(bright music) >> Welcome back to AWS re:Invent. You're watching theCUBE. And we're here with Steve Mullaney, who is the president and CEO of Aviatrix. Steve, I got to tell ya, great to see you man. >> We started the whole pandemic, last show we did was with you guys. >> Steve: Don't say we started, we didn't start it. (steve chuckles) >> Right, we kicked it off (all cross talking) >> It's going to be great. >> Our virtual coverage, that hybrid coverage that we did, how ironic? >> Steve: Yeah, was as the world was shutting down. >> So, great to see you face to face. >> Steve: Great to see you too. >> Wow, so you're two years in? >> Steve: Two and a half years yeah. >> Started, the company was standing start $2 billion valuation, raised a bunch of dough. >> Steve: Yeah. >> That's good, you got to feel good about that. >> We were 38 people, two and a half years ago, we're now 400. We had a couple million in ARR, we're now going to be over a 100 million next year, next calendar year, so significant growth. We just raised $200 million, three months ago at a $2 billion valuation. Now have 550 customers, 54 of them are fortune 500, when I started two and a half years ago, we didn't have any fortune 500s, we had probably about a 100 customers. So, massive growth, big growth (indistinct). >> Awesome, I got to ask you, I love to ask CEO's, entrepreneurs, how did you know when to scale? >> You just know it, when you see it. (indistinct) Yeah, there's no formula, you just know it and what you look for is that point where you say, okay, we've now proven the model and until you do that you minimize things and we actually just went through this. We had 12 sales teams, four months ago, we now have 50. 50, five zero and it's that step function as a company, you don't want to linearly grow 'cause you want to hold until you say, it's happening. And then once you say it's happening, okay, the dogs are eating the dog food, this is good then you flip the other way, and then you say, let's grow as fast as we possibly can and that's kind of the mode we're in right now. >> Okay, You've... >> You just know it when you see it. >> Other piece of that is how fast do you scale? And now you're sort of doing that step function as your going. >> Steve: We are going as fast as we possibly can. >> Wow, that's awesome, congratulations and I know you've got to long way to go. So okay, let's talk about the big trends that you're seeing that Aviatrix has taken advantage of, maybe explain a little bit about what you guys do. >> Yeah. So we are, what I like to call Multi- Cloud Native Networking and Network Security. So, if you think of... >> David: What is multicloud native? You got to explain that. >> I got to to explain that. Here's what's happened, it's happening and what I mean by it's happening is, enterprises at two and a half years ago, this is why I joined Aviatrix, all decided for the first time, we mean it now, we are going into Cloud 'cause before that they were just mouthing it. And they said, "We're going into the Cloud." And oh by the way, I knew two and a half years ago of course it was going to be multicloud, 'cause enterprises run workloads where they run best. That's what they do, it's sometimes it's AWS, sometimes it's ads or sometimes it's Google, it's of course going to be multicloud. And so from an enterprise perspective, they love the DevOps, they love the simplicity, the automation, the infrastructure is code, the Terraform, that Cloud operational model, because this is a business transformation, moving to Cloud is not a technology transformation it's the business. It's the CEO saying we are digitizing we have an existential threat to the survival of our company, I want to grow a market share, I want to be more competitive, we're doing this, stop laying across the tracks technology people, will run you over, we're doing this. And so when they do that as an enterprise, I'm BNY Mellon, I'm United Airlines, you name it, your favorite enterprise. I need the visibility and control from a networking and network security perspective like I used to have on-prem. Now I'm not going to do it in the horrible complex operational model the Cisco 1994 data center, do not bring that crap into my wonderful Cloud, so that ain't happening but, all I get from the Native constructs, I don't get enough of that visibility and control, it's a little bit of a black box, I don't get that. So where do I get the best of the Cloud from an operational model, but yet with the visibility and control that I need, that I used to have on-prem from networking network security, that's Aviatrix. And that's where people find us and so from a networking and network security, so that's why I call it multicloud Native because what we do is, create a layer basically an abstraction layer above all the different Clouds, we create one architecture for networking and network security with advanced services not basic services that run on AWS, Azure, Google, Oracle, Ali Cloud, Top Secret Clouds, GovClouds, you name it. And now the customer has one architecture, which is what enterprises want, I want one network, I want one network security architecture, not AWS Native, Azure Native, Google Native. >> David: Right. >> We leverage those native constructs, abstract it, and then provide a single common architecture with demand services, irrespective of what Cloud you're on. >> Dave, I've been saying this for a couple of years now, that Cloud Native... >> Does that make sense Dave? >> Absolutely. >> That abstraction layer, right? And I said, "The guys who do this, who figure this out are going to make a lot of dough." >> Yeah. >> Snowflakes obviously doing it. >> Yeah. >> You guys are doing it, it's the future. >> Yeah. >> And it's really an obvious construct when you look back at the world of call it Legacy IT for a moment... >> Steve: Yeah. >> Because did we have different networks to hookup different things in a data center? >> No, one network. >> One network of course. I don't care if the physical stack comes from Dell, HP or IBM. >> Steve: That's right, I want an attraction layer above that, yeah. >> Exactly. >> So the other thing that happens is, everybody and you'll understand this from being at Oracle, everybody wants to forget about the network. Network security, it's down in the bowels, it's like plumbing, electricity, it's just, it has to be there but people want to forget about it and so you see Datadog, you see Snowflake, you see HashiCorp going IPO in early December. Guess what? That next layer underneath that, I call it the horsemen of the multicloud infrastructure is networking and network security, that's going to be Aviatrix. >> Well, you guys make some announcements recently in that space, every company is a security company but you're really deep into it. >> Well, that's the interesting thing about it. So I said multicloud Native Networking and Network Security, it's integrated, so guess where network security is going to be done in the Cloud? In the network. >> David: Network. >> Yeah in the network. >> What a strange concept but guess what on-prem it's not, you deflect traffic to this thing called a firewall. Well, why was that? I was at Synoptics, I was at Cisco 'cause we didn't care about network security, so that's why firewall companies existed. >> Dave: Right. >> It should be integrated into the infrastructure. So now in the Cloud, your security posture is way worse than it was on-prem. You're connected to the internet by default so guess what? You want your network to do network security, so we announced two things in security; one, we're now a security competency partner for AWS, they do not give that out lightly. We were networks competency four years ago, we're now network security competency. One of the few that are both, they don't do that, that took us nine months of working with them to get there. And they only do that for the people that really are delivering value. And then what we just announced what we call, 'ThreatIQ with ThreatGuard.' So again, built into the network because we are the network, we understand the traffic, we're the control plane and the data plane, we see all traffic. We integrate into the network, we subscribe to threat databases, public databases, where we see what are the malicious IPS. If we have any traffic anywhere in your overall, and this is multicloud, not just AWS, every single Cloud, if we see that malicious traffic going some into IP guess what? It's probably BIT Mining, Bitcoin, crypto mining, it's probably some sort of data ex filtration. It could be some tour thing that you're connected to, whatever it is, you should not have traffic going. And so we do two things we alert and we show you where that all is and then with ThreatGuard, we actually will do a firewall rule right at that gateway, at that point that it's going out and immediately gone. >> You'll take the action. >> We'll take the action. >> Okay. >> And so every single customer, Dave and David, that we've shown this new capability to, it lights up like a Christmas tree. >> Yeah al bet. Okay, but now you've made some controversial statements... >> Steve: Which time? >> Okay, so you said Cisco, I think VMware... >> Dave: He's writing them down. >> I know but I can back it up. >> I think you said the risk, Cisco, VMware and Arista, they're not even in the Cloud conversation now. Arista, Jayshree Ullal is a business hero of mine, so I don't want to... >> Steve: Yeah, mine too. >> I don't want to interrogate her, she's awesome. >> Steve: Yeah. >> But what do you mean by that? Because can't Cisco come at this from their networking perspective and security and bring that in? What do you mean by they're not in the Cloud conversation? >> They're not in the conversation. >> David: Okay, defend that. >> And the reason is they were about four years ago. So when you're four years ago, you're moving into the Cloud, what's the first thing you do? I'm going to grab my CSR and I'm going to try to jam it in the Cloud. Guess what? The CSR doesn't even know it's in the Cloud, it's looking for ports, right? And so what happens is the operational model is horrendous, so all the Cloud people, it just is like oil and water, so they go, oh, that was horrendous. So no one's doing that, so what happens in the Cloud is they realize the number one thing is the Cloud operational model. I need that simplicity, I have to be a single Terraform provider, infrastructure is code. Where do I put my box with my wires? That's what the on-prem hardware people think. >> David: The selling ports your saying? >> The selling boxes. >> David: Yeah. >> And so they'll say, "Oh, we got us software version of it, it runs as a VM, it has no idea it's in the Cloud." It is not Cloud Native, I call that Cloud naive, they don't understand so then the model doesn't work. And so then they say, "Okay, I'm not going to do that." Then the only other thing they can do, is they look at the Cloud providers themselves and they say, "All right, I'm going to use Native constructs, what do you got?" And what happens basically is the Cloud providers say, "Well, we do everything and anything you'll ever need and networking and network security." And the customers, "Oh my God, it's fantastic." Then they try to use it and what they realize is you get very basic level services, and you get no visibility and control because they're a black box, you don't get to go in. How about troubleshooting, Packet Captures, simple things? How about security controls, performance traffic engineering, performance controls, visibility nothing, right? And so then they go, "Oh shit, I'm an enterprise, I'm not just some DevOps Danny three years ago, who was just spinning up workloads and didn't care about security." No, that was the Cloud three years ago. This is now United, BNY, Nike. This is like elite of elite. So when my VC was here, he said, "It's happening." That's what he meant, it's happening. Meaning enterprises, the dogs are eating the dog food and they need visibility and control, they cannot get it from the Cloud providers. >> It's happening in early days Dave. >> So Steve, we're going to stipulate that you can't jam this stuff into Cloud, but those dinosaurs are real and they're there. Explain how you... >> Steve: Well you called them dinosaurs not me but they're roaming the earth and they're going to run out of food pretty soon. (all laughing) The comet hit the earth. >> Hey, they're going to go down fighting. (all laughing) >> But the dinosaurs didn't all die the day after the comet hit the earth... >> Steve: That's right. >> They took awhile. >> Steve: They took a while. >> So, how are you going to saddle them up? That's the question because you're... >> Steve: It's over there walking dead, I don't need to do anything. >> Is it the captain Kirk to con, let them die. >> Steve: Yeah. >> Because you're in the Cloud, you're multicloud... >> Steve: Yeah. >> That's great, but 80% of my IT still on-prem and I still have Cisco switches. Isn't that just not your market or? >> When IBM and DEC did we have to do anything with IBM and DEC in the 90s, early 90s, when we created BC client server, IP architectures? No, they weren't in the conversation. >> David: Yeah. >> So, we dint compete with them, just like whatever they do on-prem, keep doing it, I wish you the best. >> But you need to integrate with them and play with them. >> Steve: No. >> Not at all? >> No, no we integrate, here is the thing that's going to happen, so to the on-prem people, it's all point of reference. They look at Cloud as off-prem, I'm going to take my operational model on-prem and I'm going to push it into the Cloud. And if I push it into multiple Clouds, they're going to call that multicloud, see we are multicloud. You're pushing your operational model into the Cloud. What's happening is Cloud has won, it won two and a half years ago with every enterprise. It's like a rock in the water. And what's going to happen is that operational model is moving out to the edge, it's moving to the branch, it's moving to the data center and it's moving into edge computing. That's what's happening... >> So outpost, so I put an outpost in my data center... >> Outpost looks like... >> Is that Aviatrix? >> Absolutely, we're going to get dragged with that... >> Dave: Okay, alright. >> Because we're the networking and network security provider, and as the company pushes out, that operational model is going to move out, not the existing on-prem OT, IT branch office then pushing in. And so, what's happening is you're coming at it from the wrong perspective. And this wave is just going to push over and so I'm just following behind this wave of AWS and Azure and Google. >> Here's the thing, you can do this and you don't have a bunch of legacy deductible debt... >> Steve: Yeah. >> So you can be Cloud Native, multicloud native, I think you called it? >> Steve: Yeah, yeah. >> I love it, you're building castles on the sand. >> Steve: Yeah. >> Jerry Chen's thing. >> Steve: Yeah. >> Now, the thing is, today's executives, they're not as naive as Ken Olsen, UNIX as, "Snake oil," who would need a PC, so they're not in denial. >> They're probably not in denial, yeah. >> Right, and so they have some resources, so the problem is they can't move as fast as you can. So, you're going to do really well. >> Steve: Yeah. >> I think they'll eventually get there Steve, but you're going to be, I don't know how many, four or five years ahead, that's a nice lead. >> That's a bet I'll take any day. >> David: Then what you don't think they'll ever get there? >> No, 10 years. (steve laughing) >> Okay, but they're not going out of business. >> No, I didn't say that. >> I know you didn't. >> What they're doing, I wish them all the best. >> Because a lot of their customers move... >> I don't compete with them. >> Yeah. We were out of time. >> Yeah. >> What did you mean by AWS is like Sandals? You mean like cool like Sandals? >> Steve: Oh, no, no, no. I don't want to... >> You mean like the vacation place? >> Have you ever been to Sandals? >> I never done it. What do you mean by that? >> There coming, there coming. Which version of sandals (indistinct)? (people cross talking) >> This is for an enterprise by the way, and look, Sandals is great for a lot of people but if you're a Cloud provider, you have to provide the common set of services for the masses because you need to make money. And oh, by the way, when you go to Sandals, go try it, like get a bottle of wine, they say, "We got red wine or white wine?" "Oh, great, what kind of red wine?" "No, red wine and it's in a box." And they hope that you won't know the difference. The problem is some people in enterprises want Four Seasons, so they want to be able to swipe the card and get a good bottle of wine. And so that's the thing with the Cloud, but the Cloud can't offer up a 200 bottle of wine to everybody. My mom loves box wine, so give her box wine. Where ISBs like us come in, is great but complimentary to the Cloud provider for that person who wants that nice bottle of wine because if AWS had to provide all this level of functionality for everybody, their instant sizes would be too big, >> Too much cost for that. (people cross talking) You're right on. And as long as you can innovate fast and stay ahead of that and keep adding value... >> Well, here's the thing, they're not going to do it for multicloud either though. >> David: I wouldn't trust them to do it with multicloud. >> No. >> David: I wouldn't. >> No enterprise would and I don't think they would ever do it anyway. >> That makes sense. Steve, we've got to go man. You're awesome, love to have you on theCUBE, come back anytime. >> Awesome, thank you. >> All right, keep it right there everybody. You're watching theCUBE, the leader in enterprise tech coverage. (bright music)

(upbeat music) >> There are many constants in the storage business, relentlessly declining costs per bit. Innovations that perpetually battle the laws of physics, a seemingly endless flow of venture capital, very intense competition. And there's one other constant in the storage industry, Eric Herzog. And he joins us today in this CUBE video exclusive to talk about IBM's recent storage announcements. Eric, welcome back to theCUBE. Great to see you, my friend. >> Great Dave, thank you very much. Of course, IBM always loves to participate with theCUBE and everything you guys do. Thank you very much for inviting us to come today. >> Really our pleasure. So we're going to cover a lot of ground. IBM Storage made a number of announcements this month around data resilience. You've got a new as a service model. You've got performance enhancements. Eric, can you give us, give us the top line summary of the hard news? >> Yeah. Top line. IBM is enhancing data and cyber resiliency across all non mainframe platforms. We already have it on the mainframe of course, and we're changing CapEx to OpEx with our storage as a service. Those are the key takeaways and the hot ticket items from an end user perspective. >> So maybe we could start with sort of the cyber piece. I mean, wow. I mean the last 18 months have been incredible and you're just seeing, you know, new levels of threats. The work from home pivot has created greater exposure. Organizations are kind of rethinking hybrid. You're seeing the ascendancy of some of the sort of hot cyber startups, but, but you're also seeing the, not only of the attack vectors winded, but the, the techniques are different. You know, threat hunting has become much more important. Your responses to threats. You have to be really careful the whole ransomware thing. So what are some of the big trends that you guys are seeing that are kind of informing how you approach the market? >> Well, first of all, it's gotten a lot worse. In fact, Fortune magazine just released the Fortune 500 a couple of weeks ago, and they had a survey that's public of CEOs, and they said, "What's the number one threat to your business? With no list just what's the number one threat?" Cyber security was number one 66% of the Fortune 500 Chief Executive Officers. Not CIOs not CTOs, but literally the CEOs of the biggest companies in the world. However, it's not just big companies. It hits the mid size, the small companies, everyone is open now to cyber threats and cyber attacks. >> Yeah. So for sure. And it's (chuckles) across the board. Let's talk about your solution, the announcement that you made here. Safeguard Copy, I think is what the branding is. >> Yeah. So what we've done is we've got a number of different technologies within our storage portfolio. For example, with our Spectrum Protect product, we can see anomalous pattern detection and backup data sets. Why would that matter? If I am going to hold theCUBE for ransom, if I don't get control of your secondary storage, snaps, replicas, and backups, you can just essentially say, I'm not paying you. You could just do a recovery, right? So we have anomalous protection there. We see encryption, we encrypt at rest with no performance penalty with our FlashSystem's family. We do air gapping. And in case of safeguarded copy, it's a form of air gapping. So we see physical air gapping with tape. logical air gapping, but to a remote location with snaps or replicas to your Cloud provider, and then local logical on-prem, which is what safeguarded copy does. We've had this technology for many years now on the mainframe platform. And we brought it down to the non mainframe environments, Linux, UNIX, and the Windows Server world by putting safeguarded copy on our FlashSystem's portfolio. >> So, okay. So part of the strategy is air gapping. So you're taking a copy, your air gapping it. You probably, you probably take those snaps, you know, at different intervals, you mix that up, et cetera. How do you manage the copies? How do you ensure if I have to do a recovery that you've got kind of a consistent data set? >> Yeah. So a couple things, first of all, we can create on a single FlashSystem array the full array up to 15,000 immutable copies, essentially they're weren't, you can't delete them, you can't change them. On a per volume basis, you can have 255. This is all managed with our storage copy manager, which can automate the entire process. Creation, deletion, frequency, and even recovery mode. So for example, I could have volume one and volume one perhaps I need to make immutable copies every four hours, while at 255 divided by four a day, I can go for many months and still be making those immutable copies. But with our Copy Services Manager, you can set up to be only 30 days, 60 days, you can set the frequency and once you set it up, it's all automated. And you can even integrate with IBM's QRadar, which is a threat detection and breach software from the security division of IBM. And when certain threats hit, it can actually automatically kick off a safeguarded copy. So what we do is make sure you've got that incredibly rapid recovery. And in fact, you can get air gapping, remotely. We have this on the main frame and a number of large global Fortune 500's actually do double air gapping, local logical, right? So they can do recovery in just a couple hours if they have an attack. And then they take that local logical and either go remote logical. Okay. Which gives them a second level of protection, or they'll go out to tape. So you can use this in a myriad of ways. You can have multiple protection. We even, by the way Dave, have three separate different admin levels. So you can have three different types of admins. One admin can't delete, one admin can. So that way you're also safe from what I'll call industrial espionage. So you can never know if someone's going to be stealing stuff from inside with multiple administrative capabilities, it makes it more difficult for someone to steal your data and then sell it to somebody. >> So, okay. Yeah, right. Because immutable is sort of, well, you're saying that you can set it up so that only one admin has control over that, is that right? If you want it... >> There's three, there's three admins with different levels of control. >> Right. >> And the whole point of having a three admins with different levels of control, is you have that extra security from an internal IT perspective versus one person, again, think of the old war movies, you know, nuclear war movies. Thank God it's never happened. Where two guys turn the key. So you've got some protection, we've got multiple admin level to do that as well. So it's a great solution with the air gapping. It's rapid recovery because it's local, but it is fully logically air gapped separated from the host. It's immutable, it's WORM, Write Once, Read Many can't delete can't change. Can't do anything. And you can automate all the management with our Copy Services Manager software that will work with safeguard copy. >> You, you talked about earlier, you could detect anomalous behavior. So, so presumably this can help with, with detecting threats, is that? >> Well, that's what our spectrum protect product does. My key point was we have all levels of data resiliency across the whole portfolio, whether it be encrypting data at rest, with our VTLs, we can encrypt in-flight. We have safeguarded copy on the mainframe, safeguarded copy on FlashSystems, any type of storage, including our competitor storage. You could air gap it to tape, right? With our spectrum virtualized software in our SAN Volume Controller, you could actually air gap out to a Cloud for 500 arrays that aren't even ours. So what we've done is put in a huge set of data and cyber resiliency across the portfolio. One thing that I've noticed, Dave, that's really strange. Storage is intrinsic to every data center, whether you're big, medium, or small. And when most people think about a cybersecurity strategy from a corporate perspective, they usually don't even think about storage. I've been shocked, but I've been in meetings with CEOs and VPs and they said, "oh, you're right, storage is, is a risk." I don't know why they don't think of it. And clearly many of the security channel partners, right? You have channel that are very focused on security and security consultants, they often don't think about the storage gaps. So we're trying to make sure, A, we've got broad coverage, primary storage, secondary storage, backup, you know, all kinds of things that we can do. And we make sure that we're talking to the end users, as well as the channel to realize that if you don't have data resilience storage, you do not have a corporate cybersecurity strategy because you just left out the storage part. >> Right on. Eric, are you seeing any use case patterns emerge in the customer base? >> Well, the main use case is prioritizing workloads. Obviously, as you do the immutable copies, you chew up capacity. Right now there's a good reason to do that. So you've got these immutable copies, but what they're doing is prioritizing workloads. What are the workloads? I absolutely have to have up and going rapidly. What are other workloads that are super important, but I could do maybe remote logical air gapping? What ones can I put out to tape? Where I have a logical, where I have a true physical air gap. But of course tape can take a long recovery time. So they're prioritizing their applications, workloads and use case to figure out what they need to have a safeguarded copy with what they could do. And by the way, they're trying to do that as well. You know, with our FlashSystem products, we could encrypt data at rest with no performance penalty. So if you were getting, you know, 30,000 database records and they were taken, you know, 10 seconds for sake of argument, when you encrypt, normally you slow that down. Well, guess what, when you encrypt with our FlashSystem product. So in fact, you know, it's interesting Dave, we have a comprehensive and free cyber resiliency assessment, no charge to the end-user, no charge to a business partner if they want to engage with us. And we will look at based on the NIST framework, any gaps. So for example, if theCUBE said, these five databases are most critical databases, then part of our cyber resilience assess and say, "ah, well, we noticed that you're not encrypting those. Why are you not encrypting those?" And by the way, that cyber resilience assessment works not only for IBM storage, but any storage estate they've got. So if they're homogenous, we can evaluate that if they're heterogeneous in their storage estate would evaluate that, and it is vendor agnostic and conforms to the NIST framework, which of course is adopted all over the world. And it's a great thing for people to get free, no obligation. You don't have to buy a single thing from IBM. It's just a free assessment of their storage and what cyber security exposure they have in their storage estate. And that's a free thing that we offer that includes safeguarded copy, encryption, air gapping, all the various functionality. And we'll say, "why are you not encrypting? Why are you not air gapping?" That if it's that important, "what, why are you leaving these things exposed?" So that's what our free cyber resilience assessment does. >> Got to love those freebies take advantage of those for sure. A lot of, a lot of organizations will charge big bucks for those. You know, maybe not ridiculously huge bucks, but you're talking tens of thousands. Sometimes you'll get up to hundreds of thousands of dollars for that type of type of assessment. So that's, you've got to take advantage of that if you're a customer out there. You know, I, I wanted to ask you about just kind of shift topics here and get into the, as a service piece of it. So you guys announced your, your as a service for storage, a lot of people have also done that. What do we need to know about the IBM Solution? And what's different from the others, maybe two part question, but what's the first part. What do we need to know? >> A couple of thing is, from an overall strategy perspective, you don't buy storage. It's a full OpEx model. IBM retains legal title. We own it. We'll do the software upgrades as needed. We may even go ahead and swap the physical system out. You buy an SLA, a tier if you will. You buy capacity, performance, we own it. So let's take an easy one. Our tier two, we give you our worst case performance at 2,250 IOPS per terabyte. Our competitors by the way, when you look at their contracts and look what they're putting out there, they will give you their best case number. So if they're two is 2,250, that's the best case. With us it's our worst case, which means if your applications or workloads get 4,000 IOPS per terabyte, it's free. We don't charge you for that. We give you the worst case scenario and our numbers are higher than our competition. So we make sure that we're differentiated true OpEx model. It's not a modified Lease model. So it's truly converts CapEx into operational expense. We have a base as everybody does, but we have a variable. And guess what? There's the base price and the variable price are the same. So if you don't use the variable, we don't charge you. We bill you for 1/4 in arrears, every feature function that's on our FlashSystem technology such as safeguarded copy, which we just talked about. AI based tiering, data at rest encryption with no performance penalty, data in compression with no performance, all those features you get, all of them, all we're doing is giving you an option. We still let you buy CapEx. We will let you lease with IBM Global Financial Services. And guess what? You could do a full OpEx model. The technology though, our flash core modules, our spectrum virtualized software is all the same. So it's all the same feature function. It's not some sort of stripped down model. We even offer Dave, 100% availability option. We give Six Nines of availability as a default, several of the competitor, which is only five minutes and 26 seconds of downtime, several of our competitors, guess what they give? Fournines. If you want five or six, you got to pay for it. We just give you six as a default differentiator, but then we're the only vendor to offer 100% availability guarantee. Now that is an option. It's the one option. But since we're already at Six Nines, when our competitors are at Four or Five Nines, we already have better availability with our storage as a service than the competition does. >> So let me just make this, make sure I'm clear on this. So you got Six Nines as part of the service. That's >> Absolutely >> Fundamental. And I get, I can pay up for 100% availability option. And, >> Yes you can. >> So what does that, what does that mean? Practically? You're putting in redundancies and, >> Right, right. So we have a technology known as HyperSwap. We have several public references by the way, at ibm.com. We've been shipping HyperSwap on both the mainframe, probably eight or nine years now. We brought it to our FlashSystem product probably five years ago. As I mentioned, we've got public references. You don't pay for the software by the way, you do have to have a dual node cluster. And HyperSwap allows you to do that. But you can do that as a service. You can buy it. You can do as CapEx, right? When you need the additional FlashSystem to go with it again, the software is free. So you're not to pay for the software. You just have to pay for the additional system level componentry, but you can do that as a service and have it completely be an OpEx model as well. We even assign a technical account manager to every account. Every account gets a technical account manager. If you will, concierge service comes with every OpEx version of our storage as a service. >> So what does that mean? What does that concierge do? Just paying attention to (indistinct) >> Concierge service will do a quarterly, a quarterly review with you. So let's say theCUBE bought 10,000 other analyst firms in the industry. You're now the behemoth. And you at theCUBE are using IBM storage as a service. You call up your technical account manager to say, "Guess what? We just bought these companies. We're going to convert them all to storage as a service, A, we need a higher tier, you could upgrade the tier B, we have a one-year contract, but you know what we'd like to extend it to two, C, we think we need more capacity." You tell your technical account manager, they'll take care of all of that for you, as well as giving you best practices. For example, if you decide you want to do safeguarded copy, which you can do, because it's built into our spectrum virtualized software, which is part of our storage as a service, we can give you best practices on that he would tell you, or she would tell you about our integration with our security visions, QRadar. So those are various best practices. So the technical account manager makes sure the software is always up to date, right? All the little things that you would have to do yourself if you own it, we take care of, because we legally own it, which is allow you to buy it as a service. So it is a true OpEx model from a financial perspective. >> In the term of the contracts are what? One, two and three years. >> One to five. >> Yeah. Okay. >> If you don't renew and you don't cancel, we'll automatically re up you at the exact tier you're at, at the exact same price. Several of our competitors, by the way, if you do that, they actually charge you a premium until you sign a contract. We do not. So if you have a contract based on tier two, right? We go buy SLA tier one, tier two, tier three. So if I have a tier two contract at theCUBE, and you forgot to get the contract done at the end of two years, but you still want it, you can go for the next 2/4. I mean, well our business partner as I should say, "Dave, don't you want to sign a contract, you said you like it." Obviously you would, but we will let you stay. You just say, now I want to keep it without a contract. And we don't charge your premium. Our competitors if you don't have a contract, they charge your premium. If you keep it installed without putting a contract in place. So little things like that clearly differentiate what we do. We don't charge a premium. If you go above the base. One of the competitors, in fact, when you go into the variable space, okay? And by the way, we provide 50% extra capacity. We over-provision. The other competitors usually do 25%. We do 50%. No charge, is just part of the service. So the other vendors, if you go into the variable space, they raised the price. So if it's $5, you know, for X capacity and you go into the, which is your base, and then you go above that, they charge you $7 and 50 cents. We don't. It's $5 at the base and $5 at the variable. Now obviously your variable can be very big or very small, but whatever the variable is, we charge you. But we do not charge you an a bigger price. Couple of competitors when you go into the variable world, they charge you more. Guess what it gets you to do, raise your base capacity. (Eric laughs) >> Yeah. I mean, that's, that should, the math should be the opposite of that, in my view. If you make a commitment to a vendor, say, okay, I'm going to commit to X. You have a nice chart on this, actually in your, in your deck. If I'm going to commit to X, and then I'm going to add on, I would think the add on price per bit should be at the same or lower. It shouldn't be higher. Right? And I get, I get what you're saying there. They're forcing you to jack up the base, but then you're taking all the risk. That's not a shared risk model. I get... >> And that's why we made sure that we don't do that. In fact, Dave, you can, you know, the fact that we don't charge you a premium if you go beyond your contract period and say, "I still wanted to do it, but I haven't done the contract yet." The other guys charge you a premium, if you go beyond your contract period. We don't do that either. So we try to be end-user friendly, customer friendly, and we've also factored in our business partners can participate in this program. At least one of our competitors came out with a program and guess what? Partners could not participate. It was all direct. And that company by happens to have about 80% of their business through the channel and their partners were basically cut out of the model, which by the way, is what a lot of Cloud providers had done in the past as well. So it was not a channel friendly model, we're channel friendly, we're end user-friendly, it's all about ease of use. In fact, when you need more capacity, it takes about 10 minutes to get the new capacity up and going, that's it? >> How long does it take to set up? How long does it take to set up initially? And how long does it take to get new capacity? >> So, first of all, we deploy either in a Colo facility that you've contracted with, including Equinix, Equinix, is part of our press release, or we install on your site. So the technical account managers is assigned, he would call up theCUBE and say, "When is it okay for us to come install the storage?" We install it. You don't install anything. You just say, here's your space. Go ahead and install. We do the installation. You then of course do the normal rationing of the capacity to this goes to this Oracle, this goes to SAP. This goes to Mongo or Cassandra, right? You do that part, but we install it. We get it up and going. We get it turned on. We hook it up to your switching infrastructure. If you've got switching infrastructure, we do all of that. And then when you need more capacity, we use our storage insights pro which automatically monitors capacity, performance, and potential tech support problems. So we give you 50% extra, right? If you drop that to 25%, so you now don't have 50% extra anymore, you only have 25% extra, we'll, the technical account manager would call you and say, "Dave, do you know that we'd like to come install extra capacity at no charge to get you back up to that 50% margin?" So we always call because it's on your site or in your Colo facility, right? We own the asset, but we set it up and you know, it takes a week or two, whatever it takes to ship to whatever location. Now by the way, our storage as a service for 2021 will be in North America and Europe only, we are really expanding our storage as a service outside into Asia and into Latin America, et cetera, but not until 2022. So we'll start out with North America and Europe first. >> So I presume part of that is figuring out just the compensation models right? And so how, how did you solve that? I mean, you can't, you know, you don't seem to be struggling with that. Like some do. I think there's some people dipping their toes in the water. Was that because, you know, IBM's got experience with like SAS pricing or how were you thinking about that and how did you deal with kind of the internal (indistinct) >> Sure. So, first of all, we've had for several years, our storage utility model. >> Right? >> Our storage utility model has been sort of a hybrid part CapEx and part OpEx. So first of all, we were already halfway there to an OpEx model with our storage utility model that's item, number one. It also gave us the experience of the billing. So for example, we bill you for a full quarter. We don't send you a monthly bill. We send you a quarterly bill. And guess what, we always bill you in arrears. So for example, since theCUBE is going to be a customer this quarter, we will send you a bill for this quarter in October for the October quarter, we'll send you a bill for that quarter in January. Okay. And if it goes up, it goes up. If it goes down, it goes down. And if you don't use any variable, there's no bill. Because what we do is the base you pay for once a year, the variable you pay for by on a quarterly basis. So if you, if you are within the base, we don't send you a bill at all because there's no bill. You didn't go into the variable capacity area at all. >> I love that. >> When you have a variable It can go up and down. >> Is that unique to some, do some competitors try to charge you up front? Like if it's a one-year term. (Dave laughs) >> Everbody charges, everybody builds yearly on the base capacity. Pretty much everyone does that. >> Okay, so upfront you pay for the base? Okay. >> Right. And the variable can be zero. If you really only use the base, then there is no variable. We only bill for it's a pay for what you use model. So if you don't use any of the variable, we never charge you for variable. Now, you know, because you guys have written about it, storage grows exponentially. So the odds of them ending up needing some of the variable is moderately high. The other thing we've done is we didn't just look at what we've done with our storage utility model, but we actually looked at Cloud providers. And in fact, not only IBM storage, but almost every of our competitors does a comparison to Cloud pricing. And when you do apples to apples, Cloud vendors are more expensive than storage as a services, not just from us, but pretty much for a moment. So let's take an example. We're Six Nines by default. Okay. So as you know, most Cloud providers provide three or Fournines as the default. They'll let you get five or Six Nines, but guess what? They charge you extra. So item number one. Second thing, performance, as you know, the performance of Cloud storage is usually very weak, but you can make it faster if you want to. They charge extra for that. We're sitting at 2,250 terabytes per IOPS, excuse me, per terabytes. That's incredible performance If you've got 100 terabytes, okay. And if your applications and workloads and that's the worst case, by the way, which differentiates from our competitors who usually quote the best case, we quote you the worst case and our worst case by the way, is almost always higher than their best cases in each of the tiers. So at their middle tier, our worst case is usually better than their best case. But the point is, if you get 4,000 IOPS per terabyte and you're on a tier two contract, it's a two-tier contract. And in fact, let's say that theCUBE has a five-year deal. And we base this on our FlashSystem technology. And so let's say for tier two, for sake of argument, FlashSystem, 7,200. We come out two years after theCUBE has it installed with the FlashSystem, 7,400. And let's say the FlashSystem, 7,400, won't deliver a 2,250 IOPS per terabyte, but 5,000, if we choose to replace it, 'cause remember it's our physical property. We own it. If we choose to replace that 7,200 with a 7,400, and now you get 5,000 IOPS per terabyte, it's free. You signed a tier two contract for five years. So two years later, if we decide to put a different physical system there and it's faster, or has four more software features, we don't charge you for any of that. You signed an SLA for tier two. >> You haven't Paid for capacity, right? All right. >> You are paying for the capacity (indistinct) performance, you don't pay for that. If we swap it out and the, the array is physically faster, and has got five new software features. You pay nothing, you pay what your original contract was based on the capacity. >> What I'm saying is you're learning from the Cloud providers 'cause you are a Cloud provider. But you know, a lot of the Cloud providers always sort of talk about how they lower prices. They lower prices, but you know, well, you worked at storage companies your whole life and they, they lower prices on a regular basis because they 'cause the cost of the curve. And so. >> Right. The cost of storage to Cloud, I mean, the average price decline in the storage industry is between 15 and 25%, depending on the year, every single year. >> Right. >> As, you know, you used to be with one of those analysts firms that used to track it by the numbers. So you've seen the numbers. >> For sure. Absolutely. >> On average it drops 15 to 25% every year. >> So, what's driving this then? If it's, it's not necessarily, is it the shift from, from CapEx to OPEX? Is it just a more convenient model than on a Cloud like model? How do you see that? >> So what's happened in IT overall is of course it started with people like salesforce.com. Well, over 10 years ago, and of course it's swept the software industry software as a service. So once that happened, then you now see infrastructure as a service, servers, switches, storage, and an IBM with our storage as a service, we're providing that storage capability. So that as a service model, getting off of the traditional licensing in the software world, which still is out there, but it's mostly now is mostly software as a service has now moved into the infrastructure space. From our perspective, we are giving our business partners and our customers, the choice. You still want to buy it. No problem. You want to lease it? No problem. You want a full OpEx model. No problem. So for us, we're able to offer any of the three options. The, as a service model that started in software has moved now into the systems world. So people want to change often that CapEx into OpEx, we can even see Global Fortune 500s where one division is doing something and a different division might do something else, or they might do it different by geography. In a certain geography, they buy our FlashSystem products and other geographies they lease them. And in other geographies it's, as a service. We are delivering the same feature, function, benefit from a performance availability software function. We just give them a different way to procure. Do you want CapEx you want leasing or OpEx you pick what you want, we'll deliver the right solution for you. >> So, you got the optionality. And that's great. You've thought that out, but, but the reason I'm asking Eric, is I'm trying to figure out this is not just for you for everybody. Is this a check-off item or is this going to be the prevailing way in which storage is consumed? So if you had, if you had a guess, let's go far out. So we're not making any near-term forecast, but end of the decade, is this going to be the dominant model or is it going to be, you know, one of the few. >> It will be one of a few, but it'll be a big few. It'll be the big, one of the biggest. So for sake of argument, there we'll still be CapEx, they'll still be OpEx they'll still be, or there will be OpEx and they're still be leasing, but I will bet you, you know, at the end of this decade, it'll be 40 to 50% will be on the OpEx model. And the other two will have the other 50%. I don't think it's going to move to everything 'cause remember, it's a little easier during the software world. In the system world, you've got to put the storage, the servers, or the networking on the prem, right? Otherwise you're not truly, you know, you got to make it a true OpEx model. There's legal restrictions. You have to make it OpEx, if not, then, you know, based on the a country's practice, depending on the country, you're in, they could say, "Well, no, you really bought that. It's not really a service model." So there's legal constraints that the software worldwise easier to get through and easier to get to bypass. Right? So, and remember, now everything is software as a service, but go back when salesforce.com was started, everyone in the enterprise was doing ELAs and all the small companies were buying some sort of contract, right, or buying by the (indistinct) basis. It took a while for that to change. Now, obviously the predominant model is software as a service, but I would argue given when salesforce.com started, which was, you know, 2007 or so, it took a good 10 years for software as a service to become the dominant level. So I think A, it won't take 10 full years because the software world has blazed a trail now for the systems world. But I do think you'll see, right. We're sitting here know halfway through 2021, that you're going to have a huge percentage. Like I said, the dominant percentage will be OpEx, but the other two will still be there as well. >> Right. >> By the way, you know in software, almost, no one's doing ELAs these days, right? A few people still do, but it's very rare, right? It's all software as a service. So we see that over time doing the same thing in the, in the infrastructure side, but we do think it will be slower. And we'll, we'll offer all three as, as long as customers want it. >> I think you're right. I think it's going to be mixed. Like, do I care more about my income statement or my balance sheet and the different companies or individual different divisions are going to have different requirements. Eric, you got to leave it there. Thanks much for your time and taking us through this announcement. Always great to see you. >> Great. Thank you very much. We really appreciate our time with theCUBE. >> All right. Thank you for watching this CUBE conversation. This is Dave Vellante and we'll see you next time. (upbeat music)

>> From around the globe, it's "theCUBE" with coverage of SUSECON Digital. Brought to you by SUSE. >> Welcome back, I'm Stu Miniman and this is CUBE's coverage of SUSECON Digital '20. Happy to welcome back to the program one of our CUBE alumni, Alan Clark, he is in the CTO office of SUSE. He works on emerging technologies and open source. Sits on many of the boards for many of those open source organizations. Alan, nice to chat with you. Thanks so much for joining us. >> Thanks for the invitation. I appreciate the opportunity. It's always fun to chat with you, Stu. >> All right, so Alan, you know, open source of course, you know, had a broad impact on the industry. Lots of talk. You know, we talk about soft breeding the world, the impact of open source. Haas on software. Give us, you know, start us a little bit kind of the state of the state as to what you're seeing broadly when it comes to. >> You know, I'm just, I keep, I enjoy this industry, 'cause it's just booming. I got into open source a long time ago, before my hair was gray, and I just can't, it just continues to surprise me and amaze me at how much it's grown. And even from, not just as projects, right? Those continue to exponentially grow, but think about the adoption, right? And from SUSE's perspective, we've got critical mission infrastructure running on open source and that is just totally amazing, right? And they've got aerospace manufacturing firms, Fortune 100s, Fortune 500s, Fortune 50s, the world's largest banks, four or five of the world's largest banks are running on SUSE Linux, right? Automotive vendors, 12 of the 15 largest automotive vendors are running on open source, running on SUSE Linux, and 10 of the largest telecommunications firms are running on SUSE, and it just goes to show that open source is really growing and is being adopted and used by critical infrastructure for the world. Particularly in these troubling days, right? >> Yeah, I mean, Alan, I've always loved diggin' into the data, you know? I haven't followed it for quite as long as you, but I've been involved for comin' up on 20 years now, and you think back 15 or 20 years it was somebody in the back room contributing some code in their spare time when they have it. When I look at the state of open source today, you mentioned lots of enterprises are using it, but lots of enterprises are contributing to it, and it's not necessarily somebody in their spare time doing it, but more and more it part of my job is leveraging and contributing back, upsource to what's happening there, so how are you seeing that? How does that impact the overall governance of open source? >> So, that's a very good question, 'cause the amount of change is huge, right? So these open source foundations have grown very large and the number of people that are contributing to them, not just in code, but in ideas, in best practices and so forth has exponentially grown, and it's amazing to see that. Plus, I guess the other part of it that I really enjoy is it's gone global, right? It used to be these projects were kind of regional, and perhaps North America to Europe, but it's, they've gone global, so these larger projects'll have 170, 180 countries that are involved. That's truly amazing. And the thing that I find very interesting, particularly given the pandemic era, we're all sitting at our homes right now. As open source developers, we're very used to this environment. We're working from home. We're scattered around the globe. We're used to working in different time zones, different geographies, and we know how to communicate and work together, so having this distance and lack of an office is actually not that much of an impediment for open source. So it's actually kind of to their advantage. >> Yeah, no, you're absolutely right. I'd done lots of interviews with developer communities and remote work is just the way they do things. Contributing code is very much an asynchronous nature of what they were doing. Alan, I love you talked about the global nature. One of the things, I was looking forward to being at this event in person was we were going to go to Dublin, you know, great city. (Alan laughs) Love to travel. When we cover a European show, it's always, "Okay, what is different "about different geographies "compared to North America?" You know, you talk about cloud adoption in general tends to be a little bit higher in North America. Any data or anecdotes that you have globally as to how open source is maybe a little bit different and culturally thought of from organizations that might be based in Europe, Asia, Latin America, or the like? >> Yeah, that's to me one of the strengths of these communities now is the difference in perspectives that you get from the different geographies, right? From Europe to Asia and so forth, and it sometimes surprises you, right? You get so used to a few vendors maybe dominating a certain area, and what you find out is they may be strong in a certain geography, but they're not globally. And as other developers and community members and users come in and start talking about their needs and their use cases, you find that their perspective is different than yours and it's kind of that "Ah ha" moment of "Oh, we need to make sure "the software works for everybody "and fits their need." And I guess the second part of that would be, you know, with this pandemic, it's causing the whole industry dynamics to change, and businesses are finding that they've got to rapidly adapt and change, and open source is one of the ways they're able to do that, right? Our customer sentiments are changing. Their purchasing habits are obviously changed. The way we shop, the way we do business, the way we're meeting people, right? We're all doing it digitally now. That's changing the services that companies need to deliver. And one of the powers of open source is being able to provide that to them and deliver those services very rapidly to them. And another dynamic here that I'm finding is interesting is customers, or consumers of open source, the businesses that are consuming open source are realizing that with these times, you know, you've got to have multiple sources for your supply chain. We have a lot more discussion about being nationalized instead of globalized, you know, when borders shut down and you can't get your supplies from another country, where are you going to get them, right? So those kinds of discussions change your source of supplies and so forth, so you have to diversify a little bit, and that's causing new types of services that are going to be created, needed. The beauty of open source, though, is it's global, and so I can get access to it whether I'm here in Salt Lake City or I'm sitting up in Dublin, wherever I'm at. And it's awesome. It's just amazing. >> Excellent, Alan. So, you know, you talked about some of the impact of what the global pandemic happening. They can leverage remote work. Open source is something that they can get ready access to. I'm curious if there's any other things in the community, you know, rallying points that you're seeing, any good stories or anecdotes that you might be able to share. >> So, I guess the other aspect of this I find extremely encouraging is, open source is amazing for individuals, not just businesses, right, to consume it, but me as an individual to learn new ideas, new technologies, try things out. And it's a great opportunity right now, particularly for home bound to go out and learn new ideas, learn about new concepts, new technologies, learn about Kubernetes, learn about containers, learn about rapid software development, right? And SUSE's actually caught onto this. This is one of the things I find really cool is they've got a couple things that are going on. First, they've created a sandbox out there where I, as an individual, for free can go out there and give rapid application development a try. It's being at home, often I don't have the full equipment that I would have at the office, right? So getting an environment set up, having the equipment and access that I need to get an environment set up to try something out, you know, like Kubernetes or application development. I may not have that at my home. So SUSE's set up some sandboxes out there where, as a developer, I can go out and give SUSE's application platform development a try. It's easy, it's all set up for me. I can go out there and I can play. Try out new concepts, see what Kubernetes is about, see what rapid development is about. And it minimizes my, you know, the task and the equipment that I need to be able to do that. The second part of that is they've opened up a lot of their online training courses for free for developers as well and operators. So it's a great time for, we're stuck at home, it's a great time to take advantage of these resources and learn more about open source. >> Great, yeah, absolutely. Alan, I spoke to your CEO, Melissa, and we talked about the importance of the developer communities. You mentioned the sandbox there. I'm curious, anything else you've seen, kind of the changing dynamic about how developers integrate with the business. One of the constant themes we talk about is IT isn't just something that's on the side, but is a clear partner with the business and often is a driver for the business, so the developers often need some education, they need communication. What do you see and how are the development communities changing? >> Oh, so I think a great part of this, this year is all the events that are going virtual. So we've got tons of resources available within these communities and through companies like SUSE, as we just talked about, and we also have these events that are going virtual, so all this content is now becoming readily accessible. I hear often from developers saying, "Well, my company doesn't give us much "for money for traveling to these events "and conferences and so forth." Now that they're all going virtual it's given 'em great access to amazing materials, and the beauty of these events is that a lot of the material is framed around helping you understand how to develop open source, how to become a part of the community, and then also about what this technology is about, where it's heading. So you, particularly as an IT organization, I get a great insight as to where the technology's going. What's the future look like? What are the ideas that are being formed by all these individuals from around the world? What's their perspectives? And then I can turn, and tying that to the business, is I can take that and take that to my business and say, "Look, here's where the technology is heading. "Here's how we can use it to enhance our business "and deliver better services to our customer." So it's a great opportunity this year. >> Yeah, you're right, Alan. There's often that gap between the people that can attend and what content is available to everyone else, and, you know, seems to be opening up. Everything from, you know, it funny, Disney is giving away the recipes for some of the things that they're doing through the conferences, typically free to attend and on demand soon after doing. All right, Alan, you're in the emerging technologies group. So, last thing I want to ask is give us a little bit look forward. What is your group looking at or the communities that you're involved in? What are some of the things that are exciting you and your peers? >> So, SUSE expanding from the edge to the cloud, to the core, right? And so we're covering things all the way from the gamut. Lot of new exciting stuff happening out on the edge with IoT and with edge services. Pretty excited about that area. SUSE's had a lot of experience in that space, particularly if you look at manufacturing providing, helping them, those businesses, the manufacturing firms meet their SLAs. Had a lot of experience in the retail space, around point of service. That, of course, is pivoting to self-service, to frictionless shopping, that types of stuff, so it's pretty exciting in those areas. So there's a lot going on in the edge. Healthcare, SUSE's been very involved, embedded in a lot of healthcare devices. That business will continue to grow, so we're seeing a lot about, on the edge. We talked a bit about rapid development. So back at the core and the cloud we're trying to make that a seamless experience so you can push those workloads, build those workloads in a containerized, micro-service manner, and distribute those pieces where it makes sense, right? So we talk about artificial intelligence gathering the data out on the edge, doing a bit of filtering and processing, moving that up to the core and the cloud, being able to mine that data, learn intelligently, then orchestrate your services, orchestrate your core appropriately, right? To meet those demands that your customers are putting on you. There's just a lot going on. We got containers. We've got hybrid cloud. We've got multicloud. We got intelligent orchestration. Then we could go on and talk a ton, we could talk for 30 minutes just about what's happening in the data space. So there's a lot to look forward to when it comes to open source and the innovation that's happening out there. >> All right, well, Alan Clark. Great to catch up with you. Thank you so much for giving us a little bit of vision. >> Thank you, Stu. >> Where we've been, and where we're going. >> Thank you very much. >> All right, I'm Stu Miniman and stay tuned for more coverage from SUSECON Digital '20. Thank you for watching "theCUBE." (calm electronic music)

(dramatic orchestral music) >> Hey, welcome back everybody. Jeff Frick, here, at theCUBE. We're at the Palo Alto studios taking a very short break in the middle of the crazy fall conference season. We'll be back on the road again next week. But we're excited to take an opportunity to take a breath. Again, meet new companies, have CUBE conversations here in the studio, and we're really excited to have our next guest. He's Apurva Dave, the CMO of Sysdig. Apurva, great to see you. >> Thanks, Jeff, thanks for having me here. >> Yea, welcome, happy Friday. >> Appreciate it, happy Friday, always worth it. >> So give us kind of the 101 on Sysdig. >> Yep, Sysdig is a really cool story. It is founded by a gentleman named Loris Degioanni. And, I think the geeks in your audience will probably know Loris in a heartbeat because he was one of the co-creators of a really famous open source project called Wireshark. It's at 20 million users worldwide, for network forensics, network visibility, troubleshooting, all that great stuff. And, way back when, in 2012, Loris realized what cloud and containers were doing to the market and how people build applications. And he stepped back and said, "We're going to need "a totally new way to monitor "and secure these applications." So he left all that Wireshark success behind, and he started another open source project, which eventually became Sysdig. >> Okay. >> Fast-forward to today. Millions of people are using the open source Sysdig and the sister project Sysdig Falco to monitor and secure these containerized applications. >> So what did Sysdig the company delineate itself from Sysdig the open source project? >> Well, you know, that's part of the challenge with open source, it's like part of your identity, right. Open source is who you are. And, what we've done is, we've taken Loris's vision and made it a reality, which is, using this open source technology and instrumentation, we can then build these enterprise class products on top for security monitoring and forensics at scales that the biggest banks in the world can use, governments can use, pharma, healthcare, insurance, all these large companies that need enterprise class products. All based on that same, original open source technology that Loris conceived so many years ago. >> So would you say, so the one that we see all the time and kind of use a base for the open source model, you kind of, Hortonworks, it's really pure, open source Hadoop. Then you have, kind of, Mapbar, you know, it's kind of proprietary on top of Hadoop. And then you have Cloudera. It's kind of open core with a wrapper. I mean, how does the open piece fit within the other pieces that you guys provide? >> That's really a really insightful question because Loris has always had a different model to open source, which is, you create these powerful open source projects that, on their own, will solve a particular problem or use case. For example, the initial Sysdig open source project is really good at forensics and troubleshooting. Sysdig Falco is really good at runtime container security. Those are useful in and of themselves. But then for enterprise class companies, you operate that at massive scale and simplicity. So we add powerful user interfaces, enterprise class management, auditing, security. We bundle that all on top. And that becomes this Cloud-Native intelligence platform that we sell to enterprise. >> And how do they buy that? >> You can, as subscription model. You can use it either as software as a service, where we operate it for you, or you can use it as on-premise software, where we deliver the bits to you and you deploy it behind your firewall. Both of those products are exactly the same functionally, and that's kind of the benefit we had as a younger company coming to market. We knew when we started, we'd need to deliver our software in both forms. >> Okay and then how does that map to, you know, Docker, probably the most broadly known container application, which rose and really disturbed everything a couple years ago. And then that's been disturbed by the next great thing, which is Kubernetes. So how do you guys fit in within those two really well-known pieces of the puzzle? >> Yeah, well you know, like we were talking about earlier, there's so much magic and stardust around Kubernetes and Docker and you just say it to an IT person anywhere and either they're working on Kubernetes, they're thinking about working on Kubernetes, or they're wondering when they can get to working on Kubernetes. The challenge becomes that, once the stardust wears off, and you realize that yeah, this thing is valuable, but there's a lot of work to actually implementing it and operationalizing it, that's when your customers realize that their entire life is going to be upended when they implement these new technologies and implement this new platform. So that's where Sysdig and other products come in. We want to help those customers actually operationalize that software. For us, that's solving the huge gaps around monitoring, security, network visibility, forensics, and so on. And, part of my goal in marketing, is to help the customers realize that they're going to need all these capabilities as they start moving to Kubernetes. >> Right, certainly, it's the hot topic. I mean, we were just at VMworld, we've been covering VMworld forever, and both Pat and Sanjay had Kubernetes as parts of their keynotes on day one and day two. So they're all in, as well, all time for Amazon, and it goes without saying with Google. >> Yeah, so it's funny is, we released initial support for Kubernetes, get this, back in 2015. And, this was the point where, basically the world hadn't yet really, they didn't really know what Kubernetes was. >> Unless they watched theCUBE. >> Unless they watched-- >> They had Craig Mcklecky-- >> Okay, alright. >> On Google cloud platform next 2014. I looked it up. >> Awesome. Very nice-- >> Told us, even the story of the ship wheel and everything. But you're right, I don't think that many people were there. It was at Mission Bay Conference Center, which is not where you would think a Google conference would be. It's a 400 person conference facility. >> Exactly, and I think this year, CubeCon is probably going to be 7,000 people. Shows you a little bit of the growth of this industry. But, even back in 2015, we kind of recognized that it wasn't just about containers, but it was about the microservices that you build on top on containers and how you control those containers. That's really going to change the way enterprises build software. And that's been a guiding principle for us, as we've built out the company and the products. >> Well, way to get ahead of the curve, I love it. So, I see it of more of a philosophical question on an open source company. It's such an important piece of the modern software world, and you guys are foundationally built on that, but I always think about when you're managing your own resources. You know, how much time do you enable the engineers to spend on the open source piece of the open source project, and how much, which is great, and they get a lot of kudos in the ecosystem, and they're great contributors, and they get to speak at conferences, and it's good, it's important. Versus how much time they need to spend on the company stuff, and managing those two resource allocations, 'cause they're very different, they're both very important, and in a company, like Sysdig, they're so intimately tied together. >> Yeah, that last point to me is the biggest driver. I think some companies deal with open source as a side project that gives engineers an outlet to do some fun, interesting things they wouldn't otherwise do. For a company like Sysdig, open source is core to what we do. We think of these two communities that we serve, the open source community and the enterprise community. But it's all based on the same technology. And our job in this mix is to facilitate the activity going on in both of these communities in a way that's appropriate for how those communities want to operate. I think most people understand how an enterprise, you know, a commercial enterprise community wants to operate. They want Sysdig to have a roadmap and deliver on that roadmap, and that's all well and good. That open source element is really kind of new and challenging. Our model has always been that the core open source technology fuels our enterprise business, and what we need to do is put as much energy as we can into the open source, such that the community is inspired to interact with us, experiment, and give back. And if we do it right, two things happen. We see massive contribution from the community, the community might even take over our open source projects. We see that happening with Sysdig Falco right now. For us, our job then is to sit back, understand how that community is innovating, and how we can add value on top of it. So coming back all the way to your question around engineers and what they should be doing, step one, always contribute to the open source. Make our open source better, so that the community is inspired to interact with us. And then from there, we'll leverage all that goodness in a way that's right for our enterprise community. >> So really getting in almost like a flywheel effect. Just investing in that core flywheel and then spin off all kinds of great stuff. >> You got it, you know, my motto's always been like, if the open source is this thing off to the side, that you're wondering, oh, should our engineers be working on it, or shouldn't they, it's going to be a tough model to sustain long-term. There has to be an integrated value to your overall organization and you have to recognize that. And then, resource it appropriately. >> Right, so let's kind of come up to the present. You guys just had a big round of funding, congratulations. >> Yep, thank you. >> So you got some new cash in the bank. So what's next for Sysdig? Now you got this new powder, if you will, so what's on the horizon, where are you guys going next? Where are you taking the company forward? >> Great question, so, we just raised a $68.5 million Series D round, led by Inside Ventures and follow-on investors from our previous investors, Accel and Bane. 68.5 doesn't happen overnight. It's certainly been a set of wins since Loris first introduced those open source projects to releasing our monitoring product, adding our security product. In fact, earlier this year, we brought on a very experienced CEO, Suresh Vasudevan, who was the previous CEO of Nimble Storage, as a partner to Loris, so that they could grow the business together. Come this summer, we're having massive success. It feels like we've hit a hockey stick late last year, where we signed up some of the largest investment banks in the world, large government organizations, Fortune 500s, all the magic is happening that you hope for, and all of a sudden, we found these investors knocking at our door, we weren't actually even out looking for funds, and we ended up with an over-subscribed round. >> Right. >> So our next goal, like what are you going to do with all that money, is first of all, we're moving to a phase where, it's not just about the product, but it's about the overall experience with Sysdig the company. We're really building that out, so that every enterprise has an incredible experience with our product and the company itself, so that they're just, you know, amazed with what Sysdig did to help make Cloud-Native a reality. >> That's great and you got to bring in an extra investor, like in a crunch phase, you guys haven't had that many investors in the company, relatively a small number of participants. >> It's been very tightly held, and we like it that way. We want to keep out community small and tight. >> Well, Apurva, exciting times, and I'm sure you're excited to have some of that money to spend on marketing going forward. >> Well, we'll do our part. >> Well, thanks for sharing your story, and have a great weekend. I'm happy it's Friday, I'm sure you are, too. >> Thanks so much, have a great weekend. Thanks for having me. >> He's Apurva, I'm Jeff, you're watching theCUBE. It's theCUBE conversation in Palo Alto, we'll be back on the road next week, so keep on watching. See you next time. (dramatic orchestral music)

(lively brass music) >> Narrator: Live from New Orleans, Louisiana. It's the Cube covering .NEXTconference 2018. Brought to you by Nutanix. >> So, you're watching the Cube and there's 55 hundred in attendance, here at the Nutanix .NEXTconference. Getting ready for a big party this evening at Mardi Gras world, get a flavor for the local cuisine and one of the things we always love at the show is, really, being able to dig in with the practitioners. Happy to welcome to the program, first, my guest Mike Spencer, vice president of hosting and managed services at ICF Olson, thanks so much for joining us. >> Well thank you very much for having me. It's been a great event, so far. Very inspiring keynote speech this morning. >> Awesome, so Mike. First of all, it's your first time here at .NEXT, tell us what brought you here and a little bit of background of yourself and your organization. >> Yep, so one of the reasons why we came here is my team is up for an award. We've been a user of the Nutanix platform for about three and a half years and it's done a lot to help us in our position in the marketplace, and so part of this is giving a little bit back, and some of it's, you know, coming to hear about what's next, so. >> So actually, could you tell us, what does this award mean to you, your team, and everything like, some people, like there's vendor awards, there's show awards, and like what's that like? >> Well, you know, I think my team is really excited to have some sort of external validation that, you know, the last three and a half or four years that we've been working towards this, you know, journey towards dev ops and infrastructures code, that somebody externally is starting to recognize that what we've done is great, and appreciating that work, so. >> Alright, so Kieth and I, I think are, you know, excited to dig in, we hear things like dev ops and infrastructures code. Something we've been documenting and talked to a lot of customers about, kind of digital transformation. Can you tell a little bit of the story? Bring us back. What was the challenge? What'd your organization look like and walk us through what you did. >> Yeah, so I think initially, very traditional IT team. Really managing things on a per-server basis, on a per-client basis and really needing that guy there to click next or to pay attention to a server. Really kind of that old adage of treating all of our servers like a pet versus more like cattle, which is where we are today and the efficiency around it. So we had some issues around stability, performance, availability, those types of things that really drove us to take a different look at the way we were doing things and so that's kind of what kicked us off on the journey to start looking at, how do we totally rethink this whole space and bring innovation in, in a space that historically doesn't have a ton of innovation. >> So let's talk about the innovation because, you know, the whole thing, whole thing services, you buy commodity hardware as cheap as possible, let it run as long as possible. When I think of Nutanix, I don't think commodity. Help bring the story together for us. >> Yep, so, you know, as architecturally, as we looked at everything we were doing, one of the unique things that we did is we decided to look at our infrastructure as more of a service-based architecture, which is very much more of a software development look at the world, versus an infrastructure look and some of the key tenants in that space are around driving for simplicity in your environment, and the Nutanix platform helped us eliminate a lot of the specialties that we needed in our area, right? So we are very much a commodity type person when it comes to servers, right? The name on the front of the server wasn't really important but what was really important for us and what Nutanix brought to the table was, they merged together all of the pieces in the server part of the stack down to the network stack. We no longer had to deal with things like storage. I didn't need to have SMEs on staff that were specialists in that space. It helped to simplify our networks. It helped us manage things through a single pane of glass, right? And we did it all in a very cost effective way. For us, it really helped us take that 25% of our labor in that space and refocus about 25% of it into really driving forward with the infrastructures code and dev ops methodologies. >> Mike, what does this mean for your business? Funny, I look at your website. It's a customer experience agency built to help you through this digital transformation. It's like, wow, it's what we're talking about at this kind of show. What does that mean to your company and, you know, your end users? >> So ICF Olson is the marketing services wing of ICF, our parent company, which is a large consulting wing but from a customer experience agency standpoint, we span everywhere from PR, brand, all the way down the stack, including managed services and hosting. A lot of our clients say, hey, you know what, you guys are really good at designing this. Why don't you guys go and run it for us? And so that's really where my art comes into place, is not just the hosting of something but also the running of something and working with the clients. It allowed us to become more of an end to end agency, right? It allowed our clients to focus on things more important like, you know, how they were going to change their brand, how they were going to look at the market, how they were going to advertise. And so from a business perspective, itself, one of the things that it did is it helped enable, you know, frankly we want a lot more business, right? Because we were willing to take these things on. We were able to repeat those types of things with a high level of success, so. >> How do you measure success? >> Success is... In our space, in particular? Honestly it is our clients not having to interact with us. (laughing) Right? We're not the sexy part of the digital ecosystem. (laughing) >> Modernization of data center is a critical piece of it. Clients are looking to you to basically make that invisible. The data center should be just something that they consume. As Nutanix has moved, you've been a customer for three years and Nutanix has moved from a hardware, software appliance, where they're selling you the entire platform to software only solution, how has, what has that meant to your business? >> Well, I mean, it's allowed us to take our focus off being experts in the hardware space. Again, something that didn't necessarily bring value, even in our private cloud. We do manage both public and private cloud but our private cloud space, it allowed us to not have to focus on the energy there and really allowed our infrastructure team to become more of a software development team. So that's been a big, big win for us. >> Talk to us a little bit about the organizational dynamics, rolling out dev ops. What did that mean for your team? You say things are invisible now. Was there a adjustment in head count, or roles, or retraining that you can share? >> Yes to all of that. In its simplest form, yeah. So a lot of people look at the implementation of dev ops being something that's kind of done to an infrastructure team. Right, it's designed to make an infrastructure team look more like a software development team or work more fluidly with a software development team and I think those things are all true but it also helped us transform our overall SDLC for software development. There's a lot of things. As we continue to build skill and trade out skill, right, continue to move up the stack, we basically became middleware developers, to where, now, our software developers for our core products and things that we sell for our clients, and support for our clients, those developers are now working on purely code and the aesthetics of things, the UX side of it. Where we are much more managing the middleware component, which interacts nicely with the hyper-converged platform. Right, Nutanix. There was a shift in labor, without a doubt. As you mature through the process you do a lot of investment in people. Right, making sure that they're kind of keeping up with the times, understanding the new methodologies. Huge shifts from the methodologies that a traditional IT team would use to what a software development team uses, right? It wasn't only moving an infrastructure team into that methodology, it was also getting the business and the software development team we work with used to us working more like them versus more like the old IT team. And so honestly, we probably caught the software development team more off guard than we did ourselves, so. (laughing) >> There's another side of that coin. As you develop that skill, as you develop that capability, retention becomes a problem. There's a natural headcount where, you know what, you don't need as many people to come in at midnight to do firmware revisions, do the low level work, but as they skill up, you look around, you know, you look at what happens in the rest of the dev ops movements, where you have entire teams leaving the fortune 500s to go to another fortune 500 to implement their dev ops. How do you encourage your team to stay? >> So to me, it's all about culture, right? Our team can work remote. They all choose to be in the office, right? They enjoy each other. It's also investing in people and investing in their growth. So it's not always about, necessarily, the size of a paycheck. It's also about work-life balance, the willingness of the organization to invest in their people, and giving them time to innovate. I mean, when you talk to the majority of infrastructure guys or even technology guys out there, what drives them every day is not necessarily their paycheck. That's a side effect of the good work they do. It's really the challenge, the pure problem solving of IT. We give them that opportunity to be able to innovate. >> Tell us a little bit about your Nutanix solution that you have, what you started with, how much you grew, what's not on your Nutanix today? >> So private cloud, we are 100% Nutanix today. We started with a four node environment that was, really, purpose built around our analytics platforms. We were looking for some way to isolate IOPS from our production environment. More of a standard, three tier architecture (clearing throat) and we did some research out there, this is at the same time that we're rethinking the architecture of everything, really kind of looking at the way we do business, and we came across several vendors, one of them being Nutanix. It was a very young company, fairly unproven, in at least our market, but their message was exactly the same message that we had developed and so we decided to take a chance on them. We put them in. You know, we did some load testing between that platform and our traditional platform and were very pleasantly surprised to find what we found. Almost a three X increase in disc IOPS and so we went live with this analytics platform, and really did a lot of testing there, right? And then we kind of started the natural process after we got comfortable with it for about six months of hey, why don't we start working through the life cycle process and bring through, bringing in Nutanix to offset? Instead of buying, you know, a storage shelf, right? I can go get a Nutanix cluster that has the same amount of storage but also brings compute with it. (clearing throat) So once we started doing that, we started putting production workloads onto the Nutanix platform and seeing great results. We expedited our journey. Within about a year and a half, we had replaced all of our traditional stand and compute plaforms. So the infrastructure guys, once they saw it in action, once the business saw the results, even the financial side of it, (laughing) you know, we were almost asked to expedite the process of moving towards Nutanix. Which, for us, it was great because it was less to manage. >> So as you guys moved to the Nutanix infrastructure, talk about the more advanced services that they've offered over the past few years. Specifically, the hypervisor, haven't you guys embraced AHV? >> So we have in dev. We are not running it as our primary hypervisor right now. In our architecture, we run VMware today. I'm not probably supposed to say that here but we run VMware. We have been looking at Acropolis. Really, the way we look at the hypervisor is as a component in our service space architecture. We are in a position where we can replace that because it's not an important part to us. We just haven't had the cycles in our roadmap to be able to put towards the replacement of VMware, yet. But it is certainly something on our roadmap and something we're marching towards because the APIs have continued to evolve on the Nutanix platform, we work quite closely with Nutanix on that. They seem to accommodate a lot of our asks but, yeah, it really has been more of a time thing, you know? There's so many things to code in this space, right now. >> You've got the award but what were you looking to really accomplish this week? Are there sessions you're looking for, are there products you're looking to dig into for you and your team? >> A lot of it was about vision, right? How well does the Nutanix vision align with our vision? And, like I said, from the keynote speeches this morning and some of the new services we see coming out, I think they're doing a great job. Their head is where our head is. They're headed the same direction we are. You know, in a lot of places where we're doing custom development, we can actually go in and say, hey, why don't we acquire this? You know, one of the exciting announcements this morning was around Beam. The ability to do compliance across our cloud platforms. We run today about 50% public cloud, 50% private cloud just depending on what the solution is we're providing, so it gives us that one pane of glass. >> What public clouds are you using and how does that, kind of the hybrid, hybrid world that Nutanix laid out this morning fit into your vision? >> Well, so. The right answer for me should be it shouldn't matter what cloud I'm running. But we are running Azure as well as AWS, just depending on the solution. So we have partnerships on both sides. But we don't necessarily look at them as being a long running relationship because, you know, this is a very, this space is changing at a very rapid pace. You know, who knows who the next person is that's going to stand up that we need to support. So we're very platform-agnostic when you look at it. When we deploy something, it really doesn't matter if it's on private cloud, public cloud, doesn't really matter. To us, it's just all building blocks that we plug in together and let code do its job. So, in that model, you guys do 50% public, 50% private. Nutanix has an opinionated view of cloud. How does that impact your business and services? >> Nutanix's approach? >> Yeah they're vision versus the...? >> Yeah, well I think their vision's great, right? Because it is a fairly agnostic vision. With them being, obviously, wanting the private cloud side of that but understanding that there is no 100%, you know, private cloud and 100% public cloud in today's world. It is all hybrid cloud environment and that certain workloads are better on prim, and certain workloads are better in the public cloud. I think that was in total alignment with everything we do. Our primary job is web hosting. So we deal with geographic workloads all the time. >> Well, Mike Spencer. I wish best of luck to the ICF Olson team. >> Yeah, thank you very much for having me. >> On the award this afternoon. You're a big winner in our books either way. Kieth Townsend, I'm Stuart Miniman. Thanks so much for watching the Cube. We'll be back with lots more. (electronic music)

(energetic classical music) >> Welcome to Cube Conversations. I'm Stu Miniman. Here with the CEO and co-founder of ThousandEyes Mohit Lad. Thanks so much for joining us here in our Palo Alto studio. >> Thanks too. I'm excited to be here. >> Alright, we always love when we get the founders on. So before we get into the company, take us back. What was the why, what were you seeing in the marketplace, and bring our audience a little bit about your background in the team and what you bring to the table. >> Sounds good. So, my background, personally, is I finished my PhD at TCLA and studied computer science, focused more on the Internet. And one of the reasons we focused-- my co-founder was my colleague as well-- and one of the reasons we focused on studying the Internet was we believed that it was going to dramatically transform our lives, and the quality of our life eventually will be highly dependent on the quality of the Internet. So that's essentially the reason we focused on researching on the Internet, on connectivity and performance. And then as we came out of grad school, and looked at the market, it was clear to us that the ship of the enterprise was dramatically changing because of the adoption of cloud, and SaaS, and infrastructure of service, and that the Internet was going to be a key component of what an enterprise looks like, and it was a black box. So our thesis behind starting the company was to really help companies understand how to manage Internet-centric WAN environments, which is what today's world looks like. >> Okay, for people that don't know ThousandEyes give us how long's the company been in business, the state of the product, how many customers you have, funding, and the like. Give us a snapshot. >> Yeah, so we started in 2010. We had an odd start in many ways because we didn't start with venture funding, so started with a small national SAANS foundation grant. And the result of that was we were very focused on customers from the early days. So for the first two years, very small, about three or four people, and then raised our first round funding in 2012 through Sequoia Capital. As of today, we're about 220 plus employees headquartered in San Francisco. And we split our engineering between San Francisco and London, so these are the two hubs. We also have offices in Austin and New York. And in terms of customers, close to 500 customers at this point of time, a heavy concentration in the mid to high end of the market, so we have more than 50 Fortune 500s, a large concentration of the top financials, and really what excites us is the fact that we're helping decode some really, really complex environments that are becoming more and more complex. >> Yeah, I loved that starting point. You find, in the networking world, there's a lot. It's government, it's scientific, need to understand this. Internet's been a distributor of architecture since the early days, but it's been going through a lot of transformations. Heck, even the TV show Silicon Valley's even talking about a "new Internet." And it's so funny for me to watch that because I'm like, oh wait, I'm talking to the people in here in Silicon Valley that are actually building that with blockchain and decentralization and the like, so its mirroring what's happening in the real world. >> Yeah, and the thing that people sometimes don't realize is the Internet was not built for enterprises. And I tell customers that when you're going to Office 365, when you're going to Amazon, you're relying on the same Internet that your kids are using to watch cat videos. And that's what's carrying your production traffic, and it's really difficult for enterprises to actually make sense of what's slowing things down, where the risk is, what's breaking, and that's where we really help companies understand and take control and thrive in these connected environments. >> It was funny, years ago we used to talk about "the consumerization of IT," and what people use at home will work its way into the enterprise, but you're right. What do businesses need that's different? ThousandEyes has, I believe you call it "Network Intelligence." How is that different than the public standard Internet that you like, and tell us a little bit about what your secret sauce is and what you're bringing to the customers. >> If you think about enterprises from 20 years ago, all the applications would be on the data centers, and it would be a pretty closed environment connected through MPLS connections and so on. So you could deploy the standard APM technologies on the data center to understand what's going on with the applications. And now if you fast forward to today, when you're using something like Office 365 or SalesForce or Workday, or so on, the applications don't sit on your premises anymore, and your network is not just your priority network, but a large portion, in fact, majority of your environment is actually the public Internet. So what is needed for you to thrive in this environment is the ability to actually understand what you depend on and be able to map out not just the user experience of applications that you don't control anymore, but the underlying factors that are impacting that application. And so what we're doing is essentially creating a huge, humongous data set on public performance of the Internet, of different components of the Internet. And we do this with some tremendous data collection but also a lot of smart heuristics that we've built on top, which makes sense of it. And then we marry this data with data we also collect from inside the enterprise. So what we're creating is this environment of a seamless network, and take off this notion of networks today are borderless, right? They really don't have any sort of borders around where the edges and so on. And what we're doing is making sure that customers can look at these hybrid environments as if it's their own private network. >> It's interesting, I think back, when we moved from the client/server era to now, the SaaS environments, like, oh, it'll just magically all work anywhere. I think back to Citrix, has a very heavy networking piece to be able to make those work anywhere. What needs to be fixed, what's kind of under the covers that most people don't understand that in a SaaS environment, solutions like yours are helping to make sure that I can have the promise of anywhere, any device, any cloud? >> Yeah, so a few different things. It's not just the applications are moving to cloud, SaaS. The users are also starting to be a lot more remote and mobile, and what that creates is an environment where a user may be unhappy with the performance of Office 365, and IT's responsible for solving that issue when the traffic is entirely bypassing the corporate environment. So it's going from a Starbucks coffee shop to Office 365 servers, and that's the environment that you're responsible for even though you don't physically control that. And as you think about that, the way we thought about the solution was not just essentially give people visibility into these complex environments, but also create an ecoystem where all these SaaS companies that you rely on as an enterprise are ThousandEyes customers. And we help them decode the Internet, and to large extent, deal with the Internet when they're delivering an application. But as an enterprise, if you're using one of these top SaaS applications, by using ThousandEyes you can not only understand the performance, but you can speak the same language with them when you are trying to troubleshoot and come into a consistent understanding of what the performance is. >> So, you're working with the SaaS providers, you're working with the enterprise, sounds like you're working with both. If I'm an enterprise CIO, and okay, yes, I'm pushing my people to work remote and everything like that, I can't worry about 10,000 employees and the network that they had. Help explain how that works. >> Right, so the requirements of a solution for today's world is beyond just giving visibility. Even if you rewind to the world from 20 years ago, you would find that when there's an issue, there's a lot of finger-pointing going on between the server team, the app team, the network team, and that finger-pointing has become worse in a multi-tenant environment, especially as you use third parties for your applications. So as an example from a few weeks ago, Amazon had a major outage in the East coast. And not only did it take down applications that were hosted on Amazon, but we had customers that were surprised that their applications were not working, and the reason they were not working was they were making, for example, API calls, where the API provider was hosted in Amazon. So they did not even realize the dependencies that they were bringing into their environment. So we had a situation where if we're using a messaging service, and I can't message the person sitting in front of me, because it's going through the Amazon environment. And so its really important in this ecoystem that we as a technology provider create something that helps you connect with each other, rather than just be a siloed solution and that's a huge part of our value chain is to make sure that we can provide you the technology that helps you see through different environments, but also establish good communications back and forth. >> Mohit, networking as an industry has tended to be one of the slower moving pieces of our market. The WAN has been going through such a transformation. You launched in 2010, from 2010 to now 2018, cloud is a much bigger piece, SDWAN wasn't part of our vocabulary. How are thing different now than when you launched the company and how has that impacted your product and your engagement with customers? >> That's a great question. One of the things that I see a lot is this shift in, at least some of the leading customers that we have, a shift towards the notion of network as a core competency. And what I mean by this is when you had environments which were static, so, you're familiar with Visio. People would use Visio to do their network topology maps. They would not change for five years, or maybe three years, depending on the customers. But if you do a Visio map of your extended environment today, it's invalid one second after it's done because the Internet is constantly changing. And so the notion of this network being a static thing is not valid anymore, and companies that need to thrive have to really treat the network as a core competency--and by network, it's not just a network, it's a skill set around networks. Coming back to the trends, the trends that you're seeing are essentially being driven by the fact that you do need to take control of the network, you do need to actually manage it, much more than you used to manage it in the past, and that will give you an edge when it comes to performance to cloud applications, better connectivities, sometimes in situations like SDVAN, it's around reducing cost through MPLS links. >> You've got kind of opposing forces when you look at that. Networking should be a core competency, but don't we have to have to have more intelligence in the network? Leverage all the analytics: machine learning and AI should manage that, 'cause it's changing so fast I can't wait for a person to do that. How do you balance that, how do your customers look at that, and how's that fit into your product? >> So absolutely right, I think networking should be a core competency but networking is not just about connecting devices and using wires to connect things. It's around really understanding what's happening, even understanding what the network actually looks like, because that's something you don't control. There's a lot of focus that we put on analytics, and one of the notions that we've developed over the many years is this notion of network intelligence. And the idea is pretty straightforward. When you're using an Amazon or an Azure, you're going through the same public environments that other customers are going through, and what we do is we essentially mine our entire data set, really understand what are the aspects of the network that are affecting multiple customers, and bridge that into a single cohesive view that is beneficial for you guys. So for example, if you have connectivity issues from the offices here at the CUBE to an Amazon, you would not only know whether it's just you, but you would have more perspective on, hey, this is a larger segment of the customer base of ThousandEyes is actually going through an issue, and here's where the specific issues are. So one of the benefits that the ThousandEyes ecosystem brings to customers is every customer that we add creates more value in the data set. >> How will some of the big waves coming like 5G, IoT, all of the Edge pieces, does that tie into the offering that you have? >> Ultimately, the common denominator for all of this is the Internet, right? Some of these technologies are more towards the last mile, but they have to go through the same core, the Internet, and it's really interesting because one of the user events we did in London a couple weeks ago, we had one of our customers, a large manufacturing company, and they were talking about how they were drilling in Texas, but the drilling was controlled through a site in Belgium, and all of this only worked because the connectivity was reliable. So they were using ThousandEyes to actually ensure that the connectivity between their giant 50 ton driller was maintained to their headquarters. So those are the kinds of applications that, we didn't build it for this specific application, but the fact is we find new ways that ThousandEyes is being used, essentially because there's more and more reliance on the Internet to make things work. >> Any other customer use cases that you want to highlight? Any customer case studies you can share? >> Yeah, so we primarily help with very broadly two sorts of use cases. So one aspect is if you are providing an online service that really depends on the Internet, has a global audience, or even a large regional audience, we help those customers really understand the user experience across the Internet and understand what parts of the Internet may be impacting the applications. So think about all the major SaaS companies that use ThousandEyes, all the major retail banks, they have an online asset that they care about, that's one use case. And then the other use case is enterprise companies. So this is everything from oil and gas, to tech enterprises, to financials. They depend more and more on the Internet when they are going into Cloud and SaaS, and for them it's really unnerving when they look at the environment they're getting into and have no visibility into this black box. So that's where we provide them intelligence into this extended environment and help them understand why a user may be having issues to Office 365 or WebEx, or all of the WYS or IP solutions that are also more and more Internet dependent. >> Mohit, how are your customers doing with the rapid pace of change here? You've talked about networking is a skillset. Finding the right skillset and training people up has always been a big challenge, but what are you seeing in the customers you're talking to? How are they doing these days? >> So the customer's very, depending on the maturity and the transition that they're going through, I still find in a lot of regions that the cloud is still new, SaaS is still new, and we're in many ways in a bubble in the value. Things happen pretty quickly here, but as you step outside you realize that some of the companies are ready to scorse and still making their first strides into SaaS and cloud, and one of the things we help these sets of customers with is essentially helping them plan towards that move. So if you have a large deployment, if you're making a large shift in your infrastructure, even, you think about, let's say a situation where I want to get rid of MPLS, I want to rely on direct Internet circuits, that's a big change, and we can help you measure the performance of MPLS performance of Internet and help you make that data-driven decision. Coming back to the notion of how our customer is doing, there are customers that have realized that network skillsets and engineering around that is core, so they invest a lot of efforts into building that core mindset. There are customers that are starting to build that, and there are customers that are looking at partners to bring that expertise in. So these customers will never build a core set of function around networking, but they look at partners, managed service providers that can bring that expertise into the environments. >> Last thing I want to ask you. You're talking about global networks, we haven't talked about security. Governance and compliance is usually some of the biggest challenges that we are having. The macroeconomic challenges of the Internet. We interviewed the president of ICANN a few years ago, and he gave a warning to our audience that said we might not have one Internet in this near future. We already are starting to see a fragmented Internet, and that could be a huge challenge. Security, governance, compliance, big topics here, but maybe bring us home on that as to what you're seeing and how that affects. >> So one of the things the Internet does, it connects people, right? And when it connects people it also makes it easy for the bad guys to reach the good guys, and so things that concern our audiences in terms of security. The way the Internet works, it's very easy for somebody to announce your address space, for example, and this has happened on several occasions, which creates a denial of service, a different denial of service where all the traffic would go to a party, which is announcing your address space, but not you. So there's all these issues where DNS mapping could be changed, the routing could be changed, and our DDoS attack that happens takes a lot of the upstream environment that you have out of the equation. And so as every day passes, there's more and more things that are being discovered in terms of how attacks can be generated, and how organizations can be brought down. So one example I'll give you which is very specific I've seen is in denial-of-service attacks, this is starting to become pretty routine in today's world. It started with the solutions being on-prem solutions that would detect the volume of traffic and try to filter traffic, and then it moved to using cloud-based solutions, because the volume of traffic would be so high, that you could not actually do this on your end. So you use these cloud-based solutions. You would turn them on when you would detect an attack, and then turn them off. And the financials in particular were always under attack, so now they've gone to a model where they're always turning these things on. A DDoS mediation service, which is based in the cloud. And what has happened, this is a really interesting phenomenon that we've seen, is, let's say, a particular bank, let's say Bank of America is under attack. The same provider that's protecting Bank of America is also protecting Wells Fargo and JP Morgan, and that infrastructure under stress could mean that Wells Fargo could actually have availability issues even though they are not under attack. So one of the things we see in the Internet is this notion of collateral damage, where you may not be the actual victim or target of an attack, but because of shared infrastructure, you're collateral damage. These are the scenarios which place more and more of an importance on gathering this intelligence on what's going on in the Internet. >> Mohit Lad, really appreciate you coming to help share with our audience everything that's happening in the WAN, network intelligence, multi-cloud, global environment world. Look forward to catching up with you more in the future. This has been a CUBE Conversation, I'm Stu Miniman, thanks for watching the CUBE. (energetic classical music)

>> Announcer: Live, from Las Vegas. It's the Cube, covering AWS re: Invent 2017, presented by AWS, Intel, and our ecosystem of partners. >> Oh, well welcome to the Cube. John Walsh, here, with Keith Townsend, talking about re: Invent, the big AWS show going on here at the Sands Expo Center and talking about 40,000 plus people. I don't know how many hundred thousand square feet of booth space we're talking about here, but this show has grown exponentially from last year to this year, and we're looking forward to being with you here for the next three days. Again, I'm John Walsh, with Keith Townsend. Keith, always a pleasure to see you sir, how ya been? >> I've been really well, I'm navigating the four hotels this conference is spanning. The last number I heard, almost 50,000 people. >> Is that right? >> Yeah, it's 48,000, 45,000, a huge conference. >> Well and quite often, for those of you who come out to Las Vegas a lot for shows, you realize that there are certain anchor centers, but as Keith pointed out, we're talking about four hotels, and even some spillover into a fifth as well. The sessions are packed, the exhibits are certainly dynamic, already attracting a lot of attention behind us and we're glad to be with you, here on the Cube. It's a pleasure now to introduce Eric Thomas, who's the Director of Cloud Products at ExtraHop and good to see you sir. Thanks for being with us. >> Thanks very much. >> Breaking your maiden on the Cube, is that correct? >> Absolutely, first time, hopefully not the last. >> We'll go easy on ya. >> Oh, thank you so much, appreciate that. >> ExtraHop, based out of Seattle. Tell us a little bit, first off, about core competencies, what you guys do, and then we'll drill down a little bit to just why you're here at AWS. >> Absolutely, so we're a platform for what we call wire data analytics. Essentially what we do is, we use the network as a data source for application intelligence, performance, security, forensics, you know whether that's sort of public or private Cloud, on-prem, hybrid set ups. We sort of sit on the network, virtual or physical network, listen to all the traffic, and then we analyze it, sort of at an application layer. So we speak web, and database, and storage, active directory, single signon, all these sort of services and protocols. Then we apply machine learning to that to surface insights to IT professionals and app developers. >> So I mean, are you looking for whether it's code issues, or maybe infiltration, or maybe performance, I mean, or everything? >> All of the above, all of the above. >> Oh, alright. >> So we sort of started off talking about IT operations, performance management, availability, downtime, and our customers then said to us, you know, once you have full visibility across the entire app delivery chain, there's real implications for security there, you know, finding intrusions, anomalies and things of that nature. And so, over the last few years, we've gotten more and more into that business. You know, as far as AWS is concerned, kind of the Cloud operations, we've been supporting AWS since 2013. That was our first product offering. And we allow our customers to maintain their visibility as they shift their workloads to AWS. And sort of the value prop here is kind of a shared responsibility model, whether you're talking about security or infrastructure. At the end of the day, the business and the customer still responsible for the application. >> So help us understand why are data in the Cloud? I mean, I'm used to taking a network analyzer and puttin' it on my wire in the data center and I can get the really smart people to look at that data and extrapolate and find really great patterns. Do I really get wire data in the Cloud? How do you guys work in AWS? >> Yeah, so the virtual wire is still a virtual network, still, you know, the same TCP connection, the same packets going across the virtual wire. So we capture that virtual network traffic, marry it with physical network traffic from the data center or on-prem, put it all together in one package. >> So across customers, you guys have to have a lot of great insights. Do you have a service where you anomolize that data and then provide that insight back to your customer base? >> Yeah, absolutely. So we sort of turn that you know, investigative workflow on its head, where we do analysis and find the interesting stuff up front so that you know, the smart people don't have to go digging through packets and network analyzers. We surface our machine learning insights by looking at behavioral anomalies. We can kinda separate those into operational versus security anomalies to kinda improve the signal to noise ratio for both IT Ops teams and security teams as well. >> But to deal with the security stuff, then, on that level then, interesting point, Keith, that you bring up. The fact that you can learn from the greater community, and apply it to specific examples. What are some of these high level findings? I don't want, don't get into specifics, or you know, too specific. But what are you finding out in terms of security concerns, and how people are best addressing and best practices to addressing this? >> So we just announced, yesterday, a new rev of ExtraHop for AWS, which enables a lot of new types of use cases or outcomes from those types of security anomalies. It's a great example. You know, you're still responsible for securing all of your storage, all of your web applications. It's easy to configure your AWS setup to let anybody in the front door of S3. >> We've see that a lot, yep. >> Right, pretty embarrassing when it happens. But ExtraHop and ExtraHop for AWS, that's an anomaly, it's a couple a clicks to find out where it's goin' on and to fix it. >> So is this more prescriptive or descriptive? Are we doin' this pre an event, or post discovery of some type of intrusion? >> So we're doing it as it happens. We talk about real time analytics and when we say real time, we mean within one second of it happening, we see it in ExtraHop. Some vendors say real time to mean 15 or 10 minutes. Not really enough, if you know, trying to find a ransomware infection and stop it, for example. With machine learning, we'll provide suggested root causes. We'll say, this looks like a security anomaly. It looks like you've opened your S3 bucket. Here's how you go fix it. >> Let's talk a little bit about ecosystem. Security, especially in the Cloud, is a really big topic. There's challenges with SSL, encryption, decryption. ExtraHop can't do it all by themselves. Are you guys partnering with other security firms to bring insights? >> Yeah, we partner with a lot of different firms. Splunk comes to mind as sort of you know, a log, analytics and aggregation vendor. A lot of sort of byte code instrumentation on the sort of performance analytics side. And if you think about it architecturally, you've got the inside out view from logs and byte code, which is great. Find out what's going on in the brains of the computer as it's self-reporting as a virtual machine or an application. We take the outside-in view. We're sort of looking at it from the outside to get more definitive about literally every single transaction and the impact of everything, from active, all the things you can't measure or instrument using classical agents and that sort of thing. So we've had those firms come to us and say, we'd like to partner with you on this ecosystem approach. >> So AWS, big conference. One of the things I've talked to a lot of folks in the community for the past coupla days. For me, this is a very different community. We have anywhere from infrastructure architects from the Big Fortune 500s, to people who've been more traditional AWS customers and are not used to going through IT and consuming these services. How does a, that ladder customer surface up at ExtraHop. >> So having been at this show since 2013, I've seen more and more enterprise customers at these shows as these, you know, sort of Cloud strategies have finally come to pass. Been talking about public Cloud since 2008 or so from a strategic perspective in the enterprise. Now, it's becoming real. Those are our customers, full-stop. The CIOs, the CSOs, the VPs of App Dev, Product Management et cetera. It's great to see them moving their workloads to the Cloud. It's also great to see that they're, you know, modernizing some of the services, while choosing to leave some of their other legacy services for later. We can monitor all of that, sort of maintain visibility, performance assurance and security, as they're moving those workloads. >> So can you talk about how you ease the pain between those two worlds, the public Cloud which is a very different operating model than what we can do in a data center. We have complete control of the infrastructure in a data center. The Cloud is abstracted away. How do you get guys help even that out and make operations simple? >> So one thing that we're seeing, sort of from a megatrend perspective with CIOs. They really want to make as many options available to their app teams, their infrastructure teams, their dev teams as possible, because the CIO's saying, I don't know what's gonna stick from a technology perspective. I'm not the one to make those decisions, I'm the one to support them. And so, I'm gonna open the floodgates. You know, you're allowed to do whatever you want with public Cloud, virtual private Cloud, I'm gonna give you all these options. Meanwhile the CSO is saying, I really wish you'd standardize. It's gettin' hard to track all these assets, all these different, you know, middleware components that you're putting out there. They need a way to audit and assess what's really going on, you know, in both the public virtual private Cloud and on-prem and that's sort of where we come in. >> So just in general, Cloud migration, you were just saying how, '08, '09, this has been eight, nine years in the making. Is it finally been kinda demystified, do ya think, to a certain degree? Or people, there's been enough trial and error that there's more confidence for those who haven't made that leap yet that okay, there's a more defined path that I'm more comfortable with it now. >> I think it's gotten more realistic in terms of the assumptions around cost savings. When people started talking about this originally, it was like, oh, great, we're gonna completely map our consumption of resources to what we really need. We're gonna save all this money, and yeah, that's true, to a degree. I think those expectations have been tempered a little bit, as you figure out, you know, where you can track that sort of performance in your capacity, or you just wanna let people run wild. So that's a tempering of expectations. There have also been these unexpected benefits around next gen application architectures, microservices, continuous integration, even continuous delivery. The Cloud enables all of that, you know, it sort inspires a level of agility in historically less agile businesses. >> And then, you mentioned the, kind of these microservices. How do you guys support microservices? We're used to the VM centric view of the Cloud, when you're talking about services that are abstracted away from the VM. How does ExtraHop play in those realms? >> So, you know, this is sort of the next iteration of the services oriented architecture, as people have realized, you know, that the sort of the best practices and sort of code patterns for developing these services. For us, you know, we auto discover systems and services running across virtual or physical networks, which means you don't have to configure things ahead of time and we can scale to elasticity very easily. We see services spin up, spin down, move from one place to another, move across availability zones, and we just track all that as it happens. >> Well Eric, we certainly appreciate the time. And we wanna know, how was the first Cube experience? You alright with it? >> So far, so good, what do you think, you tell me, you're the experts. >> We didn't beat him up enough. >> We didn't, I gotta come with tougher questions next time. >> Next time. >> There you go. >> Eric Thomas, ExtraHop, glad to have you with us here on the Cube. >> Thank you so much, 'preciate it. >> Back with more here from re: Invent. We're in Las Vegas, be here all week, back with more on the Cube, right after this. (electronic 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)