>> Announcer: From around the globe, it's theCUBE with digital coverage of IBM Think 2021 brought to you by IBM. >> Hey, welcome to the Cube's coverage of IBM Think 2021. I'm Lisa Martin. Joining me next is Dave Marmer, the vice president offering management for the cognos analytics, planning analytics and regtech portfolios at IBM. Dave, welcome to the program. >> Thank you, Lisa. Thanks for having us today. >> So lots of change in the last year, that's an Epic understatement, right? But I'm curious some of the things that you've seen from a customer's perspective, how are they utilizing planning and reporting technology and analytics to adapt to such a disruptive market? >> Quick question, the pandemic was truly a test for these organizations in terms of their resiliency and agility. But fortunately our clients were able to leverage our planning and reporting technology to do several things. They were able to re-plan their financials to integrate and reset operational areas and planning. They were able to create multiple scenarios as disruptions continue to occur and they were able to maintain confidence in insights for collaborative decision-making at truly an enterprise scale. They were easily able to increase the frequency of their planning process, moving from quarterly to monthly to even daily for their operational areas such as supply and sales. And this was really far reaching for customers like ranging from people like Perona who focuses on private employment to Vasan who is one of the largest bakeries in Europe and ancestry.com, which are the world's largest online family history resource. They're all were able to successfully navigate the radical changes in demand and in workflow and in cashflow. >> That's impressive considering things were in such a mess and still are in somewhat state of flux which is obviously different globally. You talked about the collaboration. That's one of the things that we saw so much change going on in the last year, but this dependence on technology to facilitate collaboration. Talk to me a little bit about how you've helped. Maybe those same customers that you mentioned be able to collaborate collectively across the organizations. >> So the concept that we follow which is sort of this extending planning and analysis model is this concept of decisions, financial decisions, or finance decisions being moved outside of the operational areas, the office of finance, into the areas of supply chain, into sales, into workforce management. These all had to come together far more agilely and far more connected than they ever were before. Decisions that one organization was going to make was going to impact others. And they need to bring in additional exogenous data to kind of augment the decisions they were already doing. So it came very collaborative and high participation for the people closest to the decisions. >> Excellent. So when you look at some of the things that have in the last year, what are some of your observations, that kind of things that surprised you in terms of how companies have evolved their planning and forecasting strategy in such a dynamic market? >> Well, the biggest surprise, and I guess it shouldn't be a surprise, but historical trends that they had been counting on for their planning activity, taking last year's activities and actuals and using those to plan out what would happen. Those were sort of out the window and data sources and drivers, new drivers to their business had to be considered. They hadn't had to deal with this in the past. Like our clients were kind of pleasantly surprised that they're moved to extended planning and analysis. When planning is adopted outside of the office of finance stood up to the global disruption. You know, for example, ancestry had already adopted a enterprise planning platform as a reaction to phenomenal growth they experienced years back as they were first launching their DNA product. This put them in really good shape for what happened more really recently. This allowed them to run multiple scenarios to the impact of their supply chain all the way through the labs and back to the clients. And so when the pandemic hit, the facilities were impacted but they will have to make those adjustments at quarterly and keep up a high level of customer service. >> So these seems like ancestry was already in a really good position to be able to navigate some of the massive disruption that happened so quickly. How have you helped other customers that maybe weren't as far along to do that as well and to be able to forecast and plan in a dynamic time? >> So a customer like the sun, I mentioned, they were like, one of Europe's largest bakeries, right? They live in a world of just hours, right? You're creating product that has a shelf life, a realistic shelf life. And they have much demand changes for their facilities, but also to the stores and their frozen food products that they provide in addition to how they provide them the daily fresh stuff that they do. They're very known for their rye bread, their sourdough those type of things. But they had to make a lot of changes based on what they were seeing and take into consideration, even margin. So they've been evolving and taking more advantage of AI in augmenting their human intelligence in this way. They've been able to use very sophisticated algorithms with planning analytics to allow them to plan for things like energy consumption where they calculate the expected outside temperatures and the need for the facilities, because where they are based in the Nordics, they face freezing temperatures where, you know, the facility subs health have, because there's a lot of fluctuation in seasonality to that. And so they need to adjust for that. They also really use this to take a look at the product life cycles that they had been using to get a better longterm estimate of what people would be buying instead of using human intuition, because as they said, you can get sort of into this methodical radar listening model of looking at what had occurred in the past. And they were able to start to see things months earlier that they would have normally not been able to see if they'd not augmented their human intelligence with artificial intelligence. And I think the third thing they started to use was customer purchasing behavior where they actually were just starting to see actual patterns of things that were changing. And the expected propensity was changing for repeat purchases and cross sell purchases. And they're able to make adjustments on their offerings as a result. >> If we talk about AI to augment human intelligence to empower decision making, that's a great example of that that you talked about. What's the adoption been like that around different industries and different countries in the last year? >> So we see this universally happening that there's an adoption occurring. Certain industries are definitely moving faster. It's happening in the sales and operations planning area more so than the traditional places like the financial and planning and analysis areas. So once you get into the areas like supply chain and demand planning, you know, we generally see retail and distribution, you know, companies, a high adoption of this because of the sensitivity of making sure the right product is there at the right time. We see this near a customer service. And we definitely see this as I mentioned in workforce analytics. This pandemic brought large disruption to people who had to exit the normal facilities and work in different alternative locations. And then this idea of how do we bring them back in a very managed way is a universal problem that everyone is facing and they're all starting to adopt that. So we're seeing adoptions on many of these things across all the different industries, but I'd say the ones I mentioned were certainly highly sensitive to the immediate problems that we all personally experienced. >> Right. In your opinion based on just what you've observed, what do you think the true value of integrated planning field Bay by AI? What's the true business value there? >> It's a great question. I think in business terms, the predictive capabilities like the algorithmic forecasting is really helping companies more accurately forecast their demand. And while prescriptive capabilities like decision optimization, help them determine the best way to meet that demand, typically decision optimization excels at developing scenarios and considering constraints such as time prices, cost and capacities. And those are pulled in to help augment the decisions. Whereas predictive capability really helps the forecast demand as an example, you know, man changes by season by day by hour, the prescriptive capabilities, like this is an optimization, help determine the best plan for meeting the demand. But if you think about the energy example I gave before, you have to consider things like, is it hydro? Is it coal? Is it nuclear? One of those types of things that are involved because each method has a different cost and a different capacity. So they kind of work together in that way. >> When you're having customer conversations. I'm curious what the perspective is of customers understanding the obvious business value of integrating AI with integrated planning. Is that something that they get right away? What kinds of questions do they have for you? >> Again, I think they understand the concept or scenario planning and the fact of building different scenario modeling. I think what they're getting accustomed to is the superpower that we get to augment these humans with an intent to work against their intuition. We've seen this time and time again where project planning for, you know, one of our customers who manages on behalf of the government certain projects that they would look at it and say, if it wasn't for AI, we wouldn't have detected these issues and some of the project scope, because we look at managing them in a certain way based on historical patterns. So you almost have to unlearn their historical patterns that's had to accept what the data is telling you and you're really matching properlistic and deterministic information together to get a more accurate and an informed decision to help you move and progress further. >> So for businesses, I'm curious to get your advice here. For companies that are in this state of flux as we all are and varying degrees of that across the globe, what advice do you have for those companies that are looking into utilizing planning and reporting technology to really fine tune their business performance but they don't really know where to start? >> Yeah, so from a very high level, the advice I would say is first you've got to examine your current planning process and really identify what's working well and what business questions need to be answered. Then you have to understand that planning is primarily driver-based. And because it's driver-based, you really have to understand and take a look at your current financial reports to see what's really making up the bulk of your business, what's really driving revenue, what's really driving expenses and really focusing on the drivers that have material impact. Probably you've that 80, 20 rule. What is 80% of our costs and revenues coming from? And then you need to understand the level of granularity that you need in your data to really develop the appropriate values that you want to plan again and set those targets. And you should refer to the existing spreadsheets. They have lots of value just to understand the sources of data, the calculations that get used, what's effective and not effective across the different functions and how they link together. And then you really need to determine your planning horizon. You need to understand who's going to be contributing to the plan who hasn't been doing this before, because you want people closest to the processes and the decisions to do that. And what's the frequency? As I mentioned, people moved from quarterly to monthly as a matter of fact, in a rolling forecast and they started moving to daily and you got to understand when do you recommend this kind of a model for what businesses and what's that, how much attention do you want to give to those plans on a regular basis? >> One more question for you, Dave. When you're in those customer conversations, I'm curious, is this a C-level conversation now in terms of, "Hey, we need to be able to utilize AI and predictive for planning technology and reporting technology", Has that elevated in conversation within the organization? >> So yes, the pandemic has opened up, and just disruptions in general have opened up the conversation around about the importance of better planning and business continuity and building resilience into an organization. That is a boardroom conversation that's very important. So it is definitely raised up into that level. As planning starts to sprawl outside of just the office of finance into these operational areas, those line of business executives are getting very involved and saying, you know, we need to plan to perform and setting that conversation up and using these types of new technologies and capabilities that we're kind of replacing what can't be automated by human beings, right? Or just can't be done with the amount of manual work involved. And we see this today, just the amount of sheer number of data, the amount of volume and the amount of data intersections that have to occur. You need the capabilities of something like planet windows with Watson to go to deliver something like that. >> Awesome. Well, Dave, thanks so much for joining me today sharing what you've seen in the last year and how some of the customers have been very successful at adapting to a pretty dynamic time. We appreciate you coming on the show. >> Thank you very much. I appreciate this. >> Bye Dave Marmer. I'm Lisa Martin, you're watching the cubes coverage of IBM Think. (upbeat music)

>> Narrator: From Las Vegas, it's theCUBE. Covering Qualys Security Conference 2019. Brought to you by Qualys. >> Hey, welcome back, you ready with Jeff Frick here with theCUBE. We are at the Qualys Security Conference in Las Vegas. This show's been going on, I think, 19 years. This is our first time here. We're excited to be here, and we've got, there's always these people that go between the vendor and the customer and back and forth. We've had it go one way, now we've got somebody who was at Qualys and now is out implementing the technology. We're excited to welcome Grant Johnson. He is the director of Risk and Compliance for Ancestry. Grant, great to see you. >> Thank you for having me, great to be here. >> Yeah, it is always interesting to me and there's always a lot of people at these shows that go back and forth between, and their creating the technology and delivering the technology versus implementing the technology and executing at the customer side. So, you saw an opportunity at Ancestry, what opportunity did you see and why did you make that move? >> Well it's a good question, I was really happy where I was at, I worked for here at Qualys for a long time. But, I had a good colleague of mine from way back just say, hey look, he took over as the chief information security officer at Ancestry and said, "they've got an opportunity here, do you want it?" I said, "hey sure." I mean, it was really kind of a green field. It was the ability to get in on the ground floor, designing the processes, the environment, the people and everything to, what I saw is really a really cool opportunity, they were moving to the cloud. Complete cloud infrastructure which was a few years ago, you know, a little uncommon so it was just and opportunity to learn a lot of different things and kind of be thinking through some different processes and the way to fix it. >> Right, right, so you've been there for a little while now. Over three years, what was the current state and then what was the opportunity to really make some of those changes, as kind of this new initiative with this new see, so? >> No, yeah, we were traditional. You know, a server data center kind of background and everything like that. But with the way the company was starting to go as we were growing it, really just crazy, just at a crazy clip, to where we really couldn't sustain. We wanted to go global, we wanted to move Ancertry out to Europe and to other environments and just see the growth that was going to happen there, and there just wasn't a way that we could do it with the traditional data center model. We're plugging those in all over the place, so the ideas is, we're going to go to a cloud and with going to the cloud, we could really rethink the way that we do security and vulnerability management, and as we went from a more traditional bottle which is, where you scan and tell people to patch and do things like that, to where we can try to start to bake vulnerability management into the process and do a lot of different things. And you know, we've done some pretty cool things that way, I think as a company and, always evolving, always trying to be better and better every day but it was a lot of fun and it's been really kind of a neat ride. >> So, was there a lot of app redesign and a whole bunch of your core infrastructure. Not boxes, but really kind of software infrastructure that had to be redone around a cloud focus so you can scale? >> Yeah. There absolutely was. We really couldn't lift and shift. We really had to take, because we were taking advantage of the cloud environment, if we just lifted and shifted our old infrastructure in there, it wasn't going to take advantage of that cloud expansion like we needed it to. >> Right. >> We needed it to be able to handle it tide, of high tide, low tide, versus those traffic times when we're high and low. So it really took a rewrite. And it was a lot of really neat people coming together. We basically, at the onset of this right when I started in 2016, our chief technology officer got up and said, "we're going to burn the ships." We have not signed the contract for our data center to renew at 18 months. So we have to go to the cloud. And it was really neat to see hundreds of people really come together and really make that happen. I've been involved in the corporate world for a long time in IT. And a lot of those projects fail. And it was really neat to see a big project like that actually get off the ground. >> Right, right. It's funny, the burning the ship analogy is always an interesting one. (grant laughs) Which you know, Arnold Schwarzenegger never had a plan B. (grant laughs) Because if you have plan B, you're going to fall back. So just commit and go forward. >> A lot of truth to that. Right, you're flying without a net, whatever kind of metaphor you want to use on that one. Yeah, but you have to succeed and there is a lot that'll get it done I think, if you just don't have that plan B like you said. >> Right, so talk about kind of where Ancestry now is in terms of being able to roll out apps quicker, in terms of being able to scale much larger, in terms of being able to take advantages of a lot more attack surface area, which probably in the old model was probably not good. Now those are actually new touch points for customers. >> It's a brave new world on a lot of aspects. I mean, to the first part of that, we're just a few days away from cyber Monday. Which is you know, our normal rate clip of transactions is about 10 to 12 transactions a second. >> So still a bump, is cyber Monday still a bump? >> It's still huge for us. >> We have internet at home now. We don't have to go to work to get on the internet to shop. >> You know, crazy enough, it still is. You know, over the course of the week, and kind of starting on Thanksgiving, we scale to have about 250 transactions a second. So that was one of the good parts of the cloud, do you invest and the big iron and in the big piping for your peak times of the year. Or and it sits, your 7-10% utilization during the rest of the year, but you can handle those peaks well. So I mean, we're just getting into the time of year, so that's where our cloud expansion, where a lot of the value for that has come. In terms, of attack surface, yeah, absolutely. Five years ago, I didn't even know what a container was. And we're taking advantage a lot of that technology to be able to move nimbly. You can't spin up a server fast enough to meet the demands of user online clicking things. You really have to go with containers and that also increases what you really need to be able to secure with people and the process and technology and everything like that. >> Right. >> So it's been a challenge. It's been really revitalizing and really, really neat to me to get in there and learn some new things and new stuff like that. >> That's great. So I want to ask you. It may be a little sensitive, not too sensitive but kind of sensitive right. Is with 23 and Me and Ancestry, and DNA registries, et cetera, it's opened up this whole new conversation around cold case and privacy and blah blah blah. I don't want to get into that. That's a whole different conversation, but in terms of your world and in terms of risking compliance, that's a whole different type of a data set I think that probably existed in the early days of Ancestry.com >> Yeah >> Where you're just trying to put your family tree together. So, how does that increased value, increased sensitivity, increased potential opportunity for problems impact the way that you do your job and the way that you structure your compliance systems? >> Boy. Honestly, that is part of the reason why I joined the company. Is that I really kind of saw this opportunity. Kind of be a part of really a new technology that's coming online. I'd have to say. >> Or is it no different than everyone else's personal information and those types of things? Maybe it's just higher profile in the news today. >> Not it all, no. It kind of inherent within our company. We realized that our ability to grow and stay affable or just alive as a business, we pivot on security. And security for us and privacy is at the fore front. And I think one of the key changes that's done for maybe in other companies that I get is, people from our development teams, to our operations teams, to our security department, to our executives. We don't have to sell security to em. They really get it. It's our customer privacy and their data that we're asking people to share their most personal data with us. We can give you a new credit card. Or, you can get a new credit card number issued. We can't give you a new DNA sequence. >> Right. >> So once that's out there, it's out there and it is the utmost to us. And like I said, we don't have to sell security internally, and with that we've gotten a lot of support internally to be able to implement the kind of things that we needed to implement to keep that data as secure as we can. >> Right, well that's nice to hear and probably really nice for you to be able to execute your job that you don't have to sell securities. It is important, important stuff. >> Grant: Yes, that's absolutely true. >> All right, good. So we are jamming through digital transformation. If we talk a year from now, what's on your plate for the next year? >> We just continue to evolve. We're trying to still continue the build in some of those processes that make us better, stronger, faster, as we go through, to respond to threats. And just really kind of handle the global expansion that our company's undergoing right now. Just want to keep the lights on and make sure that nobody even thinks about security when they can do this. I can't speak for them, but I think we really want to lead the world in terms of privacy and customer trust and things like that. So there are a lot of things that I think we've got coming up that we really want to kind of lead the way on. >> Good, good. I think that is a great objective and I think you guys are in a good position to be the shining light to be, kind of guiding in that direction 'cause it's important stuff, really important stuff. >> Yeah, we hope so, we really do. >> Well Grant, nothing but the best to you. Good luck and keep all that stuff locked down. >> Thank you, thank you so much! Thanks for having me. >> He's Grant, I'm Jeff. You're watching theCube. We're at the Qualys Security Conference at the Bellagio in La Vegas. Thanks for watching. We'll see you next time. (upbeat music)

(upbeat music) >> Live from Toronto, Canada. It's The Cube, covering Blockchain, futurist conference 2018. Brought to you by, The Cube. >> Hello, everyone, welcome back. The Cube's live coverage here in Toronto, Ontario, for Untraceable's Blockchain futurist conference. Two days, this is day one of two days, of Cube coverage. I'm John Furrier, your host. Our next guest is, Azam Shaghaghi, who is the director of public relations, and strategy for Shivom.io. Really interesting story, raised a bunch of money in 15 seconds in an ICO. Really interesting story, welcome to The Cube. Thanks for coming in. >> Yeah, thank you so much for having me. >> So we were just talking on camera, you studied at NASA in Northern California, where I live, and you've got this really cool venture. Before we get into it, talk about what you guys did with the ICO, then talk about what the company does. >> Sure, the project Shivom is about owning your own DNA. So, we are sequencing DNA, and storing it on the patient-friendly platform on Blockchain. Which actually give the power back to the donors, and the people that have the... I mean, and the users, basically. So basically, you can monetize, manage and... >> Control your data. >> Control your own data. >> How much did you guys raise? You did 15 seconds, give us the numbers. What happened? >> So we raised the 35 million. We reached the hard-cap, our public sell was sold out under 15 seconds. >> 15 seconds? - 15 seconds. >> And what month was that? >> It was, actually, on May, the third. >> So it was post, after, I mean, a lot of these actually just went out last year. Still, that's really a good signal, given the climate at that time. >> Exactly, and I think it's about what your actually, your intention is, in order to disrupt. We're talking about genomic information. We're talking about healthcare. At a very highly regulated industry, right? A lot of things have been untapped in that sector. So, hopefully, with the help of Blockchain, A.I., and advanced technology, we can disrupt. >> Now I...Crystal Rose, who's the CEO of Sensay Token, when I interviewed her, in Puerto Rico, she had a comment, which I love, I still use to this day. She makes kind of like A.I. chat boxes, really cool things, your brain and the Blockchain. Similar concept that you're doing, you're DNA on the Blockchain, that you can own and manage, for your own personal benefit, and/or value. >> Exactly. >> That's kind of the concept, if I get that right? >> That it is. >> Okay, who does the genoming? >> Oh, you mean the sequencing? >> Yeah, the sequencing. >> So, I mean, right now, there are companies out there that they do the, I mean, the... >> So, I've got to get it done, and then I bring it to the platform? How does that work? >> So what we, actually, we do, we have created the marketplace, for the industry players, right? For the donors, for the users, for the governments, hospitals, insurance companies, and research labs. So, basically, after you sequence your DNA, we can, you can give it us and we sequence, we manage it, and secure it, store it on the Blockchain. Obviously, we are doing a lot of partnerships with different companies and different ventures. We have an alliance, with different partners out there, that we do, we're trying to promote that, in terms of also helping to develop the kits. >> So I get this right, so a variety of touchpoints, with stakeholders, service providers would do the service, >> Exactly. >> and the users themselves...so if I get my DNA sequence... >> Why? >> If I get my DNA sequence... >> Right... >> Do I direct the provider to put it on the Blockchain, or do I take it myself and put it on the Blockchain? >> So, when you sequence, well, okay, so you just sign up in our platform, >> Got it. >> and after that you sign up in order to sequence your DNA. The kit will be sent out to you. So, it's all through Spark contract. >> So I use your marketplace and you do all the work? >> We do all the work. >> Got it, and how does the tokens work? >> So, basically... >> The better the DNA, the more tokens you get? I wish. Whoops! >> I wish it was like that. I don't think that there is a discussion of a better... >> Okay, I know I'm kidding. >> like DNA. >> I'm afraid you get my DNA sequence, I've got all of these diseases, who knows what I have. Alzheimers or, you know. >> Well that's maybe why you should figure that out, right? Why don't you just sequence your DNA? But, what was the question again? I'm sorry, I forgot. >> So I use your marketplace, and I instruct the service provider to put the DNA. How does the tokens work? >> Oh yes, so the token is OMX token. So, per transaction there is kind of like the token economics that actually has, is kind of like being managed. For example, you donate your DNA to a research lab, you get a certain amount of OMX, and each OMX is going to be worth, you know, some fraction to a varium. >> So some people might know 23 and me. >> Right. >> And do the mail-order kit, same thing. I think some other folks have, I think Ancestry.com does something similar. How do you guys differ from them? Just, decentralized, or they are centralized, obviously. >> They're very centralized, and there is also, there has been research going on, and that they even don't know what is going on, after they sequence your DNA, where that information is going, how is it being stored, so it is all, kind of like, company's property after it is... then you, kind of like, basically sign an agreement that you will give out all the authority to them, and they can do whatever they want to do with it. So basically you are on chain, and we are creating this economy of precision... so, we are promoting precision medicine, we're promoting advanced healthcare, and how we can tackle rare disease, for example, like cancer. We just kicked off, two projects, one in India, and one in Africa. So, we partner with EMQT, a not-for-profit organization, in Africa, in order to sequence 100 people that has Sickle Cell Disease. >> If I want to team the company, how big are you guys, what are you going to do with the funding, where's the product? Take us to a quick update on where you guys are at. >> Sure, we just actually, we had a shuffle in our management team after the ICO, obviously. Now we are moving towards the product development. So, we are hiring a lot of developers, we are working on product development. We are on our roadmap, and are on track. Obviously, we have initiation, re-initiated some of the partnerships, and some of the projects. We are on our marketing, get innovative, kind of like PR, strategy right now, and with a new team... >> And what's the PR strategy, you're in charge of that, is there an outreach, is it promoting the service provider, does it get the marketplace out there? >> It's everything, literally. So we are at the first thing, that our first pillar is the community. So, we want to have the community, you know, engaged in everything that we do. We keep updating them, we get them involved. That's what matters, you know, with us, and we have an organic, kind of like, community. We've already great support in Asia, in India, I mean all over the world, but we are like, very kind of like, you know, some industries favorite...market's favorite. >> Community's super important, well I love your mission. I'd love to keep in touch. It's getting loud in here, but I'd love to follow up with you guys. >> Yeah, obviously, thank you so much for your time. >> People, it's a great project, I mean, it's one of those things where this is a real example of de-centralization, where you can use your own information, and broker that for value. Be part of studies, I'd imagine. >> Exactly. >> Engage with community. >> And create an impact. >> Great, so thanks so much for coming out, appreciate it. It's The Cube coverage live, here, in Toronto, Ontario, for the Blockchain Futurist conference, John Furrier, day one, coverage. Thanks for watching. (digital music)

>>Ash it's, you know, what will that mean to my investment? And the announcement fusion IO is that, you know, we're 25 times faster on read intensive HBase applications. The combination. So as organizations are deploying Hadoop, and they're looking at technology changes coming down the pike, they can rest assured that they'll be able to take advantage of those in a much more aggressive fashion with map R than, than other distribution. >>Jack, how I got to ask you, we were talking last night at the Hadoop summit, kind of the kickoff party and, you know, everyone was there. All the top execs were there and all the developers, you know, we were in the queue. I think, I think that either Dave or myself coined the term, the big three of big data, you guys ROMs cloud Cloudera map R and Hortonworks, really at the, at the beginning of the key players early on and Charles from Cloudera was just recently on. And, and he's like, oh no, this, this enterprise grade stuff has been kicked around. It's been there from the beginning. You guys have been there from the beginning and Matt BARR has never, ever waffled on your, on your messaging. You've always been very clear. Hey, we're going to take a dupe open source a dupe and turn it into an enterprise grade product. Right. So that's clear, right? That's, that's, that's a great, that's a great, so what's your take on this because now enterprise grade is kind of there, I guess, the buzz around getting the, like the folks that have crossed the chasm implemented. So what can you comment on that about one enterprise grade, the reality of it, certainly from your perspective, you haven't been any but others. And then those folks that are now rolling it out for the first time, what can you share with them around? What does it mean to be enterprise grade? >>So enterprise grade is more about the customer experience than, than a marketing claim. And, you know, by enterprise grade, what we're talking about are some of the capabilities and features that they've grown to expect in their, their other enterprise applications. So, you know, the ability to meet full S SLA is full ha recovery from multiple failures, rolling upgrades, data protection was consistent snapshots business continuity with mirroring the ability to share a cluster across multiple groups and have, you know, volumes. I mean, there's a, there's a host of features that fall under the umbrella enterprise grade. And when you move from no support for any of those features to support to a few of them, I don't think that's going to, to ha it's more like moving to low availability. And, and there's just a lot of differences in terms of when we say enterprise grade with those features mean versus w what we view as kind of an incomplete story. So >>What do you, what do you mean by low availability? Well, I mean, it's tongue in cheek. It's nice. It's a good term. It's really saying, you know, just available when you sometimes is that what you mean? Is this not true availability? I mean, availability is 99.9%. Right? >>Right. So if you've got a, an ha solution that can't recover from multiple failures, that's downtime. If you've got an HBase application that's running online and you have data that goes down and it takes 10 to 30 minutes to have the region servers recover it from another place in the distribution, that's downtime. If you have snapshots that aren't consistent across the cluster, that doesn't provide data protection, there's no point in time recovery for, for a cluster. So, you know, there's a lot of details underneath that, but what it, what it amounts to is, do you have interruptions? Do you have downtime? Do you have the potential for losing data? And our answer is you need a series of features that are hardened and proven to deliver that. >>What about recoverability? You mentioned that you guys have done a lot of work in that area with snapshotting, that's kind of being kicked around, are our folks addressing, what are the comp what's your competition doing in those areas of recoverability just mentioned availability. Okay, got that. Recoverability security, compliance, and usability. Those are the areas that seem to be the hot focus areas what's going on in the energy. How would you give them the grade, the letter grade, if you will, candidly, compared to what you guys offer? Well, the, >>The first of all, it's take recoverability. You know, one of the tenants is you have a point in time recovery, the ability to restore to a previous point that's consistent across the cluster. And right now there's, there's no point in time recovery for, for HDFS, for the files. And there's no point in time recovery for HBase tables. So there's snapshot support. It's being talked about in the open source community with respect to snapshots, but it's being referred to in the JIRAs as fuzzy snapshots and really compared to copy table. >>So, Jack, I want to turn the conversation to the, kind of the topic we've talked about before kind of the open versus a proprietary that, that whole debate we've, we've, we've heard about that. We talked about that before here on the cube. So just kind of reiterate for us your take. I mean, we, we hear perhaps because of the show we're at, there's a lot of talk about the open source nature of Hadoop and some of the purists, as you might call them are saying, it's gotta be open a hundred percent Patrick compatible, et cetera. And then there's others that are taking a different approach, explain your approach and why you think that's the key way to make, to really spur adoption of a dupe and make it >>W w we're we're a part of the community we're, we've got, you know, commitment going on. We've, you know, pioneered and pushed a patchy drill, but we have done innovations as well. And I think that those innovations are really required to support and extend the, the whole ecosystem. So canonical distributes RN, three D distribution. We've got, you know, all our, our packages are, are available on get hub and, and open source. So it's not, it's not a binary debate. And I think the, the point being that there's companies that have jumped ahead and now that Peloton is, is, you know, pedaling faster and, and we'll, we'll catch up. We'll streamline. I think the difference is we rearchitected. So we're basically in a race car and, you know, are, are racing ahead with, with enterprise grade features that are required. And there's a lot of work that still needs to be done, needs to be accomplished before that full rearchitecture is, is in place. >>Well, I mean, I think for me, the proof is really in the pudding when you, when it comes to talk about customers that are doing real things and real production, grade mission, critical applications that they're running. And to me that shows the successor or relative success of a given approach. So I know you guys are working with companies like ancestry.com, live nation and Quicken loans. Maybe you could, could you walk us through a couple of those scenarios? Let's take ancestry.com. Obviously they've got a huge amount of data based on the kind of geological information, where do you guys do >>With them? Yeah, so they've got, I mean, they've got the world's largest family genealogy services available on the web. So there's a massive amount of data that they make accessible and, and, you know, ability for, for analysis. And then they've rolled out new features and new applications. One of which is to ship a kit out, have people spit in a tube, returned back and they do DNA matching and reveal additional details. So really some really fabulous leading edge things that are being done with, with the use of, of Hadoop. >>Interesting. So talk about when you went to, to work with them, what were some of their key requirements? Was it around, it was more around the enterprise enterprise, grade security and uptime kind of equation, or was it more around some of the analytics? What, what, what's the kind of the killer use case for them? >>It's kind of, you know, it's, it's hard with a specific company or even, you know, to generalize across companies. Cause they're really three main areas in terms of ease of use and administration dependability, which includes the full ha and then, and then performance. And in some cases, it's, it's just one of those that kind of drives it. And it's used to justify, in other cases, it's kind of a collection. The ease of use is being able to use a cluster, not only as Hadoop, but to access it and treat it like enterprise storage. So it's a complete POSIX compliance file system underneath that allows the, the mounting and access and updates and using it in dynamic read-write. So what that means from an application level, it's, it's faster, it's much easier to administer and it's much easier and reliable for developers to, to utilize. >>I got to ask you about the marketing question cause I see, you know, map our, you guys have done a good job of marketing. Certainly we want to be thankful to you guys is supporting the cube in the past and you guys have been great supporters of our mission, but now the ecosystem's evolving a lot more competition. Claudia mentioned those eight companies they're tracking in quote Hadoop, and certainly Jeff and I, and, and SiliconANGLE by look at there's a lot more because Hadoop washing has been going on now for the term Hadoop watching me and jumping in and doing Hadoop, slapping that onto an existing solution. It's not been happening full, full, full bore for a year. At least what's the next for you guys to break above the noise? Obviously the communities are very active projects are coming online. You guys have your mission in the enterprise. What's the strategy for you guys going forward is more of the same and anything new even share. >>Yeah, I, I, I think as far as breaking above the noise, it will be our customers, their success and their use cases that really put the spotlight on what the differences are in terms of, of, you know, using a big data platform. And I think what, what companies will start to realize is I'd rather analogy between supply chain and the big, the big revolution in supply chain was focusing on inventory at each stage in the supply chain. And how do you reduce that inventory level and how do you speed the, the flow of goods and the agility of a company for competitive advantage. And I think we're going to view data the same way. So companies instead of raw data that they're copying and moving across different silos, if they're able to process data in place and send small results sets, they're going to be faster, more agile and more competitive. >>And that puts the spotlight on what data platform is out there that can support a broad set of applications and it can have the broadest set of functionality. So, you know, what we're delivering is a mission grade, you know, enterprise grade mission, critical support platform that supports MapReduce and does that high performance provides NFS POSIX access. So you can use it like a file system integrates, you know, enterprise grade, no SQL applications. So now you can do, you know, high-speed consistent performance, real time operations in addition to batch streaming, integrated search, et cetera. So it's, it's really exciting to provide that platform and have organizations transform what they're doing. >>How's the feedback on with Ted Dunning? I haven't seen a lot of buzz on the Twittersphere is getting positive feedback here. He's a, a tech athlete. He's a guru, he's an expert. He's got his hands in all the pies. He's a scientist type. What's he up to? What's his, what's his role within Mapa and he's obviously playing in the open-source community. What's he up to these days, >>Chief application architect, he's on the leading edge of my house. So machine learning, so, you know, sharing insights there, he was speaking at the storm meetup two nights ago and sharing how you can integrate long running batch, predictive analytics with real-time streaming and how the use of snapshots really that, that easy and possible. He travels the world and is helping organizations understand how they can take some very complex, long running processes and really simplify and shorten those >>Chance to meet him in New York city had last had duke world at a, at a, a party and great guy, fantastic geek, and certainly is doing a great work and shout out to Ted. Congratulations, continue up that support. How's everyone else doing? How's John and Treevis doing how's the team at map are we're pedaling as best as you can growing >>Really quickly. No, we're just shifting gears. Would it be on pedaling >>Engine? >>Yeah. Give us an update on the company in terms of how the growth and kind of where you guys are moving that. >>Yeah. We're, we're expanding worldwide, you know, just this, you know, last few months we've opened up offices and in London and Munich and Paris, we're expanding in Asia, Japan and Korea. So w our, our sales and services and engineering, and basically across the whole company continues to expand rapidly. Some really great, interesting partnerships and, and a lot of growth Natalie's we add customers, but it's, it's nice to see customers that continue to really grow their use of map are within their organization, both in terms of amount of data that they're analyzing and the number of applications that they're bringing to bear on the platform. >>Well, that a little bit, because I think, you know, one of the, one of the trends we do see is when a company brings in big data, big data platform, and they might start experiment experimenting with it, build an application. And then maybe in the, maybe in the marketing department, then the sales guys see it and they say, well, maybe we can do something with that. How is that typically the kind of the experience you're seeing and how do you support companies that want to start expanding beyond those initial use cases to support other departments, potentially even other physical locations around the world? How do you, how do you kind of, >>That's been the beauty of that is if you have a platform that can support those new applications. So if you know, mission critical workloads are not an issue, if you support volumes so that you can logically separate makes it much easier, which we have. So one of our customers Zions bank, they brought in Matt BARR to do fraud detection. And pretty soon the fact that they were able to collect all of that data, they had other departments coming to them and saying, Hey, we'd like to use that to do analysis on because we're not getting that data from our existing system. >>Yeah. They come in and you're sitting on a goldmine, there are use cases. And you also mentioned kind of, as you're expanding internationally, what's your take on the international market for big data to do specifically is, is the U S kind of a leaps and bounds ahead of the rest of the world in terms of adoption of the technology. What are you seeing out there in terms of where, where the rest of the, >>I wouldn't say leaps and bounds, and I think internationally, they're able to maybe skip some of the experimental steps. So we're seeing, we're seeing deployment of class financial services and telecom, and it's, it's fairly broad recruit technologies there. The largest provider of recruiting services, indeed.com is one of their subsidiaries they're doing a lot with, with Hadoop and map are specifically, so it's, it's, it's been, it's been expanding rapidly. Fantastic. >>I also, you know, when you think about Europe, what's going on with Google and some of the, the privacy concerns even here, or I should say, is there, are there different regulatory environments you've got to navigate when you're talking about data and how you use data when you're starting to expand to other, other locales? >>Yeah. There's typically by vertical, there's different, different requirements, HIPAA and healthcare, and basal to, and financial services. And so all of those, and it, it, it basically, it's the same theme of when you're bringing Hadoop into an organization and into a data center, the same sorts of concerns and requirements and privacy that you're applying in other areas will be applied on Hindu. >>I'm now kind of turning back to the technology. You mentioned Apache drill. I'd love to get an update on kind of where, where that stands. You know, it's put, then put that into context for people. We hear a lot about the SQL and Hadoop question here, where does drill fit into that, into that equation? >>Well, the, the, you know, there's a lot of different approaches to provide SQL access. A lot of that is driven by how do you, how do you leverage some of the talent and organization that, you know, speak SQL? So there's developments with respect to hive, you know, there's other projects out there. Apache drill is an open source project, getting a lot of community involvement. And the design center there is pretty interesting. It started from the beginning as an open source project. And two main differences. One was in looking at supporting SQL it's, let's do full ANSI SQL. So it's full 2003 ANSI, sequel, not a SQL like, and that'll support the greatest number of applications and, you know, avoid a lot of support and, and issues. And the second design center is let's support a broad set of data sources. So nested sources like Jason scheme on discovery, and basically fitting it into an enterprise environment, which sometimes is kinda messy and can get messy as acquisitions happen, et cetera. So it's complimentary, it's about, you know, enabling interactive, low latency queries. >>Jack, I want to give you the final word. We are out of time. Thanks for coming on the cube. Really preached. Great to see you again, keep alumni, but final word. And we'll end the segment here on the cube is your quick thoughts on what's happening here at Hadoop world. What is this show about? Share with the audience? What's the vibe, the summary quick soundbite on Hadoop. >>I think I'll go back to how we started. It's not, if you used to do putz, how you use to do and, you know, look at not only the first application, but what it's going to look like in multiple applications and pay attention to what enterprise grade means. >>Okay. They were secure. We got a more coverage coming, Jack Norris with map R I'll say one of the big three original, big three, still on the, on the list in our mind, and the market's mind with a unique approach to Hadoop and the mid-June great. This is the cube I'm Jennifer with Jeff Kelly. We'll be right back after this short break, >>Let's settle the PR program out there and fighting gap tech news right there. Plenty of the attack was that providing a new gadget. Let's talk about the latest game name, but just the.

>>Okay. We're back here, live in New York city for big data week. This is siliconangle.tvs, exclusive coverage of Hadoop world strata plus Hadoop world big event, a big data week. And we just wrote a blog post on siliconangle.com calling this the south by Southwest for data geeks and, and, um, it's my prediction that this is going to turn into a, quite the geek Fest. Uh, obviously the crowd here is enormous packed and an amazing event. And, uh, we're excited. This is siliconangle.com. I'm the founder John ferry. I'm joined by cohost update >>Volante of Wiki bond.org, where people go for free research and peers collaborate to solve problems. And we're here with Jack Norris. Who's the vice president of market marketing at map are a company that we've been tracking for quite some time. Jack, welcome back to the cube. Thank you, Dave. I'm going to hand it to you. You know, we met quite a while ago now. It was well over a year ago and we were pushing at you guys and saying, well, you know, open source and nice look, we're solving problems for customers. We got the right model. We think, you know, this is, this is our strategy. We're sticking to it. Watch what happens. And like I said, I have to hand it to you. You guys are really have some great traction in the market and you're doing what you said. And so congratulations on that. I know you've got a lot more work to do, but >>Yeah, and actually the, the topic of openness is when it's, it's pretty interesting. Um, and, uh, you know, if you look at the different options out there, all of them are combining open source with some proprietary. Uh, now in the case of some distributions, it's very small, like an ODBC driver with a proprietary, um, driver. Um, but I think it represents that that any solution combining to make it more open is, is important. So what we've done is make innovations, but what we've made those innovations we've opened up and provided API. It's like NFS for standard access, like rest, like, uh, ODBC drivers, et cetera. >>So, so it's a spectrum. I mean, actually we were at Oracle open world a few weeks ago and you listen to Larry Ellison, talk about the Oracle public cloud mix of actually a very strong case that it's open. You can move data, it's all Java. So it's all about standards. Yeah. And, uh, yeah, it from an opposite, but it was really all about the business value. That's, that's what the bottom line is. So, uh, we had your CEO, John Schroeder on yesterday. Uh, John and I both were very impressed with, um, essentially what he described as your philosophy of we, we not as a product when we have, we have customers when we announce that product and, um, you know, that's impressive, >>Is that what he was also given some good feedback that startup entrepreneurs out there who are obviously a lot of action going on with the startup community. And he's basically said the same thing, get customers. Yeah. And that's it, that's all and use your tech, but don't be so locked into the tech, get the cutters, understand the needs and then deliver that. So you guys have done great. And, uh, I want to talk about the, the show here. Okay. Because, uh, you guys are, um, have a big booth and big presence here at the show. What, what did you guys are learning? I'll say how's the positioning, how's the new news hitting. Give us a quick update. So, >>Uh, a lot of news, uh, first started, uh, on Tuesday where we announced the M seven edition. And, uh, yeah, I brought a demo here for me, uh, for you all. Uh, because the, the big thing about M seven is what we don't have. So, uh, w we're not demoing Regents servers, we're not demoing compactions, uh, we're not demoing a lot of, uh, manual administration, uh, administrative tasks. So what that really means is that we took this stack. And if you look at HBase HBase today has about half of dupe users, uh, adopting HBase. So it's a lot of momentum in the market, uh, and, you know, use for everything from real-time analytics to kind of lightweight LTP processing. But it's an infrastructure that sits on top of a JVM that stores it's data in the Hadoop distributed file system that sits on a JVM that stores its data in a Linux file system that writes to disk. >>And so a lot of the complexity is that stack. And so as an administrator, you have to worry about how data gets permit, uh, uh, you know, kind of basically written across that. And you've got region servers to keep up, uh, when you're doing kind of rights, you have things called compactions, which increased response time. So it's, uh, it's a complex environment and we've spent quite a bit of time in, in collapsing that infrastructure and with the M seven edition, you've got files and tables together in the same layer writing directly to disc. So there's no region servers, uh, there's no compactions to deal with. There's no pre splitting of tables and trying to do manual merges. It just makes it much, much simpler. >>Let's talk about some of your customers in terms of, um, the profile of these guys are, uh, I'm assuming and correct me if I'm wrong, that you're not selling to the tire kickers. You're selling to the guys who actually have some experience with, with a dupe and have run into some of the limitations and you come in and say, Hey, we can solve some of those problems. Is that, is that, is that right? Can you talk about that a little bit >>Characterization? I think part of it is when you're in the evaluation process and when you first hear about Hadoop, it's kind of like the Gartner hype curve, right. And, uh, you know, this stuff, it does everything. And of course you got data protection, cause you've got things replicated across the cluster. And, uh, of course you've got scalability because you can just add nodes and so forth. Well, once you start using it, you realize that yes, I've got data replicated across the cluster, but if I accidentally delete something or if I've got some corruption that's replicated across the cluster too. So things like snapshots are really important. So you can return to, you know, what was it, five minutes before, uh, you know, performance where you can get the most out of your hardware, um, you know, ease of administration where I can cut this up into, into logical volumes and, and have policies at that whole level instead of at an individual file. >>So there's a, there's a bunch of features that really resonate with users after they've had some experience. And those tend to be our, um, you know, our, our kind of key customers. There's a, there's another phase two, which is when you're testing Hadoop, you're looking at, what's possible with this platform. What, what type of analytics can I do when you go into production? Now, all of a sudden you're looking at how does this fit in with my SLS? How does this fit in with my data protection, uh, policies, you know, how do I integrate with my different data sources? And can I leverage existing code? You know, we had one customer, um, you know, a large kind of a systems integrator for the federal government. They have a million lines of code that they were told to rewrite, to run with other distributions that they could use just out of the box with Matt BARR. >>So, um, let's talk about some of those customers. Can you name some names and get >>Sure. So, um, actually I'll, I'll, I'll talk with, uh, we had a keynote today and, uh, we had this beautiful customer video. They've had to cut because of times it's running in our booth and it's screaming on our website. And I think we've got to, uh, actually some of the bumper here, we kind of inserted. So, um, but I want to shout out to those because they ended up in the cutting room floor running it here. Yeah. So one was Rubicon project and, um, they're, they're an interesting company. They're a real-time advertising platform at auction network. They recently passed a Google in terms of number one ad reach as mentioned by comScore, uh, and a lot of press on that. Um, I particularly liked the headline that mentioned those three companies because it was measured by comScore and comScore's customer to map our customer. And Google's a key partner. >>And, uh, yesterday we announced a world record for the Hadoop pterosaur running on, running on Google. So, um, M seven for Rubicon, it allows them to address and replace different point solutions that were running alongside of Hadoop. And, uh, you know, it simplifies their, their potentially simplifies their architecture because now they have more things done with a single platform, increases performance, simplifies administration. Um, another customer is ancestry.com who, uh, you know, maybe you've seen their ads or heard, uh, some of their radio shots. Um, they're they do a tremendous amount of, of data processing to help family services and genealogy and figure out, you know, family backgrounds. One of the things they do is, is DNA testing. Uh, so for an internet service to do that, advanced technology is pretty impressive. And, uh, you know, you send them it's $99, I believe, and they'll send you a DNA kit spit in the tube, you send it back and then they process that and match and give you insights into your family background. So for them simplifying HBase meant additional performance, so they could do matches faster and really simplified administration. Uh, so, you know, and, and Melinda Graham's words, uh, you know, it's simpler because they're just not there. Those, those components >>Jack, I want to ask you about enterprise grade had duped because, um, um, and then, uh, Ted Dunning, because he was, he was mentioned by Tim SDS on his keynote speech. So, so you have some rockstars stars in the company. I was in his management team. We had your CEO when we've interviewed MC Sri vis and Google IO, and we were on a panel together. So as to know your team solid team, uh, so let's talk about, uh, Ted in a minute, but I want to ask you about the enterprise grade Hadoop conversation. What does that mean now? I mean, obviously you guys were very successful at first. Again, we were skeptics at first, but now your traction and your performance has proven this is a market for that kind of platform. What does that mean now in this, uh, at this event today, as this is evolving as Hadoop ecosystem is not just Hadoop anymore. It's other things. Yeah, >>There's, there's, there's three dimensions to enterprise grade. Um, the first is, is ease of use and ease of use from an administrator standpoint, how easy does it integrate into an existing environment? How easy does it, does it fit into my, my it policies? You know, do you run in a lights out data center? Does the Hadoop distribution fit into that? So that's, that's one whole dimension. Um, a key to that is, is, you know, complete NFS support. So it functions like, uh, you know, like standard storage. Uh, a second dimension is undependability reliability. So it's not just, you know, do you have a checkbox ha feature it's do you have automated stateful fail over? Do you have self healing? Can you handle multiple, uh, failures and, and, you know, automated recovery. So, you know, in a lights out data center, can you actually go there once a week? Uh, and then just, you know, replace drives. And a great example of that is one of our customers had a test cluster with, with Matt BARR. It was a POC went on and did other things. They had a power field, they came back a week later and the cluster was up and running and they hadn't done any manual tasks there. And they were, they were just blown away to the recovery process for the other distributions, a long laundry list of, >>So I've got to ask you, I got to ask you this, the third >>One, what's the third one, third one is performance and performance is, is, you know, kind of Ross' speed. It's also, how do you leverage the infrastructure? Can you take advantage of, of the network infrastructure, multiple Knicks? Can you take advantage of heterogeneous hardware? Can you mix and match for different workloads? And it's really about sharing a cluster for different use cases and, and different users. And there's a lot of features there. It's not just raw >>The existing it infrastructure policies that whole, the whole, what happens when something goes wrong. Can you automate that? And then, >>And it's easy to be dependable, fast, and speed the same thing, making HBase, uh, easy, dependable, fast with themselves. >>So the talk of the show right now, he had the keynote this morning is that map. Our marketing has dropped the big data term and going with data Kozum. Is that true? Is that true? So, Joe, Hellerstein just had a tweet, Joe, um, famous, uh, Cal Berkeley professor, computer science professor now is CEO of a startup. Um, what's the industry trifecta they're doing, and he had a good couple of epic tweets this week. So shout out to Joe Hellerstein, but Joel Hellison's tweet that says map our marketing has decided to drop the term big data and go with data Kozum with a shout out to George Gilder. So I'm kind of like middle intellectual kind of humor. So w w w what's what's your response to that? Is it true? What's happening? What is your, the embargo, the VP of marketing? >>Well, if you look at the big data term, I think, you know, there's a lot of big data washing going on where, um, you know, architectures that have been out there for 30 years or, you know, all about big data. Uh, so I think there's a, uh, there's the need for a more descriptive term. Um, the, the purpose of data Kozum was not to try to coin something or try to, you know, change a big data label. It was just to get people to take a step back and think, and to realize that we are in a massive paradigm shift. And, you know, with a shout out to George Gilder, acknowledging, you know, he recognized what the impact of, of making available compute, uh, meant he recognized with Telekom what bandwidth would mean. And if you look at the combination of we've got all this, this, uh, compute efficiency and bandwidth, now data them is, is basically taking those resources and unleashing it and changing the way we do things. >>And, um, I think, I think one of the ways to look at that is the new things that will be possible. And there's been a lot of focus on, you know, SQL interfaces on top of, of Hadoop, which are important. But I think some of the more interesting use cases are taking this machine J generated data that's being produced very, very rapidly and having automated operational analytics that can respond in a very fast time to change how you do business, either, how you're communicating with customers, um, how you're responding to two different, uh, uh, risk factors in the environment for fraud, et cetera, or, uh, just increasing and improving, um, uh, your response time to kind of cost events. We met earlier called >>Actionable insight. Then he said, assigning intent, you be able to respond. It's interesting that you talk about that George Gilder, cause we like to kind of riff and get into the concept abstract concepts, but he also was very big in supply side economics. And so if you look at the business value conversation, one of things we pointed out, uh, yesterday and this morning, so opening, um, review was, you know, the, the top conversations, insight and analytics, you know, as a killer app right now, the app market has not developed. And that's why we like companies like continuity and what you guys are doing under the hood is being worked on right at many levels, performance units of those three things, but analytics is a no brainer insight, but the other one's business value. So when you look at that kind of data, Kozum, I can see where you're going with that. >>Um, and that's kind of what people want, because it's not so much like I'm Republican because he's Republican George Gilder and he bought American spectator. Everyone knows that. So, so obviously he's a Republican, but politics aside, the business side of what big data is implementing is massive. Now that I guess that's a Republican concept. Um, but not really. I mean, businesses is, is, uh, all parties. So relative to data caused them. I mean, no one talks about e-business anymore. We talking to IBM at the IBM conference and they were saying, Hey, that was a great marketing campaign, but no one says, Hey, uh, you and eat business today. So we think that big data is going to have the same effect, which is, Hey, are you, do you have big data? No, it's just assumed. Yeah. So that's what you're basically trying to establish that it's not just about big. >>Yeah. Let me give you one small example, um, from a business value standpoint and, uh, Ted Dunning, you mentioned Ted earlier, chief application architect, um, and one of the coauthors of, of, uh, the book hoot, which deals with machine learning, uh, he dealt with one of our large financial services, uh, companies, and, uh, you know, one of the techniques on Hadoop is, is clustering, uh, you know, K nearest neighbors, uh, you know, different algorithms. And they looked at a particular process and they sped up that process by 30,000 times. So there's a blog post, uh, that's on our website. You can find out additional information on that. And I, >>There's one >>Point on this one point, but I think, you know, to your point about business value and you know, what does data Kozum really mean? That's an incredible speed up, uh, in terms of, of performance and it changes how companies can react in real time. It changes how they can do pattern recognition. And Google did a really interesting paper called the unreasonable effectiveness of data. And in there they say simple algorithms on big data, on massive amounts of data, beat a complex model every time. And so I think what we'll see is a movement away from data sampling and trying to do an 80 20 to looking at all your data and identifying where are the exceptions that we want to increase because there, you know, revenue exceptions or that we want to address because it's a cost or a fraud. >>Well, that's what I, I would give a shout out to, uh, to the guys that digital reasoning Tim asked he's plugged, uh, Ted. It was idolized him in terms of his work. Obviously his work is awesome, but two, he brought up this concept of understanding gap and he showed an interesting chart in his keynote, which was the date explosion, you know, it's up and, you know, straight up, right. It's massive amount of data, 64% unstructured by his calculation. Then he showed out a flat line called attention. So as data's been exploding over time, going up attention mean user attention is flat with some uptick maybe, but so users and humans, they can't expand their mind fast enough. So machine learning technologies have to bridge that gap. That's analytics, that's insight. >>Yeah. There's a big conversation now going on about more data, better models, people trying to squint through some of the comments that Google made and say, all right, does that mean we just throw out >>The models and data trumps algorithms, data >>Trumps algorithms, but the question I have is do you think, and your customer is talking about, okay, well now they have more data. Can I actually develop better algorithms that are simpler? And is it a virtuous cycle? >>Yeah, it's I, I think, I mean, uh, there are there's, there are a lot of debate here, a lot of information, but I think one of the, one of the interesting things is given that compute cycles, given the, you know, kind of that compute efficiency that we have and given the bandwidth, you can take a model and then iterate very quickly on it and kind of arrive at, at insight. And in the past, it was just that amount of data in that amount of time to process. Okay. That could take you 40 days to get to the point where you can do now in hours. Right. >>Right. So, I mean, the great example is fraud detection, right? So we used the sample six months later, Hey, your credit card might've been hacked. And now it's, you know, you got a phone call, you know, or you can't use your credit card or whatever it is. And so, uh, but there's still a lot of use cases where, you know, whether is an example where modeling and better modeling would be very helpful. Uh, excellent. So, um, so Dana custom, are you planning other marketing initiatives around that? Or is this sort of tongue in cheek fun? Throw it out there. A little red meat into the chum in the waters is, >>You know, what really motivated us was, um, you know, the cubes here talking, you know, for the whole day, what could we possibly do to help give them a topic of conversation? >>Okay. Data cosmos. Now of course, we found that on our proprietary HBase tools, Jack Norris, thanks for coming in. We appreciate your support. You guys have been great. We've been following you and continue to follow. You've been a great support of the cube. Want to thank you personally, while we're here. Uh, Matt BARR has been generous underwriter supportive of our great independent editorial. We want to recognize you guys, thanks for your support. And we continue to look forward to watching you guys grow and kick ass. So thanks for all your support. And we'll be right back with our next guest after this short break. >>Thank you. >>10 years ago, the video news business believed the internet was a fat. The science is settled. We all know the internet is here to stay bubbles and busts come and go. But the industry deserves a news team that goes the distance coming up on social angle are some interesting new metrics for measuring the worth of a customer on the web. 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