>> From the Hard Rock Hotel in Las Vegas, it's theCUBE! Covering HoshoCon 2018! Brought to you by Hosho. >> Okay, welcome back everyone, we're here live in Las Vegas for the first security blockchain conference's inaugural event, HoshoCon, and it's all about the top brains in the industry coming together, with experience and tech chops to figure out the future in security. I'm John Furrier, the host of theCUBE. Our next guest, Andre McGregor, who's the partner and head of global security for TLDR. Welcome to theCUBE, thanks for joining me. >> Thank you for having me. >> So you have a background, we were just talking off-camera, FBI, you've been doing the cyber for a long time, cyber-security, mostly enterprise-grade, large-scale. Now we're in crypto, where you have small set of teams, running massive scale, with money involved. >> Correct. So guess what, money attracts. >> Right. People who want it, want that money. Lot of hacks, $400 million in Japan, plus 60 million over here, you add it all up, there's a billion so far this year, who knows what really the number is, it's pretty big. >> It is, and what's concerning and the reason why I came over in this space was the number of hacks that were happening. My company, we get probably a call a week, whether it's high net worth individuals, CEO, exchanges, we've helped a couple, some that you'd know of if I told you who they were, trying to get out of a very bad situation. And interim response has been big, but what we've learned is that it's the same old fraud, the same old security tactics that are being used against some of these crypto-companies. >> And we've seen it all the time, everyone's had fraud alerts on their credit card, this is like classic blocking and tackling, at a whole 'nother level. >> It is, because if you think about it from, like a traditional start-up, you have a company that's small, they have time to develop their MVP, they go out and do maybe a seed round, friends and family, they're sort of ramping up over time, whereas we basically flipped the model upside-down, the same six founders now have $10 million worth of crypto, and they're not protecting it in the ways they think they should, because they're in hyper-growth mode. So the bad guys have determined that as a great place to target, and now as we see in the news, it's actually happening. >> Yeah, and Hartej, the co-founder of Hosho, was just one talking about physical security, in the sense of you got to watch out where you go too now, it's not just online security, it's physical security. So start-ups have that kind of fast and loose kind of culture. >> Well, if you think about it, traditional security in corporations, I can put everyone in a building, I have this similar or same network egress points, I can protect those, I can do the gates, guards, guns, perimeters around, but I got people working from home now in the crypto space, everyone's got their own setup. If someone's in an audience, they say oh, I've been in the blockchain space since 2010 or 11, I can make assumptions about them, about their financial worth, and other people are doing the same, but having nefarious reasons. >> Yeah, you connected the dots okay, it was $0.22 in 2011, so therefore, if they had kept a little bit of Bitcoin-- >> They would be doing very well. >> They're a target. >> Therefore, they're a target now. So when you think about it, you put all those scams together, it becomes sort of a hot topic for-- >> I just got into crypto. (laughs) >> Good answer, good answer. >> Alright, so let's talk about this security hack. Because obviously, in the enterprise tech, we cover a lot of those events across the year. IoT Edge is a huge topic, cloud computing booming, so now you have a lot of compute, which is good, and for bad actors too. So you have now a service area that's now, no perimeter, there's no egress points to manage. Is there a digital way to kind of map this out, and does blockchain give us any advantages or is there anything on the horizon that you see, where we can, in digital form? >> Well, I mean the true reason I came to the blockchain space, having worked hundreds of victim notifications and several dozen actual intrusions, from large intrusions at banks that are top five in the world, all the way down to small core defense contractors, you realize it's always a server you didn't know about, credentials that had more access than they should, obviously gaining access to a centralized server, that then gets exposed and allows that data to be leaked out. So the idea of blockchain and being able to decentralize, distribute that data, own it, and keep it cryptographically pure, and also being able to essentially remove the single source of failure that we saw in a lot of these hacks is exciting. Obviously, blockchain is also not the answer to everything. So in some ways, the spread sheet is still a spread sheet, and the MongoDB will still be the MongoDB, but-- >> The post-it next to your computer, your private key on it. >> But at the same point in time, it all comes down to cyber-hygiene, right? I mean, the stuff that we're looking at, the hacks that we're seeing, the hacks that I'm dealing with and my company dealing with, day in and day out, are not sophisticated. They may be sophisticated actors, but they're using insophisticated means, and of course, I hate to harp on it, but e-mail is still the number one intrusion vector, we all have it, we all use it. You could take stats from the FBI that says 92%, you could take stats from Verizon that says 93%, but that will be the number one way in. >> And phishing is the classic attack point. >> It will always be, because-- >> It's easy. >> I can manipulate people, I find the right opportunity, I always say even I've been phished. It happens, the way your mind is, it's just how you react, is what we need to teach people. >> It's really clicking on that one thing, that just takes one time. >> Yep. >> A PDF that you think is a document from work, or potentially a job opportunity, a new thing, sports scores, your favorite team, girlfriend, boyfriend, whatever, I mean, you don't know! >> But, I'm going to challenge you on this, you get, you click on that bad link, or you feel like your computer has been hacked, who do you call? Do you actually have someone that you can call? There's no cyber 911. Unless you are a high net worth individual, or being targeted by a nation-state, you're not calling the FBI. So who do you call? And that's a problem that we have in our industry right now. I mean, I guess I've been the person that people have been calling, which is fine, I want to help them. 12 years as a firefighter on top of my FBI career, I'm used to helping people in time of need. But really, in the grand scheme of things, there's not enough Mandiants or Verizons are too big. So for these smaller, six-person companies, that don't have $500,000 to spend on instant response, they actually have no one to call when they actually do click something bad. >> And the people they punch in a call, the ones that aren't actually there to help them. Sometimes they get honey-potted into another vector. >> Sure. >> Which is hey, how can I help you? >> Or I even challenge it a bit further. You call any of these companies when your phone has been hacked, you SIM-swap, whatever it is, and you need to sign a master services agreement, you need to go through all the legalese, while you're actively being hacked. Like, it's happening hour after hour, and you're seeing it, your accounts are being compromised and being taken over, and you're trying to find outside counsel to do redline. So in emergency services, we say, don't exchange business cards at the disaster site. It's not the time that you should be saying hi, I'm introducing myself, we should figure out all the retainers, inter-response, legal questions beforehand, so that at 2:00 in the morning, someone calls, and you have someone pick up the phone. >> Yeah, and you know what the costs are going to be, 'cause it's solve the problem at hand, put out that fire, if you will. Okay, so I got to ask you a question on how do people protect themselves? 'Cause we know Michael Terpin's doing a fireside chat, it's well known that he sued AT&T, he had his phone SIM swapped out, this is a known vector in the crypto community. Most people maybe in the mainstream might not know it. But you know, your phone can be hacked. >> Yes. >> Simple two-factor authentication's not enough. >> Correct. >> What is the state-of-the-art solution for people who want to hold crypto, any meaningful amount, could be casual money, to high net worth individual wants to have a lot of crypto. >> I mean, I spent a good amount of my time talking about custody. We've sort of pivoted off to a new part of our business line, that deals specifically around institutional custody solutions, and helping people get through this particular process. But we all know, especially from that particular case, that SMS compromises, after account takeover of a phone, is high. Hardware tokens are always going to be something that I'm going to, Harp or YubiKey, or something like that, where I'm still having the ability to keep a remote adversary away from being able to attack my system that has my private keys, or whatever high-value data I have on it. But if I think about it at the end of the day, I'm going to need to transfer that risk. I would like to say that we can transfer all risk, but instead for the people that have a lot of crypto, you're going to need to look for a good custody solution, you're going to need to look and trust the team, you're going to need to look and trust the technology they have, and you're going to have to get insurance. Because there are so many vectors, in a certain point in time, we can't go back to the wild west, where we're actually >> The insider job is, is really popular now too. >> It is, but there are ways around the collusion, counterparty, third party risk of ensuring that not one person can take the billion dollars worth of crypto and run away off to Venezuela and never appear again. But again, it comes down to basic hygiene. I ask people, I've surveyed hundreds of people in the crypto space, and I ask simple questions like VPNs, and I'm still getting a third to a half of people are using VPNS. Very simple things that people are not doing. When you looks at password for example, if anyone still has a password under 12 characters, then game over. I mean, there are a variety of ways of hacking them. I can use GPU servers to do them very quickly. I won't go into all the different options that are there. People still-- >> So 12 characters, alphanumeric obviously, with-- >> With special characters as well. >> Special characters. >> But the assumption, let's just make the assumption, that either those passwords have been cracked already, because they've already been dumped, people share passwords, they get used again, and then the entropy is exponentially higher with every single character after 12. So my password's 22 characters, sure it's a pain to type it in, but when you think about it, at the end of the day, when I combine that with a password manager that also has a YubiKey that's a hardware token, and I require that access all the time, then I don't run into the problem that someone's going to compromise a single system to get into multiple systems. >> And then also, I know there's a lot of Google people as well, they're looking at security at the hardware level, down to the firmware. >> Sure, sure. >> There's all kinds of-- >> I mean, obviously, you could use the TPM chip as well, and that's something that we should be better at, as a society. >> So while I got you here, I might as well ask you about the China super micro modchip baseboard management controller, BMC, that was reported in Bloomberg, debunked, Apple and Amazon both came out and said no, that's been confirmed. They shift their story a little bit too, the reality probably there is some mods going on, it's manufactured in China. I mean, it's a zero-margin business going to zero, why not just let the Chinese continue to develop, and have a higher-value security solution somewhere else, that's what some people are discussing, like okay, like the DRAM market was. >> Yep. >> Let the Japanese own that, they did, and then Intel makes the Pentium. Wall Street Journal reported that, Andy Kessler. So the shifts in the industry, certainly China's manufacturing the devices. There's no surprise when you go to China, and if you turn on your iPhone, it says Apple would like to push an update, but that's not Apple, it's a forged certificate, pretty much public knowledge. The DNS is controlled by China, and a certificate, these are things that they can control, that's, this is the new normal. >> It, it-- >> If you know the hardware, you can exploit it. >> We've been dealing with supply-chain issues since Maxtor hard drives in Indonesia. So was I shocked when I hear stories about that? No, I'm sort of scared myself into a corner, working in skiffs over the years and reading the various reports that come out about supply chain poisoning. >> Certainly possible. >> It's happening. I mean, it's just to what extent is still something that may or may not be known to its full extent, but it's something that will happen, always happens, and will continue to happen. And so at a certain point in time, capitalism does step in and says alright, well, guess what, China, the way I see it is, China wants to be a super-power. At a certain point, they know that people are looking at them, and saying we can't trust you. So they're going to clean up their house, just like anyone else. >> It's inevitable for them. >> It is inevitable. Because they need to show that they can be a trusting force, in the world economy. And at the same time, we're going to have competition out there that's essentially going to say, alright, we can actually prove to have a much better, stronger, validated supply chain that you'll use. >> I mean, IoT and blockchain, great solutions for supply chain. >> 100%. >> I mean, so this is where-- >> I mean, we're talking, I mean, I was actually on a plane flying from Phoenix, to Santa Fe, New Mexico, and I was sitting next to a guy, who was just like, I just want to use a blockchain to be able to deal with a supply chain around compromised food. So in the sense that if you think about it, fish for example, there's a lot of fake fish, fake type of tuna and other stuff that's out there, that people don't know the difference. But the restaurants are paying double, triple the amount of money for it. You start taking things like elephant tusks, you take things like just being able to track things that no one's really thinking about, and you're just like huh, I never thought of it that way. So at the end of the day, I still get surprised with what people are thinking about, that they can do with the blockchain. >> So Andre, question for you here, this event, what's the impact of this event and for the industry, in your opinion? Obviously, a lot of smart people here talking, candidly, sometimes maybe a little bit contentious about philosophies, regulation, no regulation, self-governance, lot of different things being discussed as exploration, to a new proficiency level that we need to get to. What are some of the hallway conversations you're hearing, and involved in? >> A lot of mine are obviously around custody. That is the topic of the moment. And for me, I'm in learning mode. I recognize that I've spent a lot of time in cyber-security. However, whereas it relates to blockchain and digital asset custody, whether it's utility tokens or security tokens, I'm on the CFTC Technology Advisory Committee, specifically, with cyber-security and custody, and so I want to take in as much information as I can, bring it back to the committee, bring it back to the commissioners, and help them create the proper regulations and standards, whether it's through an SRO, or it's through the government itself. >> For the folks that may watch this video later, that are new to the area, what does custody actually mean? Obviously, holding crypto, but define custody in context of these conversations, what is it, what's the threshold issues that are being discussed? >> Sure. I mean, to break it down, custody is very similar to a bank. So you are, you're saying I have a lot of X. It could be baseball cards, it could be gold bars, it could be fiat cash. And I want to have someone hold it, and I'm going to trust them with that. Of course, I'm transferring that risk, and with that, I have an expectation to have a qualified custodian, that has rules and regulations of how they're going to actually manage it, how they're going to control it, ensure that the risk, that people aren't going to take it. It could be, again, the Monet, it could be the Johnny Bench Ricky card, it could be 100 million blocks of gold. But I also want to have a level of insurance. That insurance could come from the insurance industry themselves, and allowing me to protect it in case something does happen to that, or the government. The FDIC, $250,000 for your bank account is a type of insurance that people are using. By the end of the day, from an institutional perspective, you want a pure custodian that takes all the risk. The government wants to say a certain point, that that custodian can allow for margin call, so that the client can't come in and say, well I'm not going to pay out $100 million worth of crypto, and I'm going to seize, or seizure of funds as well. And that's what's being set up right now. Traditional banks are not ready to handle that. Traditional auditing firms, like PWC or Ernst & Young, are still trying to figure out how they'd even be given a qualified opinion, as it relates to how-- >> So it's not so much that they are not have the appetite to do it, they don't have systems, they don't have expertise, >> They don't have systems, they don't have expertise, >> They don't have workflows. >> And right now, things are so new and so volatile, that they're sort of almost putting their toe in the water, but really not sure what the temperature is yet of the water to hop in. >> If someone wants to go to court, you say hey, prove it. Well, it's encrypted, I don't know who did it. >> Well, and the thing is is that when you have 53 states and territories with different money-transmitting laws, on top of the countless federal agencies and departments that are managing that, it is hard to come to consensus. It is much easier in a place like Bermuda, where the government is small enough where everyone can get together pretty quickly, have consensus on an opinion of how they want to deal with the crypto market, deal with custody, pass a regulation, and what's nice about Bermuda is it has crown ascendancy, so the UK government still approves it. >> And they move fast on the regulation side. They literally just passed-- >> They are the only jurisdiction that has a fully complete law surrounding cryptocurrency. >> You're bullish on Bermuda. >> I am, because I saw the efficiency there. And I expressed my same opinion with the CFTC, when I was doing my hearing last week, that it's nice to see the speed, but it's also a small island that allows for that speed. >> And they have legitimate practices that have been going on for years in other industries. >> Right, so there's no dirty money, there's no anything that people are sort of concerned with, they have the same AML, KYC, anti-money laundering and know your customer regulations that you would expect if you had your money in the United States. >> Yeah, we had a chance to interview the honorable charge there. >> Premier Burt, oh very nice. >> Yeah, he's great, and Toronto, so it's awesome. >> Nice. >> Alright, so final takeaway, for this show here, what's your takeaway about this event, the impact to the industry? >> This is a very important event, because I think people are still trying to get their footing around blockchain, they're still trying to get their footing around digital asset protections. And if we can get the smart people in one room, and they can share knowledge, and then we can come together as a community, and create some standards that make sense, then we're protecting the world. >> Well Andre, I'm glad you're in the industry, 'cause your expertise and background on the commercial side and government side certainly lend well to the needs. (laughs) So to speak. We need you, we need more of you. Thanks for coming on theCUBE, really appreciate your commentary and your insight. It's theCUBE, bringing the insights here, we are live in Las Vegas for HoshoCon, I'm John Furrier with theCUBE, we'll be back with more coverage after this short break. (upbeat music)

>> Announcer: Live from Santa Fe, California, it's the Cube covering Big Data Silicon Valley 2017. >> Welcome, back, everyone. We're here live in Silicon Valley in San Jose for Big Data SV in conjunct with stratAHEAD Hadoop part two. Three days of coverage here in Silicon Valley and Big Data. It's our eighth year covering Hadoop and the Hadoop ecosystem. Now expanding beyond just Hadoop into AI, machine learning, IoT, cloud computing with all this compute is really making it happen. I'm John Furrier with my co-host George Gilbert. Our next guest is Arik Pelkey who is the senior director of product marketing at Pentaho that we've covered many times and covered their event at Pentaho world. Thanks for joining us. >> Thank you for having me. >> So, in following you guys I'll see Pentaho was once an independent company bought by Hitachi, but still an independent group within Hitachi. >> That's right, very much so. >> Okay so you guys some news. Let's just jump into the news. You guys announced some of the machine learning. >> Exactly, yeah. So, Arik Pelkey, Pentaho. We are a data integration and analytics software company. You mentioned you've been doing this for eight years. We have been at Big Data for the past eight years as well. In fact, we're one of the first vendors to support Hadoop back in the day, so we've been along for the journey ever since then. What we're announcing today is really exciting. It's a set of machine learning orchestration capabilities, which allows data scientists, data engineers, and data analysts to really streamline their data science processes. Everything from ingesting new data sources through data preparation, feature engineering which is where a lot of data scientists spend their time through tuning their models which can still be programmed in R, in Weka, in Python, and any other kind of data science tool of choice. What we do is we help them deploy those models inside of Pentaho as a step inside of Pentaho, and then we help them update those models as time goes on. So, really what this is doing is it's streamlining. It's making them more productive so that they can focus their time on things like model building rather than data preparation and feature engineering. >> You know, it's interesting. The market is really active right now around machine learning and even just last week at Google Next, which is their cloud event, they had made the acquisition of Kaggle, which is kind of an open data science. You mentioned the three categories: data engineer, data science, data analyst. Almost on a progression, super geek to business facing, and there's different approaches. One of the comments from the CEO of Kaggle on the acquisition when we wrote up at Sylvan Angle was, and I found this fascinating, I want to get your commentary and reaction to is, he says the data science tools are as early as generations ago, meaning that all the advances and open source and tooling and software development is far along, but now data science is still at that early stage and is going to get better. So, what's your reaction to that, because this is really the demand we're seeing is a lot of heavy lifing going on in the data science world, yet there's a lot of runway of more stuff to do. What is that more stuff? >> Right. Yeah, we're seeing the same thing. Last week I was at the Gardener Data and Analytics conference, and that was kind of the take there from one of their lead machine learning analysts was this is still really early days for data science software. So, there's a lot of Apache projects out there. There's a lot of other open source activity going on, but there are very few vendors that bring to the table an integrated kind of full platform approach to the data science workflow, and that's what we're bringing to market today. Let me be clear, we're not trying to replace R, or Python, or MLlib, because those are the tools of the data scientists. They're not going anywhere. They spent eight years in their phD program working with these tools. We're not trying to change that. >> They're fluent with those tools. >> Very much so. They're also spending a lot of time doing feature engineering. Some research reports, say between 70 and 80% of their time. What we bring to the table is a visual drag and drop environment to do feature engineering a much faster, more efficient way than before. So, there's a lot of different kind of desperate siloed applications out there that all do interesting things on their own, but what we're doing is we're trying to bring all of those together. >> And the trends are reduce the time it takes to do stuff and take away some of those tasks that you can use machine learning for. What unique capabilities do you guys have? Talk about that for a minute, just what Pentaho is doing that's unique and added value to those guys. >> So, the big thing is I keep going back to the data preparation part. I mean, that's 80% of time that's still a really big challenge. There's other vendors out there that focus on just the data science kind of workflow, but where we're really unqiue is around being able to accommodate very complex data environments, and being able to onboard data. >> Give me an example of those environments. >> Geospatial data combined with data from your ERP or your CRM system and all kinds of different formats. So, there might be 15 different data formats that need to be blended together and standardized before any of that can really happen. That's the complexity in the data. So, Pentaho, very consistent with everything else that we do outside of machine learning, is all about helping our customers solve those very complex data challenges before doing any kind of machine learning. One example is one customer is called Caterpillar Machine Asset Intelligence. So, their doing predictive maintenance onboard container ships and on ferry's. So, they're taking data from hundreds and hundreds of sensors onboard these ships, combining that kind of operational sensor data together with geospatial data and then they're serving up predictive maintenance alerts if you will, or giving signals when it's time to replace an engine or complace a compressor or something like that. >> Versus waiting for it to break. >> Versus waiting for it to break, exactly. That's one of the real differentiators is that very complex data environment, and then I was starting to move toward the other differentiator which is our end to end platform which allows customers to deliver these analytics in an embedded fashion. So, kind of full circle, being able to send that signal, but not to an operational system which is sometimes a challenge because you might have to rewrite the code. Deploying models is a really big challenge within Pentaho because it is this fully integrated application. You can deploy the models within Pentaho and not have to jump out into a mainframe environment or something like that. So, I'd say differentiators are very complex data environments, and then this end to end approach where deploying models is much easier than ever before. >> Perhaps, let's talk about alternatives that customers might see. You have a tool suite, and others might have to put together a suite of tools. Maybe tell us some of the geeky version would be the impendent mismatch. You know, like the chasms you'd find between each tool where you have to glue them together, so what are some of those pitfalls? >> One of the challenges is, you have these data scientists working in silos often times. You have data analysts working in silos, you might have data engineers working in silos. One of the big pitfalls is not really collaborating enough to the point where they can do all of this together. So, that's a really big area that we see pitfalls. >> Is it binary not collaborating, or is it that the round trip takes so long that the quality or number of collaborations is so drastically reduced that the output is of lower quality? >> I think it's probably a little bit of both. I think they want to collaborate but one person might sit in Dearborn, Michigan and the other person might sit in Silicon Valley, so there's just a location challenge as well. The other challenge is, some of the data analysts might sit in IT and some of the data scientists might sit in an analytics department somewhere, so it kind of cuts across both location and functional area too. >> So let me ask from the point of view of, you know we've been doing these shows for a number of years and most people have their first data links up and running and their first maybe one or two use cases in production, very sophisticated customers have done more, but what seems to be clear is the highest value coming from those projects isn't to put a BI tool in front of them so much as to do advanced analytics on that data, apply those analytics to inform a decision, whether a person or a machine. >> That's exactly right. >> So, how do you help customers over that hump and what are some other examples that you can share? >> Yeah, so speaking of transformative. I mean, that's what machine learning is all about. It helps companies transform their businesses. We like to talk about that at Pentaho. One customer kind of industry example that I'll share is a company called IMS. IMS is in the business of providing data and analytics to insurance companies so that the insurance companies can price insurance policies based on usage. So, it's a usage model. So, IMS has a technology platform where they put sensors in a car, and then using your mobile phone, can track your driving behavior. Then, your insurance premium that month reflects the driving behavior that you had during that month. In terms of transformative, this is completely upending the insurance industry which has always had a very fixed approach to pricing risk. Now, they understand everything about your behavior. You know, are you turning too fast? Are you breaking too fast, and they're taking it further than that too. They're able to now do kind of a retroactive look at an accident. So, after an accident, they can go back and kind of decompose what happened in the accident and determine whether or not it was your fault or was in fact the ice on the street. So, transformative? I mean, this is just changing things in a really big way. >> I want to get your thoughts on this. I'm just looking at some of the research. You know, we always have the good data but there's also other data out there. In your news, 92% of organizations plan to deploy more predictive analytics, however 50% of organizations have difficulty integrating predictive analytics into their information architecture, which is where the research is shown. So my question to you is, there's a huge gap between the technology landscapes of front end BI tools and then complex data integration tools. That seems to be the sweet spot where the value's created. So, you have the demand and then front end BI's kind of sexy and cool. Wow, I could power my business, but the complexity is really hard in the backend. Who's accessing it? What's the data sources? What's the governance? All these things are complicated, so how do you guys reconcile the front end BI tools and the backend complexity integrations? >> Our story from the beginning has always been this one integrated platform, both for complex data integration challenges together with visualizations, and that's very similar to what this announcement is all about for the data science market. We're very much in line with that. >> So, it's the cart before the horse? Is it like the BI tools are really driven by the data? I mean, it makes sense that the data has to be key. Front end BI could be easy if you have one data set. >> It's funny you say that. I presented at the Gardner conference last week and my topic was, this just in: it's not about analytics. Kind of in jest, but it drove a really big crowd. So, it's about the data right? It's about solving the data problem before you solve the analytics problem whether it's a simple visualization or it's a complex fraud machine learning problem. It's about solving the data problem first. To that quote, I think one of the things that they were referencing was the challenging information architectures into which companies are trying to deploy models and so part of that is when you build a machine learning model, you use R and Python and all these other ones we're familiar with. In order to deploy that into a mainframe environment, someone has to then recode it in C++ or COBOL or something else. That can take a really long time. With our integrated approach, once you've done the feature engineering and the data preparation using our drag and drop environment, what's really interesting is that you're like 90% of the way there in terms of making that model production ready. So, you don't have to go back and change all that code, it's already there because you used it in Pentaho. >> So obviously for those two technologies groups I just mentioned, I think you had a good story there, but it creates problems. You've got product gaps, you've got organizational gaps, you have process gaps between the two. Are you guys going to solve that, or are you currently solving that today? There's a lot of little questions in there, but that seems to be the disconnect. You know, I can do this, I can do that, do I do them together? >> I mean, sticking to my story of one integrated approach to being able to do the entire data science workflow, from beginning to end and that's where we've really excelled. To the extent that more and more data engineers and data analysts and data scientists can get on this one platform even if their using R and WECCA and Python. >> You guys want to close those gaps down, that's what you guys are doing, right? >> We want to make the process more collaborative and more efficient. >> So Dave Alonte has a question on CrowdChat for you. Dave Alonte was in the snowstorm in Boston. Dave, good to see you, hope you're doing well shoveling out the driveway. Thanks for coming in digitally. His question is HDS has been known for mainframes and storage, but Hitachi is an industrial giant. How is Pentaho leveraging Hitatchi's IoT chops? >> Great question, thanks for asking. Hitatchi acquired Pentaho about two years ago, this is before my time. I've been with Pentaho about ten months ago. One of the reasons that they acquired Pentaho is because a platform that they've announced which is called Lumata which is their IoT platform, so what Pentaho is, is the analytics engine that drives that IoT platform Lumata. So, Lumata is about solving more of the hardware sensor, bringing data from the edge into being able to do the analytics. So, it's an incredibly great partnership between Lumata and Pentaho. >> Makes an eternal customer too. >> It's a 90 billion dollar conglomerate so yeah, the acquisition's been great and we're still very much an independent company going to market on our own, but we now have a much larger channel through Hitatchi's reps around the world. >> You've got IoT's use case right there in front of you. >> Exactly. >> But you are leveraging it big time, that's what you're saying? >> Oh yeah, absolutely. We're a very big part of their IoT strategy. It's the analytics. Both of the examples that I shared with you are in fact IoT, not by design but it's because there's a lot of demand. >> You guys seeing a lot of IoT right now? >> Oh yeah. We're seeing a lot of companies coming to us who have just hired a director or vice president of IoT to go out and figure out the IoT strategy. A lot of these are manufacturing companies or coming from industries that are inefficient. >> Digitizing the business model. >> So to the other point about Hitachi that I'll make, is that as it relates to data science, a 90 billion dollar manufacturing and otherwise giant, we have a very deep bench of phD data scientists that we can go to when there's very complex data science problems to solve at customer sight. So, if a customer's struggling with some of the basic how do I get up and running doing machine learning, we can bring our bench of data scientist at Hitatchi to bear in those engagements, and that's a really big differentiator for us. >> Just to be clear and one last point, you've talked about you handle the entire life cycle of modeling from acquiring the data and prepping it all the way through to building a model, deploying it, and updating it which is a continuous process. I think as we've talked about before, data scientists or just the DEV ops community has had trouble operationalizing the end of the model life cycle where you deploy it and update it. Tell us how Pentaho helps with that. >> Yeah, it's a really big problem and it's a very simple solution inside of Pentaho. It's basically a step inside of Pentaho. So, in the case of fraud let's say for example, a prediction might say fraud, not fraud, fraud, not fraud, whatever it is. We can then bring that kind of full lifecycle back into the data workflow at the beginning. It's a simple drag and drop step inside of Pentaho to say which were right and which were wrong and feed that back into the next prediction. We could also take it one step further where there has to be a manual part of this too where it goes to the customer service center, they investigate and they say yes fraud, no fraud, and then that then gets funneled back into the next prediction. So yeah, it's a big challenge and it's something that's relatively easy for us to do just as part of the data science workflow inside of Pentaho. >> Well Arick, thanks for coming on The Cube. We really appreciate it, good luck with the rest of the week here. >> Yeah, very exciting. Thank you for having me. >> You're watching The Cube here live in Silicon Valley covering Strata Hadoop, and of course our Big Data SV event, we also have a companion event called Big Data NYC. We program with O'Reilley Strata Hadoop, and of course have been covering Hadoop really since it's been founded. This is The Cube, I'm John Furrier. George Gilbert. We'll be back with more live coverage today for the next three days here inside The Cube after this short break.