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

>> Narrator: TheCUBE's live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (upbeat music intro) (logo background tingles) >> Hi everybody, welcome back to day three of MWC23, my name is Dave Vellante and we're here live at the Theater of Barcelona, Lisa Martin, David Nicholson, John Furrier's in our studio in Palo Alto. Lot of buzz at the show, the Mobile World Daily Today, front page, Netflix chief hits back in fair share row, Greg Peters, the co-CEO of Netflix, talking about how, "Hey, you guys want to tax us, the telcos want to tax us, well, maybe you should help us pay for some of the content. Your margins are higher, you have a monopoly, you know, we're delivering all this value, you're bundling Netflix in, from a lot of ISPs so hold on, you know, pump the brakes on that tax," so that's the big news. Lockheed Martin, FOSS issues, AI guidelines, says, "AI's not going to take over your job anytime soon." Although I would say, your job's going to be AI-powered for the next five years. We're going to talk about data, we've been talking about the disaggregation of the telco stack, part of that stack is a data layer. John Kreisa is here, the CMO of Couchbase, John, you know, we've talked about all week, the disaggregation of the telco stacks, they got, you know, Silicon and operating systems that are, you know, real time OS, highly reliable, you know, compute infrastructure all the way up through a telemetry stack, et cetera. And that's a proprietary block that's really exploding, it's like the big bang, like we saw in the enterprise 20 years ago and we haven't had much discussion about that data layer, sort of that horizontal data layer, that's the market you play in. You know, Couchbase obviously has a lot of telco customers- >> John: That's right. >> We've seen, you know, Snowflake and others launch telco businesses. What are you seeing when you talk to customers at the show? What are they doing with that data layer? >> Yeah, so they're building applications to drive and power unique experiences for their users, but of course, it all starts with where the data is. So they're building mobile applications where they're stretching it out to the edge and you have to move the data to the edge, you have to have that capability to deliver that highly interactive experience to their customers or for their own internal use cases out to that edge, so seeing a lot of that with Couchbase and with our customers in telco. >> So what do the telcos want to do with data? I mean, they've got the telemetry data- >> John: Yeah. >> Now they frequently complain about the over-the-top providers that have used that data, again like Netflix, to identify customer demand for content and they're mopping that up in a big way, you know, certainly Amazon and shopping Google and ads, you know, they're all using that network. But what do the telcos do today and what do they want to do in the future? They're all talking about monetization, how do they monetize that data? >> Yeah, well, by taking that data, there's insight to be had, right? So by usage patterns and what's happening, just as you said, so they can deliver a better experience. It's all about getting that edge, if you will, on their competition and so taking that data, using it in a smart way, gives them that edge to deliver a better service and then grow their business. >> We're seeing a lot of action at the edge and, you know, the edge can be a Home Depot or a Lowe's store, but it also could be the far edge, could be a, you know, an oil drilling, an oil rig, it could be a racetrack, you know, certainly hospitals and certain, you know, situations. So let's think about that edge, where there's maybe not a lot of connectivity, there might be private networks going in, in the future- >> John: That's right. >> Private 5G networks. What's the data flow look like there? Do you guys have any customers doing those types of use cases? >> Yeah, absolutely. >> And what are they doing with the data? >> Yeah, absolutely, we've got customers all across, so telco and transportation, all kinds of service delivery and healthcare, for example, we've got customers who are delivering healthcare out at the edge where they have a remote location, they're able to deliver healthcare, but as you said, there's not always connectivity, so they need to have the applications, need to continue to run and then sync back once they have that connectivity. So it's really having the ability to deliver a service, reliably and then know that that will be synced back to some central server when they have connectivity- >> So the processing might occur where the data- >> Compute at the edge. >> How do you sync back? What is that technology? >> Yeah, so there's, so within, so Couchbase and Couchbase's case, we have an autonomous sync capability that brings it back to the cloud once they get back to whether it's a private network that they want to run over, or if they're doing it over a public, you know, wifi network, once it determines that there's connectivity and, it can be peer-to-peer sync, so different edge apps communicating with each other and then ultimately communicating back to a central server. >> I mean, the other theme here, of course, I call it the software-defined telco, right? But you got to have, you got to run on something, got to have hardware. So you see companies like AWS putting Outposts, out to the edge, Outposts, you know, doesn't really run a lot of database to mind, I mean, it runs RDS, you know, maybe they're going to eventually work with companies like... I mean, you're a partner of AWS- >> John: We are. >> Right? So do you see that kind of cloud infrastructure that's moving to the edge? Do you see that as an opportunity for companies like Couchbase? >> Yeah, we do. We see customers wanting to push more and more of that compute out to the edge and so partnering with AWS gives us that opportunity and we are certified on Outpost and- >> Oh, you are? >> We are, yeah. >> Okay. >> Absolutely. >> When did that, go down? >> That was last year, but probably early last year- >> So I can run Couchbase at the edge, on Outpost? >> Yeah, that's right. >> I mean, you know, Outpost adoption has been slow, we've reported on that, but are you seeing any traction there? Are you seeing any nibbles? >> Starting to see some interest, yeah, absolutely. And again, it has to be for the right use case, but again, for service delivery, things like healthcare and in transportation, you know, they're starting to see where they want to have that compute, be very close to where the actions happen. >> And you can run on, in the data center, right? >> That's right. >> You can run in the cloud, you know, you see HPE with GreenLake, you see Dell with Apex, that's essentially their Outposts. >> Yeah. >> They're saying, "Hey, we're going to take our whole infrastructure and make it as a service." >> Yeah, yeah. >> Right? And so you can participate in those environments- >> We do. >> And then so you've got now, you know, we call it supercloud, you've got the on-prem, you've got the, you can run in the public cloud, you can run at the edge and you want that consistent experience- >> That's right. >> You know, from a data layer- >> That's right. >> So is that really the strategy for a data company is taking or should be taking, that horizontal layer across all those use cases? >> You do need to think holistically about it, because you need to be able to deliver as a, you know, as a provider, wherever the customer wants to be able to consume that application. So you do have to think about any of the public clouds or private networks and all the way to the edge. >> What's different John, about the telco business versus the traditional enterprise? >> Well, I mean, there's scale, I mean, one thing they're dealing with, particularly for end user-facing apps, you're dealing at a very very high scale and the expectation that you're going to deliver a very interactive experience. So I'd say one thing in particular that we are focusing on, is making sure we deliver that highly interactive experience but it's the scale of the number of users and customers that they have, and the expectation that your application's always going to work. >> Speaking of applications, I mean, it seems like that's where the innovation is going to come from. We saw yesterday, GSMA announced, I think eight APIs telco APIs, you know, we were talking on theCUBE, one of the analysts was like, "Eight, that's nothing," you know, "What do these guys know about developers?" But you know, as Daniel Royston said, "Eight's better than zero." >> Right? >> So okay, so we're starting there, but the point being, it's all about the apps, that's where the innovation's going to come from- >> That's right. >> So what are you seeing there, in terms of building on top of the data app? >> Right, well you have to provide, I mean, have to provide the APIs and the access because it is really, the rubber meets the road, with the developers and giving them the ability to create those really rich applications where they want and create the experiences and innovate and change the way that they're giving those experiences. >> Yeah, so what's your relationship with developers at Couchbase? >> John: Yeah. >> I mean, talk about that a little bit- >> Yeah, yeah, so we have a great relationship with developers, something we've been investing more and more in, in terms of things like developer relations teams and community, Couchbase started in open source, continue to be based on open source projects and of course, those are very developer centric. So we provide all the consistent APIs for developers to create those applications, whether it's something on Couchbase Lite, which is our kind of edge-based database, or how they can sync that data back and we actually automate a lot of that syncing which is a very difficult developer task which lends them to one of the developer- >> What I'm trying to figure out is, what's the telco developer look like? Is that a developer that comes from the enterprise and somebody comes from the blockchain world, or AI or, you know, there really doesn't seem to be a lot of developer talk here, but there's a huge opportunity. >> Yeah, yeah. >> And, you know, I feel like, the telcos kind of remind me of, you know, a traditional legacy company trying to get into the developer world, you know, even Oracle, okay, they bought Sun, they got Java, so I guess they have developers, but you know, IBM for years tried with Bluemix, they had to end up buying Red Hat, really, and that gave them the developer community. >> Yep. >> EMC used to have a thing called EMC Code, which was a, you know, good effort, but eh. And then, you know, VMware always trying to do that, but, so as you move up the stack obviously, you have greater developer affinity. Where do you think the telco developer's going to come from? How's that going to evolve? >> Yeah, it's interesting, and I think they're... To kind of get to your first question, I think they're fairly traditional enterprise developers and when we break that down, we look at it in terms of what the developer persona is, are they a front-end developer? Like they're writing that front-end app, they don't care so much about the infrastructure behind or are they a full stack developer and they're really involved in the entire application development lifecycle? Or are they living at the backend and they're really wanting to just focus in on that data layer? So we lend towards all of those different personas and we think about them in terms of the APIs that we create, so that's really what the developers are for telcos is, there's a combination of those front-end and full stack developers and so for them to continue to innovate they need to appeal to those developers and that's technology, like Couchbase, is what helps them do that. >> Yeah and you think about the Apples, you know, the app store model or Apple sort of says, "Okay, here's a developer kit, go create." >> John: Yeah. >> "And then if it's successful, you're going to be successful and we're going to take a vig," okay, good model. >> John: Yeah. >> I think I'm hearing, and maybe I misunderstood this, but I think it was the CEO or chairman of Ericsson on the day one keynotes, was saying, "We are going to monetize the, essentially the telemetry data, you know, through APIs, we're going to charge for that," you know, maybe that's not the best approach, I don't know, I think there's got to be some innovation on top. >> John: Yeah. >> Now maybe some of these greenfield telcos are going to do like, you take like a dish networks, what they're doing, they're really trying to drive development layers. So I think it's like this wild west open, you know, community that's got to be formed and right now it's very unclear to me, do you have any insights there? >> I think it is more, like you said, Wild West, I think there's no emerging standard per se for across those different company types and sort of different pieces of the industry. So consequently, it does need to form some more standards in order to really help it grow and I think you're right, you have to have the right APIs and the right access in order to properly monetize, you have to attract those developers or you're not going to be able to monetize properly. >> Do you think that if, in thinking about your business and you know, you've always sold to telcos, but now it's like there's this transformation going on in telcos, will that become an increasingly larger piece of your business or maybe even a more important piece of your business? Or it's kind of be steady state because it's such a slow moving industry? >> No, it is a big and increasing piece of our business, I think telcos like other enterprises, want to continue to innovate and so they look to, you know, technologies like, Couchbase document database that allows them to have more flexibility and deliver the speed that they need to deliver those kinds of applications. So we see a lot of migration off of traditional legacy infrastructure in order to build that new age interface and new age experience that they want to deliver. >> A lot of buzz in Silicon Valley about open AI and Chat GPT- >> Yeah. >> You know, what's your take on all that? >> Yeah, we're looking at it, I think it's exciting technology, I think there's a lot of applications that are kind of, a little, sort of innovate traditional interfaces, so for example, you can train Chat GPT to create code, sample code for Couchbase, right? You can go and get it to give you that sample app which gets you a headstart or you can actually get it to do a better job of, you know, sorting through your documentation, like Chat GPT can do a better job of helping you get access. So it improves the experience overall for developers, so we're excited about, you know, what the prospect of that is. >> So you're playing around with it, like everybody is- >> Yeah. >> And potentially- >> Looking at use cases- >> Ways tO integrate, yeah. >> Hundred percent. >> So are we. John, thanks for coming on theCUBE. Always great to see you, my friend. >> Great, thanks very much. >> All right, you're welcome. All right, keep it right there, theCUBE will be back live from Barcelona at the theater. SiliconANGLE's continuous coverage of MWC23. Go to siliconangle.com for all the news, theCUBE.net is where all the videos are, keep it right there. (cheerful upbeat music outro)

>>Good morning, everyone from Chicago Live. The Cube is live at Ansible Fast 2022. Lisa Martin and John Ferer are here for two days of multiple coverage on the cube. Very excited to be back in person. Ansible's 10th anniversary, the first in-person event. John, since 2019. Yeah, great to be perfect. One of the nuggets dropped this morning and I know you was Opss code. >>Yeah, we're gonna hear about that OPSIS code here in this segment. We're gonna get in, but the leader of the, the business unit at Ansible, part of Red Hat. So look forward >>To this. Exactly. Tom Anderson joins us, one of our alumni. Welcome back to the program. Thank you. The VP and general manager of Red Hat. First of all, how great is it to be back in person with live guests and an engaged audience and then robust community? >>It is amazing. It really is. I kind of question whether this day was ever gonna come again after three years of being apart, but to see the crowd here and to see, like you said, the energy in the room this morning and the keynotes, it's fantastic. So it's fa I just couldn't be happier. >>So opsis code nugget drop this morning. Yep. We wanna dissect that with you as, as that was mentioned in the keynote this morning. As Ansible is pushing into the cloud and and into the edge, what does OPSIS code mean for end users and how is it gonna help them to use a term that was used a lot in the keynote level up their automation? >>Yeah, so what we see is, look, the day zero, day one provisioning of infrastructure. There's lots of tools, there's lots of ways to do that. Again, it's just the company's ambition and dedication to doing it. The tools are there, they can do that. We see the next big opportunity for automation is in day two operations. And what's happening right now in ops is that you have multiple clouds, you've got multiple data centers and now you've got edge environments. The number of things to manage on a day-to-day basis is only increasing. The complexity is only increasing this idea of a couple years ago where we're gonna do shift everything left onto the developer. It's nice idea, but you still have to operate these environments on a day two basis. So we see this opportunity as opsis code, just like we did infrastructures code, just like we did configuration as code. We see the next frontier as operations code. >>Yeah, and this is really a big trend as you know with cube reporting a lot on the cloud native velocity of the modern application developer these days, they're under, they're, it's a great time to be a software developer because all the open source goodness is happening, but they're going faster. They want self-service, they want it built in secure, They need guardrails, they need, they need faster ops. So that seems to be the pressure point. Is ops as code going to be that solution? Because you have a lot of people talking about multi-cloud, multiple environments, which sounds great on paper, but when you try to execute it, Yeah, there's complexity. So you know, the goal of complexity management has really been one of the key things around ops. How do I keep speed up and how do I reduce the complexities? These are big. How does, how does ops code fit into that? >>Yeah, so look, we, we see Ansible as this common automation back plane, if you will, that goes across all of these environments. It provides a common abstraction layer so that whether you're running on Azure, whether you're a GCP or whether you're AWS or whether you're, you know, a PLC out on a shop industrial edge floor with a plc, each of those things need to be automated. If we can abstract that into a common automation language, then that allows these domain experts to be able to offer their services to developers in a way that promotes the acceleration, if you will, of those developers tasks. And that developer doesn't have to know about the underlying complexities of storage or database or cloud or edge. They can just do their >>Job. You know, Tom, one of the things I observed in Keynote, and it comes across every time I, we have an event and in person it's more amplified. Cause you see it, the loyalty of the customer base. You have great community. It's very not corporate like here. It's very no big flashy news. But there's some news, hard news, It's very community driven. Check the box there. So continuing on the roots, I wanna get your thoughts on how now the modern era we're in, in this world, the purchasing power, again, I mentioned multicloud looks good on paper, which every CX I wanna be multiple clouds. I want choice now. Now you talk to the people running things like, whoa, hold on, boss. Yeah, the bottoms up is big part of the selection process of how people select and buying consume technology with open source, you don't need to like do a full buy. You can use open source and then get Ansible. Yeah. This is gonna be a big part of how the future of buying product is and implementing it. So I think it's gonna be a groundswell, bottoms up market in this new cloud native with O in the ops world. What's your reaction to that? What's your thoughts? >>So here, here's my thoughts. The bulk of the people here are practitioners. They love Ansible, they use Ansible in their day to day job. It's how it helped, makes 'em successful. Almost every executive that I go out and talk to and our customers, they tell me one of their number one pro or their number one problem is attracting you talent and retaining the talent that they have. And so how can they do that? They can give them the tools to do their job, the tools that they actually like. So not a top down, you know, old fashioned systems management. You're gonna use this tool whether you like it or not. But that bottoms up swell of people adopting open source tools like Ansible to do their job and enjoy it. So I see it as a way of the bottoms up addressing the top down initiative of the organization, which is skills retention, skills enhancement. And that's what we focus on here at this event. Are the practitioners, >>Is that the biggest customer conversation topic these days? Is this the skills gap, retention, attraction talent? Would you say it's more expansive as the organizations are so different? >>Well, so a lot of the folks that I meet are, you know, maybe not sea level, but they're executives in the organization, right? So they're struggling with attract, you know, pretty much everywhere I go, I was in Europe this summer, conversation was always the same. We got two problems. Tracking people. We can't find people, people we find we can't afford. So we need to automate what they would do. And, and then the second piece is the complexity of our environment is growing, right? I'm being asked to do more and I can't find more people to do it. What's my solution? It's automation, you know, at the end of the day, that's what it comes down to. >>It's interesting, the people who are gonna be involved in the scaling horizontally with automation are gonna have the keys to the kingdom. The old joke when it was, you know, they run everything. They power the business now the business is digital. You gotta be hybrid. So we see hybrids a steady state right now, hybrid cloud. When you bring the edge into the equation, how do you see that developing? Because we think it's gonna be continually be hybrid and that's gonna extend out on the edge. What is the ansible's view on how the edge evolves? What's, what's going on there? Can you share your thoughts on the expansion to the edge? >>There's a, our experience is there's a rapid modernization happening out at the edge, industrial edge, you know, oil and gas platforms, retail locations, industrial floors, all that kind of stuff. We see this convergence of OT and IT happening right now where some of the disciplines that enterprises have used in the IT area are gonna expand out into ot. But some of the requirements of ot of not having skilled IT resources, you know, in the store, in the fast food restaurant, on the oil platform, needing to have the tools to be able to automate those changes remotely. We're seeing a real acceleration of that right now. And frankly, Ansible's playing a big role in that. And it's connecting a lot of the connective tissue is around network. What is the key piece that connects all of this environment as network and those number of endpoints that need to be managed. Ansible is, you know, >>It's way use case for Ansible because Ansible built their business on configuration automation, which was don't send someone out to that branch office back in the old days. Exactly. Do it. Manual versus automation. Hey, automation every time. Yes. This is at large scale. I mean the scale magnitude, can you scope the scale of what's different? I mean go even go back 10 years, okay, where we were and how we got here, where we are today. Scope the size of the scale that's happening here. >>You know, hundreds of thousands of endpoints and things. That's not even the API points, but that's the kind of compute points, the network points, the servers it's in. It's, it's, you know what we would've never thought, you know, 10 years ago, a thousand endpoints was a lot or 10,000 endpoints was a lot of things to manage when you start talking about network devices. Yeah, yeah. Home network devices for employees that are remote employees that need to be in a secured network. Just the order of magnitude, maybe two orders of magnitude larger than it has been in the past. And so again, coming home to the automation world, >>The world's spun in your front, your front door right now. >>Yeah, yeah, yeah, >>Absolutely. Talk about, you talked about the acceleration. If we think of about the proliferation of, of devices online, especially the last two years, when, to your point, so many people shifted to remote and are still there. What are some of the, the changes in automation that we've seen as businesses have had to pivot and change so frequently and so many times to be successful? >>Yeah, so here's what we've seen, which is it's no longer acceptable for the owner of the network team or the ownership of the database or of the storage facility to, you can't wait for them to offer their service to people. Self-service is now the rule of thumb, right? So how can those infrastructure owners be able to offer their services to non IT people in a way that manages their compliance and makes them feel that they can get those resources without having to come and ask. And they do that by automating with Ansible and then offering those as package services out to their developers, to their QE teams, to their end users, to be able to consume and subscribe to that infrastructure knowing that they are the ones who are controlling how it's being provisioned, how it's being used. >>What are some of the, there were some great customers mentioned this morning in the keynote, but do you have a favorite example of a customer, regardless of industry that you think really shows the value and, and the evolution of the Ansible platform in its first 10 years and that really articulates the business value that automation delivers to a company? >>Yeah, no, it's a great question. I would think that, you know, if you wound the clock back 10 years, Ansible was all about server configuration management, right? That's what it was about was per provisioning, provisioning, you know, VMware infrastructure, vSphere, and then loading on VMs on top of that as it's expanded into network, into security and to storage and to database into cloud. It's become a much broader platform, if you will. And a good example is we have a customer, large oil and gas customer who is modernizing their oil platforms. I can imagine I not, I've not been on one, but I imagine the people that are out working on that oil platforms have greasy hands that are pushing on things. And they had this platform that the technology modernization included Azure. So connecting to data on Azure, rolling out new application updates, has to have a firewall, has to have network capabilities, has to have underlying OS to be able to do that. And Ansible was the glue that brought all that together to be able to modernize that oil platform. And so for me, that's the kind of thing where it sort of makes it real. You know, the actual businesses, >>The common set of services, this is, this is where we're seeing multi-cloud. Yeah. You start to have that conversation where, okay, I got this edge, it kind of looks the same, I gotta make it work. I'm a developer, I want some compute, I want to put this together. I have containers and orchestration behind it and kind of seeing the same kind of pattern. Yeah. Evolving at scale. So you guys have the platform, okay, I'm an open source. I love the open source. I got the platform 2.3, I see supply chain management in there. You got trusted signatures. That's a supply chain. We've been hearing a lot about security in the code. What else is in the platform that's updated? Can you share the, the, the new things that people should pay attention to in the platform? >>Yeah, we're gonna talk about a couple of things smaller around event driven Ansible, which is bringing Ansible into that really day two ops world where it's sort of hands free automation and, and, and operations where rather than someone pushing a button to trigger or initiate a piece of, of automation, an event will take place. I've detected an outta space condition, I've detected a security violation, I've detected something. Go to a rule book. That rule book will kick off in automation close that remediate that problem and close the thing without anyone ever having to do anything with that. So that's kind of one big area. And we're gonna talk tomorrow. We've got a real special announcement tomorrow with our friends from IBM research that I'm gonna, >>We'll have you on 10 30 Martha Calendars. >>But there's some really great stuff going on on the platform as we start to expand these use cases in multiple directions and how we take Ansible out to more and more people, automation out to more and more people from the inside, experts out to the consumers of automation, make it easier to create automation. >>Yeah. And one of the things I wanted to follow up on that and the skill gap, tying that together is you seeing heard in the keynote today around Stephanie was talking about enterprise architecture. It's not, I won't say corner case answer. I mean it's not one niche or narrow focus. Expanding the scope was mentioned by Katie, expand your scope grow, you got a lot of openings. People are hire now, Now Ansible is part of the enterprise architecture. It's not just one thing, it's, it's a complete, Explain what that means for the folks out there. Yeah. >>So when you start to connect what I call the technology domains, so the network team uses Ansible to automate their network infrastructure and configure all their systems. And the compute team uses it to deploy new servers on aws. And the security ops team use it to go out and gather facts when they have a threat detection happening and the storage team is using it to provision storage. When you start to then say, Okay, we have all these different domains and we want to connect those together into a set of workflows that goes across all of those domains. You have this common language and we're saying, okay, so it's not just the language, it's also the underlying platform that has to be scalable. It's gotta be secure. We talked about signing content. I mean, people don't understand the risk of an automation gone wild. You can, you can do a lot of damage to your infrastructure real fast with automation, just like you can do repair, right? So is what's running in my environment secure? Is it performant and is it scalable? I mean, those are the two, those are the three areas that we're really looking at with the platform right >>Now. Automation gone wild, it sounds like the next reality TV show. Yeah, I >>May, I may regret saying that. >>Sounds >>Like great. Especially on live tv. Great, >>Great podcast title right there. I made a mental note. Automation Gone Wild episode one. Here we are >>Talk about Ansible as is really being the, the catalyst to allow organizations to truly democratize automation. Okay. You, you talked about the different domains there and it seems to me like it's, it's positioned to really be the catalyst that's the driver of that democratization, which is where a lot of people wanna get to. >>Yeah. I mean for us, and you'll see in our sessions at Ansible Fest, we talk a lot about the culture, the culture of automation, right? And saying, okay, how do you include more and more people in your organization in this process? How can you get them to participate? So we talk about these ideas of communities of practice. So we bring the open source, the concepts of open source communities down into enterprises to build their own internal communities of practice around Ansible, where they're sharing best practices, skills, reusable content. That is one of the kind of key factors that we see as a success in inside organizations is the scales, is sort of bringing everybody into that culture of automation and not being afraid of automation saying, Look, it's not gonna take my job, it's gonna help me do my job better. >>Exactly. That automation argument always went, went to me crazy. Oh yeah, automating is gonna take my job away. You know, bank teller example, there's more bank tellers now than ever before. More atm. So the, the job shifts, I mean the value shifts. Yeah. This is kind of where the, where the automation helps. What's real quick, final minute we have left. Where does that value shift? I'm the person being automated away or job. Yeah. Where do you see the value job? Cause it's still tons of openings for people's skills, >>You know? So we see the shift from, particularly in operations from, here's my job, I look at a ticket queue, I grab a ticket, it's got a problem, I go look at a log, I look for a string and a log, I find out the air and I go, configuration change that. That's not a really, I wouldn't call that a fund existence for eight or 10 hours a day, but the idea, if I can use automation to do that for me and then focus on innovating, creating new capabilities in my environment, then you start to attract a new, you know, the next generation of operations people into a much more exciting role. >>Yeah. Architects too, they turned into architects that turned into the multiple jobs scope. It's like multi-tool player. It's like >>A, you know, Yeah, yeah. The five tool player, >>Five tool player in baseball is the best of the best. But, but kind of that's what's >>Happening. That's exactly what's happening, right? That's exactly what's happening. And it helps address that skills challenge. Yeah. And the talent challenge that organizations have as well. >>And everybody wants to be able to focus on delivering value to the organization. I have to get the end of the day. That's a human component that we all want. So it sounds like Ansible is well on its way to helping more and more organizations across industries achieve just that. Tom, it's great to have you back on the program. Sounds like you're coming back tomorrow, so we get day two of Tom. All right, excellent. Look forward to it. Congratulations on the first in-person event in three years and we look forward to talking to you >>Tomorrow. Thank you so much. >>All right, for our guests and John Furrier, I'm Lisa Martin. You're watching The Cube Live from Chicago, Day one of our coverage of Ansible Fest 2022. Stick around. John and I welcome back another Cube alumni next.

(upbeat music) >> Hi, everyone. Welcome to theCUBE's presentation of the AWS Startup Showcase. This is season two, episode four of our ongoing series, where we're talking with exciting partners in the AWS ecosystem. This topic on this episode is cybersecurity. Detect and protect against threats. I have two guests here with me today from Hunters. Please welcome Lital Asher-Dotan, the CMO. And Ofer Gayer, the VP of product management. Thank you both so much for joining us today. >> Thank you for having us, Lisa. >> Our pleasure. Lital, let's go ahead and start with you. Give the audience an overview of Hunters. What does it do, when was it founded, what's the vision? All that good stuff. >> So Hunters was founded in 2018. Two co-founders coming out of Unit 8200 in the Israeli Defense Force. The founders and our people in engineering and R&D are mostly coming from both offensive cybersecurity as well as defensive threat hunting, advanced operations, or being able to see and response to advance attack. And with the knowledge that they came with, they wanted to enable security teams in organizations, not just those that are coming from, you know, military background but those that actually need to defend day in and day out against the growing cyber-attacks that are growing in sophistication, in the numbers of attacks. And we all know that every organization nowaday is being targeted, is it ransomware, more sophisticated attacks. So this thing has become a real challenge. And we all know those challenges that the industry is facing with talent scarcity, with lack of the knowledge and expertise needed to address this. So came in with this mindset of we want to bring our expertise into the field, build it into a platform, into a tool that will actually serve security teams in organizations around the world to defend against cyber attacks. So born and raised in Tel Aviv, became a global company. Recently raised a serious CO funding. Funded by the world's greatest VCs, from Stripes, Wild Ventures, supported by Snowflake data breaks and Microsoft M12, also as strategic partners. And we now have broad variety of customers from all industries around the world, from tech to retail to e-commerce to banks that we work closely with. So very exciting times. And we're very excited to share today how we work with AWS customers to support the environments. >> Yeah, we're going to unpack that. So really solid foundation the company was built on, only a few years ago. Lital was there, why a new approach? Was there a compelling event? Obviously, we've seen dramatic changes in the threat landscape in recent years. Ransomware becoming a, when it happens to us, not if. But any sort of compelling event that really led the founders to go, "Ah! This new approach, we got to go this direction." >> Absolutely. We've seen a tremendous shift of organizations from cloud adoption to adoption of more security tools. Both create a scenario which the toolsets that are currently being used by security organizations, the security teams are not efficient anymore. They cannot deal with the plethora of a variety of data. They cannot deal with the scale that is needed. And the security teams are really under a tremendous burden of tweaking tools that they have in their environment without too much of automation, with a lot of manual work processes. So we've seen a lot of points where the current technology is not supporting the people and the processes that need to support security operations. And with that, Ofer, and his product team kind of set a vision of what a new platform should come to replace and enhance what teams are using these days. >> Excellent. Ofer, that's a perfect segue to bring you into the conversation. Talk about that vision and some of those really key challenges and problems that Hunters is solving for organizations across any industry. >> Yeah. So as Lital mentioned, it was very rightful. The problem with the SIM space, that the space that we're disrupting is the well-known secret around is it's a broken space. There's a lot of competitors. There's a lot of vendors out there. It's one of the most mature, presumably mature markets in cybersecurity. But it seems like that every single customer and organization we talk to, they don't really like their existing solution. It doesn't really fit what they need. It's a very painful process and it's painful all across their workflow from the time they ingest the data. Everybody knows if you ever had a SIM solution or a SOC platform, just getting the data into your environment can take the most amount of your time, the lion's share of whatever your engineers are working on will go to getting the data into the system, and then keeping it there. It's this black hole that you have to keep feeding with more and more resources as you go along. It's an endless task with a lot of moving pieces, and it's very very painful before you even get a single moment of value of security use case from your product. That's a big, painful piece. What you then see is, once they set it up, their detection engineering is so far behind the curve because of all the different times of things they need to take care of. It used to be a limited attack surface. We all know the attack surface here today is enormous, especially when you talk about something like AWS, there's new services, new things all the time, more accounts, more things. It keeps moving a lot, and keeping track of that and having someone that can actually look into a new threat when it's released, look into a new attack surface, analyze it, deploying the detections in time, test and tweak, and all those things. Most organizations don't even how to start approaching this problem, and that's a big pain for them. When they finally get to investigating something, there lacks the context and the knowledge of how to investigate. They have very limited information coming to them and they go on this hunting chase of not hunting the attackers but hunting the data, looking for the bits and pieces they're missing to complete the picture. It's like this bad boss that gives you very little instructions or guidelines, and then you need to kind of try to figure out what is it that they asked, right? That's the same thing with trying to do triaging with very minimal context. You look at the IP and then you try to figure out, you look at the Hash, you look at all these different artifacts and you try to figure out yourself. You have very limited insights. And the worst is when you're under the gun, when there's a new emerging threat that happens like a Log4Shell, and now you're under the gun and the entire company's looking at you and saying, "Are we impacted? What's going on? What should we doing?" So from start to finish, it's a very painful process that impacts everybody in the security organization. A lot of cumbersome work with a lot of frustration. >> And it's companies in any industry, Ofer, don't have time. You talked about some of the time involved here in the lag. And there isn't time in the very dynamic threat landscape that customers are living in. Lital, question for you, is your primary target audience existing SIM customers? 'Cause Ofer mentioned the disruption of the SIM market. I'm just wanting to understand in terms of who you're targeting, what does that look like? >> Definitely looking for customers that have a SIM and don't like it, don't find that it helps them improve the security posture. We also have organizations that are young, emerging, have a lot of data, a lot of tech companies that have grown in the last 10, 15 years, or even five years. With Snowflake as a customer, they're booming. They have so much data that going the direction of traditional tools to aggregate the logs, cross-correlate them doesn't make any sense with the scale that they need. They need the cloud-based approach, SaaS approach that is capable of taking care of the environment. So we both cater to those organizations that we're shifting from on-prem to cloud and need visibility into those two environments and into those cloud natives. Born to the cloud don't want to even think of a traditional SIM. >> You mentioned Snowflake. We were just at Snowflake Summit a couple of months ago, I think that was. And tremendous company that massive growth, massive growth in data across the board though. So I'm curious, Ofer, if we go back to you, if we can dig into some of these data challenges. Obviously, data volume and variety, it's only going to continue to grow and proliferate and expand. Data in silos is still a problem. What are some of those main data challenges that Hunters helps customers to just eliminate? >> Definitely. So the data challenge starts with getting the right data in. The fact that you have so many different products across so many different environments and you need to try to get them in some location to try to use them for running your queries, your rules, your correlation. It's a big prompt. There's no unified standard for anyone, even if there was, you would have a lot of legacy things on-premises, as well as your AWS environment. You need to combine all these. You can keep things only on-prem. You can own... Mostly a lot of, most organizations are still in hybrid mode. They have, they're shifting most of their things to AWS. You still have a lot of things on-prem that they're going to shift in the next 3, 4, 5 years. So that hybrid approach is definitely a problem for gathering the data. And when they gather the data, a lot of the times their existing solutions are very cost prohibitive and scale prohibitive from pushing all the data in essential location. So they have these data silos. They'll put some of it there, some of it here, some of that in a different location, hot storage, cold storage, long-term storage. They don't really, they end up not knowing really where the data is especially when they need it the most becomes a huge problem for them. Now with analytics, it's very hard to know upfront what data I'll need not tomorrow, but maybe in three months to look back and query. Making these decisions is very hard. Changing them later is even harder. Keeping track of all these moving pieces. You know, you have a device, you have some vendor sending you some logs, they changed their APIs. Who's in charge of fixing it? Who's in charge of changing your schema? You move from one EDR vendor to the other. How are you making sure that you keep the same level of protection? All these data challenges are very problematic for most customers. The most important thing is to be able to gather as much data as possible, putting it in a centralized location, and having good monitoring in a continuous flow of, I know what data I'm getting in. I know how much I'm using, and I'm making sure that it's working and flowing. It's going to a central place where I can use it at any time that I want. >> We've seen, if I can add- >> So, Lital- >> Sorry. >> Yes, please. >> You wanted to add on that? We've seen too much compromise on data that because of prohibitive costs, structure of tools, or because of inability to manage the scale, teams are compromising or making choices and are paying a price of the latency of being able to then go search if an incident happened, that if you are impacted by something. It all means money and time at the end of the day when you actually need to answer yourself, am I breached or not? We want to break out from this compromise. We think that data is something that should not be compromised. It's a commodity today. Everything should be retained, kept, and used as appropriately without the team needing to ration what they're going to use versus what they're not going to use. >> Correct (faintly speaking). >> That's a great point. >> Go ahead. >> Yeah. And we've seen customers either having entire teams dedicated to just doing this and, or leveraging products and companies that actually build a business around helping you filter the data that you need to put in different data silos, which to me is, shows how much problem, pain, and how much this space is broken with what it provides with customers that you have these makeshift solutions to go around the problem instead of facing it head on and saying, "Okay, let's build something that you're put all your data as much as you want, not have to compromise on security." >> You both bring up such a great point where data and security is concerned. No business can afford to compromise. Usually compromise is a good thing, but in that case, it's really not. Companies can't afford that. We know with the threat landscape, the risk, all of the incentives for bad actors that companies need to ensure that they're doing the right things in a timely manner. Lital, I'm curious, you mentioned the target markets that you're going after. Where were customer conversations? Is this a C-suite conversation from a data security perspective? I would this is more than the CISO. >> It's a CISO conversation, as well as we talk on a daily basis with those that lead security operations, head of SOCs. Those that actually see how the analyst are being overworked, are tired, have so many false positives that they need to deal with, noise day in, day out, becoming enslaved with the tools that they need to work on and tweak. So we have seen that the ones that are most enlightened by a solution like Hunters are actually the ones that have the SOC reporting to them. They know the daily pain and how much the process is broken. And this is probably one of the... We all talk about, you know, job satisfaction or dissatisfaction, the greatest, the great resignation, people are living. This is the real problem in security. And the SOC is one of these places that we see this alert, fatigue, people are struggling. It's a stressful work. And if there is anything that we can do to offload the work that is less appealing and have them work on what they sign up for, which is dealing with real threat, solving them, instead of dealing with false positives. This is where we can actually help. >> Can you add a little bit on that, Lital? And you mentioned the cybersecurity skills gap, which is massive. We talked about that a lot because it's a huge problem. How is Hunters a facilitator of companies that might be experiencing that? >> Absolutely. So we come with approach of, we call it the 80/20 of detection and response. Basically, there are about 80%, probably more, it's actually something like 95% of the threats are shared across all organizations in the world. Also, 80 to 90% of the environments are similar. People are using similar tools. They're on similar cloud services. We think that everything that goes around detection of threats, around those common attacks, scenarios in common attack landscape should come out of the box from the vendor like Hunters. So we automate, we write the rules, we cross-correlate. We provide those services out of the box once you sign in to use our solution. Your data flows in and we basically do the processing and the analysis of all the data, so that your team can actually focus on the 20%, or the 15, or the 5% that are very unique to your organization. If you are developing a specific app and you have the knowledge about the DevSecOps that needs to take place to defend it. Great, have your team focus on that. If you are a specific actor in a specific space and specific threats that are unique to you, you build your own detections into our tool. But the whole idea that we have the knowledge, we see attacks across industries and across industries we have the researchers and the capabilities to be on top of those things, so your team doesn't need to do it on a daily basis because new attacks come almost on a daily basis. Now, we read them in the news, we see them. So we do it, so your team doesn't have to. >> And nobody wants to be that next headline where a breach is concerned. Lital, close this out here with outcomes. I noticed some big stats on your website. I always gravitate towards that. What are some of the key outcomes that Hunters customers are achieving and then specifically AWS customers? >> Absolutely. Well, we already talked a lot about data and being able to ingest it. So we give our customers the predictability, the ability to ingest the data knowing what the cost is going to be in a very simple cost model. So basically you can ingest everything that you have across all IT tools that you have in your environment. And that helped companies reduce up to 75% of the data cost. We've seen with large customer, how much it change when they moved from traditional SIMs to using Hunters. Specifically, AWS customers can actually use the AWS Credits to buy Hunters if they're interested. Just go to AWS Marketplace, search for Hunters and come to a website, you can use your credits for that. I think we talked also about the security burden, the time spent on writing rules plus correlating incidents. We have seen sometimes a change in, instead of investigating an incident for two days, it is being cut for 20 minutes because we give them the exact story of the entire attack. What are the involved assets? What are the users that are involved, that they can just go see what's happening and then immediately go and remediate it. So big shift in meantime to detect meantime to respond. And I'm sure Ofer has a more kind of insights that he's seen with some of our customers around that. >> Yeah. So some great examples recently there. So there's two things that I've been chatting to customers about. One thing they really get a benefit of is we talked about the problem with talent. And where that really matters the most is that under the gun mode, we have a service that is, we see it as the natural progression of the service that we provide called Team Axon. What Team Axon does for you is when you're under the gun, when something like Log4Shell happens and everybody's looking at you, and time is ticking, instead of trying to figure out on yourself, Team Axon will come in, figure out the threat, will devise a report for all the customers, run queries on your behalf on your data, and give it to you within 24 hours. You'll have something to show your CEO or your executive team, your board even, this is where we got impacted or not impacted. This is what we did. Here's the mitigation thing, step that we need to take from world-class experts that you might not get access to for every single attack out there. That really helps customers kind of feel like they're safe. There's someone there to help them. There's a big brother there. I call it sometimes the Bat-Signal when we need it the most. The other thing is on the day-to-day, a lot of solution, we'll kind of talk about out-of-the-box security. Now, the problem with out-of-the-box security is keeping it up to date, that's what a lot of people miss. You have to think that you installed a year ago, but security doesn't stay put, you need to keep updating it. And you need to keep the updated pretty pretty frequently to stay ahead of the curve. If you're behind couple of months on your security updates, you know what happens. Same thing with your SOC platform on your SIM rule base. The reason that customers don't update is because if they usually do, then it might blow up the amount of alerts they're getting 'cause they need to tweak them. With the approach that we take that we tested on our customer's data transparently for them, and make sure to release them without false positives. We're just allowing them to push the updates transparently directly to their account. They don't need to do anything. And one customer, one of our biggest accounts, they have dozens of subsidiaries and multiple SOCs and one of the largest e-commerce companies in the world. And the person running security, he said, "If I had to do what Hunters gives me out of the box myself, I have to hire 20 people and put them to work for 18 months for what you give me out of the box." So for me, it's a very- >> That's huge. >> What we give customers and the kind of challenges that we're able to solve for them. >> Big challenges. Lital and Ofer, thank you so much for joining us on theCUBE today as part of this AWS Startup Showcase, talking about what Hunters does, why the vision and the value in it for customers. We appreciate your time and your insights. >> Thank you so much. >> For having us. >> My pleasure. For my guests, I'm Lisa Martin. Thank you for watching this episode of the AWS Startup Showcase. We'll see you soon. (cheerful music)

(upbeat music) >> Hey, welcome back everyone to theCube's live coverage of the Monaco Crypto Summit here in Monaco. I'm John Furrier, host of theCube, and Lauren Bissell here, founder and CEO of Immutable Industries, focused on the advancement of technologies in art, entertainment, blockchain across multiple sectors. Great background in entertainment music, complying that into the convergence and to crypto. Welcome to theCube. I appreciate it. >> Thank you so much. Thank you guys for having me. It's been an incredible day so far. >> So we were just talking before we came on camera, your background and just the people you've worked with in the music industry. You've been there for a very long part of your career, from the beginning. Now you're on the wave of Web3, crypto, DeFi. There's a confluence of refactoring businesses. We're seeing that impact. And I think a lot of people, finance and entrepreneurial, the best brains are coming into the sector because it's an opportunity, clearly, to reset and refactor old antiquated business models and practices, in a new way to achieve the same things. Better, faster, cheaper >> Exactly. Better, faster, cheaper, is good sometimes, other times that's... We will see. But I think for me, coming in from the music industry was something that, I honestly never expected to be involved in blockchain and futuristic tech. It's always something that I admired, but I didn't really see, "Okay. Here's how I can be involved in that." I was obsessed with it. But as I was sort of progressing my career as a music producer, I saw so many issues with the industry. The way capital came in, the way that it was distributed. I mean, these things are still happening today. But I was just constantly looking around for better solutions and how to make this work in a better way. In 2017, when I started really diving into crypto, that was something where I saw a huge opportunity for the entire industry. The music industry is notorious for just sort of being behind the curve when it comes to new tech. And it's a shame. When you're in an industry that's full of art and innovation, you would think that it's something... It's an industry that would embrace this position. Maybe some people do this, and I applaud those people very much. But in general, the music industry is kind of behind. We live a little bit in the Wild West. Not in the futures way, but kind of in the old way. I'm just really excited to be able to bring these things into the industry. >> It's interesting. I'm not in the industry, in the music side, but I've been on the software industry, where you had the proprietary software, the rights, and people used to build software. And then when the company went under, the software was gone, lost forever. And in around the late eighties, nineties, open source movement happened, and it just changed everything. And I think, to me, I feel like this is a similar structural inflection point in change, where rights are changing. People are still holding onto like, "He can't use the copyright." And I even saw a stat that said, with AI now, you can actually copyright every single melody, every single note in music. So that means like, "Who the hell's going to develop anything?" So are even rights even matter? So rights, ownership, art, mixing. Funny story of my son, a year and a half ago, mixed an old song from a band that wasn't around, and it became a TikTok sensation. Hundreds of millions of listens, and then the Spotify and Apple account was making like 20,000 a week, and DistroKid cut him off. Because someone went back and claimed the copyrights. But it was a mix of a couple of different pieces of the song for a new melody. But because that wasn't his work, the middle man killed the account. >> Right. But if there had been maybe an easier solution for him to go get those rights. So I actually used to be a rights and royalties negotiation specialist. I was on the phone with labels, every second of every day. From a producer standpoint, you're trying to find something that works for the artist, something that works for the label, something that you can arrange in perpetuity, if possible. But it's just... Again, there's so many people that have to just get on the phone- >> Like a busy gen system of like- >> Yeah. >> Weirdness >> Right. >> What's the solution? >> I mean, right now one of the favorite... It's super simple. Smart contracts related to publishing and royalties. Now you still need, probably in the interim, someone to go out and... The old school job for someone in rights and royalties is sitting in a restaurant and listening to see if the music is being played, and then you write it all down on a piece of paper. I mean, that's quite old school, but that still happens in a lot of places. So we can kind of move into smart contracts for the payment systems, and eventually we can move into AI, to actually detect what music is being played where. Just to go, not really on a tangent, but it's like, "Okay. Well, are we taking a job away from someone who's supposed to sit in a restaurant and listen to the music?" Well, I think we're developing a lot of new jobs by needing to generate this software. This is more- >> I've heard that. We've heard that argument before, "Oh! Bank tellers are going to be put out of business by the ATM machine." Turns out there's more branches now. >> Right. >> Okay. There's a total waste there. I mean, people say that are like... I mean, but it does bring up the next gen, the creator, the young artist, the ability to collaborate with smart contracts, the removal of the middle person in all this, the intermediaries. That's really the key, right? >> I think it is the key. And like I said, before removal of the middle person, some people would look down on that. I think it's more efficient systems. When you have more efficient systems, you have more efficient societies, you can create bigger and better things. So is there a change process that has to happen there? Yeah, of course. But this is humanity, this is history, this is what happens. >> Okay. So you're a pro, you've been through- >> I just embrace that. >> You've been through the business, you got the scar tissue, you got the experience, you got the brains. Now you're here in the front of a new generation, a lot of pioneering going on, a lot of chaos, a lot of confusion. Some people... Blood's spilling on the ground. There's a lot of stuff going on, that is opportunity. What are you up to? How are you attacking this market, how do you look at it, what's on your mind? >> Yeah. I mean, so what's funny, I've actually been spending the last few years, sort of directly advising individuals and companies in the music industry. So everyone from artists to label executives, content distribution executives, licensing teams and publishers, and sort of explaining, "Here's how things work. Here's how we think they're going to go. And here's how, instead of running away from that and trying to block your artists from using that system, we can actually use this to enhance the financial pie of the music industry, instead of just trying to steal a piece of everyone else's pie." That's what I really want to do, is, the industry pie can get bigger. We don't need to steal your blueberries. It's just- >> They're picking up crumbs and fighting over crumbs >> Exactly. The industry changed, and I understand why it's scary. I really, really do. I've lived through this. But it's going to be- >> What do they say? What's your advice to them, and what's their reaction? Is it like, "Yeah, you said that you'd get lip service." Or like, "Yeah, we're trying my best. I'll stop drinking, I promise." I mean, I've heard... I tried last week. I mean, are they actually getting it done, or they don't know what to do? >> Yeah. Well, I think it starts with individuals. I actually spent a lot of time working with individuals on education and how they can take that information to their companies or implement that in their companies. It's on sort of a corporate level. It is slower. That's okay. That's expected. But educating sort of individuals, like I said, that's what I've been doing for the past few years, is what's really been helpful. Because if you just kind of do this overnight, I understand it's not going to happen overnight. But being able, like I said, to figure out, "Okay. We grow the financial pie for the whole industry." This accumulates, this helps the health of the industry. Like I said, I grew up in the industry. I care a lot about the industry. I actually want to see good things happen- >> Positive change. >> It's in my heart, in my soul, to make the music industry- >> So Lauren, I got to ask you. So as you see the industry changing, and it's going to be hard to get people to go through transformation. >> Yeah. >> They have to get there. Otherwise, they'll be extinct. And we kind of see that. Is there new brands emerging that have a clean sheet of paper? Because I'm a far young artist, I'm saying to myself, "Okay. If I can write my own ticket..." And by the way, brands become platforms is a big trend you're seeing with NFTs and- >> Yeah. >> And these great Web3 platforms. So I got more social power, I got collective intelligence, I got network effect, I got fans. All that's tappable now from a monetization standpoint. >> Yeah. >> Are there new agencies, new brands, emerging that's artists friendly like this? >> I mean, that's one of the reasons we're here, to begin with. I'm obviously just going to mention Digital Bits, because they're literally creating NFTs for brands. I'm here because I believe in what they're building. Their model is applicable to brands, it's applicable to artists and athletes. I actually truly believe in what they're building and how they're doing it. NFTs is a faster way to achieve what we thought we were going to achieve with sort of the tokenization of a person or an individual brand. NFTs, I think, is a better way to do that. Obviously NFTs are tokens as well, but it's a different type of thing than an ICO. >> It has more versatility and it's got the same kind of characteristics- >> Yeah. I think you can build more community with it, you can maintain the value of the token itself, the non-punchable token itself, a little bit better, and you can build community around it. >> What are some of the companies you're advising and people you're advising? Are they record labels, are they executive, like an executive coach on one end, business consultant on the other? >> Yeah. >> What's some of the range of... >> So I actually advise a couple of brands, I can't completely speak about in the music industry, but from the executive position, I do advise individual executives from the label and the content distribution side, on sort of how to implement futurist tech into their company a little bit better, and sort of what the real things that are going on, the new things that are going on. I actually just took on a role for a company called Cyber Yachts, which I'm really excited about. This one's just going to be fun. International music, entertainment, fun. >> Do you need some media up there? We'll have to do interviews on both- >> Yeah. You can come on the metaverse yacht and the physical yacht, if you want to. But- >> Monaco's a great place for that. >> We will be here. >> All right. >> Absolutely. >> So tell me about the future of some of these big agencies you mentioned? Because if you look at the market right now, if you zoom out, content is king, distribution is Kong. That's what they say. There's a lot more distribution now more than, it seems, content. That's maybe on some perspectives. But it seems like there's a lot more outlets looking for better content. >> Always. >> Do you agree that distribution's hungry for the content, or is there more content than distribution? >> I think it just depends on the type of content. If you look at the content that's being distributed over, say social media, for example, there's a plethora of content. >> Yeah. I guess I'm not- >> There's actually, now, this new hierarchy there, where you have to really scrap to get to the top. So in a weird way, you're seeing that sort of mimic. We see how societies work. So now that's become very hierarchical, and that's almost mimicking the way the traditional industry has been developed. So we go through these cycles. >> It must be hard for a record label to try to do the A and R job, when you have more artists emerging from TikTok, Instagram, the social networks, or- >> I would say their job's probably gotten easier. >> Do you think because of the filtering? >> Well, yeah. Now you can view so much talent in a tiny amount of time online. Now, do I know what they are like lives, do I know how they perform? No, I got to go figure that out. But before you had to go to clubs and sit in there, and run around a city. You can only be in so many places at one time. >> You got to chase content down, look it down. >> Yeah. >> All right, so what's the most exciting thing that you think is happening in the whole crypto world, that's people should pay attention to, that's going to impact some of the mainstream? What's the most important things, do you think? >> Well, something that's actually, somewhat unrelated to music, which is government adoption. Sorry, but hands down, that is the most exciting and important thing that's going on right now. >> Adopting it and embracing it is important. >> Adopting it, embracing it, new regulations coming out. >> Are you happy with the progress? >> Yeah. I mean, it takes time. But right now we're the biggest sort of country that sun is, El Salvador. >> And now Monaco's leaning in. >> Now Monaco is obviously leaning in, that's... It's exciting. It's really exciting. >> Well, to me, I think Digital Bits, so when you climbed in earlier, is that, there's a legitimate crossover between the physical asset, digital asset world, and now the kind of the tough parts, the in between the details and the gaps, the contracts, the royalties. >> Yeah. >> Compliance. What does that even mean? >> Right. >> How is that going to get sorted out? Do you think this is going to settle itself out on its own or self govern, a little bit of a iron hand in there, or... >> It'll be a mix. I mean, there's a lot of trial and error going on right now, as far as governments. Like I said, there's really only a few places in the world that are doing it. I applaud these places for their bravery because... Don't get me wrong. It's going to be a struggle. There's going to be failures and successes, and being willing to be one of the countries that does that, that shows some grit. I really respect it. >> And the upside is if they get it right, it's huge. Lauren, final question. What are you up to next, what's on your mind? What are you working on beyond this consultancy? What's around the corner for you? Where do you see the self dots connecting in the future? >> Well, I'm really... Right now I travel quite a bit. I spend a lot of different... A lot of time at different conferences. I spoke earlier a little bit about an education program that I'm developing with an alliance with Draper University in El Salvador. So I want to finish the programming for that. We're going to scale that out across multiple countries. And that's everything from education for governments and education for people that, maybe just recently heard of Bitcoin and they don't even know how to go about seeing what it is. >> 5G in emerging countries is pretty potential there. >> It is. Absolutely. >> Great stuff. Lauren, thanks for coming on theCube sharing. >> Thank you so much. >> I appreciate it. Lauren Bissell here on theCube, I'm John Furrier, live in Monaco, for the Monaco Crypto Summit, Digital Bits. We got a big gala event tonight with Prince Albert in attendance. A lot of action, a lot of big news happening here. All the players are gathered for the inaugural Monaco Crypto Summit. I'm John Furrier. We'll have more live coverage after this short break. (upbeat music)

>>Hello, everyone. Welcome to the cubes coverage here. New York city for AWS Amazon web services summit 2022. I'm John furrier, host of the cube with Dave ante. My co-host. We are breaking it down, getting an update on the ecosystem. As the GDP drops, inflations up gas prices up the enterprise continues to grow. We're seeing exceptional growth. We're here on the ground floor. Live at the Summit's packed house, 10,000 people. Eric Bradley's here. Chief STR at ETR, one of the premier enterprise research firms out there, partners with the cube and powers are breaking analysis that Dave does check that out as the hottest podcast in enterprise. Eric. Great to have you on the cube. Thanks for coming on. >>Thank you so much, John. I really appreciate the collaboration always. >>Yeah. Great stuff. Your data's amazing ETR folks watching check out ETR. They have a unique formula, very accurate. We love it. It's been moving the market. Congratulations. Let's talk about the market right now. This market is booming. Enterprise is the hottest thing, consumers kind of in the toilet. Okay. I said that all right, back out devices and, and, and consumer enterprise is still growing. And by the way, this first downturn, the history of the world where hyperscalers are on full pumping on all cylinders, which means they're still powering the revolution. >>Yeah, it's true. The hyperscalers were basically at this two sun system when Microsoft and an AWS first came around and everything was orbiting around it. And we're starting to see that sun cool off a little bit, but we're talking about a gradient here, right? When we say cool off, we're not talking to shutdown, it's still burning hot. That's for sure. And I can get it to some of the macro data in a minute, if that's all right. Or do you want me to go right? No, go go. Right. Yeah. So right now we just closed our most recent survey and that's macro and vendor specific. We had 1200 people talk to us on the macro side. And what we're seeing here is a cool down in spending. We originally had about 8.5% increase in budgets. That's cool down is 6.5 now, but I'll say with the doom and gloom and the headlines that we're seeing every day, 6.5% growth coming off of what we just did the last couple of years is still pretty fantastic as a backdrop. >>Okay. So you, you started to see John mentioned consumer. We saw that in Snowflake's earnings. For example, we, we certainly saw, you know, Walmart, other retailers, the FA Facebooks of the world where consumption was being dialed down, certain snowflake customers. Not necessarily, they didn't have mentioned any customers, but they were able to say, all right, we're gonna dial down, consumption this quarter, hold on until we saw some of that in snowflake results and other results. But at the same time, the rest of the industry is booming. But your data is showing softness within the fortune 500 for AWS, >>Not only AWS, but fortune 500 across the board. Okay. So going back to that larger macro data, the biggest drop in spending that we captured is fortune 500, which is surprising. But at the same time, these companies have a better purview into the economy. In general, they tend to see things further in advance. And we often remember they spend a lot of money, so they don't need to play catch up. They'll easily more easily be able to pump the brakes a little bit in the fortune 500. But to your point, when we get into the AWS data, the fortune 500 decrease seems to be hitting them a little bit more than it is Azure and GCP. I >>Mean, we're still talking about a huge business, right? >>I mean, they're catching up. I mean, Amazon has been transforming from owning the developer cloud startup cloud decade ago to really putting a dent on the enterprise as being number one cloud. And I still contest that they're number one by a long ways, but Azure kicking ass and catching up. Okay. You seeing people move to Azure, you got Charlie bell over there, Sean, by former Amazonians, Theresa Carlson, people are going over there, there there's lift over at Azure. >>There certainly is. >>Is there kinks in the arm or for AWS? There's >>A couple of kinks, but I think your point is really good. We need to take a second there. If you're talking about true pass or infrastructure is a service true cloud compute. I think AWS still is the powerhouse. And a lot of times the, the data gets a little muddied because Azure is really a hosted platform for applications. And you're not really sure where that line is drawn. And I think that's an important caveat to make, but based on the data, yes, we are seeing some kinks in the armor for AWS. Yes. Explain. So right now, a first of all caveat, 40% net score, which is our proprietary spending metric across the board. So we're not like raising any alarms here. It's still strong that said there are declines and there are declines pretty much across the board. The only spot we're not seeing a decline at all is in container, spend everything else is coming down specifically. We're seeing it come down in data analytics, data warehousing, and M I, which is a little bit of a concern because that, that rate of decline is not the same with Azure. >>Okay. So I gotta ask macro, I see the headwinds on the macro side, you pointed that out. Is there any insight into any underlying conditions that might be there on AWS or just a chronic kind of situational thing >>Right now? It seems situational. Other than that correlation between their big fortune 500, you know, audience and that being our biggest decline. The other aspect of the macro survey is we ask people, if you are planning to decline spend, how do you plan on doing it? And the number two answer is taking a look at our cloud spend and auditing it. So they're kind say, all right, you know, for the last 10 years it's been drunken, sail or spend, I >>Was gonna use that same line, you know, >>Cloud spend, just spend and we'll figure it out later, who cares? And then right now it's time to tighten the belts a little bit, >>But this is part of the allure of cloud at some point. Yeah. You, you could say, I'm gonna, I'm gonna dial it down. I'm gonna rein it in. So that's part of the reason why people go to the cloud. I want to, I wanna focus in on the data side of things and specifically the database. Let, just to give some context if, and correct me if I'm, I'm a little off here, but snowflake, which hot company, you know, on the planet, their net score was up around 80% consistently. It it's dropped down the last, you know, quarter, last survey to 60%. Yeah. So still highly, highly elevated, but that's relative to where Amazon is much larger, but you're saying they're coming down to the 40% level. Is that right? >>Yeah, they are. And I remember, you know, when I first started doing this 10 years ago, AWS at a 70%, you know, net score as well. So what's gonna happen over time is those adoptions are gonna get less and you're gonna see more flattening of spend, which ultimately is going to lower the score because we're looking for expansion rates. We wanna see adoption and increase. And when you see flattening a spend, it starts to contract a little bit. And you're right. Snowflake also was in the stratosphere that cooled off a little bit, but still, you know, very strong and AWS is coming down. I think the reason why it's so concerning is because a it's within the fortune 500 and their rate of decline is more than Azure right >>Now. Well, and, and one of the big trends you're seeing in database is this idea of converging function. In other words, bringing transaction and analytics right together at snowflake summit, they added the capability to handle transaction data, Mongo DB, which is largely mostly transactions added the capability in June to bring in analytic data. You see data bricks going from data engineering and data science now getting into snowflake space and analytics. So you're seeing that convergence Oracle is converging with my SQL heat wave and their core databases, couch base couch base is doing the same. Maria do virtually all these database companies are, are converging their platforms with the exception of AWS. AWS is still the right tool for the right job. So they've got Aurora, they've got RDS, they've got, you know, a dynamo DV, they've got red, they've got, you know, going on and on and on. And so the question everybody's asking is will that change? Will they start to sort of cross those swim lanes? We haven't seen it thus far. How is that affecting the data >>Performance? I mean, that's fantastic analysis. I think that's why we're seeing it because you have to be in the AWS ecosystem and they're really not playing nicely with others in the sandbox right now that now I will say, oh, Amazon's not playing nicely. Well, no, no. Simply to your point though, that there, the other ones are actually bringing in others at consolidating other different vendor types. And they're really not. You know, if you're in AWS, you need to stay within AWS. Now I will say their tools are fantastic. So if you do stay within AWS, they have a tool for every job they're advanced. And they're incredible. I think sometimes the complexity of their tools hurts them a little bit. Cause to your point earlier, AWS started as a developer-centric type of cloud. They have moved on to enterprise cloud and it's a little bit more business oriented, but their still roots are still DevOps friendly. And unless you're truly trained, AWS can be a little scary. >>So a common use case is I'm gonna be using Aurora for my transaction system and then I'm gonna ETL it into Redshift. Right. And, and I, now I have two data stores and I have two different sets of APIs and primitives two different teams of skills. And so that is probably causing some friction and complexity in the customer base that again, the question is, will they begin to expand some of those platforms to minimize some of that friction? >>Well, yeah, this is the question I wanted to ask on that point. So I've heard from people inside Amazon don't count out Redshift, we're making, we're catching up. I think that's my word, but they were kind of saying that right. Cuz Redshift is good, good database, but they're adding a lot more. So you got snowflake success. I think it's a little bit of a jealousy factor going on there within Redshift team, but then you got Azure synapse with the Synap product synapse. Yep. And then you got big query from Google big >>Query. Yep. >>What's the differentiation. What are you seeing for the data for the data warehouse or the data clouds that are out there for the customers? What's the data say, say to us? >>Yeah, unfortunately the data's showing that they're dropping a little bit whose day AWS is dropping a little bit now of their data products, Redshift and RDS are still the two highest of them, but they are starting to decline. Now I think one of the great data points that we have, we just closed the survey is we took a comparison of the legacy data. Now please forgive me for the word legacy. We're gonna anger a few people, but we Gotter data Oracle on-prem, we've got IBM. Some of those more legacy data warehouse type of names. When we look at our art survey takers that have them where their spend is going, that spends going to snowflake first, and then it's going to Google and then it's going to Microsoft Azure and, and AWS is actually declining in there. So when you talk about who's taking that legacy market share, it's not AWS right now. >>So legacy goes to legacy. So Microsoft, >>So, so let's work through in a little context because Redshift really was the first to take, you know, take the database to the cloud. And they did that by doing a one time license deal with par XL, which was an on-prem database. And then they re-engineered it, they did a fantastic job, but it was still engineered for on-prem. Then you along comes snowflake a couple years later and true cloud native, same thing with big query. Yep. True cloud native architecture. So they get a lot of props. Now what, what Amazon did, they took a page outta of the snowflake, for example, separating compute from storage. Now of course what's what, what Amazon did is actually not really completely separating like snowflake did they couldn't because of the architecture, they created a tearing system that you could dial down the compute. So little nuances like that. I understand. But at the end of the day, what we're seeing from snowflake is the gathering of an ecosystem in this true data cloud, bringing in different data types, they got to the public markets, data bricks was not able to get to the public markets. Yeah. And think is, is struggling >>And a 25 billion evaluation. >>Right. And so that's, that's gonna be dialed down, struggling somewhat from a go to market standpoint where snowflake has no troubles from a go to market. They are the masters at go to market. And so now they've got momentum. We talked to Frank sluman at the snowflake. He basically said, I'm not taking the foot off the gas, no way. Yeah. We, few of our large, you know, consumer customers dialed things down, but we're going balls to the >>Wall. Well, if you look at their show before you get in the numbers, you look at the two shows. Snowflake had their summit in person in Vegas. Data bricks has had their show in San Francisco. And if you compare the two shows, it's clear, who's winning snowflake is blew away from a, from a market standpoint. And we were at snowflake, but we weren't at data bricks, but there was really nothing online. I heard from sources that it was like less than 3000 people. So >>Snowflake was 1900 people in 2019, nearly 10,000. Yeah. In 2020, >>It's gonna be fun to sort of track that as a, as an odd caveat to say, okay, let's see what that growth is. Because in fairness, data, bricks, you know, a little bit younger, Snowflake's had a couple more years. So I'd be curious to see where they are. Their, their Lakehouse paradigm is interesting. >>Yeah. And I think it's >>And their product first company, yes. Their go to market might be a little bit weak from our analysis, but that, but they'll figure it out. >>CEO's pretty smart. But I think it's worth pointing out. It's like two different philosophies, right? It is. Snowflake is come into our data cloud. That's their proprietary environment. They're the, they think of the iPhone, right? End to end. We, we guarantee it's all gonna work. And we're in control. Snowflake is like, Hey, open source, no, bring in data bricks. I mean data bricks, open source, bring in this tool that too, now you are seeing snowflake capitulate a little bit. They announce, for instance, Apache iceberg support at their, at the snowflake summit. So they're tipping their cap to open source. But at the end of the day, they're gonna market and sell the fact that it's gonna run better in native snowflake. Whereas data bricks, they're coming at it from much more of an open source, a mantra. So that's gonna, you know, we'll see who look at, you had windows and you had apple, >>You got, they both want, you got Cal and you got Stanford. >>They both >>Consider, I don't think it's actually there yet. I, I find the more interesting dynamic right now is between AWS and snowflake. It's really a fun tit for tat, right? I mean, AWS has the S three and then, you know, snowflake comes right on top of it and announces R two, we're gonna do one letter, one number better than you. They just seem to have this really interesting dynamic. And I, and it is SLT and no one's betting against him. I mean, this guy's fantastic. So, and he hasn't used his war chest yet. He's still sitting on all that money that he raised to your point, that data bricks five, their timing just was a little off >>5 billion in >>Capital when Slootman hasn't used that money yet. So what's he gonna do? What can he do when he turns that on? He finds the right. >>They're making some acquisitions. They did the stream lit acquisitions stream. >>Fantastic >>Problem. With data bricks, their valuation is underwater. Yes. So they're recruiting and their MNAs. Yes. In the toilet, they cannot make the moves because they don't have the currency until they refactor the multiple, let the, this market settle. I I'm, I'm really nervous that they have to over factor the >>Valuation. Having said that to your point, Eric, the lake house architecture is definitely gaining traction. When you talk to practitioners, they're all saying, yeah, we're building data lakes, we're building lake houses. You know, it's a much, much smaller market than the enterprise data warehouse. But nonetheless, when you talk to practitioners that are actually doing things like self serve data, they're building data lakes and you know, snow. I mean, data bricks is right there. And as a clear leader in, in ML and AI and they're ahead of snowflake, right. >>And I was gonna say, that's the thing with data bricks. You know, you're getting that analytics at M I built into it. >>You know, what's ironic is I remember talking to Matt Carroll, who's CEO of auDA like four or five years ago. He came into the office in ma bro. And we were in temporary space and we were talking about how there's this new workload emerging, which combines AWS for cloud infrastructure, snowflake for the simple data warehouse and data bricks for the ML AI, and then all now all of a sudden you see data bricks yeah. And snowflake going at it. I think, you know, to your point about the competition between AWS and snowflake, here's what I think, I think the Redshift team is, you know, doesn't like snowflake, right. But I think the EC two team loves it. Loves it. Exactly. So, so I think snowflake is driving a lot of, >>Yeah. To John's point, there is plenty to go around. And I think I saw just the other day, I saw somebody say less than 40% of true global 2000 organizations believe that they're at real time data analytics right now. They're not really there yet. Yeah. Think about how much runway is left and how many tools you need to get to real time streaming use cases. It's complex. It's not easy. >>It's gonna be a product value market to me, snowflake in data bricks. They're not going away. Right. They're winning architectures. Yeah. In the cloud, what data bricks did would spark and took over the Haddo market. Yeah. To your point. Now that big data, market's got two players, in my opinion, snow flicking data, bricks converging. Well, Redshift is sitting there behind the curtain, their wild card. Yeah. They're wild card, Dave. >>Okay. I'm gonna give one more wild card, which is the edge. Sure. Okay. And that's something that when you talk about real time analytics and AI referencing at the edge, there aren't a lot of database companies in a position to do that. You know, Amazon trying to put outposts out there. I think it runs RDS. I don't think it runs any other database. Right. Snowflake really doesn't have a strong edge strategy when I'm talking the far edge, the tiny edge. >>I think, I think that's gonna be HPE or Dell's gonna own the outpost market. >>I think you're right. I'll come back to that. Couch base is an interesting company to watch with Capella Mongo. DB really doesn't have a far edge strategy at this point, but couch base does. And that's one to watch. They're doing some really interesting things there. And I think >>That, but they have to leapfrog bongo in my >>Opinion. Yeah. But there's a new architecture emerging at the edge and it's gonna take a number of years to develop, but it could eventually from an economic standpoint, seep back into the enterprise arm base, low end, take a look at what couch base is >>Doing. They hired an Amazon guard system. They have to leapfrog though. They need to, they can't incrementally who's they who >>Couch >>Base needs to needs to make a big move in >>Leap frog. Well, think they're trying to, that's what Capella is all about was not only, you know, their version of Atlas bringing to the cloud couch base, but it's also stretching it out to the edge and bringing converged database analytics >>Real quick on the numbers. Any data on CloudFlare, >>I was, I've been sitting here trying to get the word CloudFlare out my mouth the whole time you guys were talking, >>Is this another that's innovated in the ecosystem. So >>Platform, it was really simple for them early on, right? They're gonna get that edge network out there and they're gonna steal share from Akamai. Then they started doing exactly what Akamai did. We're gonna start rolling out some security. Their security is fantastic. Maybe some practitioners are saying a little bit too much, cuz they're not focused on one thing or another, but they are doing extremely well. And now they're out there in the cloud as well. You >>Got S3 compare. They got two, they got an S3 competitor. >>Exactly. So when I'm listening to you guys talk about, you know, a, a couch base I'm like, wow, those two would just be an absolute fantastic, you know, combination between the two of them. You mean >>CloudFlare >>Couch base. Yeah. >>I mean you got S3 alternative, right? You got a Mongo alternative basically in my >>Opinion. And you're going and you got the edge and you got the edge >>Network with security security, interesting dynamic. This brings up the super cloud date. I wanna talk about Supercloud because we're seeing a trend on we're reporting this since last year that basically people don't have to spend the CapEx to be cloud scale. And you're seeing Amazon enable that, but snowflake has become a super cloud. They're on AWS. Now they're on Azure. Why not tan expansion expand the market? Why not get that? And then it'll be on Google next, all these marketplaces. So the emergence of this super cloud, and then the ability to make that across a substrate across multiple clouds is a strategy we're seeing. What do you, what do you think? >>Well, honestly, I'm gonna be really Frank here. The, everything I know about the super cloud I know from this guy. So I've been following his lead on this and I'm looking forward to you guys doing that conference and that summit coming up from a data perspective. I think what you're saying is spot on though, cuz those are the areas we're seeing expansion in without a doubt. >>I think, you know, when you talk about things like super cloud and you talk about things like metaverse, there's, there's a, there, there look every 15 or 20 years or so this industry reinvents itself and a new disruption comes out and you've got the internet, you've got the cloud, you've got an AI and VR layer. You've got, you've got machine intelligence. You've got now gaming. There's a new matrix, emerging, super cloud. Metaverse there's something happening out there here. That's not just your, your father's SAS or is or pass. Well, >>No, it's also the spend too. Right? So if I'm a company like say capital one or Goldman Sachs, my it spend has traditionally been massive every year. Yes. It's basically like tons of CapEx comes the cloud. It's an operating expense. Wait a minute, Amazon has all the CapEx. So I'm not gonna dial down my budget. I want a competitive advantage. So next thing they know they have a super cloud by default because they just pivoted their, it spend into new capabilities that they then can sell to the market in FinTech makes total sense. >>Right? They're building out a digital platform >>That would, that was not possible. Pre-cloud >>No, it wasn't cause you weren't gonna go put all that money into CapEx expenditure to build that out. Not knowing whether or not the market was there, but the scalability, the ability to spend, reduce and be flexible with it really changes that paradigm entire. >>So we're looking at this market now thinking about, okay, it might be Greenfield in every vertical. It might have a power law where you have a head of the long tail. That's a player like a capital one, an insurance. It could be Liberty mutual or mass mutual that has so much it and capital that they're now gonna scale it into a super cloud >>And they have data >>And they have the data tools >>And the tools. And they're gonna bring that to their constituents. Yes, yes. And scale it using >>Cloud. So that means they can then service the entire vertical as a service provider. >>And the industry cloud is becoming bigger and bigger and bigger. I mean, that's really a way that people are delivering to market. So >>Remember in the early days of cloud, all the banks thought they could build their own cloud. Yeah. Yep. Well actually it's come full circle. They're like, we can actually build a cloud on top of the cloud. >>Right. And by the way, they can have a private cloud in their super cloud. Exactly. >>And you know, it's interesting cause we're talking about financial services insurance, all the people we know spend money in our macro survey. Do you know the, the sector that's spending the most right now? It's gonna shock you energy utilities. Oh yeah. I was gonna, the energy utilities industry right now is the one spending the most money I saw largely cuz they're playing ketchup. But also because they don't have these type of things for their consumers, they need the consumer app. They need to be able to do that delivery. They need to be able to do metrics. And they're the they're, they're the one spending right >>Now it's an arms race, but the, the vector shifts to value creation. So >>It's it just goes back to your post when it was a 2012, the trillion dollar baby. Yeah. It's a multi-trillion dollar baby that they, >>The world was going my chassis post on Forbes, headline trillion dollar baby 2012. You know, I should add it's happening. That's >>On the end. Yeah, exactly. >>Trillions of babies, Eric. Great to have you on the key. >>Thank you so much guys. >>Great to bring the data. Thanks for sharing. Check out ETR. If you're into the enterprise, want to know what's going on. They have a unique approach, very accurate in their survey data. They got a great market basket of, of, of, of, of data questions and people and community. Check it out. Thanks for coming on and sharing with. >>Thank you guys. Always enjoy. >>We'll be back with more coverage here in the cube in New York city live at summit 22. I'm John fur with Dave ante. We'll be right back.

>>Hello. And we're here at the cube startup showcase made possible by a Ws. Thanks so much for joining us today. You know when Jim McDaid Ghani was formulating her ideas around data mesh, She wasn't the only one thinking about decentralized data architecture. Hello, Fresh was going into hyper growth mode and realized that in order to support its scale, it needed to rethink how it thought about data. Like many companies that started in the early part of last decade, Hello Fresh relied on a monolithic data architecture and the internal team. It had concerns about its ability to support continued innovation at high velocity. The company's data team began to think about the future and work backwards from a target architecture which possessed many principles of so called data mesh even though they didn't use that term. Specifically, the company is a strong example of an early but practical pioneer of data mission. Now there are many practitioners and stakeholders involved in evolving the company's data architecture, many of whom are listed here on this on the slide to are highlighted in red are joining us today, we're really excited to welcome into the cube Clements cheese, the Global Senior Director for Data at Hello Fresh and christoph Nevada who's the Global Senior Director of data also, of course. Hello Fresh folks. Welcome. Thanks so much for making some time today and sharing your story. >>Thank you very much. Hey >>steve. All right, let's start with Hello Fresh. You guys are number one in the world in your field, you deliver hundreds of millions of meals each year to many, many millions of people around the globe. You're scaling christoph. Tell us a little bit more about your company and its vision. >>Yeah. Should I start or Clements maybe maybe take over the first piece because Clements has actually been a longer trajectory yet have a fresh. >>Yeah go ahead. Climate change. I mean yes about approximately six years ago I joined handle fresh and I didn't think about the startup I was joining would eventually I. P. O. And just two years later and the freshman public and approximately three years and 10 months after. Hello fresh was listed on the German stock exchange which was just last week. Hello Fresh was included in the Ducks Germany's leading stock market index and debt to mind a great great milestone and I'm really looking forward and I'm very excited for the future for the future for head of fashion. All our data. Um the vision that we have is to become the world's leading food solution group and there's a lot of attractive opportunities. So recently we did lounge and expand Norway. This was in july and earlier this year we launched the U. S. Brand green >>chef in the U. K. As >>well. We're committed to launch continuously different geographies in the next coming years and have a strong pipe ahead of us with the acquisition of ready to eat companies like factor in the U. S. And the planned acquisition of you foods in Australia. We're diversifying our offer now reaching even more and more untapped customer segments and increase our total addressable market. So by offering customers and growing range of different alternatives to shop food and consumer meals. We are charging towards this vision and the school to become the world's leading integrated food solutions group. >>Love it. You guys are on a rocket ship, you're really transforming the industry and as you expand your tam it brings us to sort of the data as a as a core part of that strategy. So maybe you guys could talk a little bit about your journey as a company specifically as it relates to your data journey. You began as a start up. You had a basic architecture like everyone. You made extensive use of spreadsheets. You built a Hadoop based system that started to grow and when the company I. P. O. You really started to explode. So maybe describe that journey from a data perspective. >>Yes they saw Hello fresh by 2015 approximately had evolved what amount of classical centralized management set up. So we grew very organically over the years and there were a lot of very smart people around the globe. Really building the company and building our infrastructure. Um This also means that there were a small number of internal and external sources. Data sources and a centralized the I team with a number of people producing different reports, different dashboards and products for our executives for example of our different operations teams, christian company's performance and knowledge was transferred um just via talking to each other face to face conversations and the people in the data where's team were considered as the data wizard or as the E. T. L. Wizard. Very classical challenges. And those et al. Reserves indicated the kind of like a silent knowledge of data management. Right? Um so a central data whereas team then was responsible for different type of verticals and different domains, different geographies and all this setup gave us to the beginning the flexibility to grow fast as a company in 2015 >>christoph anything that might add to that. >>Yes. Um Not expected to that one but as as clement says it right, this was kind of set up that actually work for us quite a while. And then in 2017 when L. A. Freshman public, the company also grew rapidly and just to give you an idea how that looked like. As was that the tech department self actually increased from about 40 people to almost 300 engineers And the same way as a business units as Clemens has described, also grew sustainable, sustainably. So we continue to launch hello fresh and new countries launching brands like every plate and also acquired other brands like much of a factor and with that grows also from a data perspective the number of data requests that centrally we're getting become more and more and more and also more and more complex. So that for the team meant that they had a fairly high mental load. So they had to achieve a very or basically get a very deep understanding about the business. And also suffered a lot from this context switching back and forth, essentially there to prioritize across our product request from our physical product, digital product from the physical from sorry, from the marketing perspective and also from the central reporting uh teams. And in a nutshell this was very hard for these people. And this that also to a situation that, let's say the solution that we have became not really optimal. So in a nutshell, the central function became a bottleneck and slowdown of all the innovation of the company. >>It's a classic case, isn't it? I mean Clements, you see you see the central team becomes a bottleneck and so the lines of business, the marketing team salesman's okay, we're going to take things into our own hands. And then of course I I. T. And the technical team is called in later to clean up the mess. Uh maybe, I mean was that maybe I'm overstating it, but that's a common situation, isn't it? >>Yeah. Uh This is what exactly happened. Right. So um we had a bottleneck, we have the central teams, there was always a little of tension um analytics teams then started in this business domains like marketing, trade chain, finance, HR and so on. Started really to build their own data solutions at some point you have to get the ball rolling right and then continue the trajectory um which means then that the data pipelines didn't meet the engineering standards. And um there was an increased need for maintenance and support from central teams. Hence over time the knowledge about those pipelines and how to maintain a particular uh infrastructure for example left the company such that most of those data assets and data sets are turned into a huge step with decreasing data quality um also decrease the lack of trust, decreasing transparency. And this was increasing challenge where majority of time was spent in meeting rooms to align on on data quality for example. >>Yeah. And and the point you were making christoph about context switching and this is this is a point that Jemaah makes quite often is we've we've we've contextualized are operational systems like our sales systems, our marketing system but not our our data system. So you're asking the data team, Okay. Be an expert in sales, be an expert in marketing, be an expert in logistics, be an expert in supply chain and it start stop, start, stop, it's a paper cut environment and it's just not as productive. But but on the flip side of that is when you think about a centralized organization you think, hey this is going to be a very efficient way, a cross functional team to support the organization but it's not necessarily the highest velocity, most effective organizational structure. >>Yeah, so so I agree with that. Is that up to a certain scale, a centralized function has a lot of advantages, right? That's clear for everyone which would go to some kind of expert team. However, if you see that you actually would like to accelerate that and specific and this hyper growth, right, you wanna actually have autonomy and certain teams and move the teams or let's say the data to the experts in these teams and this, as you have mentioned, right, that increases mental load and you can either internally start splitting your team into a different kind of sub teams focusing on different areas. However, that is then again, just adding another peace where actually collaboration needs to happen busy external sees, so why not bridging that gap immediately and actually move these teams and to end into into the function themselves. So maybe just to continue what, what was Clements was saying and this is actually where over. So Clements, my journey started to become one joint journey. So Clements was coming actually from one of these teams to build their own solutions. I was basically having the platform team called database housed in these days and in 2019 where basically the situation become more and more serious, I would say so more and more people have recognized that this model doesn't really scale In 2019, basically the leadership of the company came together and I identified data as a key strategic asset and what we mean by that, that if we leverage data in a proper way, it gives us a unique competitive advantage which could help us to, to support and actually fully automated our decision making process across the entire value chain. So what we're, what we're trying to do now or what we should be aiming for is that Hello, Fresh is able to build data products that have a purpose. We're moving away from the idea. Data is just a by problem products, we have a purpose why we would like to collect this data. There's a clear business need behind that. And because it's so important to for the company as a business, we also want to provide them as a trust versi asset to the rest of the organization. We say there's the best customer experience, but at least in a way that users can easily discover, understand and security access high quality data. >>Yeah, so and and and Clements, when you c J Maxx writing, you see, you know, she has the four pillars and and the principles as practitioners you look at that say, okay, hey, that's pretty good thinking and then now we have to apply it and that's and that's where the devil meets the details. So it's the four, you know, the decentralized data ownership data as a product, which we'll talk about a little bit self serve, which you guys have spent a lot of time on inclement your wheelhouse which is which is governance and a Federated governance model. And it's almost like if you if you achieve the first two then you have to solve for the second to it almost creates a new challenges but maybe you could talk about that a little bit as to how it relates to Hello fresh. >>Yes. So christophe mentioned that we identified economic challenge beforehand and for how can we actually decentralized and actually empower the different colleagues of ours. This was more a we realized that it was more an organizational or a cultural change and this is something that somebody also mentioned I think thought words mentioned one of the white papers, it's more of a organizational or cultural impact and we kicked off a um faced reorganization or different phases we're currently and um in the middle of still but we kicked off different phases of organizational reconstruct oring reorganization, try unlock this data at scale. And the idea was really moving away from um ever growing complex matrix organizations or matrix setups and split between two different things. One is the value creation. So basically when people ask the question, what can we actually do, what shall we do? This is value creation and how, which is capability building and both are equal in authority. This actually then creates a high urge and collaboration and this collaboration breaks up the different silos that were built and of course this also includes different needs of stuffing forward teams stuffing with more, let's say data scientists or data engineers, data professionals into those business domains and hence also more capability building. Um Okay, >>go ahead. Sorry. >>So back to Tzemach did johnny. So we the idea also Then crossed over when she published her papers in May 2019 and we thought well The four colors that she described um we're around decentralized data ownership, product data as a product mindset, we have a self service infrastructure and as you mentioned, Federated confidential governance. And this suited very much with our thinking at that point of time to reorganize the different teams and this then leads to a not only organisational restructure but also in completely new approach of how we need to manage data, show data. >>Got it. Okay, so your business is is exploding. Your data team will have to become domain experts in too many areas, constantly contact switching as we said, people started to take things into their own hands. So again we said classic story but but you didn't let it get out of control and that's important. So we actually have a picture of kind of where you're going today and it's evolved into this Pat, if you could bring up the picture with the the elephant here we go. So I would talk a little bit about the architecture, doesn't show it here, the spreadsheet era but christoph maybe you can talk about that. It does show the Hadoop monolith which exists today. I think that's in a managed managed hosting service, but but you you preserve that piece of it, but if I understand it correctly, everything is evolving to the cloud, I think you're running a lot of this or all of it in A W. S. Uh you've got everybody's got their own data sources, uh you've got a data hub which I think is enabled by a master catalog for discovery and all this underlying technical infrastructure. That is really not the focus of this conversation today. But the key here, if I understand it correctly is these domains are autonomous and not only that this required technical thinking, but really supportive organizational mindset, which we're gonna talk about today. But christoph maybe you could address, you know, at a high level some of the architectural evolution that you guys went through. >>Yeah, sure. Yeah, maybe it's also a good summary about the entire history. So as you have mentioned, right, we started in the very beginning with the model is on the operation of playing right? Actually, it wasn't just one model is both to one for the back end and one for the for the front and and or analytical plane was essentially a couple of spreadsheets and I think there's nothing wrong with spreadsheets, right, allows you to store information, it allows you to transform data allows you to share this information. It allows you to visualize this data, but all the kind of that's not actually separating concern right? Everything in one tool. And this means that obviously not scalable, right? You reach the point where this kind of management set up in or data management of isn't one tool reached elements. So what we have started is we've created our data lake as we have seen here on Youtube. And this at the very beginning actually reflected very much our operational populace on top of that. We used impala is a data warehouse, but there was not really a distinction between borders, our data warehouse and borders our data like the impala was used as a kind of those as the kind of engine to create a warehouse and data like construct itself and this organic growth actually led to a situation as I think it's it's clear now that we had to centralized model is for all the domains that will really lose kimball modeling standards. There was no uniformity used actually build in house uh ways of building materialized use abuse that we have used for the presentation layer, there was a lot of duplication of effort and in the end essentially they were missing feedbacks, food, which helped us to to improve of what we are filled. So in the end, in the natural, as we have said, the lack of trust and that's basically what the starting point for us to understand. Okay, how can we move away and there are a lot of different things that you can discuss of apart from this organizational structure that we have said, okay, we have these three or four pillars from from Denmark. However, there's also the next extra question around how do we implement our talking about actual right, what are the implications on that level? And I think that is there's something that we are that we are currently still in progress. >>Got it. Okay, so I wonder if we could talk about switch gears a little bit and talk about the organizational and cultural challenges that you faced. What were those conversations like? Uh let's dig into that a little bit. I want to get into governance as well. >>The conversations on the cultural change. I mean yes, we went through a hyper growth for the last year since obviously there were a lot of new joiners, a lot of different, very, very smart people joining the company which then results that collaboration uh >>got a bit more difficult. Of course >>there are times and changes, you have different different artifacts that you were created um and documentation that were flying around. Um so we were we had to build the company from scratch right? Um Of course this then resulted always this tension which I described before, but the most important part here is that data has always been a very important factor at l a fresh and we collected >>more of this >>data and continued to improve use data to improve the different key areas of our business. >>Um even >>when organizational struggles, the central organizational struggles data somehow always helped us to go through this this kind of change. Right? Um in the end those decentralized teams in our local geography ease started with solutions that serve the business which was very very important otherwise wouldn't be at the place where we are today but they did by all late best practices and standards and I always used sport analogy Dave So like any sport, there are different rules and regulations that need to be followed. These rules are defined by calling the sports association and this is what you can think about data governance and compliance team. Now we add the players to it who need to follow those rules and bite by them. This is what we then called data management. Now we have the different players and professionals, they need to be trained and understand the strategy and it rules before they can play. And this is what I then called data literacy. So we realized that we need to focus on helping our teams to develop those capabilities and teach the standards for how work is being done to truly drive functional excellence in a different domains. And one of our mission of our data literacy program for example is to really empower >>every employee at hello >>fresh everyone to make the right data informs decisions by providing data education that scaled by royal Entry team. Then this can be different things, different things like including data capabilities, um, with the learning paths for example. Right? So help them to create and deploy data products connecting data producers and data consumers and create a common sense and more understanding of each other's dependencies, which is important, for example, S. S. L. O. State of contracts and etcetera. Um, people getting more of a sense of ownership and responsibility. Of course, we have to define what it means, what does ownership means? But the responsibility means. But we're teaching this to our colleagues via individual learning patterns and help them up skill to use. Also, there's shared infrastructure and those self self service applications and overall to summarize, we're still in this progress of of, of learning, we are still learning as well. So learning never stops the tele fish, but we are really trying this um, to make it as much fun as possible. And in the end we all know user behavior has changed through positive experience. Uh, so instead of having massive training programs over endless courses of workshops, um, leaving our new journalists and colleagues confused and overwhelmed. >>We're applying um, >>game ification, right? So split different levels of certification where our colleagues can access, have had access points, they can earn badges along the way, which then simplifies the process of learning and engagement of the users and this is what we see in surveys, for example, where our employees that your justification approach a lot and are even competing to collect Those learning path batteries to become the # one on the leader board. >>I love the game ification, we've seen it work so well and so many different industries, not the least of which is crypto so you've identified some of the process gaps uh that you, you saw it is gloss over them. Sometimes I say paved the cow path. You didn't try to force, in other words, a new architecture into the legacy processes. You really have to rethink your approach to data management. So what what did that entail? >>Um, to rethink the way of data management. 100%. So if I take the example of Revolution, Industrial Revolution or classical supply chain revolution, but just imagine that you have been riding a horse, for example, your whole life and suddenly you can operate a car or you suddenly receive just a complete new way of transporting assets from A to B. Um, so we needed to establish a new set of cross functional business processes to run faster, dry faster, um, more robustly and deliver data products which can be trusted and used by downstream processes and systems. Hence we had a subset of new standards and new procedures that would fall into the internal data governance and compliance sector with internal, I'm always referring to the data operations around new things like data catalog, how to identify >>ownership, >>how to change ownership, how to certify data assets, everything around classical software development, which we know apply to data. This this is similar to a new thinking, right? Um deployment, versioning, QA all the different things, ingestion policies, policing procedures, all the things that suffer. Development has been doing. We do it now with data as well. And in simple terms, it's a whole redesign of the supply chain of our data with new procedures and new processes and as a creation as management and as a consumption. >>So data has become kind of the new development kit. If you will um I want to shift gears and talk about the notion of data product and, and we have a slide uh that we pulled from your deck and I'd like to unpack it a little bit. Uh I'll just, if you can bring that up, I'll read it. A data product is a product whose primary objective is to leverage on data to solve customer problems where customers, both internal and external. So pretty straightforward. I know you've gone much deeper and you're thinking and into your organization, but how do you think about that And how do you determine for instance who owns what? How did you get everybody to agree? >>I can take that one. Um, maybe let me start with the data product. So I think um that's an ongoing debate. Right? And I think the debate itself is an important piece here, right? That visit the debate, you clarify what we actually mean by that product and what is actually the mindset. So I think just from a definition perspective, right? I think we find the common denominator that we say okay that our product is something which is important for the company has come to its value what you mean by that. Okay, it's it's a solution to a customer problem that delivers ideally maximum value to the business. And yes, it leverages the power of data and we have a couple of examples but it had a fresh year, the historical and classical ones around dashboards for example, to monitor or error rates but also more sophisticated ways for example to incorporate machine learning algorithms in our recipe recommendations. However, I think the important aspects of the data product is a there is an owner, right? There's someone accountable for making sure that the product that we are providing is actually served and is maintained and there are, there is someone who is making sure that this actually keeps the value of that problem thing combined with the idea of the proper documentation, like a product description, right that people understand how to use their bodies is about and related to that peace is the idea of it is a purpose. Right? You need to understand or ask ourselves, Okay, why does this thing exist does it provide the value that you think it does. That leads into a good understanding about the life cycle of the data product and life cycle what we mean? Okay from the beginning from the creation you need to have a good understanding, we need to collect feedback, we need to learn about that. We need to rework and actually finally also to think about okay benefits time to decommission piece. So overall, I think the core of the data product is product thinking 11 right that we start the point is the starting point needs to be the problem and not the solution and this is essentially what we have seen what was missing but brought us to this kind of data spaghetti that we have built there in in Russia, essentially we built at certain data assets, develop in isolation and continuously patch the solution just to fulfill these articles that we got and actually these aren't really understanding of the stakeholder needs and the interesting piece as a result in duplication of work and this is not just frustrating and probably not the most efficient way how the company should work. But also if I build the same that assets but slightly different assumption across the company and multiple teams that leads to data inconsistency and imagine the following too narrow you as a management for management perspective, you're asking basically a specific question and you get essentially from a couple of different teams, different kind of grass, different kind of data and numbers and in the end you do not know which ones to trust. So there's actually much more ambiguity and you do not know actually is a noise for times of observing or is it just actually is there actually a signal that I'm looking for? And the same is if I'm running in a B test right, I have a new future, I would like to understand what has it been the business impact of this feature. I run that specific source in an unfortunate scenario. Your production system is actually running on a different source. You see different numbers. What you've seen in a B test is actually not what you see then in production typical thing then is you're asking some analytics tend to actually do a deep dive to understand where the discrepancies are coming from. The worst case scenario. Again, there's a different kind of source. So in the end it's a pretty frustrating scenario and that's actually based of time of people that have to identify the root cause of this divergence. So in a nutshell, the highest degree of consistency is actually achieved that people are just reusing Dallas assets and also in the media talk that we have given right, we we start trying to establish this approach for a B testing. So we have a team but just providing or is kind of owning their target metric associated business teams and they're providing that as a product also to other services including the A B testing team, they'll be testing team can use this information defines an interface is okay I'm joining this information that the metadata of an experiment and in the end after the assignment after this data collection face, they can easily add a graph to the dashboard. Just group by the >>Beatles Hungarian. >>And we have seen that also in other companies. So it's not just a nice dream that we have right. I have actually worked in other companies where we worked on search and we established a complete KPI pipeline that was computing all this information. And this information was hosted by the team and it was used for everything A B test and deep dives and and regular reporting. So uh just one of the second the important piece now, why I'm coming back to that is that requires that we are treating this data as a product right? If you want to have multiple people using the things that I am owning and building, we have to provide this as a trust mercy asset and in a way that it's easy for people to discover and actually work with. >>Yeah. And coming back to that. So this is to me this is why I get so excited about data mesh because I really do think it's the right direction for organizations. When people hear data product they say well, what does that mean? Uh but then when you start to sort of define it as you did, it's it's using data to add value, that could be cutting costs, that could be generating revenue, it could be actually directly you're creating a product that you monetize, So it's sort of in the eyes of the beholder. But I think the other point that we've made is you made it earlier on to and again, context. So when you have a centralized data team and you have all these P NL managers a lot of times they'll question the data because they don't own it. They're like wait a minute. If they don't, if it doesn't agree with their agenda, they'll attack the data. But if they own the data then they're responsible for defending that and that is a mindset change, that's really important. Um And I'm curious uh is how you got to, you know, that ownership? Was it a was it a top down with somebody providing leadership? Was it more organic bottom up? Was it a sort of a combination? How do you decide who owned what in other words, you know, did you get, how did you get the business to take ownership of the data and what is owning? You know, the data actually mean? >>That's a very good question. Dave I think this is one of the pieces where I think we have a lot of learnings and basically if you ask me how we could start the feeling. I think that would be the first piece. Maybe we need to start to really think about how that should be approached if it stopped his ownership. Right? It means somehow that the team has a responsibility to host and self the data efforts to minimum acceptable standards. This minimum dependencies up and down string. The interesting piece has been looking backwards. What what's happening is that under that definition has actually process that we have to go through is not actually transferring ownership from the central team to the distributor teams. But actually most cases to establish ownership, I make this difference because saying we have to transfer ownership actually would erroneously suggests that the data set was owned before. But this platform team, yes, they had the capability to make the changes on data pipelines, but actually the analytics team, they're always the ones who had the business understands, you use cases and but no one actually, but it's actually expensive expected. So we had to go through this very lengthy process and establishing ownership. We have done that, as in the beginning, very naively. They have started, here's a document here, all the data assets, what is probably the nearest neighbor who can actually take care of that and then we we moved it over. But the problem here is that all these things is kind of technical debt, right? It's not really properly documented, pretty unstable. It was built in a very inconsistent over years and these people who have built this thing have already left the company. So there's actually not a nice thing that is that you want to see and people build up a certain resistance, e even if they have actually bought into this idea of domain ownership. So if you ask me these learnings, but what needs to happen as first, the company needs to really understand what our core business concept that they have, they need to have this mapping from. These are the core business concept that we have. These are the domain teams who are owning this concept and then actually link that to the to the assets and integrated better with both understanding how we can evolve actually, the data assets and new data build things new in the in this piece in the domain. But also how can we address reduction of technical death and stabilizing what we have already. >>Thank you for that christoph. So I want to turn a direction here and talk about governance and I know that's an area that's passionate, you're passionate about. Uh I pulled this slide from your deck, which I kind of messed up a little bit sorry for that, but but by the way, we're going to publish a link to the full video that you guys did. So we'll share that with folks. But it's one of the most challenging aspects of data mesh, if you're going to decentralize you, you quickly realize this could be the Wild West as we talked about all over again. So how are you approaching governance? There's a lot of items on this slide that are, you know, underscore the complexity, whether it's privacy, compliance etcetera. So, so how did you approach this? >>It's yeah, it's about connecting those dots. Right. So the aim of the data governance program is about the autonomy of every team was still ensuring that everybody has the right interoperability. So when we want to move from the Wild West riding horses to a civilised way of transport, um you can take the example of modern street traffic, like when all participants can manoeuvre independently and as long as they follow the same rules and standards, everybody can remain compatible with each other and understand and learn from each other so we can avoid car crashes. So when I go from country to country, I do understand what the street infrastructure means. How do I drive my car? I can also read the traffic lights in the different signals. Um, so likewise as a business and Hello Fresh, we do operate autonomously and consequently need to follow those external and internal rules and standards to set forth by the redistribution in which we operate so in order to prevent a car crash, we need to at least ensure compliance with regulations to account for society's and our customers increasing concern with data protection and privacy. So teaching and advocating this advantage, realizing this to everyone in the company um was a key community communication strategy and of course, I mean I mentioned data privacy external factors, the same goes for internal regulations and processes to help our colleagues to adapt to this very new environment. So when I mentioned before the new way of thinking the new way of um dealing and managing data, this of course implies that we need new processes and regulations for our colleagues as well. Um in a nutshell then this means the data governance provides a framework for managing our people the processes and technology and culture around our data traffic. And those components must come together in order to have this effective program providing at least a common denominator, especially critical for shared dataset, which we have across our different geographies managed and shared applications on shared infrastructure and applications and is then consumed by centralized processes um for example, master data, everything and all the metrics and KPI s which are also used for a central steering. Um it's a big change day. Right. And our ultimate goal is to have this noninvasive, Federated um ultimatum and computational governance and for that we can't just talk about it. We actually have to go deep and use case by use case and Qc buy PVC and generate learnings and learnings with the different teams. And this would be a classical approach of identifying the target structure, the target status, match it with the current status by identifying together with the business teams with the different domains have a risk assessment for example, to increase transparency because a lot of teams, they might not even know what kind of situation they might be. And this is where this training and this piece of illiteracy comes into place where we go in and trade based on the findings based on the most valuable use case um and based on that help our teams to do this change to increase um their capability just a little bit more and once they hand holding. But a lot of guidance >>can I kind of kind of trying to quickly David will allow me I mean there's there's a lot of governance piece but I think um that is important. And if you're talking about documentation for example, yes, we can go from team to team and tell these people how you have to document your data and data catalog or you have to establish data contracts and so on the force. But if you would like to build data products at scale following actual governance, we need to think about automation right. We need to think about a lot of things that we can learn from engineering before. And that starts with simple things like if we would like to build up trust in our data products, right, and actually want to apply the same rigor and the best practices that we know from engineering. There are things that we can do and we should probably think about what we can copy and one example might be. So the level of service level agreements, service level objectives. So that level indicators right, that represent on on an engineering level, right? If we're providing services there representing the promises we made to our customers or consumers, these are the internal objectives that help us to keep those promises. And actually these are the way of how we are tracking ourselves, how we are doing. And this is just one example of that thing. The Federated Governor governance comes into play right. In an ideal world, we should not just talk about data as a product but also data product. That's code that we say, okay, as most as much as possible. Right? Give the engineers the tool that they are familiar basis and actually not ask the product managers for example to document their data assets in the data catalog but make it part of the configuration. Have this as a, as a C D C I, a continuous delivery pipeline as we typically see another engineering task through and services we say, okay, there is configuration, we can think about pr I can think about data quality monitoring, we can think about um the ingestion data catalog and so on and forest, I think ideally in the data product will become of a certain templates that can be deployed and are actually rejected or verified at build time before we actually make them deploy them to production. >>Yeah, So it's like devoPS for data product um so I'm envisioning almost a three phase approach to governance and you kind of, it sounds like you're in early phases called phase zero where there's there's learning, there's literacy, there's training, education, there's kind of self governance and then there's some kind of oversight, some a lot of manual stuff going on and then you you're trying to process builders at this phase and then you codify it and then you can automate it. Is that fair? >>Yeah, I would rather think think about automation as early as possible in the way and yes, there needs to be certain rules but then actually start actually use case by use case. Is there anything that small piece that we can already automate? It's as possible. Roll that out and then actually extended step by step, >>is there a role though that adjudicates that? Is there a central Chief state officer who is responsible for making sure people are complying or is it how do you handle that? >>I mean from a from a from a platform perspective, yes, we have a centralized team to uh implement certain pieces they'll be saying are important and actually would like to implement. However, that is actually working very closely with the governance department. So it's Clements piece to understand and defy the policies that needs to be implemented. >>So Clements essentially it's it's your responsibility to make sure that the policy is being followed. And then as you were saying, christoph trying to compress the time to automation as fast as possible percent. >>So >>it's really it's uh >>what needs to be really clear that it's always a split effort, Right? So you can't just do one thing or the other thing, but everything really goes hand in hand because for the right automation for the right engineering tooling, we need to have the transparency first. Uh I mean code needs to be coded so we kind of need to operate on the same level with the right understanding. So there's actually two things that are important which is one its policies and guidelines, but not only that because more importantly or even well equally important to align with the end user and tech teams and engineering and really bridge between business value business teams and the engineering teams. >>Got it. So just a couple more questions because we gotta wrap I want to talk a little bit about the business outcome. I know it's hard to quantify and I'll talk about that in a moment but but major learnings, we've got some of the challenges that you cited. I'll just put them up here. We don't have to go detailed into this, but I just wanted to share with some folks. But my question, I mean this is the advice for your peers question if you had to do it differently if you had a do over or a Mulligan as we like to say for you golfers, what would you do differently? Yeah, >>I mean can we start with from a from the transformational challenge that understanding that it's also high load of cultural change. I think this is this is important that a particular communication strategy needs to be put into place and people really need to be um supported. Right? So it's not that we go in and say well we have to change towards data mesh but naturally it's in human nature, you know, we're kind of resistance to to change right? Her speech uncomfortable. So we need to take that away by training and by communicating um chris we're gonna add something to that >>and definitely I think the point that I have also made before right we need to acknowledge that data mesh is an architecture of scale, right? You're looking for something which is necessary by huge companies who are vulnerable, data productive scale. I mean Dave you mentioned it right, there are a lot of advantages to have a centralized team but at some point it may make sense to actually decentralized here and at this point right? If you think about data Mash, you have to recognize that you're not building something on a green field. And I think there's a big learning which is also reflected here on the slide is don't underestimate your baggage. It's typically you come to a point where the old model doesn't doesn't broke anymore and has had a fresh right? We lost our trust in our data and actually we have seen certain risks that we're slowing down our innovation so we triggered that this was triggering the need to actually change something. So this transition implies that you typically have a lot of technical debt accumulated over years and I think what we have learned is that potentially we have decentralized some assets to earlier, this is not actually taking into account the maturity of the team where we are actually distributed to and now we actually in the face of correcting pieces of that one. Right? But I think if you if you if you start from scratch you have to understand, okay, is are my team is actually ready for taking on this new uh, this news capabilities and you have to make sure that business decentralization, you build up these >>capabilities and the >>teams and as Clements has mentioned, right, make sure that you take the people on your journey. I think these are the pieces that also here, it comes with this knowledge gap, right? That we need to think about hiring and literacy the technical depth I just talked about and I think the last piece that I would add now which is not here on the flight deck is also from our perspective, we started on the analytical layer because that's kind of where things are exploding, right, this is the thing that people feel the pain but I think a lot of the efforts that we have started to actually modernize the current state uh, towards data product towards data Mash. We've understood that it always comes down basically to a proper shape of our operational plane and I think what needs to happen is is I think we got through a lot of pains but the learning here is this need to really be a commitment from the company that needs to happen and to act. >>I think that point that last point you made it so critical because I I hear a lot from the vendor community about how they're gonna make analytics better and that's that's not unimportant, but but through data product thinking and decentralized data organizations really have to operationalize in order to scale. So these decisions around data architecture an organization, their fundamental and lasting, it's not necessarily about an individual project are why they're gonna be project sub projects within this architecture. But the architectural decision itself is an organizational, its cultural and what's the best approach to support your business at scale. It really speaks to to to what you are, who you are as a company, how you operate and getting that right, as we've seen in the success of data driven driven companies is yields tremendous results. So I'll ask each of you to give give us your final thoughts and then we'll wrap maybe >>maybe it quickly, please. Yeah, maybe just just jumping on this piece that you have mentioned, right, the target architecture. If we talk about these pieces right, people often have this picture of mind like OK, there are different kind of stages, we have sources, we have actually ingestion layer, we have historical transformation presentation layer and then we're basically putting a lot of technology on top of that kind of our target architecture. However, I think what we really need to make sure is that we have these different kind of viewers, right? We need to understand what are actually the capabilities that we need in our new goals. How does it look and feel from the different kind of personas and experience view? And then finally, that should actually go to the to the target architecture from a technical perspective um maybe just to give an outlook but what we're what we're planning to do, how we want to move that forward. We have actually based on our strategy in the in the sense of we would like to increase that to maturity as a whole across the entire company and this is kind of a framework around the business strategy and it's breaking down into four pillars as well. People meaning the data, cultural, data literacy, data organizational structure and so on that. We're talking about governance as Clements has actually mentioned that, right, compliance, governance, data management and so on. You talk about technology and I think we could talk for hours for that one. It's around data platform, better science platform and then finally also about enablement through data, meaning we need to understand that a quality data accessibility and the science and data monetization. >>Great, thank you christophe clement. Once you bring us home give us your final thoughts. >>Can't can just agree with christoph that uh important is to understand what kind of maturity people have to understand what the maturity level, where the company where where people organization is and really understand what does kind of some kind of a change replies to that those four pillars for example, um what needs to be taken first and this is not very clear from the very first beginning of course them it's kind of like Greenfield you come up with must wins to come up with things that we really want to do out of theory and out of different white papers. Um only if you really start conducting the first initiatives you do understand. Okay, where we have to put the starts together and where do I missed out on one of those four different pillars? People, process technology and governance. Right? And then that kind of an integration. Doing step by step, small steps by small steps not boiling the ocean where you're capable ready to identify the gaps and see where either you can fill um the gaps are where you have to increase maturity first and train people or increase your text text, >>you know Hello Fresh is an excellent example of a company that is innovating. It was not born in Silicon Valley which I love. It's a global company. Uh and I gotta ask you guys, it seems like this is an amazing place to work you guys hiring? >>Yes, >>definitely. We do >>uh as many rights as was one of these aspects distributing. And actually we are hiring as an entire company specifically for data. I think there are a lot of open roles serious. Please visit or our page from better engineering, data, product management and Clemens has a lot of rules that you can speak about. But yes >>guys, thanks so much for sharing with the cube audience, your, your pioneers and we look forward to collaborations in the future to track progress and really want to thank you for your time. >>Thank you very much. Thank you very much. Dave >>thank you for watching the cubes startup showcase made possible by A W. S. This is Dave Volonte. We'll see you next time. >>Yeah.

>> From around the globe, it's theCUBE, covering Data Citizens'21. Brought to you by Collibra. >> We're back talking all things data at Data Citizens '21. My name is Dave Vellante and you're watching theCUBE's continuous coverage, virtual coverage #DataCitizens21. I'm here with Jim Cushman who is Collibra's Chief Product Officer who shared the company's product vision at the event. Jim, welcome, good to see you. >> Thanks Dave, glad to be here. >> Now one of the themes of your session was all around self-service and access to data. This is a big big point of discussion amongst organizations that we talk to. I wonder if you could speak a little more toward what that means for Collibra and your customers and maybe some of the challenges of getting there. >> So Dave our ultimate goal at Collibra has always been to enable service access for all customers. Now, one of the challenges is they're limited to how they can access information, these knowledge workers. So our goal is to totally liberate them and so, why is this important? Well, in and of itself, self-service liberates, tens of millions of data lyric knowledge workers. This will drive more rapid, insightful decision-making, it'll drive productivity and competitiveness. And to make this level of adoption possible, the user experience has to be as intuitive as say, retail shopping, like I mentioned in my previous bit, like you're buying shoes online. But this is a little bit of foreshadowing and there's even a more profound future than just enabling a self-service, that we believe that a new class of shopper is coming online and she may not be as data-literate as our knowledge worker of today. Think of her as an algorithm developer, she builds machine learning or AI. The engagement model for this user will be, to kind of build automation, personalized experiences for people to engage with data. But in order to build that automation, she too needs data. Because she's not data literate, she needs the equivalent of a personal shopper. Someone that can guide her through the experience without actually having her know all the answers to the questions that would be asked. So this level of self-service goes one step further and becomes an automated service. One to really help find the best unbiased in a labeled training data to help train an algorithm in the future. >> That's, okay please continue. >> No please, and so all of this self and automated service, needs to be complemented with kind of a peace of mind that you're letting the right people gain access to it. So when you automate it, it's like, well, geez are the right people getting access to this. So it has to be governed and secured. This can't become like the Wild Wild West or like a data, what we call a data flea market or you know, data's everywhere. So, you know, history does quickly forget the companies that do not adjust to remain relevant. And I think we're in the midst of an exponential differentiation in Collibra data intelligence cloud is really kind of established to be the key catalyst for companies that will be on the winning side. >> Well, that's big because I mean, I'm a big believer in putting data in the hands of those folks in the line of business. And of course the big question that always comes up is, well, what about governance? What about security? So to the extent that you can federate that, that's huge. Because data is distributed by its very nature, it's going to stay that way. It's complex. You have to make the technology work in that complex environment, which brings me to this idea of low code or no code. It's gaining a lot of momentum in the industry. Everybody's talking about it, but there are a lot of questions, you know, what can you actually expect from no code and low code who were the right, you know potential users of that? Is there a difference between low and no? And so from your standpoint, why is this getting so much attention and why now, Jim? >> You don't want me to go back even 25 years ago we were talking about four and five generational languages that people were building. And it really didn't re reach the total value that folks were looking for because it always fell short. And you'd say, listen, if you didn't do all the work it took to get to a certain point how are you possibly going to finish it? And that's where the four GLs and five GLs fell short as capability. With our stuff where if you really get a great self-service how are you going to be self-service if it still requires somebody right though? Well, I guess you could do it if the only self-service people are people who write code, well, that's not bad factor. So if you truly want the ability to have something show up at your front door, without you having to call somebody or make any efforts to get it, then it needs to generate itself. The beauty of doing a catalog, new governance, understanding all the data that is available for choice, giving someone the selection that is using objective criteria, like this is the best objective cause if it's quality for what you want or it's labeled or it's unbiased and it has that level of deterministic value to it versus guessing or civic activity or what my neighbor used or what I used on my last job. Now that we've given people the power with confidence to say, this is the one that I want, the next step is okay, can you deliver it to them without them having to write any code? So imagine being able to generate those instructions from everything that we have in our metadata repository to say this is exactly the data I need you to go get and perform what we call a distributed query against those data sets and bringing it back to them. No code written. And here's the real beauty Dave, pipeline development, data pipeline development is a relatively expensive thing today and that's why people spend a lot of money maintaining these pipelines but imagine if there was zero cost to building your pipeline would you spend any money to maintain it? Probably not. So if we can build it for no cost, then why maintain it? Just build it every time you need it. And it then again, done on a self-service basis. >> I really liked the way you're thinking about this cause you're right. A lot of times when you hear self self-service it's about making the hardcore developers, you know be able to do self service. But the reality is, and you talk about that data pipeline it's complex a business person sitting there waiting for data or wants to put in new data and it turns out that the smallest unit is actually that entire team. And so you sit back and wait. And so to the extent that you can actually enable self-serve for the business by simplification that is it's been the holy grail for a while, isn't it? >> I agree. >> Let's look a little bit dig into where you're placing your bets. I mean, your head of products, you got to make bets, you know, certainly many many months if not years in advance. What are your big focus areas of investment right now? >> Yeah, certainly. So one of the things we've done very successfully since our origin over a decade ago, was building a business user-friendly software and it was predominantly kind of a plumbing or infrastructure area. So, business users love working with our software. They can find what they're looking for and they don't need to have some cryptic key of how to work with it. They can think about things in their terms and use our business glossary and they can navigate through what we call our data intelligence graph and find just what they're looking for. And we don't require a business to change everything just to make it happen. We give them kind of a universal translator to talk to the data. But with all that wonderful usability the common compromise that you make as well, its only good up to a certain amount of information, kind of like Excel. You know, you can do almost anything with Excel, right? But when you get to into large volumes, it becomes problematic and now you need that, you know go with a hardcore database and application on top. So what the industry is pulling us towards is far greater amounts of data not that just millions or even tens of millions but into the hundreds of millions and billions of things that we need to manage. So we have a huge focus on scale and performance on a global basis and that's a mouthful, right? Not only are you dealing with large amounts at performance but you have to do it in a global fashion and make it possible for somebody who might be operating in a Southeast Asia to have the same experience with the environment as they would be in Los Angeles. And the data needs to therefore go to the user as opposed to having the user come to the data as much as possible. So it really does put a lot of emphasis on some of what you call the non-functional requirements also known as the ilities and so our ability to bring the data and handle those large enterprise grade capabilities at scale and performance globally is what's really driving a good number of our investments today. >> I want to talk about data quality. This is a hard topic, but it's one that's so important. And I think it's been really challenging and somewhat misunderstood when you think about the chief data officer role itself, it kind of emerged from these highly regulated industries. And it came out of the data quality, kind of a back office role that's kind of gone front and center and now is, you know pretty strategic. Having said that, the you know, the prevailing philosophy is okay, we got to have this centralized data quality approach and that it's going to be imposed throughout. And it really is a hard problem and I think about, you know these hyper specialized roles, like, you know the quality engineer and so forth. And again, the prevailing wisdom is, if I could centralize that it can be lower cost and I can service these lines of business when in reality, the real value is, you know speed. And so how are you thinking about data quality? You hear so much about it. Why is it such a big deal and why is it so hard in a priority in the marketplace? You're thoughts. >> Thanks for that. So we of course acquired a data quality company, not burying delete, earlier this year LGQ and the big question is, okay, so why, why them and why now, not before? Well, at least a decade ago you started hearing people talk about big data. It was probably around 2009, it was becoming the big talk and what we don't really talk about when we talk about this ever expanding data, the byproduct is, this velocity of data, is increasing dramatically. So the speed of which new data is being presented the way in which data is changing is dramatic. And why is that important to data quality? Cause data quality historically for the last 30 years or so has been a rules-based business where you analyze the data at a certain point in time and you write a rule for it. Now there's already a room for error there cause humans are involved in writing those rules, but now with the increased velocity, the likelihood that it's going to atrophy and become no longer a valid or useful rule to you increases exponentially. So we were looking for a technology that was doing it in a new way similar to the way that we do auto classification when we're cataloging attributes is how do we look at millions of pieces of information around metadata and decide what it is to put it into context? The ability to automatically generate these rules and then continuously adapt as data changes to adjust these rules, is really a game changer for the industry itself. So we chose OwlDQ for that very reason. It's not only where they had this really kind of modern architecture to automatically generate rules but then to continuously monitor the data and adjust those rules, cutting out the huge amounts of costs, clearly having rules that aren't helping you save and frankly, you know how this works is, you know no one really complains about it until there's the squeaky wheel, you know, you get a fine or exposes and that's what is causing a lot of issues with data quality. And then why now? Well, I think and this is my speculation, but there's so much movement of data moving to the cloud right now. And so anyone who's made big investments in data quality historically for their on-premise data warehouses, Netezzas, Teradatas, Oracles, et cetera or even their data lakes are now moving to the cloud. And they're saying, hmm, what investments are we going to carry forward that we had on premise? And which ones are we going to start a new from and data quality seems to be ripe for something new and so these new investments in data in the cloud are now looking up. Let's look at new next generation method of doing data quality. And that's where we're really fitting in nicely. And of course, finally, you can't really do data governance and cataloging without data quality and data quality without data governance and cataloging is kind of a hollow a long-term story. So the three working together is very a powerful story. >> I got to ask you some Colombo questions about this cause you know, you're right. It's rules-based and so my, you know, immediate like, okay what are the rules around COVID or hybrid work, right? If there's static rules, there's so much unknown and so what you're saying is you've got a dynamic process to do that. So and one of the my gripes about the whole big data thing and you know, you referenced that 2009, 2010, I loved it, because there was a lot of profound things about Hadoop and a lot of failings. And one of the challenges is really that there's no context in the big data system. You know, the data, the folks in the data pipeline, they don't have the business context. So my question is, as you it's and it sounds like you've got this awesome magic to automate, who would adjudicates the dynamic rules? How does, do humans play a role? What role do they play there? >> Absolutely. There's the notion of sampling. So you can only trust a machine for certain point before you want to have some type of a steward or a assisted or supervised learning that goes on. So, you know, suspect maybe one out of 10, one out of 20 rules that are generated, you might want to have somebody look at it. Like there's ways to do the equivalent of supervised learning without actually paying the cost of the supervisor. Let's suppose that you've written a thousand rules for your system that are five years old. And we come in with our ability and we analyze the same data and we generate rules ourselves. We compare the two themselves and there's absolutely going to be some exact matching some overlap that validates one another. And that gives you confidence that the machine learning did exactly what you did and what's likelihood that you guessed wrong and machine learning guessed wrong exactly the right way that seems pretty, pretty small concern. So now you're really saying, well, why are they different? And now you start to study the samples. And what we learned, is that our ability to generate between 60 and 70% of these rules anytime we were different, we were right. Almost every single time, like almost every, like only one out of a hundred where was it proven that the handwritten rule was a more profound outcome. And of course, it's machine learning. So it learned, and it caught up the next time. So that's the true power of this innovation is it learns from the data as well as the stewards and it gives you confidence that you're not missing things and you start to trust it, but you should never completely walk away. You should constantly do your periodic sampling. >> And the secret sauce is math. I mean, I remember back in the mid two thousands it was like 2006 timeframe. You mentioned, you know, auto classification. That was a big problem with the federal rules of civil procedure trying to figure out, okay, you know, had humans classifying humans don't scale, until you had, you know, all kinds of support, vector machines and probabilistic, latent semantic indexing, but you didn't have the compute power or the data corpus to really do it well. So it sounds like a combination of you know, cheaper compute, a lot more data and machine intelligence have really changed the game there. Is that a fair assumption? >> That's absolutely fair. I think the other aspect that to keep in mind is that it's an innovative technology that actually brings all that compute as close into the data as possible. One of the greatest expenses of doing data quality was of course, the profiling concept bringing up the statistics of what the data represents. And in most traditional senses that data is completely pulled out of the database itself, into a separate area and now you start talking about terabytes or petabytes of data that takes a long time to extract that much information from a database and then to process through it all. Imagine bringing that profiling closer into the database, what's happening in the NAPE the same space as the data, that cuts out like 90% of the unnecessary processing speed. It also gives you the ability to do it incrementally. So you're not doing a full analysis each time, you have kind of an expensive play when you're first looking at a full database and then maybe over the course of a day, an hour, 15 minutes you've only seen a small segment of change. So now it feels more like a transactional analysis process. >> Yeah and that's, you know, again, we talked about the old days of big data, you know the Hadoop days and the boat was profound was it was all about bringing five megabytes of code to a petabyte of data, but that didn't happen. We shoved it all into a central data lake. I'm really excited for Collibra. It sounds like you guys are really on the cutting edge and doing some really interesting things. I'll give you the last word, Jim, please bring us on. >> Yeah thanks Dave. So one of the really exciting things about our solution is, it trying to be a combination of best of breed capabilities but also integrated. So to actually create a full and complete story that customers are looking for, you don't want to have them worry about a complex integration in trying to manage multiple vendors and the times of their releases, et cetera. If you can find one customer that you don't have to say well, that's good enough, but every single component is in fact best of breed that you can find in it's integrated and they'll manage it as a service. You truly unlock the power of your data, literate individuals in your organization. And again, that goes back to our overall goal. How do we empower the hundreds of millions of people around the world who are just looking for insightful decision? Did they feel completely locked it's as if they're looking for information before the internet and they're kind of limited to whatever their local library has and if we can truly become somewhat like the internet of data, we make it possible for anyone to access it without controls but we still govern it and secure it for privacy laws, I think we do have a chance to to change the world for better. >> Great. Thank you so much, Jim. Great conversation really appreciate your time and your insights. >> Yeah, thank you, Dave. Appreciate it. >> All right and thank you for watching theCUBE's continuous coverage of Data Citizens'21. My name is Dave Vellante. Keep it right there for more great content. (upbeat music)

(upbeat music) >> Hello, and welcome to this Cube special presentation of Cube On CloudStartups with AWS Showcase. I'm John Furrier, your host of theCUBE. This session is the accelerate digital transformation and simplify AWS with autonomous cloud operations with Venkat Krishnamachari, who's the CEO and co-founder here with me on remote. Venkat, good to see you. >> Great to see you, John. >> So this is a session on, essentially DAY2 operations. Something that we've been covering on theCUBE as you know, for a long time. But the big trend is as DevOps becomes much more mainstream, intelligent applications or agile applications, have to connect with intelligent infrastructure and your company MontyCloud has the solution that literally turns IT pros into cloud powerhouses as you guys say, it's your tagline. This is a super important area. I want to get your thoughts and showcase what you guys are doing as one of the hot 10 startups. Thanks for coming on. So take a minute to explain real quick. What is MontyCloud all about? >> Great, thank you again for the opportunity. Hey everybody, I'm Venkat Krishnamachari. I represent mandate team at MontyCloud. We are an intelligent cloud management platform company. What we help customers do, is we help them simplify their cloud operations so they can go innovate and develop intelligent applications. Our platform is called DAY2, because everything after the day one of going to Cloud, needs a lot of expertise and we decided that's a fun area to go solve for our customers. We solve everything on starting DAY2 from simplifying provisioning, to management, to operations, to autonomous cloud operations. Our platform does this for our customers so they can innovate faster and they can close the cloud skills gap that is required to empower the developers. >> Venkat, I want to get your thoughts on DAY2 operations. There's been a trend that people talk about for a long time. As people move to the cloud and see the economic advantage of certainly with COVID-19, the market has said, "Hey, if you're on cloud native, you win." Andy Jassy at re:Invent last Keynote really laid out how companies can be proficient in becoming cloud-scale advantages. One of them was have expertise in cloud. So everyone is kind of doing that. You're starting to see enterprises all build the muscle for cloud operations. That's day one, they get started. Then that's kind of the challenges and the opportunities kick in when you have to continue in production. You have things that go on in the software. The underlying scaling infrastructure needs to be scaled out or all these kinds of things happen. This is what DAY2 is all about, keeping track of and maintaining high availability, uptime and keep the cost structure in line. This is what people discover. If they don't think properly about the architecture, they have huge problems. You guys solve this problem. Could you explain why this is important. >> Sure thing, John. So cloud operations, as you described, it's a continuous operations and continuous improvement in cloud environments. What efficient cloud operations does for customers is it accelerates innovation, reduces the risk, and more importantly, all the period of time that they are using their applications in the cloud, which is future, reduces the total cost of cloud operations. This is important because there is a huge gap in cloud skills. The surface area of cloud that customers need to manage is growing by the day. And most importantly, developers are increasingly and rightfully so, getting a seat at the table in defining and accelerating company's cloud journey. Which means, now they're proposing, microservices based application, container based application. Traditional applications are still in the mix. Now the surface area becomes a challenge for the IT operators to manage. That's why it's very important to start right. See, we ask this question to our customers. Having listened to our customers as hundreds of them, one thing is clear, when we ask this question to our customers, ever wonder why and how large scale companies like AWS are able to deliver massively scalable services and operate massive data centers with fewer people? Because it's automation. And it's important to think about, as you scale, automate a way things that must be automated, eliminate undifferentiated heavy lifting and help your developers move fast. All of this is vital in the day and age we live in, John. >> Yeah, I want to double down on that because I think this idea of integrating into operations is a critical key point for where success and failure kind of happen. We've seen with cloud, certainly IT departments and enterprise is going okay, cost optimization, check. Get cloud native, getting the cloud, lift and shift, I thought it through, I put some stuff in the cloud and then they go great, now I need resilience. I need resiliency, and I want to make sure things are now working okay, water flowing through the pipes, cloud's working. Then they say, "Well this is good, I got to need to integrate in with my own premises or edge or other things that are happening." Then they try to integrate into their core operations. McKinsey calls this the value driver three, integrating into core operators. We heard from them earlier in the program here at this event. This is key, it's not trivial to integrate cloud into your operations. This is what DAY2 and beyond is all about. Talk more about that. >> Yeah, that's a great point. And that's something that we've been working with customers to hands-on help learn and build it for them, right? So the acceleration of cloud adoption during the pandemic and ongoing adoption, it's going to shift the software security compliance and operational landscape dramatically. There's no escaping it. Cloud operations will no longer be an afterthought. DevOps will integrate with CloudOps. It'll provide a seamless feedback loop so that a box can be found sooner, fixed sooner, and uptime can be guaranteed. I'll give an example. One of our customers is a university. During the pandemic, their core examination application went down and they couldn't fix it on time because of lack of resources. For them, it's vital to have adopted cloud operations sooner but the runway they had was very little. Fortunately, we had the solution for them there. Within a week, they were able to take their entire on-prem application online, not just take the application but provide an autonomous cloud operations layer to their existing IT team with our platform, upscale them, and then about 14,000 students took their exams without any disruption. Now this customer and customers such as themselves have come to expect that level of integrated cloud operations into their application portfolio. It's important to address that with a platform that simplifies it. >> Venkat, real quick. Define, what is autonomous CloudOps platform? What does that mean? >> So let's take an example here, right? Customers who are trying to move an existing workload to cloud bring a traditional set of application. Then customers who are born in the cloud build microservices or server less based applications. Then there is containers. Now, all three the person surface areas that customers, particularly the IT teams have to manage. With the growing surface area, with the adoption of infrastructure as core, it becomes more nuanced to think about, how do we simplify? And in simplification comes automation. When a developer provision certain resource, previously, they used to be filing a ticket. Central IT team has to respond. Developers don't want that anymore. They want to innovate faster but at the same time Central IT team wants to have some governance in play. The best way to get out of the way of developers is automating it. And providing autonomous cloud operations means developers can deploy newer workloads faster, but with a level of guaranteed guardrail on security compliance and costs that sets them free. This is what we mean by autonomous cloud operations, closing the gap in skills, closing the gap in tooling, empowering your developers without thinking about the traditional model but enabling them to do things that's more in a rapid pace. That's what we mean by autonomous cloud operations. >> You had a great market opportunity. I think this is obviously a no brainer. As people say in the industry "cloud is scale is proven". Even post COVID if people don't have a cloud growth strategy they're pretty much going to be toast. McKinsey calls this a trillion dollar at a minimum not including potential new use cases, new pioneering applications coming. So pretty much, well the verdict is there, this is cloud. I got to ask you about MontyCloud as you guys have a business. Give or take a quick minute to explain the business of MontyCloud, some vitals or how people buy the product, the business model. Take a quick minute to explain MontyCloud business. >> Sure thing. John, see, our entire goal is to simplify cloud operations. Because what we learned is what seems to be complex about cloud adoption is that everybody is expected to be an expert on everything in the new era, but most teams are not ready to run efficient cloud operations at scale, as the cloud footprint is growing. This means we have to redefine certain conversations here. We talk directly to infrastructure architects, cloud architects, application owners. And in general, we talk to people who are leading their IT digital transformation for their companies. What we are enabling our customers is, they must demand that the traditional operation model must change to enable newer application patterns. For this, we are expecting customers want to standardize things, right? IT leaders are beginning to say, "All right, I got to standardize my provisioning, standardize my operations, reduce the heavy lifting that comes with infrastructure's code, and enable the business team and the application team to work closely together." The best way to do that is to go solve this problem with automation. So our platform is able to go help such customers, particularly leaders who demand digital transformation. With clear KPIs, our platform can help them ask the why question easily. And then our platform can also go perform, the how part of automation. That's what we solve. Those are the kinds of customers we really have been working with, John. >> So if I'm a customer, how do I know when I need to call MontyCloud? Is it because my cloud footprint is growing which is a natural sign of growth, or is it because I have more events happening, more things to manage? When do I know I have the need to call you guys? What's the signal? What's the sign? >> So we call it the day one mindset, and also the DAY2 mindset. Customers deciding to go to cloud on day one, should think about DAY2. Because without thinking about DAY2, it can become very expensive, right? When a customer's thinking about digital transformation, could be a lift and shift or it could be starting a new application pattern in the cloud, we can certainly help starting right that day because there are a couple of things they have to do, right? They have to standardize the cloud operations which means setting up the cloud accounts, setting up guardrails, enabling teams to go provision with self service. You want to start the right way. So we are happy to help on the day one journey itself and we can automate DAY2 along with it. So standardizing infrastructure operations, standardizing provisioning, security, visibility, compliance, cost. If any of this is an important milestone that customers have to achieve in their cloud journey, we can help. >> By the way, I would just point out that we were just talking on another session around lift and shift is not a no-brainer either if not thought through and remediated correctly that cost could go through the roof. I mean, we've seen evidence of lift and shift fails just because they didn't think it through. Just to your point. I mean, that's not a no brainer. Quickly explain why lift and shift is not as easy as it looks. >> Sure thing. So lift and shift is great to get started, but why sometimes it fails is that the connotations about wanting to keep your Opex down while giving up CapEx is at odds with each other, right? Cloud is great for reducing your Capex. But ongoing operations, of the DAY2 operations, can add a lot of burden to the operational expenses. What customers find out is after moving to the cloud, the cost overruns are happening because of resources that are not provisioned correctly, resources that should not be running. Wild Wild West kind of scenarios, where everybody has access to everything and they over provision. All of this together end up impacting customers' ability to go control the Opex. Then digital transformation projects are looked at from three different angles at least, right? Cost is definitely one, security is another, and then the ongoing operational tax with respect to monitoring, governance, remediation. All three when it simultaneously hits our customers, they look at lift and shift and saying, "Hey, this was cheaper on prem." But actually in the long run, this will be not just cheaper on the cloud, it can also be more efficient if they do it right. We can talk about some examples on how we help some customers with that helpful, John. >> Well, I want to get into the cloud operations, the whole dashboard in cloud operation administration. Is there anything that you could share because people are wanting more and more analytics. I mean, they're buying everything in sight. I mean, cyber security, you name it. There's more and more dashboards. No one wants another dashboard. So this is something that you guys have a strong opinion on how to think this through. Because again, at the end of the day, if you're instrumenting your network properly and your applications, your intelligence, things are changing, where's the data? Take us through your thinking around that. >> Sure thing. You are spot on. Nobody wants another dashboard that is just spewing data at them because data, without context is irrelevant in our mind, right? We want to be able to provide context, we want to be able to provide data within the context. And the dashboard to us means a customer that's looking at it, an IT leader looking at it should be able to ask the why question without working too hard at it, right? Let's bring up our dashboard. I would love to show and tell, although it's a dashboard, it is a tool that can enable IT leaders do things differently. >> John: Right, here it is. This is it right here. Okay, so this is the dashboard. Take me through it, what does it mean? >> Venkat: Yeah, let's (indistinct) right? The chart in the middle is the most important piece there. What we help our leaders, IT leaders do is, all the fullness of time of cloud adoption, we know the cloud's footprint is going to grow. The gray chart in the back, the stock chart represents the cloud footprint. As the cloud footprint continues to grow, we would like our leaders to demand that their security issues go down, their compliance issues go down and their costs to become more and more optimum. When leaders demand this, they can make things happen and our platform can help reduce all three and leaders can have this kind of dashboard to ask the why question. For example, they can compare one department with another department, ask that why question. They can compare an application that is similar in one department in another department and ask the why question, why is it more expensive? Why is it having more compliance issues? This is the kind of why questions our dashboard helps our customers perform and ask those questions, and they don't have to lift a finger, right? This entire dashboard comes to life within few minutes of them connecting their cloud accounts, where we provide visibility into operational issues, trend lines of data on how much consumption happens. And over a couple of months, they can see for themselves, make overall operation cost going down. Is my IT infrastructure now in cloud more resilient? And doesn't take more people to do it or am I able to turn on MontyClouds DAY2 bonds to go start reducing that burden or the period of time. This is what we mean by putting the power of autonomous CloudOps in our hands for customers. >> And this is what you mean by the IT powerhouse for the cloud. Is this on Amazon? So if I want to consume the product, what do I need to do to engage with you guys? What does it mean to me? Am I buying a service? Is it native? Is there agents involved? Take me through, what do I need to do? >> It's a great question. We are born in the cloud startup, which means we are super thankful for amazing technologies like Amazon infrastructure as core and the venting platform that's out there. So our platform is fully hosted, managed SaaS platform. A customer does not need to do anything but log onto montycloud.com, click a bunch of buttons, and connect their database account. They get started in under five minutes, self-service. And as they go through the platform, the guided experience where they can get to that dashboard I showed you in just a few clicks. They can get visibility, security posture assessment, compliance posture assessment, all in those few clicks. And when they decide to start using the platform more to automate and leverage the bots, they can always buy into additional services in the platform. So it's a easy to use get started in 10 minutes tops, if you will, that kind of platform >> Okay, great stuff. I want you to take me through the intelligent application flywheel that's going on here. So I can imagine that as the flywheel of success happens. Okay, got some intelligent apps, I see the dashboard, I'm getting some more visibility on the value creation, unlocking more value, new use case, all the things that happen in cloud, all good. And then I start growing, but I got builders trying to build more applications, more demand for more applications, more pressure on the infrastructure. The next question's, how do you guys simplify the cloud operation equation? Because I got to add more VPCs, I got to do more infrastructure, is it more EC Two? It can get complicated. How do you guys solve that problem? Because if the cloud footprint starts to grow because of more intelligent applications, how do you guys make it easier and simpler to scale up the intelligent infrastructure? >> Oh, that's a great question again, John. I'm going to go into a little bit of a detailed slide here. But before I do that, let's talk about two customers that we helped, right? This slide on the left, talks to those, both the customers. So what we have learned working with customers is, they have to build cloud accounts, manage cloud regions, user onboarding. Then they have to build networking infrastructure. Then they have to enable application infrastructure on top of the networking infrastructure. Application infrastructure could mean they want high-performance computing workloads or elastic services, such as queuing services, storage, or traditional VMS databases. That's a lot to build in the application infrastructure with infrastructure scope. On top of that, our customers have to deal with visibility, security, compliance costs. You get it, right? The path to intelligent applications is not easy because cloud is powerful, but it's broad, and the talent required is deep. We are able to say, how can we help our customers automate everything below the intelligent application layer. If we can do that, which we do, we can now propel our developers to go build intelligent applications without having the of also managing the underlying infrastructure. And we can help the IT operations team become cloud powerhouses because they get out of the way and enabled. Give you two examples here, right? One of our customers is a fortune 200 large ISP. They have about 10,000 servers in a particular department. And previously, when the servers were on premises, they had about a four member team managing compliance for it. When they lifted and shifted these servers into the cloud, the same model they wanted to... There are leaders that asked "Why should we continue with the same model?" They wanted MontyCloud. Now there is a DAY2 compliance board that's running, managing the 10,000 servers automatically watching on for compliance drifts, notifying them in a Slack channel, gets approval, remediates and fixes it. They were able to take those four folks and put them on the intelligent application side, I suppose to continuous infrastructure management site. Another example, a fortune 200 global networking company. It's an interesting situation, John. So on cyber Monday, they wanted to go big of obviously the cyber Monday was very important for them. The Thursday before cyber Monday, their on-premises data center and application went down and their teams wanted to move the application to cloud. And the partner that we work with, that brought this challenge to us saying hey, this fortune customer wants to go to cloud and we have this weekend. Well, we were able to go guide the partner and with our platform they were able to not only take their application from on-prem to cloud, they set up the cloud infrastructure, the networking, the application layer, the monitoring layer, the operations layer, all of that within a day. And on Monday that application delivered three X sales for this customer, without that partner or the customer being a cloud expert. That's what we mean by putting that kind of power in the hands of customers. >> Yeah, and I want to go back to that slide 'cause I think there's a second section I want to look at because what you just referred to is, I think this builds into the next comment on the right-hand side, this DAY2 kind of console vision here. The idea of getting in the weeds and getting into the troubleshooting of say, that cyber Monday example is exactly the non agility scenario, right? Because, if anyone's ever worked in tech knows when you have to get to root cause on something, it can take a while, right? So you need to have the system architecture built out. So here, classic cloud architecture on the left moves to a simple kind of console model. That's kind of what you guys are offering. Am I getting that right, Venkat? Is that kind of how this works? >> Yeah, that's kind of how it works, but the path to that maybe, a quick explanation though. We look at what's on the right--- >> Put that slide back up, let's get that slide back. Okay, there it is. >> Venkat: So what's on the right side here is, every layer on the left requires specialized talent and specialized tooling. That's all customers are currently experienced in the cloud. They either have to buy into a expensive monitoring tool or buy into an expensive security posture management tool. They have to hire, you know... It's hard to find cloud talent, right? And then they have to use infrastructure as code solutions. Sometimes that is, that can get more complex to maintain. What we have in MontyCloud is that, every layer there, they can provision by clicking away. For example, when they provision their cloud accounts setting up AWS best practices, budget guardrails, security, logging and monitoring, they can click away and do it. Setting up network infrastructure like VPC is setting up AWS transit gateway, VPNs, there's templates they can click and do it. The application infrastructure, which is a growing set of application infrastructure. Imagine this John, if a developer can come in and request the IT team they would like to set up an RDS database, right? The IT team can now with DAY2, can provide the developer options of, do you want it in dev stage prod? And do you want snapshots, backup, high availability? These are all check boxes and the developer can pick and choose and they can provision what they want without additional help from the IT team. And the IT team does not have to automate any one of those because it's pre automated in our platform. >> Yeah, this is the promise of infrastructure as code. You don't got to get in to the architecture and start throwing switches and all kinds of weird stuff can happen. Someone doesn't turn off, they don't enable auto-scale and they tested for this they forgot to revert back. I mean, there's a zillion things that could go wrong, human error, as well as automation. So once you set it up, then you provide a consumable developer friendly approach. That seems to be what's happening. Okay, cool. All right, well Venkat, this is fantastic. Final minutes we have left. I want to get your thoughts on the momentum and the vision. Talk about the momentum that you guys have now in the marketplace and what's the vision for the next five years. >> Great, it's a great question. From a momentum perspective John, we take an approach of, let's work with customers and understand that we can solve some problems for them. We've been working backups with customers. We have customers that are startups, that are born in the cloud, we have customers that are enterprise customers who are having a large footprint on-prem. Then we have everybody in between like university customers who are transitioning off. So what we did is from a momentum perspective, we worried more about, do we understand the talent gap and the tooling gap that exists across the board of all customers? Because every customer, once they go to cloud, they look to achieving the same level of efficiency and simplicity like modern cloud companies. A traditional company that moves to cloud wants to act and behave like the one in the cloud customer. For us it was very important to understand a variety of customers, a variety of use cases, and then automated away. So momentum is that we are able to go help a customer that is a Greenfield customer to go to cloud easily. And we're also able to go help brownfield customers, ensure they can reduce the total cost of cloud operations on an ongoing basis. So we've been seeing customers of all sizes, even helping customers of all sizes move fast. And there's a bunch of case studies out there in our website. We are a startup, so we've been able to help those customers and earn their trust by delivering results for them. So the momentum is that, we are able to go scale up now, and scale up fast for our customers without us being in the way, technically. Or customers can go to our platform help themselves and accelerate the platform. That's the momentum we have. From a future perspective, you asked, where things are headed, right? There are a couple of things. First things first, it's important to not just predict the future, we got to create it, right? About two years back when we founded MontyCloud, the question my team asked me, my CTO asked me is, what really matters in cloud ranking, right? So we said, all right, this is provisioning automation management. Yeah, they all matter. But what seemed to really matter is there are three things that matter. That's how we came to... One is events. The cloud itself is an eventing machine, right? More than ever, the cloud infrastructure emits events at every turn, every resource, every activity is expressed as an event. So we made an early bet on building an event driven platform from the ground up. We are the only platform that is even driven. Every other platform is seen to try and solve problems which is awesome to have, but they take an approach of an API based model or an inference into log based model. So the future, we believe, belongs to eventing model because it's lightweight on the customer's infrastructure, it goes easy on the cloud providers. More importantly, it gets the customer as close as possible to when the event happens, right? That's very important, to be able to be even event-driven. If you noticed Cloud Native Foundation came up and announced recently cloud events is the right way to deal with modern SaaS platforms. We've been in cloud events from day one for us, right? So the future is in eventing model. >> And that's where the data angle, I think, connects here for this event and why you guys are a hot startup is, observability, all these things. It's all about a event driven infrastructure. It's all events. It's monitoring, it's management, it's data. At the end of the day, the data is the instrumentation, is what it is. Developers are coding. Media's data. Everything's data. Everything has to do with data. You guys have a unique approach. Venkat Krishnamachari, thank you for coming on. Appreciate it, and thanks for sharing your story here at the AWS Showcase. First inaugural Cube On CloudStartups, part of the 10 hot startups categories. Thanks for sharing. >> Thanks for the opportunity. And we hope to help a lot more customers, simply for the cloud operations and innovate with some intelligent applications that's going to change the world. >> Check out Venkat and his company all on Twitter, on Facebook, they're on every channel, all the channels are open, of course. theCUBE we're bringing you all the hot startups, extracting the signal from the noise. I'm John furrier. Thanks for watching. (Upbeat music)

>>from around the globe. It's the Cube covering HP. Discover Virtual experience Brought to you by HP. >>Welcome to the Cube's coverage of HP Discover 2020 Virtual Experience. I'm Lisa Martin, and I'm pleased to be joined by two guests from HP longtime partner Tech Data. We have calling Blair the vice president of sales and marketing of I. O. T. And Data Solutions and David Smith, H P E Pre Sales Field Solutions are common. And David, Welcome to the Cube. Thanks, Lisa. Great to see. So let's start with the calling. HP and Technical have been partners for over 40 years, but tell our audience a little bit about tech data before we get into the specifics of what you're doing and some of the cool I o. T. Stuff with HP. I >>think that the Tech data is a Fortune 100 distributor. We continued to evolved to be a solutions aggregator in these next generation technology businesses. As you've mentioned, we've been serving the I T distribution markets globally for for 40 plus years, and we're now moving into next generation technologies like Wild Analytics, I O. T and Security bubble Lifecycle Management services. But to be able todo position ourselves with our customer base and the needs of their clients have. So I'm excited to be here today to talk a little bit about what we're doing in I, O. T. And Analytics with David on the HPC side >>and in addition to the 40 plus years of partnership calling that you mentioned that Detected and HP have you've got over 200 plus hp. Resource is David, you're one of those guys in the field. Talk to us about some of the things that you're working on with Channel Partners Table David to enable them, especially during such crazy times of living and now >>absolutely, absolutely so. What we can do is we can provide strong sales and technical enablement if your team, for example, wants to better understand how to position HP portfolio if they require assistance and architect ing a secure performance i o t. Solution. We can help ensure that you're technical team is fully capable of having that conversation, and it's one that they're able to have of confidence, weaken validate the proposed HP solutions with the customers, technical requirements and proposed use case. We can even exist on a customer calls, if it would, would benefit our partner to kind of extend out to that. We also have a a a deep technical bench that Colin can speak to in the OT space toe lean on as well. For so solution is that kind of span into the space beyond where HP typically operates, which would be edge, compute computing and network. Sic security. >>Excellent call and tell me a little bit about Tech Data's investments in I o. T. When did this start? What are you guys doing today? >>Sure, we started in the cloud space. First tackle this opportunity in data center modernization and hybrid cloud. That was about seven years ago. Shortly thereafter we started investing very materially in the security cyber security space. And then we follow that with Data Analytics and then the Internet of things. Now we've been in those spaces with our long term partners for some time. But now that we're seeing this movement to the intelligent edge and a real focus on business outcomes and specialization, we've kind of tracked with the market, and we feel like we've invested a little bit ahead of where the channel is in terms of supporting our ecosystem of partners in this space. >>So the intelligent edge has been growing for quite some time. Poland in the very unique times that we're living in in 2020 how are you seeing that intelligent edge expand even more? And what are some of the pressing opportunities that tech data and HPC i O T solutions together can address? >>So a couple. So the first is a Xai mentioned earlier just data center modernization. And so, in the middle of code 19 and perhaps postcode 19 we're going to see a lot of clients that are really focused on monetizing the things that they've got. But doing so to drive business outcomes. We believe that increasingly, the predominance of use cases and compute and analytics is going to move to the edge. And HP has got a great portfolio for not just on premise high performance computing but also hybrid cloud computing. And then when we get into the edge with edge line and networking with Aruba and devices that need to be a digitized and sense arised, it's a really great partnership. And then what we're able to do also, Lisa, is we've been investing in vertical markets since 2000 and seven, and I've been a long the ride with that team, most all of that way. So we've got deep specialization and healthcare and industrial manufacturing, retail and then public sector. And then the last thing we've kind of turned on here recently just last month is a strategic partnership in the smarter cities space. So we're able to leverage a lot of those vertical market capabilities. Couple that with our HP organization and really drive specialized repeatable solutions in these vertical markets, where we believe increasingly, customers are going to be more interested in a repeatable solutions that can drive quick proof of value proof of concepts with minimal viable what kinds of products. And that's that's kind of the apartment today with RHB Organization and the HP Corporation >>David. Let's double click into some of those of vertical markets that Colin mentioned some of the things that pop into minor healthcare manufacturing. As we know, supply chains have been very challenged during covered. Give us an insight into what you're hearing from channel partners now virtually, but what are some of the things that are pressing importance? >>So from a pressing and important to Collins exact point, and your exact point as well is really it's all about the edge computing space now from a product perspective Azaz Colin had mentioned earlier. HP has their edge line converged systems, which is kind of taking the functionality of OT and edge T Excuse me of OT and I t and combine it into a single edge processing compute solution. You kind of couple that with the ability to configure components such as Tesla GP, use in specific excellent offerings to offer an aid and things like realtime, video processing and analytics. Uh, and a perfect example of this is, ah so for dissing and covert space. If if I need to be able to analyze a group of people to ensure they're staying as far apart as possible or, you know within self distant guidelines, that is where kind of the real time that's like an aspect of things can be taken advantage of same things with with the leveraging cameras where you could actually take temperature detection as as well, so it's really kind of best to think of Edge Lines Solutions is data center computing at the edge kind of transition into the Aruba space. Uh Rubio says offerings aid in the island Security is such a clear pass device inside, which allows for device discovery of network and monitoring of wired and wireless devices. There's also Aruba asset tracking and real time location of solutions, and that's particularly important in the healthcare space as well. If I have a lot of high value assets, things like wheelchairs, things like ventilation devices, where these things low located within my facilities and how can I keep keep track of them? They also, and by that I mean HP. They also kind of leveraging expanse ecosystem of partners. As an example, they leverage thing works allow their i o t solutions as well, when you kind of tying it all together with HP Point. Next to the end, customers provided with comprehensive loyalty solution. >>So, Colin, how ready? Our channel partners and the end user customers to rapidly pivot and start either deploying more technologies at the edge to be able to deliver some of the capabilities that David talked about in terms of analytics and sensors for social distancing. How ready are the channel partners and customers to be able to understand, adopt and execute this technology. >>So I think on the understanding side, I think the partners are there. We've been talking about digital transformation in the channel for a couple of years now, and I think what's happened through the 19 Pandemic is that it's been a real spotlight on the need for those business outcomes to to solve for very specific problems. And that's one of the values that we serve in the channel. So we've got a solution offering that we call our solution factory. And what we do really says is we leverage a process to look outside the industry. At Gartner, Magic Quadrant Solutions forced a Wave G two crowd. You know, top leaders, visionaries and understand What are those solutions that are in demand in these vertical markets that we talked about? And then we do a lot of work with David and his team internally in the HP organization to be able to do that and then build out that reference architectures so that we know that there's a solution that drives a bill of materials and a reference architecture that's going to work that clients are going to need and then we can do it quickly. You know, Tech data. Everything's about being bold, acting now getting scale. And we've got a large ecosystem partners that already have great relationships. So we pride ourselves on being able to identify what are those solutions that we can take to our partners that they can quickly take to their end users where you know we've We've kind of developed out what we think the 70 or 80% of that solution is going to look like. And then we drive point next and other services capabilities to be able to complete that last mile, if you will, of some of the customization. So we're helping them. For those who aren't ready, we're helping them. For those who already have very specific use cases and a practice that they drive with repeatable solutions were coming alongside them and understanding. What can we do? Using a practice builder approach, which is our consultative approach to understand where our partners are going in the market, who their clients are, what skill sets do they have? What supplier affinities do they want to drive? What brand marketing or demand generation support do they need? And that's where we can take some of these solutions, bring them to bear and engage in that consultative engagement to accelerate being ready as, as you rightly say, >>so tech. It has a lot of partners. You in general. You also have a lot of partners in the i o T space calling What? How do you from a marketing hat perspective? How do you describe the differentiation that Tech data and HP ease Iot solutions delivered to the channel to the end user? >>A couple of different things? I think that's that's differentiation. And that's one of the things that we strive for in the channel is to be specialized and to be competitively differentiated. And so the first part, I say to all of my team, Lisa, is you know, whether it's our solution consultants or our technical consultants, our solutions to the developers or the software development team that works my organization. Our goal is to be specialized in such a way that we're having relevant value added conversations not only our channel partners, but also end users of our partners want to bring us into those conversations, and many do. The next is really education and enablement as you would expect. And so there's a lot of things that are specialized in our technical. We drive education certification programs, roadshows, seminars, one of the things that we're seeing a lot of interest now. Lisa is for a digital marketing, and we're driving. Some really need offerings around digital marketing platforms that not only educate our partners but also allow our partners to bring their end users and tour some of this some of these technologies. So whether it's at our Clearwater office, where we've got an I. O T. Solution center, that we we take our partners and their clients through or we're using our facilities Teoh to do executive briefings and ideation as a service that, you know, kind of understanding the art of the possible. With both our resellers and their clients work, we're using our solution. Our solution catalogs that we've built an interactive pdf that allows our partners to understand over 50 solutions that we've got and then be able to identify. Where would they like to bring in David and his team and then my consultants to do that, that deep planning on business development, uh, that we talked about a little bit earlier. >>So the engagement right now is maybe even more important than it has been in a while because it's all hands off and virtual David. Talk to me about some of the engagement and the enablement piece that call and talked about. How are you able to really keep a channel partner and their end user customers engaged and interested in what you're able to deliver through this from New Virtual World? >>That's a great, great question. And we work in conjunction with our marketing teams to make sure that as new technologies and quite in I O. T space as well as within the HP East base as well that that our channel partners are educated and aware that these solutions exist. I know for a fact that for the majority of them you kind of get this consistent bombardment of new technology. But being able to actually have someone go out and explain it and then being able to correspondingly position it's use case and it's functionality and why it would provide value for your end customer is one of the benefits of tech data ads to kind of build upon that previous statement. The fact that We have such a huge portfolio of partners, so you kind of have HP and the edge compute space. But we have so many different partners in the OT space where it's really just a phone call, an email, a Skype message, a way to have that conversation around interoperability and then provide those responses back to our partners. >>Excellent. One more question before we go. Colin for you, A lot of partners. Why HP fry Mt. >>So a couple of reasons? One of the one of the biggest reasons as HP is just a great partner. And so when you look at evaluating I. O. T solutions that tend to be pretty comprehensive in many cases, Lisa it takes 10 or 12 partners to complete a really i o t solution and address that use case that that's in the field. And so when you have a partner like HP who's investing in these programs, investing in demand generation, investing in the spectrum of technology, whether it's hybrid Cloud Data Center, compute storage or your edge devices and Iot gateways, then to be able to contextualize those into what we call market ready solutions in each one of these vertical markets where there's references and there's use cases. And there were coupling education that specific rest of solutions. You know HP can do all of those things, and that's very important. Because in this new world, no one can go it alone anymore. It takes it takes partnerships, and we're all better together. And HP really does embrace that philosophy. And they've been a great partner for us in the Iot space. >>Excellent. Well, Colin and David, thank you so much for joining me today on the Cube Tech data. H p e i o t better together. Thank you so much. It's been a pleasure talking with you. >>Thank you. >>Thank you. Lisa. >>And four Collet and David. I am Lisa Martin. You're watching the Cube's virtual coverage of HP Discover 2020. Thanks for watching. Yeah, yeah, yeah, yeah.

>> Announcer: From San Francisco, it's theCUBE, covering Accenture Tech Vision 2020. Brought to you by Accenture. >> Hey, welcome back, everybody. Jeff Frick here from theCUBE. We are high atop San Francisco at the Accenture Innovation Hub, 33rd floor of the Salesforce Tower. It's a beautiful night, but we're here for a very special occasion. It's the Tech Vision 2020 reveal, and we are happy to have the guy that runs the whole thing, he's going to reveal on stage a little bit later, but we got him in advance. He's Paul Daugherty, the chief technology and innovation officer for Accenture. Paul, great to see you as always. >> Great to see you, Jeff, too. It is a beautiful evening here, looking out over the Bay. >> If only we could turn the cameras around, but, sorry, we can't do that. >> Yeah. >> All right, so you've been at this now, the Tech Vision's been going on for 20 years, we heard earlier today. >> Yeah. >> You've been involved for almost a decade. How has this thing evolved over that time? >> Yeah, you know, we've been doing the Vision for 20 years, and what we've been trying to do is forecast what's happening with business and technology in a way that's actionable for executives. There's lots of trend forecasts and lists and things, but if you just see a list of cloud, or-- >> Jeff: Mobile's going to be really big. (laughs) >> AI, mobile, it doesn't really help you. We're trying to talk a little bit about the impact on business, impact to the world, and the decisions that you need to make. What's changed over that period of time is just the breadth of the impact that technology's having on people, so we focus a lot of our Visions on the impact on humans, on individuals, what's happening with technology, what the impact on business, we can talk about that a little bit more, but business is certainly not the back office of companies anymore. It's not just the back office and front office, either. In business, it's instrumental in the fabric of how every part of the company operates, their strategy, their operations, their products and services, et cetera, and that's really the trajectory we've seen. As technology advances, we have this accelerating exponential increase in technology, the implications for executives and the stakes just get higher and higher. >> It's weird, there are so many layers to this. One of the things we've talked about a lot is trust, and you guys talk about trust a lot. But what strikes me as kind of this dichotomy is on one hand, do I trust the companies, right? Do I trust Mark Zuckerberg with my data, to pick on him, he gets picked on all the time. That might be a question, but do I trust that Facebook is going to work? Absolutely. And so, our reliance on the technology, our confidence in the technology, our just baseline assumption that this stuff is going to work, is crazy high, up to and including people taking naps in their Teslas, (laughs) which are not autonomous vehicles! >> Not an advisable practice. >> Not autonomous vehicles! So it's this weird kind of split where it's definitely part of our lives, but it seems like kind of the consciousness is coming up as kind of the second order. What does this really mean to me? What does this mean to my data? What are people actually doing with this stuff? And am I making a good value exchange? >> Well, that's the, we talk in the Vision this year about value versus values, and the question you're asking is getting right at that, the crux between value and values. You know, businesses have been using technology to drive value for a long time. That's how applying different types of technology to enterprise, whether it be back to the mainframe days or ERP packages, cloud computing, et cetera, artificial intelligence. So value is what they were talking about in the Vision. How do you drive value using the technology? And one thing we found is there's a big gap. Only 10% of organizations are really getting full value in the way they're applying technology, and those that are are getting twice the revenue growth as companies that aren't, so that's one big gap in value. And this values point is really getting to be important, which is, as technology can be deployed in ways that are more pervasive and impact our experience, they're tracking our health details-- >> Right, right. >> They know where we are, they know what we're doing, they're anticipating what we might do next. How does that impact the values? And how are the values of companies important in other ways? The values you have around sustainability and other things are increasingly important to new generations of consumers and consumers who are thinking in new ways. This value versus values is teeing up what we call a tech-clash, which isn't a tech-lash, just, again, seeing people reacting against tech companies, as you said earlier, it's a tech-clash, which is the values that consumer citizens and people want sometimes clashing with the value of the models that companies have been using to deliver their products and services. >> Right. Well, it seems like it's kind of the "What are you optimizing for?" game, and it seems like it was such an extreme optimization towards profitability and shareholder value, and less, necessarily, employees, less, necessarily, customers, and certainly less in terms of the social impact. So that definitely seems to be changing, but is it changing fast enough? Are people really grasping it? >> Well, I think the data's mixed on that. I think there's a lot of mixed data on "What do people really want?" So people say they want more privacy, they say they want access and control of their data, but they still use a lot of the services that it may be inconsistent with the values that they talk about, and the values that come out in surveys. So, but that's changing. So consumers are getting more educated about how they want their data to be used. But the other thing that's happening is that companies are realizing that it's really a battle for experience. Experience is what, creating broader experiences, better experiences for consumers is what the battleground is. A great experience, whether you're a travel company or a bank or a manufacturing company, or whatever you might be, creating the experience requires data, and to get the data from an individual or another company, it takes trust. So this virtuous circle of experience, data, and trust is something that companies are realizing is essential to their competitive advantage going forward. We say trust is the currency of the digital and post-digital world that we're moving into. >> Right, it's just how explicit is that trust, or how explicit does it need to be? And as you said, that's unclear. People can complain on one hand, but continue to use the services, so it seems to be a little bit kind of squishy. >> It's a sliding scale. It's really a value exchange, and you have to think about it. What's the value exchange and the value that an individual consumer places on their privacy versus free access to a service? That's what's being worked out right now. >> Right, so I'm going to get your take on another thing, which is exponential curves, and you've mentioned time and time again, the pace of change is only accelerating. Well, you've been saying that, probably, for (laughs) 20 years. (Paul laughs) So the curve's just getting steeper. How do you see that kind of playing out over time? Will we eventually catch up? Is it just presumed that this is kind of the new normal? Or how is this going to shake out? 'Cause people aren't great at exponential curves. It's just not really in our DNA. >> Yeah, but I think that's the world we're operating in now, and I think the exponential potential is going to continue. We don't see a slowdown in the exponential growth rates of technology. So artificial intelligence, we're at the early days. Cloud computing, only about 20% enterprise adoption, a lot more to go. New adoptions are on the horizon, things like central bank digital currencies that we've done some research and done some work on recently. Quantum computing and quantum cryptography for networking, et cetera. So the pace of innovation is going to accelerate, and the challenge for organizations is rationalizing that and deciding how to incorporate that into their business, change their business, and change the way that they're leveraging their workforce and change the way that they're interacting with customers. And that's why what we're trying to address in the Vision is provide a little bit of that road map into how you digest it down. Now, there's also technology foundations of this. We talk about something at Accenture called living systems. Living systems is a new way of looking at the architecture of how you build your technology, because you don't have static systems anymore. Your systems have to be living and biological, adapting to the new technology, adapting to the business, adapting to new data over time. So this concept of living systems is going to be really important to organizations' success going forward. >> But the interesting thing is, one of the topics is "AI and Me," and traditional AI was very kind of purpose-built. For instance, Google Photos, can you find the cat? Can I find the kids at the beach? But you're talking about models where the AI can evolve and not necessarily be quite so data-centric around a specific application, but much more evolutionary and adaptable, based on how things change. >> Yeah, I think that's the future of AI that we see. There's been a lot of success in applying AI today, and a lot of it's been based on supervised learning, deep learning techniques that require massive amounts of data. Solving problems like machine vision requires massive amounts of data to do it right. And that'll continue. There'll continue to be problem sets that need large data. But what we're also seeing is a lot of innovation and AI techniques around small data. And we actually did some research recently, and we talk about this a little bit in our Vision, around the future being maybe smaller data sets and more structured data and intelligence around structured data, common-sense AI, and things that allow us to make breakthroughs in different ways. And that's, we used to look at "AI and Me," which is the trend around the workforce and how the workforce changes. It's those kinds of adaptations that we think are going to be really important. >> So another one is robotics, "Robots in the Wild." And you made an interesting comment-- >> Paul: Not "Robots Gone Wild," "Robots in the Wild," "Robots in the Wild." >> Well, maybe they'll go wild once they're in the wild. You never know. Once they get autonomy. Not a lot of autonomy, that's probably why. But it's kind of interesting, 'cause you talk about robots being designed to help people do a better job, as opposed to carving out a specific function for the robot to do without a person, and it seems like that's a much easier route to go, to set up a discrete thing that we can carve out and program the robot to do. Probably early days of manufacturing and doing spot welding in cars, et cetera. >> Right. >> So is it a lot harder to have the robot operate with its human partner, if you will, but are the benefits worth it? How do you kind of see that shaking out, versus, "Ah, I can carve out one more function"? >> Yeah, I think it's going to be a mix. I think there'll be, we see a lot of application of the robots paired with people in different ways, cobots in manufacturing being a great example, and something that's really taking off in manufacturing environments, but also, you have robots of different forms that serve human needs. There's a lot of interesting things going on in healthcare right now. How can you support autistic children or adults better using human-like robots and agents that can interact in different ways? A lot of interesting things around Alzheimer's and dealing with cognitive impairment and such using robots and robotics. So I think the future isn't, there's a lot of robots in the wild in the form of C-3POs and R2-D2s and those types of robots, and we'll see some of those. And those are being used widely in business today, even, in different contexts, but I think the interesting advance will be looking at robots that complement and augment and serve human needs more effectively. >> Right, right, and do people do a good enough job of getting some of the case studies? Like, you just walked through kind of the better use cases, the more humane use cases, the kind of cool medical breakthroughs, versus just continued optimization of getting me my Starbucks coupon when I walk by out front? (Paul laughs) >> Yeah, I'm not sure. >> Doesn't seem like I get the pub, like they just don't get the pub, I don't think. >> Yeah, yeah, yeah, maybe not. A little mixology is another (Jeff laughs) inflection that robots are getting good at. But I think that's what we're trying to do, is through the effort we do with the Vision, as well as our Tech for Good work and other things, is look at how we amplify and highlight some of the great work that is happening in those areas. >> So, you've been doing it for a decade. What struck you this year as being a little bit different, a little bit unexpected, not necessarily something you may have anticipated? >> I think the thing that is maybe a tipping point that I see in this Vision that I didn't anticipate is this idea that every company's really becoming a technology company. We said eight years ago, "Every business "will be a digital business," and that was, while ridiculed by some at the time, that really came true, and every business and every industry really is becoming digital or has already become digital. But I think we might've gotten it slightly wrong. Digital was kind of a step, but every company is deploying technology in the way they serve their customers, in the way they build their products and services. Every product and service is becoming technology-enabled. The ecosystem of technology providers is critical to companies in every industry. So every company's really becoming a technology company. Maybe every company needs to be as good as a digital native company at developing products and services, operating them. So I think that this idea of every company becoming a technology company, every CEO becoming a technology CEO, technology leader, is something that I think will differentiate companies going forward as well. >> Well, really, good work, you, Michael, and the team. It's fun to come here ever year, because you guys do a little twist. Like you said, it's not "Cloud's going to be really big, "mobile's going to be really big," but a little bit more thoughtful, a little bit more deep, a little bit longer kind of thought cycles on these trends. >> Yeah, and I think the, if you read through the Vision, we're trying to present a complete story, too, so it's, as you know, "We, the post-digital people." But if you look at innovation, "The I in Experience" is about serving your customers differently. "The Dilemma of Smart Machines" and "Robots in the Wild" is about your new products and services and the post-digital environment powered by technology. "AI and Me" is about the new workforce, and "Innovation DNA" is about driving continuous innovation in your organization, your culture, as you develop your business into the future. So it really is providing a complete narrative on what we think the future looks like for executives. >> Right, good, still more utopian than dystopian, I like it. >> More utopia than dystopia, but you got to steer around the roadblocks. (Jeff chuckles) >> All right, Paul, well, thanks again, and good luck tonight with the big presentation. >> Thanks, Jeff. >> All right, he's Paul, I'm Jeff. You're watching theCUBE. We're at the Accenture innovation reveal 2020, when we're going to know everything with the benefit of hindsight. Thanks for watching, (laughs) we'll see you next time. (upbeat pop music)

(upbeat music) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We are high atop San Franciscso, in the Salesforce Tower in the brand new Accenture, the Innovation Hub. It opened up, I don't know, six months ago or so. We were here for the opening. It's a really spectacular space with a really cool Cinderella stair, so if you come, make sure you check that out. We're talking about cloud and the evolution of cloud, and hybrid cloud, and clearly, two players that are right in the middle of this, helping customers get through this journey, and do these migrations are Accenture and HPE. So we're excited to have our next guest, Melissa Besse. She is the Managing Director, Intelligent Cloud and Infrastructure Strategic Partnerships, at Accenture. Melissa, welcome. >> Thanks Jeff. >> And joining us from HP is David Stone. He is the VP of Ecosystem Sales. David great to see you. >> Great, thanks for having me. >> So, let's just jump into it. The cloud discussion has taken over for the last 10 years, but it's really continuing to evolve. It was kind of this new entrance, with AWS coming on the scene, one of the great lines that Jeff Bezos talks about, is they had no competition for seven years. Nobody recognized that the bookseller, out on the left hand edge, was coming in to take their infrastructure business. But as things have moved to public cloud, now there's hybrid cloud, now all applications, or work loads, are right for public clouds, so now, all the Enterprises are trying to figure this out, they want to make their moves but it's complicated. So, first of all, let's talk about some of the vocabulary, hybrid cloud versus Multi-Cloud. What do those terms mean to you and your customers? Let's start with you, Melissa. >> Sure. So when you think of Multi-Cloud, right, we're seeing a big convergence of, I would say, a Multi-Cloud operating model, that really has to integrate across all the clouds. So, you have your public cloud providers, you have your SaaS, like Salesforce, work day, you have your PAS, right. And so when you think of Multi-Cloud, any customer is going to have a plethora, of all of these types of clouds. And really being able to manage across those, becomes critical. When you think of Hybrid-Cloud, Hybrid-Cloud is really thinking about the placement of Ous. We usually look at it from a data perspective, right. Are you going to in the public, or in the private space? And you kind of look at it from that perspective. And it really enables that data movement across both, of those clouds. >> So what do you see, David, in your customers? >> I see a lot of the customers, that we see today, are confused, right? The people who have gone to the Public Cloud, had scratched their heads and said, "Geez, what do I do?", "It's not as cheap as I thought it was going to be." So, the ones who are early adopters, are confused. The ones who haven't moved, yet, are really scratching their head as well, right. Because if you don't the right strategy, you'll end up getting boxed in. You'll pay a ton of money to get your data in, and you'll pay a ton of money to get your data out. And so, all of our customers, you know, want the right hybrid strategy. And, I think that's where the market, and I know Accenture and HPE, clearly see the market really becoming a hybrid world. >> It's interesting, you said it's based on the data, and you just talked about moving data in and out. Where we more often here it talked about workload, this kind of horses for courses, you know. It's a workload specific, should be deployed in this particular, kind of infrastructure configuration. But you both mention data, and there's a lot of conversation, kind of pre-cloud, about data gravity and how expensive it is to move the data, and the age old thing, do you move the compute to the data, or move the data to the compute? There's a lot of advantages, if you have that data in the cloud, but you're highlighting a couple of the real negatives, in terms of potential cost implications, and we didn't even get into regulations, and some of the other things that drive workloads to stay, in the data center. So, how should people start thinking about these variables, when they're trying to figure out what to do next? >> Accenture's position definitely, like when we started off on our Hybrid Cloud journey, was to capture the workload, right. And, once you have that workload, you could really balance the public benefits of speed, innovation, and consumption, with the private benefits of, actual regulation, data gravity, and performance, right. And so, our whole approach and big bet, has been to- Basically, we had really good leading public capabilities, cause we got into the market early. But we knew our customers were not going to be able to, migrate their entire estate over to public. And so in doing that, we said okay, if we create a hybrid capability, that is highly automated, that is consumed like public, and that is standard, we'd be able to offer our customers a way to pick really, the right workload, in the right place, at the right price. And that was really what our whole goal was. >> Go ahead. >> Yeah, and so just to add on to what Melissa said, I think we also think about, at least, you know, keeping the data in a place that you want, but then being cloud adjacent, so getting in the right data centers, and we often use a cloud saying, to bring the cloud to the data. So, if you have the right hybrid strategy, you put the data where it makes the most sense. Where you want to maintain the security and privacy, but then have access to the APIs, and whatever else you might need to get the full advantages, of the public cloud. >> Yeah, and we here a lot of the data center providers like, Equinix and stuff, talking about features, like direct connect and, you know, to have this proximity between the public cloud, and the stuff that's in your private cloud, so that you do have, you know, low latency, and you can, when you do have to move things, or you do need to access that data, it's not so far away. I'm curious about the impact of companies like, Salesforce in the Salesforce tower, here in San Francisco, at the center offices, and office 365, and Work Day, on how can the adoption of the SaaS applications, have changed the conversation about cloud, and what's important and not important, it used to be security, I don't trust anything outside my data center, and know I might argue that public clouds are more secure, in some ways that private cloud, you don't have disgruntled employees per se, running around the data centers unplugging things. So, how it the adoption of things like Office 365, clearly Microsoft's leveraged that in a big way, to grow their own cloud presence, change the conversation about what's good about cloud, what's not good about cloud, why should we move in this direction. David, you have a thought? >> No, look, I think it's a great question, and I think if you think about the, as Melissa said, the used cases, right. And, how Microsoft has successfully pivoted, their business to it as a service model, right. And so what I think it's done, it's opened up innovation, and a lot of the Salesforces of the world, have adapted their business models. And that's truly to your point, a SaaS based offer, and so when you can do a Work Day, or Salesforce.com implementation, sure, it's been built, it's tested and everything else. I think what then becomes the bigger question, and the bigger challenge is, most companies are sitting on a thousand applications, that have been built over time. And what do you do with those, right? And so, in many cases you need to be connected, to those SaaS space providers, but you need the right hybrid strategy, again, to be able to figure out, how to connect those SaaS space services, to whatever you're going to do, with those thousand workloads. And those thousand workloads, running on different things, you need the right strategy, to figure out where to put the actual workloads. And, as people are trying to go, I know one of the questions that comes up is, do you migrate? Or do you modernize? >> David: And so, as people put that strategy together, I think how you tie to those SaaS space services, clearly ties into your hybrid strategy. >> I would agree, and so, as David mentioned, right. That's where the cloud adjacency, you're seeing a lot of blur, between public and private, I mean, Google's providing Bare-metal as a service. So it is actually dedicated, hybrid cloud capabilities, right. So you're seeing a lot of everyone, and as David talked about, all of the surrounding applications around your SAP, around your oracle. When we created our Exensor Hyper Cloud, we were going after the Enterprise workload. But there's a lot of legacy and other ones, that need that data, and or, the Salesforce data. Whatever the data is, right. And really be able to utilize it when they need to, in a real low latency. >> So, I was wondering I we could unpack, the Accenture Hybrid Cloud. >> Melissa: Sure. >> What is that? Is that your guys own cloud? Is this, you know, kind of the solution set? I've heard that mentioned a couple times. So what is the Accenture Hybrid Cloud? >> So Accenture Hybrid Cloud, was a big bet that we made, as we saw the convergence of MultiCloud. We really said, we know, everything is not going to go public. And in some cases, it's all coming back. And so, customers really needed a way, to look at all of their workloads, right. Because part of the issue with, the getting the cost and benefits out of public is, the workload goes but you really aren't able, to get out of the data center. We term it the "Wild Animal Park", because there's a lot of applications that, right, are you going to modernize, are you going to let them to end of life. So there's a lot of things you have to consider, to truly exit the data center strategy. And so, Accenture Hybrid Cloud is actually, a big bet we made, it is a highly automated, standard private cloud capability, that really augments all of the leading capability, we had in the cloud area. It is, it's differentiated, we made a big bet with HPE, it's differentiated on it's hardware. One of the reasons, when we were going after the Enterprise, was they need large compute, and large storage requirements. And what we're able to do is, when we created this, use some of our automation differentiation. We have actually a client, that we had in the existing I-O-N environment, and we were actually able to achieve, some significant benefits, just from the automation. We got 50 percent in the provisioning of applications. We got 40 percent in the provisioning of the V.M. And we were able to take a lot of what I'll call, the manual tasks, and down to, it was like 62 percent reduction in the effort. As well as, 33 percent savings overall, in getting things production ready. So, this capability is highly automated. It will actually repeat the provisioning, at the application level, because we're going after the Enterprise workloads. And it will create these, it's an ASA that came from government, so it's highly secured, and it really was able to preserve, I think what our customer needed. And being able to span that public/private, capability they need out there in the hybrid world. >> Yeah, I was going to say, I don't know that there's enough talk, about the complexity of the management in these worlds. Nobody ever wants to talk about writing, the CIS Admin piece of the software, right? It's all about the core functionality. Let's shift gears a little bit and talk about HPC, a lot of conversation about high performance computing, a lot going on with A.I. and machine learning now. Which, you know, most of those benefits are going to be, realized in a specific application, right? It's machine learning or artificial intelligence, applied to a specific application. So, again, you guys make big iron, and have been making big iron for a long time, what is this kind of hybrid cloud open up, in terms of, for HPE to have the big heavy metal, and still have kind of the agility and flexibility, of a cloud type of infrastructure. >> Yeah, no, I think it's a great question. I think if you think about HPE's strategy has been, in this area of high performance compute. That we bought the company S.G.I. And as you have seen the announcements, we're hopefully going to close on the Cray acquisition as well. And so we in the world of the data continuing to expand, and at huge volumes. The need to have incredible horsepower to drive that, that's associated with it, now all of this really requires, where's your data being created, and where's it actually being consumed? And so, you need to have the right edge, to cloud strategy in everything. And so, in many cases, you need enough compute at the edge, to be able to compute and do stuff in real time. But in many cases you need to feed all that data, back into another cloud or some sort of mother. HPE, you know, type of high performance compute environment, that can actually run the more, advanced A.I. machine learning type of applications, to really get the insights and tune the algorithms. And then, push some of those APIs and applications, back to the edge. So, it's an area of huge investment, it's an area where because of the latency, you know, things like the autonomous driving, and things like that. You can't put all that stuff into the public cloud. But you need the public cloud, or you need cloud type capability, if you will, to be able to compute and make the right decisions, at the right time. So, it's about having the right compute technology, at the right place, at the right time, at the right cost, and the right perform. >> A lot of rights, good opportunity for Accenture. So, I mean it's funny as we talk about hybrid cloud, and that kind of new, verbs around cloud-like things. Is where we're going to see the same thing, kind of the edge versus the data center comparison, in terms of where the data is, where the processing is, because it's going to be this really dynamic situation, and how much can you push out of the edge, cause, you know, there's no air conditioning a lot of times, and the power might not be that great, and maybe connectivity is a little bit limited. So, you know, Edge offers a whole bunch of, different challenges that you can control for, in a data center but it is going to be this crazy, kind of hybrid world there too, in terms of where the allocation of those resources are. You guys get into the deeper end of that model, Melissa? >> Yeah, so we're definitely working with HPE, to create some of, I'll call it our edge managed services, again, going back to what we were saying about the data, right, we saw the centralization of data with the cloud, with the initial entrance into the cloud, now we're seeing the decentralization of that data, back out to the Edge. With that, right, in these hybrid cloud models, you're really going to need- They require a lot of high performance compute, especially for certain industries, right? If you take a look at gas, oil, and exploration, if you look at media processing, right, all of these need to be able to do that. One of the things, and depending on where it's located, if it's on the Edge, how you're going to feedback the data as we talked about. And so, we're looking at, how do you take this foundation, right, this, I'll call it Exensor Hybrid architecture, right. Take that, and play that intermediate role. I'm going to call it intermediary, right, because you really need a really good, you know, global data map, you need a good supply chain, right. Really to make sure that the data, no matter where it's coming from, is going to be available for that application, at the right time. With, right, the ability to do it at speed. And so, all of these things are factors, as you look at our whole Exensor Hybrid Cloud strategy, right. And being able to manage that, Edge to core and then back up to Cloud, etcetera. >> Right, now I wonder if you could share some stories, cause the value proposition around Cloud, is significantly shifted for those who are paying attention, right. But it's not about cost, it's not about cost savings, I mean there's a lot of that in there and that's good, but really the opportunity is about speed. Speed and innovation. And enabling more innovation across your Enterprise, with more people having more access to more data, to build more apps, and really, to react. Are people getting that? Or, are they still, the customer still kind of encumbered, by this kind of transition phase, they're still trying to sort it out, or do they get it? That really this opportunity is about speed, speed, speed. >> No, go ahead. I mean we use a phrase first off, it's, "fear no cloud", right. To your point, you know, how do you figure out the right strategy. But, I think within that you get, what's the right application? And how do you, you know, fit it in to the overall strategy, of what you're trying to do. >> Yeah. >> And I think the other thing that we're seeing is, you know, customers are trying to figure that out. We have a whole, right, when you start with that application map, you know, there could be 500 to 1000 workloads, right, and applications, and how are you going to, some you're going to retain, some you're going to retire, some you're going to (stutters) refactor for the cloud, or for your private cloud capability. Whatever it is, you're going to be looking at doing, I think, you know, we're seeing early adopters, like even the hyperscalers, themselves, right. They recognize the speed, so you know, we're working with Google for instance. They wanted to get into the Bare-metal, as a service capability, right. Them actually building it, getting it out to market would take so much longer. We already had this whole Exensor Hybrid Cloud architecture, that was cloud adjacent, so we had sub-millisecond latency, right. And so, they're the ones, right, everyone's figuring out that utilizing all of these, I'll call it platforms and prebook capabilities. Many of our partners have them as well, is really allowing them that innovation, get products to market sooner, be able to respond to their customers. Because it is, as we talked about in this multicloud world, lots of things that you have to manage, if you can get pieces from multiple, you know, from a partner, right, that can provide more of the services that you need, it really enables the management of those clouds sources. >> Right, so we're going to wrap it up, but I just want to give you the last word in terms of, what's the most consistent blind spot, that you see when you're first engaging with a customer, who's relatively early on this journey, that they miss, that you see over, and over, and over, and you're like, you know, these are some of the thing you really got to think about, that they haven't thought about. >> Yeah so, for me, I think it's- the cloud isn't about a destination, it's about an experience. And so, how do you get- you talked about the operations, but how do you provide that overall experience? I like to use this simple analogy, that if you and I needed a car, for five or 10, or 15 minutes, you go get an Uber. Cause it's easy, it's quick. If you need a car for a couple days, you do a rental car. You need a car for a year, you might do a lease. You need a car for three, four year, you probably by it, right? And so, if you use that analogy and think, Hmmm, I need a workload application for five/six years, putting something at a persistent workload, that you know about on a public cloud, may be the right answer, but it might be a lot more cost prohibited. But, if you need something, that you can stand up in five minutes, and shut it right back down, the public cloud is absolutely, the right way to go, as long as you can deal with the security requirements, and stuff. And so, if you think about, what are the actual requirements, is it cost, is it performance, you've talked about speed and everything else. It's really trying to figure out how you get an experience, and the only experience that can really hit you, what you need to do today, is having the right hybrid strategy. And every company, I know Accenture was out, way in front of the market on public cloud, and now they've come to the realization, so has many other places. The world is going to be hybrid, it's going to be multicloud. And as long as you can have an experience, and a partner, that can manage, you know, help you define the right path, you'll be on the right journey. >> Jeff: Melissa. >> I think blind spot we run into is, it does start off as a cost savings activity. And there really, it really is so much more about, how are you going to manage that enterprise workload? How are you going to worry about the data? Are you going to have access to it? Are you going to be able to make it fluid, right? The whole essence of cloud, right, what it disrupted was the thought, that something had to stay in one place, right. And, where the real time decisions were being made. Where things needed to happen. Now, through all the different clouds, as well as, that you had to own it yourself, right. I mean, everyone always thought, okay, I'll take all the, you know, I.T. department, and very protective of everything that it wanted to keep. Now, it's about saying, all right, how do I utilize, the best of each of these multiclouds, to stand up, what I'll call, what their core capability is as a customer, right. Are they doing the next chip design? Are they, you know, doing financial market models, right? That requires a high performance capability, right. So, when you start to think about all of this stuff, right, that's the true power, is having a strategy that looks at those outcomes. What am I trying to achieve in getting my products, and services to market, and touching the customers I need. Versus, oh, I'm going to move this out to an infrastructure, because that's what cloud, it'll save me money, right. That's typically the downfall we see, because they're not looking at it from the workload, or the application. >> Same old story, right? Focus on your core differentiator, and outsource the heavy lifting on the stuff, (laughs) that's not your core. Alright, well Melissa, David, thanks for taking a minute, and I really enjoyed the conversation. >> Thanks, Jeff. >> She's Melissa, He's David, and I'm Jeff Frick, you're watching theCUBE. We are high above the San Francisco skyline, in the Salesforce tower at the Accenture Innovation Hub. Thanks for watching, we'll see you next time. (tech music)

(upbeat music) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We are high atop San Franciscso, in the Salesforce Tower in the brand new Accenture, the Innovation Hub. It opened up, I don't know, six months ago or so. We were here for the opening. It's a really spectacular space with a really cool Cinderella stair, so if you come, make sure you check that out. We're talking about cloud and the evolution of cloud, and hybrid cloud, and clearly, two players that are right in the middle of this, helping customers get through this journey, and do these migrations are Accenture and HPE. So we're excited to have our next guest, Melissa Besse. She is the Managing Director, Intelligent Cloud and Infrastructure Strategic Partnerships, at Accenture. Melissa, welcome. >> Thanks Jeff. >> And joining us from HP is David Stone. He is the VP of Ecosystem Sales. David great to see you. >> Great, thanks for having me. >> So, let's just jump into it. The cloud discussion has taken over for the last 10 years, but it's really continuing to evolve. It was kind of this new entrance, with AWS coming on the scene, one of the great lines that Jeff Bezos talks about, is they had no competition for seven years. Nobody recognized that the bookseller, out on the left hand edge, was coming in to take their infrastructure business. But as things have moved to public cloud, now there's hybrid cloud, now all applications, or work loads, are right for public clouds, so now, all the Enterprises are trying to figure this out, they want to make their moves but it's complicated. So, first of all, let's talk about some of the vocabulary, hybrid cloud versus Multi-Cloud. What do those terms mean to you and your customers? Let's start with you, Melissa. >> Sure. So when you think of Multi-Cloud, right, we're seeing a big convergence of, I would say, a Multi-Cloud operating model, that really has to integrate across all the clouds. So, you have your public cloud providers, you have your SaaS, like Salesforce, work day, you have your PAS, right. And so when you think of Multi-Cloud, any customer is going to have a plethora, of all of these types of clouds. And really being able to manage across those, becomes critical. When you think of Hybrid-Cloud, Hybrid-Cloud is really thinking about the placement of Ous. We usually look at it from a data perspective, right. Are you going to in the public, or in the private space? And you kind of look at it from that perspective. And it really enables that data movement across both, of those clouds. >> So what do you see, David, in your customers? >> I see a lot of the customers, that we see today, are confused, right? The people who have gone to the Public Cloud, had scratched their heads and said, "Geez, what do I do?", "It's not as cheap as I thought it was going to be." So, the ones who are early adopters, are confused. The ones who haven't moved, yet, are really scratching their head as well, right. Because if you don't the right strategy, you'll end up getting boxed in. You'll pay a ton of money to get your data in, and you'll pay a ton of money to get your data out. And so, all of our customers, you know, want the right hybrid strategy. And, I think that's where the market, and I know Accenture and HPE, clearly see the market really becoming a hybrid world. >> It's interesting, you said it's based on the data, and you just talked about moving data in and out. Where we more often here it talked about workload, this kind of horses for courses, you know. It's a workload specific, should be deployed in this particular, kind of infrastructure configuration. But you both mention data, and there's a lot of conversation, kind of pre-cloud, about data gravity and how expensive it is to move the data, and the age old thing, do you move the compute to the data, or move the data to the compute? There's a lot of advantages, if you have that data in the cloud, but you're highlighting a couple of the real negatives, in terms of potential cost implications, and we didn't even get into regulations, and some of the other things that drive workloads to stay, in the data center. So, how should people start thinking about these variables, when they're trying to figure out what to do next? >> Accenture's position definitely, like when we started off on our Hybrid Cloud journey, was to capture the workload, right. And, once you have that workload, you could really balance the public benefits of speed, innovation, and consumption, with the private benefits of, actual regulation, data gravity, and performance, right. And so, our whole approach and big bet, has been to- Basically, we had really good leading public capabilities, cause we got into the market early. But we knew our customers were not going to be able to, migrate their entire estate over to public. And so in doing that, we said okay, if we create a hybrid capability, that is highly automated, that is consumed like public, and that is standard, we'd be able to offer our customers a way to pick really, the right workload, in the right place, at the right price. And that was really what our whole goal was. >> Go ahead. >> Yeah, and so just to add on to what Melissa said, I think we also think about, at least, you know, keeping the data in a place that you want, but then being cloud adjacent, so getting in the right data centers, and we often use a cloud saying, to bring the cloud to the data. So, if you have the right hybrid strategy, you put the data where it makes the most sense. Where you want to maintain the security and privacy, but then have access to the APIs, and whatever else you might need to get the full advantages, of the public cloud. >> Yeah, and we here a lot of the data center providers like, Equinix and stuff, talking about features, like direct connect and, you know, to have this proximity between the public cloud, and the stuff that's in your private cloud, so that you do have, you know, low latency, and you can, when you do have to move things, or you do need to access that data, it's not so far away. I'm curious about the impact of companies like, Salesforce in the Salesforce tower, here in San Francisco, at the center offices, and office 365, and Work Day, on how can the adoption of the SaaS applications, have changed the conversation about cloud, and what's important and not important, it used to be security, I don't trust anything outside my data center, and know I might argue that public clouds are more secure, in some ways that private cloud, you don't have disgruntled employees per se, running around the data centers unplugging things. So, how it the adoption of things like Office 365, clearly Microsoft's leveraged that in a big way, to grow their own cloud presence, change the conversation about what's good about cloud, what's not good about cloud, why should we move in this direction. David, you have a thought? >> No, look, I think it's a great question, and I think if you think about the, as Melissa said, the used cases, right. And, how Microsoft has successfully pivoted, their business to it as a service model, right. And so what I think it's done, it's opened up innovation, and a lot of the Salesforces of the world, have adapted their business models. And that's truly to your point, a SaaS based offer, and so when you can do a Work Day, or Salesforce.com implementation, sure, it's been built, it's tested and everything else. I think what then becomes the bigger question, and the bigger challenge is, most companies are sitting on a thousand applications, that have been built over time. And what do you do with those, right? And so, in many cases you need to be connected, to those SaaS space providers, but you need the right hybrid strategy, again, to be able to figure out, how to connect those SaaS space services, to whatever you're going to do, with those thousand workloads. And those thousand workloads, running on different things, you need the right strategy, to figure out where to put the actual workloads. And, as people are trying to go, I know one of the questions that comes up is, do you migrate? Or do you modernize? >> David: And so, as people put that strategy together, I think how you tie to those SaaS space services, clearly ties into your hybrid strategy. >> I would agree, and so, as David mentioned, right. That's where the cloud adjacency, you're seeing a lot of blur, between public and private, I mean, Google's providing Bare-metal as a service. So it is actually dedicated, hybrid cloud capabilities, right. So you're seeing a lot of everyone, and as David talked about, all of the surrounding applications around your SAP, around your oracle. When we created our Exensor Hyper Cloud, we were going after the Enterprise workload. But there's a lot of legacy and other ones, that need that data, and or, the Salesforce data. Whatever the data is, right. And really be able to utilize it when they need to, in a real low latency. >> So, I was wondering I we could unpack, the Accenture Hybrid Cloud. >> Melissa: Sure. >> What is that? Is that your guys own cloud? Is this, you know, kind of the solution set? I've heard that mentioned a couple times. So what is the Accenture Hybrid Cloud? >> So Accenture Hybrid Cloud, was a big bet that we made, as we saw the convergence of MultiCloud. We really said, we know, everything is not going to go public. And in some cases, it's all coming back. And so, customers really needed a way, to look at all of their workloads, right. Because part of the issue with, the getting the cost and benefits out of public is, the workload goes but you really aren't able, to get out of the data center. We term it the "Wild Animal Park", because there's a lot of applications that, right, are you going to modernize, are you going to let them to end of life. So there's a lot of things you have to consider, to truly exit the data center strategy. And so, Accenture Hybrid Cloud is actually, a big bet we made, it is a highly automated, standard private cloud capability, that really augments all of the leading capability, we had in the cloud area. It is, it's differentiated, we made a big bet with HPE, it's differentiated on it's hardware. One of the reasons, when we were going after the Enterprise, was they need large compute, and large storage requirements. And what we're able to do is, when we created this, use some of our automation differentiation. We have actually a client, that we had in the existing I-O-N environment, and we were actually able to achieve, some significant benefits, just from the automation. We got 50 percent in the provisioning of applications. We got 40 percent in the provisioning of the V.M. And we were able to take a lot of what I'll call, the manual tasks, and down to, it was like 62 percent reduction in the effort. As well as, 33 percent savings overall, in getting things production ready. So, this capability is highly automated. It will actually repeat the provisioning, at the application level, because we're going after the Enterprise workloads. And it will create these, it's an ASA that came from government, so it's highly secured, and it really was able to preserve, I think what our customer needed. And being able to span that public/private, capability they need out there in the hybrid world. >> Yeah, I was going to say, I don't know that there's enough talk, about the complexity of the management in these worlds. Nobody ever wants to talk about writing, the CIS Admin piece of the software, right? It's all about the core functionality. Let's shift gears a little bit and talk about HPC, a lot of conversation about high performance computing, a lot going on with A.I. and machine learning now. Which, you know, most of those benefits are going to be, realized in a specific application, right? It's machine learning or artificial intelligence, applied to a specific application. So, again, you guys make big iron, and have been making big iron for a long time, what is this kind of hybrid cloud open up, in terms of, for HPE to have the big heavy metal, and still have kind of the agility and flexibility, of a cloud type of infrastructure. >> Yeah, no, I think it's a great question. I think if you think about HPE's strategy has been, in this area of high performance compute. That we bought the company S.G.I. And as you have seen the announcements, we're hopefully going to close on the Cray acquisition as well. And so we in the world of the data continuing to expand, and at huge volumes. The need to have incredible horsepower to drive that, that's associated with it, now all of this really requires, where's your data being created, and where's it actually being consumed? And so, you need to have the right edge, to cloud strategy in everything. And so, in many cases, you need enough compute at the edge, to be able to compute and do stuff in real time. But in many cases you need to feed all that data, back into another cloud or some sort of mother. HPE, you know, type of high performance compute environment, that can actually run the more, advanced A.I. machine learning type of applications, to really get the insights and tune the algorithms. And then, push some of those APIs and applications, back to the edge. So, it's an area of huge investment, it's an area where because of the latency, you know, things like the autonomous driving, and things like that. You can't put all that stuff into the public cloud. But you need the public cloud, or you need cloud type capability, if you will, to be able to compute and make the right decisions, at the right time. So, it's about having the right compute technology, at the right place, at the right time, at the right cost, and the right perform. >> A lot of rights, good opportunity for Accenture. So, I mean it's funny as we talk about hybrid cloud, and that kind of new, verbs around cloud-like things. Is where we're going to see the same thing, kind of the edge versus the data center comparison, in terms of where the data is, where the processing is, because it's going to be this really dynamic situation, and how much can you push out of the edge, cause, you know, there's no air conditioning a lot of times, and the power might not be that great, and maybe connectivity is a little bit limited. So, you know, Edge offers a whole bunch of, different challenges that you can control for, in a data center but it is going to be this crazy, kind of hybrid world there too, in terms of where the allocation of those resources are. You guys get into the deeper end of that model, Melissa? >> Yeah, so we're definitely working with HPE, to create some of, I'll call it our edge managed services, again, going back to what we were saying about the data, right, we saw the centralization of data with the cloud, with the initial entrance into the cloud, now we're seeing the decentralization of that data, back out to the Edge. With that, right, in these hybrid cloud models, you're really going to need- They require a lot of high performance compute, especially for certain industries, right? If you take a look at gas, oil, and exploration, if you look at media processing, right, all of these need to be able to do that. One of the things, and depending on where it's located, if it's on the Edge, how you're going to feedback the data as we talked about. And so, we're looking at, how do you take this foundation, right, this, I'll call it Exensor Hybrid architecture, right. Take that, and play that intermediate role. I'm going to call it intermediary, right, because you really need a really good, you know, global data map, you need a good supply chain, right. Really to make sure that the data, no matter where it's coming from, is going to be available for that application, at the right time. With, right, the ability to do it at speed. And so, all of these things are factors, as you look at our whole Exensor Hybrid Cloud strategy, right. And being able to manage that, Edge to core and then back up to Cloud, etcetera. >> Right, now I wonder if you could share some stories, cause the value proposition around Cloud, is significantly shifted for those who are paying attention, right. But it's not about cost, it's not about cost savings, I mean there's a lot of that in there and that's good, but really the opportunity is about speed. Speed and innovation. And enabling more innovation across your Enterprise, with more people having more access to more data, to build more apps, and really, to react. Are people getting that? Or, are they still, the customer still kind of encumbered, by this kind of transition phase, they're still trying to sort it out, or do they get it? That really this opportunity is about speed, speed, speed. >> No, go ahead. I mean we use a phrase first off, it's, "fear no cloud", right. To your point, you know, how do you figure out the right strategy. But, I think within that you get, what's the right application? And how do you, you know, fit it in to the overall strategy, of what you're trying to do. >> Yeah. >> And I think the other thing that we're seeing is, you know, customers are trying to figure that out. We have a whole, right, when you start with that application map, you know, there could be 500 to 1000 workloads, right, and applications, and how are you going to, some you're going to retain, some you're going to retire, some you're going to (stutters) refactor for the cloud, or for your private cloud capability. Whatever it is, you're going to be looking at doing, I think, you know, we're seeing early adopters, like even the hyperscalers, themselves, right. They recognize the speed, so you know, we're working with Google for instance. They wanted to get into the Bare-metal, as a service capability, right. Them actually building it, getting it out to market would take so much longer. We already had this whole Exensor Hybrid Cloud architecture, that was cloud adjacent, so we had sub-millisecond latency, right. And so, they're the ones, right, everyone's figuring out that utilizing all of these, I'll call it platforms and prebook capabilities. Many of our partners have them as well, is really allowing them that innovation, get products to market sooner, be able to respond to their customers. Because it is, as we talked about in this multicloud world, lots of things that you have to manage, if you can get pieces from multiple, you know, from a partner, right, that can provide more of the services that you need, it really enables the management of those clouds sources. >> Right, so we're going to wrap it up, but I just want to give you the last word in terms of, what's the most consistent blind spot, that you see when you're first engaging with a customer, who's relatively early on this journey, that they miss, that you see over, and over, and over, and you're like, you know, these are some of the thing you really got to think about, that they haven't thought about. >> Yeah so, for me, I think it's- the cloud isn't about a destination, it's about an experience. And so, how do you get- you talked about the operations, but how do you provide that overall experience? I like to use this simple analogy, that if you and I needed a car, for five or 10, or 15 minutes, you go get an Uber. Cause it's easy, it's quick. If you need a car for a couple days, you do a rental car. You need a car for a year, you might do a lease. You need a car for three, four year, you probably by it, right? And so, if you use that analogy and think, Hmmm, I need a workload application for five/six years, putting something at a persistent workload, that you know about on a public cloud, may be the right answer, but it might be a lot more cost prohibited. But, if you need something, that you can stand up in five minutes, and shut it right back down, the public cloud is absolutely, the right way to go, as long as you can deal with the security requirements, and stuff. And so, if you think about, what are the actual requirements, is it cost, is it performance, you've talked about speed and everything else. It's really trying to figure out how you get an experience, and the only experience that can really hit you, what you need to do today, is having the right hybrid strategy. And every company, I know Accenture was out, way in front of the market on public cloud, and now they've come to the realization, so has many other places. The world is going to be hybrid, it's going to be multicloud. And as long as you can have an experience, and a partner, that can manage, you know, help you define the right path, you'll be on the right journey. >> Jeff: Melissa. >> I think blind spot we run into is, it does start off as a cost savings activity. And there really, it really is so much more about, how are you going to manage that enterprise workload? How are you going to worry about the data? Are you going to have access to it? Are you going to be able to make it fluid, right? The whole essence of cloud, right, what it disrupted was the thought, that something had to stay in one place, right. And, where the real time decisions were being made. Where things needed to happen. Now, through all the different clouds, as well as, that you had to own it yourself, right. I mean, everyone always thought, okay, I'll take all the, you know, I.T. department, and very protective of everything that it wanted to keep. Now, it's about saying, all right, how do I utilize, the best of each of these multiclouds, to stand up, what I'll call, what their core capability is as a customer, right. Are they doing the next chip design? Are they, you know, doing financial market models, right? That requires a high performance capability, right. So, when you start to think about all of this stuff, right, that's the true power, is having a strategy that looks at those outcomes. What am I trying to achieve in getting my products, and services to market, and touching the customers I need. Versus, oh, I'm going to move this out to an infrastructure, because that's what cloud, it'll save me money, right. That's typically the downfall we see, because they're not looking at it from the workload, or the application. >> Same old story, right? Focus on your core differentiator, and outsource the heavy lifting on the stuff, (laughs) that's not your core. Alright, well Melissa, David, thanks for taking a minute, and I really enjoyed the conversation. >> Thanks, Jeff. >> She's Melissa, He's David, and I'm Jeff Frick, you're watching theCUBE. We are high above the San Francisco skyline, in the Salesforce tower at the Accenture Innovation Hub. Thanks for watching, we'll see you next time. (tech music)

>> Announcer: Live from San Diego, California it's The Cube! Covering Cisco Live US 2019. Brought to you by Cisco and it's ecosystem partners. >> Welcome back to The Cube's coverage of Cisco Live Day 2 from sunny San Diego. I'm Lisa Martin joined by Dave Vallante. Dave and I have an alumni, a Cube alumni back with us, Jeff Moncrief, consulting systems engineer from Cisco. Jeff, welcome back! >> Thank you very much, it's great to be back! >> So, we're in the DevNet Zone, loads of buzz going on behind us. This community is nearly 600,000 strong. We want to talk with you about Stealthwatch. You did a very interesting talk yesterday. You said, it had a couple hundred folks in there. War stories from real networks. War stories ... strong descriptor. Talk to us about what that means, what some of those war stories are, and how Stealthwatch can help customers learn from that and eradicate those. >> So it's called Saved by Stealthwatch. It was a really good session. This is the third Cisco Live that I've presented this session at. And it's really just stories from actual customer networks where I've actually deployed Stealthwatch into. I've been selling Stealthwatch for about five years now. And I've compiled quite a list of stories, right? And it really ... if you think about advanced threats and insider threats and those kinds of exciting things, the presentation was really about getting back to fundamentals. Getting back to the fact that in all these years that I've been working with customers and using Stealthwatch, a lot of the scary things that I have found have nothing to do with that. With the advanced type threat stuff. It really has to do with the fact that they're forgetting the basics. Their firewalls are wide open, their networks are flat. Their segmentation boundaries aren't being adhered to. So it's allowed us to come in and expose a lot of scary things that were going on and they were just completely oblivious to it. >> Why are those gaps there? Is it because of a change management issue? Technology's moving so quickly? Lack of automation? >> Yeah, I think there's a couple reasons that I've seen. It's a recurring theme really. Limited resources ... number one. Number two, limited budgets, so your priorities have to shift. But I think a big one that I've seen a lot is turnover and attrition. A lot of times we'll go in with Stealthwatch and we'll kick off an evaluation or whatnot and the customer will say, I just don't know what's there. I don't know if I have 100 machines that need visibility or for a thousand. And I'm a Stealthwatch cloud consulting systems engineer so the cloud world is where I spend a lot of my time now and what I'm seeing as it relates to the cloud realm is that's exponentially worse now. Because now you've got things like devops and shadow IT that are all playing in the customer's public cloud environment deploying workloads, deploying instances and building things that the security team has no awareness of. So there's a lot of things that are living and breathing on the network that they just don't know about. >> And so the tribal knowledge leaves the building, how do you guys help solve that problem? >> So we come in ... and you know the last time that you and I spoke, you used the term cockroaches, I think, which I loved. I actually have used that a lot since then, so thank you for that. >> Dave: Yeah, you're welcome. >> No, but, you know ... we come in and we actually, we turn the customer's network infrastructure ... Whether it's on-prem or in the public cloud into a giant security sensor grid. And we leverage something called NetFlow, which you've probably heard of. And it's essentially allowing us to account for every conversation throughout the entire infrastructure, whether or not it's on-prem or in the public cloud or maybe even in a private cloud. We've got you covered in that area. And it allows us to expose every one of those living, breathing things. And then we can just query the system. So think of us like a giant network DVR on steroids. We see everything, you can't hide from us, because we're using the network to look at everything. And then we can just set little trip wires up. And that's kind of what I go into in my presentation also is how you can set these trip wires ahead of time to find things that are going on that you just didn't know about and frankly, they're probably going to scare ya. >> One of the stories that you shared in your talk yesterday. You talk about people really forgetting the basics. A university that had a vending machine breach. You just think, a vending machine in a cafeteria? >> Jeff: That's right. >> Really? Tell us about that. What kind of data was exposed from a vending machine? >> So that's one of my favorite stories to tell. We had gone in and we'd installed Stealthwatch at a small university in the US. And they had a very small team. Okay, you're going to see that recurring theme. Limited staff. And they really just had a firewall. Okay, that was what they were doing for security. So we came in, we enabled NetFlow, we kind of let Stealthwatch do it's thing for a couple of days, and I just queried the system. Okay, it's not rocket science, it's not AI a lot of times, it's really the fundamentals. And I just said, tell me anything talking on remote desktop protocols inside the network out to the internet. And lo and behold, there was one IP address that had communication from it to every bad country you can imagine ... actively. And I said to them ... I said, what is this IP address? What's it doing? And that was in the conference room in the university with their staff and the guy looked it up in the asset inventory system, and he looked at me and he goes, that's a vending machine. And I said, a vending machine? And he said, yeah. And then I was like, okay, well that's a first, I've never heard of that before. And he goes wait a minute, it's a dirty tray return machine. You ever heard of one of those? >> Lisa: No. >> I hadn't either. >> Lisa: Explain. >> So for loss prevention, I guess universities and other public institutions, they will buy these unique vending machines that are designed for loss prevention. So that the college students don't go around and you know, steal or throw away the trays from the cafeteria. You have to return the tray to get a coin. There's a common supermarket chain that does the same thing with their shopping carts. And it's for loss prevention. So I said, okay, that's pretty strange. Even stranger than just vending machine. And I said, well did you realize that it was talking to a remote desktop all over the world? And he said no. And I said so, can you tell me what it has access to? So he looked it up in the firewall manager right there and he said, it has access to the entire network. Flat network, no segmentation. No telling how long this had been going on, and we exposed it. >> And Stealthwatch exposes those gaps with just kind of old school knock on the door. >> Yeah, it really is. We're talking about fundamental network telemetry that we're gathering off the route switch infrastructure itself. You know, obviously, we're at Cisco Live, we work really well with Cisco gear. Cisco actually invented NetFlow about 20 years ago. And we leveraged that to give visibility footprint that allow us to expose things like the vending machine. I've found hospital x-ray machines that were scanning all the US military, for instance. I find things in the cloud that are just completely wide open from a security ACL standpoint. So we've got that fundamental level of visibility with Stealthwatch, and then we kick in some really cool machine learning and statistical analytics and machine running analytics and that allows us to look for anomalies that would be indicators of compromise. So we're taking that visibility footprint and we're taking it to that next level looking for threats that might be in the customer's environment. >> So before we get to the machine intelligence, I presume that cloud and containers only makes this problem worse. What are you seeing in the field? How are you dealing with that? >> So we're in a landscape today where we've got a lot of customers that might be cloud averse. But we've also got a lot of customers that are on the wide other side of that spectrum and they're very cloud progressive. And a lot of them are doing things like server-less micro services, containers and, when you think of containers you think of container orchestration ... kubernetes. So Stealthwatch Cloud is actually in that realm right now today, able to protect and illuminate those environments. That's really the Wild West right now, is trying to protect those very abstract server-less and containerized environments but yeah, we come in, we are able to deploy inside kubernetes clusters or AWS or azure or GCP, and tell the Stealthwatch story in those environments, find segmentation violations, find firewall holes just like we would on premise, and then look for anomalies that would be interesting. >> So the security paradigm for those three you mentioned, those three cloud vendors, and you're on-prem, and maybe even some of your partners, is a lot of variability there. How should customers deal with maintaining the edicts of the organization and sort of busting down those silos? >> Yeah, so you think about like Stealthwatch Cloud which is the product that I'm a CSE for, we're really focusing on automation, high efficacy and accuracy. All right, we're not going to be triggering hundreds or thousands of alerts whenever you plug us in. It's going to further bog down a limited team. They've got limited time and they have to change their priorities constantly. This solution is designed to work immediately out of the box quickly deploy within a matter of hours. It's all SAAS based so actually it lives in the cloud. And it really takes that burden off of the organization of having to go and set a bunch of policies and trip wires and alerts. It does it automatically. It's going to let you know when you need to take a look at it so that you can focus on your other priorities. >> So curious where your conversations are within an organization - whether it's a hospital, or a university when what you're finding is in this multi-cloud world that we live in where there's attrition and all of these other factors contributing to organizations that don't know what they have with multi-cloud edge comes this very amorphous perimeter, right? Where are those conversations because if data is the lifeblood of an organization, if it's not secure and protected, if it's exposed there's a waterfall of problems that could come with that. So is this being elevated into the C-Suite of an organization? How do you start those conversations? >> So it's not just the C-Suite and the executive type structure that we're having to talk to now, traditionally we would go in with the Stealthwatch opportunity and talk to the teams in the organization it's going to be the InfoSec team, right? As we move to the cloud though, we're talking about a whole bunch of different teams. You've got the InfoSec team, you've got the network operations team now, they're deploying those workloads. The big one though that we've really got to think about and what we've really got to educate our customers on is the Dev Ops teams. Because the Dev Ops teams, they're really the ones that are deploying those cloud workloads now. You've got to think about ... they've got API access, they've got direct console login access. So you've got multiple different entry points now into all these different heterogeneous environments. And a lot of times, we'll go in and we'll turn on Stealthwatch and we show the organization, yeah, you knew that Dev Ops was in the VPC's deploying things, but you didn't know the extent that they were deploying them. >> Lights up like a Christmas tree? >> Yeah, lights up like a Christmas tree and like a conversation I had last week with a customer. I asked them, I said, all right so you're in AWS, are we talking do you have 50 instances or do you have 500? He said, I have no idea. Because I'm not the one deploying these instances. I'm just lucky enough to get permission to have access to them to let you plug your stuff in to show me what's going on in that environment. But yet they're in charge of securing that data. So it's quite frightening. >> So you've got discovery, you've got ways to expose the gaps, and then you're obviously advising on remediation activity. And you're also bringing in machine intelligence. So what's the endgame there? Is it automation? Is it systems of agency where the machine is actually taking action? Can you explain that? So when the statistical analysis comes in and the anomaly detection comes in, it's really that network DVR, so we've got the data, now let's do some really cool things with it. And that's where we're in actually, for every single one of these entities, and I do stress entities because the days of operating systems and IP addresses are going away. Face it, it's happening. Things are becoming more and more abstract. You know, API keys, user accounts, lambda's and runtime compute, we have to think about those. So what we do for all these different entities is we build a model for each one of these, and that model, that's where all the math and the AI comes in. We're going to learn Known Good for it. Who do they talk to? How much data's sent or received? And then we start looking for activity in that infrastructure as it relates to that entity that's outside of that Known Good model. So that would be the anomaly detection and you know, our anomaly detection, it really can be attributed to two different major categories. Number one is going to be, we're looking for things that cross the cyber kill chain. So those different IOC's as a threat actually manifests. That's what the anomaly detection's doing. And then we're also looking for just straight compliance and configuration violations in the customer's cloud infrastructure, for instance, that would just be a flat out security risk today, day one, forget base lining anomaly detection, it should just not be configured that way. >> Let's see, roughly 25% of Cisco's revenue is in services, what role does the customer service team play in all this? How do you interact ... how do the product guys and the service guys work together? >> So we've got a great customer experience team, customer services team for Stealthwatch and it doesn't matter if we're talking Stealthwatch on-premise or the Stealthwatch cloud, they cover both. And what will happen is we'll come in from a pre-sales standpoint, we do the evaluation, show good value, and then we've got a good relationship with the CX team where we'll hand that off to them, and then we'll work with the CX team to make sure that customer is good to go, they're taken care of, and it's not we've sold this and we're just going to forget you type scenario. They do a good job of coming in, they make sure that the customer's needs are met, any feature requests that they like taken care of. You know, they have routine touchpoints with the customers and they make sure that the product, for all intents and purposes, doesn't lose interest or visibility in the customer's environment. That they're using it, they're getting good value out of it, and we're going to build a relationship. I call it cradle to grave. We're going to be with that customer cradle to grave. >> Now Jeff, one of the things I didn't talk to you about at Google Next was ... first I got to ask you, you're a security guy, right? Have you always been a security guy? >> Yeah, security for about 20 years now, dating back to internet security systems. >> The question I often ask security guys is who's your favorite superhero? >> My favorite superhero ... I'd say Batman. >> Dave: Batman? >> Yeah. >> I like Batman. (chuckles) The reason I ask is that somebody told me one time that true security guys, they love superheroes because they grew up kind of wanting to save the world and protect the innocent. So ... just had to ask. >> Yeah there you go .. Batman. >> I'm sensing a tattoo coming. Last question for you Jeff is in terms of time to business impact, the vending machine story is just so polarizing because it's such a shocking massive exposure point, did they ever discover how long it had been open and in terms of being able to remedy that, how quickly can Stealthwatch come in, identify these- >> So very quick operation wise. So like the vending machine story, that's something that if you turn on Flow, and you send it to Stealthwatch right now, we can pick that up in 10 minutes. That quick to visibility and value. Now how long has it been going on? A lot of times they can't answer that question because they've never had anything to illuminate that to begin with. But moving forward, now they've got a forensic incident response audit trail capability with Stealthwatch which is actually a pretty common use case. Especially if you think about things like PCI that have got auto requirements and whatnot. A lot of organizations if they're not using a Flow based security analytics tool, they can't always meet those audit and forensic requirements. So at least from the point of installing Stealthwatch they'll be good to go from that point forward. >> So if they can find an anomaly that needs to be rectified in 10 minutes, what's the next step for them to actually completely close that gap? >> So like with Cisco Identity Services engine, we've got a great integration there where we can actually take action, shut off that machine instantly. We can shut off a switch port. We can isolate that machine to an isolated sandboxed VLAN, get it off the network, and then in the cloud, we can do things like automated remediation. We can use things like Amazon and Lambda to actually shut off an instance that might be compromised. We can actually use Lambda's to insert firewall rules. So if we find a hole, we can plug it. Very easily, automated- >> Ship a function to it and plug a hole. >> Batman slash detective. I think you need a tattoo and a badge. >> I can work on that, I like it. >> Jeff thank you so much for joining Dave and me on The Cube this afternoon. >> My pleasure. >> Really interesting stuff, we appreciate your time. >> Absolutely. >> For Dave Vallante, I'm Lisa Martin. You're watching The Cube's second day of coverage of Cisco Live from San Diego. Thanks for watching. (upbeat music)

>> Live from San Diego, California, it's the Cube. Covering Cisco Live US 2019. Brought to you by Cisco and its ecosystem partners. >> Hi, welcome back to Cisco Live from sunny San Diego. I'm Lisa Martin with Dave Vellante and David are joined by a couple of guests from DXC. To my right we've got Jake Sager, principal client executive TMT, Tech Media Telecom. Jake, great to have you on the program. >> Thank you. >> Now we're broadcasting from the sun. And Randy Redman, the director of security services Product Management. Randy welcome. >> Thank you very much. Glad to be here. >> So we're in the definite zone. You can imagine all of the exciting conversations going on behind us here. Guys, I just noticed that DXC, guys have been around for a couple of years IT services company with 25 billion in annual revenue, but you guys were just named, I think it's this morning, number three on CLUS 2019 solution provider list up from number 10 last year. Pretty good momentum. Jake, we'll start with you. What do you see in feed on the street, in the market with respect to digital transformation, what are customers pains and how is the DXC helping knock him out of the park? >> Well, I think you know, DXC has a long legacy history over 60 years of business together from CSC, EDS, and obviously HP heritage. So we've kind of seen it all and seen the business transform from a highly on the ground business to now a lot of things in the cloud. With that obviously customers are looking to do business in different ways. There's a lot of digital disruptors out there. So they're looking to find the new solution that's going to shade off the competition, kind of skirt it, find the newest best thing before they can and find customer driven solutions rather than just cost driven solutions and other things like that. >> So when you say customer driven solution, let's dig into that a little bit more. What does that mean? And how is it actually, how does it manifest? >> Well, I think the customer can be a lot of different things to a lot of different people. In retail, it can be somebody walking into your store and banking, it can be somebody using an app. But what does that end consumer want? What's going to make their life easier and make them go to you versus another company? And that's really what companies need to be looking at. There's no one answer to anything. But it's a lot of thought-lead leadership to try to come up with something brand new, that is not going to be disrupted by the next Airbnb or Uber. >> So you are a CEO, Michael, talks a lot about digital transformation. >> Right. >> Right here in the security side of things. So we going to dig into that a little bit. But in terms of the evolution of digital transformation, generally and specifically, how people are rethinking security as a result, because we often say, what's the difference between a business and a digital business? Well, it's how they use data. Okay, well and that opens up a whole can of worms on security. So what are you seeing in terms of the evolution of the so called digital transformation, but specifically how it's affecting their posture towards security? >> Yeah, absolutely, because in a digital environment, customers are completely rethinking both how their infrastructure is deployed and how their applications are deployed. And so really, it's opening up whole new avenues for security threats to enter their environments. At the same time, there are so many individual security technologies and customers are really struggling with what are the right technology choices to make and then more importantly how to operate them effectively, how to implement appropriate security policies, how to actually monitor effectively for threats across the environment. So digital transformation is changing their business environment, but it's really completely opening up the sphere on the security side of the house. >> So Jake, we were talking and I had asked you what your favorite topics are, you said, smart city, IoT and connected cars. Sounds like a security nightmare. >> Yeah. >> But it's an opportunity as well for you guys. >> Absolutely. >> So you go in, what's the customer conversation like? I mean, pick one or all three, if you can generalize, in terms of I mean, these are all new things, right? It's the Wild West right now. What's customers mindset? Like you said, they don't want to get disrupted. They're looking at new opportunities. What are they looking at? How are you guys helping them? >> Well, it depends industry by industry. You know, when it comes to healthcare, we can help with remote telemedicine, operating medical equipment remotely. But again, that's going to bring in a whole bunch of new security threats, which Randy is going to be more than equipped to talk about. But I think securing that is really a big problem. When you start talking about massive IoT, you're talking about thousands and thousands of sensors out there in a smart city or oil mining gas utility, like they were talking about earlier today. You're talking about tons of different entry points, lots of different vulnerabilities. So that's definitely a huge issue for them. It's also a ton of new data that they don't know how to manage, that they don't know how to make sense out of, through artificial intelligence or other means. So for a company like us that really has strength in security, artificial intelligence, machine learning, as well as a strong background of data center, data lake management, helping them kind of figure out what data to use and how to use it most effectively. That's really where we shine. Cause we're not necessarily the company providing the hardware. We're not the company writing the software. But we're really the glue that integrates it all together, and brings all those multi solutions together. 'Cause in IoT, it's an ecosystem. It's not solution in a box. >> Let's dig into the Smart City concept. It's so fascinating. I've read up on the Las Vegas city of Las Vegas, which is been on the Cube. Done a lot to really transform that city. But to your point take about data, I think Chuck Robbins said this morning in the keynote that organizations are only really getting insight from less than 1% of their data. >> Right. >> It must be one of those where do we start? >> Right. >> So you are talking about working with municipalities on becoming smart cities and being able to apply some of your expertise and AI. Where do you start that conversation? >> Well, I mean, the terms over abused, I think data is a new oil, right? So if you don't know which data you're getting it from and you're only getting 10%, you're not doing a very good job as an oil producer, right? So our company is very good at identifying where the data is. 'Cause a lot of times, that's half the problem, is finding where that data resides, getting it into a place where you can actually ingest it, and then actually analyze it and get something useful out of it. Companies typically don't know where all their data is, they don't know how to analyze it and they definitely don't know how to turn it into something useful. So that's something DXC does across the board. >> What about the partnership with Cisco? So Cisco, obviously, it's got the networks, it's got, you know, packets flying around. It's got to secure those. What's the partnership like? Are you leveraging their products? I'm sure you are. You guys use everybody's products. >> Right. >> What's the partnership like? And what specifically are you doing in the security area Randy? >> Yeah, so in terms of the partnership with Cisco, we're certainly looking in several areas frankly, because right, we're looking with our clients at a solution letter approach, right. And that's one of the things that we like with Cisco is the broad portfolio meshes with our broad portfolio. So certainly key areas of focus for us right now are in the Unified Communication space and how we're helping with collaboration for our clients, but also in the security area, technologies, such as Cisco stealth watch, which is helping provide more visibility to what's happening in networks today. Because more and more our view is that security as we were just talking about, even in the IoT space becomes more of an analytics exercise. It's less about really being able to detect what you already know, it's really about being able to drive detection from the unknown. And so the more data that we can get, the more visibility into network environments the better. >> How do you work with Cisco? 25% of Cisco's revenue is they called services. So, where do they leave off? I mean they're a product company. You guys are a services firm, but they have services. >> Right. >> How do you interact with them? You don't compete, I presume. At least there's maybe some overlap. But, where do they leave off and you guys pick up? >> Yeah, so certainly, we're not competing with Cisco from a services perspective. We're certainly relying on Cisco services for hardware and professional support around their technology. We're really there to provide overall solution design, architecture installation and we'll leverage Cisco professional services where that's appropriate. And then we provide managed services on the back end as well. >> So you're saying their role is to make sure it's architected properly and it's working, in the way it's promised. Your role is to say it my way and you can correct me is help the customer figure out how to apply those technologies to create business value. >> Well, exactly and also typically in a client solution. Cisco maybe one of several technologies that are involved in a broader solutions-- >> you got to make it all work together tomorrow-- >> And part of our role is to act as that integrator to bring the core Cisco elements with the DXC services and-- >> So your jobs getting harder and harder and harder. >> Fully it is. It's a security perspective. >> Dave: As a consumer things are getting easier, right? Oh, yeah, Google, Facebook, Instagram is so easy. But the back end with, you know, cloud and DevOps, the pace of change. How have you seen that affect your business? How are you dealing with that rapid change? >> Yeah, so I think that from a couple of perspectives here. One is that it's changing how we go about the process in terms of developing services and capabilities for our clients. Just as Agile has taken over actually in the application space, It's really driving how we think about actually developing offerings now around getting technology out into the market more quickly, evolving and growing capability from there. And so really, it's all about how we get proof of value for our clients quickly by getting technology into their hands as quickly as possible. >> Lisa: So let's talk about some of these waves of innovation Cisco was talking about this morning. Talking about this explosion of 5G, Wi-Fi 6 being able to have this access that works really well indoors outdoors, how that's changing even Jake you know, consumer demand. What opportunities, and Jake I'll start with you, what opportunities and some of the things that Cisco was talking about with respect to connectivity, AI with GPUs being everywhere, edge mobile, architectures becoming so a Morpheus opportunity for DXC to help customers really not just integrate the technologies but to excel and accelerate themselves to define new services, new business models. What's your differentiation point there? >> I mean, our main differentiation point from DXC is agnostic to the technology. We really specialize in being vendor agnostic, finding the best of breed companies out there and integrating it into our portfolio and offering it to our clients. If our client wants Azure, we're not going to try to sell them on Google Cloud. If they want one or the other, we're going to be hand in hand with the customer either way. With these new technologies that come around, it's just going to open the doors for so many new types of business, so many more disruptive businesses. No matter what comes along our goal is to have that portfolio in hand, which Cisco rounds out to be able to offer to our over 6000 enterprise clients. So we need to be able to manage every shape, size, variety, industry, anything you can think of. >> What's the trend? Is the trend, yeah, we want as you say, okay, we'll make it make it work for you or is the trend like, you guys figure it out. We're not sure what the right fit is. How much of that is going on? >> I'd say you probably see 50 50. (Jake laughs) >> I think we're seeing a lot of that. Certainly as clients are migrating applications to the cloud. They may be starting with a particular cloud platform, but clients are really frankly fairly agnostic in terms of the cloud platform they're migrating to. They're taking advantage of more and more SAS applications. So one of the trends that we're definitely seeing is how to address client security concerns in a hybrid cloud environment because that's more and more what we expect the future to be, even if clients are focusing on a particular cloud platform as their starting point today. >> So as data is traversing the network and one of the one of the things that I heard this morning from Chuck Robbins keynote was that the common denominator as all of these changes and waves in innovation are coming is the network. Data is traversing the network. Given that is a given and there's only going to be more and more data and more connected devices, more mobile data traffic. Randy question for you. How can DXC, how can you help customers leverage your expertise and say security and AI, as you mentioned, to extract more value from their data and allow them to become far more secure as the it's no longer acceptable, you can't just simply put a firewall around a perimeter that has so many a Morpheus points? >> Yeah and absolutely. And as we mentioned, with all of the data that's available today, it really becomes more of an analytics problem. And one of the investments that the DXC is making is specifically in our security platform that allows us to ingest data from pretty much any infrastructure data source and be able to leverage capabilities to provide analytics, machine learning and automation on top of that, to help clients leverage the power of the data and specifically from a security perspective, not just drive detection, because that's interesting. The question I get from clients is well now, what do I do about it? >> Right. >> And we're leveraging investment, our platform automation is actually to begin to take automated actions on behalf of our clients in order to solve security problems. >> Excellent, guys. Well, thank you so much, Jake, and Randy for stopping by the Cube and talking with Dave and me about what you guys are doing at DXC. The next time we'll have to talk about connected cars. >> Sure. >> Thank you. >> Alright. For Dave Vellante I'm Lisa Martin, you're watching the Cube live from Cisco Live in sunny San Diego. Thanks for watching. (techy music)