment, of positive sentiment. Now, let's also realize, and we'll talk about this again in a bit, there's a lot of players out there. There will probably be continued consolidation in the security space, that we'll see what happens, but it's an area where spending is increasing, there is a lot of vendors out there to play with, and I do believe we'll see consolidation in that space. >> Yes. No question. A highly fragmented business. A lack of skills is a real challenge. Automation is a big watch word and so I would expect, which brings us, Erik, to prediction number five. Can be hard to do prediction posts without talking about M&A. We see the trend toward increased tech spending driving more IPOs, SPACs and M&A. We've seen some pretty amazing liquidity events this year. Snowflake, obviously a big one. Airbnb, DoorDash, outside of our enterprise tech but still notable. Palantir, JFrog, number of others. UiPath just filed confidentially and their CEO said, "Over the next 12 to 18 months, I would think Automation Anywhere is going to follow suit at some point." Hashicorp was a company we called out in our 2020 predictions as one to watch along with Snowflake and some others, and, Erik, we've seen some real shifts in observability. The ELK Stack gaining prominence with Elastic, ChaosSearch just raised 40 million, and everybody's going after 5G. Lots of M&A opportunities. What are your thoughts? >> I think if we're going to make this a prediction show, I'm going to say that was a great year, but we're going to even have a better year next year. There is a lot of cash on the balance sheet. There are low interest rates. There is a lot of spending momentum in enterprise IT. The three of those set up for a perfect storm of more liquidity events, whether it be continued IPOs, whether it could be M&A, I do expect that to continue. You mentioned a lot of the names. I think you're 100% right. Another one I would throw out there in that observability space, is it's Grafana along with the ELK Stack is really making changes to some of the pure plays in that area. I've been pretty vocal about how I thought Splunk was having some problems. They've already made three acquisitions. They are trying really hard to get back up and keep that growth trajectory and be the great company they always have been, so I think the observability area is certainly one. We have a lot of names in that space that could be taken out. The other one that wasn't mentioned, however, that I'd like to mention is more in the CDN area. Akamai being the grandfather there, and we'll get into it a little bit too, but CloudFlare has a huge market cap, Fastly running a little bit behind that, and then there's Limelight, and there's a few startups in that space and the CDN is really changing. It's not about content delivery as much as it is about edge compute these days, and they would be a real easy takeout for one of these large market cap names that need to get into that spot. >> That's a great call. All right, let's bring up number six, and this is one that's near and dear to my heart. It's more of a longer-term prediction and that prediction is in the 2020s, 75% of large organizations are going to re-architect their big data platforms, and the premise here is we're seeing a rapid shift to cloud database and cross-cloud data sharing and automated governance. And the prediction is that because big data platforms are fundamentally flawed and are not going to be corrected by incremental improvements in data lakes and data warehouses and data hubs, we're going to see a shift toward a domain-centric ownership of the data pipeline where data teams are going to be organized around data product or data service builders and embedded into lines of business. And in this scenario, the technology details and complexity will become abstracted. You've got hyper-specialized data teams today. They serve multiple business owners. There's no domain context. Different data agendas. Those, we think, are going to be subsumed within the business lines, and in the future, the primary metric is going to shift from the cost and the quality of the big data platform outputs to the time it takes to go from idea to revenue generation, and this change is going to take four to five years to coalesce, but it's going to begin in earnest in 2021. Erik, anything you'd add to this? >> I'm going to let you kind of own that one 'cause I completely agree, and for all the listeners out there, that was Dave's original thought and I think it's fantastic and I want to get behind it. One of the things I will say to support that is big data analytics, which is what people are calling it because they got over the hype of machine learning, they're sick of vendors saying machine learning, and I'm hearing more and more people just talk about it as we need big data analytics, we need 'em at the edge, we need 'em faster, we need 'em in real time. That's happening, and what we're seeing more is this is happening with vendor-agnostic tools. This isn't just AWS-aligned. This isn't just GCP-aligned or Azure-aligned. The winners are the Snowflakes. The winners are the Databricks. The winners are the ones that are allowing this interoperability, the portability, which fully supports what you're saying. And then the only other comment I would make, which I really like about your prediction, is about the lines of business owning it 'cause I think this is even bigger. Right now, we track IT spending through the CIO, through the CTO, through IT in general. IT spending is actually becoming more diversified. IT spending is coming under the purview of marketing, it's coming under the purview of sales, so we're seeing more and more IT spending, but it's happening with the business user or the business lines and obviously data first, so I think you're 100% right. >> Yeah, and if you think about it, we've contextualized our operational systems, whether it's the CRM or the supply chain, the logistics, the business lines own their respective data. It's not true for the analytics systems, and we talked about Snowflake and Databricks. I actually see these two companies who were sort of birds of a feather in the early days together, applying Databricks machine learning on top of Snowflake, I actually see them going in diverging places. I see Databricks trying to improve on the data lake. I see Snowflake trying to reinvent the concept of data warehouse to this global mesh, and it's going to be really interesting to see how that shakes out. The data behind Snowflake, obviously very, very exciting. >> Yeah, it's just, real quickly to add on that if we have time, Dave. >> Yeah, sure. >> We all know the valuation of Snowflake, one of the most incredible IPOs I've seen in a long time. The data still supports it. It still supports that growth. Unfortunately for Databricks, their IPO has been a little bit more volatile. If you look at their stock chart every time they report, it's got a little bit of a roller coaster ride going on, and our most recent data for Databricks is actually decelerating, so again, I'm going to use the caveat that we only have about 950 survey responses in. We'll probably get that up to 1,300 or so, so it's not done yet, but right now we are putting Databricks into a category where we're seeing it decelerate a little bit, which is surprising for a company that's just right out of the gate. >> Well, it's interesting because I do see Databricks as more incremental on data lakes and I see Snowflake as more transformative, so at least from a vision standpoint, we'll see if they can execute on that. All right, number seven, let's bring up number seven. This is talking about the cloud, hybrid cloud, multi-cloud. The battle to define hybrid and multi-cloud is going to escalate in 2021. It's already started and it's going to create bifurcated CIO strategies. And, Erik, spending data clearly shows that cloud is continuing its steady margin share gains relative to on-prem, but the definitions of the cloud, they're shifting. Just a couple of years ago, AWS, they never talk about hybrid, just like they don't talk about multi-cloud today, yet AWS continues now to push into on-prem. They treat on-prem as just another node at the edge and they continue to win in the marketplace despite their slower growth rates. Still, they're so large now. 45 billion or so this year. The data is mixed. This ETR data shows that just under 50% of buyers are consolidating workloads, and then a similar, in the cloud workloads, and a similar percentage of customers are spreading evenly across clouds, so really interesting dynamic there. Erik, how do you see it shaking out? >> Yeah, the data is interesting here, and I would actually state that overall spend on the cloud is actually flat from last year, so we're not seeing a huge increase in spend, and coupled with that, we're seeing that the overall market share, which means the amount of responses within our survey, is increasing, certainly increasing. So cloud usage is increasing, but it's happening over an even spectrum. There's no clear winner of that market share increase. So they really, according to our data, the multi-cloud approach is happening and not one particular winner over another. That's just from the data perspective that various do point on AWS. Let's be honest, when they first started, they wanted all the data. They just want to take it from on-prem, put it in their data center. They wanted all of it. They never were interested in actually having interoperability. Then you look at an approach like Google. Google was always about the technology, but not necessarily about the enterprise customer. They come out with Anthos which is allowing you to have interoperability in more cloud. They're not nearly as big, but their growth rate is much higher. Law of numbers, of course. But it really is interesting to see how these cloud players are going to approach this because multi-cloud is happening whether they like it or not. >> Well, I'm glad you brought up multi-cloud in a context of what the data's showing 'cause I would agree we're, and particularly two areas that I would call out in ETR data, VMware Cloud on AWS as well as VM Cloud Foundation are showing real momentum and also OpenStack from Red Hat is showing real progress here and they're making moves. They're putting great solutions inside of AWS, doing some stuff on bare metal, and it's interesting to see. VMware, basically it's the VMware stack. They want to put that everywhere. Whereas Red Hat, similarly, but Red Hat has the developer angle. They're trying to infuse Red Hat in throughout everybody's stack, and so I think Red Hat is going to be really interesting to, especially to the extent that IBM keeps them, sort of lets them do their own thing and doesn't kind of pollute them. So, so far so good there. >> Yeah, I agree with that. I think you brought up the good point about it being developer-friendly. It's a real option as people start kicking a little bit more of new, different developer ways and containers are growing, growing more. They're not testing anymore, but they're real workloads. It is a stack that you could really use. Now, what I would say to caveat that though is I'm not seeing any net new business go to IBM Red Hat. If you were already aligned with that, then yes, you got to love these new tools they're giving you to play with, but I don't see anyone moving to them that wasn't already net new there and I would say the same thing with VMware. Listen, they have a great entrenched base. The longer they can kick that can down the road, that's fantastic, but I don't see net new customers coming onto VMware because of their alignment with AWS. >> Great, thank you for that. That's a good nuance. Number eight, cloud, containers, AI and ML and automation are going to lead 2021 spending velocity, so really is those are the kind of the big four, cloud, containers, AI, automation, And, Erik, this next one's a bit nuanced and it supports our first prediction of a rebound in tech spending next year. We're seeing cloud, containers, AI and automation, in the form of RPA especially, as the areas with the highest net scores or spending momentum, but we put an asterisk around the cloud because you can see in this inserted graphic, which again is preliminary 'cause the survey's still out in the field and it's just a little tidbit here, but cloud is not only above that 40% line of net score, but it has one of the higher sector market shares. Now, as you said, earlier you made a comment that you're not necessarily seeing the kind of growth that you saw before, but it's from a very, very large base. Virtually every sector in the ETR dataset with the exception of outsourcing and IT consulting is seeing meaningful upward spending momentum, and even those two, we're seeing some positive signs. So again, with what we talked about before, with the freezing of the IT projects starting to thaw, things are looking much, much better for 2021. >> I'd agree with that. I'm going to make two quick comments on that, one on the machine learning automation. Without a doubt, that's where we're seeing a lot of the increase right now, and I've had a multiple number of people reach out or in my interviews say to me, "This is very simple. These projects were slated to happen in 2020 and they got paused. It's as simple as that. The business needs to have more machine learning, big data analytics, and it needs to have more automation. This has just been paused and now it's coming back and it's coming back rapidly." Another comment, I'm actually going to post an article on LinkedIn as soon as we're done here. I did an interview with the lead technology director, automation director from Disney, and this guy obviously has a big budget and he was basically saying UiPath and Automation Anywhere dominate RPA, and that on top of it, the COVID crisis greatly accelerated automation, greatly accelerated it because it had to happen, we needed to find a way to get rid of these mundane tasks, we had to put them into real workloads. And another aspect you don't think about, a lot of times with automation, there's people, employees that really have friction. They don't want to adopt it. That went away. So COVID really pushed automation, so we're going to see that happening in machine learning and automation without a doubt. And now for a fun prediction real quick. You brought up the IT outsourcing and consulting. This might be a little bit more out there, the dark horse, but based on our data and what we're seeing and the COVID information about, you said about new projects being unwrapped, new hiring happening, we really do believe that this might be the bottom on IT outsourcing and consulting. >> Great, thank you for that, and then that brings us to number nine here. The automation mandate is accelerating and it will continue to accelerate in 2021. Now, you may say, "Okay, well, this is a lay-up," but not necessarily. UiPath and Automation Anywhere go public and Microsoft remains a threat. Look, UiPath, I've said UiPath and Automation Anywhere, if they were ready to go public, they probably would have already this year, so I think they're still trying to get their proverbial act together, so this is not necessarily a lay-up for them from an operational standpoint. They probably got some things to still clean up, but I think they're going to really try to go for it. If the markets stay positive and tech spending continues to go forward, I think we can see that. And I would say this, automation is going mainstream. The benefits of taking simple RPA tools to automate mundane tasks with software bots, it's both awakened organizations to the possibilities of automation, and combined with COVID, it's caused them to get serious about automation. And we think 2021, we're going to see organizations go beyond implementing point tools, they're going to use the pandemic to restructure their entire business. Erik, how do you see it, and what are the big players like Microsoft that have entered the market? What kind of impact do you see them having? >> Yeah, completely agree with you. This is a year where we go from small workloads into real deployment, and those two are the leader. In our data, UiPath by far the clear leader. We are seeing a lot of adoptions on Automation Anywhere, so they're getting some market sentiment. People are realizing, starting to actually adopt them. And by far, the number one is Microsoft Power Automate. Now, again, we have to be careful because we know Microsoft is entrenched everywhere. We know that they are good at bundling, so if I'm in charge of automation for my enterprise and I'm already a Microsoft customer, I'm going to use it. That doesn't mean it's the best tool to use for the right job. From what I've heard from people, each of these have a certain area where they are better. Some can get more in depth and do heavier lifting. Some are better at doing a lot of projects at once but not in depth, so we're going to see this play out. Right now, according to our data, UiPath is still number one, Automation Anywhere is number two, and Microsoft just by default of being entrenched in all of these enterprises has a lot of market share or mind share. >> And I also want to do a shout out to, or a call out, not really a shout out, but a call out to Pegasystems. We put them in the RPA category. They're covered in the ETR taxonomy. I don't consider them an RPA vendor. They're a business process vendor. They've been around for a long, long time. They've had a great year, done very, very well. The stock has done well. Their spending momentum, the early signs in the latest survey are just becoming, starting to moderate a little bit, but I like what they've done. They're not trying to take UiPath and Automation Anywhere head-on, and so I think there's some possibilities there. You've also got IBM who went to the market, SAP, Infor, and everybody's going to hop on the bandwagon here who's a software player. >> I completely agree, but I do think there's a very strong line in the sand between RPA and business process. I don't know if they're going to be able to make that transition. Now, business process also tends to be extremely costly. RPA came into this with trying to be, prove their ROI, trying to say, "Yeah, we're going to cost a little bit of money, but we're going to make it back." Business process has always been, at least the legacies, the ones you're mentioning, the Pega, the IBMs, really expensive. So again, I'm going to allude to that article I'm about to post. This particular person who's a lead tech automation for a very large company said, "Not only are UiPath and AA dominating RPA, but they're likely going to evolve to take over the business process space as well." So if they are proving what they can do, he's saying there's no real reason they can't turn around and take what Appian's doing, what IBM's doing and what Pega's doing. That's just one man's opinion. Our data is not actually tracking it in that space, so we can't back that, but I did think it was an interesting comment for and an interesting opportunity for UiPath and Automation Anywhere. >> Yeah, it's always great to hear directly from the mouths of the practitioners. All right, brings us to number 10 here. 5G rollouts are going to push new edge IoT workloads and necessitate new system architectures. AI and real-time inferencing, we think, require new thinking, particularly around processor and system design, and the focus is increasingly going to be on efficiency and at much, much lower costs versus what we've known for decades as general purpose workloads accommodating a lot of different use cases. You're seeing alternative processors like Nvidia, certainly the ARM acquisition. You've got companies hitting the market like Fungible with DPAs, and they're dominating these new workloads in the coming decade, we think, and they continue to demonstrate superior price performance metrics. And over the next five years they're going to find their way, we think, into mainstream enterprise workloads and put continued pressure on Intel general purpose microprocessors. Erik, look, we've seen cloud players. They're diversifying their processor suppliers. They're developing their own in-house silicon. This is a multi-year trend that's going to show meaningful progress next year, certainly if you measure it in terms of innovations, announcements and new use cases and funding and M&A activity. Your thoughts? >> Yeah, there's a lot there and I think you're right. It's a big trend that's going to have a wide implication, but right now, it's there's no doubt that the supply and demand is out of whack. You and I might be the only people around who still remember the great chip famine in 1999, but it seems to be happening again and some of that is due to just overwhelming demand, like you mentioned. Things like IoT. Things like 5G. Just the increased power of handheld devices. The remote from work home. All of this is creating a perfect storm, but it also has to do with some of the chip makers themselves kind of misfired, and you probably know the space better than me, so I'll leave you for that on that one. But I also want to talk a little bit, just another aspect of this 5G rollout, in my opinion, is we have to get closer to the edge, we have to get closer to the end consumer, and I do believe the CDN players have an area to play in this. And maybe we can leave that as there and we could do this some other time, but I do believe the CDN players are no longer about content delivery and they're really about edge compute. So as we see IoT and 5G roll out, it's going to have huge implications on the chip supply. No doubt. It's also could have really huge implications for the CDN network. >> All right, there you have it, folks. Erik, it's great working with you. It's been awesome this year. I hope we can do more in 2021. Really been a pleasure. >> Always. Have a great holiday, everybody. Stay safe. >> Yeah, you too. Okay, so look, that's our prediction for 2021 and the coming decade. Remember, all these episodes are available as podcasts. All you got to do is search Breaking Analysis podcast. You'll find it. We publish each week on wikibon.com and siliconangle.com, and you got to check out etr.plus. It's where all the survey action is. Definitely subscribe to their services if you haven't already. You can DM me @dvellante or email me at david.vellante@siliconangle.com. This is Dave Vellante for Erik Bradley for theCUBE Insights powered by ETR. Thanks for watching, everyone. Be well and we'll see you next time. (relaxing music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE in ETR. This is Breaking Analysis with Dave Vellante. >> Cloud computing has been the single most transformative force in IT over the last decade. As we enter the 2020s, we believe that cloud will become the underpinning of a ubiquitous, intelligent and autonomous resource that will disrupt the operational stacks of virtually every company in every industry. Welcome to this week's special edition of Wikibon's CUBE Insights Powered by ETR. In this breaking analysis, and as part of theCUBE365's coverage of AWS re:Invent 2020, we're going to put forth our scenario for the next decade of cloud evolution. We'll also drill into the most recent data on AWS from ETR's October 2020 survey of more than 1,400 CIOs and IT professionals. So let's get right into it and take a look at how we see the cloud of yesterday, today and tomorrow. This graphic shows our view of the critical inflection points that catalyze the cloud adoption. In the middle of the 2000s, the IT industry was recovering from the shock of the dot-com bubble and of course 9/11. CIOs, they were still licking their wounds from the narrative, does IT even matter? AWS launched its Simple Storage Service and later EC2 with a little fanfare in 2006, but developers at startups and small businesses, they noticed that overnight AWS turned the data center into an API. Analysts like myself who saw the writing on the wall and CEO after CEO, they poo-pooed Amazon's entrance into their territory and they promised a cloud strategy that would allow them to easily defend their respective turfs. We'd seen the industry in denial before, and this was no different. The financial crisis was a boon for the cloud. CFOs saw a way to conserve cash, shift CAPEX to OPEX and avoid getting locked in to long-term capital depreciation schedules or constrictive leases. We also saw shadow IT take hold, and then bleed in to the 2010s in a big way. This of course created problems for organizations rightly concerned about security and rogue tech projects. CIOs were asked to come in and clean up the crime scene, and in doing so, realized the inevitable, i.e., that they could transform their IT operational models, shift infrastructure management to more strategic initiatives, and drop money to the bottom lines of their businesses. The 2010s saw an era of rapid innovation and a level of data explosion that we'd not seen before. AWS led the charge with a torrent pace of innovation via frequent rollouts or frequent feature rollouts. Virtually every industry, including the all-important public sector, got into the act. Again, led by AWS with the Seminole, a CIA deal. Google got in the game early, but they never really took the enterprise business seriously until 2015 when it hired Diane Green. But Microsoft saw the opportunity and leaned in heavily and made remarkable strides in the second half of the decade, leveraging its massive software stake. The 2010s also saw the rapid adoption of containers and an exit from the long AI winter, which along with the data explosion, created new workloads that began to go mainstream. Now, during this decade, we saw hybrid investments begin to take shape and show some promise. As the ecosystem realized broadly that it had to play in the AWS sandbox or it would lose customers. And we also saw the emergence of edge and IoT use cases like for example, AWS Ground Station, those emerge. Okay, so that's a quick history of cloud from our vantage point. The question is, what's coming next? What should we expect over the next decade? Whereas the last 10 years was largely about shifting the heavy burden of IT infrastructure management to the cloud, in the coming decade, we see the emergence of a true digital revolution. And most people agree that COVID has accelerated this shift by at least two to three years. We see all industries as ripe for disruption as they create a 360 degree view across their operational stacks. Meaning, for example, sales, marketing, customer service, logistics, etc., they're unified such that the customer experience is also unified. We see data flows coming together as well, where domain-specific knowledge workers are first party citizens in the data pipeline, i.e. not subservient to hyper-specialized technology experts. No industry is safe from this disruption. And the pandemic has given us a glimpse of what this is going to look like. Healthcare is going increasingly remote and becoming personalized. Machines are making more accurate diagnoses than humans, in some cases. Manufacturing, we'll see new levels of automation. Digital cash, blockchain and new payment systems will challenge traditional banking norms. Retail has been completely disrupted in the last nine months, as has education. And we're seeing the rise of Tesla as a possible harbinger to a day where owning and driving your own vehicle could become the exception rather than the norm. Farming, insurance, on and on and on. Virtually every industry will be transformed as this intelligent, responsive, autonomous, hyper-distributed system provides services that are ubiquitous and largely invisible. How's that for some buzzwords? But I'm here to tell you, it's coming. Now, a lot of questions remain. First, you may even ask, is this cloud that you're talking about? And I can understand why some people would ask that question. And I would say this, the definition of cloud is expanding. Cloud has defined the consumption model for technology. You're seeing cloud-like pricing models moving on-prem with initiatives like HPE's GreenLake and now Dell's APEX. SaaS pricing is evolving. You're seeing companies like Snowflake and Datadog challenging traditional SaaS models with a true cloud consumption pricing option. Not option, that's the way they price. And this, we think, is going to become the norm. Now, as hybrid cloud emerges and pushes to the edge, the cloud becomes this what we call, again, hyper-distributed system with a deployment and programming model that becomes much more uniform and ubiquitous. So maybe this s-curve that we've drawn here needs an adjacent s-curve with a steeper vertical. This decade, jumping s-curves, if you will, into this new era. And perhaps the nomenclature evolves, but we believe that cloud will still be the underpinning of whatever we call this future platform. We also point out on this chart, that public policy is going to evolve to address the privacy and concentrated industry power concerns that will vary by region and geography. So we don't expect the big tech lash to abate in the coming years. And finally, we definitely see alternative hardware and software models emerging, as witnessed by Nvidia and Arm and DPA's from companies like Fungible, and AWS and others designing their own silicon for specific workloads to control their costs and reduce their reliance on Intel. So the bottom line is that we see programming models evolving from infrastructure as code to programmable digital businesses, where ecosystems power the next wave of data creation, data sharing and innovation. Okay, let's bring it back to the current state and take a look at how we see the market for cloud today. This chart shows a just-released update of our IaaS and PaaS revenue for the big three cloud players, AWS, Azure, and Google. And you can see we've estimated Q4 revenues for each player and the full year, 2020. Now please remember our normal caveats on this data. AWS reports clean numbers, whereas Azure and GCP are estimates based on the little tidbits and breadcrumbs each company tosses our way. And we add in our own surveys and our own information from theCUBE Network. Now the following points are worth noting. First, while AWS's growth is lower than the other two, note what happens with the laws of large numbers? Yes, growth slows down, but the absolute dollars are substantial. Let me give an example. For AWS, Azure and Google, in Q4 2020 versus Q4 '19, we project annual quarter over quarter growth rate of 25% for AWS, 46% for Azure and 58% for Google Cloud Platform. So meaningfully lower growth rates for AWS compared to the other two. Yet AWS's revenue in absolute terms grows sequentially, 11.6 billion versus 12.4 billion. Whereas the others are flat to down sequentially. Azure and GCP, they'll have to come in with substantially higher annual growth to increase revenue from Q3 to Q4, that sequential increase that AWS can achieve with lower growth rates year to year, because it's so large. Now, having said that, on an annual basis, you can see both Azure and GCP are showing impressive growth in both percentage and absolute terms. AWS is going to add more than $10 billion to its revenue this year, with Azure growing nearly 9 billion or adding nearly 9 billion, and GCP adding just over 3 billion. So there's no denying that Azure is making ground as we've been reporting. GCP still has a long way to go. Thirdly, we also want to point out that these three companies alone now account for nearly $80 billion in infrastructure services annually. And the IaaS and PaaS business for these three companies combined is growing at around 40% per year. So much for repatriation. Now, let's take a deeper look at AWS specifically and bring in some of the ETR survey data. This wheel chart that we're showing here really shows you the granularity of how ETR calculates net score or spending momentum. Now each quarter ETR, they go get responses from thousands of CIOs and IT buyers, and they ask them, are you spending more or less than a particular platform or vendor? Net score is derived by taking adoption plus increase and subtracting out decrease plus replacing. So subtracting the reds from the greens. Now remember, AWS is a $45 billion company, and it has a net score of 51%. So despite its exposure to virtually every industry, including hospitality and airlines and other hard hit sectors, far more customers are spending more with AWS than are spending less. Now let's take a look inside of the AWS portfolio and really try to understand where that spending goes. This chart shows the net score across the AWS portfolio for three survey dates going back to last October, that's the gray. The summer is the blue. And October 2020, the most recent survey, is the yellow. Now remember, net score is an indicator of spending velocity and despite the deceleration, as shown in the yellow bars, these are very elevated net scores for AWS. Only Chime video conferencing is showing notable weakness in the AWS data set from the ETR survey, with an anemic 7% net score. But every other sector has elevated spending scores. Let's start with Lambda on the left-hand side. You can see that Lambda has a 65% net score. Now for context, very few companies have net scores that high. Snowflake and Kubernetes spend are two examples with higher net scores. But this is rarefied air for AWS Lambda, i.e. functions. Similarly, you can see AI, containers, cloud, cloud overall and analytics all with over 50% net scores. Now, while database is still elevated with a 46% net score, it has come down from its highs of late. And perhaps that's because AWS has so many options in database and its own portfolio and its ecosystem, and the survey maybe doesn't have enough granularity there, but in this competition, so I don't really know, but that's something that we're watching. But overall, there's a very strong portfolio from a spending momentum standpoint. Now what we want to do, let's flip the view and look at defections off of the AWS platform. Okay, look at this chart. We find this mind-boggling. The chart shows the same portfolio view, but isolates on the bright red portion of that wheel that I showed you earlier, the replacements. And basically you're seeing very few defections show up for AWS in the ETR survey. Again, only Chime is the sore spot. But everywhere else in the portfolio, we're seeing low single digit replacements. That's very, very impressive. Now, one more data chart. And then I want to go to some direct customer feedback, and then we'll wrap. Now we've shown this chart before. It plots net score or spending velocity on the vertical axis and market share, which measures pervasiveness in the dataset on the horizontal axis. And in the table portion in the upper-right corner, you can see the actual numbers that drive the plotting position. And you can see the data confirms what we know. This is a two-horse race right now between AWS and Microsoft. Google, they're kind of hanging out with the on-prem crowd vying for relevance at the data center. We've talked extensively about how we would like to see Google evolve its business and rely less on appropriating our data to serve ads and focus more on cloud. There's so much opportunity there. But nonetheless, you can see the so-called hybrid zone emerging. Hybrid is becoming real. Customers want hybrid and AWS is going to have to learn how to support hybrid deployments with offerings like outposts and others. But the data doesn't lie. The foundation has been set for the 2020s and AWS is extremely well-positioned to maintain its leadership, in our view. Now, the last chart we'll show takes some verbatim comments from customers that sum up the situation. These quotes were pulled from several ETR event roundtables that occurred in 2020. The first one talks to the cloud compute bill. It spikes and sometimes can be unpredictable. The second comment is from a CIO at IT/Telco. Let me paraphrase what he or she is saying. AWS is leading the pack and is number one. And this individual believes that AWS will continue to be number one by a wide margin. The third quote is from a CTO at an S&P 500 organization who talks to the cloud independence of the architecture that they're setting up and the strategy that they're pursuing. The central concern of this person is the software engineering pipeline, the cICB pipeline. The strategy is to clearly go multicloud, avoid getting locked in and ensuring that developers can be productive and independent of the cloud platform. Essentially separating the underlying infrastructure from the software development process. All right, let's wrap. So we talked about how the cloud will evolve to become an even more hyper-distributed system that can sense, act and serve, and provides sets of intelligence services on which digital businesses will be constructed and transformed. We expect AWS to continue to lead in this build-out with its heritage of delivering innovations and features at a torrid pace. We believe that ecosystems will become the main spring of innovation in the coming decade. And we feel that AWS has to embrace not only hybrid, but cross-cloud services. And it has to be careful not to push its ecosystem partners to competitors. It has to walk a fine line between competing and nurturing its ecosystem. To date, its success has been key to that balance as AWS has been able to, for the most part, call the shots. However, we shall see if competition and public policy attenuate its dominant position in this regard. What will be fascinating to watch is how AWS behaves, given its famed customer obsession and how it decodes the customer's needs. As Steve Jobs famously said, "Some people say, give the customers what they want. "That's not my approach. "Our job is to figure out "what they're going to want before they do." I think Henry Ford once asked, "If I'd ask customers what they wanted, "they would've told me a faster horse." Okay, that's it for now. It was great having you for this special report from theCUBE Insights Powered by ETR. Keep it right there for more great content on theCUBE from re:Invent 2020 virtual. (cheerful music)

>> From The Cube studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a Cube Conversation. >> The isolation economy has created substantial momentum for certain cybersecurity companies, notably, as of the big stock market sell off on June 11th, relative to our last cyber report, which we did in February, the S and P 500, and the NASDAQ are off 11% and 3% respectively. But the valuations of three companies that we cited as four-star firms in our February cyber report are up significantly. In particular, Okta's valuation is up 34% since our last look in February. CrowdStrike, almost 50%, and Zscaler over 60%. Yet several other companies that were named as four-star players have really either tracked the S and P or even performed more poorly, despite still showing decent strength and spending momentum based on survey data from ETR. Welcome, everybody, to this week's Wickibon Cube Insights powered by ETR. My name is Dave Vellante and in this breaking analysis, we want to update you on our cybersecurity outlook and try to answer several questions, such as what has changed in the cybersecurity landscape. since our last report. Much has, as you know, Has the isolation economy created a permanent shift in security spend, or are these upticks just anomalies? What can we learn from the ETR spending data, and is the divergence and valuations amongst security leaders justified? Let's start by taking a look at what has changed since our last cyber report. Now, we produce this just ahead of the RSA conference in February, and one of the last physical conferences. So there's some big changes going on in the market. We really want to understand, are they systematic? In other words, are there fundamental changes to the system and its underlying principles, and by many accounts, the answer appears to be yes. Recently I listened in to a number of CSOs. of it was a call with ETR's Eric Bradley. And we heard the executives echo some of the themes that we've been discussing previously. It was notion of the work-from-home pivot, creating a focus on things like zero trust networks, changes in identity and access management, and way more focus on cloud, and of course, as a service, really reducing reliance on traditional firewalls and appliances that would reside in organizations' data centers. You know, we've gone from a world where digital transformation was an important strategic initiative to one where if you weren't digital, you largely couldn't transact business. Now, people are, the question they have is that is the longterm viability of VPNs makes sense? And even things like SD-WAN are being called into question, as corporate offices are empty and the internet is becoming the new private network. Now, one thing that hasn't changed is there are still a lot of technologies in this space. And that seems to be continuing as buyers need solutions to problems quickly to plug holes, and on balance IT budgets, they are contracting, so most companies still have to justify security spending based on the amount of risk reduction versus the cost. Of course, it's easier to justify for securing remote workers. So what I want to do now is take a pause and let's look back at some of the ETR data that we shared back in February. Now remember, this data is from the January ETR survey, ETR surveys organizations once every quarter. And if you recall, we keyed on two key metrics, some of our favorite metrics. Net Score, which is a measure of spending momentum, and Market Share, which measures pervasive per, sorry, pervasiveness in the dataset. Now, as you might recall, the left most chart here shows the cyber players and we sorted them by Net Score. The right hand side, that sorts those companies on Shared N, which measures the number of mentions of that company within the cybersecurity sector. Now, at the time, we named several four-star companies, actually we started this last year when we initiated coverage in the security space. These four-star security firms, really based on their rankings within both of those metrics, Net Score and Shared N. So you could see the four stars, Microsoft, Splunk, Palo Alto Networks, Proofpoint, Okta, CrowdStrike, and we added Zscaler as new, and then CyberArk. And we gave Cisco and Fortinet two stars, as they were kind of on the cusp. Now let's look at some of these companies from the April survey that ETR did. So this chart shows a subset of the vendors that we showed before. Now remember, this survey was taken at the height of the lockdown, from kind of early part of March to the early part of April. Budgets were under immense pressure. Nonetheless, look at Microsoft, Cisco, Palo Alto, Fortinet, and Zscaler all held up pretty evenly. CrowdStrike also held steadily and maintain a very high level. Okta dipped somewhat, but from a pretty high level as well. Only Proofpoint is one of the ones that showed decline notably from 48% to a 40% Net Score relative to the chart I showed earlier. Now, SailPoint didn't make the four-star cut because it doesn't have the presence in the dataset, but it's Net Score is solid, and the Shared N jumped from 66 last survey to 88 in the latest checkpoint. So this identity and access management player, it seems to be one to watch. We'll come back to that in future episodes. Now let's plot some of these players in context, you know, using this two-dimensional axis that we often show. This chart shows that that view that we like to share. It plots Net Score, or spending velocity, on the Y axis, and then market share on the X axis. Remember, our market share is calculated by dividing the number of mentions for a company by the total number of mentions within that sector. So it's not like true IDC market share, it's market share within the survey. So you can see here a continued theme of Microsoft momentum, very high Net Score, or high Net Score and big presence. We plotted IBM and Dell EMC, which is really the legacy RSA business, just for context. And these are two companies with strong security brands, but as you can see, they're really not the giants that they used to be in cybersecurity software. So a couple of points on this graphic. CrowdStrike really jumps out as the momentum play on this chart. And that's really no surprise given its focus on endpoint security and the pivot to work-from-home. Okta has a focus on cloud-based identity management and they continue to show very strong. And CyberArk, with a focus on privileged access is also very important in this remote worker environment. We'll talk about that some more later. And you can see Zscaler, quite strong and steady from the last survey, but that company saw some of the biggest action in the stock market, which we're going to try to explain in a moment. Proofpoint, we talked about a deceleration in Net Score, but they're right in the mix as is Fortinet. Now finally, Palo Alto, you know, they remain strong. And Cisco, like many of its businesses, very credible with a Net Score that's decent and a large market presence as always. Now, as we've reported, security is one of the brightest spots in that Cisco portfolio. So the big takeaway from the ETR data is that despite the pandemic, cybersecurity software has held up very well from a spending standpoint. But now let's look a little bit deeper into what's happening in the stock market with these firms. And first as we know, there's a clear disconnect between what's happening in financial markets and the fundamentals of the economy. You know, Wall Street versus Main Street is kind of that narrative. And within the security sector, there's also a dissonance between companies, and we want to discuss that next. Here's an updated chart that we showed in February from our last cybersecurity episode. It compares the performance of the S and P 500 and the NASDAQ as of February 19th, with the performance of four-star cyber players from that date to Thursday, June 11th, the day that saw an 1800 point drop in the Dow. So some of the steam has been let out of the market, but the story really isn't going to change that much. First, the S and P is off 11% since that time, but the NAS is only off of 3%, tech heavy. But look at the deltas of our four-star companies. Let me start with Splunk. I didn't show Splunk earlier on the charts, but the value metrics of Splunk, they really haven't moved much since our February report. Splunk's Net Score was down somewhat in the sector, but remember, Splunk does more than just security. It's really becoming a critical big data player in analytics. I think people maybe don't like the tepid 2% revenue growth that Splunk showed, but remember Splunk is transitioning to an ARR model, an annual recurring revenue model, and that's going to take some time. It acquired SignalFx late last year to give it a stronger SaaS play in monitoring, and of course the analytics. I like Splunk, just like Adobe and Tableau had to make a similar transition, and ultimately they powered through it because they're great companies with really loyal customers, and I think that really does apply to Splunk. Let's take a look now at Palo Alto Networks and Fortinet. Now, you might remember in our last security update, we spent a fair amount of time explaining the valuation divergence between Palo Alto and Fortinet due to some of the cloud challenges that Palo Alto was facing, even some of the sales motions. So we said Fortinet at the time had done a better job transitioning to a cloud, but Palo Alto really had a good quarter. It beat earnings revenue, and it gave guidance, and the stock moved up very nicely. But then it ran into resistance, and you can see it's a tracking about what the S and P 500 over this period of time. And you can see the revenue multiples show the valuations divergence between those two companies. It's even more stark. So you've got Fortinet's kind of holding firm, and Palo Alto, dipping a little bit. Now, let me make some comments here. I mean, I like Palo Alto Networks. Not only are they solid in the ETR dataset, despite the COVID pandemic, but anecdotal evidence in discussions with IT leaders suggests that organizations want to do business with Palo Alto. They're really considered a thought leader in the space. And I personally, I think they're going to do very well this decade. So now maybe there's some technical aspects going on with the stock. I'm not really qualified to address that. But they clearly saw some resistance despite bouncing on the strong quarter. Just couldn't hold. Now, let me skip over the green box, and I want to quickly comment on the last two here. I'm going to start with CyberArk. They are underperforming, this group, even though you would think with the focus on privileged access security, they'd do well in this environment. And they beat last quarter, but they suspended guidance, and they cited exposure to some hard hit industries on their earnings call. And as well, it just is interesting, the company is aggressively hiring. And so that increased op ex substantially. The thing in management is confidence, you know, what do they know that the street doesn't know? And they're just being cautious, you know, but they are taking a valuation hit as a result. We'll see how that plays out. Now, Proofpoint has also taken a valuation hit in our period of analysis back from February to now, despite beating estimates last quarter. You know, maybe not as strong as a work-from-home play, but again, a beat in this environment is definitely a positive. Now I want to come back to the three key companies highlighted in the green, Okta, CrowdStrike and Zscaler. Zscaler, remember, we added new in February to our four-star list, which we initiated last year. The valuation of these three companies has soared since the pandemic, and they've reported tailwinds as a result of the new reality. Okta with its identity management focus, CrowdStrike with endpoint, and Zscaler with its security cloud, are all seeing momentum. And it makes sense that these three are very focused and they're aligned with our remote worker economy, and of course, a shift to the cloud. As well, they all beat earnings and management had a pretty sanguine outlook going forward. But I want to call your attention to the revenue multiples of these three companies and take a look and compare them to their peers. You know, are these justified? Well, as I said before, there's really a difference between the stock market and what's happening in the real world today. So I would say, you know, I want to see these companies continue to outperform their estimates, and their strong guidance. And frankly, at these revenue multiples, I'd expect, you know, even higher growth rates of, especially from Okta and Zscaler. So we'll see. The point is, the market's exuberance, it's really based on future expectations. And I do think there was a bit of, you know, FOMO, fear of missing out, at play here with investors hopping on the bandwagon. Remember, look, the data from ETR shows that these companies are pretty strong, and of course, much of the stock action is based on performance relative to earnings estimates. So we'll see if this can continue. I mean, to me, it does feel a little frothy even after that recent sell off. All right, let's wrap up. So the disconnect between financial markets and the real world economy, it creates uncertainty in the market. So you got to be cautious, really, if, especially if you're chasing momentum. I just want to say, I know a lot of young investors who reach out to me and they comment to me in these segments. And look, I'm not qualified to tell you where to invest. I just report on the fundamentals and I try to tie in financial trends, and market trends, of course, But you got to do your own research, you know, be patient, do your dollar cost averaging thing. You got a long life to live. Now, the after COVID AC economy and the remote work-from-home momentum will not be a rising tide that's going to lift all ships in this segment. But there's no doubt that CSOs are rethinking cyber. We've said for years that protecting the perimeter was going to change as the main focus. And it has to a degree. But I'll tell ya, I think the mindset has changed more in the last 90 days than in the previous three years. The scourge of VPNs, and even the efficacy of SD-WAN are being called into question as security technologies that exploit the internet and cloud appear to be very sensible to CSOs and have momentum. You know, we're also seeing more collaboration between organizational boundaries, and even many CIOs are becoming much more involved in security as their line of business tends. And even some CSOs reporting it to CIO's. As we've said many times, cyber has become and will continue to be a board level agenda item and topic. On near term, we really don't see the fragmentation of the products that we've talked about for years changing. If anything, the shiny new security tools, you know, might even increase granularity in the marketplaces organizations, they can't just unplug their legacy infrastructure as much as they they'd like to. But longer term, there will be more consolidation in this market, as the whales are going to buy companies to fill holes in their lines. I mean, look at VMware, there's a good example of a company we really haven't talked about trying to elbow its way into the security space. And the cloud, as well, was going to attack some of the problems of complexity, which in part stems from too many tools, and that will foster some of this collaboration expectation. Okay, well, that's it for this week. Remember, these episodes are all available as podcasts. So please subscribe. I publish weekly on wikibon.com and siliconangle.com. So check that out and please do comment on my LinkedIn posts. You can email me as well, at david.vellante@siliconangle.com. This is Dave Vellante for The Cube Insights powered by ETR. Thanks for watching, everyone. We'll see you next time. (mellow digital music)

>>Yeah, >>from the Cube Studios in Palo Alto and Boston. It's the Cube covering IBM. Think brought to you by IBM. >>Welcome back to IBM. Think 2020. The global experience. My name, Stupid man. And happy to welcome to the program. Aaron Bobick, who is the vice president and chief design officer for the IBM Cloud Data and AI portfolios. Thank you so much for joining us. >>Thank you, Steven. Great to >>be here. Alright. So I always love talking to design people. My background is engineering. I said on the Cube a couple of times I feel they didn't really teach us in school enough about design. We all know on the consumer side, when you have >>a >>phenomenal technology and beautiful designed together, it's an amazing experience. So you've got a brought purview. You've had a very diverse background. Help us understand. You know what a chief design office they're across, you know, cloud and Data and ai is responsible for >>so in a in a just my job is to really ensure that we design and develop usable and meaningful experiences for our users. Finds customers and partners in the little mawf cloud in the eye both evolving technologies. Um, adoption challenges here and there, and our job is to simplify >>the complex and the network. Okay, that's awesome. You know, I think back, you know, early web days, you know, we were happy if we just had a u I let alone Didn't think about the ux experience there. So you know, what are some of the important things? You know, what? What's IBM looking at? To make sure that that user interface is something that is Yeah. >>So I'll take a step back. And question is doing Say that, you know, in the sounding times while we're still figuring out new ways stood up So to get work done and really get the essence off being more productive design is there to help figure out a solution to these human, because at the end of it, design is really an expression of intent and intend to help solve the problem and overcome everyday challenges. So, you know, be at IBM is basically focusing on helping our users and partners and customers be more productive. And the feeling is that design has become really important to IBM, not just IBM does. Other landed companies are having great advantages. So if I just call it a few studies in a recent guard from the study found that 89% of companies that they would focus and you extend them apart. So this is about differentiation by design the second Forrester Little study, and they found that 70% of projects fail because of poor us, and that's a huge number. There's also city by the GM of the Design Management Institute that says that design that companies are poor home S and P 500 by 20. So all in all this is that design is now a very important aspect of how we go to market, and it's essential. The good news. IBM has always been part of Indiana money for ponderous Thomas. What Jr said, Good design is good business, though We're in it for the long run. >>Yeah, obviously a long history. There are over 100 years of focus on that. So one of the big themes we've heard the last couple of years, you know, see X. That's about that customer experience and not only the external customers but the internal customers we're talking about, you know, support agents and the like. So how is IBM making sure that it is on the leading edge for the >>great questions to over the last? I would say a good 10 years. We really work hard to develop a culture off designing, design, thinking and close by IBM. Whether it's product development, the services we offer support. We work with customers pretty much every touch point of the user has with us. Design has had an influence in it. To get to where we are today, we had to go hire a whole bunch of formally trained designers. We're working across more than 50 plus global design studio to bring in diversity and part of an idea. And at the end of the day, it's not about this confidence in craft. It's also what the baby work. So we had to hire designers, but we also changing the way IBM offers across organizations work. The level of the strain were called the Enterprise Design Thinking Framework, which is essentially our take a human centered design. Build a scale for the enterprise, so the enterprise is a key element here. The practices we've developed using those frameworks helps our team collaborate better keeping the users and their need at the center of everything we do. But it's not just for us. We also developed it for generally everyone. So if anyone wants to take it up, they could try IBM dot com slash design thinking and give it a shot. And through all of these, we have managed to see some incredible progress internally across organizations with alignment and go to market. But we've also seen some great progress that internally as well, case in point over 20 international designer words for design in the Enterprise. But with the last two years across the portfolio, So it's been a fun ride and our focus for customer experience because the endpoints, all the touchpoints has really given us >>a lot of minutes. Well, congratulations on the award is there. We know enterprises are particular and challenging there. They're not necessarily the first to deploy something new. But one of the big discussions we've had for years when you talk about Cloud and AI is a skill set and training. So what are some of the unique challenges that you have from a design stand point in the enterprise? >>I think the answer to your question is in your question, and it comes down to the enterprise. Enterprise is unique in many different ways, right? First of all, it's about mission critical needs, and second is about productivity. Our minds and the users are coming to us to help them solve these massive, complex challenges and problems, from data management to automation to modernization, to being on the cloud or adopting AI. They're really looking detained, the way they work and at scale. This means that we, as designers and at IBM, have to really take the time to understand the users, to see what their pain points are detected environments and the context of the working so that IBM can ultimately >>help solve the conflict. >>No, that's one part second because it's in the enterprise but also dealing with the fact that technology is evolving at a very rapid pace. Thinking about containers, ai Blockchain, you name it and we know that in order to meet the needs of this modern day age workers, we really need to think out of the box and be a little bit ahead of the curve designed for collaboration and the adoption of these emerging technologies without adding a huge learning curve, but that's a challenge as well. How do we adopt technologies without adding learning curves? So as a profession in design, we have to keep up with it, adopt and constantly lead with innovation. In essence, you know, designing for the enterprise brings interesting and unique challenges, and IBM is >>up for it. Well, you know, it sounds great to talk about just having a design that is super easy. And people get, um I'm wondering if you have any, any tips that you could have out there because, you know, I know myself. I'm always Frank, talk to other people, understand what they're doing. And sometimes it's like, Oh, well, today I learned this, and I wish I had learned this two years ago because, boy, you saved me, you know, an hour, a week of my time when I did this. And it's one of things I enjoy doing is trying to help people with short cuts or new ways of doing things. So we get set in our ways when we learn a new technology that tends to be where it fossilized in our brain, and it's upto look at something with fresh eyes and say, Oh, I got an update G. Maybe I should press that button and or float over and to understand what it does. Is there any any guidance that you can have? Is how do you make it simple and intuitive yet overcoming all of the legacy that we have when when we come into it with what interfaces were used? >>I do think that designers have this unique talent of being able to connect the dots, and that's our superpower. So in terms of tips I would take get to know your users get to know them really, really well, think about what exactly are their blockers and then think about technology and see how it can solve that over to connect the dots. So just to give an example. And I was talking about sort of design being broader than this interface design, you know in IBM started reacting to over 19. We need a lot of things. One of the things we did was we kinda defined solution to improve human computer interaction, very using sort of AI technologies like Watson Assistant and Children's Hospitals to help answer the huge number of questions coming in around 19. So from that standpoint, design is about beyond interfaces. And I feel if we take a step back and figure out, what problem are we trying to solve here? And how do we ensure that the users mental model off the things that they used to using in the everyday use, like 20 maps? How can you bring in those innovations back in the enterprise? That issue? >>Okay, you mentioned technologies are changing so fast, you know, AI containers loud. How's your team keeping up with all of this? You know, the pace of change and stop for a drop. You know, we're in S T I C D model these days. So what's the role of the designer in both? Keeping up with the new things and making sure that you know you're helping the user along the way. >>Fortunately, IBM we have a few advantages in having a broader organization called IBM Research. And IBM Research is a little bit forward facing, and they try to predict the uptake of technology that we have a little bit of a heads up on stage now that is a quantum computing, and such as Well, we got enough up there to as a designer. The inherent trade for designers to be curious and Barbara curiosity is to make sure that we learned, and we can combine them and instead of you bring in a sponge. And I think the fact that designers have this golden acid of empathy is very tender and used, and these superpowers to work with designers in other parts of the business, depending the doctor. But how can we not only solve? The problem is we see it but also solve the problems that are not visible. So the later needs of users. So I feel in a lot of different ways. Designers, you know, >>I >>have to be curious there to solve complex problems, and they have to keep up with technology. It's decimated. >>Yeah, I'm curious. It's exciting times. What excites you about the field of design these days? >>I had no Let me take a step back. Your question at the heart of it. I believe that I'm a designer because I believe we can design solutions that impacts people's lives. So in some ways we are adding to a value of human life, and that's what you mean to design and especially in enterprise design, is about that complexity if the messiness off, complex infrastructure and business use cases and localization and globalization is a really hairy problem. So I feel from an intellectual standpoint, this gives me a way to use my that are curious mind as well as my expertise to help solve this problem. So that's what drew me into >>delight. Excellent. Well, so much going on at IBM Think this week I want to give you the final word. What message do you want to share with IBM users, customers and business partners? >>Thank you. Stupid opportunity. Of course. I want to say thank you. Thank you for believing in us for being a North Star. You are The reason why we've invested so much in design and user experience really make our lives better and your willingness to sort of work alongside us every step of the way. It's really appreciate it. I mean, we tend to really feel that you see with us, so help us innovate, help us bring in great experiences that help you get your business are so on that note. If I could do a little shout out to want to be for our customers and prospects here who are listening in the joining on the user experience program. So we can co create experiences with you to solve your problems and hopefully build solutions that you love. Check out the link IBM that based on these experiences, the easy sign up and the second thing that popped a little bit of a user research like invite you to join in on the research about your journey here is that it's still involving field. I understand we're all going to challenges in adopting AI. Let's all learn, share and help each other and infusing AI in your enterprise. Thank you for being >>part of our innovation journey. Excellent. Well, thank you so much for sharing with our community. This update love the fusion of technology and design co creations. One of our favorite words when we talk about this part of the model that we do on the Cube. So thank you so much for joining us. Thank you. All right. Lots more coverage from IBM. Think 2020 The global experience. I'm stupid, man. And thank you for watching the Cube. >>Yeah, Yeah, yeah, yeah

(upbeat digital music) >> Hey welcome back, get ready. Jeff Frick here with theCUBE. We're in our Palo Alto studio for a Cube Conversation. Again, we love talking with little companies, emerging companies, kind of maybe technology you haven't heard of before and we're excited to have our next guest 'cause he's right in the heart of security space, which is always a hot topic, continues to be a hot topic and will never go away 'cause the bad guys they just keep working hard to try to break everything that we create. So our next guest is Ambuj Kumar, the co-founder and CEO of Fortanix. Ambuj welcome. >> Thank you, Jeff. >> So give, for the people who aren't familiar with Fortanix kind of the basic 101. >> Yeah, so if you look at all the security today, it falls into three categories. One is protecting your data address. So what that means is, if somebody steals your laptop, how do you protect your hard drive from getting exposed? >> Right. >> So we use encryption for that. Similarly, we also use encryption to secure our data in use. So we connect to some bank website and our data goes encrypted through TELUS and so what that means is if somebody's doing wiretapping our data is protected. However once the applications start to run, whether it's in your data center or public cloud, then the data applications are being exposed. So to fix that Runtime vulnerabilities what the industry has done so far is to secure the infrastructure, try to secure the infrastructure and that is $80 billion per year industry. But we have failed to that because infrastructure is just so vastly complex. So what we do is we use something called Runtime encryption and idea is that your data and applications remain encrypted, so even when people who are running your cloud they're untrusted and they want to get your data, they can't do anything with it. >> So, a lot of stuff there to unpack. So first off we know the perimeter systems don't work anymore. >> Yeah >> I mean you got to put them up they do some level of stuff But you can't secure the perimeter anymore. So it is all this kind of working your security >> Yeah and the encryption all the way through the process. But this is pretty interesting I've never heard of encryption actually at Runtime, I mean it begs the question, you know how does the microprocessor run the encrypted data? >> That's right So it's a long research problem in security. People had been working on something called Fully homomorphic encryption and the idea is that: Can I take my program encrypted data encrypted and run in totally untrusted environment and give you the result that you can decrypt. Chances are that you can do that with very simple programs, like if you're adding some numbers, multiplying those numbers and even in those cases slow by many orders of magnitude. So what normally some operations takes one second will it will take three years. >> Okay >> Not good. >> Laughs >> So what we do is we use some new instructions from Intel called Software Guard Extension, Intel SGX and your data and your programs, they get decrypted in a secure region of CPU So all the memory, all the operating systems accessible things, anything that can be touched by any other process, they only can look at encrypted stuff. Your data get decrypted right when instructions are working on them and at that point it is accessible only to your write process. >> Right. >> So you use this hardware capability to accelerate the encryption decryption. So we can provide all the benefits of fully owned morphic encryption at a performance that is totally acceptable to our customers. >> So let me make sure I understand, So it decrypts it literally at the last possible obviously not second >> Yeah but last possible (laughs) in microprocessor time >> Yeah cycle, runs that process and then is write only to the output of that process. And is that immediately encrypted again >> Exactly >> On the write side as well? >> Yeah Yeah, exactly. Exactly. >> (laughs) So you mentioned the Intel instructions So is this relatively new, the SGX? >> Yeah, so we were first vendor to commercialize Intel SGX, its a new technology, but it's coming in all their CPU's so right now it's in all client CPU's, and some of the data centers CPU's But five years from now all the CPU's you will get from Intel will hopefully have this technology >> Right So obviously Skylake >> Yeah Skylake has it and all newer architecture. >> Wow So a little bit more about the company How long you guys been around, how long you been working on this problem you know funding kind of give us the overview on the company. >> Yeah >> So I have been working on encryption for last seven years the company was founded two years ago >> Okay >> We are funded by some well known security VC's including Foundation Capital and NeoTribe Ventures >> Okay >> We are widely recognized as the pioneers in this field that we are creating Runtime encryption. Recognized by Gartner's Cool Vendor we came number two in RSA Innovation Sandbox you know hundreds of security companies. We have several S&P 500 customers already so we are deployed in their products and environment, we are securing trillions of dollars of assets in realtime. Our goal is to convince CIA to run their most prestigious most sensitive applications on some untrusted cloud in some enemy country. >> Laughs >> It's a long shot >> Are you doing like a POC of something like that with them? Are you in active conversations or is that more of kind of a philosophical goal? >> I cannot confirm of deny that >> Okay, fair enough >> But that's our goal. And until we achieve that, we have something to keep working on. >> Okay. And then where do you guys sit kind of in the world of public clouds with AWS and Azure and Google versus either private (mumbles) or multiple clouds inside the company or you know some of these other kind of options like we hear like the Equinix which I think is one of the places >> Yeah >> How's that work? >> Yeah So our goal is to extricate security from infrastructure So in the end, our goal is that infrastructure will provide you compute cycles and the security will come from the customers, end customers who are developing the applications and deploying the applications. >> Right >> So its cloud agnostic security so meaning that we will go after on-prem customers, we'll go after public cloud, colo and all of that >> Right >> So in the meantime for our go-to market what we did was we partnered with two of really well known strong forces in the industry, one is IBM Cloud >> Yeah where IBM is putting this servers and running our technology and with Equinix, which is world's largest data provider and so if you are in any of the public cloud, if you are in IBM cloud you get our security by default so you are continuous running encryption >> Right >> Isolated from all the threats that might be there, or if you are in some other public cloud you can use it Equinix colo so if you have some applications that you don't want to be hacked you can use our SAS service to run those applications encrypted. >> Right And of course Equinix has got the direct connect to all the public clouds >> Yeah >> So minimum latency integration >> Couple of milliseconds. >> with all the other stuff >> in the public cloud. >> Yeah exactly. So what's the expense, both kind of the overhead expense on the computing side to do this when it's done properly and then what's the expense to run this is this something that is expensive can only be used for the most critical applications, or do you see this several times being more general purpose execution? >> So its will be used to secure anything that you don't want to be hacked and the cost of using Runtime encryption is minimal so I expect it to be wisely adopted and we make it really easy for developers and security organizations to use this technology. So you have to bring in your container and then Fortanix process attaches to your container you don't need to recompile your source code we never get to look at your source code there's no binary transfers nothing like that. And then so it's a simple millisecond long process and we give you modified container and now you can take this modified container run on any cloud you want and if it runs it runs securely. From that point onwards. >> Right And today you just have to make sure its got right microprocessor >> Yeah and in the future hopefully that will be more general purpose. >> Yeah >> Alright So what's next? What are you working on, what's a priority for the balance of 2018? >> Yeah, so we have lots of integration work going on VIA World is coming next week We have support for something called Kermit that allows you to secure your estorial box v send et cetera with Fortanix. Now we are also running integration with some data bases some multi party computers and things like that. So our goal is to make our technology more widely available to a large variety of customers. >> Alight, well Ambuj very interesting story, Encryption at Runtime so >> Yeah >> So we look forward to watching the story unfold. >> Awesome, yeah This is a decade long journey and I think when we have done infrastructure security will be irrelevant. So its going to be very exciting for all the parties involved. >> Alright, we'll keep eye, thanks for stopping by. >> Thanks >> Alrighty, Ambuj Kumar You're watching theCube from our Palo Alto studios See you next time. And thanks for watching. (epic orchestra music)

>> Announcer: Live from Nassau in the Bahamas, it's the Cube. Covering Polycon '18. Brought to you by Polymath. >> Welcome back, everyone. This is the Cube's exclusive coverage. We're live in the Bahamas, here for day two of our wall to wall coverage of Polycon '18. It's a security token conference, securitizing, you know, token economics, cryptography, cryptocurrency. All this is in play. Token economics powering the world. New investors are here. I'm John Furrier, Dave Vellante. Our next guest is Nithin Eapen Who's the Chief Investment Officer for Arcadia Crypto Ventures. Welcome to the Cube. >> Thank you very much gentlemen. >> Thanks for joining us. >> Thanks for coming out. >> Excited to have you on for a couple reasons. One, we've been talking since day one, lot of hallway conversations. Small, intimate conference, so we've had a chance to talk. Folks haven't heard that yet, so let's kind of get some of the key things we discussed. You are very bullish and long on cryptocurrency and Blockchain. You guys are doing a variety of deals. You're also advising companies and you guys are rolling your sleeves up. So kind of interesting dynamics. So take a minute to explain what you guys are doing, your model. >> Okay. >> And we're going to try to get some of your partners on later. You have a great team. >> Yep. >> Experienced pros in investing. And you got wales, you got pros. So you got a nice balance. >> Yes we do. >> So take a minute to explain Arcadia, your approach and philosophy. >> Okay. Okay. So Arcadia Crypto Ventures primarily we are a private fund. We invest other money. We believe in the whole crypto space. We believe this market is expanding and it is growing and it's going to be the biggest thing that ever happened. It's going to be this fusion of internet and PC and mobile. And everything is going to go batshit, okay. We believe in the whole tokenization world. Everything is going to be tokenized. So as a whole, we believe this space is going to go very big. Okay, so that's one piece and because of that, we invest in the space, the whole space. Not one bitcoin or Ethereum, but everything in the space that makes sense. People who have a use case. Now the second piece of it is we advised great founders. We want to get founders to come out and build these new things because this is the new internet of the new era and people have to come out and build these things. And so many of them are traditional businesses and we have to explain to them why this matters, why you should come to this space and be decentralized and reach the whole world. Because initially, the internet came. The idea of the internet was everybody gets information. Now information did get everywhere. You don't have to worry that the mailman is there to deliver your email anymore. Even if it's a Sunday, your mail will get delivered. So that part was good. But now you have these few companies that's holding all your data. It's okay for most people, but they do censor a lot of people. So that is one point. That censorship. We want a censorship-resistant world where everybody's ideas get out. So that way, we believe that's how this whole internet space itself is going to change because of that. See this is if I explained in one word, this is the greatest sociopolitical economic experimental revolution ever that has happened in humankind. >> In the history of the world. I mean this is important. I'd said that on my opening today. >> Uh-huh. >> Dave and I were riffing and Dave and I have always been studying. We've been entre-- We are entrepreneurs. We live in Silken Valleys in Boston and so you seeing structural change going on. So it's not just make money. >> Nope. >> There's mission-based, younger demographics. So you starting to see really great stuff. So I want to ask you specifically, 'cause you guys are unique in the sense that you're investing in a lot of things. But startups, pure-playing startups? >> Which had only one path before, or two paths. >> Right, yeah. >> Cashflow financing and venture capital. >> Okay. >> So that's a startup model. The growing companies that are transform their growth business with token economics, those would have long odds. Those are the best deals. >> Okay. Then there's like the third deal. Well we're out of business, throw the Hail Mary, repivot. (laughs) Right, so categorically, you're starting to see the shape of the kinds of swim lanes of deals. >> Okay. >> Okay, pivoting, that Hail Mary. Okay, you can evaluate that pretty much straight up on that. Startups need nurturing, right? >> Yeah. >> So the VC1 al-oc-chew works really well for startups because of the product market fits going to be developed. You got cloud computing so you can go faster. So you guys are nurturing startups. At the same time, you're also doing growth deals. >> We do. >> Explain the dynamic between those kinds of deals, how you guys approach them. What's the dynamic? What are the key things that you're bringing? Is it just packaging? Is it tech? So on, so forth. >> So with a lot of people, when they are on the advisory side. Primarily we look at the founder and the tech. What are they trying to solve? That is key. If it's a turd, you can't package it. No matter how you package it, that's not going to work. >> You can't package dog you-know-what. >> Yeah, exactly, okay. >> So that's one thing that we look at. The founders and their idea. Now their idea, can it be decentralized? Some models are meant to be centralized maybe so it doesn't work, okay. Like, see it all boils down to-- Let me break it down. We look at it. Okay, do you have an asset? Behind the scenes, is there an asset? Is that asset being transferred among parties? If you have an asset and it's being transferred, is there some central mechanism in between? Because if there is a central mechanism in between, that means you're going to be paying rent to that. Okay, all right. You have these things. Okay, great. Now you have your asset. Do you have that in between party? But in some of them, let's say you have money in your pocket. You walk, it falls down. Somebody else pick ups the money. It's his. It's a bearer asset, okay? So that's where bitcoin solved a very big problem. It was bearer asset. >> Unless they hack your wallet, then they take your money. >> Right. That happens in real life too, right? Somebody can take money from your wallet. So it can happen in bitcoin. They can hack your wallet. All right. So bitcoin was solving that problem. Now the second piece is a registered asset. And I mean by registered asset is take your car. You buy your car, you go to the DMV, stand in line, register. There's a record of data at the DMV in their central database. If somebody steals your car, the car is still not his. It's only if they can change the record over there in DMV. Then it becomes his. Now there maybe you do want the DMV to be there. Or maybe we can-- But the DMV being there, now you have a problem. They're going to charge you rent and they can decide, oh you know what? John, I'm not going to give him a license or a car in the state of California. They can decide, right? So that is where now you decide do you want to go the centralized route or the decentralized route? So we break it down to the asset. >> So there could be a fit for decentralized. I get that. >> Yeah. >> Let me ask you a tactical question, because I know a lot of entrepreneurs out there. They're watching and they'll hear this. A big strategic decision up front is, obviously, token selection. >> So it's pretty clear that security token works really well for funding and whatnot. Then there's a role for security tokens. I mean utility tokens. >> Yes. >> So do people, should they start from a risk management standpoint, a new company. So let's just say we had an existing business. Entrepreneur says, "Hey, you know what? We're doing well. We're doing 10 million dollars in revenue and I want to do tokenize 'cause we're a decentralized business. That's a perfect fit." Do they start a new company or do they just use the security token with their existing stable company? >> I would suggest, usually at that time, that's more of a legal question at that time. I don't know if I'm a lawyer to answer that. I tell them, you have a business. The business model is going well. If you're happy with it, let that be there. Make a new company. If your business model was not doing good, you might as well start from there because you figure out it's not working. But again, at that time, we tried to come up with this question. Are you trying to put the old wine in a new bottle kind of thing? If the wine is old, it ain't going to work. You have to get to that realization. So, here. >> People are being sued. So mainly the legal question is do I want to risk being. >> All right, let me hop in here. I wanted to ask, go back to something you said about censorship. I had this conversation with my kid the other day. I was explaining Google essentially censors your search results based on what they think you're going to click on. >> They do that. >> He's like no and then he thought about it and he's like okay, yeah they kind of do that. Okay, so that's an underpinning of we're going to take back the internet, right? >> Yeah. >> Okay, I just wanted to sort of clarify that. From an investment philosophy standpoint, you're technical, yet you don't exclusively vet or invest in infrastructure protocols and dig deep into what-- You read the white papers, but there are some folks out there hedge funds, et cetera. All they do is just invest in utility tokens. They're trying to invest in stuff that's going to be infrastructure for the next internet. Your philosophy is different. You're saying, we talked about this, we don't really know what's going to win, but we make prudent investments in areas that we think will win. We like to spread it around a little bit. Why that philosophy? May reduce your return, but it also reduces your risk. Maybe you could describe that a little bit. >> Sure. See, in general, picking winners in the long run has been-- It's a proved fact that nobody could pick winners. Like if you take active hedge fund managers. Active hedge fund managers, in the long run, if you take 10 to 20 years, they lag the S and P. So if you had money, if you give it to an active hedge fund manager, and so that you just had to buy the S and P, you will have beaten 93%. >> That's Buffet's advice. Buy an S and P 500. >> Buffet made a bet for a billion dollars or something where, you know. So take Warren Buffet for that matter, his fund is lagging too. In reality, all his stock investments are down. He put it in IBM at $200 after eight years, it's at the 143 or something, right? So realistically,-- There's a lot of luck element, okay. You can do all of the analysis and you could still end up buying Enron, Lehman, and Bear Stearns, right? >> Right, yeah. >> And at that time, see they were using some models that they knew 'til then. Most people, investment comes from, you have this background that you know, okay this is what I look at. Cash flow, discounted cash flow. Great. If that is there, price to earnings, I'm going to buy. But then an Amazon came, most of the traditional investors never invested in Amazon. They were like, it's a loss- making company. They never going to survive. But they forgot the fact that companies like that there's this network effect and once the people are there, at any point, Jeff Bezos can just turn off the switch and take off the discount. You're not going to change your shopping from Amazon at that point because this month I lost my 15%. We're so used to it so people missed that. Nowadays they see that, but when it came to Blockchain they're like, oh, no, no, this is a fad. That's what most people said. >> So we talked about discounted cashflow as a classic valuation method. I see guys trying to do DCF on these investments. I mean, we were joking about that. (laughs) How do you-- What's your reaction to that? >> If anybody's saying that if they come to me and I'm like you-- I don't know what Kool-Aid do you drink at that point because what cashflow are they discounting? There's no cashflow. It's not like you're going to get dividends from these tokens. There's no dividends. It's like can you find out how many people are going to use it. What is the network effect? And again, for that, a lot of people are coming with a lot of these matrices or matrix right now. But I think even that, they're trying to retrofit into it. They're like, oh I can use this matrix. But, really we don't know. >> So people tend to want metrics. Dave and I talk about this all the time. When people part with their money, they need to know what they're betting on. So the question is when you look at investments, when you spend cash, when you write checks, what is your valuation technique? Do you look for the l-- How do you play that long game? What's the criteria? Besides like the normal stuff like founders, disruptive, like you got to write the check, let's say. Okay, buying a token. It's got to be worth something in the future, obviously. >> So we look at that space, where invariably they are trying to disrupt. Is there a big market? And even if it's a niche market, okay? So we're doing an error chain token. It's a very niche market. It's just the pilot, the maintenance folks, and the charter people, or the plain charter guys. It's a very small market, but that's good enough. It's very niche. They can have an ecosystem between themselves rather than being incentivized to long game miles and stuff like that, right? It doesn't have to be a very big market. We just look at it, okay. Founder is good, he has an idea, it is a space that can be decentralized and people can come in and they feel that they're part of the ecosystem. See the whole thing with the token economy and a traditional economy like let's say I'm spending money to buy a stock. So I buy stock. As an investor, what do I want? I want maximum returns. The employee, he wants to get maximum pay. And the consumer who's buying the product, he wants to get it at the cheapest price. So there's a-- It start aligned, okay? The moment you give 'em the cheapest price, my profits go down. If I increase the employees' salary, my profits go down. So we are all three of us are totally misaligned. >> If I for an important point, do you favor certain asset classes, you know, token, security tokens, or utility tokens, or you looking for equity? I mean, maybe just ... >> Right now, we've moved away from the whole equity bonds, or any of those things. We are totally concentrated on the utility or security tokens. We don't mind if it's a security token or utility token. >> And if it's a security token, are you looking for dividends, are you looking for >> At that point it's some kind of dividend. >> So you're not expecting equity as part of that security token? >> No, I like to expect equity, but if they are saying okay my token, if people buy and if they pay me $10, and out of that you're going to get $1 back, okay that's fine. We don't mind that as long as it's legal and all those things we're fine because it just makes the process easier. Earlier you invest and you didn't know when you could get out of your investment. At this point, it's become so liquid, at any point of time within two or three months, the token is less to people are either buying and selling. We know, otherwise, earlier when we used to do Ren Chain investments, we would get into our product, have it it's time seven to 10 years to get out. And in the meanwhile, they say great stories. Oh we're doing great. Who do I check with that we are doing great? I'm not getting any dividends. Nobody's buying this from me. How do I know? Where am I? I really don't know. I can make these values up and on my Excel sheet and say okay we valuing this company at a billion. >> So your technique is to say okay look at the equity plays the long game. You need an exit on liquidity, either M and A or IPO. >> Yes. >> Now you have a new liquidity market, so you play the game differently. I won't say spray and pray, but you have multiple bets going on so you can monitor liquidity opportunity. So that's a new calculation. >> And it's a great calculation, also. Because see we're in the market and now we know at any point of time, we don't have things on our books that are like we don't know what the value is. We know what that price is because the market is there, the exchange is there. What other people are willing to pay for us doesn't surprise. It's like saying my house is worth a million dollars. Actually it might be worth to me. It depends on what people are willing to pay me. >> Right exactly. >> If I have to synthesize this, you're taking high frequency trading techniques with classic venture investing, handling token from those two perspectives. >> Yes. >> High frequency trading meaning I'm looking at volatility and then option to abandon and get rid of whatever or whatever. >> The only thing is, we're not exiting our positions. We are in the long game. We believe the score market is supposed to at least reach eight trillion. When we started this whole investing, at that time, the whole market was at six billion and we said okay this market, based on our thesis, is supposed to reach eight trillion. Until then, we keep buying, okay? >> But to your HFT, you're not really arbitraging. >> No, no, we're not doing any of those. Because see >> They're applying real time techniques to token evaluations so they're game is try to get into a winner. >> Yes. >> With some tokens. >> A lot of the funds, they're doing this arbitrage more. They're trying to do arbitrage. But the problem is they're missing the big picture that way. So, arbitrage works in a very tight market. So S and P, let's say, somebody's doing 5% return on S and P. The guy with a arbitrage is coming and saying I made five point three, 5.5% or 6%. That's great in the equity world. Now, I want returns last year are 10 x or 30 x or 50 x. And somebody comes and tells me I made an extra 0.2%, doesn't really matter to me. I'm like instead of wasting that time doing arbitrage and paying taxes, I might just hold it. >> You believe in the fundamentals. >> You guys are in New York. Obviously, Arcadia Crypto Ventures, that's how they get ahold of you guys. Final question for you to end the segment. As new real pros come in, and let's take New York as a since you're in New York. The New York crowd comes in or the Silken Valley comes crowd existing market players other markets come in here. How important is optics packaging and compatibility with the sector, meaning I just can't throw my weight around on the hedge fund scene. We do it this way, I got money. Because people here have money. So what's the dynamic of pros coming in, we're seeing institutional folks come in, we're seeing real pros come in. They've never been to Burning Man. So, you know, they get that Burning Man culture exists, but this is not a Burning Man industry. >> Right, right. >> Business doesn't run like Burning Man. Maybe it should, that's a debate we'll have. Your take. >> So the new funds that are coming in, so they have a fear that they have missed out. They are missing the picture that this is just the beginning. So they've seen that this industry has gone from six billion to 500 billion in a year or year and a half. They're like, oh my god, I missed it. >> It's got to be over. >> So I have to write these big checks to get this. We don't write big checks. We write much smaller checks because we believe that if a founder is raising money, he has to raise it through small checks from everybody. That means all those people are really interested in this. And they're all of them really want the token to go up. Whether it's the investor, the user, and the employee who is working there because all of them they're interests are aligned. The moment you give a big check, so let's say you could raise 10 million from 10,000 people or you could raise it from one person. So when the big check is there, let's say I go to raise my money. There's this fund who's missed it and he says here's 10 million dollars. Okay, now I've got me and the fund and my tokens. Nobody else knows about my tokens. My tokens are as good as valueless. Now the funders looking okay, I need to exit. Nobody knows about my tokens. The fund is the only guy who has my tokens, he's trying to exit. Obviously the market is going to crash. There's no market. And he's like why did I get into this. So he missed that point that you need people around you. It's not just you alone. See, earlier days when ... >> This is your point about understanding how token economics works. >> Yes. >> So having more people in actually creates a game mechanic for trading. >> Because then you know that you're not the only guy interested in this. And earlier venture capital space there were these bunch of few venture capitals who wanted to capture that whole thing and tried to sell it to the next guy. Here, I'm what I'm saying is, we all have to come in together. We all can be together at the same price, which is good because the small person has, the common man has a chance to be a VC right now. Earlier you could never be a VC. I could only see Google, after IPO. I could never get it at what KPCB or Sequoia got it at. I had to wait 'til they got through CDA, CDB, which they bought at five cents. I would get at about $40 maybe. In this case, the big fund has a lot more money than me, but I can have my small 5,000 or 10,000. I can invest in the ICO. >> If you picked the right spot and you were there at the right place, the right time. 'Cause you are seeing guys come in and try to buy up all the tokens early on. >> They're trying to do that. They don't get it, but they will understand. So it is a learning (mumbles). Even they will evolve. They're like okay this is not how it works. And you have to make mistakes. >> Sorry, got to ask you one final, final since you brought it up. More people the better. So we're hearing rumors inside the hallways here that big wales are buying full allocations and then sharing them with all their friends. >> Possible, it is possible. >> We see some of that behavior. Dave calls it steel on steel, you know. Groups, you know. I'm going to take this whole deal down. We see that in venture capital. Used to be syndicates. Now you seeing Andreessen Horowitz doing the whole deals. That kind of creates some alienation, my opinion, but what's your take on that? I'm a big wale. I'm taking down the whole allocation. >> It's okay. Some of those things are going to happen, okay. It is fine. The only problem is usually when that happens the big wale who takes it he will realize very quickly. >> He's got to get more people. >> He needs more people otherwise he might be able to exit to his five buddies who were always taking it from him. Now those guys, they also have to exit at some point. Nobody knows about the product. Might as well just take a small piece, even the founders in this case typically in a token model. Founders who've taken 20% or 10% have done better than founders who took 60% of the whole tokens. >> Right. Nithin, great to have you on. Love your business model. Arcadia Crypto Ventures. They got real pros, they got a wale, they got people who know what they're doing, and they're active. They understand the ethos. I think you guys are well-aligned and you're not trying to come in and saying this is how we did it in New York before. You get the culture. You're aligned and you're making investments. Great perspective. Thanks for sharing. >> Thank you so much. >> This is the Cube, bringing the investor perspective live here in the Bahamas. More exclusive Cube coverage. Token economics, huge opportunity for entrepreneurs and investors to create value and capture it. That's Blockchain, that's crypto, that's token economics. I'm John with Dave Vallante. We'll be back with more coverage after this short break. (futuristic digital music)

>> Announcer: From Denver, Colorado, it's theCUBE, covering Super Computing 17. Brought to you by Intel. (soft electronic music) Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're gettin' towards the end of the day here at Super Computing 2017 in Denver, Colorado. 12,000 people talkin' really about the outer limits of what you can do with compute power and lookin' out into the universe and black holes and all kinds of exciting stuff. We're kind of bringin' it back, right? We're all about democratization of technology for people to solve real problems. We're really excited to have our last guest of the day, bringin' the energy, Armughan Ahmad. He's SVP and GM, Hybrid Cloud and Ready Solutions for Dell EMC, and a many-time CUBE alumni. Armughan, great to see you. >> Yeah, good to see you, Jeff. So, first off, just impressions of the show. 12,000 people, we had no idea. We've never been to this show before. This is great. >> This is a show that has been around. If you know the history of the show, this was an IEEE engineering show, that actually turned into high-performance computing around research-based analytics and other things that came out of it. But, it's just grown. We're seeing now, yesterday the super computing top petaflops were released here. So, it's fascinating. You have some of the brightest minds in the world that actually come to this event. 12,000 of them. >> Yeah, and Dell EMC is here in force, so a lot of announcements, a lot of excitement. What are you guys excited about participating in this type of show? >> Yeah, Jeff, so when we come to an event like this, HBC-- We know that HBC is also evolved from your traditional HBC, which was around modeling and simulation, and how it started from engineering to then clusters. It's now evolving more towards machine learning, deep learning, and artificial intelligence. So, what we announced here-- Yesterday, our press release went out. It was really related to how our strategy of advancing HBC, but also democratizing HBC's working. So, on the advancing, on the HBC side, the top 500 super computing list came out. We're powering some of the top 500 of those. One big one is TAC, which is Texas Institute out of UT, University of Texas. They now have, I believe, the number 12 spot in the top 500 super computers in the world, running an 8.2 petaflops off computing. >> So, a lot of zeros. I have no idea what a petaflop is. >> It's very, very big. It's very big. It's available for machine learning, but also eventually going to be available for deep learning. But, more importantly, we're also moving towards democratizing HBC because we feel that democratizing is also very important, where HBC should not only be for the research and the academia, but it should also be focused towards the manufacturing customers, the financial customers, our commercial customers, so that they can actually take the complexity of HBC out, and that's where our-- We call it our HBC 2.0 strategy, off learning from the advancements that we continue to drive, to then also democratizing it for our customers. >> It's interesting, I think, back to the old days of Intel microprocessors getting better and better and better, and you had Spark and you had Silicon Graphics, and these things that were way better. This huge differentiation. But, the Intel I32 just kept pluggin' along and it really begs the question, where is the distinction now? You have huge clusters of computers you can put together with virtualization. Where is the difference between just a really big cluster and HBC and super computing? >> So, I think, if you look at HBC, HBC is also evolving, so let's look at the customer view, right? So, the other part of our announcement here was artificial intelligence, which is really, what is artificial intelligence? It's, if you look at a customer retailer, a retailer has-- They start with data, for example. You buy beer and chips at J's Retailer, for example. You come in and do that, you usually used to run a SEQUEL database or you used to run a RDBMS database, and then that would basically tell you, these are the people who can purchase from me. You know their purchase history. But, then you evolved into BI, and then if that data got really, very large, you then had an HBC cluster, would which basically analyze a lot of that data for you, and show you trends and things. That would then tell you, you know what, these are my customers, this is how many times they are frequent. But, now it's moving more towards machine learning and deep learning as well. So, as the data gets larger and larger, we're seeing datas becoming larger, not just by social media, but your traditional computational frameworks, your traditional applications and others. We're finding that data is also growing at the edge, so by 2020, about 20 billion devices are going to wake up at the edge and start generating data. So, now, Internet data is going to look very small over the next three, four years, as the edge data comes up. So, you actually need to now start thinking of machine learning and deep learning a lot more. So, you asked the question, how do you see that evolving? So, you see an RDBMS traditional SQL evolving to BI. BI then evolves into either an HBC or hadoop. Then, from HBC and hadoop, what do you do next? What you do next is you start to now feed predictive analytics into machine learning kind of solutions, and then once those predictive analytics are there, then you really, truly start thinking about the full deep learning frameworks. >> Right, well and clearly like the data in motion. I think it's funny, we used to make decisions on a sample of data in the past. Now, we have the opportunity to take all the data in real time and make those decisions with Kafka and Spark and Flink and all these crazy systems that are comin' to play. Makes Hadoop look ancient, tired, and yesterday, right? But, it's still valid, right? >> A lot of customers are still paying. Customers are using it, and that's where we feel we need to simplify the complex for our customers. That's why we announced our Machine Learning Ready Bundle and our Deep Learning Ready Bundle. We announced it with Intel and Nvidia together, because we feel like our customers either go to the GPU route, which is your accelerator's route. We announced-- You were talking to Ravi, from our server team, earlier, where he talked about the C4140, which has the quad GPU power, and it's perfect for deep learning. But, with Intel, we've also worked on the same, where we worked on the AI software with Intel. Why are we doing all of this? We're saying that if you thought that RDBMS was difficult, and if you thought that building a hadoop cluster or HBC was a little challenging and time consuming, as the customers move to machine learning and deep learning, you now have to think about the whole stack. So, let me explain the stack to you. You think of a compute storage and network stack, then you think of-- The whole eternity. Yeah, that's right, the whole eternity of our data center. Then you talk about our-- These frameworks, like Theano, Caffe, TensorFlow, right? These are new frameworks. They are machine learning and deep learning frameworks. They're open source and others. Then you go to libraries. Then you go to accelerators, which accelerators you choose, then you go to your operating systems. Now, you haven't even talked about your use case. Retail use case or genomic sequencing use case. All you're trying to do is now figure out TensorFlow works with this accelerator or does not work with this accelerator. Or, does Caffe and Theano work with this operating system or not? And, that is a complexity that is way more complex. So, that's where we felt that we really needed to launch these new solutions, and we prelaunched them here at Super Computing, because we feel the evolution of HBC towards AI is happening. We're going to start shipping these Ready Bundles for machine learning and deep learning in first half of 2018. >> So, that's what the Ready Solutions are? You're basically putting the solution together for the client, then they can start-- You work together to build the application to fix whatever it is they're trying to do. >> That's exactly it. But, not just fix it. It's an outcome. So, I'm going to go back to the retailer. So, if you are the CEO of the biggest retailer and you are saying, hey, I just don't want to know who buys from me, I want to now do predictive analytics, which is who buys chips and beer, but who can I sell more things to, right? So, you now start thinking about demographic data. You start thinking about payroll data and other datas that surround-- You start feeding that data into it, so your machine now starts to learn a lot more of those frameworks, and then can actually give you predictive analytics. But, imagine a day where you actually-- The machine or the deep learning AI actually tells you that it's not just who you want to sell chips and beer to, it's who's going to buy the 4k TV? You're makin' a lot of presumptions. Well, there you go, and the 4k-- But, I'm glad you're doin' the 4k TV. So, that's important, right? That is where our customers need to understand how predictive analytics are going to move towards cognitive analytics. So, this is complex but we're trying to make that complex simple with these Ready Solutions from machine learning and deep learning. >> So, I want to just get your take on-- You've kind of talked about these three things a couple times, how you delineate between AI, machine learning, and deep learning. >> So, as I said, there is an evolution. I don't think a customer can achieve artificial intelligence unless they go through the whole crawl walk around space. There's no shortcuts there, right? What do you do? So, if you think about, Mastercard is a great customer of ours. They do an incredible amount of transactions per day, (laughs) as you can think, right? In millions. They want to do facial recognitions at kiosks, or they're looking at different policies based on your buying behavior-- That, hey, Jeff doesn't buy $20,000 Rolexes every year. Maybe once every week, you know, (laughs) it just depends how your mood is. I was in the Emirates. Exactly, you were in Dubai (laughs). Then, you think about his credit card is being used where? And, based on your behaviors that's important. Now, think about, even for Mastercard, they have traditional RDBMS databases. They went to BI. They have high-performance computing clusters. Then, they developed the hadoop cluster. So, what we did with them, we said okay. All that is good. That data that has been generated for you through customers and through internal IT organizations, those things are all very important. But, at the same time, now you need to start going through this data and start analyzing this data for predictive analytics. So, they had 1.2 million policies, for example, that they had to crunch. Now, think about 1.2 million policies that they had to say-- In which they had to take decisions on. That they had to take decisions on. One of the policies could be, hey, does Jeff go to Dubai to buy a Rolex or not? Or, does Jeff do these other patterns, or is Armughan taking his card and having a field day with it? So, those are policies that they feed into machine learning frameworks, and then machine learning actually gives you patterns that they can now see what your behavior is. Then, based on that, eventually deep learning is when they move to next. Deep learning now not only you actually talk about your behavior patterns on the credit card, but your entire other life data starts to-- Starts to also come into that. Then, now, you're actually talking about something before, that's for catching a fraud, you can actually be a lot more predictive about it and cognitive about it. So, that's where we feel that our Ready Solutions around machine learning and deep learning are really geared towards, so taking HBC to then democratizing it, advancing it, and then now helping our customers move towards machine learning and deep learning, 'cause these buzzwords of AIs are out there. If you're a financial institution and you're trying to figure out, who is that customer who's going to buy the next mortgage from you? Or, who are you going to lend to next? You want the machine and others to tell you this, not to take over your life, but to actually help you make these decisions so that your bottom line can go up along with your top line. Revenue and margins are important to every customer. >> It's amazing on the credit card example, because people get so pissed if there's a false positive. With the amount of effort that they've put into keep you from making fraudulent transactions, and if your credit card ever gets denied, people go bananas, right? The behavior just is amazing. But, I want to ask you-- We're comin' to the end of 2017, which is hard to believe. Things are rolling at Dell EMC. Michael Dell, ever since he took that thing private, you could see the sparkle in his eye. We got him on a CUBE interview a few years back. A year from now, 2018. What are we going to talk about? What are your top priorities for 2018? >> So, number one, Michael continues to talk about that our vision is advancing human progress through technology, right? That's our vision. We want to get there. But, at the same time we know that we have to drive IT transformation, we have to drive workforce transformation, we have to drive digital transformation, and we have to drive security transformation. All those things are important because lots of customers-- I mean, Jeff, do you know like 75% of the S&P 500 companies will not exist by 2027 because they're either not going to be able to make that shift from Blockbuster to Netflix, or Uber taxi-- It's happened to our friends at GE over the last little while. >> You can think about any customer-- That's what Michael did. Michael actually disrupted Dell with Dell technologies and the acquisition of EMC and Pivotal and VMWare. In a year from now, our strategy is really about edge to core to the cloud. We think the world is going to be all three, because the rise of 20 billion devices at the edge is going to require new computational frameworks. But, at the same time, people are going to bring them into the core, and then cloud will still exist. But, a lot of times-- Let me ask you, if you were driving an autonomous vehicle, do you want that data-- I'm an Edge guy. I know where you're going with this. It's not going to go, right? You want it at the edge, because data gravity is important. That's where we're going, so it's going to be huge. We feel data gravity is going to be big. We think core is going to be big. We think cloud's going to be big. And we really want to play in all three of those areas. >> That's when the speed of light is just too damn slow, in the car example. You don't want to send it to the data center and back. You don't want to send it to the data center, you want those decisions to be made at the edge. Your manufacturing floor needs to make the decision at the edge as well. You don't want a lot of that data going back to the cloud. All right, Armughan, thanks for bringing the energy to wrap up our day, and it's great to see you as always. Always good to see you guys, thank you. >> All right, this is Armughan, I'm Jeff Frick. You're watching theCUBE from Super Computing Summit 2017. Thanks for watching. We'll see you next time. (soft electronic music)

>> Announcer: Live from Berlin, Germany, it's theCUBE! Covering NetApp Insight 2017. Brought to you by NetApp. (rippling music) Welcome back to theCUBE's live coverage of NetApp Insight. I'm Rebecca Knight, your host, along with my cohost Peter Burris. We are joined by Brett Roscoe. He is the Vice President for Solutions and Service Marketing at NetApp, and Laura Dubois, who is a Group Vice President at IDC. Thanks so much for coming on the show. Yeah, thanks for having us. Thank you for having us. So, NetApp and IDC partner together and worked on this big research project, as you were calling it, a thought leadership project, to really tease out what the companies that are thriving and being successful with their data strategies are doing, and what separates those from those that are merely just surviving. Do you want to just lay the scene for our viewers and explain why you embarked on this? Well, you know, it's interesting. NetApp has embarked on its own journey, right, its own transformation. If you look at where the company's been really over the past few years in terms of becoming a traditional storage company to a truly software, cloud-focused, data-focused company, right? And that means a whole different set of capabilities that we provide to our customers. It's a different, our customers are looking at data in a different way. So what we did was look at that and say we know that we're going through a transformation, so we know our customers are going through a journey themselves. And whatever their business model is, it's being disrupted by this digital economy. And we wanted a way to work with IDC and really help our customers understand what that journey might look like, where they might be on that path, and what are the tools and what are the engagement models for us to help them along that journey? So that was really the goal, was really, it's engagement with our customers, it's looking and being curious about where they are on their journey on digital, and how do they move forward in that, in doing all kinds of new things like new customer opportunities and new business and cost optimization, all that kind of stuff. So that's really what got us interested in the project to begin with. Yeah, and I would just add to that. Revenue's at risk of disruption across pretty much every industry, and what's different is the amount of revenue that's at risk within one industry to the next. And all of this revenue that's at risk, is really as a consequence of new kinds of business models, new kinds of products and services that are getting launched new ways of engaging with customers. And these are some of the things that we see thrivers doing and outperforming merely just survivors, or even just data resisters. And so we want to understand the characteristics of data thrivers, and what are they doing that's uniquely different, what are their attributes versus companies that are just surviving. So let's tease that out a little bit. What are these data thrivers doing differently? What are some of the best practices that have emerged from this study? Well I mean, I think if you look at there's a lot of great information that came out of the study for us in terms of what they're doing. I think in a nutshell, it's really they put a focus on their data and they look at it as an asset to their business. Which means a lot of different things in terms of how is the data able to drive opportunities for them. I mean, there's so many companies now that are getting insights from their data, and they're able to push that back to their customer. I mean, NetApp is a perfect example of that. We actually do that with our customers. All the telemetry data we collect from our own systems, we provide that information back to our customers so they can help plan and optimize their own environments. So I think data is certainly, it's validated our theory, our message of where we're going with data, but I think the data focus, I mean, there's lot of other attributes, there's the focus of hiring chief data officers within the company, there's certainly lots of other attributes, Laura, that you can comment on. Yeah, I mean, we see new roles emerging around data, right, and so we see the rise of the data management office. We see the emergence of a Chief Data Officer, we see data architects, certainly data scientists, and this data role that's increasingly integrated into sort of the traditional IT organization, enterprise, architecture. And so enterprise, architecture and these data roles very, very closely aligned is one, I would say, example of a best practice in terms of the thriver organizations, is having these data champions, if you will, or data visionaries. And certainly there's a lot of things that need to be done to have a successful execution, and a data strategy as a first place, but then a successful execution around data. And there's a lot of challenges that exist around data as well. So the survey highlighted that obviously data's distributed, it's dynamic and it's diverse, it's not only in your private cloud but in the public cloud, I think it's at 34% on average of data is in a public cloud. So, how to deal with these challenges is, I think, also one of the things that you guys wanted to highlight. Yeah, and I think the other big revelation was the thrivers, one of the aspects, so not their data focus but also they're making business decisions with their data. They tend to use that data in terms of their operations and how they drive their business. They tend to look for new ways to engage with their customers through a digital or data-driven experience. Look at the number of mobile apps coming out of consumer, really B to C kind of businesses. So there's more and more digital focus, there's more and more data focus, and there's business decisions made around that data. So, I want to push you guys on this a little bit. 'Cause we've always used data in business, so that's not new. There's always been increasing amounts of data being used. So while the volume's certainly new, it's very interesting, it's by itself not that new. What is new about this? What is really new about it that's catalyzing this change right now? Have you got some insights into that? Well, I would just say if you look at some of the largest companies that are no longer here, so you've got Blockbuster, you've got Borders Books and Music, you've got RadioShack, look at what Amazon has done to the retail industry. You look at what Uber is doing to the transportation industry. Look at every single industry, there's disruption. And there's the success of this new innovative company, and I think that's why now. Yes, data has always been an important attribute of any kind of business operation. As more data gets digital, combine that with innovation and APIs that allow you to, and the public cloud, allow you to use that as a launch pad for innovation. I think those are some of the things about why now. I mean, that would be my take, I don't know-- Yeah, I think there's a couple things. Number one, I think yes, businesses have been storing data for years and using data for years, but what you're seeing is new ways to use the data. There's analytics now, it is so easy to run analytics compared to what it was just years ago, that you can now use data that you've been storing for years and run historical patterns on that, and figure out trends and new ways to do business. I think the other piece that is very interesting is the machine learning, the artificial intelligence, right? So much of the industry now, I mean, look at the automotive industry. They are collecting more information than I bet they ever thought they would, because the autonomous driving effort, all of that, is all about collecting information, doing analytics on information, and creating AI capabilities within their products. So there's a whole new business that's all new, there's whole new revenue streams that are coming up as a result of leveraging insights from data. So let me run something by ya, 'cause I was looking for something different. It used to be that the data we were working was what I call stylized data. You can't go out here in Berlin and wander the streets and find Accounting. It doesn't exist, it's human-made, it's contrived. HR is contrived. We have historically built these systems based on transactions, highly stylized types of data. There's only so much you can do with it. But because of technology, mobile, IOT, others, we now are utilizing real world data. So we're collecting an entirely new class of data that has a dramatic impact in how we think about business and operations. Does that comport with what the study said, that study respondents focusing on new types of data as opposed to just traditional sources of data? We certainly looked at correlations of what data thrivers are doing by different types of data. I would say, in terms of the new types of data that are emerging, you've got time series data, stream data, that's increasingly important. You've got machine-generated data from sensors. And I would say that one thing that the thrivers do better than merely just survivors, is have processes and procedures in place to action the data. To collect it and analyze it, as Brett pointed out, is accessible, and it's easy. But what's not easy to is to action results out of that data to drive change and business processes, to drive change in how things are brought to market, for example. So, those are things that data thrivers are doing that maybe data survivors aren't. I don't know if you have anything to add to that. Yeah, no, I think that's exactly right. I think, yes, traditional data, but it's interesting because even those traditional data sets that have been sitting there for years have untapped value. >> Peter: Wikibon knew types of data. That's right. But we've also been doing data warehousing, analytics for a long time. So it seems as though, I would guess, that the companies that are leading, many that you mentioned, are capturing data differently, they're using analytics and turning data into value differently, and then they are taking action based on that data differently. And I'm wondering if across the continuum that you guys have identified, of thrivers all the way down to survivors, and you mentioned one other, data-- >> Laura: resisters. resisters, and there was, anyways. So there's some continuum of data companies. Do they fall into that pattern, where I'm good at capturing data, I'm good at generating analytics, but I'm not good at taking action on it? Is that what a data resister is? So a data resister is sort of the one extreme. Companies that don't have well-aligned processes where they're doing digital transformation on a very ad hoc basis, it's not repeatable. They're somewhat resistant to change. They're really not embracing that there's disruption going on that data can be a source of enablement to do the disrupting, not being disrupted. So they're kind of resisting those fundamental constructs, I would say. They typically tend to be very siloed. Their IT's in a very siloed architecture where they're not looking for ways to take advantage of new opportunities across the data they're generating, or the data they're collecting, rather. So that would be they're either not as good at creating business value out of the data they have access to. Yes, that's right, that's right. And then I think the whole thing with thrivers is that they are purposeful. They set a high level objective, a business-level objective that says we're going to leverage data and we're going to use digital to help drive our business forward. We are going to look to disrupt our own business before somebody disrupts it for us. So how do you help those data resistors? What's your message to them, particularly if they may not even operate with the belief that data is this asset? I mean, that's the whole premise of the study. I think the data that comes out, like you know, hey data thrivers, you're two times more likely to draw two times more profitability to there's lots of great statistics that we pulled out of this to say thrivers have a lot more going for them. There is a direct corelation that says if you are taking a high business value of your data, and high business value of the digital transformation that you are going to be more profitable, you're going to generate more revenue, and you're going to be more relevant in the next 10 to 20 years. And that's what we want to use that, to say okay where are you on this journey? We're actually giving them tools to measure themselves by taking assessments. They can take an assessment of their own situation and say okay, we are a survivor Okay, how do we move closer to being a thriver? And that's where NetApp would love to come in and engage and say let us show you best practices, let us show you tools and capabilities that we can bring to bear to your environment to help you go a little bit further on that journey, or help you on a path that's going to lead you to a data thriver. Yeah, that's right, I agree with that. (laughs) What is the thing that keeps you up at night for the data resister, though, in the sense of someone who is not, does not have, maybe not even capturing and storing the data but really has no strategy to take whatever insights the data might be giving them to create value? I don't know, that's a hard question. I don't know, what keeps you up at night? Well, I think if I were looking at a data resister, I think the stats, the data's against them. I mean, right? If you look at a Fortune 500 company in the 1950s, their average lifespan was something like 40 years. And by the year 2020, the average lifespan of an S&P 500 company is going to be seven years, and that's because of disruption. Now, historically that may have been industrial disruption, but now it's digital disruption, and that right there is, if you're feeling like you're just a survivor, that ought to keep a survivor up at night. If I can ask too. It's, for example, one of the reasons why so many executives say you have to hire millennials, because there's this presumption that millennials have a more natural affinity with data, than older people like me. Now, there's not necessarily a lot of stats that definitely prove that, but I think that's one of the, the misperceptions, or one of the perceptions, that I have to get more young people in because they'll be more likely to help me move forward in an empirical style of management than some older people who are used to a very, very different type of management practice. But still there are a lot of things that companies, I would presume, would need to be able to do to move from one who's resisting these kinds of changes to actually taking advantage of it. Can I ask one more question? Is it that, did the research discover that data is the cause of some of these, or just is correlated with success? In other words, you take a company like Amazon, who did not have to build stores like traditional retailers, didn't have to carry that financial burden, didn't have to worry so much about those things, so that may be starting to change, interestingly enough. Is that, so they found a way to use data to alter that business, but they also didn't have to deal with the financial structure of a lot of the companies they were competing with. They were able to say our business is data, whereas others had said our business is serving the customer with these places in place. So, which is it? Do you think it's a combination of cause and effect, or is it just that it's correlated? Hmm. I would say it's probably both. We do see a correlation, but I would say the study included companies whose business was data, as well as companies that were across a variety of industries where they're just leveraging data in new ways. I would say there's probably some aspects of both of that, but that wasn't like a central tenent of the study per se, but maybe that will be phase two. Maybe we'll mine the data and try and find some insights there. Yeah, there's a lot more information that we can glean from this data. We think this'll be an ongoing effort for us to kind of be a thought leader in this area. I mean, the data proved that there was 11% of those 800 respondents that are thrivers, which means most people are not in that place yet. So I think it's going to be a journey for everyone. Yes, I agree that some companies may have some laws of physics or some previous disruptions like brick and mortar versus online retail, but it doesn't mean there's not ways that traditional companies can't use technology. I mean, you look at, in the white paper, we used examples like General Electric and John Deere. These are very traditional companies that are using technology to collect data to provide insights into how customers are using their products. So that's kind of the thought leadership that any company has to have, is how do I leverage digital capabilities, online capabilities, to my advantage and keep being disruptive in the digital age? I think that's kind of the message that we want them to hear. Right, and I would just add to that. It's not only their data, but it's third-party data. So it's enriching their data, say in the case of Starbucks. So Starbucks is a company that certainly has many physical assets. They're taking their customer data, they're taking partner data, whether that be music data, or content from the New York Times, and they're combining that all to provide a customer experience on their mobile app that gives them an experience on the digital platform that they might have experienced in the physical store. So when they go to order their coffee in their mobile pay app, they don't have to wait in line for their coffee, it's already paid for and ready when they go to pick it up. But while they're in their app, they can listen to music or they can read the New York Times. So there's a company that is using their own data plus third party data to really provide a more enriched experience for their company, and that's a traditional, physical company. And they're learning about their customers through that process too. Exactly, exactly, right. Are there any industries that you think are struggling more with this than others? Or is it really a company-specific thing? Well, the research shows that companies in ever industry are facing disruption, and the research shows that companies in every industry are reacting to that disruption. There are some industries that tend to have, obviously by industry they might have more thrivers or more resisters, but nothing I can per se call out by industry. I think retail is the one that you can point to and say there's an industry that's really struggling to really keep up with the disruption that the large, people like Amazon and others have really leveraged digital well advanced of them, well in advance of their thought process. So I think the white paper actually breaks down the data by industry, so you can kind of look at that, I think that will provide some details. But I think every, there is no industry immune, we'll just put it that way. And the whole concept of industry is undergoing change as well. That's true, that is true, everything's been disrupted. Great, well, Brett and Laura thank you so much for coming on our show. We had a great conversation. Thank you. Enjoy your time. You're watching theCUBE, we'll have more from NetApp Insight after this. (rippling music)

>> Announcer: Live from New York City it's The Cube, covering IBM data science for all. Brought to you by IBM. (light digital music) >> Welcome back to data science for all. It's a whole new game. And it is a whole new game. >> Dave Vellante, John Walls here. We've got quite a distinguished panel. So it is a new game-- >> Well we're in the game, I'm just happy to be-- (both laugh) Have a swing at the pitch. >> Well let's what we have here. Five distinguished members of our panel. It'll take me a minute to get through the introductions, but believe me they're worth it. Jennifer Shin joins us. Jennifer's the founder of 8 Path Solutions, the director of the data science of Comcast and part of the faculty at UC Berkeley and NYU. Jennifer, nice to have you with us, we appreciate the time. Joe McKendrick an analyst and contributor of Forbes and ZDNet, Joe, thank you for being here at well. Another ZDNetter next to him, Dion Hinchcliffe, who is a vice president and principal analyst of Constellation Research and also contributes to ZDNet. Good to see you, sir. To the back row, but that doesn't mean anything about the quality of the participation here. Bob Hayes with a killer Batman shirt on by the way, which we'll get to explain in just a little bit. He runs the Business over Broadway. And Joe Caserta, who the founder of Caserta Concepts. Welcome to all of you. Thanks for taking the time to be with us. Jennifer, let me just begin with you. Obviously as a practitioner you're very involved in the industry, you're on the academic side as well. We mentioned Berkeley, NYU, steep experience. So I want you to kind of take your foot in both worlds and tell me about data science. I mean where do we stand now from those two perspectives? How have we evolved to where we are? And how would you describe, I guess the state of data science? >> Yeah so I think that's a really interesting question. There's a lot of changes happening. In part because data science has now become much more established, both in the academic side as well as in industry. So now you see some of the bigger problems coming out. People have managed to have data pipelines set up. But now there are these questions about models and accuracy and data integration. So the really cool stuff from the data science standpoint. We get to get really into the details of the data. And I think on the academic side you now see undergraduate programs, not just graduate programs, but undergraduate programs being involved. UC Berkeley just did a big initiative that they're going to offer data science to undergrads. So that's a huge news for the university. So I think there's a lot of interest from the academic side to continue data science as a major, as a field. But I think in industry one of the difficulties you're now having is businesses are now asking that question of ROI, right? What do I actually get in return in the initial years? So I think there's a lot of work to be done and just a lot of opportunity. It's great because people now understand better with data sciences, but I think data sciences have to really think about that seriously and take it seriously and really think about how am I actually getting a return, or adding a value to the business? >> And there's lot to be said is there not, just in terms of increasing the workforce, the acumen, the training that's required now. It's a still relatively new discipline. So is there a shortage issue? Or is there just a great need? Is the opportunity there? I mean how would you look at that? >> Well I always think there's opportunity to be smart. If you can be smarter, you know it's always better. It gives you advantages in the workplace, it gets you an advantage in academia. The question is, can you actually do the work? The work's really hard, right? You have to learn all these different disciplines, you have to be able to technically understand data. Then you have to understand it conceptually. You have to be able to model with it, you have to be able to explain it. There's a lot of aspects that you're not going to pick up overnight. So I think part of it is endurance. Like are people going to feel motivated enough and dedicate enough time to it to get very good at that skill set. And also of course, you know in terms of industry, will there be enough interest in the long term that there will be a financial motivation. For people to keep staying in the field, right? So I think it's definitely a lot of opportunity. But that's always been there. Like I tell people I think of myself as a scientist and data science happens to be my day job. That's just the job title. But if you are a scientist and you work with data you'll always want to work with data. I think that's just an inherent need. It's kind of a compulsion, you just kind of can't help yourself, but dig a little bit deeper, ask the questions, you can't not think about it. So I think that will always exist. Whether or not it's an industry job in the way that we see it today, and like five years from now, or 10 years from now. I think that's something that's up for debate. >> So all of you have watched the evolution of data and how it effects organizations for a number of years now. If you go back to the days when data warehouse was king, we had a lot of promises about 360 degree views of the customer and how we were going to be more anticipatory in terms and more responsive. In many ways the decision support systems and the data warehousing world didn't live up to those promises. They solved other problems for sure. And so everybody was looking for big data to solve those problems. And they've begun to attack many of them. We talked earlier in The Cube today about fraud detection, it's gotten much, much better. Certainly retargeting of advertising has gotten better. But I wonder if you could comment, you know maybe start with Joe. As to the effect that data and data sciences had on organizations in terms of fulfilling that vision of a 360 degree view of customers and anticipating customer needs. >> So. Data warehousing, I wouldn't say failed. But I think it was unfinished in order to achieve what we need done today. At the time I think it did a pretty good job. I think it was the only place where we were able to collect data from all these different systems, have it in a single place for analytics. The big difference between what I think, between data warehousing and data science is data warehouses were primarily made for the consumer to human beings. To be able to have people look through some tool and be able to analyze data manually. That really doesn't work anymore, there's just too much data to do that. So that's why we need to build a science around it so that we can actually have machines actually doing the analytics for us. And I think that's the biggest stride in the evolution over the past couple of years, that now we're actually able to do that, right? It used to be very, you know you go back to when data warehouses started, you had to be a deep technologist in order to be able to collect the data, write the programs to clean the data. But now you're average causal IT person can do that. Right now I think we're back in data science where you have to be a fairly sophisticated programmer, analyst, scientist, statistician, engineer, in order to do what we need to do, in order to make machines actually understand the data. But I think part of the evolution, we're just in the forefront. We're going to see over the next, not even years, within the next year I think a lot of new innovation where the average person within business and definitely the average person within IT will be able to do as easily say, "What are my sales going to be next year?" As easy as it is to say, "What were my sales last year." Where now it's a big deal. Right now in order to do that you have to build some algorithms, you have to be a specialist on predictive analytics. And I think, you know as the tools mature, as people using data matures, and as the technology ecosystem for data matures, it's going to be easier and more accessible. >> So it's still too hard. (laughs) That's something-- >> Joe C.: Today it is yes. >> You've written about and talked about. >> Yeah no question about it. We see this citizen data scientist. You know we talked about the democratization of data science but the way we talk about analytics and warehousing and all the tools we had before, they generated a lot of insights and views on the information, but they didn't really give us the science part. And that's, I think that what's missing is the forming of the hypothesis, the closing of the loop of. We now have use of this data, but are are changing, are we thinking about it strategically? Are we learning from it and then feeding that back into the process. I think that's the big difference between data science and the analytics side. But, you know just like Google made search available to everyone, not just people who had highly specialized indexers or crawlers. Now we can have tools that make these capabilities available to anyone. You know going back to what Joe said I think the key thing is we now have tools that can look at all the data and ask all the questions. 'Cause we can't possibly do it all ourselves. Our organizations are increasingly awash in data. Which is the life blood of our organizations, but we're not using it, you know this a whole concept of dark data. And so I think the concept, or the promise of opening these tools up for everyone to be able to access those insights and activate them, I think that, you know, that's where it's headed. >> This is kind of where the T shirt comes in right? So Bob if you would, so you've got this Batman shirt on. We talked a little bit about it earlier, but it plays right into what Dion's talking about. About tools and, I don't want to spoil it, but you go ahead (laughs) and tell me about it. >> Right, so. Batman is a super hero, but he doesn't have any supernatural powers, right? He can't fly on his own, he can't become invisible on his own. But the thing is he has the utility belt and he has these tools he can use to help him solve problems. For example he as the bat ring when he's confronted with a building that he wants to get over, right? So he pulls it out and uses that. So as data professionals we have all these tools now that these vendors are making. We have IBM SPSS, we have data science experience. IMB Watson that these data pros can now use it as part of their utility belt and solve problems that they're confronted with. So if you''re ever confronted with like a Churn problem and you have somebody who has access to that data they can put that into IBM Watson, ask a question and it'll tell you what's the key driver of Churn. So it's not that you have to be a superhuman to be a data scientist, but these tools will help you solve certain problems and help your business go forward. >> Joe McKendrick, do you have a comment? >> Does that make the Batmobile the Watson? (everyone laughs) Analogy? >> I was just going to add that, you know all of the billionaires in the world today and none of them decided to become Batman yet. It's very disappointing. >> Yeah. (Joe laughs) >> Go ahead Joe. >> And I just want to add some thoughts to our discussion about what happened with data warehousing. I think it's important to point out as well that data warehousing, as it existed, was fairly successful but for larger companies. Data warehousing is a very expensive proposition it remains a expensive proposition. Something that's in the domain of the Fortune 500. But today's economy is based on a very entrepreneurial model. The Fortune 500s are out there of course it's ever shifting. But you have a lot of smaller companies a lot of people with start ups. You have people within divisions of larger companies that want to innovate and not be tied to the corporate balance sheet. They want to be able to go through, they want to innovate and experiment without having to go through finance and the finance department. So there's all these open source tools available. There's cloud resources as well as open source tools. Hadoop of course being a prime example where you can work with the data and experiment with the data and practice data science at a very low cost. >> Dion mentioned the C word, citizen data scientist last year at the panel. We had a conversation about that. And the data scientists on the panel generally were like, "Stop." Okay, we're not all of a sudden going to turn everybody into data scientists however, what we want to do is get people thinking about data, more focused on data, becoming a data driven organization. I mean as a data scientist I wonder if you could comment on that. >> Well I think so the other side of that is, you know there are also many people who maybe didn't, you know follow through with science, 'cause it's also expensive. A PhD takes a lot of time. And you know if you don't get funding it's a lot of money. And for very little security if you think about how hard it is to get a teaching job that's going to give you enough of a pay off to pay that back. Right, the time that you took off, the investment that you made. So I think the other side of that is by making data more accessible, you allow people who could have been great in science, have an opportunity to be great data scientists. And so I think for me the idea of citizen data scientist, that's where the opportunity is. I think in terms of democratizing data and making it available for everyone, I feel as though it's something similar to the way we didn't really know what KPIs were, maybe 20 years ago. People didn't use it as readily, didn't teach it in schools. I think maybe 10, 20 years from now, some of the things that we're building today from data science, hopefully more people will understand how to use these tools. They'll have a better understanding of working with data and what that means, and just data literacy right? Just being able to use these tools and be able to understand what data's saying and actually what it's not saying. Which is the thing that most people don't think about. But you can also say that data doesn't say anything. There's a lot of noise in it. There's too much noise to be able to say that there is a result. So I think that's the other side of it. So yeah I guess in terms for me, in terms of data a serious data scientist, I think it's a great idea to have that, right? But at the same time of course everyone kind of emphasized you don't want everyone out there going, "I can be a data scientist without education, "without statistics, without math," without understanding of how to implement the process. I've seen a lot of companies implement the same sort of process from 10, 20 years ago just on Hadoop instead of SQL. Right and it's very inefficient. And the only difference is that you can build more tables wrong than they could before. (everyone laughs) Which is I guess >> For less. it's an accomplishment and for less, it's cheaper, yeah. >> It is cheaper. >> Otherwise we're like I'm not a data scientist but I did stay at a Holiday Inn Express last night, right? >> Yeah. (panelists laugh) And there's like a little bit of pride that like they used 2,000, you know they used 2,000 computers to do it. Like a little bit of pride about that, but you know of course maybe not a great way to go. I think 20 years we couldn't do that, right? One computer was already an accomplishment to have that resource. So I think you have to think about the fact that if you're doing it wrong, you're going to just make that mistake bigger, which his also the other side of working with data. >> Sure, Bob. >> Yeah I have a comment about that. I've never liked the term citizen data scientist or citizen scientist. I get the point of it and I think employees within companies can help in the data analytics problem by maybe being a data collector or something. I mean I would never have just somebody become a scientist based on a few classes here she takes. It's like saying like, "Oh I'm going to be a citizen lawyer." And so you come to me with your legal problems, or a citizen surgeon. Like you need training to be good at something. You can't just be good at something just 'cause you want to be. >> John: Joe you wanted to say something too on that. >> Since we're in New York City I'd like to use the analogy of a real scientist versus a data scientist. So real scientist requires tools, right? And the tools are not new, like microscopes and a laboratory and a clean room. And these tools have evolved over years and years, and since we're in New York we could walk within a 10 block radius and buy any of those tools. It doesn't make us a scientist because we use those tools. I think with data, you know making, making the tools evolve and become easier to use, you know like Bob was saying, it doesn't make you a better data scientist, it just makes the data more accessible. You know we can go buy a microscope, we can go buy Hadoop, we can buy any kind of tool in a data ecosystem, but it doesn't really make you a scientist. I'm very involved in the NYU data science program and the Columbia data science program, like these kids are brilliant. You know these kids are not someone who is, you know just trying to run a day to day job, you know in corporate America. I think the people who are running the day to day job in corporate America are going to be the recipients of data science. Just like people who take drugs, right? As a result of a smart data scientist coming up with a formula that can help people, I think we're going to make it easier to distribute the data that can help people with all the new tools. But it doesn't really make it, you know the access to the data and tools available doesn't really make you a better data scientist. Without, like Bob was saying, without better training and education. >> So how-- I'm sorry, how do you then, if it's not for everybody, but yet I'm the user at the end of the day at my company and I've got these reams of data before me, how do you make it make better sense to me then? So that's where machine learning comes in or artificial intelligence and all this stuff. So how at the end of the day, Dion? How do you make it relevant and usable, actionable to somebody who might not be as practiced as you would like? >> I agree with Joe that many of us will be the recipients of data science. Just like you had to be a computer science at one point to develop programs for a computer, now we can get the programs. You don't need to be a computer scientist to get a lot of value out of our IT systems. The same thing's going to happen with data science. There's far more demand for data science than there ever could be produced by, you know having an ivory tower filled with data scientists. Which we need those guys, too, don't get me wrong. But we need to have, productize it and make it available in packages such that it can be consumed. The outputs and even some of the inputs can be provided by mere mortals, whether that's machine learning or artificial intelligence or bots that go off and run the hypotheses and select the algorithms maybe with some human help. We have to productize it. This is a constant of data scientist of service, which is becoming a thing now. It's, "I need this, I need this capability at scale. "I need it fast and I need it cheap." The commoditization of data science is going to happen. >> That goes back to what I was saying about, the recipient also of data science is also machines, right? Because I think the other thing that's happening now in the evolution of data is that, you know the data is, it's so tightly coupled. Back when you were talking about data warehousing you have all the business transactions then you take the data out of those systems, you put them in a warehouse for analysis, right? Maybe they'll make a decision to change that system at some point. Now the analytics platform and the business application is very tightly coupled. They become dependent upon one another. So you know people who are using the applications are now be able to take advantage of the insights of data analytics and data science, just through the app. Which never really existed before. >> I have one comment on that. You were talking about how do you get the end user more involved, well like we said earlier data science is not easy, right? As an end user, I encourage you to take a stats course, just a basic stats course, understanding what a mean is, variability, regression analysis, just basic stuff. So you as an end user can get more, or glean more insight from the reports that you're given, right? If you go to France and don't know French, then people can speak really slowly to you in French, you're not going to get it. You need to understand the language of data to get value from the technology we have available to us. >> Incidentally French is one of the languages that you have the option of learning if you're a mathematicians. So math PhDs are required to learn a second language. France being the country of algebra, that's one of the languages you could actually learn. Anyway tangent. But going back to the point. So statistics courses, definitely encourage it. I teach statistics. And one of the things that I'm finding as I go through the process of teaching it I'm actually bringing in my experience. And by bringing in my experience I'm actually kind of making the students think about the data differently. So the other thing people don't think about is the fact that like statisticians typically were expected to do, you know, just basic sort of tasks. In a sense that they're knowledge is specialized, right? But the day to day operations was they ran some data, you know they ran a test on some data, looked at the results, interpret the results based on what they were taught in school. They didn't develop that model a lot of times they just understand what the tests were saying, especially in the medical field. So when you when think about things like, we have words like population, census. Which is when you take data from every single, you have every single data point versus a sample, which is a subset. It's a very different story now that we're collecting faster than it used to be. It used to be the idea that you could collect information from everyone. Like it happens once every 10 years, we built that in. But nowadays you know, you know here about Facebook, for instance, I think they claimed earlier this year that their data was more accurate than the census data. So now there are these claims being made about which data source is more accurate. And I think the other side of this is now statisticians are expected to know data in a different way than they were before. So it's not just changing as a field in data science, but I think the sciences that are using data are also changing their fields as well. >> Dave: So is sampling dead? >> Well no, because-- >> Should it be? (laughs) >> Well if you're sampling wrong, yes. That's really the question. >> Okay. You know it's been said that the data doesn't lie, people do. Organizations are very political. Oftentimes you know, lies, damned lies and statistics, Benjamin Israeli. Are you seeing a change in the way in which organizations are using data in the context of the politics. So, some strong P&L manager say gets data and crafts it in a way that he or she can advance their agenda. Or they'll maybe attack a data set that is, probably should drive them in a different direction, but might be antithetical to their agenda. Are you seeing data, you know we talked about democratizing data, are you seeing that reduce the politics inside of organizations? >> So you know we've always used data to tell stories at the top level of an organization that's what it's all about. And I still see very much that no matter how much data science or, the access to the truth through looking at the numbers that story telling is still the political filter through which all that data still passes, right? But it's the advent of things like Block Chain, more and more corporate records and corporate information is going to end up in these open and shared repositories where there is not alternate truth. It'll come back to whoever tells the best stories at the end of the day. So I still see the organizations are very political. We are seeing now more open data though. Open data initiatives are a big thing, both in government and in the private sector. It is having an effect, but it's slow and steady. So that's what I see. >> Um, um, go ahead. >> I was just going to say as well. Ultimately I think data driven decision making is a great thing. And it's especially useful at the lower tiers of the organization where you have the routine day to day's decisions that could be automated through machine learning and deep learning. The algorithms can be improved on a constant basis. On the upper levels, you know that's why you pay executives the big bucks in the upper levels to make the strategic decisions. And data can help them, but ultimately, data, IT, technology alone will not create new markets, it will not drive new businesses, it's up to human beings to do that. The technology is the tool to help them make those decisions. But creating businesses, growing businesses, is very much a human activity. And that's something I don't see ever getting replaced. Technology might replace many other parts of the organization, but not that part. >> I tend to be a foolish optimist when it comes to this stuff. >> You do. (laughs) >> I do believe that data will make the world better. I do believe that data doesn't lie people lie. You know I think as we start, I'm already seeing trends in industries, all different industries where, you know conventional wisdom is starting to get trumped by analytics. You know I think it's still up to the human being today to ignore the facts and go with what they think in their gut and sometimes they win, sometimes they lose. But generally if they lose the data will tell them that they should have gone the other way. I think as we start relying more on data and trusting data through artificial intelligence, as we start making our lives a little bit easier, as we start using smart cars for safety, before replacement of humans. AS we start, you know, using data really and analytics and data science really as the bumpers, instead of the vehicle, eventually we're going to start to trust it as the vehicle itself. And then it's going to make lying a little bit harder. >> Okay, so great, excellent. Optimism, I love it. (John laughs) So I'm going to play devil's advocate here a little bit. There's a couple elephant in the room topics that I want to, to explore a little bit. >> Here it comes. >> There was an article today in Wired. And it was called, Why AI is Still Waiting for It's Ethics Transplant. And, I will just read a little segment from there. It says, new ethical frameworks for AI need to move beyond individual responsibility to hold powerful industrial, government and military interests accountable as they design and employ AI. When tech giants build AI products, too often user consent, privacy and transparency are overlooked in favor of frictionless functionality that supports profit driven business models based on aggregate data profiles. This is from Kate Crawford and Meredith Whittaker who founded AI Now. And they're calling for sort of, almost clinical trials on AI, if I could use that analogy. Before you go to market you've got to test the human impact, the social impact. Thoughts. >> And also have the ability for a human to intervene at some point in the process. This goes way back. Is everybody familiar with the name Stanislav Petrov? He's the Soviet officer who back in 1983, it was in the control room, I guess somewhere outside of Moscow in the control room, which detected a nuclear missile attack against the Soviet Union coming out of the United States. Ordinarily I think if this was an entirely AI driven process we wouldn't be sitting here right now talking about it. But this gentlemen looked at what was going on on the screen and, I'm sure he's accountable to his authorities in the Soviet Union. He probably got in a lot of trouble for this, but he decided to ignore the signals, ignore the data coming out of, from the Soviet satellites. And as it turned out, of course he was right. The Soviet satellites were seeing glints of the sun and they were interpreting those glints as missile launches. And I think that's a great example why, you know every situation of course doesn't mean the end of the world, (laughs) it was in this case. But it's a great example why there needs to be a human component, a human ability for human intervention at some point in the process. >> So other thoughts. I mean organizations are driving AI hard for profit. Best minds of our generation are trying to figure out how to get people to click on ads. Jeff Hammerbacher is famous for saying it. >> You can use data for a lot of things, data analytics, you can solve, you can cure cancer. You can make customers click on more ads. It depends on what you're goal is. But, there are ethical considerations we need to think about. When we have data that will have a racial bias against blacks and have them have higher prison sentences or so forth or worse credit scores, so forth. That has an impact on a broad group of people. And as a society we need to address that. And as scientists we need to consider how are we going to fix that problem? Cathy O'Neil in her book, Weapons of Math Destruction, excellent book, I highly recommend that your listeners read that book. And she talks about these issues about if AI, if algorithms have a widespread impact, if they adversely impact protected group. And I forget the last criteria, but like we need to really think about these things as a people, as a country. >> So always think the idea of ethics is interesting. So I had this conversation come up a lot of times when I talk to data scientists. I think as a concept, right as an idea, yes you want things to be ethical. The question I always pose to them is, "Well in the business setting "how are you actually going to do this?" 'Cause I find the most difficult thing working as a data scientist, is to be able to make the day to day decision of when someone says, "I don't like that number," how do you actually get around that. If that's the right data to be showing someone or if that's accurate. And say the business decides, "Well we don't like that number." Many people feel pressured to then change the data, change, or change what the data shows. So I think being able to educate people to be able to find ways to say what the data is saying, but not going past some line where it's a lie, where it's unethical. 'Cause you can also say what data doesn't say. You don't always have to say what the data does say. You can leave it as, "Here's what we do know, "but here's what we don't know." There's a don't know part that many people will omit when they talk about data. So I think, you know especially when it comes to things like AI it's tricky, right? Because I always tell people I don't know everyone thinks AI's going to be so amazing. I started an industry by fixing problems with computers that people didn't realize computers had. For instance when you have a system, a lot of bugs, we all have bug reports that we've probably submitted. I mean really it's no where near the point where it's going to start dominating our lives and taking over all the jobs. Because frankly it's not that advanced. It's still run by people, still fixed by people, still managed by people. I think with ethics, you know a lot of it has to do with the regulations, what the laws say. That's really going to be what's involved in terms of what people are willing to do. A lot of businesses, they want to make money. If there's no rules that says they can't do certain things to make money, then there's no restriction. I think the other thing to think about is we as consumers, like everyday in our lives, we shouldn't separate the idea of data as a business. We think of it as a business person, from our day to day consumer lives. Meaning, yes I work with data. Incidentally I also always opt out of my credit card, you know when they send you that information, they make you actually mail them, like old school mail, snail mail like a document that says, okay I don't want to be part of this data collection process. Which I always do. It's a little bit more work, but I go through that step of doing that. Now if more people did that, perhaps companies would feel more incentivized to pay you for your data, or give you more control of your data. Or at least you know, if a company's going to collect information, I'd want you to be certain processes in place to ensure that it doesn't just get sold, right? For instance if a start up gets acquired what happens with that data they have on you? You agree to give it to start up. But I mean what are the rules on that? So I think we have to really think about the ethics from not just, you know, someone who's going to implement something but as consumers what control we have for our own data. 'Cause that's going to directly impact what businesses can do with our data. >> You know you mentioned data collection. So slightly on that subject. All these great new capabilities we have coming. We talked about what's going to happen with media in the future and what 5G technology's going to do to mobile and these great bandwidth opportunities. The internet of things and the internet of everywhere. And all these great inputs, right? Do we have an arms race like are we keeping up with the capabilities to make sense of all the new data that's going to be coming in? And how do those things square up in this? Because the potential is fantastic, right? But are we keeping up with the ability to make it make sense and to put it to use, Joe? >> So I think data ingestion and data integration is probably one of the biggest challenges. I think, especially as the world is starting to become more dependent on data. I think you know, just because we're dependent on numbers we've come up with GAAP, which is generally accepted accounting principles that can be audited and proven whether it's true or false. I think in our lifetime we will see something similar to that we will we have formal checks and balances of data that we use that can be audited. Getting back to you know what Dave was saying earlier about, I personally would trust a machine that was programmed to do the right thing, than to trust a politician or some leader that may have their own agenda. And I think the other thing about machines is that they are auditable. You know you can look at the code and see exactly what it's doing and how it's doing it. Human beings not so much. So I think getting to the truth, even if the truth isn't the answer that we want, I think is a positive thing. It's something that we can't do today that once we start relying on machines to do we'll be able to get there. >> Yeah I was just going to add that we live in exponential times. And the challenge is that the way that we're structured traditionally as organizations is not allowing us to absorb advances exponentially, it's linear at best. Everyone talks about change management and how are we going to do digital transformation. Evidence shows that technology's forcing the leaders and the laggards apart. There's a few leading organizations that are eating the world and they seem to be somehow rolling out new things. I don't know how Amazon rolls out all this stuff. There's all this artificial intelligence and the IOT devices, Alexa, natural language processing and that's just a fraction, it's just a tip of what they're releasing. So it just shows that there are some organizations that have path found the way. Most of the Fortune 500 from the year 2000 are gone already, right? The disruption is happening. And so we are trying, have to find someway to adopt these new capabilities and deploy them effectively or the writing is on the wall. I spent a lot of time exploring this topic, how are we going to get there and all of us have a lot of hard work is the short answer. >> I read that there's going to be more data, or it was predicted, more data created in this year than in the past, I think it was five, 5,000 years. >> Forever. (laughs) >> And that to mix the statistics that we're analyzing currently less than 1% of the data. To taking those numbers and hear what you're all saying it's like, we're not keeping up, it seems like we're, it's not even linear. I mean that gap is just going to grow and grow and grow. How do we close that? >> There's a guy out there named Chris Dancy, he's known as the human cyborg. He has 700 hundred sensors all over his body. And his theory is that data's not new, having access to the data is new. You know we've always had a blood pressure, we've always had a sugar level. But we were never able to actually capture it in real time before. So now that we can capture and harness it, now we can be smarter about it. So I think that being able to use this information is really incredible like, this is something that over our lifetime we've never had and now we can do it. Which hence the big explosion in data. But I think how we use it and have it governed I think is the challenge right now. It's kind of cowboys and indians out there right now. And without proper governance and without rigorous regulation I think we are going to have some bumps in the road along the way. >> The data's in the oil is the question how are we actually going to operationalize around it? >> Or find it. Go ahead. >> I will say the other side of it is, so if you think about information, we always have the same amount of information right? What we choose to record however, is a different story. Now if you want wanted to know things about the Olympics, but you decide to collect information every day for years instead of just the Olympic year, yes you have a lot of data, but did you need all of that data? For that question about the Olympics, you don't need to collect data during years there are no Olympics, right? Unless of course you're comparing it relative. But I think that's another thing to think about. Just 'cause you collect more data does not mean that data will produce more statistically significant results, it does not mean it'll improve your model. You can be collecting data about your shoe size trying to get information about your hair. I mean it really does depend on what you're trying to measure, what your goals are, and what the data's going to be used for. If you don't factor the real world context into it, then yeah you can collect data, you know an infinite amount of data, but you'll never process it. Because you have no question to ask you're not looking to model anything. There is no universal truth about everything, that just doesn't exist out there. >> I think she's spot on. It comes down to what kind of questions are you trying to ask of your data? You can have one given database that has 100 variables in it, right? And you can ask it five different questions, all valid questions and that data may have those variables that'll tell you what's the best predictor of Churn, what's the best predictor of cancer treatment outcome. And if you can ask the right question of the data you have then that'll give you some insight. Just data for data's sake, that's just hype. We have a lot of data but it may not lead to anything if we don't ask it the right questions. >> Joe. >> I agree but I just want to add one thing. This is where the science in data science comes in. Scientists often will look at data that's already been in existence for years, weather forecasts, weather data, climate change data for example that go back to data charts and so forth going back centuries if that data is available. And they reformat, they reconfigure it, they get new uses out of it. And the potential I see with the data we're collecting is it may not be of use to us today, because we haven't thought of ways to use it, but maybe 10, 20, even 100 years from now someone's going to think of a way to leverage the data, to look at it in new ways and to come up with new ideas. That's just my thought on the science aspect. >> Knowing what you know about data science, why did Facebook miss Russia and the fake news trend? They came out and admitted it. You know, we miss it, why? Could they have, is it because they were focused elsewhere? Could they have solved that problem? (crosstalk) >> It's what you said which is are you asking the right questions and if you're not looking for that problem in exactly the way that it occurred you might not be able to find it. >> I thought the ads were paid in rubles. Shouldn't that be your first clue (panelists laugh) that something's amiss? >> You know red flag, so to speak. >> Yes. >> I mean Bitcoin maybe it could have hidden it. >> Bob: Right, exactly. >> I would think too that what happened last year is actually was the end of an age of optimism. I'll bring up the Soviet Union again, (chuckles). It collapsed back in 1991, 1990, 1991, Russia was reborn in. And think there was a general feeling of optimism in the '90s through the 2000s that Russia is now being well integrated into the world economy as other nations all over the globe, all continents are being integrated into the global economy thanks to technology. And technology is lifting entire continents out of poverty and ensuring more connectedness for people. Across Africa, India, Asia, we're seeing those economies that very different countries than 20 years ago and that extended into Russia as well. Russia is part of the global economy. We're able to communicate as a global, a global network. I think as a result we kind of overlook the dark side that occurred. >> John: Joe? >> Again, the foolish optimist here. But I think that... It shouldn't be the question like how did we miss it? It's do we have the ability now to catch it? And I think without data science without machine learning, without being able to train machines to look for patterns that involve corruption or result in corruption, I think we'd be out of luck. But now we have those tools. And now hopefully, optimistically, by the next election we'll be able to detect these things before they become public. >> It's a loaded question because my premise was Facebook had the ability and the tools and the knowledge and the data science expertise if in fact they wanted to solve that problem, but they were focused on other problems, which is how do I get people to click on ads? >> Right they had the ability to train the machines, but they were giving the machines the wrong training. >> Looking under the wrong rock. >> (laughs) That's right. >> It is easy to play armchair quarterback. Another topic I wanted to ask the panel about is, IBM Watson. You guys spend time in the Valley, I spend time in the Valley. People in the Valley poo-poo Watson. Ah, Google, Facebook, Amazon they've got the best AI. Watson, and some of that's fair criticism. Watson's a heavy lift, very services oriented, you just got to apply it in a very focused. At the same time Google's trying to get you to click on Ads, as is Facebook, Amazon's trying to get you to buy stuff. IBM's trying to solve cancer. Your thoughts on that sort of juxtaposition of the different AI suppliers and there may be others. Oh, nobody wants to touch this one, come on. I told you elephant in the room questions. >> Well I mean you're looking at two different, very different types of organizations. One which is really spent decades in applying technology to business and these other companies are ones that are primarily into the consumer, right? When we talk about things like IBM Watson you're looking at a very different type of solution. You used to be able to buy IT and once you installed it you pretty much could get it to work and store your records or you know, do whatever it is you needed it to do. But these types of tools, like Watson actually tries to learn your business. And it needs to spend time doing that watching the data and having its models tuned. And so you don't get the results right away. And I think that's been kind of the challenge that organizations like IBM has had. Like this is a different type of technology solution, one that has to actually learn first before it can provide value. And so I think you know you have organizations like IBM that are much better at applying technology to business, and then they have the further hurdle of having to try to apply these tools that work in very different ways. There's education too on the side of the buyer. >> I'd have to say that you know I think there's plenty of businesses out there also trying to solve very significant, meaningful problems. You know with Microsoft AI and Google AI and IBM Watson, I think it's not really the tool that matters, like we were saying earlier. A fool with a tool is still a fool. And regardless of who the manufacturer of that tool is. And I think you know having, a thoughtful, intelligent, trained, educated data scientist using any of these tools can be equally effective. >> So do you not see core AI competence and I left out Microsoft, as a strategic advantage for these companies? Is it going to be so ubiquitous and available that virtually anybody can apply it? Or is all the investment in R&D and AI going to pay off for these guys? >> Yeah, so I think there's different levels of AI, right? So there's AI where you can actually improve the model. I remember when I was invited when Watson was kind of first out by IBM to a private, sort of presentation. And my question was, "Okay, so when do I get "to access the corpus?" The corpus being sort of the foundation of NLP, which is natural language processing. So it's what you use as almost like a dictionary. Like how you're actually going to measure things, or things up. And they said, "Oh you can't." "What do you mean I can't?" It's like, "We do that." "So you're telling me as a data scientist "you're expecting me to rely on the fact "that you did it better than me and I should rely on that." I think over the years after that IBM started opening it up and offering different ways of being able to access the corpus and work with that data. But I remember at the first Watson hackathon there was only two corpus available. It was either the travel or medicine. There was no other foundational data available. So I think one of the difficulties was, you know IBM being a little bit more on the forefront of it they kind of had that burden of having to develop these systems and learning kind of the hard way that if you don't have the right models and you don't have the right data and you don't have the right access, that's going to be a huge limiter. I think with things like medical, medical information that's an extremely difficult data to start with. Partly because you know anything that you do find or don't find, the impact is significant. If I'm looking at things like what people clicked on the impact of using that data wrong, it's minimal. You might lose some money. If you do that with healthcare data, if you do that with medical data, people may die, like this is a much more difficult data set to start with. So I think from a scientific standpoint it's great to have any information about a new technology, new process. That's the nice that is that IBM's obviously invested in it and collected information. I think the difficulty there though is just 'cause you have it you can't solve everything. And if feel like from someone who works in technology, I think in general when you appeal to developers you try not to market. And with Watson it's very heavily marketed, which tends to turn off people who are more from the technical side. Because I think they don't like it when it's gimmicky in part because they do the opposite of that. They're always trying to build up the technical components of it. They don't like it when you're trying to convince them that you're selling them something when you could just give them the specs and look at it. So it could be something as simple as communication. But I do think it is valuable to have had a company who leads on the forefront of that and try to do so we can actually learn from what IBM has learned from this process. >> But you're an optimist. (John laughs) All right, good. >> Just one more thought. >> Joe go ahead first. >> Joe: I want to see how Alexa or Siri do on Jeopardy. (panelists laugh) >> All right. Going to go around a final thought, give you a second. Let's just think about like your 12 month crystal ball. In terms of either challenges that need to be met in the near term or opportunities you think will be realized. 12, 18 month horizon. Bob you've got the microphone headed up, so I'll let you lead off and let's just go around. >> I think a big challenge for business, for society is getting people educated on data and analytics. There's a study that was just released I think last month by Service Now, I think, or some vendor, or Click. They found that only 17% of the employees in Europe have the ability to use data in their job. Think about that. >> 17. >> 17. Less than 20%. So these people don't have the ability to understand or use data intelligently to improve their work performance. That says a lot about the state we're in today. And that's Europe. It's probably a lot worse in the United States. So that's a big challenge I think. To educate the masses. >> John: Joe. >> I think we probably have a better chance of improving technology over training people. I think using data needs to be iPhone easy. And I think, you know which means that a lot of innovation is in the years to come. I do think that a keyboard is going to be a thing of the past for the average user. We are going to start using voice a lot more. I think augmented reality is going to be things that becomes a real reality. Where we can hold our phone in front of an object and it will have an overlay of prices where it's available, if it's a person. I think that we will see within an organization holding a camera up to someone and being able to see what is their salary, what sales did they do last year, some key performance indicators. I hope that we are beyond the days of everyone around the world walking around like this and we start actually becoming more social as human beings through augmented reality. I think, it has to happen. I think we're going through kind of foolish times at the moment in order to get to the greater good. And I think the greater good is using technology in a very, very smart way. Which means that you shouldn't have to be, sorry to contradict, but maybe it's good to counterpoint. I don't think you need to have a PhD in SQL to use data. Like I think that's 1990. I think as we evolve it's going to become easier for the average person. Which means people like the brain trust here needs to get smarter and start innovating. I think the innovation around data is really at the tip of the iceberg, we're going to see a lot more of it in the years to come. >> Dion why don't you go ahead, then we'll come down the line here. >> Yeah so I think over that time frame two things are likely to happen. One is somebody's going to crack the consumerization of machine learning and AI, such that it really is available to the masses and we can do much more advanced things than we could. We see the industries tend to reach an inflection point and then there's an explosion. No one's quite cracked the code on how to really bring this to everyone, but somebody will. And that could happen in that time frame. And then the other thing that I think that almost has to happen is that the forces for openness, open data, data sharing, open data initiatives things like Block Chain are going to run headlong into data protection, data privacy, customer privacy laws and regulations that have to come down and protect us. Because the industry's not doing it, the government is stepping in and it's going to re-silo a lot of our data. It's going to make it recede and make it less accessible, making data science harder for a lot of the most meaningful types of activities. Patient data for example is already all locked down. We could do so much more with it, but health start ups are really constrained about what they can do. 'Cause they can't access the data. We can't even access our own health care records, right? So I think that's the challenge is we have to have that battle next to be able to go and take the next step. >> Well I see, with the growth of data a lot of it's coming through IOT, internet of things. I think that's a big source. And we're going to see a lot of innovation. A new types of Ubers or Air BnBs. Uber's so 2013 though, right? We're going to see new companies with new ideas, new innovations, they're going to be looking at the ways this data can be leveraged all this big data. Or data coming in from the IOT can be leveraged. You know there's some examples out there. There's a company for example that is outfitting tools, putting sensors in the tools. Industrial sites can therefore track where the tools are at any given time. This is an expensive, time consuming process, constantly loosing tools, trying to locate tools. Assessing whether the tool's being applied to the production line or the right tool is at the right torque and so forth. With the sensors implanted in these tools, it's now possible to be more efficient. And there's going to be innovations like that. Maybe small start up type things or smaller innovations. We're going to see a lot of new ideas and new types of approaches to handling all this data. There's going to be new business ideas. The next Uber, we may be hearing about it a year from now whatever that may be. And that Uber is going to be applying data, probably IOT type data in some, new innovative way. >> Jennifer, final word. >> Yeah so I think with data, you know it's interesting, right, for one thing I think on of the things that's made data more available and just people we open to the idea, has been start ups. But what's interesting about this is a lot of start ups have been acquired. And a lot of people at start ups that got acquired now these people work at bigger corporations. Which was the way it was maybe 10 years ago, data wasn't available and open, companies kept it very proprietary, you had to sign NDAs. It was like within the last 10 years that open source all of that initiatives became much more popular, much more open, a acceptable sort of way to look at data. I think that what I'm kind of interested in seeing is what people do within the corporate environment. Right, 'cause they have resources. They have funding that start ups don't have. And they have backing, right? Presumably if you're acquired you went in at a higher title in the corporate structure whereas if you had started there you probably wouldn't be at that title at that point. So I think you have an opportunity where people who have done innovative things and have proven that they can build really cool stuff, can now be in that corporate environment. I think part of it's going to be whether or not they can really adjust to sort of the corporate, you know the corporate landscape, the politics of it or the bureaucracy. I think every organization has that. Being able to navigate that is a difficult thing in part 'cause it's a human skill set, it's a people skill, it's a soft skill. It's not the same thing as just being able to code something and sell it. So you know it's going to really come down to people. I think if people can figure out for instance, what people want to buy, what people think, in general that's where the money comes from. You know you make money 'cause someone gave you money. So if you can find a way to look at a data or even look at technology and understand what people are doing, aren't doing, what they're happy about, unhappy about, there's always opportunity in collecting the data in that way and being able to leverage that. So you build cooler things, and offer things that haven't been thought of yet. So it's a very interesting time I think with the corporate resources available if you can do that. You know who knows what we'll have in like a year. >> I'll add one. >> Please. >> The majority of companies in the S&P 500 have a market cap that's greater than their revenue. The reason is 'cause they have IP related to data that's of value. But most of those companies, most companies, the vast majority of companies don't have any way to measure the value of that data. There's no GAAP accounting standard. So they don't understand the value contribution of their data in terms of how it helps them monetize. Not the data itself necessarily, but how it contributes to the monetization of the company. And I think that's a big gap. If you don't understand the value of the data that means you don't understand how to refine it, if data is the new oil and how to protect it and so forth and secure it. So that to me is a big gap that needs to get closed before we can actually say we live in a data driven world. >> So you're saying I've got an asset, I don't know if it's worth this or this. And they're missing that great opportunity. >> So devolve to what I know best. >> Great discussion. Really, really enjoyed the, the time as flown by. Joe if you get that augmented reality thing to work on the salary, point it toward that guy not this guy, okay? (everyone laughs) It's much more impressive if you point it over there. But Joe thank you, Dion, Joe and Jennifer and Batman. We appreciate and Bob Hayes, thanks for being with us. >> Thanks you guys. >> Really enjoyed >> Great stuff. >> the conversation. >> And a reminder coming up a the top of the hour, six o'clock Eastern time, IBMgo.com featuring the live keynote which is being set up just about 50 feet from us right now. Nick Silver is one of the headliners there, John Thomas is well, or rather Rob Thomas. John Thomas we had on earlier on The Cube. But a panel discussion as well coming up at six o'clock on IBMgo.com, six to 7:15. Be sure to join that live stream. That's it from The Cube. We certainly appreciate the time. Glad to have you along here in New York. And until the next time, take care. (bright digital music)