>> from Cambridge, Massachusetts. It's the Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back to M I. T. Everybody. This is the Cube. The leader in live coverage. My name is David wanted. I'm here with my co host, Paul Gill, in this day to coverage of the M I t cdo I Q conference. A lot of acronym stands for M I. T. Of course, the great institution. But Chief Data officer information quality event is his 13th annual event. Lars to Maria's here is the managing partner of Brass Rat Capital. Cool name Lars. Welcome to the Cube. Great. Very much. Glad I start with a name brass around Capitol was That's >> rat is reference to the M I t school. Okay, Beaver? Well, he is, but the students call it a brass rat, and I'm third generation M i t. So it's just seen absolutely appropriate. That is a brass rods and capital is not a reference to money, but is actually referenced to the intellectual capital. They if you have five or six brass rats in the same company, you know, we Sometimes engineers arrive and they could do some things. >> And it Boy, if you put in some data data capital in there, you really explosions. We cause a few problems. So we're gonna talk about some new regulations that are coming down. New legislation that's coming down that you exposed me to yesterday, which is gonna have downstream implications. You get ahead of this stuff and understand it. You can really first of all, prepare, make sure you're in compliance, but then potentially take advantage for your business. So explain to us this notion of open government act. >> Um, in the last five years, six years or so, there's been an effort going on to increase the transparency across all levels of government. Okay, State, local and federal government. The first of federal government laws was called the the Open Data Act of 2014 and that was an act. They was acted unanimously by Congress and signed by Obama. They was taking the departments of the various agencies of the United States government and trying to roll up all the expenses into one kind of expense. This is where we spent our money and who got the money and doing that. That's what they were trying to do. >> Big picture type of thing. >> Yeah, big picture type thing. But unfortunately, it didn't work, okay? Because they forgot to include this odd word called mentalities. So the same departments meant the same thing. Data problem. They have a really big data problem. They still have it. So they're to G et o reports out criticizing how was done, and the government's gonna try and correct it. Then in earlier this year, there was another open government date act which said in it was signed by Trump. Now, this time you had, like, maybe 25 negative votes, but essentially otherwise passed Congress completely. I was called the Open as all capital O >> P E >> n Government Data act. Okay, and that's not been implemented yet. But there's live talking around this conference today in various Chief date officers are talking about this requirement that every single non intelligence defense, you know, vital protection of the people type stuff all the like, um, interior, treasury, transportation, those type of systems. If you produce a report these days, which is machine, I mean human readable. You must now in two years or three years. I forget the exact invitation date. Have it also be machine readable. Now, some people think machine riddle mil means like pdf formats, but no, >> In fact, what the government did is it >> said it must be machine readable. So you must be able to get into the reports, and you have to be able to extract out the information and attach it to the tree of knowledge. Okay, so we're all of sudden having context like they're currently machine readable, Quote unquote, easy reports. But you can get into those SEC reports. You pull out the net net income information and says its net income, but you don't know what it attaches to on the tree of knowledge. So, um, we are helping the government in some sense able, machine readable type reporting that weaken, do machine to machine without people being involved. >> Would you say the tree of knowledge You're talking about the constant >> man tick semantic tree of knowledge so that, you know, we all come from one concept like the human is example of a living thing living beast, a living Beeston example Living thing. So it also goes back, and they're serving as you get farther and farther out the tree, there's more distance or semantic distance, but you can attach it back to concept so you can attach context to the various data. Is this essentially metadata? That's what people call it. But if I would go over see sale here at M I t, they would turn around. They call it the Tree of Knowledge or semantic data. Okay, it's referred to his semantic dated, So you are passing not only the data itself, but the context that >> goes along with the data. Okay, how does this relate to the financial transparency? >> Well, Financial Transparency Act was introduced by representative Issa, who's a Republican out of California. He's run the government Affairs Committee in the House. He retired from Congress this past November, but in 2017 he introduced what's got referred to his H R 15 30 Um, and the 15 30 is going to dramatically change the way, um, financial regulators work in the United States. Um, it is about it was about to be introduced two weeks ago when the labor of digital currency stuff came up. So it's been delayed a little bit because they're trying to add some of the digital currency legislation to that law. >> A front run that Well, >> I don't know exactly what the remember soul coming out of Maxine Waters Committee. So the staff is working on a bunch of different things at once. But, um, we own g was asked to consult with them on looking at the 15 30 act and saying, How would we improve quote unquote, given our technical, you know, not doing policy. We just don't have the technical aspects of the act. How would we want to see it improved? So one of the things we have advised is that for the first time in the United States codes history, they're gonna include interesting term called ontology. You know what intelligence? Well, everyone gets scared by the word. And when I read run into people, they say, Are you a doctor? I said, no, no, no. I'm just a date. A guy. Um, but an intolerant tea is like a taxonomy, but it had order has important, and an ontology allows you to do it is ah, kinda, you know, giving some context of linking something to something else. And so you're able Thio give Maur information with an intolerant that you're able to you with a tax on it. >> Okay, so it's a taxonomy on steroids? >> Yes, exactly what? More flexible, >> Yes, but it's critically important for artificial intelligence machine warning because if I can give them until ology of sort of how it goes up and down the semantics, I can turn around, do a I and machine learning problems on the >> order of 100 >> 1000 even 10,000 times faster. And it has context. It has contacts in just having a little bit of context speeds up these problems so dramatically so and it is that what enables the machine to machine? New notion? No, the machine to machine is coming in with son called SP R M just standard business report model. It's a OMG sophistication of way of allowing the computers or machines, as we call them these days to get into a standard business report. Okay, so let's say you're ah drug company. You have thio certify you >> drugged you manufactured in India, get United States safely. Okay, you have various >> reporting requirements on the way. You've got to give extra easy the FDA et cetera that will always be a standard format. The SEC has a different format. FERC has a different format. Okay, so what s p r m does it allows it to describe in an intolerant he what's in the report? And then it also allows one to attach an ontology to the cells in the report. So if you like at a sec 10 Q 10 k report, you can attach a US gap taxonomy or ontology to it and say, OK, net income annual. That's part of the income statement. You should never see that in a balance sheet type item. You know his example? Okay. Or you can for the first time by having that context you can say are solid problem, which suggested that you can file these machine readable reports that air wrong. So they believe or not, There were about 50 cases in the last 10 years where SEC reports have been filed where the assets don't equal total liabilities, plus cheryl equity, you know, just they didn't add >> up. So this to, >> you know, to entry accounting doesn't work. >> Okay, so so you could have the machines go and check scale. Hey, we got a problem We've >> got a problem here, and you don't have to get humans evolved. So we're gonna, um uh, Holland in Australia or two leaders ahead of the United States. In this area, they seem dramatic pickups. I mean, Holland's reporting something on the order of 90%. Pick up Australia's reporting 60% pickup. >> We say pick up. You're talking about pickup of errors. No efficiency, productivity, productivity. Okay, >> you're taking people out of the whole cycle. It's dramatic. >> Okay, now what's the OMG is rolling on the hoof. Explain the OMG >> Object Management Group. I'm not speaking on behalf of them. It's a membership run organization. You remember? I am a >> member of cold. >> I'm a khalid of it. But I don't represent omg. It's the membership has to collectively vote that this is what we think. Okay, so I can't speak on them, right? I have a pretty significant role with them. I run on behalf of OMG something called the Federated Enterprise Risk Management Group. That's the group which is focusing on risk management for large entities like the federal government's Veterans Affairs or Department offense upstairs. I think talking right now is the Chief date Officer for transportation. OK, that's a large organization, which they, they're instructed by own be at the, um, chief financial officer level. The one number one thing to do for the government is to get an effective enterprise worst management model going in the government agencies. And so they come to own G let just like NIST or just like DARPA does from the defense or intelligence side, saying we need to have standards in this area. So not only can we talk thio you effectively, but we can talk with our industry partners effectively on space. Programs are on retail, on medical programs, on finance programs, and so they're at OMG. There are two significant financial programs, or Sanders, that exist once called figgy financial instrument global identifier, which is a way of identifying a swap. Its way of identifying a security does not have to be used for a que ce it, but a worldwide. You can identify that you know, IBM stock did trade in Tokyo, so it's a different identifier has different, you know, the liberals against the one trading New York. Okay, so those air called figgy identifiers them. There are attributes associated with that security or that beast the being identified, which is generally comes out of 50 which is the financial industry business ontology. So you know, it says for a corporate bond, it has coupon maturity, semi annual payment, bullets. You know, it is an example. So that gives you all the information that you would need to go through to the calculation, assuming you could have a calculation routine to do it, then you need thio. Then turn around and set up your well. Call your environment. You know where Ford Yield Curves are with mortgage backed securities or any portable call. Will bond sort of probabilistic lee run their numbers many times and come up with effective duration? Um, And then you do your Vader's analytics. No aggregating the portfolio and looking at Shortfalls versus your funding. Or however you're doing risk management and then finally do reporting, which is where the standardized business reporting model comes in. So that kind of the five parts of doing a full enterprise risk model and Alex So what >> does >> this mean for first? Well, who does his impact on? What does it mean for organizations? >> Well, it's gonna change the world for basically everyone because it's like doing a clue ends of a software upgrade. Conversion one's version two point. Oh, and you know how software upgrades Everyone hates and it hurts because everyone's gonna have to now start using the same standard ontology. And, of course, that Sarah Ontology No one completely agrees with the regulators have agreed to it. The and the ultimate controlling authority in this thing is going to be F sock, which is the Dodd frank mandated response to not ever having another chart. So the secretary of Treasury heads it. It's Ah, I forget it's the, uh, federal systemic oversight committee or something like that. All eight regulators report into it. And, oh, if our stands is being the adviser Teff sock for all the analytics, what these laws were doing, you're getting over farm or more power to turn around and look at how we're going to find data across the three so we can come up consistent analytics and we can therefore hopefully take one day. Like Goldman, Sachs is pre payment model on mortgages. Apply it to Citibank Portfolio so we can look at consistency of analytics as well. It is only apply to regulated businesses. It's gonna apply to regulated financial businesses. Okay, so it's gonna capture all your mutual funds, is gonna capture all your investment adviser is gonna catch her. Most of your insurance companies through the medical air side, it's gonna capture all your commercial banks is gonna capture most of you community banks. Okay, Not all of them, because some of they're so small, they're not regularly on a federal basis. The one regulator which is being skipped at this point, is the National Association Insurance Commissioners. But they're apparently coming along as well. Independent federal legislation. Remember, they're regulated on the state level, not regularly on the federal level. But they've kind of realized where the ball's going and, >> well, let's make life better or simply more complex. >> It's going to make life horrible at first, but we're gonna take out incredible efficiency gains, probably after the first time you get it done. Okay, is gonna be the problem of getting it done to everyone agreeing. We use the same definitions >> of the same data. Who gets the efficiency gains? The regulators, The companies are both >> all everyone. Can you imagine that? You know Ah, Goldman Sachs earnings report comes out. You're an analyst. Looking at How do I know what Goldman? Good or bad? You have your own equity model. You just give the model to the semantic worksheet and all turn around. Say, Oh, those numbers are all good. This is what expected. Did it? Did it? Didn't you? Haven't. You could do that. There are examples of companies here in the United States where they used to have, um, competitive analysis. Okay. They would be taking somewhere on the order of 600 to 7. How 100 man hours to do the competitive analysis by having an available electronically, they cut those 600 hours down to five to do a competitive analysis. Okay, that's an example of the type of productivity you're gonna see both on the investment side when you're doing analysis, but also on the regulatory site. Can you now imagine you get a regulatory reports say, Oh, there's they're out of their way out of whack. I can tell you this fraud going on here because their numbers are too much in X y z. You know, you had to fudge numbers today, >> and so the securities analyst can spend Mme. Or his or her time looking forward, doing forecasts exactly analysis than having a look back and reconcile all this >> right? And you know, you hear it through this conference, for instance, something like 80 to 85% of the time of analysts to spend getting the data ready. >> You hear the same thing with data scientists, >> right? And so it's extent that we can helped define the data. We're going thio speed things up dramatically. But then what's really instinct to me, being an M I t engineer is that we have great possibilities. An A I I mean, really great possibilities. Right now, most of the A miles or pattern matching like you know, this idea using face shield technology that's just really doing patterns. You can do wonderful predictive analytics of a I and but we just need to give ah lot of the a m a. I am a I models the contact so they can run more quickly. OK, so we're going to see a world which is gonna found funny, But we're going to see a world. We talk about semantic analytics. Okay. Semantic analytics means I'm getting all the inputs for the analysis with context to each one of the variables. And when I and what comes out of it will be a variable results. But you also have semantics with it. So one in the future not too distant future. Where are we? We're in some of the national labs. Where are you doing it? You're doing pipelines of one model goes to next model goes the next mile. On it goes Next model. So you're gonna software pipelines, Believe or not, you get them running out of an Excel spreadsheet. You know, our modern Enhanced Excel spreadsheet, and that's where the future is gonna be. So you really? If you're gonna be really good in this business, you're gonna have to be able to use your brain. You have to understand what data means You're going to figure out what your modeling really means. What happens if we were, You know, normally for a lot of the stuff we do bell curves. Okay, well, that doesn't have to be the only distribution you could do fat tail. So if you did fat tail descriptions that a bell curve gets you much different results. Now, which one's better? I don't know, but, you know, and just using example >> to another cut in the data. So our view now talk about more about the tech behind this. He's mentioned a I What about math? Machine learning? Deep learning. Yeah, that's a color to that. >> Well, the tech behind it is, believe or not, some relatively old tech. There is a technology called rd F, which is kind of turned around for a long time. It's a science kind of, ah, machine learning, not machine wearing. I'm sorry. Machine code type. Fairly simplistic definitions. Lots of angle brackets and all this stuff there is a higher level. That was your distracted, I think put into standard in, like, 2000 for 2005. Called out. Well, two point. Oh, and it does a lot at a higher level. The same stuff that already f does. Okay, you could also create, um, believer, not your own special ways of a communicating and ontology just using XML. Okay, So, uh, x b r l is an enhanced version of XML, okay? And so some of these older technologies, quote unquote old 20 years old, are essentially gonna be driving a lot of this stuff. So you know you know Corbett, right? Corba? Is that what a maid omg you know, on the communication and press thing, do you realize that basically every single device in the world has a corpus standard at okay? Yeah, omg Standard isn't all your smartphones and all your computers. And and that's how they communicate. It turns out that a lot of this old stuff quote unquote, is so rigidly well defined. Well done that you can build modern stuff that takes us to the Mars based on these old standards. >> All right, we got to go. But I gotta give you the award for the most acronyms >> HR 15 30 fi G o m g s b r >> m fsoc tarp. Oh, fr already halfway. We knew that Owl XML ex brl corba, Which of course >> I do. But that's well done. Like thanks so much for coming. Everyone tried to have you. All right, keep it right there, everybody, We'll be back with our next guest from M i t cdo I Q right after this short, brief short message. Thank you

(lively peaceful music) >> Live, from New York, it's The Cube. Covering "The Cube New York City 2018." Brought to you by SiliconANGLE Media and its ecosystem partners. >> Well, everyone, welcome back to The Cube special conversation here in New York City. We're live for Cube NYC. This is our ninth year covering the big data ecosystem, now evolved into AI, machine learning, cloud. All things data in conjunction with Strata Conference, which is going on right around the corner. This is the Cube studio. I'm John Furrier. Dave Vellante. Our next guest is Mick Hollison, who is the CMO, Chief Marketing Officer, of Cloudera. Welcome to The Cube, thanks for joining us. >> Thanks for having me. >> So Cloudera, obviously we love Cloudera. Cube started in Cloudera's office, (laughing) everyone in our community knows that. I keep, keep saying it all the time. But we're so proud to have the honor of working with Cloudera over the years. And, uh, the thing that's interesting though is that the new building in Palo Alto is right in front of the old building where the first Palo Alto office was. So, a lot of success. You have a billboard in the airport. Amr Awadallah is saying, hey, it's a milestone. You're in the airport. But your business is changing. You're reaching new audiences. You have, you're public. You guys are growing up fast. All the data is out there. Tom's doing a great job. But, the business side is changing. Data is everywhere, it's a big, hardcore enterprise conversation. Give us the update, what's new with Cloudera. >> Yeah. Thanks very much for having me again. It's, it's a delight. I've been with the company for about two years now, so I'm officially part of the problem now. (chuckling) It's been a, it's been a great journey thus far. And really the first order of business when I arrived at the company was, like, welcome aboard. We're going public. Time to dig into the S-1 and reimagine who Cloudera is going to be five, ten years out from now. And we spent a good deal of time, about three or four months, actually crafting what turned out to be just 38 total words and kind of a vision and mission statement. But the, the most central to those was what we were trying to build. And it was a modern platform for machine learning analytics in the cloud. And, each of those words, when you unpack them a little bit, are very, very important. And this week, at Strata, we're really happy on the modern platform side. We just released Cloudera Enterprise Six. It's the biggest release in the history of the company. There are now over 30 open-source projects embedded into this, something that Amr and Mike could have never imagined back in the day when it was just a couple of projects. So, a very very large and meaningful update to the platform. The next piece is machine learning, and Hilary Mason will be giving the kickoff tomorrow, and she's probably forgotten more about ML and AI than somebody like me will ever know. But she's going to give the audience an update on what we're doing in that space. But, the foundation of having that data management platform, is absolutely fundamental and necessary to do good machine learning. Without good data, without good data management, you can't do good ML or AI. Sounds sort of simple but very true. And then the last thing that we'll be announcing this week, is around the analytics space. So, on the analytic side, we announced Cloudera Data Warehouse and Altus Data Warehouse, which is a PaaS flavor of our new data warehouse offering. And last, but certainly not least, is just the "optimize for the cloud" bit. So, everything that we're doing is optimized not just around a single cloud but around multi-cloud, hybrid-cloud, and really trying to bridge that gap for enterprises and what they're doing today. So, it's a new Cloudera to say the very least, but it's all still based on that core foundation and platform that, you got to know it, with very early on. >> And you guys have operating history too, so it's not like it's a pivot for Cloudera. I know for a fact that you guys had very large-scale customers, both with three letter, letters in them, the government, as well as just commercial. So, that's cool. Question I want to ask you is, as the conversation changes from, how many clusters do I have, how am I storing the data, to what problems am I solving because of the enterprises. There's a lot of hard things that enterprises want. They want compliance, all these, you know things that have either legacy. You guys work on those technical products. But, at the end of the day, they want the outcomes, they want to solve some problems. And data is clearly an opportunity and a challenge for large enterprises. What problems are you guys going after, these large enterprises in this modern platform? What are the core problems that you guys knock down? >> Yeah, absolutely. It's a great question. And we sort of categorize the way we think about addressing business problems into three broad categories. We use the terms grow, connect, and protect. So, in the "grow" sense, we help companies build or find new revenue streams. And, this is an amazing part of our business. You see it in everything from doing analytics on clickstreams and helping people understand what's happening with their web visitors and the like, all the way through to people standing up entirely new businesses based simply on their data. One large insurance provider that is a customer of ours, as an example, has taken on the challenge and asked us to engage with them on building really, effectively, insurance as a service. So, think of it as data-driven insurance rates that are gauged based on your driving behaviors in real time. So no longer simply just using demographics as the way that you determine, you know, all 18-year old young men are poor drivers. As it turns out, with actual data you can find out there's some excellent 18 year olds. >> Telematic, not demographics! >> Yeah, yeah, yeah, exactly! >> That Tesla don't connect to the >> Exactly! And Parents will love this, love this as well, I think. So they can find out exactly how their kids are really behaving by the way. >> They're going to know I rolled through the stop signs in Palo Alto. (laughing) My rates just went up. >> Exactly, exactly. So, so helping people grow new businesses based on their data. The second piece is "Connect". This is not just simply connecting devices, but that's a big part of it, so the IOT world is a big engine for us there. One of our favorite customer stories is a company called Komatsu. It's a mining manufacturer. Think of it as the ones that make those, just massive mines that are, that are all over the world. They're particularly big in Australia. And, this is equipment that, when you leave it sit somewhere, because it doesn't work, it actually starts to sink into the earth. So, being able to do predictive maintenance on that level and type and expense of equipment is very valuable to a company like Komatsu. We're helping them do that. So that's the "Connect" piece. And last is "Protect". Since data is in fact the new oil, the most valuable resource on earth, you really need to be able to protect it. Whether that's from a cyber security threat or it's just meeting compliance and regulations that are put in place by governments. Certainly GDPR is got a lot of people thinking very differently about their data management strategies. So we're helping a number of companies in that space as well. So that's how we kind of categorize what we're doing. >> So Mick, I wonder if you could address how that's all affected the ecosystem. I mean, one of the misconceptions early on was that Hadoop, Big Data, is going to kill the enterprise data warehouse. NoSQL is going to knock out Oracle. And, Mike has always said, "No, we are incremental". And people are like, "Yeah, right". But that's really, what's happened here. >> Yes. >> EDW was a fundamental component of your big data strategies. As Amr used to say, you know, SQL is the killer app for, for big data. (chuckling) So all those data sources that have been integrated. So you kind of fast forward to today, you talked about IOT and The Edge. You guys have announced, you know, your own data warehouse and platform as a service. So you see this embracing in this hybrid world emerging. How has that affected the evolution of your ecosystem? >> Yeah, it's definitely evolved considerably. So, I think I'd give you a couple of specific areas. So, clearly we've been quite successful in large enterprises, so the big SI type of vendors want a, want a piece of that action these days. And they're, they're much more engaged than they were early days, when they weren't so sure all of this was real. >> I always say, they like to eat at the trough and then the trough is full, so they dive right in. (all laughing) They're definitely very engaged, and they built big data practices and distinctive analytics practices as well. Beyond that, sort of the developer community has also begun to shift. And it's shifted from simply people that could spell, you know, Hive or could spell Kafka and all of the various projects that are involved. And it is elevated, in particular into a data science community. So one of additional communities that we sort of brought on board with what we're doing, not just with the engine and SPARK, but also with tools for data scientists like Cloudera Data Science Workbench, has added that element to the community that really wasn't a part of it, historically. So that's been a nice add on. And then last, but certainly not least, are the cloud providers. And like everybody, they're, those are complicated relationships because on the one hand, they're incredibly valuable partners to it, certainly both Microsoft and Amazon are critical partners for Cloudera, at the same time, they've got competitive offerings. So, like most successful software companies there's a lot of coopetition to contend with that also wasn't there just a few years ago when we didn't have cloud offerings, and they didn't have, you know, data warehouse in the cloud offerings. But, those are things that have sort of impacted the ecosystem. >> So, I've got to ask you a marketing question, since you're the CMO. By the way, great message UL. I like the, the "grow, connect, protect." I think that's really easy to understand. >> Thank you. >> And the other one was modern. The phrase, say the phrase again. >> Yeah. It's the "Cloudera builds the modern platform for machine learning analytics optimized for the cloud." >> Very tight mission statement. Question on the name. Cloudera. >> Mmhmm. >> It's spelled, it's actually cloud with ERA in the letters, so "the cloud era." People use that term all the time. We're living in the cloud era. >> Yes. >> Cloud-native is the hottest market right now in the Linux foundation. The CNCF has over two hundred and forty members and growing. Cloud-native clearly has indicated that the new, modern developers here in the renaissance of software development, in general, enterprises want more developers. (laughs) Not that you want to be against developers, because, clearly, they're going to hire developers. >> Absolutely. >> And you're going to enable that. And then you've got the, obviously, cloud-native on-premise dynamic. Hybrid cloud and multi-cloud. So is there plans to think about that cloud era, is it a cloud positioning? You see cloud certainly important in what you guys do, because the cloud creates more compute, more capabilities to move data around. >> Sure. >> And (laughs) process it. And make it, make machine learning go faster, which gives more data, more AI capabilities, >> It's the flywheel you and I were discussing. >> It's the flywheel of, what's the innovation sandwich, Dave? You know? (laughs) >> A little bit of data, a little bit of machine itelligence, in the cloud. >> So, the innovation's in play. >> Yeah, Absolutely. >> Positioning around Cloud. How are you looking at that? >> Yeah. So, it's a fascinating story. You were with us in the earliest days, so you know that the original architecture of everything that we built was intended to be run in the public cloud. It turns out, in 2008, there were exactly zero customers that wanted all of their data in a public cloud environment. So the company actually pivoted and re-architected the original design of the offerings to work on-prim. And, no sooner did we do that, then it was time to re-architect it yet again. And we are right in the midst of doing that. So, we really have offerings that span the whole gamut. If you want to just pick up you whole current Cloudera environment in an infrastructure as a service model, we offer something called Altus Director that allows you to do that. Just pick up the entire environment, step it up onto AWUS, or Microsoft Azure, and off you go. If you want the convenience and the elasticity and the ease of use of a true platform as a service, just this past week we announced Altus Data Warehouse, which is a platform as a service kind of a model. For data warehousing, we have the data engineering module for Altus as well. Last, but not least, is everybody's not going to sign up for just one cloud vendor. So we're big believers in multi-cloud. And that's why we support the major cloud vendors that are out there. And, in addition to that, it's going to be a hybrid world for as far out as we can see it. People are going to have certain workloads that, either for economics or for security reasons, they're going to continue to want to run in-house. And they're going to have other workloads, certainly more transient workloads, and I think ML and data science will fall into this camp, that the public cloud's going to make a great deal of sense. And, allowing companies to bridge that gap while maintaining one security compliance and management model, something we call a Shared Data Experience, is really our core differentiator as a business. That's at the very core of what we do. >> Classic cloud workload experience that you're bringing, whether it's on-prim or whatever cloud. >> That's right. >> Cloud is an operating environment for you guys. You look at it just as >> The delivery mechanism. In effect. Awesome. All right, future for Cloudera. What can you share with us. I know you're a public company. Can't say any forward-looking statements. Got to do all those disclaimers. But for customers, what's the, what's the North Star for Cloudera? You mentioned going after a much more hardcore enterprise. >> Yes. >> That's clear. What's the North Star for you guys when you talk to customers? What's the big pitch? >> Yeah. I think there's a, there's a couple of really interesting things that we learned about our business over the course of the past six, nine months or so here. One, was that the greatest need for our offerings is in very, very large and complex enterprises. They have the most data, not surprisingly. And they have the most business gain to be had from leveraging that data. So we narrowed our focus. We have now identified approximately five thousand global customers, so think of it as kind of Fortune or Forbes 5000. That is our sole focus. So, we are entirely focused on that end of the market. Within that market, there are certain industries that we play particularly well in. We're incredibly well-positioned in financial services. Very well-positioned in healthcare and telecommunications. Any regulated industry, that really cares about how they govern and maintain their data, is really the great target audience for us. And so, that continues to be the focus for the business. And we're really excited about that narrowing of focus and what opportunities that's going to build for us. To not just land new customers, but more to expand our existing ones into a broader and broader set of use cases. >> And data is coming down faster. There's more data growth than ever seen before. It's never stopping.. It's only going to get worse. >> We love it. >> Bring it on. >> Any way you look at it, it's getting worse or better. Mick, thanks for spending the time. I know you're super busy with the event going on. Congratulations on the success, and the focus, and the positioning. Appreciate it. Thanks for coming on The Cube. >> Absolutely. Thank you gentlemen. It was a pleasure. >> We are Cube NYC. This is our ninth year doing all action. Everything that's going on in the data world now is horizontally scaling across all aspects of the company, the society, as we know. It's super important, and this is what we're talking about here in New York. This is The Cube, and John Furrier. Dave Vellante. Be back with more after this short break. Stay with us for more coverage from New York City. (upbeat music)

from San Francisco it's the queue covering Red Hat summit 2018 brought to you by Red Hat hey welcome back everyone's two cubes live coverage here in San Francisco California at Moscone West I'm John for a co-host of the cube with my analyst this week co-host John Troy a co-founder of tech reckoning our next guest is Dave Abrams executive general manager of data at Insurance Australia group welcome to the cube thanks for having me we were just you know talking on an off-camera before we came on about the challenges of data as cloud scale you guys have been around for many many years yeah you're dealing with a lot of legacy yeah you guys out right on the front step what's going on with you take a minute to explain what you guys do in your role in your environment absolutely now it's you know so we're we're large insurance trying we we've got offices in New Zealand and across Southeast Asia so we're kind of expanding out in our in our reach but um we've been around for a hundred odd years and and we've really grown a lot through merger and acquisition over time and so what that's meant ah this is a bit of a byproduct of those kind of merge and acquisition process is that data has been siloed and fragmented in different brands and different products and so it's been hard to get for example just a holistic view of a customer what does the customer have all the products they hold you know are they a personal customer as well as a business caste and all that sort of stuff doesn't kind of line up so we've had that big challenge in we've been working over the last couple of years to even just kind of consolidate all that unify that data into one platform so that we can see across the group from from a holistic perspective and and build that single view of customer and that's now helped us sort of understand you know what our customers are doing in and what's important to them and how we can better support them and yeah and offer better services and what are you doing here at Red Hat this week what's what's the objective what are you doing what do you have you know I'm speaking you talking the folk what's the what's the solution with Red Hat well so yeah we're primarily here as a result of the Innovation Awards so we you know we were nominated and we're successful in our in our award for that category in our region which was wonderful we we're really honored with that so we're here because of that we sharing our customer story with the rest of the Red Hat team and the rest of the open-source community around really what it's meant for us to use open source within a big corporate that's kind of traditionally been based on a lot of vendor technology right a live Ben driven predominantly by the big tech vendors you know that have come in and sort of helped us build big solutions and platforms which which were great and wonderful in the fact that you know they they were there and they lasted like ten years plus and that was all good but now because things are changing so fast we need to be more adaptable and and unfortunately those platforms become so entrenched into the organization and and and sort of lock you in that it's a to adjust into it to be adaptable you can't you can't take it out very easily it doesn't even stack up sometimes from a business case so why would we take that technology out we'll just have to dig deeper and we'll just have to spend more right so we're trying to we're trying to re reverse-engineer some of that and the role open source for you guys have been part of new systems recruiting talent everything director what's been a benefit the impact of absolutely it's huge inand you're right I think one of the biggest benefits for us that that really plays out is there is in the talent side right for our people to say not only are we transitioning our organization as a whole and the way we the way we operate but we're really transitioning out people we're transition from kind of the work force that we that we had and they've got us to where we are today but we're now setting ourselves up for the workforce of the future and it is a different skill set it is a different way of approaching problems so you know bringing bring this new technology to the table and allowing people to experiment to learn and to update their skills and capabilities exactly what we what we need for our company so we're pushing that hard yeah that's great it's like a real cultural shift give me maybe transfer transfer over a little bit to the actual tech problem you had right so you multiple countries multiple data warehouses multiple systems yours so what were you looking at and then what was the solution that you kind of figured out and then when yeah when so when I first started the roll a couple of years back we had something like 23 different separate individual data warehouses there were all sort of interconnected and dependent on each other and had copies of each other in each other and it was just it was a little bit of a mess so so the first challenge was to really sort of rationalize and clean up a lot of that so so that's that's what we spent a fair bit of time upfront doing which was basically really acquiring the organization's data from a massive amount of call source systems so in the vicinity of I think we take data from roughly about 150 to 200 call systems and we want to take that data essentially in as close to real time as we possibly can and pump that into her into a and to a new clean unified data Lake right just to make that data all line up so that was the big challenge in the first instance and then the second instance was really a scale problem right so getting the right technology that would help us scale into you know because we've predominately been using our own data centers and keeping a lot of stuff you know in that sort of on-prem mode but we really wanted to be able you know self scale to not only to be able to you know take advantage of cloud infrastructure just to give us that extra computing that extra storage and processing but really also to be able to leverage the the commoditization that's happening in cloud right because you know all all cloud companies around the world commoditizing technology like machine learning and you know artificial intelligence so that it's it's it's available to lots of organizations and the way we see it is really that that we're not going to be able to compete or out engineer those those companies so we need to make it you know accessible and available for our people to be able to use and leverage that innovation on our work as well as is you know do some some smart stuff ourselves are using infrastructures of service OpenStack or what's your solution I mean what are you guys doing solution is yet to use I've been stack is is our first sort of real step into infrastructure-as-a-service so that's really helped us set up like I was showing this morning set up the capability for us to turn our scale in a really cost-efficient way and we've ported a lot of our traditional dedicated you know applications on infrastructure that you know was like appliance based and things like that on to OpenStack now so that we can it gives us a lot more portability and we can move that around and put that in the place where we think gets us the best value so so that's really helped I'm kind of curious you work with Red Hat consulting and was I was I was curious about that process did you was that the result of a kind of a bake-off or we were already Red Hat customers and said oh hey by the way can you give us some advice yeah it really came about I mean we've been working with Red Hat for many years you know and it started back just sort of in the support area of Linux and and rel and using that kind of capability and rit has been there for us for quite a long time now and I think we've sort of done some some Explorer exploratory type exercise with them around you know I've been shifting and The Container well but but what really started the stick was just getting their expertise in from our OpenStack perspective and when you that was a key platform that we really wanted to dive into an enable and so having them there is our partner and helping us provide that extra consulting knowledge and expertise was was what we really needed helped us deliver on that project and we delivered in a mazing ly tight timeframe so it was a fast delivery faster live what about the business impact why people look at OpenStack and some of these new technologies and certainly with the legacy stuff going on you have got all these things everywhere what was the actual business benefits can you highlight like did you get like faster time-to-market was it like a claims issue and what were the key things that you look back and saying well we kicked ass and we did these three things I mean really what it boils down to as faster time-to-market right and just the ability to move quicker so to give you an example the way we used to work is it would take you say probably weeks maybe even longer to to provision and get infrastructure stood up and ready to go for different projects so I meant that there was all this lead time that projects nearly go through before they could start to write code and even start to add value to to customer so we wanted to sort of take that away and and and and that was a that was a big hindrance to to be able to experiment and to be on a play we think so again we want to take that out of the picture in and really free people up to sort of say well the infrastructure is done and it spins up in a matter of seconds now on OpenStack and you can get on with the job of trying something out experimenting and actually delivering and writing code that will that will produce an outcome to launch new applications what was a specific outcome that came from standing up putting that over stack together I see you experimenting result not adding yeah not only in the app spice but more so the biggest the biggest sort of benefit with God is really in the data space where we've now been able to essentially stand up our entire data stack using open source technology and we've never been able to do that before and this is you know this is this is the environment it's allowed us to do that by just allowing for us to do that test and trial and say you know he's kafir you're gonna be the right tool for us is it you know is he gonna we're gonna use Post Chris whatever that is it's allowed us to sort of really do that in a rapid way and then figure that thing out and start to move forward so you know ask our kiss you guys have done a lot of work out there good work so I gotta ask you the question with kubernetes containers now part of the discussion as a real viable way to handle legacy but also new software development projects how do you look at that what it's what's the your your reaction to that as that practitioner yeah you guys excited yeah yeah things in motion what's your what's your color um absolutely it's in fact it's been something that we've kind of had on the radar for quite a while because we've we've we've been working with containers so dock in particular and and and one of the things that you know you come across this just management of containers and just ongoing maintenance of of those kind of things where they start to get a little bit unwieldy a little bit out of control so you know we've been trying to we try to start which started off trying to build our own you know in solution to that is there's a lot of corporates are doing quickly found out less that's it that's a huge engineering challenge so things like kubernetes that have now come along and the investment that's been put in that platform will really open up that avenue for and even seeing just the the new innovation that's been put into our OpenShift here that sort of takes a lot of that management and service you know administration out of the out of the equation few is wonderful for a company like us because at the end of the day we're an insurance company right we're not a we're not a technology engineering company while although we have some capability it's never going to be our our strengths right we're really here to service our customers and and to help them in the times when they need our help you guys are a data company data is critical for any trivet yeah how how is you how we've become more data-driven as a result of all this yeah so so now that we've got our data all in one place and we're able to get their single views of customers we're able to put that data now into the hands of people that can really add value to us so for example into our analytics teams and get them to look for optimization in price or in service claims processing all those kind of good things that that are helping our customers reduce the the time frames that they would normally go through in that part of that experience and I think one of the other things is not only that but also enrich our digital capability right and rich that digital channel so make it more convenient for customers you know where it used to be that customers would come along and it's literally like coming to the organization for the first time every time you know I say fill in that form again from blank you're like we don't know anything about you but now we're able to enrich your form exactly it's very painful I see your name and you know you wanted to show your house tell us all about that house you know what does it made of you know what what type of roof material what's the wall we know all that we've probably seen that house ten times already so why wouldn't we just be able to pre-populate that kind of information and make it more convenient forecasting personalization becomes critical absolutely absolutely I like the way you underscored and told the story just like with cloud you just can't take your broken old IT apps and just throw them up at the cloud you had to you had to do a data exercise and you had to do a consolidation and the cleaning strong and sure that involved open source but you didn't get the tech stack first first you have to picture picture data app and and that was a key part here yeah so that's difficult and that's you know that's one of the things that I think we really we really invested in it was because a lot of the time what we've seen is organizations have sort of attacked the low-hanging fruit like the the the kind of the external the digital data that they might be able to get but not that offline data that's been you know one and and generated by the branch and the call centers and all those kind of areas and we dug in deep and invested in that space and got that right first which really helped us a lot to accelerate and now we're I think we're in a better position we can definitely take advantage of that yeah thanks for sharing your insights here in the cube I gotta ask you a final question as the folks watching that they're looking at you say wow this guy he got down and dirty fixed some things he's gone forward innovative what advice would you give someone watching is pregnant practitioner what have you learned what's the learnings that you've that have been magnified out of this process for you and your view going forward yeah yeah there's a there's a lot of learnings we can share but I think some of the key ones is you know I think there's sometimes a bit of a bit of a sort of attempt to try and solve everything yourself right and and we definitely did that where I try and build it all yourself and do everything right but it's it's a challenge and and use partners and look for look for you know things that are kind of gonna help you accelerate and give you some of the foundational work you don't have to build yourself right you don't have to build everything yourself and I think that acknowledgement is really key so that was one of the big things for us the other thing is you know just just investing early and getting things right upfront life pulling your data and consolidating it into into a single platform even though that takes a lot of time and and it's and it's quite challenging to sort of go back and redo things that's actually a huge investment in a big winter to really help you accelerate at the end that investment upfront does does pay off so congratulations on your Innovation Award thank you Davis is general manager at I I AG insurance Australia group here inside the cube sharing the best practices it's it's a world you got to do the homework upfront open source is the way it's and it's an operating model for innovation the cube bringing you all the action here on day two of coverage stay with us for more live right after this short break

(upbeat music) >> Announcer: Live from Las Vegas. It's theCUBE. Covering ServiceNow Knowledge 2018. Brought to you by ServiceNow. >> Welcome back to theCUBE's live coverage of ServiceNow Knowledge 18, #know18. I'm your host, Rebecca Knight along with Dave Vellante. We're joined by Gemma Kyle. She's the head of management assurance at MLC. She's straight from Sydney. So welcome Gemma. >> Thank you. Thank you very much. >> Let's start off by having you tell our viewers a little bit about MLC. >> Sure. MLC is a life insurance company. It's interestingly Australia's oldest and newest life insurance company. We were recently sold by National Australia Bank to Nipon Life, a Japanese life insurance company. And we are now, thanks to the investment of capital from Nipon into MLC Life Insurance, Australia's newest stand-alone life insurance company. So we come as a 130 year old company with 1.4 million customers. But also we're investing in new technology, new infrastructure, new processes, new business operations. >> So, insurance is one of those industries that hasn't been radically disrupted. And I wonder what the conversation is like internally. Is there a complacency? "Not in our industry," "Not in my lifetime," "I'll be retired by then." Or is there paranoia? >> It's a great question. Look, it hasn't been disrupted, but it will be. I can't talk for the American market, but certainly in the Australian market we have 17 players right now and we know that they are going to consolidate down to eight or nine and we want to be one of those eight or nine. The disruption is going to come from the fact that previously there had been complacency. Customers had not been communicated with, invariably because their life insurance sits within a broader wealth management product called superannuation. Now, what's happening is customers are becoming more informed, more demanding and want more access to more flexible and innovative products that can follow them through their life cycles of marriage and children and mortgage. We really need to be on the front foot to offer the right kind of products and life insurance products to meet our customers' need. >> So, superannuation was not a term that I was familiar with. And I don't want to go too deep into it, but it's basically Australia's version of Social Security, except you can see your money. You can invest, you have control over where it goes and it's yours. >> Gemma: That's correct. >> It's not just some black hole. >> Yeah, that's correct. >> Okay, nice. >> I was just thinking about when you were talking about how customers are starting to demand more. So, when you think about digital transformation, is that what's leading the charge, would you say? >> Absolutely, absolutely. When we talk about digital transformation, it's really about breaking down the silos that previously existed within companies. And it's not just life insurance companies, it's all types of financial services. So previously, we would have the actuaries who do the pricing of our products and the advisors who sell our products, were the gods of the insurance industry. Now, when we look towards digital transformation, it becomes technology, it becomes process and it becomes risk management and control that are in the ascendancy. This is really critical because customers are looking to self-serve. They want to make their own choices. So that's where we need to meet them. We need to break down the traditional barriers between the silos and have a single platform that we can use a data analytics to better serve our customers. >> So, the advisors are getting disrupted by the whole self service trend. The actuaries. Are we getting robo-actuaries now with machines? >> Well, I don't see why not. >> Right, the data's there. >> Yeah, absolutely. You know, we are actually required under regulation to have a chief actuary, and that absolutely makes sense. But what we're starting to have now is automatic underwriting engines. So, previously you would apply for an insurance policy and the underwriter would come in, who has an actuarial background, and they'd price the risk you present to the business. These days we have sufficient data, that as soon as you put your information into the system, we can automatically approve a policy for you. That automated underwriting is an example of the type of disruption we're starting to see. >> But humans are still the last mile, if necessary. Is that right? >> Gemma: Absolutely, absolutely. Advisors are important because they help our customers understand their financial need. And advisors are very strongly regulated within the Australian market. They're required to have a qualification. And we've recently seen changes to our legislation around the requirement to, evidence that they are treating customers honestly, efficiently and fairly. And selling them products that they need, not that they have been encouraged to sell by another supplier. >> What's your biggest challenge? (laughs) Top three. >> Yeah, yeah, top three. Look, without a doubt, it's cultural change. Cultural change. By cultural change, I mean the behaviors and the beliefs that surround not just internally how we manage risk and compliance, but also externally around how customers perceive the insurance industry. We definitely suffer from a lack of trust. Now, the disruption that we're facing is that customers are saying "We don't trust you," and it's not well founded. It actually doesn't bear out in the data in terms of how we pay our claims and how we service our customers. But there's certainly an image problem there. So, we think we need to service, we think we need to address this cultural issue from the inside out. We need to fix ourselves and make sure that we, can with integrity, defend the decisions we make around how we service our customers and then in turn have customers really trust us. See what we do. Trust us by how we behave, not just what we say. >> When you're talking about the behaviors, changing the behaviors, how is security, risk, compliance, how is that all perceived within your company? >> Yeah, yeah. Well, like I said, the actuaries are king. They have sophisticated data models through which they can price policies. And, where we've traditionally been with risk and compliance is very much in a qualitative space around actions that are undertaken, or senses that things aren't working as well as they should do. What we've started to do, and this service now has been absolutely critical to this journey, we're starting to shift the conversation away from risk and compliance and towards business process and control. We're simply shifting the conversation away from a focus on risk exposures, or the things you must do and onto the cost benefit analysis of control investment options. That allows our executive and our board to start to use data and analytics to drive decision making around where we need to focus our efforts. >> So you're turning all of this kind of back office risk oriented stuff into a value proposition for the organization. >> Gemma: Exactly right. >> Can you talk a little bit more on how ServiceNow participates in that process? >> Sure. Ultimately, the value proposition. It is about behaviors, but the value proposition is all around being able to defend the decisions that you make. Being able to demonstrate with data and analytics. And being able to put a quantified amount of money on the bottom line around what's the value to actually changing our behaviors, or changing the way we manage your process. ServiceNow is obviously critical to that, because they have this amazing performance analytics engine that enables us to draw data out of the system as it relates to business process. As it relates to operational loss events. As it relates to customer complaints. As it relates to asset management. And integrate it to tell a story of where we're most exposed. To loss today and potential loss tomorrow. It's a very powerful tool that even our, surprisingly, our CEO, not only does he now use his app that we've created for him, but he personally calls people in the office to say "Hey, I see you've got "an overdue action here, what are you doing about it?" Now, I know, nobody wants that call. (laughs) Nobody wants that call. So consequently, we've got this incredible tone from the top that reinforces how important it is to pay attention to your controls, to your obligations, to genuinely own them. That's when you start to see the cultural change. >> You don't do business in Europe, do you? >> Gemma: No. >> Is there a GDPR equivalent in Australia, if you're familiar with GDPR? >> Gemma: No, sorry. >> Okay, so, it's all about privacy. So, the GDPR? >> Gemma: Oh, oh. Yes, yes. >> The fines go into effect this month and that doesn't affect you because you're not doing business in Europe. But is there something similar in Australia where if a customer says "I want to know "what data you have on me." Or, "I want you to delete that data." You have to prove that. It's quite onerous. But, is there anything similar for you guys? >> Gemma: Absolutely, absolutely. So, we've actually got two things. First of all, we do have the privacy act. And under the privacy act that's been in place for quite some time, all individuals, whether you're an employee or a customer, you have access to your data. And, you also have the right to be taken off lists and call trees and the like. The government's just recently introduced CPS147, which is a prudential standard around data breaches. Now previously, if there was a breach of data. Say for example, we accidentally send a letter to the wrong customer, and in that letter it has personal details about somebody's medical history. Now previously that was not okay from a privacy perspective, but it wasn't notifiable to the regulator. With CPS147, we now have a notifiable data breach system. It's just come in place. And we have to notify the regulator when we breach somebody's privacy, somebody's data. And we could be subject to fines. >> And does the ServiceNow platform play a role in that, in terms of just tracking the notification, or compliance, or? >> Absolutely, absolutely. So, when the change in legislation was introduced, we simply added literally another little tick box into our operational loss event module to say, "Is this a data breach?" And just simply by doing that, now when you log a loss event, and you tick that little box, we can see from across the company where are our data breaches happening? And if they're a cluster. Is there something here that's telling us that we've got a systemic problem that we need to fix? As soon as it came in, we were automatically reporting on it. >> One of the things we're hearing is that there's so much great customer-on-customer learning that takes place at Knowledge. Are you finding that? Are you talking with a lot of customers, and about how you use the platform? And success? >> Absolutely. This is a really exciting conference. I'm really having such a good time and sort of overcoming my jet lag, to tell the truth. It is very exciting. In Australia, we've already started some groups. So, we work with other, myself and my systems manager, Greg Dominich, the two of us tend to go to a lot of companies to talk about our experiences with the implementation of ServiceNow. What worked well, what didn't work well and what we would do better. Because we want to create a community of practice. We want to lift the practice of risk management above where it currently sits. And so, walking around here there's so many networking events, and this hall in particular, is wonderful. So yes, talking to lots of other customers. Just sharing innovations. It's very exciting. >> How long have you, when did you go live with ServiceNow? >> Okay, so, we went live, let's see, probably in, the first GIC module went live in June, 2017. We then had another iteration in September, 2017. So, we've basically spread it out to make sure. The next modules we're looking to introduce are business continuity management. Each time, we follow the same, we use the PPM tool, that ServiceNow provides to actually implement the modules. But then we have a process that we follow ourselves in terms of putting in the data, cleaning it, categorizing it, making sure we've got the analytics right and then we step to the next module. Interestingly enough, cultural change doesn't happen once you've implemented the system. Cultural change starts at the very point where you recognize there's an opportunity to do better. So, as we implement each module, we're also maturing our practices. And we're also changing the culture of how the business approaches risk and compliance. >> What's your relationship with IT in all of this? How does that all work? >> Look, it's very close. It's part of the transformation journey that those silos still exist. And they exist because we all create our own languages for understanding our world and how we engage with a business. Now, it's about breaking down those barriers. So, we work very closely with them on security management, on business continuity management and on incident management. And we're going through the process now of aligning our language so that once we have that shared language, we have the shared data and we can really become quite powerful. >> Rebecca: Great. Well Gemma, thanks so much for coming on theCUBE. It's been a really fun conversation. >> Thank you very much. It's been nice to hear. >> I'm Rebecca Knight for Dave Vellante. We will have more from.

>> Announcer: Live from Las Vegas, it's theCUBE. Covering IBM Think 2018. Brought to you by IBM. >> Welcome back to theCUBE live at the inaugural IBM Think 2018 event. I'm Lisa Martin with Dave Vellante. Excited to be joined by a guest from down under, Garrett McDonald, the head of Enterprise Architecture at the Department of Human Services in Australia. Welcome to theCUBE. >> Thank you very much. >> Great to have you. So tell us about the Department of Human Services, DHS. You guys touch 99 percent of the Australian population. >> Yeah, we do. We sit within federal government, we're a large service delivery organization. So through a range of programs and services we touch pretty much every Australian citizen on an annual basis. And within our organization we're responsible for delivery of our national social welfare system, and that picks up people pretty much across the entire course of their lives at different points, we're also responsible for delivering the federally administered portion of our national health system, and that picks up pretty much every Australian every time you go to a doctor, a pharmacy, a hospital, a path lab, indirectly both the provider and the citizen are engaging with our services. We're responsible for running the child support system, but then we also provide IT services for other government departments, so we implement and operate for the Department of Veterans Affairs, and also the National Disability Insurance Agency. And then finally we also run Whole-of-government capabilities, so DHS we operate the myGov platform, that's a Whole-of-government capability for citizens who government authentication and within out program we have 12 million active users and that number continues to grow year on year, and that's the way that you access authenticated services for most of the major interactions that a citizen would have online with government. >> And your role is formerly CTO, right? >> Yep. >> You've got a new role. Can you explain it? >> Yeah, I'm a bit of a jack-of-all-trades within the senior executive at DHS, I've had roles in ICT infrastructure, the role of CTO, the role of national manager for Enterprise Architecture, and I've also had application delivery roles as well. >> Okay, so let's get into the healthcare talk because the drivers in that industry are so interesting, you've got privacy issues, in this country it's HIPAA, I'm sure you're got similar restrictions on data. Um, what's driving your business? You've got that regulation environment plus you've got the whole digital disruption thing going on. You've got cloud, private cloud, what's driving your organization from a technology perspective? >> I think there's two main factors there. We have changing citizen expectations, like we've got this continued explosion in the rate of changing technology, and through that people are becoming increasingly comfortable with the integration of technology in their lives, we've got people who are living their lives through social media platforms and have come to expect a particular user experience when engaging through those platforms, and they're now expecting the same experience when they interact with government. How do I get that slick user experience, how do I take the friction out of the engagement, and how do I take the burden out of having to interact with government? But at the same time, given we are a government agency and we do have data holdings across the entire Australian population, whether it's social welfare, whether it's health or a range of other services, there's this very very high focus on how do we maintain privacy and security of data. >> Yeah, I can't imagine the volumes of transactional data for 12 million people. What are some of the things that DHS is using or leveraging that relationship with IBM for to manage these massive volumes of data? You mentioned like different types of healthcare security requirements alone. What is that like? >> We've been using IBM as our dominant security partner for quite some years now, and it's been the use of data power appliances and ISM power appliances out at the edge to get the traffic into the organization. We're deploying Qradar as our Next Gen SIEM and we're slowly transitioning over to that. And then as we work out way through the mid-range platform through our investment in the power fleet and back to our System Z, we've been using Db2 on Z for quite some years in the health domain to provide that security, the reliability and the performance that we need to service the workloads that hit us on a day-to-day basis. >> So you got a little IoT thing going on. Right? You got the edge, you got the mainframe, you got Db2. Talk a little bit about how, because you've been a customer for a long time, talk about how that platform has evolved. Edge data, modernization of the mainframe, whether it's Linux, blockchain, AI, discuss that a little bit. >> Okay, so over the past three years we've been developing our Next Gen infrastructure strategy. And that really started off around about three years ago, we decided to converge on Enterprise Linux as our preferred operating system. We had probably five or six operating systems in use prior to that, and by converging down on Linux it's given us a, the ability to run same operating system whether it's on x86, on Power, or Z Linux, and that's allowed us to develop a broader range of people with deep skills in Linux, and that's really then given us a common platform upon which we can build an elastic private cloud to service our Next Gen application workloads. >> Now you've talked off-camera. No public cloud. Public cloud bad word (laughs) But you've chosen not to. Maybe discuss why and what you're doing to get cloud-like experiences. >> Yeah, so we are building out a private cloud and we do have a view towards public cloud at a point in the future, but given mandatory requirements we need to comply with within the Australian government around the use of the Cloud, given the sensitivity of the data that we hold. At this point we're holding all data on premise. >> Can we talk a little bit more about what you guys are doing with analytics and how you're using that to have a positive social impact for these 12 million Australians? >> Yeah, we've got a few initiatives on the go there. On how do we apply whether it's machine learning, AI, predictive analytics, or just Next Gen advanced analytics on how do we change the way we're delivering services to the citizens of Australia, how do we make it a more dynamic user experience, how do we make it more tailored? And on here that we're exploring at the moment is this considerable flexibility in our systems and how citizens can engage with them, so for example in the social welfare space we have a requirement for you to provide an estimate of the income you expect to learn over the next 12 months, and then based on what you actually earn through the year there can be an end-of-year true-up. Right, so that creates a situation where if you overestimate at the start of the year you can end up with an overpayment at the end of the year and we need to recover that. So what we're looking at doing is well how do we deploy predictive analytics so that we can take a look an an individual's circumstances and say well, what do we think the probability is that you may end up with an inadvertent overpayment, and how can we engage with you proactively throughout the year to help true that up so that you don't reach the end of the year and have an overpayment that we need to recover. >> So I wonder if we could talk about the data model. You talk about analytics, but what about the data model? As you get pressure from, you know, digital, let's call it. And healthcare is an industry that really hasn't been dramatically or radically transformed. It hasn't been Uberized. But the data model has largely been siloed, at least in my experience working with the healthcare industry. What's the situation in Australia, and specifically with regard to how do you get your data model in shape to be able to leverage it for this digital world? And I know you're coming at it from a standpoint of infrastructure, but maybe you could provide that context. >> Well, given for privacy reasons we continue to maintain a pretty strong degree of separation between categories of health data for a citizen, and we also have an initiative being deployed nationally around an electronic health record that the citizen is able to control, right, so when you create your citizen record, health record, there is a portion of data that is uploaded from our systems into that health record, and then a citizen can opt in around, well what information when you visit the general practitioner is available in that health record. When you go to a specialist you're able to control through privacy settings what information you're willing to share, so it's still a federated model, but there's a very, very strong focus on well how do we put controls in place so that the citizen is in control of their data. >> I want to follow up in that, this is really important, so okay, if I hear you correctly, the citizen essentially has access to and controls his or her own healthcare information. >> Yeah, that's right. And they're able to control what information are they willing to share with a given health practitioner. >> And it's pretty facile, it's easy for the citizen to do that. >> Yeah. >> And you are the trusted third party, is that right? Or -- >> It's a federated model, so we are a contributor to that service. We provide some of the functionality, we feed some of the data in, but we do have another entity that controls the overarching federation. >> Do you, is there a discussion going on around blockchain? I mean could you apply blockchain to sort of eliminate the need for that third party? And have a trustless sort of network? What's the discussion like there? >> We've been maintaining a watching brief on blockchain for a good couple of years now. We've been trying to explore, well how do we find an initial use case where we can potentially apply block chain where it provides a value and it meets the risk profile. And given it does need to be a distributed ledger, how do we find the right combination of parties where we can undertake a joint proof of technology to identify can we make this work. So not so much in HealthSpace, there are other areas where we're exploring at the moment. >> Okay, so you see the potential of just trying to figure out where it applies? >> Yeah, absolutely, and we're also watching the market to see well what's going to become the dominant distribution, how a regulatory framework's going to catch up and ensure that, you know apart from the technical implementation how do we make sure that it's governed, it's administered -- >> Do you own any Bitcoin? No, I'm just kidding. (laughter) How do you like in the Melbourne Cup? So, let's talk a little bit about the things that excite you as a technologist. We talked about a bunch of them, cloud, AI, blockchain, what gets you excited? >> I think the AI and machine learning is a wonderful area of emerging technology. So we've also been pushing quite hard with virtual assistants over the past two to three years, and we have six virtual assistants in the production environment. And those span both the unauthenticated citizen space, how do we assist them in finding information about the social welfare system, once you authenticate we have some additional virtual assistants that help guide you through the process, and then we've also been deploying virtual assistants into the staff-facing side. Now we have one there, she's been in production around about 18 months, and we've got very very complex social welfare legislation, policy, business rules, and when you're on the front line and you have a customer sitting in front of you those circumstances can be really quite complex. And you need to very quickly work through what areas of the policy are relevant, how do I apply them, how does this line up with the legislation, so what we've done is we've put a virtual assistant in place, it's a chat-based VA, and you can ask the virtual assistant some quite complex questions and we've had a 95 percent success rate on the virtual assistant answering a query on the first point of contact without the need to escalate to a subject matter expert and we figure that if we saved, we've had it round about a million questions answered in the last year, and if you think that each one of those probably saves around three minutes of time, engaging in SME, giving them the context and then sorting through to an answer, that's three million minutes of effort that our staff have been able to apply to ensuring that we get the best outcome for our citizen rather than working through how do I find the right answer. So that's a bit of a game-changer for us. >> What are some of the things that you're, related to AI, machine learning, cloud, that you're excited about learning this week at the inaugural IBM Think? And how it may really help your government as a service initiative, et cetera. >> Yeah, so I think I see a lot more potential in the space between say machine learning and predictive analytics. On based on what we know about an individual and based on what we know about similar individuals, how do we help guide that individual back to self-sufficiency? Right, so for many many years we've been highly effective and very efficient at the delivery of our services, but ultimately if we can get someone back to self-sufficiency, they're engaged in society, they're contributing to the economy, and I think that puts everyone in a pretty good place. >> Alright, so I got to ask you, I know again, architecture and infrastructure person, but I always ask everybody in your field. How long before machines are going to be able to make better diagnoses than doctors? >> Uh, not so sure about doctors, but within our space our focus has been on how do we use artificial intelligence and machine learning to augment human capability? Like, the focus is on within our business lines within our business lines we have room for discretion and human judgment. Right, so, we don't expect that the machines will be making the decisions, but given the complexity and the volume of the policy and legislation, we do think there's a considerable opportunity to use that technology to allow an individual to make the most informed and the most consistent and the most accurate decision. >> So then in your term you don't see that as a plausible scenario? >> No. >> Maybe not in our lifetime. >> As I said the focus is very much on, well, how do we augment human capability with emerging technology. >> So Garrett, last question and we've got about a minute left. What are some of the things that you are excited about in your new role as head of Enterprise Architecture for 2018 that you see by the end by the time we get to December, your summertime, that you will have wanted to achieve? >> Okay, so, over the last roughly two years I've been developing the future state technology design that will reshape out social welfare system for probably the next 30 years. This is a generational refresh we're undertaking in that space, so I think it's been a hard slog getting to this point, we're now starting to build on our new digital engagement layer, we've got a new enrichment layer starting to come to life where we do put that machine learning and AI in place and then we're also starting to rebuild the core of our social welfare system, so this is the year for me where we go from planning through to execution, and it brings me an immense sense of pleasure and pride to see the work that you've been pouring yourself into for many years start to come to fruition, start to engage with citizens, start to engage with other government agencies, and start to deliver the value that we know that it's capable of delivering. >> Well, sounds like a very exciting year ahead. We want to thank you so much, Garrett, for stopping by theCUBE and sharing the insights, what you guys are doing to help impact the lives of 12 million Australians. >> Thank you very much. >> Have a great event. >> Thank you. >> And for Dave Vellante I'm Lisa Martin. You're watching theCUBE's live coverage of the inaugural IBM Think 2018. Stick around, we'll be back with our next guest after a short break.