>> Narrator: Live from Nice, France. It's theCUBE, covering .NEXT Conference 2017 Europe. Brought to you by Nutanix. Hi, I'm Stu Miniman and this is SiliconANGLE Media's coverage of theCUBE at Nutanix .NEXT in East Frant. Really excited to have on the program Rob Lloyd who is the CEO of Hyperloop One. Off of the keynote this morning Rob, thank you so much for finding time to join us. It's great to join you. So, it's interesting. I've been watching Hyperloop since the day one when it got announced. I'm study mechanical engineering. So transportation's something I looked at. But I've been in the tech world. I knew you back from your Cisco days. When I talked to some friends of mine that didn't know about Hyperloop, you kind of explain it was like, "Oh! Remember the bank pneumatic tubes? "It's like we're going to do that with people." And they're like, "That sounds crazy!" And then you say, "Well, Elon Musk is behind it." And they're like, "Well, OK, another Elon Musk thing "that's probably going to be near impossible, "but they will find a way to make it work." >> Rob: Mm-hmm. You talked a little about your journey in the keynote this morning, but let's start, Hyperloop One. Pre-revenue, give kind of a thumbnail of the company and where you are today. So it's a three year old company. Literally in a garage, in the very late part of 2014, our founder, co-founder, started on November 2nd. So, just an idea. A white paper that Elon wrote, which was the concepts of something very different. A new mode of transportation. Slightly outraged by the expense and archaic nature of the High-Speed Rail in California proposal. So, that's the starting point. A company that was founded, co-founded by Shervin Pishevar, a venture capitalist, and some brilliant engineer, Josh Giegel. Now, from that point, 300 people, 245 million dollars raised and just this summer, having only started on the designs, tested some concepts: a magnetic levitation, a custom-design linear electric motor, evacuating a tube to the equivalent of 200,000 feet above the Earth's surface. We built a full-scale prototype. 500 meters prove the tech is working, and the cool part is that the speed with which this engineering and development is occurring is like nothing else. So, it's kind of DevOps for hardware. And we saw what happened when people kind of went to Agile Development Methodologies. We saw it in tech. But it really hasn't hit the traditional methods of transportation, where people build in silos. They're not closely associated with a fabricator or a welder. And we have mechanical engineers working with fabricators working with welders, and you make amazing progress when you see that happen. You stated, it's been over 100 years since we had a kind of major new transportation model. The tooling that allows you to prototype this... I know, it's kind of, friend of mine, it's watching the space stuff and watching the videos that you put out. Everything from testing the engine, to the pod. If I remember, wasn't there a contest around the pods, too? Well, actually, yeah. The tools we have today, the analytics tools, the way we can model things, didn't exist when we did our first moonshots, when the United States said we're going to put a man on the moon and NASA was mobilized and the country was excited. We didn't have the tools we have today. So we have much, much better tools. We have methodologies and approaches that are not accepted everywhere but are embraced in our company. And you make things happen that were taking five years from an engineering perspective. And you can build a full-scale prototype in 10 months. That really changes the speed with which this can occur. Most people say this would take a decade. This is going to take three years. As I said, like other Elon Musk companies, you've got strong conviction at Hyperloop One. Some of the things that kind of skeptics come up with reminds me a lot of autonomous vehicles. "Ah, well, regulators are never going to let it happen, "and, gosh, safety." It's like "I'm putting software? "It's going to do this? We're going to be going at what speeds?" And you know, "How fast?" And all these things. How long's this going to take before its reality? Can you give us a little bit of that road map, as to how we make sure that once somebody actually goes in this, that they're not going to end up just completely flattened? So, the way that we're approaching this is actually different than the way in which most new technologies are regulated in transportation. We're going to partner with one or two countries. We're going to partner with the regulator while we're designing the commercial version of our technology. So while we commercialize, which is the next two to three years of our road map, we know the tech works. Now you build a commercial offer. You build the car. You build the pod. We will commercialize this. We're going to work with them now, so we don't come to them in three to five years and say, "Would you please certify this?" And in doing so, we actually bring a huge opportunity to the countries that go first. We ran a global competition, to kind of AKA Peter Diamandis, who's on our board, like the X Prize. We actually asked countries, "Who'd like to build the world's first Hyperloop?" 2,600 people registered. 100 serious submissions. Dozens of them are now real projects moving forward with government support. So, the short answer is, we have to do it differently. We're going to partner with a regulator while we're commercializing the tech. And then when we get there, of course you want it to be safe. Of course you'll need certification. But you do that now rather than later. And you'll end up bringing benefits to a country that chooses to go first. Did I hear right that the first solution is probably going to be in the Middle East? There is a good probability that's the case. The land is fairly flat. We can build along existing right-of-ways. There's massive investments in airports and ports there. Wherever there's a very dense transportation hub today, airports, downtown centers, connections to metro or train stations, that's where we want to put kind of a Hyperloop portals. So think of it as the backbone between two data centers. All the activity going on in the data center, we want to connect those high-density locations. But it's not just one-to-one. We can branch on and branch off. So it's sort of like point-to-point packet switching. One of the things that really excited me in your presentation that I didn't know as well is you talked about kind of the sustainability, the energy of this compared to other options, as well as the affordability. Something that really could help a lot of environments. Could you speak to those? Yeah, so... There is absolute science about the substitution rates that will go to a faster mode of transportation if the price is right. So, our model as we analyze opportunities around the world, in the United States, in Europe, Northern Europe, Canada, India, and the Middle East, where we see a lot of our projects today... If we price at the same price level of the current mode of transport, you'll get almost 100% conversion, because why not? Why wouldn't I go, in nine minutes, to Abu Dhabi from Dubai, instead of what could be a two-hour car drive? But why not price it for the ticket of a metro ride? Then you'll get really high ridership, high utilization. The economics of building infrastructure, a PPP structure that would bring private equity, debt, pension funds, sovereign funds together, to invest in that new infrastructure, that's how it's going to work. So that's the passenger case. And then on the freight side, you know, seriously, we forgot that this on-demand economy is based on a transportation network that effectively is 100 to 200 years old. Steel cans, right? This idea of a container was invented, a standard-sized container that goes on ships. The ships unload them in ports. They sit a couple of days. Then a truck puts it on the back, and they drive through our cities. Or it goes on the back of a train and takes seven to ten days to get to its consumer. That doesn't work anymore, in this world of Amazon, on-demand, Alibaba commerce. The only option they have is to pay for air freight, which is five to six times more than it would cost to carry those same packages and goods in a Hyperloop cargo system. Huge opportunity. Rob, speak about sustainability, kind of the energy required for this compared to other modes of transportation. We take some energy to remove the pressure inside the tube which obviously reduces resistance. It's an all-electric motor. Because we have little resistance and no friction, because we're floating on magnets, effectively floating on a magnetic cushion, once you're up to speed, you're pretty much gliding, like gliding in space. >> Stu: What speed do we think that'll be? Well, by the way, this really is, I'm being very candid, it depends on the route. It depends on how straight we can get a right-of-way. It depends on the levels, so flat and straight means you can go fast. If you're going to go 300 kilometers, we can go six, seven, eight hundred miles an hour which is faster than an aircraft. And obviously city center to city center, then we don't have the drive of an hour and a half, the vagaries of weather, and all that other stuff, which has made air travel for most of us just a somewhat demoralizing experience. So, solar power, wind power, and in some environments where we do have a lot of sun, we can just have the tube covered with solar panels and make the entire thing energy neutral, which is really, really amazing. A new mode of transportation that doesn't consume any energy. Yeah, maybe Elon can help with some of the solar stuff. Elon's got that stuff. How much is Elon involved? So he's not involved in our company. His idea, right? His brainchild. Our company was formed to commercialize that, and there are others that are now in this market. I think we're the leader. No, I know we're the leader. We've demonstrated the technology no one else has. And we're there. I mean, this is a go-for-it business. So we're going for it. Well you just had a new partnership with Virgin announced recently. So Richard Branson, you know... Yeah, so Virgin Hyperloop One, a brand that actually has been known for customer experience, thinking of the customer, delivering an experience, taking on the giants as he did with Virgin Atlantic, putting people into space now from a commercial perspective, as well as satellites. So think of his companies and transportation and how that brings comfort to governments and investors, that we're here to actually really make something big happen, and Richard's done that. He's a serial entrepreneur. And that brand typically stands for an excellent experience. Yeah. He has pretty good track record as a risk taker out there, too. Some of the extreme things that he had done, but absolutely, the comfort and the brand there... Pre-revenue, you said a couple of years until we're there, but you mentioned even that you've got kind of a pipeline of orders already, so sounds like-- Well the projects are big, so this is infrastructure. We won't be financing that. That will be done by people that find governments and pension funds and sovereign funds and insurance companies that invest in infrastructure. But if you take a look at the projects because they're big, they start with billions and they go up from there. So it's kind of fun to think that you're first order could be three billion. It's kind of neat to go from this pre-revenue stage to the size of projects that we'll have. That three billion will be spent on, some will be on contractors, some will be on infrastructure, but for us, the revenues that will come will be high margin. We're building a software platform that will connect with other modes of transport and manage the massive amounts of data we'll be collecting off the pod and the track, the headway between these vehicles, which could be as close as ten seconds traveling at that speed, and then obviously you've got to have a whole lot of control software and a whole IoT Platform built in. Last question I have for you. We're here at a technology show. Just throw out there: software, massive amounts of data, I've got to have the analytics going into it, and there, is the tech all ready? How's the industry doing to support some of these kind of moonshot-type of activities? High-speed networking is going to be a big deal for us. So we probably need kind of an evolution of 5G because we're moving so fast inside that enclosed area that we're going to need some radio technology to keep all of those devices connected. That's a little bit of a push. Listen, we're starting from scratch. So we have a clean sheet. So we have legacy to integrate. That's typically an advantage. We're not trying to do mechanical switching. We'll do a digital switch, which means you'll actually just bump a vehicle off onto an on-ramp and weave it back in with software, kind of like packets on the network. But clean sheet. I think we have tools required to do everything that we're looking for today, an industry that's evolved, has developed around IoT, and a plethora of options that our architects and engineers are working on today. Gosh, all my background thinking about packet loss and things like that, it gets me a little bit nervous, but I know you've got lots of engineers working to solve that problem. Rob Lloyd, Hyperloop One. Really appreciate you joining us. I'm Stu Miniman. We'll be back with lots more coverage here from Nutanix .NEXT in Nice, France. You're watching theCUBE.

>>Well, hi everybody. I'm John Walls here and welcome back to the cubes, continuing coverage and splunk.com 21. And we've talked a lot about data, obviously, um, and a number of partnerships and the point of resources that it's going on in this space. And certainly a very valuable partnership that Splunk has right now is one with Intel. And with me to talk a little bit more about that is Rick Echavarria, who is the vice president of sales and the marketing group at Intel. Rick. Good to see it today. Thanks for joining us on the queue. It's >>Good to see you, John, and thanks for having us. >>You bet. No glad to have you as part of the.com coverage as well. Um, well, first off, let's just for folks at home, uh, who would like to learn more about this relationship, the Splunk Intel partnership, if you would give us that the 30,000 foot picture of it right now, in terms of, of how it began and how it's evolved to the point where it resides today. >>Yeah. Uh, sure. Glad to do that. You know, Splunk is had for many years, uh, position as, as one of the world's best, uh, security information and event management platform. So just like many customers in the cybersecurity space, they're probably trying to retire their technical debt. And, and what are the areas of important focuses to SIM space, right? The SIM segment within cybersecurity. And so the initial engagement between Intel and Splunk started with the information security group at Intel, looking to, again, retire the technical debt, bring next generation SIM technology. And that started, uh, the engagement with Splunk again, to go solve the cybersecurity challenges. One of the things that we quickly learned is that, uh, those flung offers a great platform, you know, from a SIM point of view, as you know, the cyber security segment, the surface area of attack, the number of attacks kids were increased. >>And we quickly realized that this needed to be a collaboration in order for us to be able to work together, to optimize our infrastructure. So it could scale, it could be performance, it could be reliable, uh, to protect Intel's business. And as we started to work with Splunk, we realized, Hey, this is a great opportunity. Intel is benefiting from it. Why don't we start working together and create a reference architecture so that our joint customers also benefit from the collaboration that we have in the cybersecurity space, as we were building the Intel cybersecurity infrastructure platform. So that re that was really the beginning of, uh, of the collaboration around described here and a little bit more, >>Right? So, so you had this, this good working relationship and said, Hey, why don't we get together? Let's get the band together and see what we can do for our car joint clients down the road. Right. So, so what about those benefits that, because you've now you've got this almost as force multiplier right. Of, of Intel's experience. And then what Splunk has been able to do in the data analytics world. Um, what kind of values are being derived, do you think with that partnership? >>Well, obviously we feel much better about our cyber security posture. Um, and, uh, and what's sort of interesting, John, is that we realized that we were what started out as a conversation on SIM. Uh, it really turned out to be an opportunity for us to look at Splunk as a data platform. And, you know, in the technology world, you sometimes hear people talk about the horizontal capabilities. Then the vertical usage is really the security. Uh, the SIM technology. It really became one of several, sorry about the noise in the background. One, uh, became a vertical application. And then we realized that we can apply this platform to some other usages. And in addition to that, you know, when you think about cybersecurity and what we use for SIM that tends to be part of your core systems in it, we started to explore what can we do with what could we do with other data types for other different types of applications. >>And so what we, what we decided to do is we would go explore usages of this data at the edge, uh, of, of the network, and really started to move into much more of that operational technology space. When we realized that Splunk could really, uh, that we could integrate that we can ingest other types of data. And that started a second collaboration around our open Vino technology and our AI capabilities at the edge with the ingestion and the machine learning capabilities of Splunk, so that we can take things like visual data and start creating dashboards for, for example, uh, managing the flow of people, you know, especially in COVID environment. So, uh, and understanding utilization of spaces. So it really started with SIM is moved to the edge. And now we realized that there's a continuum in this data platform that we can build other usages around. >>What was that learning curve like when you went out to the edge, because a lot of people are talking about it, right. And there was a lot of banter about this is where we have to be, but you guys put your money where your mouth was, right? Yeah. You went out, you, you explored that frontier. And, and so what was that like? And, and, and what I guess maybe kind of being early in, uh, what advantage do you think that has given you as that process has matured a little bit? >>Well, it's really interesting John, because what really accelerated our engagement with Splunk in that space was the pandemic. And we had, uh, in 2020 Intel announced the pandemic response technology initiative, where we decided we were going to invest $50 million in accelerating technologies and solutions and partnerships to go solve some of the biggest challenges that depend on them. It was presenting to the world at large. And one of those areas was around companies trying to figure out how to, how to manage spaces, how to manage, you know, the number of people that are in a particular space and social distancing and things of that nature. And, you know, we ended up engaging with Splunk and this collaboration, again, to start looking at visual data, right, integrating that with our open Vino platform and again, their machine learning and algorithms, and start then creating what you would call more operational technology types of application based on visual data. Now these will have other applications that could be used for security usages. It could be used for, again, social distancing, uh, the utilization of acids, but their pandemic and that program that ends the launch is really what became the catalyst for our collaboration with Splunk that allowed us to expand into space. >>Right. And you've done a tremendous amount of work in the healthcare space. I mean, especially in the last year and a half with Penn and the pandemic, um, can you give just a couple of examples of that maybe the variety of uses and the variety of, uh, processes that you've had an influence in, because I think it's pretty impressive. >>Yeah. We, um, there's quite a bit of breadth in the types of solutions we've deployed as part of the pandemic response. John, you can think of some of the, I wouldn't call these things basic things, but you think about telehealth and that improving the telehealth experience all the way to creating privacy aware or sorry, solutions for privacy sensitive usage is where you're doing things like getting multiple institutions to share their data with the right privacy, uh, which, you know, going back to secure and privacy with the right, uh, protections for that data, but being allowed, allowing organization a and organization B partner together use data, create algorithms that both organizations benefit from it. An example of that is, is work we've done around x-ray, uh, and using x-rays to detect COVID on certain populations. So we've gone from those, you know, data protection, algorithm, development, development type of solutions to, to work that we've done in tele-health. So, uh, and, and a lot of other solutions in between, obviously in the high-performance, uh, space we've invested in high-performance computing for, to help the researchers, uh, find cures, uh, for the current pandemic and then looking at future pandemic. So it's been quite a breadth of, uh, uh, of solutions and it's really a Testament also to the breadth of Intel's portfolio and partnerships to be able to, uh, enable so much in such a short amount of time. >>I totally agree, man. Just reading it a little bit about it, about that work, and you talk about the, the breadth of that, the breadth and the depth of that is certainly impressive. So just in general, we'll just put healthcare in this big lump of customers. So what, what do you think the value proposition of your partnership with Splunk is in terms of providing, you know, ultimate value to your customers, because you're dealing with so many different sectors. Um, but if you could just give a summary from your perspective, this is what we do. This is why this power. >>Yeah. Well, customers, uh, talk about transformation. You know, there's a lot of conversation around transformation, right before the pandemic and through and center, but there's a lot of talk about companies wanting to transform and, you know, in order to be able to transform what are the key elements of that is, uh, to be able to capture the right data and then take, turn that data into the right outcomes. And that is something that requires obviously the capabilities and the ability to capture, to ingest, to analyze the data and to do that on an infrastructure that is going to scale with your business, that is going to be reliable. And that is going to be, to give you the flexibility for the types of solutions that you're wanting to apply. And that's really what this blog, uh, collaboration with Intel is going to do. It's, it's just a great example, John, uh, of the strategy that our CEO, pat Gelsinger recently talked about the importance of software to our business. >>This plump collaboration is right in the center of that. They have capabilities in SIM in it observability, uh, in many other areas that his whole world is turning data into, you know, into outcomes into results. But that has to be done on an infrastructure that again, will scale with your business, just like what's the case with Intel and our cybersecurity platform, right? We need to collaborate to make sure that this was going to scale with the demand demands of our business, and that requires close integration of, of hardware and software. The other point that I will make is that the, what started out as a collaboration with between Intel and Splunk, it's also expanding to other partners in the ecosystem. So I like to talk to you a little bit on a work stream that we have ongoing between Intel Splunk, HPE and the Lloyd. >>And why is that important is because, uh, as customers are deploying solutions, they're going to be deploying applications and they're going to have data in multiple environments on premise across multiple clouds. And we have to give, uh, these customers the ability to go gather the data from multiple sources. And that's part of the effort that we're developing with HPE and the Lloyd's will allow people to gather data, perform their analytics, regardless, regardless of their where their data is and be able to deploy the Splunk platform across these multiple environments, whether it's going to be on prem or it's going to be in a pure cloud environment, or it's going to be in a hybrid with multiple clouds, and you're willing to give our customers the most flexibility that we can. And that's where that collaboration with Deloitte and HP is going to come into play. >>Right. And you understand Splunk, right? You will get the workload. I mean, it's, it's totally, there's great familiarity there, which is a great value for that customer base, because you could apply that. So, so, um, obviously you're giving us like multiple thumbs up about the partnership. What excites you the most about going forward? Because as you know, it's all about, you know, where are we going from here? Yes. Now where we've been. So in terms of where you're going together in that partnership, well, what excites you about that? >>Well, first of all, we're excited because it's just a great example of the value that we can deliver to customers when you really understand their pain points and then have the capability to integrate solutions that encompass software and hardware together. So I think that the fact that we've been able to do the work on, on that core SIM space, where we now have a reference architecture that shows how you could really scale and deliver that a Splunk solution for your cybersecurity needs in a, in a scale of one reliable and with high levels of security, of course. And the fact that we then also been able to co-develop fairly quickly solutions for the edge, allows customers now to have that data platform that can scale and can access a lot of different data types from the edge to the cloud. That is really unique. I think it provides a lot of flexibility and it is applicable to a lot of vertical industry segments and a lot of customers >>And be attractive to a lot of customers. That's for sure rec edge of area. We appreciate the time, always a good to see you. And we certainly appreciate your joining us here on the cube to talk about.com for 21. And your relationship with the folks at Splunk. >>Yeah. Thank you, John. >>You bet. Uh, talking about Intel spot, good partnership. Long time, uh, partnership that has great plans going forward, but we continue our coverage here of.com 21. You're watching the cube.

>>Yeah, >>yeah. Hello and welcome to this track to creating engaging analytics experiences for all. I'm Hannah Sinden Thought spots Omiya director of marketing on. I'm delighted to have you here today. A boy Have we got to show for you now? I might be a little bit biased as the host of this track, but in my humble opinion, you've come to a great place to start because this track is all about everything. Thought spot. We'll be talking about embedded search in a I thought spot one spot I. Q. We've got great speakers from both thoughts about andare customers as well as some cool product demos. But it's not all product talk. We'll be looking at how to leverage the tech to give your users a great experience. So first up is our thoughts about one deep dive. This session will be showing you how we've built on our already superb search experience to make it even easier for users across your company to get insight. We've got some great speakers who are going to be telling you about the cool stuff they've been working on to make it really fantastic and easy for non technical people to get the answers they need. So I'm really delighted to introduce Bob Baxley s VP of design and experience That thought spot on Vishal Kyocera Thought spots director of product management. So without further ado, I'll hand it over to Bob. Thanks, >>Hannah. It's great to be here with everybody today and really excited to be able to present to you thought spot one. We've been working on this for months and months and are super excited to share it before we get to the demo with Shawl, though, I just want to set things up a little bit to help people understand how we think about design here. A thought spot. The first thing is that we really try to think in terms of thought. Spot is a consumer grade product, terms what we wanted. Consumer grade you x for an analytics. And that means that for reference points rather than looking at other enterprise software companies, we tend to look at well known consumer brands like Google, YouTube and WhatsApp. We firmly believe that people are people, and it doesn't matter if they're using software for their own usage or thought are they're using software at work We wanted to have a great experience. The second piece that we were considering with thoughts about one is really what we call the desegregation of bundles. So instead of having all of your insights wraps strictly into dashboards, we want to allow users to get directly to individual answers. This is similar to what we saw in music. Were instead of you having to buy the entire album, of course, you could just buy individual songs. You see this in iTunes, Spotify and others course. Another key idea was really getting rid of gate keepers and curators and kind of changing people from owning the information, helping enable users to gather together the most important and interesting insights So you can follow curator rather than feeling like you're limited in the types of information you can get. And finally, we wanted to make search the primary way, for people are thinking about thought spot. As you'll see, we've extended search from beyond simply searching for your data toe, also searching to be able to find pin boards and answers that have been created by other people. So with that, I'll turn it over to my good friend Rachel Thio introduce more of thought, spot one and to show you a demo of the product. >>Thank you, Bob. It's a pleasure to be here to Hello, everyone. My name is Michelle and Andy, product management for Search. And I'm really, really excited to be here talking about thoughts about one our Consumer analytics experience in the Cloud. Now, for my part of the talk, we're gonna first to a high level overview of thoughts about one. Then we're going to dive into a demo, and then we're gonna close with just a few thoughts about what's coming next. So, without any today, let's get started now at thought spot. Our mission is to empower every user regardless of their expertise, to easily engage with data on make better data driven decisions. We want every user, the nurse, the neighborhood barista, the teacher, the sales person, everyone to be able to do their jobs better by using data now with thoughts about one. We've made it even more intuitive for all these business users to easily connect with the insights that are most relevant for them, and we've made it even easier for analysts to do their jobs more effectively and more efficiently. So what does thoughts about one have? There's a lot off cool new features, but they all fall into three main categories. The first main category is enhanced search capabilities. The second is a brand new homepage that's built entirely for you, and the third is powerful tools for the analysts that make them completely self service and boost their productivity. So let's see how these work Thought Spot is the pioneer for search driven analytics. We invented search so that business users can ask questions of data and create new insights. But over the years we realized that there was one key piece off functionality that was missing from our search, and that was the ability to discover insights and content that had already been created. So to clarify, our search did allow users to create new content, but we until now did not have the ability to search existing content. Now, why does that matter? Let's take an example. I am a product manager and I am always in thought spot, asking questions to better understand how are users are using the product so we can improve it now. Like me, A lot of my colleagues are doing the same thing. Ah, lot of questions that I asked have already been answered either completely are almost completely by many of my colleagues, but until now there's been no easy way for me to benefit from their work. And so I end up recreating insights that already exists, leading to redundant work that is not good for the productivity off the organization. In addition, even though our search technology is really intuitive, it does require a little bit of familiarity with the underlying data. You do need to know what metric you care about and what grouping you care about so that you can articulate your questions and create new insights. Now, if I consider in New employees product manager who joins Hotspot today and wants to ask questions, then the first time they use thought spot, they may not have that data familiarity. So we went back to the drawing board and asked ourselves, Well, how can we augment our search so that we get rid off or reduced the redundant work that I described? And in addition, empower users, even new users with very little expertise, maybe with no data familiarity, to succeed in getting answers to their questions the first time they used Hot Spot, and we're really proud and excited to announce search answers. Search answers allows users to search across existing content to get answers to their questions, and its a great compliment to search data, which allows them to search the underlying data directly to create new content. Now, with search answers were shipping in number of cool features like Answer Explainer, Personalized search Results, Answer Explorer, etcetera that make it really intuitive and powerful. And we'll see how all of these work in action in the demo. Our brand new homepage makes it easier than ever for all these business users to connect with the insights that are most relevant to them. These insights could be insights that these users already know about and want to track regularly. For example, as you can see, the monitor section at the top center of the screen thes air, the KP eyes that I may care most about, and I may want to look at them every day, and I can see them every day right here on my home page. By the way, there's a monitoring these metrics in the bankrupt these insights that I want to connect with could also be insights that I want to know more about the search experience that I just spoke about ISS seamlessly integrated into the home page. So right here from the home page, I can fire my searchers and ask whatever questions I want. Finally, and most interestingly, the homepage also allows me to connect with insights that I should know about, even if I didn't explicitly ask for them. So what's an example? If you look at the panel on the right, I can discover insights that are trending in my organization. If I look at the panel on the left, I can discover insights based on my social graph based on the people that I'm following. Now you might wonder, How do we create this personalized home page? Well, our brand new, personalized on boarding experience makes it a piece of cake as a new business user. The very first time I log into thought spot, I pay three people I want to follow and three metrics that I want to follow, and I picked these from a pool of suggestions that Ai has generated. And just like that, the new home page gets created. And let's not forget about analysts. We have a personalized on boarding experience specifically for analysts that's optimized for their needs. Now, speaking of analysts, I do want to talk about the tools that I spoke off earlier that made the analysts completely self service and greatly boost their productivity's. We want analysts to go from zero to search in less than 30 minutes, and with our with our new augmented data modeling features and thoughts about one, they can do just that. They get a guided experience where they can connect, model and visualize their data. With just a few clicks, our AI engine takes care off a number of tasks, including figuring out joints and, you know, cleaning up column names. In fact, our AI engine also helps them create a number of answers to get started quickly so that these analysts can spend their time and energy on what matters most answering the most complicated and challenging and impactful questions for the business. So I spoke about a number of different capabilities off thoughts about one, but let's not forget that they are all packaged in a delightful user experience designed by Bob and his team, and it powers really, really intuitive and powerful user flows, from personalized on boarding to searching to discover insights that already exist on that are ranked based on personalized algorithms to making refinements to these insights with a assistance to searching, to create brand new insights from scratch. And finally sharing all the insights that you find interesting with your colleagues so that it drives conversations, decisions and, most importantly, actions so that your business can improve. With that said, let's drive right into the demo for this demo. We're going to use sales data set for a company that runs a chain off retail stores selling apparel. Our user is a business user. Her name is Charlotte. She's a merchandiser, She's new to this company, and she is going to be leading the genes broader category. She's really excited about job. She wants to use data to make better decisions, so she comes to thought spot, and this is what she sees. There are three main sections on the home page that she comes to. The central section allows you to browse through items that she has access to and filter them in various ways. Based for example, on author or on tags or based on what she has favorited. The second section is this panel on the right hand side, which allows her to discover insights that are trending within her company. This is based on what other people within her company are viewing and also personalized to her. Finally, there's this search box that seamlessly integrated into the home page. Now Charlotte is really curious to learn how the business is doing. She wants to learn more about sales for the business, so she goes to the search box and searches for sales, and you can see that she's taken to a page with search results. Charlotte start scanning the search results, and she sees the first result is very relevant. It shows her what the quarterly results were for the last year, but the result that really catches her attention is regional sales. She'd love to better understand how sales are broken down by regions. Now she's interested in the search result, but she doesn't yet want to commit to clicking on it and going to that result. She wants to learn more about this result before she does that, and she could do that very easily simply by clicking anywhere on the search result card. Doing that reveals our answer. Explain our technology and you can see this information panel on the right side. It shows more details about the search results that she selected, and it also gives her an easy to understand explanation off the data that it contains. You can see that it tells her that the metrics sales it's grouped by region and splitter on last year. She can also click on this preview button to see a preview off the chart that she would see if she went to that result. It shows her that region is going to be on the X axis and sales on the Y axis. All of this seems interesting to her, and she wants to learn more. So she clicks on this result, and she's brought to this chart now. This contains the most up to date data, and she can interact with this data. Now, as she's looking at this data, she learns that Midwest is the region with the highest sales, and it has a little over $23 million in sales, and South is the region with the lowest sales, and it has about $4.24 million in sales. Now, as Charlotte is looking at this chart, she's reminded off a conversation she had with Suresh, another new hire at the company who she met at orientation just that morning. Suresh is responsible for leading a few different product categories for the Western region off the business, and she thinks that he would find this chart really useful Now she can share this chart with Suresh really easily from right here by clicking the share button. As Charlotte continues to look at this chart and understand the data, she thinks, uh, that would be great for her to understand. How do these sales numbers across regions look for just the genes product category, since that's the product category that she is going to be leading? And she can easily narrow this data to just the genes category by using her answer Explorer technology. This panel on the right hand side allows her to make the necessary refinements. Now she can do that simply by typing in the search box, or she can pick from one off the AI generated suggestions that are personalized for her now. In this case, the AI has already suggested genes as a prototype for her. So with just a single click, she can narrow the data to show sales data for just jeans broken down by region. And she can see that Midwest is still the region with the highest sales for jeans, with $1.35 million in sales. Now let's spend a minute thinking about what we just saw. This is the first time that Charlotte is using Thought spot. She does not know anything about the data sources. She doesn't know anything about measures or attributes. She doesn't know the names of the columns. And yet she could get to insights that are relevant for her really easily using a search interface that's very much like Google. And as she started interacting with search results, she started building a slightly better understanding off the underlying data. When she found an insight that she thought would be useful to a colleague offers, it was really seamless for her to share it with that colleague from where she Waas. Also, even though she's searching over content that has already been created by her colleagues in search answers. She was in no way restricted to exactly that data as we just saw. She could refine the data in an insight that she found by narrowing it. And there's other things you can do so she could interact with the data for the inside that she finds using search answers. Let's take a slightly more complex question that Charlotte may have. Let's assume she wanted to learn about sales broken down by, um, by category so that she can compare her vertical, which is jeans toe other verticals within the company. Again, she can see that the very first result that she gets is very relevant. It shows her search Sorry, sales by category for last year. But what really catches her attention about this result is the name of the author. She's thrilled to note that John, who is the author of this result, was also an instructor for one off for orientation sessions and clearly someone who has a lot of insight into the sales data at this company. Now she would love to see mawr results by John, and to do that, all she has to do is to click on his name now all of the search results are only those that have been authored by John. In fact, this whole panel at the top of the results allow her to filter her search results or sort them in different ways. By clicking on these authors filter, she can discover other authors who are reputed for the topic that she's searching for. She can also filter by tags, and she can sort these results in different ways. This whole experience off doing a search and then filtering search results easily is similar to how we use e commerce search engines in the consumer world. For example, Amazon, where you may search for a product and then filter by price range or filter by brand. For example, Let's also spend a minute talking about how do we determine relevance for these results and how they're ranked. Um, when considering relevance for these results, we consider three main categories of things. We want to first make sure that the result is in fact relevant to the question that the user is asking, and for that we look at various fields within the result. We look at the title, the author, the description, but also the technical query underpinning that result. We also want to make sure that the results are trustworthy, because we want users to be able to make business decisions based on the results that they find. And for that we look at a number of signals as well. For example, how popular that result is is one of those signals. And finally, we want to make sure the results are relevant to the users themselves. So we look at signals to personalize the result for that user. So those are all the different categories of signals that we used to determine overall ranking for a search result. You may be wondering what happens if if Charlotte asks a question for which nobody has created any answer, so no answers exist. Let's say she wants to know what the total sales of genes for last year and no one's created that well. It's really easy for her to switch from searching for answers, which is searching for content that has already been created to searching the data directly so she can create a new insight from scratch. Let's see how that works. She could just click here, and now she's in the search data in her face and for the question that I just talked about. She can just type genes sales last year. And just like that, she could get an answer to her question. The total sales for jeans last year were almost $4.6 million. As you can see, the two modes off search searching for answers and searching, the data are complementary, and it's really easy to switch from one to the other. Now we understand that some business users may not be motivated to create their own insights from scratch. Or sometimes some of these business users may have questions that are too complicated, and so they may struggle to create their own inside from scratch. Now what happens usually in these circumstances is that these users will open a ticket, which would go to the analyst team. The analyst team is usually overrun with these tickets and have trouble prioritizing them. And so we started thinking, How can we make that entire feedback loop really efficient so that analysts can have a massive impact with as little work as possible? Let me show you what we came up with. Search answers comes with this system generated dashboard that analysts can see to see analytics on the queries that business users are asking in search answers so it contains high level K P. I is like, You know how many searches there are and how many users there are. It also contains one of the most popular queries that users are asking. But most importantly, it contains information about what are popular queries where users are failing. So the number on the top right tells you that about 10% off queries in this case ended with no results. So the user clearly failed because there were no results on the table. Right below it shows you here are the top search queries for original results exist. So, for example, the highlighted row there says jean sales with the number three, which tells the analysts that last week there were three searches for the query jean sales and the resulted in no results on search answers. Now, when an analyst sees a report like this, they can use it to prioritize what kind of content they could be creating or optimizing. Now, in addition to giving them inside into queries which led to no results or zero results. This dashboard also contains reports on creatives that lead to poor results because the user did get some results but didn't click on anything, meaning that they didn't get the answer that they were looking for. Taking all these insights, analysts can better prioritize and either create or optimize their content to have maximum impact for their business users with the least amount of for. So that was the demo. As you can see with search answers, we've created a very consumer search interface that any business user can use to get the answers to their questions by leveraging data or answers that have already been created in the system by other users in their organization. In addition, we're creating tools that allow analysts toe create or optimized content that can have the highest impact for these business users. All right, so that was the demo or thoughts about one and hope you guys liked it. We're really excited about it. Now Let me just spend a minute talking about what's coming next. As I've mentioned before, we want to connect every business user with the insights that are most relevant for them, and for that we will continue to invest in Advanced AI and personalization, and some of the ways you will see it is improved relevance in ranking in recommendations in how we understand your questions across the product within search within the home page everywhere. The second team that will continue to invest in is powerful analyst tools. We talked about tools and, I assure you, tools that make the analysts more self service. We are committed to improving the analyst experience so that they can make the most off their time. An example of a tool that we're really excited about is one that allows them to bridge the vocabulary difference that this even business user asks questions. A user asked a question like revenue, but the column name for the metric in the data set its sales. Now analysts can get insights into what are the words that users air using in their questions that aren't matching anything in the data set and easily create synonyms so that that vocabulary difference gets breached. But that's just one example of how we're thinking about empowering the analysts so that with minimal work, they can amplify their impact and help their business users succeed. So there's a lot coming, and we're really excited about how we're planning to evolve thoughts about one. With all that said, Um, there's just, well, one more thing that my friend Bob wants to talk to you guys about. So back to you, Bob. >>Thanks, Michelle. It's such a great demo and so fun to see all the new work that's going on with thought. Spot one. All the happenings for the new features coming out that will be under the hood. But of course, on the design side, we're going to continue to evolve the front end as well, and this is what we're hoping to move towards. So here you'll see a new log in screen and then the new homepage. So compared to the material that you saw just a few minutes ago, you'll notice this look is much lighter. A little bit nicer use of color up in the top bar with search the features over here to allow you to switch between searching against answers at versus creating new answers, the settings and user profile controls down here and then on the search results page itself also lighter look and feel again. Mork color up in the search bar up the top. A little bit nicer treatments here. We'll continue to evolve the look and feel the product in coming months and quarters and look forward to continue to constantly improving thoughts about one Hannah back to you. >>Thanks, Bob, and thank you both for showing us the next generation of thought spot. I'd love to go a bit deeper on some of the points you touched on there. I've got a couple of questions here. Bob, how do you think about designing for consumer experience versus designing for enterprise solutions? >>Yes, I mentioned Hannah. We don't >>really try to distinguish so much between enterprise users and consumer users. It's really kind of two different context of use. But we still always think that users want some product and feature and experience that's easy to use and makes sense to them. So instead of trying to think about those is two completely different design processes I think about it may be the way Frank Lloyd Wright would approached architecture. >>Er I >>mean, in his career, he fluidly moved between residential architecture like falling water and the Robie House. But he also designed marquis buildings like the Johnson wax building. In each case, he simply looked at the requirements, thought about what was necessary for those users and designed accordingly. And that's really what we do. A thought spot. We spend time talking to customers. We spend time talking to users, and we spent a lot of time thinking through the problem and trying to solve it holistically. And it's simply a possible >>thanks, Bob. That's a beautiful analogy on one last question for you. Bischel. How frequently will you be adding features to this new experience, >>But I'm glad you asked that, Hannah, because this is something that we are really really excited about with thoughts about one being in the cloud. We want to go really, really fast. So we expect to eventually get to releasing new innovations every day. We expect that in the near future, we'll get to, you know, every month and every week, and we hope to get to everyday eventually fingers crossed on housing. That can happen. Great. Thanks, >>Michelle. And thank you, Bob. I'm so glad you could all join us this morning to hear more about thoughts about one. Stay close and get ready for the next session. which will be beginning in a few minutes. In it will be introduced to thoughts for >>everywhere are >>embedded analytics product on. We'll be hearing directly from our customers at Hayes about how they're using embedded analytics to help healthcare providers across billing compliance on revenue integrity functions. To make more informed decisions on make effective actions to avoid risk and maximize revenue. See you there.

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. >>Hey, welcome back already, Jeffrey here with the Cube coming to you from Palo Alto studios today for our ongoing coverage of aws reinvent 2020. It's a digital event like everything else in 2020. We're excited for our next segment, so let's jump into it. We're joined in our next segment by Andrew Rafa. He is the principal and zero trust offering lead at the Light and Touche LLP. Andrew, great to see you. >>Thanks for having me. >>Absolutely. And joining him is Robbie Deval. He is the AWS cyber risk lead for Deloitte and Touche LLP. Robbie, Good to see you as well. >>Hey, Jeff, good to see you as well. >>Absolutely. So let's jump into it. You guys are all about zero trust and I know a little bit about zero trust I've been going to are safe for a number of years and I think one of the people that you like to quote analysts chase Cunningham from Forrester, who's been doing a lot of work around zero trust. But for folks that aren't really familiar with it. Andrew, why don't you give us kind of the 101? About zero trust. What is it? What's it all about? And why is it important? >>Sure thing. So is your trust is, um, it's a conceptual framework that helps organizations deal with kind of the ubiquitous nature of modern enterprise environments. Um, and then its course. Your trust commits to a risk based approach to enforcing the concept of least privileged across five key pillars those being users, workloads, data networks and devices. And the reason we're seeing is your trust really come to the forefront is because modern enterprise environments have shifted dramatically right. There is no longer a defined, clearly defined perimeter where everything on the outside is inherently considered, considered untrusted, and everything on the inside could be considered inherently trusted. There's a couple what I call macro level drivers that are, you know, changing the need for organizations to think about securing their enterprises in a more modern way. Um, the first macro level driver is really the evolving business models. So as organizations are pushing to the cloud, um, maybe expanding into into what they were considered high risk geography is dealing with M and A transactions and and further relying on 3rd and 4th parties to maintain some of their critical business operations. Um, the data and the assets by which the organization, um transact are no longer within the walls of the data center. Right? So, again, the perimeter is very much dissolved. The second, you know, macro level driver is really the shifting and evolving workforce. Um, especially given the pandemic and the need for organizations to support almost an entirely remote workforce nowadays, um, organizations, they're trying to think about how they revamp their traditional VPN technologies in order to provide connectivity to their employees into other third parties that need to get access to, uh, the enterprise. So how do we do so in a secure, scalable and reliable way and then the last kind of macro level driver is really the complexity of the I t landscape. So, you know, in legacy environment organizations on Lee had to support managed devices, and today you're seeing the proliferation of unmanaged devices, whether it be you know, B y o d devices, um, Internet of things, devices or other smart connected devices. So organizations are now, you know, have the need to provide connectivity to some of these other types of devices. But how do you do so in a way that, you know limits the risk of the expanding threat surface that you might be exposing your organization to by supporting from these connected devices? So those are some three kind of macro level drivers that are really, you know, constituting the need to think about security in a different >>way. Right? Well, I love I downloaded. You guys have, ah zero trust point of view document that that I downloaded. And I like the way that you you put real specificity around those five pillars again users, workloads, data networks and devices. And as you said, you have to take this kind of approach that it's kind of on a need to know basis. The less, you know, at kind of the minimum they need to know. But then, to do that across all of those five pillars, how hard is that to put in place? I mean, there's a There's a lot of pieces of this puzzle. Um, and I'm sure you know, we talk all the time about baking security and throughout the entire stack. How hard is it to go into a large enterprise and get them started or get them down the road on this zero trust journey? >>Yeah. So you mentioned the five pillars. And one thing that we do in our framework because we put data at the center of our framework and we do that on purpose because at the end of the day, you know, data is the center of all things. It's important for an organization to understand. You know what data it has, what the criticality of that data is, how that data should be classified and the governance around who and what should access it from a no users workloads, uh, networks and devices perspective. Um, I think one misconception is that if an organization wants to go down the path of zero trust, there's a misconception that they have to rip out and replace everything that they have today. Um, it's likely that most organizations are already doing something that fundamentally aligned to the concept of these privilege as it relates to zero trust. So it's important to kind of step back, you know, set a vision and strategy as faras What it is you're trying to protect, why you're trying to protect it. And what capability do you have in place today and take more of an incremental and iterative approach towards adoption, starting with some of your kind of lower risk use cases or lower risk parts of your environment and then implementing lessons learned along the way along the journey? Um, before enforcing, you know more of those robust controls around your critical assets or your crown jewels, if you >>will. Right? So, Robbie, I want to follow up with you, you know? And you just talked about a lot of the kind of macro trends that are driving this and clearly covert and work from anywhere is a big one. But one of the ones that you didn't mention that's coming right around the pike is five g and I o t. Right, so five g and and I o. T. We're going to see, you know, the scale and the volume and the mass of machine generated data, which is really what five g is all about, grow again exponentially. We've seen enough curves up into the right on the data growth, but we've barely scratched the surface and what's coming on? Five G and I o t. How does that work into your plans? And how should people be thinking about security around this kind of new paradigm? >>Yeah, I think that's a great question, Jeff. And as you said, you know, I UT continues to accelerate, especially with the recent investments and five G that you know pushing, pushing more and more industries and companies to adopt a coyote. Deloitte has been and, you know, helping our customers leverage a combination of these technologies cloud, Iot, TML and AI to solve their problems in the industry. For instance, uh, we've been helping restaurants automate their operations. Uh, we've helped automate some of the food safety audit processes they have, especially given the code situation that's been helping them a lot. We are currently working with companies to connect smart, wearable devices that that send the patient vital information back to the cloud. And once it's in the cloud, it goes through further processing upstream through applications and data. Let's etcetera. The way we've been implementing these solutions is largely leveraging a lot of the native services that AWS provides, like device manager that helps you onboard hundreds of devices and group them into different categories. Uh, we leveraged device Defender. That's a monitoring service for making sure that the devices are adhering to a particular security baseline. We also have implemented AWS green grass on the edge, where the device actually resides. Eso that it acts as a central gateway and a secure gateway so that all the devices are able to connect to this gateway and then ultimately connect to the cloud. One common problem we run into is ah, lot of the legacy i o t devices. They tend to communicate using insecure protocols and in clear text eso we actually had to leverage AWS lambda Function on the edge to convert these legacy protocols. Think of very secure and Q t t protocol that ultimately, you know, sense data encrypted to the cloud eso the key thing to recognize. And then the transformational shift here is, um, Cloud has the ability today to impact security off the device and the edge from the cloud using cloud native services, and that continues to grow. And that's one of the key reasons we're seeing accelerated growth and adoption of Iot devices on did you brought up a point about five G and and that's really interesting. And a recent set of investments that eight of us, for example, has been making. And they launched their AWS Waveland zones that allows you to deploy compute and storage infrastructure at the five G edge. So millions of devices they can connect securely to the computer infrastructure without ever having to leave the five g network Our go over the Internet insecurely talking to the cloud infrastructure. Uh, that allows us to actually enable our customers to process large volumes of data in a short, near real time. And also it increases the security of the architectures. Andi, I think truly, uh, this this five g combination with I o t and cloudy, I m l the are the technologies of the future that are collectively pushing us towards a a future where we're gonna Seymour smart cities that come into play driverless connected cars, etcetera. >>That's great. Now I wanna impact that a little bit more because we are here in aws re invent and I was just looking up. We had Glenn Goran 2015, introducing a W S s I O T Cloud. And it was a funny little demo. They had a little greenhouse, and you could turn on the water and open up the windows. But it's but it's a huge suite of services that you guys have at your disposal. Leveraging aws. I wonder, I guess, Andrew, if you could speak a little bit more suite of tools that you can now bring to bear when you're helping your customers go to the zero trust journey. >>Yeah, sure thing. So, um, obviously there's a significant partnership in place, and, uh, we work together, uh, pretty tremendously in the market, one of the service are one of solution offering that we've built out which we dub Delight Fortress, um is a is a concept that plays very nicely into our zero trust framework. More along the kind of horizontal components of our framework, which is really the fabric that ties it all together. Um s o the two horizontal than our framework around telemetry and analytics. A swell the automation orchestration. If I peel back the automation orchestration capability just a little bit, um, we we built this avoid fortress capability in order for organizations to kind of streamline um, some of the vulnerability management aspect of the enterprise. And so we're able through integration through AWS, Lambda and other functions, um, quickly identify cloud configuration issues and drift eso that, um, organizations cannot only, uh, quickly identify some of those issues that open up risk to the enterprise, but also in real time. Um, take some action to close down those vulnerabilities and ultimately re mediate them. Right? So it's way for, um, to have, um or kind of proactive approach to security rather than a reactive approach. Everyone knows that cloud configuration issues are likely the number one kind of threat factor for Attackers. And so we're able to not only help organizations identify those, but then closed them down in real time. >>Yeah, it's interesting because we hear that all the time. If there's a breach and if if they w s involved often it's a it's a configuration. You know, somebody left the door open basically, and and it really drives something you were talking about. Ravi is the increasing important of automation, um, and and using big data. And you talked about this kind of horizontal tele metrics and analytics because without automation, these systems are just getting too big and and crazy for people Thio manage by themselves. But more importantly, it's kind of a signal to noise issue when you just have so much traffic, right? You really need help surfacing. That signals you said so that your pro actively going after the things that matter and not being just drowned in the things that don't matter. Ravi, you're shaking your head up and down. I think you probably agree with this point. >>Yeah, yeah, Jeff and definitely agree with you. And what you're saying is truly automation is a way off dealing with problems at scale. When when you have hundreds of accounts and that spans across, you know, multiple cloud service providers, it truly becomes a challenge to establish a particular security baseline and continue to adhere to it. And you wanna have some automation capabilities in place to be able to react, you know, and respond to it in real time versus it goes down to a ticketing system and some person is having to do you know, some triaging and then somebody else is bringing in this, you know, solution that they implement. And eventually, by the time you're systems could be compromised. So ah, good way of doing this and is leveraging automation and orchestration is just a capability that enhances your operational efficiency by streamlining summed Emmanuel in repetitive tasks, there's numerous examples off what automation and orchestration could do, but from a security context. Some of the key examples are automated security operations, automated identity provisioning, automated incident response, etcetera. One particular use case that Deloitte identified and built a solution around is the identification and also the automated remediation of Cloud security. Miss Consideration. This is a common occurrence and use case we see across all our customers. So the way in the context of a double as the way we did this is we built a event driven architectures that's leveraging eight of us contribute config service that monitors the baselines of these different services. Azzan. When it detects address from the baseline, it fires often alert. That's picked up by the Cloudwatch event service that's ultimately feeding it upstream into our workflow that leverages event bridge service. From there, the workflow goes into our policy engine, which is a database that has a collection off hundreds of rules that we put together uh, compliance activities. It also matched maps back to, ah, large set of controls frameworks so that this is applicable to any industry and customer, and then, based on the violation that has occurred, are based on the mis configuration and the service. The appropriate lambda function is deployed and that Lambda is actually, uh, performing the corrective actions or the remediation actions while, you know, it might seem like a lot. But all this is happening in near real time because it is leveraging native services. And some of the key benefits that our customers see is truly the ease of implementation because it's all native services on either worse and then it can scale and, uh, cover any additional eight of those accounts as the organization continues to scale on. One key benefit is we also provide a dashboard that provides visibility into one of the top violations that are occurring in your ecosystem. How many times a particular lambda function was set off to go correct that situation. Ultimately, that that kind of view is informing. Thea Outfront processes off developing secure infrastructure as code and then also, you know, correcting the security guard rails that that might have drifted over time. Eso That's how we've been helping our customers and this particular solution that we developed. It's called the Lloyd Fortress, and it provides coverage across all the major cloud service providers. >>Yeah, that's a great summary. And I'm sure you have huge demand for that because he's mis configuration things. We hear about him all the time and I want to give you the last word for we sign off. You know, it's easy to sit on the side of the desk and say, Yeah, we got a big security and everything and you got to be thinking about security from from the time you're in, in development all the way through, obviously deployment and production and all the minutes I wonder if you could share. You know, you're on that side of the glass and you're out there doing this every day. Just a couple of you know, kind of high level thoughts about how people need to make sure they're thinking about security not only in 2020 but but really looking down the like another road. >>Yeah, yeah, sure thing. So, you know, first and foremost, it's important to align. Uh, any transformation initiative, including your trust to business objectives. Right? Don't Don't let this come off as another I t. Security project, right? Make sure that, um, you're aligning to business priorities, whether it be, you know, pushing to the cloud, uh, for scalability and efficiency, whether it's digital transformation initiative, whether it be a new consumer identity, Uh uh, an authorization, um, capability of china built. Make sure that you're aligning to those business objectives and baking in and aligning to those guiding principles of zero trust from the start. Right, Because that will ultimately help drive consensus across the various stakeholder groups within the organization. Uh, and build trust, if you will, in the zero trust journey. Um, one other thing I would say is focus on the fundamentals. Very often, organizations struggle with some. You know what we call general cyber hygiene capabilities. That being, you know, I t asset management and data classifications, data governance. Um, to really fully appreciate the benefits of zero trust. It's important to kind of get some of those table six, right? Right. So you have to understand, you know what assets you have, what the criticality of those assets are? What business processes air driven by those assets. Um, what your data criticality is how it should be classified intact throughout the ecosystem so that you could really enforce, you know, tag based policy, uh, decisions within, within the control stack. Right. And then finally, in order to really push the needle on automation orchestration, make sure that you're using technology that integrate with each other, right? So taken a p I driven approach so that you have the ability to integrate some of these heterogeneous, um, security controls and drive some level of automation and orchestration in order to enhance your your efficiency along the journey. Right. So those were just some kind of lessons learned about some of the things that we would, uh, you know, tell our clients to keep in mind as they go down the adoption journey. >>That's a great That's a great summary s So we're gonna have to leave it there. But Andrew Robbie, thank you very much for sharing your insight and and again, you know, supporting this This move to zero trust because that's really the way it's got to be as we continue to go forward. So thanks again and enjoy the rest of your reinvent. >>Yeah, absolutely. Thanks for your time. >>All right. He's Andrew. He's Robbie. I'm Jeff. You're watching the Cube from AWS reinvent 2020. Thanks for watching. See you next time.

>>Hello. My name is Isaac twang and I am on the faculty at MIT in electrical engineering and computer science and in physics. And it is a pleasure for me to be presenting at today's NTT research symposium of 2020 to share a little bit with you about programmable quantum simulators theory and practice the simulation of physical systems as described by their Hamiltonian. It's a fundamental problem which Richard Fineman identified early on as one of the most promising applications of a hypothetical quantum computer. The real world around us, especially at the molecular level is described by Hamiltonians, which captured the interaction of electrons and nuclei. What we desire to understand from Hamiltonian simulation is properties of complex molecules, such as this iron molded to them. Cofactor an important catalyst. We desire there are ground States, reaction rates, reaction dynamics, and other chemical properties, among many things for a molecule of N Adams, a classical simulation must scale exponentially within, but for a quantum simulation, there is a potential for this simulation to scale polynomials instead. >>And this would be a significant advantage if realizable. So where are we today in realizing such a quantum advantage today? I would like to share with you a story about two things in this quest first, a theoretical optimal quantum simulation, awkward them, which achieves the best possible runtime for generic Hamiltonian. Second, let me share with you experimental results from a quantum simulation implemented using available quantum computing hardware today with a hardware efficient model that goes beyond what is utilized by today's algorithms. I will begin with the theoretically optimal quantum simulation uncle rhythm in principle. The goal of quantum simulation is to take a time independent Hamiltonian age and solve Schrodinger's equation has given here. This problem is as hard as the hardest quantum computation. It is known as being BQ P complete a simplification, which is physically reasonable and important in practice is to assume that the Hamiltonian is a sum over terms which are local. >>For example, due to allow to structure these local terms, typically do not commute, but their locality means that each term is reasonably small, therefore, as was first shown by Seth Lloyd in 1996, one way to compute the time evolution that is the exponentiation of H with time is to use the lead product formula, which involves a successive approximation by repetitive small time steps. The cost of this charterization procedure is a number of elementary steps, which scales quadratically with the time desired and inverse with the error desired for the simulation output here then is the number of local terms in the Hamiltonian. And T is the desired simulation time where Epsilon is the desired simulation error. Today. We know that for special systems and higher or expansions of this formula, a better result can be obtained such as scaling as N squared, but as synthetically linear in time, this however is for a special case, the latest Hamiltonians and it would be desirable to scale generally with time T for a order T time simulation. >>So how could such an optimal quantum simulation be constructed? An important ingredient is to transform the quantum simulation into a quantum walk. This was done over 12 years ago, Andrew trials showing that for sparse Hamiltonians with around de non-zero entries per row, such as shown in this graphic here, one can do a quantum walk very much like a classical walk, but in a superposition of right and left shown here in this quantum circuit, where the H stands for a hazard market in this particular circuit, the head Mar turns the zero into a superposition of zero and one, which then activate the left. And the right walk in superposition to graph of the walk is defined by the Hamiltonian age. And in doing so Childs and collaborators were able to show the walk, produces a unitary transform, which goes as E to the minus arc co-sign of H times time. >>So this comes close, but it still has this transcendental function of age, instead of just simply age. This can be fixed with some effort, which results in an algorithm, which scales approximately as towel log one over Epsilon with how is proportional to the sparsity of the Hamiltonian and the simulation time. But again, the scaling here is a multiplicative product rather than an additive one, an interesting insight into the dynamics of a cubit. The simplest component of a quantum computer provides a way to improve upon this single cubits evolve as rotations in a sphere. For example, here is shown a rotation operator, which rotates around the axis fi in the X, Y plane by angle theta. If one, the result of this rotation as a projection along the Z axis, the result is a co-sign squared function. That is well-known as a Ravi oscillation. On the other hand, if a cubit is rotated around multiple angles in the X Y plane, say around the fee equals zero fee equals 1.5 and fee equals zero access again, then the resulting response function looks like a flat top. >>And in fact, generalizing this to five or more pulses gives not just flattered hops, but in fact, arbitrary functions such as the Chevy chef polynomial shown here, which gets transplants like bullying or, and majority functions remarkably. If one does rotations by angle theta about D different angles in the X Y plane, the result is a response function, which is a polynomial of order T in co-sign furthermore, as captured by this theorem, given a nearly arbitrary degree polynomial there exists angles fi such that one can achieve the desired polynomial. This is the result that derives from the Remez exchange algorithm used in classical discreet time signal processing. So how does this relate to quantum simulation? Well recall that a quantum walk essentially embeds a Hamiltonian insight, the unitary transform of a quantum circuit, this embedding generalize might be called and it involves the use of a cubit acting as a projector to control the application of H if we generalize the quantum walk to include a rotation about access fee in the X Y plane, it turns out that one obtains a polynomial transform of H itself. >>And this it's the same as the polynomial in the quantum signal processing theorem. This is a remarkable result known as the quantum synchrony value transformed theorem from contrast Julian and Nathan weep published last year. This provides a quantum simulation auger them using quantum signal processing. For example, can start with the quantum walk result and then apply quantum signal processing to undo the arc co-sign transformation and therefore obtain the ideal expected Hamiltonian evolution E to the minus I H T the resulting algorithm costs a number of elementary steps, which scales as just the sum of the evolution time and the log of one over the error desired this saturates, the known lower bound, and thus is the optimal quantum simulation algorithm. This table from a recent review article summarizes a comparison of the query complexities of the known major quantum simulation algorithms showing that the cubitus station and quantum sequel processing algorithm is indeed optimal. >>Of course, this optimality is a theoretical result. What does one do in practice? Let me now share with you the story of a hardware efficient realization of a quantum simulation on actual hardware. The promise of quantum computation traditionally rests on a circuit model, such as the one we just used with quantum circuits, acting on cubits in contrast, consider a real physical problem from quantum chemistry, finding the structure of a molecule. The starting point is the point Oppenheimer separation of the electronic and vibrational States. For example, to connect it, nuclei, share a vibrational mode, the potential energy of this nonlinear spring, maybe model as a harmonic oscillator since the spring's energy is determined by the electronic structure. When the molecule becomes electronically excited, this vibrational mode changes one obtains, a different frequency and different equilibrium positions for the nuclei. This corresponds to a change in the spring, constant as well as a displacement of the nuclear positions. >>And we may write down a full Hamiltonian for this system. The interesting quantum chemistry question is known as the Frank Condon problem. What is the probability of transition between the original ground state and a given vibrational state in the excited state spectrum of the molecule, the Frank content factor, which gives this transition probability is foundational to quantum chemistry and a very hard and generic question to answer, which may be amiable to solution on a quantum computer in particular and natural quantum computer to use might be one which already has harmonic oscillators rather than one, which has just cubits. This has provided any Sonic quantum processors, such as the superconducting cubits system shown here. This processor has both cubits as embodied by the Joseph's injunctions shown here, and a harmonic oscillator as embodied by the resonant mode of the transmission cavity. Given here more over the output of this planar superconducting circuit can be connected to three dimensional cavities instead of using cubit Gates. >>One may perform direct transformations on the bull's Arctic state using for example, beam splitters, phase shifters, displacement, and squeezing operators, and the harmonic oscillator, and may be initialized and manipulated directly. The availability of the cubit allows photon number resolve counting for simulating a tri atomic two mode, Frank Condon factor problem. This superconducting cubits system with 3d cavities was to resonators cavity a and cavity B represent the breathing and wiggling modes of a Triumeq molecule. As depicted here. The coupling of these moles was mediated by a superconducting cubit and read out was accomplished by two additional superconducting cubits, coupled to each one of the cavities due to the superconducting resonators used each one of the cavities had a, a long coherence time while resonator States could be prepared and measured using these strong coupling of cubits to the cavity. And Posana quantum operations could be realized by modulating the coupling cubit in between the two cavities, the cavities are holes drilled into pure aluminum, kept superconducting by millikelvin scale. >>Temperatures microfiber, KT chips with superconducting cubits are inserted into ports to couple via a antenna to the microwave cavities. Each of the cavities has a quality factor so high that the coherence times can reach milliseconds. A coupling cubit chip is inserted into the port in between the cavities and the readout and preparation cubit chips are inserted into ports on the sides. For sake of brevity, I will skip the experimental details and present just the results shown here is the fibrotic spectrum obtained for a water molecule using the Pulsonix superconducting processor. This is a typical Frank content spectrum giving the intensity of lions versus frequency in wave number where the solid line depicts the theoretically expected result and the purple and red dots show two sets of experimental data. One taken quickly and another taken with exhaustive statistics. In both cases, the experimental results have good agreement with the theoretical expectations. >>The programmability of this system is demonstrated by showing how it can easily calculate the Frank Condon spectrum for a wide variety of molecules. Here's another one, the ozone and ion. Again, we see that the experimental data shown in points agrees well with the theoretical expectation shown as a solid line. Let me emphasize that this quantum simulation result was obtained not by using a quantum computer with cubits, but rather one with resonators, one resonator representing each one of the modes of vibration in this trial, atomic molecule. This approach represents a far more efficient utilization of hardware resources compared with the standard cubit model because of the natural match of the resonators with the physical system being simulated in comparison, if cubit Gates had been utilized to perform the same simulation on the order of a thousand cubit Gates would have been required compared with the order of 10 operations, which were performed for this post Sonic realization. >>As in topically, the Cupid motto would have required significantly more operations because of the need to retire each one of the harmonic oscillators into some max Hilbert space size compared with the optimal quantum simulation auger rhythms shown in the first half of this talk, we see that there is a significant gap between available quantum computing hardware can perform and what optimal quantum simulations demand in terms of the number of Gates required for a simulation. Nevertheless, many of the techniques that are used for optimal quantum simulation algorithms may become useful, especially if they are adapted to available hardware, moving for the future, holds some interesting challenges for this field. Real physical systems are not cubits, rather they are composed from bolt-ons and from yawns and from yawns need global anti-Semitism nation. This is a huge challenge for electronic structure calculation in molecules, real physical systems also have symmetries, but current quantum simulation algorithms are largely governed by a theorem, which says that the number of times steps required is proportional to the simulation time. Desired. Finally, real physical systems are not purely quantum or purely classical, but rather have many messy quantum classical boundaries. In fact, perhaps the most important systems to simulate are really open quantum systems. And these dynamics are described by a mixture of quantum and classical evolution and the desired results are often thermal and statistical properties. >>I hope this presentation of the theory and practice of quantum simulation has been interesting and worthwhile. Thank you.

>>Yeah, yeah, yeah! >>Everybody, welcome back to the special digital presentation where we are tracking the transformation of Oracle consulting. And really, it's rebirth. Aaron Millstone is back. He's the senior vice president of consulting, joined by Jeff Davis. Who's ah, principal at Deloitte. He's the chief commercial officer for Oracle at Deloitte. Gentlemen, good to see you. Welcome. >>Thank you very much. >>Thanks for having me back. >>You're welcome, guys. Jeff, let me start with you. I've got the obvious question is why would Deloitte World Class? Yes, I well known why you partnering with Oracle Consultant? >>We're really It was a perfect match. The fact that we were looking to grow our oracle practice and really new and innovative ways around Oracle's cloud technology. Uh, in discussions with the oil, coal and specifically with Aaron Millstone, we discovered that we really had complementary capabilities and very little overlapping capabilities. So it was natural for us to find a way to work together. And specifically we found that there were strategic assets we had and there were tactical assets that Oracle had the mixture of two made a really unique and compelling value proposition for the customer base >>and Aaron. I mean, we've talked about the shift from from staff augmentation to much more strategic partnering with your customers. But you're not trying to compete with the big size of there's, there's it sounds like there's not a lot of overlap there. Where do you pick up and leave off for Deloitte? You describe that? >>Sure. I mean, we're You're right, right? We're not. We're not ever going to try to compete with the Deloitte. It's not our that's not in our DNA. It's not our intention. We exist to drive Oracle's to drive success for our customers on Oracle's cloud. That's that's our mantra. That's what we focus in on. So for us, right, we're deep technologists. We're We understand our cloud. We understand how cloud works within our various product suites that we migrate to the cloud. We understand how to manage it. We understand how to build paths extensions to it, but we don't have big program management. We don't understand non oracle components that well, you know, we've got some expertise here and there. But if we need to expand, you know, on Oracle solution to coexist with a Microsoft azure solution, we can't do that without going to a partner and as we bigger and the transformation that they're gonna have to change management and big, big transformation journey capabilities. Like again, That's not That's not expertise. >>Yeah, so Jeff will come back to you. So we see a lot of these deals. Sometimes we call them Barney deals. I love you. You love me. There's a press release, and that's it. But so one of the things we look for okay is their teeth behind this. You guys have come out with what you call elevate. What is elevate? How did it get started? And I have some follow up questions. >>Yeah, well, elevate really got started when Aaron and I started to look at the assets that each of the firms possessed on the Deloitte side, as Aaron suggested, We have deep capabilities and a broad range of technologies, some of them competing technologies with Oracle at the same time. Uh, we didn't have a great deal of depth in Oracle's technical products, Oracle Cloud Infrastructure and Oracle Autonomous. Our bench was not as big as Aaron's, and Aaron also had access to your local development at a level that we didn't have access to. So we really found ourselves in a situation where we could put those two capabilities together and we could offer something to our clients and a broad range of customers. Oracle customers in the field. They had access to all of the Lloyds capabilities, which includes great project management, great change management, real skill around the strategic aspects of cloud migration. And Aaron had tools on had resource is trained and developed around the late historical technology. They'd always be a step ahead of any s I So together we felt this was really a differentiation for marketplace, right, Erin? >>Yeah, absolutely right. And if I don't think I would add to it is that if you if you look at Deloitte approaches client conversation from, ah, business value perspective, you know, the work consulting teams tends to focus conversation. It tends to approach conversations with a focus on How do you want to do the technology? Um, both are helpful. But, you know, quite frankly, as we get into the bigger information in place, we need to lead with the Lloyd model of how do we How do we drive your business value and then begin from a technologist perspective, that's when we show up. So it really has been a very logical, very complimentary match. >>So you and I have talked about, you know, data centers and building data centers and investing. It's not just it's just not a good use of capital today. There's so many other things that organizations can do. You guys have identified data center. Consolidation is, is I'll call it Ah, you know, an initiative that you're seeing customers. I wonder if you could talk about that a little bit. Is that kind of a starting point for conversations? >>Yeah, well, it's definitely starting point, right? So we call it a referred to his infrastructure led transformation, Um, and appetite. The appetite for that is certainly high. We were seeing an increased focus on um, you know what customers need to do to take not just a workload here and there. But how do they get out of the data center business full? So it's a foregone conclusion, right? Like you just said, it's not. It's not really a question of should we invest in another data center? Where should we invest in up to in their data centers? The question has changed to Let's move the cloud. How do we get there and let's move in a big way? And that's why we're seeing that dialogue across all of our customers. And we find even for Oracle, it's been a learning for us, right? We started with on Oracle workload conversation, which is, Do you want to move this work? Work loads of oracle? But you want to move that Oracle workload works. And really, what we're finding is it's a wholesale transformation of everything in the data center, too. One or more clouds, right again, often often it's a multi cloud strategy, and that's okay. And we, you know, we were having more bigger conversations. The thing that has been really interesting is these conversations have evolved, and especially as we work with our partners at Deloitte, has been that, you know, we think that the combination of our our cloud technology, the consulting services that Oracle Consulting and Deloitte can bring to bear and then Oracle's ability to finance the whole deal makes the very compelling conversations for customers because you can walk in to a CEO to a CFO and say, Look on day one, you can actually have a lower spend that what you have today in your data center and get a cop transformation underway at the same time. >>So I want to come back to that business case and member Jeff, before we do, I want to ask you. So we heard Erin, you know, talking about the catalyst. You know, that sort of infrastructure transformation. But you're in the outcomes business, right in both. The bush has been deployed especially so So what is that North Star that you're seeing with customers? You know, it's not about the tech. They're not starting there. Um, that will often tell you that's kind of the easy part. But then we see tech coming and going, and it's the It's the business process. That's the people issues lining everybody. So what are you seeing is so the outcomes. What's that conversation like with your customers? >>Yeah, well, really, this conversation starts with business leadership. Um, if you think about it, there's a strong value proposition in infrastructure renewal. It's not at the top of mind, but once you start to understand the value that's created, it does raise two ah, high priority. Now, our experiences that virtually every board is looking for the C suite toe have a cloud strategy of some kind. People recognize the value of cloud in, uh in many of our clients and many of Oracle's customers, so the boards are pressing the C suite for a cloud strategy. Among those things are the value that cloud brings, including virtually unlimited scalability. Is is being tested real time now with a lot of current events. So when you see the scalability when you know you need a cloud strategy of some kind, your business advisors impressing you, the value proposition starts well, how do we get there? And what does it take to be successful? Our perspective is that it's it's fair to believe that the cloud will reduce infrastructure. Spend significantly. It's a great opportunity for consolidation. It also adds a layer of security, resiliency and scalability that you simply couldn't do on your own. So it addresses a lot of business needs Aziz well as a number of technical needs that need to be addressed. >>So let's talk a little bit more about that business cases that generally what you're seeing, where it starts is let's take some costs right out, and then Aaron, you and I talked about maybe investing that in the future of it. But is that really the starting point for the vast majority of customers? Let's let's let's cut some costs right away and get a payback immediately. >>So I'd like to share our perspective, which is, you know, nobody spends money for the sake of spending money on technology. It's got to have meaningful business value. So the conversation starts with really renewal and a path to the cloud. But there's a natural opportunity for savings in consolidation that we take advantage. We're not simply shifting from your hardware to the cloud we're actually modernizing, which will result in significant savings. But it also gives the business something that they don't have today at a level of security and scalability and ability to run a modern technology much faster, much better. Ah, and much more scalable. >>So a lot of people might again I go back to these deals. I think of this as a sales play. One of the things we look for is there. Is there any other integration? Are you doing co engineering in this case, maybe not, co engineering But are there tools that you're developing that you're taking to market, that you're actually leveraging? Eric, can you talk about that a little bit? Convinces. That's not just the sales play. >>Yeah, sure. And Jeff alluded to some of this earlier, too, right? So we definitely each had our respective tool. Angry Deloitte's investments in tools, what was built out of data that we have seen used quite a few times now we've been investing in something we call the Oracle soar. You know, our tools are, as you'd imagine, heavily Oracle focus. It's about moving Oracle technology to Oracle Cloud out of data and some of the tools that Deloitte's invested in our focus more comprehensively on holistically, looking at everything in a data center and everything that's across data centers and start to develop a set of facts around this stuff. But in both cases, we actually looked at these things and we said, You know what? If you combine these together, we get a very comprehensive view of what exactly it is, but we're looking at with a customer so we can tell everything from the types of traffic we see in the network to the specific versions of stuff you start to identify whether there's risk associated with having things, not aster on a supporter and get a very conference of you that's based on facts. And so, you know, we took those tools. We combined them together so that we can go into a customer and give a complete end and view from both on Oracle and Delight Perspective. And quite frankly, it doesn't matter whether the Lloyd leads or whether Oracle leads. We've developed these tools together. We're going to market together. And we've even got you know, the templates you'd expect consultancies tohave, right? So when you look at business cases, we've got joint business case templates that we've created together and that we're using actively with customers and therefore then we're refining them, improving them each time we do it. But, you know, we're at a point now where our tools are combined, templates are combined, and we even at this, you know, we're even Jeff in our poll earlier yesterday actually even got a joint Ah, war room that's constantly engaging with different account teams and making sure that we structurally approach things in a consistent way so that we're driving business value and using the tools appropriately. >>You know, I think, um, migration risk is probably one of the most significant factors in a business case. I mean, many don't understand it, but those in I t. And certainly hopefully in the executive office do you understand it? It sounds like that's a part of your tooling, anyway is designed to mitigate that's significant migration risk. When you talk about that a little bit, >>yeah, so we, you know, we approach migration from, you know, we start with the conversation. I'm almost always some type of log of what? The list of applications, what versions of things running they've been maintained by some might department somewhere, right? Or the collective? It's in varying degrees of accuracy is what we find. We don't rely on that. We go in and our our tools, our combined tooling across oracle, Deloitte interrogate the systems. We come back with actual information from the actual systems themselves. And then we started the plan. And so the funny thing is, with the migration, you know, probably 80% of the effort. 90% of the effort is in the planning stages and making sure that we understand exactly what we're moving exactly. When again, we're not. We're not dealing with the edge applications. Typically, we're dealing with the mission critical applications that are supporting the heart of a supply chain or a finance operation. And you can't. You just can't afford the down time that maybe you could afford on something that might be a consumer facing or a little less mission. Critical. So, yeah, we start finding very early and interrogate aggressively with actual data. >>Jeff, can you give us a sense as to how far you're into this elevate journey? May be thinking about a couple of customers either specifically or generically gonna where you're at with them. How far along? Maybe even some examples that you feel are representative. >>Sure. Um, you know, the the relationship has been probably about six Ah, close to seven months of maturity. In that time, we've had an opportunity to work on several key clients at scale. Uh, we've worked together in collaboration with one of the nation's largest retailers in the grocery business. We've worked collaboratively in aerospace and defense and also in the hospitality industry. In these cases, what we're finding and one is each one is in the various stage of maturity. One is done, one is in midstream on one is at the early stages and current economic conditions or driving a huge pipeline. Right now, I think our challenge right now is making sure that we identify those clients that can best take a value, take advantage of our services and our joint offering to deal with that pipeline. Right now, what we're finding is that the savings are at least as we projected. In some cases, we're finding even more. What people say they have and what people say they do isn't necessarily what you find when you get in there. But almost every case we're finding that there's unused equipment, unused capacity that they currently have redundancy, low utilization of their current assets. We can go a long way and streamlining that. Plus, I can't emphasize enough that ah, these days security is a major concern and we're adding a layer of security that they could never achieve themselves with soft. >>How do you guys on how the customers wanna approach the transaction? Is it a Bixby is a T and M. Is it a situation where you participate in some of the some of the savings of the game. How does the pricing work? >>So we have Go >>ahead. Um, I'll start off by saying each deal is really custom built around what a customer really needs, what they're trying to get out of it right now. As an example, Op X is very important. So we're engineering deals in a way that helps customers deal with their financial challenges, especially around op Ex. There are other structures that we can put in place. We have the backing of Oracle Finance, so we can be very innovative on deals they could be. When value was attained. They could be milestone based. There's just, uh, I think, a wide variety I don't want to say unlimited, but a wide variety of different options that we can offer our clients in order to be able to deal with whatever financial challenge or opportunity that may be looking at >>perfect, perfect. And you want >>to add to that >>and everything looking at other than you know, the there are. There are always things that are discovered during a personal project, and so, you know, we we also we do factor and things that allow some flexibility. Right? So even if we have a fixed price deal will include a bucket of ours to deal with, you know, unanticipated changes or even innovation. It doesn't have to be, You know, contingency could be Hey, we want to go out and spend and invest some money on artificial intelligence machine learning analytics over in this space since we've already moved these applications. All right, so we're approaching it again from a very flexible standpoint, and we're just point right. We can we can custom craft. Ah, deal to match what? The clients. Best business outcome. Okay. >>Yeah, that makes sense. That client might see some adjacent opportunity that they want to pursue, and they want that to be covered in the agreement I'm gonna end. Um, if you start with you, Aaron and then Jeff go to you. How? What do you guys see? A success? What does success look like? You know, when you were, you know, just less than a year in when you're 234 let's say five years and you look back, What does success look like? >>So, to me, successful success is gonna look like we've gotten a number of these big transformation deals in play. It's in motion, naturally between our organizations, not necessarily driven entirely by Jeff and I going out and driving the organization behave the right way. It's more in our DNA. But more importantly, I think we've gone into We've gone beyond the conversation of Let's Move workloads. We've gone into conversations off. Let's really talk about how to reimagine your business on top of Oracle's cloud and have an ongoing dialogue that looks at that transformation. Once we hit that 0.345 years from now, right, that will be a wild success, Jeff. >>But really, it's been around for 135 years. This is our birthday, uh, this year and in that time, what we've learned is there's no substitute for impact and value added to our clients. In our perspective, what this would success looks like his client success find success means improved scalability of their operations, uh, securing their technology and their data at a substantially lower cost, so that they can focus on what their core businesses and focus less on technology. That success to deploy >>right guys, thanks so much. Great session We're not only witnessing the rebirth of Oracle Consulting, but there's clearly a transformation going on. And it's cultural. Gentlemen, congratulations on your partnership. And thanks so much for coming on the Cube. >>Thank you so much >>for having us. >>You're welcome. Alright, Keep right there, everybody. We're back with our next guest covering Oracle Consulting North America. This is Dave Vellante with the Cube. Thanks for watching. >>Yeah, Yeah, yeah, yeah, yeah, >>yeah.

>>live from Las Vegas. It's the Q covering you. I pat Forward America's 2019. Brought to you by you, I path Welcome >>back to Las Vegas. Everybody's is Day two of the Cubes coverage of you AI Path forward. Three. This is the third year of North American Conference, and second year the Cube is covered. This Anthony at Batista's here, Cuba. Lami was on last year from from Deloitte. He's a principal there, Anthony. Good to see again. >>Great to be here. Great. >>Yes. So it is. I mean, we've seen the growth of our P A. Generally you AI path, the whole automation were starting to talk about intelligent automation. A. I has its wings, and it's starting toe sore. But give us the update from a year ago. We talked about, you know, accelerating last year. I think it was you had a really good statements around looking, Yes, go on Fast is good, but you wanna accelerate the right things, you know, speeding up for bad processes. Paving the cow path, as I sometimes call it, is really not the way to go. But what's new? >>So I do think there's still some issues around getting programs t to scale and thinking about automation at scale, which has been a major theme here. The conference is still in front of us. People are still figuring out how the climate that curve well, I think is new is way Thought about automation before it was, it was a whore statement was that humans or automation is about going to replace the human on. I really think we've no lights. Always had a campaign about I t a. I that that we kicked off a couple of years ago and said, How do we have automation and humans interact with each other? And I don't just mean attended, attended bots, But how do we actually start to use automation as sort of the glue that hang together a much more rich experience to start to put the components there? So that leads us to the age of with, which is how we how we use technology along with humans, to change their role in there been some great talks. One of my partners earlier they was here with Walmart's, his client on. They talked about how they're redefining the HR processes at Wal Mart on that was That was a really good presentation because they changed the workers work. They didn't replace workers. >>So how was this concept of the age of with how is that different than attended? Boss, can you maybe talk about a possible use case or example? >>So if you think about a call center way, know who's coming in? We used to just look them up and say, Hey, do we know who's calling? Now we can say that we know is calling. Do they have a history with us? Way can use data, and that's another part of the width. Is Dave plus analytics with automation? And we could say, Well, what else do we know about this person to have a history of calling us? They have an open ticket. Have they had some issues or complaints in the past that we can deal with or get in front of on and basically start to put the intelligence in the front end? And that could be unattended, right? That could just be some screen pops around automation way start to introduce natural language. We start to introduce some advanced analytics, and that would be a simple, simple way of enhancing that process. >>So let me double click on that so normally what you would get this year in the other end of the line of the call center. And it's like, Hold on, I'm just reading the notes and you know, they're scanning these notes. It's like an eye test, you know, and they can't. They can't ever get to see. It's a faster for you to just explain. Let me tell you what what I'm imagining is in a different experience where this is happening in near real time, getting pop ups or some other messaging. Is that absolutely experience on how real is this today? >>This Israel. And you know, I I always like to say all them anything. All the main thing is easy if you just take the process, repave the cow path. But it's very real because the natural language components they work up front. Now you can ask some questions you could start to do pre searches on which materials might might help with that type of question. You also can train the process over time. So daily overtime. What's the call satisfaction? Did you actually complete what it was? The call got started about on how quickly you do that so you could train these models and start to use machine learning to actually improve that experience even further. So I think that's left again, back to the whip. It's adding these components. >>I like talking to folks with a consulting background because you know, when you're talking to the vendor community, they get very excited about our why and how you know, lack of disruption to install some software, right? And so that's one of the advantages, I guess, of our P A. As you can pop it into an existing process, good or bad, and get going right away. We've seen this time and time again in the industry. When you have when you have a big force people to change, you know it's slow When you can show Immediate Roo. I start to see these rocket ships at the same time as a consultant, you really want to have a bigger impact on business you don't want to just repeat in automate Bad process is. So how do you work with clients to sort of manage that that insatiable desire for quick R A y, and then the transformative components that. You know, I could maybe defend you from disruption or allow you to be an incumbent disruptor. >>So I think what's interesting is transformation. Use the word we were really good transformation program. So starting to say how that we think of automation first as we do a traditional transformation program is is very near and dear to us now. And instead of saying, Hey, we're gonna bolt the ear piece system and then figure out if we can get some improvement by automating later. We're saying, you know what? Let's sort of double go backwards. Maybe it's a little fashion, but what is this whole process look like? And can we put automation and launch not is a process improvement after lunch? So I think we think of these transformation programmes, But AARP programs for ready and they're doing at automation is now on the tip of the front end of the program rather than afterthought. Reporting used to be >>right, so I mean, >>you guys >>have to be technology agnostic in your business. I mean, we happen to be a U IE path conference, but there, you know, if our p a generally you iPad specifically, it's not a panacea for all problems. I mean, we've talked about a I we talked about other automation process automation capabilities. You've got existing systems. All this stuff has to work together. So so and people always say technology last people process first. You guys lived that, Um So how are you seeing automation evolved in in terms of adoption of the how people are dealing with existing systems and some of the other technologies that you're having to bring together. >>So I think the first thing is, the technology has to work. It has to be bulletproof, resilient. If you're going to put it in these processes and make it make it part of your work life reserving clients or that sort of thing. So first it needs to be bulletproof. That's becoming a given second. I'd like to think that's, well, architected more. Maura's. You bring in a I or other advanced components. You need thio. Be ready to have a changing ecosystem. You know, the best document processing right now might not be the best in six months. So starting to think of your automation solution is that the technical glue and this is allow you to swap out the trade components as you as you refined processes going forward or something new hits the market. So now we're working ecosystem, I think, for the r p a. Vendors that are having great success in a market like you have have they sort of give you that platform, and they give you the off ramps and the on ramps to integrate the other technologies. And like I said, I think that's table stakes in addition, being bulletproof. But the next piece of that is how we get various people involved in the value proposition of creating automation. So various tools and studios, some for the business user that might not be as technical, maybe self designed about it, eh? Process description level on, then maybe a more technical work bench for the technical body builder. So I'm starting to see that in the product suite and somebody announcements here this week. Hallie, we tailor the tools to different users and engage them in that process from one into the other. >>So you mentioned scaling before what the blockers, what's the challenges of scaling? Why's it seemed to be so hard? It's clearly an area of focus here at this event. >>So I think first of all, the technology is is still new to some areas. They're still back and forth with the business or I t led initiative. I think there are some scars and wounds over the last few years of automation where people might have gotten started on the wrong foot. There's even some reduced to learn from. So I think people are looking for the business case. They're getting more comfortable with it. So the job sizes, deal sizes, air getting bigger for the FDA vendors and for us. But I think it's just an evolution. And, like I said, there a lot of stubbed toes early on a nomination. >>What are >>some of the big mistakes that you've seen? People make >>people thinking that it's only a business tool, or only a technology tool or technology to the point that they get started on something that becomes either a real technology problem, a real business problem? Maybe you told the body out in the business, and you attach it to your ear piece system and you cause performance problems or you have security problems on. Then it becomes a real I t problem also seeing the reverse where you know, when I t group will start and say Let's do some automation And they pushed into some departments it might have a fully big business case, might now have good support, and it becomes a technology science project rather than delivery in the real value. >>I was tryingto a week sort of Think about analogies. Analogous ascendance sees in software. I use service now a little bit, but that was kind of a heavy lift. It started an I t. It was very clear. You know, I t You're seeing this massive rapid growth of you ai path fastest growing probably the fastest growing software segment in history and striking to me that we're just now starting to see Cloud come into the play here. If we just you iPad that big announced this week. It's got this new SAS capability, which you would think you would, you know, be born in the cloud. But people have explained why that is. Do you have concerns about the pace of growth and a company like you I path and its competitors their ability to sort of keep up and continue to deliver quality. I mean, a big part of what you guys do is sort of risk management. Well, so how do you manage that risk? >>So I think what you look for if you're going to be in the lion's partner, if you're going the work together and pursue things together first you have to have the basics. It has to be bulletproof. It has to work. When you hit bumps in the road, you have to have escalation pass. That makes sense. And there's growing pains in any firm, or any company that grows grows as quickly as you tap. On the other hand, the question is, your culture is the line. Do you know the fix problems? Do you put your customers first? I think that's what we look like. Look at in the lions, which is how we have a partner with. People have similar DNA about customers first, and you put everything else aside, roll your sleeves up and do the right thing. So that's what we look for in lines like This >>Way. Always talked about the buzzwords of digital transformation, which conferences like this, it is kind of buzzy, but when you talk to customers, they're actually going through digital transformations. And then a couple years ago, they started experimenting. They bought one of everything and they'd run things in parallel with, you know, legacy systems. But now they're starting to place their bets, saying, actually, we've got some use cases that are working. We're gonna double down on the stuff that, you know, we think works. Our p a in some cases fits there. We're gonna unplug some of the legacy stuff and try to deal with our technical debt. But I guess my question is, where do you see our P? A fitting in to that whole digital transformation? Major, I like to think of a matrix where you've got different sets of service is and you've got different industries that are tapping, you know, all data centric that that are tapping these new capabilities and formulating new businesses. News industries. That's how you see this disruption happening. And then the incumbent saying, Hey, we've got assets to we're gonna tap that same matrix and whether it's open source software or cloud or new security paradigms or data and analytics. So where do you see our P? A fitting into that matrix? >>So I think at the glue level. At the architectural level, it can be the orchestrator of the experience of taking a variety technologies integrating them, providing again on ramps and off ramps, doing with a human canoe, looking at screens, analyzing content so it could be the glue that orchestrates those processes orchestrates. Maybe some of the so it was used to be a void between legacy systems and new systems on darky A helps take all that away or level the playing field on. That s So that's has another set of eyes and ears for process integration, our technology integration. And I think that's what it's probably it's best place now. Are there good process tools there? Can we get, you know, community developments? A big discussion right now. I think some people have been successful at it, but it requires a lot of care and feeding and planning to have your community hand the rails or stay between the curbs and do useful things. So I think we're in the beginning of how far can we go with community development? I think the technology is really the glue. >>So community of elven terms of best practice sharing >>and users have developing their own bots. You know, what are the guardrails? Does the process? They're automating matter. Does it introduced a risk? Eyes going to perform. How do you make sure your bots are an evil that people are creating? It's a pretty powerful technology. >>Is their I p in there that you don't want it? We talked about this last year that you don't want to necessarily share with others. So, um, now your role used to have focused specifically in financial service is now you're more horizontal. But how does the light look at this opportunity? Is there is it an automation practice? Is it you cut across all industries with automation, or is it sort of broader than that? >>So my colleague here runs the offering, which is Do we have the people, the training, the tools that delivery centers in the know how to go out and do this kind of work? And we've scaled tremendously in the automation space. The second part is, how do we look to the Jason sees? So we work very closely with our colleagues in a I and ML when we say how we go do the next generation of this out of the gate, How we experiment, how we say, Do you want fries with that as we as we do some of this work. But then we look for the industry in the intersection, and that's where a firm like Lloyd we've got deep, deep industry expertise, way say, well, those intersections where we can go make something happen way come work with our partners are lions you know, partners in making making something happen at an industry specific level, or can we go solve a specific problem? So I think that's what we bring that unique. But we do it both ways. >>It's kind of off off the topic here, but I was talking about that matrix before and again. I'm envisioning technology, horizontal technologies and then vertical industries, and it used to be for decades if you were in it. And if you're in financial service is, you are pretty much stuck in financial service is you had a value chain that was specific to financialservices, and you knew it inside and out, whether it was product development or marketing or sales distribution, whatever it was. That same thing for automobiles on manufacturing, an education on and on and on, and you develop these industry areas of expertise and domain experts with in there. And you guys have built up a global powerhouse doing, But you're seeing a CZ digital. It's cos. Become digital. What's the difference in the business in a digital business? That's how they use data. Data is at the core, and you're now seeing organizations Company's tech company specifically traverse different industries. You're seeing Amazon, you know, in content you're seeing Apple and financialservices other companies getting into health care. >>How is >>that? First of all, you see that and what do you think it was driving that? And how does that affect your business? Or your clients asking youto help you traverse new new industries, get into new industries or defend against others? You know, these big tech companies tryingto with a duel, disruption agenda, trying to take him >>over, and the center of all that you mentioned a little. But the center of that is who the ultimate customers, and we'll experience that they want how they want that experience integrated, so it's not channel by channel anymore. It's which pieces fit together and how I want to buy things and how I want to be serviced. You're getting whole crossed economies around what the consumer wants, unable by technology. I think the other thing that plays into that is you start thinking of the Internet of things and how connected people are. And how do you use monetize and integrate data about particular people and how they want to be served to make that a better experience? I think the consumer ultimately is driving. A lot of that technology is in the billions. >>Yeah, is you think about that picture again. You'd like to use a metaphor of a matrix. I mean, I see our p a is just, you know, one piece of that. You know, there's so many others you mentioned. I o t We talk about a I all the time we talk about Blockchain. It's how you put those different capabilities together and apply them to your business. That really makes the difference. Not that RPG right now feels very tactical, but it's part of a much more strategic agenda. >>Absolutely on again. It could be the glue in an ecosystem of emerging technologies. I do see there's the eyes and ears. The fact that what you get out of the box from regular p. A vendor. Really? Integrate some really, really painful things. Looking at spreadsheets and thinking the guys with green visors column numbers. It's really good at that stuff as, ah, base task. >>Yeah, nothing wrong with tactical and quick. Roo, I So, Anthony, thanks very much for coming on The Cube. Really appreciate your time. >>Thank you. Great to be here >>to welcome. All right, Keep right, everybody. We're back with our next guest. Day two from you. I path forward in Las Vegas. You watching the cue?

>> from New York City. It's the cue covering automation anywhere. Imagine, brought to you by automation anywhere you >> were in midtown Manhattan, at the automation anywhere. Imagine Crawford's twenty nineteen really psyched to be here. Fifteen hundred people talking about our P A. But really, the Rp story is much more than just robotic process. Automation is really a new way to work, which we hear about all over the place and really reimagining what this technology can do. We're excited to have our next guest. She's Shelly Kramer, an analyst and partner for feature research. Shelley, great to see you. >> Great to see you, too. And you got it. >> I got a right. >> You got it. >> Well, you're a very busy lady. Got all kinds of stuff going on, which is what we like. So first off, just kind of Have you been here before? General impressions of the show. >> This is my first, Uh, this is my first, uh, automation anywhere event. And so it's exciting. I write a lot about our PPA and about the future of work and work force transformation. So it's great to be >> here. Yeah. And you just wrote not a super uplifting article linked in about, you know, employee dissatisfaction and some of the issues with employee retention. We talked before. We turn the cameras on about things like calling him human resource is, you know, and human capital there, they're people. And I thought my here really touched on it. Well, in the keynote today that this is not a rip and replace technology for people. This is a tool to help us do our jobs better, just like our laptops and our mobile phones and our application. So are you excited about the opportunity? See, it is this transformative. >> I do see this transformative. And I think that before you talk about what we talked about, the technology that we have to talk about people and Simpson and I'm pulling this out of my memory banks. So on average, about eighty two percent of employees within any organization are disengaged. OK, eighty two percent. >> So the ping pong tables and the tables are not doing it. >> And so when you think about it and engaged workforce are people who wake up in the morning or whatever it is, they go to work and who are excited about what they're doing excited about their company. They're working for love, the culture that they're working within. I love all those things that they're doing. And so when you realize that eighty two percent of people are disengaged, that's problematic. Right then, you're talking about the toughest talent acquisition markets that we've had in a really long time. So we're all fighting for top talent, not just top tech talent but any kind of talent. And so focusing on how we can make the workforce better and create better cultures and put systems and processes in place that can make people do their jobs more efficiently, more productively and actually like them. That's to me. That's the beginning of where we get to this technology piece and what our P a Khun Dio How? Aye aye plays a role in there and how you can. How employees can partner you think of technology as a partner, as opposed to worrying about technology replacing them. So I think it's an exciting time in the workforce, and when you think about it that way, it makes a lot of sense. >> Do you think the difference in kind of the expectations that people have when they Goto work to be engaged as a function of the millennials who are looking for something that's more mission lead. Is it a function of just the competition? It's so robust that before people could get away with having, you know, maybe a less compelling work experience. What do you think is driving? The changer hasn't changed, and maybe now we're just paying more attention to it. >> You know, I think it's changed in a little bit. I think that you and I are old enough that when we were coming of age and we were working our way up the corporate ladder, you know, you kind of sold a little bit of your soul to the company store. No matter. I mean, I gripped in advertising, right, and, you know, I work crazy hours, and I loved it. But I never questioned that there were dues that I had to pay, and that's what you think. And I think that people don't necessarily expect the world on a platter. But I think especially the more skilled you are, whether it's a knowledge of tack or whatever it is in today's market, I think that ueno and again it could be someone my age. It could be someone that's twenty five. It could be someone that's forty. I can find something else. So minute, this isn't doing it for me, right? I can go find something else right now. That said, there are also people who are punching a clock. You know what I'm saying? And I don't mean ship workers. Necessarily. As much as I need to pay the mortgage, I need to get my kid's bed. You know, I don't love this job. Maybe it's a path to something. One of my daughters works for an insurance company. She has a very non glamorous job. She doesn't love it, but she knows she has to do it for X amount of time before she can be considered for this different promotion. And she is watching the clock on literally. I'm getting to that milestone and asking for her promotion. And if that doesn't happen, she'LL leave. So so I think that when you can, people don't feel like they need to be stuck, right? So I think that way. As a CZ leaders and his executives in the workforce, I think you always have to be mindful about what the work environment that you're creating is and focus on. How do we keep how do we get people? And how do we build the value props that we used when we entice them to say yes to our offer? How do we get them to stay? >> S o many things there, But But, you know, what things you just mentioned is is I don't think they accept it like we did. Maybe when we're coming up, which is, you know, you hire somebody and you hire them for the act tributes that they're bringing in the organization than it used to be. Then you give him the employee rulebook and you basically squash, you know, kind of all the individual creativity and ingenuity and enthusiasm, which is why you hired him in the first place. And we don't see that as much anymore. But at the same token, you know, not everyone's worried about robots taken their job at the same time. There's so many unfulfilled Rex out there. And as you said, it's the most competitive labor market out there. How our employers supposed to kind of square that circle because they need to bring the automation they want to keep the people happy. It's a hyper competitive market, and they need mission. But, you know, we gotta pay the bills and get the products out. >> I think that So I think that we can never forget that people that work for our companies need to pay the bills too. Right? So when you can give them something that they could be excited about, Tio dio that helps. Right? But it just kind of like I'm thinking back Teo, uh, presentation and I can't remember his name. But the V p of product did a presentation about today on a loan mortgage loan application. Okay, that has to be like one of the most boring things, right? If you're in that mortgage loan processing, do this. Do this. Do this villain this spreadsheet love about, By the way, I hate creating spreadsheets. I just want to look at a finished one. But anyway, it was so cool to just look at this, and I shot a video of it, shared it on Twitter. If you want to see what I'm talking about, but which is so cool that you can do this and do this and do this and you know you create this process. And in no time the technology has done all the work and all the calculations, and you've got a recommendation approved, not approved. And so if you're in the mortgage loan business, way to think about that leased, the way that I think to think about it is, doesn't mean that your job is going to go away. It's just like my daughter doing that not very exciting job that she's doing. If automation could fuel some of the mundane, repeatable, banal tasks so that she could focus on the other part of the equation where it's more interesting and more exciting, I think then that's really the value equation there. But I think as I think, what businesses have todo is be transparent and very honest with their employees and tell them, you know, this is our This is why we're doing this. This is what our means, and this is how it's going to add value. It we're not doing this to necessarily replace humans. This is so that we can make this work better, efficient more, you know. And I think that you know, I'll step back and say for a personal example. We went through a process last year where we evaluated all of our business processes, and we looked at how much time our employees were spending sweet track time doing certain tasks. And then we were realizing, you know, the value there. We were actually paying too much in terms of the time, investment or tasks that didn't make sense to. Then we set out integrating automation into our processes, and it was it was a big project, right? And people were kind of worried, you know, and they were kind of worried about it. But one of the things that way told them early on with, like, This is not so that we don't have work for you. This is so that we can make what it is you're doing more efficient and you could do things you like better, >> right? Right. >> And so and that has happened and way didn't lose any of our team. And a lot of those task that they were doing are now automated. They're doing stuff that they like more. So I think that I think that's really the challenge for businesses. Two is the messaging right and then involving your teams in the process, appointment of any kind of >> technology. I think it's just the soul crushing. You know, expression is so it's so valid for for these types of activities. And I think again may hear had a great stat. Four percent of US jobs required a medium level of creativity, which you know people want to get out from under that. But if we can define it as a tool and is a thinking like personal digital assistants, my body will. That used to be just my palm three was my p d. A. Right wow how no. One No one was threatened by the Palm taking the job away. So I think you know, you're right. If we can put it in the context of it's just another tool that's just gonna help you get your job done. That's a very different way to frame the problem versus kind of just ripping replace narrative, which we hear kind of over and over again. >> Well, and I think it also goes beyond Jeff. It's goes beyond, and I think that employees at every level have to understand this. It goes beyond just helping you get your job done. It really is about, you know, cos that survived today and tomorrow are the companies who transformed. And, you know, we talk a lot about digital transformation. And you know, I'm out there on the front lines all day, every day, and I can promise you there are many, many companies who are far from really understanding and embracing this and understanding what it takes from a technology standpoint and the value, that data ad and how to use that data and and the impact that that has on customer experience and all that sort of thing. So So I think it's really is about much more than this will make your jobs suck less >> right, right, Right. >> I think it's about this is how our company stay successful. This is how you helped make your job in the role you play within our organization, what you want it to be, right? And I think that you could probably telling, you know, I've been marketing because I'm always thinking about you know how I know how you spend something and I don't mean in a spin like a PR way, but I think we all have to step back and think about it in terms of the whole equation. And there are a whole lot of companies that don't exist anymore, right? You know whether you're talking about the financial services sector and you know and every business everywhere is being disrupted. I told this story. I was I was talking with me here earlier this morning and I was telling him a story about how my husband, I just bought a new car and we expected to get a loan for that car from our community. It's not a community from our our local bank local. Our local bank has been recently purchased by a bigger bank in the last couple of years, but we run all of our corporate money. I mean, everything that we've ever done is here in this way. You know your spell. ITA loan application. No problem. Give us an interest rate. No problem. But they made every part. My husband I vote travel a lot for business, and they're every part of the process required us to be somewhere together to have an official closing. To do this, to do that and it was just like way could never purchased this car because they were making it so difficult for us. So we enter death talking with the car dealer who said I'm not God. We can fix you up, financed it through their banking partner, which is a huge national bank approved in five minutes. Loan documents in five minutes. Hey, come on out. You sign this tomorrow? He consigned this when he gets, you know. And when I talked to my bank after the transaction, they said, Here's the deal. I wanted to do business with you. But when you make it difficult for a customer to give you their money, they're not gonna hesitate to give somebody else there money, Right? So So I think the banking industry is one example of the these processes that air so cumbersome that in some ways can be automated, but it just it doesn't make sense. And customers today you and I are impatient people as our people younger than we are way. No, there has to be a better way. An analogy could give us a better >> win it right, And, you know, they could use different data sets. And I mean, I've bought himself recently, and you just push the button on the phone and it takes a minute. The wheel spins and then your approved right and you're done if you're done, and it's it's a completely different experience. But the part about the digital transformation I want to follow because it came up today where a lot of times people are the integration point between these systems very similar to the example you just used and you can't digitally transform. If all these automated systems ultimately have the bottleneck through some person to take this piece Veda and stick it over there, right? So it's it's absolutely critical to get those people out of the way, right? So as you look forward twenty nineteen, what are some of the big trends beyond our P A and kind of personal digital assistants not called palm, uh, that you're seeing and that you're excited about? >> Well, I think that, you know, it's hard not to be excited about our p A. Just simply because of, you know, it's predicted to be a one point nine billion this year and to almost double by twenty twenty one. I mean, it makes sense that companies like automation anywhere doubling down on that right. It also makes sense that gigantic companies like IBM and Jo Lloyd and you know why. I mean, you >> know, hearing for >> all here, right? There's a reason for that, right? Because IBM customers want this and Microsoft's customers want this. So So I think that in general I think that technology is fueling our world. Our personal lives are business world, and I think that probably one of the biggest things that I pay attention to to that we pay attention to is that technology alone isn't the answer. It's the partnership of human beings and their skill sets and capabilities and data and automation and artificial intelligence and all those things. So I think that I think that it's an incredibly exciting time. It's kind of like, you know, you you go back to the video that we saw this morning in the bit about the Internet, and I don't know if you remember this. I don't know, probably. I don't know how much older I am than you, but, you know, I remember that Internet machine and wow, this is like I could send an email, you know? And then when you think about right how and those comments, You know that Matt Lauer and Katie Couric we're making it like that weren't down comments we didn't didn't know, right? Know the impact Internet could have would have, you know. And so I think the same is true of this kind of technology today. This this next generation of technology. So there's not just one thing I'm interested. I'm interested in Element. >> Gotta keep learning right because way have a hard time with were very linear and everything is growing exponentially. So you got a got to be willing. Teo learned the next thing because it's right around the corner, >> and I think that's so key. That's that's a great rap. I think that people who are happiest today are people who actually love change and who love learning. And I would say I would posit that most that those air not inherently things that people trades that people possess. I'm lucky because I do. You see what I'm saying? I think it's >> an interesting question, whether that's inherent. If there's just people that liked the learning, our curious all the time, and that and then they got to stick in the muds and Candice stick in the muds, change your attitude and become learners again. >> Maybe they won't. I mean, you know, I think that they're I do think that there are are people who are wired to like change and two are curious and who loved learning. And I think there are a whole bunch of people who are not. And I would I truly believe that for success in today's world and moving forward for young people and not so young people, you better get there if that's not your deal, because I don't think that I did it. And I have stumbled across conversations of people having like, you know, that's not gonna happen, you know? So I don't have to worry about it because I'm gonna be outta here by then. Or and you know what? There are a whole bunch of people that have that mindset, and that's a okay, but especially for young people making their way, >> they okay mindset. But it's not the fact that that's the problem. It's it's happening now. I mean, the future is here, and it's happening at a faster pace. Well, Shelley, we could go on and on and on, but we're going to leave it there. And I appreciate you taking a few minutes out of your day. >> Absolutely. My pleasure. >> All right. She Shelly, I'm Jeff. You're watching the Cube. Where? Automation anywhere. Imagine in Midtown Manhattan. Thanks for watching. See you next time.

>> fly from San Francisco. It's the Cube covering Google Club next nineteen, right Tio by Google Cloud and its ecosystem partners. >> Okay, welcome back, everyone. We are here live in San Francisco for cubes. Coverage of Google next twenty nineteen. Hashtag Google. Next nineteen, Google's Cloud Conference, where their customers, developers all come together Cubes. Three days of coverage. Day one. I'm John forward, my Coast, Dave Aloft as well. Astute many men Who's out there doing some reporter? Next guess Allison. Wagon filled is the CMO of Google Cloud. Great to see you. Thanks for joining us. >> Thanks for having me. I'm glad to be here, >> so I got to say, looking out on the floor here, we're in the middle of the floor. Great demographics. A lot of developers, lot of enterprise customers. A lot of you know, sea levels will also enterprise architects and cloud architects. So this is not just a developer fest. This is a business developer conference. >> Yes. So that's been a real change this year. Not only have we increase the numbers I think I mentioned earlier that we have thirty thousand people are actually able even more than that. We had a cap registration we sold out last week. But the composition is different this year because this year we have over seventy percent from enterprise companies and then within enterprise Cos it's Dev's decision makers, business leaders. And then we have a whole executive track of leader Circle program as well. So it's been a really great mix of different energy, different questions in different sessions. >> You guys do a great job in event kudos to the team original Google Io was a great event that continues to be the consumer side on Google. You guys have that same kind of grew swing going on a lot of sessions. Take him in to explain the theme of the show. What's going on around the events? Breakouts? What's the focus? >> Yes, so the focus? Well, there's a theme and a couple different levels. The broad theme is a cloud like no other, because we've introduced a lot of new, different features and products and programs. We introduced Antos this morning, which was really revolutionary way of using containers broadly multi cloud, high but cloud. So it's from a product standpoint, but it's also a cloud like no other, because it's about the community that's here, and it's truly a partnership with our customers and our partners about building this cloud together, and we see the community as a really key part of that. It's really corta Google's values around openness, open source technology and really embracing the broader community to build the cloud together. >> And I thought was interesting. The Kino was phenomenal. You had the CEO of Google come out Sundar Pichai and the new CEO on the job for ten weeks. T K >> Sommers. Korean. Yes. Lot of action >> going on a Google right now. >> Yeah, it's been great to have Thomas. Diane was phenomenal and building the business. It's wonderful. Have Sundar here. He's got a lot of commitment, really engaged with our customers. And so it's a lot of energy and a lot of excitement. A Google. >> I thought the vory class act of Thomas Curry and his first words on stage at the CEO was to give props. The Diane Green very, very respected, that was >> great, was very gracious of, Thomas >> said. Sorry, he said. The press, sir, that one of things I really like about Google is not afraid of hard problems, So I wanted to ask you a CMO I always asked the most about brand promise. What's the brand promise? That you want customers and the community to take away from an event like this? >> So the brand promise has a couple different areas. First and foremost, we want our customers to be successful with their customers. And so we think, really holistically about lessons. Make sure that we're delivering the cloud technologies so that customers can really serve everyone that they want to serve, whether it be a retailer that wants to create a wonderful, offline and online experience, whether it's a health care provider that wants to ensure that every doctor, it knows all of the right data about all the patients or within a hospital. And so that's the way we're always thinking is how do we ensure that we help our customers set up to be successful? >> So one of the big teams we heard this morning was the industry focus, and you just referenced that again. It seems to be an increasingly important part of the messaging and the technologies that you're creating, and it ties into digital transformation. You seeing every industry transform data is at the heart of that transformation. You're seeing big companies traverse different industries. So what if you could talk about the industry focus? Uh, where'd that come from? Where do you see it going? >> Yes, So there's really three core parts of what we've been talking about today. First and foremost is the infrastructure and ensuring that we have the world's best infrastructure. Then, on top of that, it's ensuring that we have all the right applications to help with digital transformation. And then, as part of that further, is the industry solutions. Because in our six focus industries, we want to make sure that we're really developing the right applications with the right solutions and half a deep expertise that companies are looking for so that we can really part with partner with them and really, truly be innovative. And we could feel much more comfortable being innovative. But we really understand our customer problems >> keep Part of that is the global s eyes. You look out here, you see all the big names I won't name because I'll forget one. But there's two obvious ones right there because once you start to see those guys come into the ecosystem, that's when you can partner and get really deep industry expertise globally, >> I agree. And so we do have a great partnerships that said here with Accenture in tow, Lloyd and Antos or three of them, many more that we were working really closely with. And there really are an extension of what we want to build because we know that we will not be able Teo create every single last mile industry solution and every single industry, and working with those companies really helps us. >> I was on the plane last night watching the game. Of course, I love you guys got to see it. You're probably appear busy, but I focused. Google was all over the this year, >> so this is our second year of our partnership with the law, and it's been great. There's a couple dimensions to that partnership. First and foremost, we help them analyze eighty years worth of data. And through all of that analysis, we've been working with him about making predictions about games in helping them understand players and coaches and teams better. Everything from creating brackets. Teo, how do you fan experience? And then as part of that, we also had opportunity to do some advertising within their games. So you may have seen some of the TV spots that we did, which was about analyzing that data. We put ourselves on the line by making predictions during the game about what we thought would happen based on all of our analysis. And then the Big Chef this year was we included students, so it was really studies. Last year we created all these models, but we did it within Google. We had Google, Debs and Google engineers creating prediction models. We said, like, What if we brought students in tow? Help us? So we recruited thirty or so all star students around the country from their schools, brought them together. They learned DCP like that. It was awesome. And then they started working together doing predictions. And so a lot of what you saw in the Games and on our hub was actually students using Google Claude platform to make predictions about the games. >> So just get this right. The reference on stage by T K students. So you had data from the that was exposed to the students. They had a hackathon. How much lead time that they have? What was that >> did everything with thirty days. So they hack it on was about two months ago or so. But within the last thirty days, they did all of these different projects and they were actually doing really creative things about trying to come up with new types of stats like explosiveness. What does that mean? Does that mean that you move in closer to the basket or does it mean that here they're coming up, the stats around pace of game and different elements of the place? It was really fun. >> How many slam dunk this, Miss Fowles? So >> question, Who do you who you're rooting for? I was >> writing from Virginia. You know, Let's say I >> was right for >> Virginia after my bracket got busted, so I was allowed to kind of change a little bit. And they're Michigan. Once they were gone, I was like, >> So I use no way. I but I hit ninety ninth percentile. So you go. I had Michigan in Michigan State rather in Virginia in my Final Four for Michigan State. Lost, but still, I would have been >> That's pretty good >> night, point nine. So what is with what kind of predictions were the students doing well, >> predictions about everything from, well, last night we had some predictions about the number two point last. We had about how many different times we're going to exchange like the ball will go back and forth between teams. We had predictions about three pointers and one game everything. So it's been really fun. Teo work with >> that kind of in game predictions. To see that a lot. >> You probably saw some stats real >> probability of, ah, victory, which of course, last night. Forget it. I mean, it's changed so quickly. >> Great program. One of those I want to ask you change gears is you have a book in the press room called customer Voices. So this has been a focus, and I think a lot of people have been Lego Google's great tact, but not a lot of customers, which you guys air debunking with. Not only this, but here to show shown the logo slide really kind of showing the traction from a customer's standpoint. >> Yes, about >> the focus on the customer. How does that change? How you doing your job? How is the tech rolling out? Can you share some insight into customer focused. >> Yeah, this has been a really big step change this year. We have over four hundred customers speaking throughout this event, and then we have a number of them that are on stage in the keynotes telling real stories. Two years ago, we had some customers speaking and they would say, I'm looking. I'm dabbling and this But now they're making rial kind of bet The company decisions using our technology. And so this customer voices is looking at those companies. We have something called the customer innovation serious this afternoon, where the CIA of HSBC will be talking about their evolution and Gogo Cloud. Two years ago, Darrell West was on stage talking about just kind of what they will be getting. Two Dio with Google Cloud Platform And now here we are two years later, when they've made a lot of progress and we'LL be sharing their stories that the custom innovation Siri's is one of my favorite parts. It next, >> you know, we cover a lot of events. David eyes were like two ESPN of tech or game day. We've gotten the shows, we see a lot of events and you kind of hear the key words over and over again. Soon these events here we're hearing scale, which we've heard all the time. Google scales, scales, scales solve all our problems. But we're hearing more about customers. OK, this has been a big focus. How have you guys shifted internally? Because this seems to been around for a while. Like you said, I think it's a step function from what we're seeing as well. What's going on internally. How you guys mobilizing, How you guys taking this to the mark? Because you've got great partition. So Cisco onstage VM wears even up there. You got an ecosystem developing a lot of momentum. >> So we're truly this year Enterprise ready to use a buzz word that comes up. So two years ago, we still had some holes in some of our technology stack, and we're still really building to go to market teams. We still vastly scaling that so absolutely growing there. But we're in a whole different place as a business where we are able to serve really large enterprises at scale. McKesson just announced sixth largest company that they are moving and working with us a Google cloud. I mean, so these air major companies that are making big decisions to work with us. And so it's at a whole different level this year, and we're really proud that the customers have chosen to work with us, and we're building the organization to ensure that their successful. So that's our customer success program. That's ensuring we have the right kind of customer engineers working hand in hand with our customers. So it's a big focus ever. Whole group. It's a focus where Thomas Kurian has a lot of background serving enterprise customers at Oracle for twenty years, bringing that expertise. So you'LL see that everywhere. So I'm glad you picked up on that and feel it because it's really permeates everything we're doing at Google clouds, >> and it's been a good, positive change. The results of their What's the focus for you As you look forward, It's a lot to do. You guys are a great opportunity. I always say Google's dark horse now Samson's got a good lead out there being first in, but you guys have a lot of tech. You got the customer focus. You got a lot of momentum on the tech side. Cloud native Open source. Partner ecosystem Developing customer ecosystem. So kind of ball's in your court, so to speak. >> You feel really well, position we It's early. So in the whole market, people seem to think that I like all these decisions, but it's really still eighty percent of workload Zoran data centers of these big enterprises, everybody who's here with us right now. And most companies were choosing a multi club strategy this morning. We announced a major product and those that really enables the multi cloud strategy so enables Google to really be at the center of that multi cloud and provide the services using containers and a lot of the biggest best advances right now. And so as we scale our go to market, we can really bring this technology that way here, over and over again, is the best technology in the business. Yeah, we had it really had to go to market in place to bring it to customers. And this is really where we're taking it so we can help get this awesome technology. It's so fun is a marketer to them, bring it to everybody. >> I always say it so early. The wave is just getting started more ways behind it. I'm very impressed. That intrigue also by the rebranding of the Google Cloud platform what you guys announced last kind of hybrid and those is interesting because it's a rebrand slash new set of integration points Sisco again on stage kind of integrating with your container platform is a key key story that I think is nuanced but kind of points to a whole new Google. What was behind the rebranding? Can you just share some insight that what the commerce she's like Google Cloud Platforms is descriptive. But I mean, >> sister, thanks >> Cloud Services platform when we chose that name last year is when we wanted to Alfa with a product and frankly, within the marketing team, he kind of knew was always a placeholder name. And then the debate was, What do we change the name when you go to Beta, which we did a couple months ago? Or when we go to went to Gaea and we decided this would be a great opportunity to change the name, so we always knew it was going to change the name. Picking a name is always complicated, and so we spent a lot of time thinking about what way wanted that name too mean and what we wanted to stand for. And we really liked Anthros. It's a Greek word. It is a nod to the Greek aspects of the history of the product. With Cooper, Netease, Andhis, Teo and other areas. It means the blossom it means to grow. It means all. And so you many words like Anthology and things like that. So we'd liked both what it meant, And we also liked that with all Namie decisions, it's easy to spell. It's easy to find. It's all great, >> and it's super >> booming in California. Here as we speak. Well, ironic. >> It has an international flavor to it. But you guys, you guys are taking this show overseas, right? They've got a big show in London in November, I know and yes, >> be in Tokyo in July at next and then London in November. And then we do it between all of these. What we call Clouds Summit Siri's, which are in country slightly smaller. But we bring a lot of the same technology, and speakers and sessions just have a slightly scaled down version. >> Intimate. We really appreciate your support. We love doing the Cube hearing a lot of Czech athletes, as we say here on the show floor. Lot of knowledge, good customer converses. Alison's Thanks for sharing the inside congratulates on the great >> show, so I left be here. Thanks >> for rebranding as the market shifts. Great time to have a rebrand, certainly when it means something more. Multi cloud hybrid cloud Google Cloud Platform now and those that cube bring you live coverage here from the floor at Google next twenty nineteen. Stay with us for more after this short break.

>>live from Las Vegas. It's the Cube covering. Inform Attica, World 2018 Not you. Buy in for Monica. >>I am Peter Burroughs Wellcome. Back to Day two. Coverage on a cube of in from Attica, World 2018. We're broadcasting from the Venetian here in beautiful Las Vegas. Certainly a lot of excitement. A lot of the buzz just heard the general session empty. Probably 1000 people in the room looking at various moods. Excited to be here this morning. We're being joined by my co host. Jim Kabila's Jim is lead analyst to Wicked Bond. Silicon Angle. Looking at a lot of the data and data practice issues on our first guest is want Te'o One is a principle of data management on architecture at Deloitte one. Welcome to the Cube. >>Great. Thank you, guys. Thank you guys for having me. >>So let's kick it off. What's do you do with Deloitte? What's interesting? What a customer is talking about? >>Yeah. No, absolutely. I mean, I think you know, we are absolutely at the what I would call inflection point around the importance of data. And so my role at the Lloyd is to lead our data management and architecture practice, which essentially deals with everything from data strategy today to execution and how we enable all their transformational initiatives right to truly take advantage of the power that data has to unlock. You know, better business processes to unlock better insights, right to take better action, right? I mean everything that we've been historically talking about, right? In terms of what can organizations do around their data asset? My job is to ensure that we are leading guiding, driving and developing these solutions for our clients. >>So here's a simple question. Just kind of kick it off and see where it goes way. Think that data is becoming more important? You think the day is coming on or important? Are you finding yourself still talking to people that are data administrators or you finding yourselves being pulled into higher level conversations within the business? Talk about data asset, date ass, information, data, asset returns. How is that changing? >>I would say it's evolving, right? I mean, if I and so I have the privilege of running or practice nationally, right? So I have the approach of looking at all of the various industries and sectors right. And so I think, you know, if you take the financial service is life science, healthcare industries, right where there's a lot more regulatory demand on data ensuring that you know what it is, where it's coming from. It's got the right data standards and qualities. I would say they they've gotten it long ago, right? And they've put in place data management organizations. We hear the chief data officer, right? I would say those industries and sectors are a lot more prominent on DSO the conversations absolutely at the executive level, right? There is an executive owner that's responsible for ensuring that the data is correct. >>Tell us about changing data landscape one. Why do enterprises need to change their data strategy and architecture? What do you What do you hear from clients telling them? >>Yeah, I think it's quite simple, right? It is so absolutely enable their business strategy right. You can no longer enable your business strategy without without the data dimension, right? I mean, for many, many years we've talked about, you know, people process technology, right? Well, now there's 1/4 dimension, right? People process technology and data on dhe. That's how we like to think about it. Is that important? Right? You need that executive, and I'll use two words very, very distinctly, right. You don't need an executive data sponsor. You need an executive data owner. Right? And that's the transformation, right? In the evolution that we're seeing in the market and that we're actually advocating for right to truly unlocked that business strategy, that business outcome that they're looking >>for. So let's talk about if we're gonna do that, then we need tools to do it. Yeah, absolutely. So we're talking about data we're talking about data owners we're talking about practice is to actually create generate value out of data. That's not something we're going to manually, right? Talk about some of the tools generally that your clients are starting to apply to improve their productivity of doing these things. >>Yeah. I mean, I would say there's a sort of standard spectrum of data management tools ride from, you know, the database to master data management to quality to meta data management. Right. So each of these technical capabilities and tools right provide the capabilities required to manage that sort of data supply chain right? There is infinite sources of data and there's infinite sources of demand, right? And it is the responsibility of, you know, the data management organization, too, to manage that supply chain. And obviously you need tools and you need technology to sort of support that entire life cycle. >>What is the one thing that you tell clients that need to do with their data in order to stay competitive? Is there one imperative thing that they all need to do with their data just to stay in the thick of whatever it is they do in their industry? >>Yes. So the one thing I always advise our clients is all data is not created equal, right? So fine and identify the data that truly Dr Value for your organization. Because that's been, I would say, one of the biggest challenges in this space, right is everyone's drowning in data, right? And so to bring all these capabilities for your entire, you know, sort of landscape in your organization, it's massive, right? It's just too big, right? So ty value and outcomes to the data that matters, right? So I'll give an example, right? So in retail, right, I mean their values around knowing their customers and the products that they So to those customers, right? So let's start double clicking underneath that and figuring out and ensuring that that data right has all the rights standards is up to quality so it can meet those business strategies, right? Don't go after everything, Right, map business outcome and value to the data that supports that. >>What's the role of the chief data officer and the other C level executives in driving that sort of transformation? Yeah. How is their role changing? >>So I would say the chief date officer role is again evolving and still maturing. Not everyone has it, but I do see them as the when the next executive sea level rose. That will truly be a catalyst for change and innovation. Right where, you know, I think we traditionally think about the CTO or the C I o. Or the chief strategy officer, right? Sort of back to the now four dimensions. It's no longer three their ability to understand the business strategy, understand where their data is to support that and bring new, innovative ways to enable that, right? So it's absolutely critical. >>So what we think ultimately on justice on you is that a chief is a is an executive that's responsible for demonstrating that they're generating, return and share older capital. Exactly. Chief data officer. Therefore, be the individual that's demonstrating that they're generating return on the company state assets. When you take an asset approach, you could think about portfolio. But think about portfolio now. You're discriminating, which values most valuable. Which date is less valuable. If you agree, that suggest that there is a new class of tool that has to be bought in around this notion of port folio catalogs, minute master data management and give us a sense of that kind of new tool kit that's gonna be at the core of not just managing data inside an application like a D B. M s right, but something that's actually managing data assets, >>right? Yeah, I think it's It's the entire ecosystem of how we bring it together and how we prove we create. What I would say is, products and service is around data right so back to this construct of your managing the data supply chain, right? And so the responsibility of the CDO and how you measure and manage that too, you know, outcomes. Right and shareholder value is I've just created a product around this data, and we talked a lot about data monetization. Andi. I would say It's from a outside in perspective. Am I selling my data? Am I making money? Right? Well, and of course, that's one angle. But I would say there's also the inside out view where your monetizing to create value back to your organization, Right? So increase, you know, customer cells, right? Reduced turn right. All those things matter. And so time data products to those business outcomes. I think how you get to, you know, the return on investment shareholder value as it relates to this role in the products and service is that it's creating. >>All right, we're out of time. I want a oh, principal date architecture er and management management architecture. Sorry at Deloitte. Thank you very much for being on the Cube. >>Thank you. >>All right, so we'll be right back with another event or another segment from in Dramatic World 2018 here in Las Vegas.

>>live from Las Vegas. It's the Cube covering. Inform Attica World 2018 Not you. Buy. Inform Attica. >>Welcome back and run. Live here in Las Vegas at the Venetian Cubes coverage of In From Attica, World 2018. I'm John for the coast to queue with by host the next two days. Peter Barrister, head of research for Wicked Bonds with an Angle and the Cube. Our next two guests from Deloitte. Leo Cabrera, who's senior manager. And Rajeev Krishna, who's the specialist leader on the engineering side. CDO side guys, Thanks for joining us. Thank you, John. Thank you, Lloyd. The leader in a lot of areas, absolutely doing a lot of cutting edge stuff from c'mon, the Blockchain crypto side tax side also in the I t side. You guys have been in a great top customers here in data in from Atticus, leading the charge, looking good with the trends. But the cloud is here. Cloud scale ecosystems developing. How do you guys see in from Attica? Evolving. Going forward, Mostly great messaging. But they still got customers out there that have sold stuff. They want to bring in cloud native new data. What's what's the prospects were in from Attica. >>Foreign Formica, Saudi lawyer. We have this nuanced article data advantage and basically would consider the inflection point between what we call in just 3.0, industry for point. And it's basically now we want to get value out of the data and our data advantage strategy Focus on three pillars. They have engineering wilderness and enable men for as Informatica Isa great component and a great supporter in each of these areas. Right, So, through these study we offer video service is we offer data governance. Studio chief did offer sheet state all of it. Yeah, on. And we partner with Informatica to profile the data to understand what will be the points in which we can find value over the data on off course with the new enterprise catalog to tool to do better governance for our clients. >>I want to get under the hood. I see the catalog is getting a lot of great reviews. Some people think that this is the next big wave in data management, similar to what we've seen in other ways like well, what? Relational databases and every way that comes on cap this catalogue New kind of catalogs emerging. What's your view on this? Is it away? Visit like recycled catalog, is it? >>So get a cataloguing and data. Curation has bean going on for decades, right? But it's never gained traction on, and it's never given Klein's the value because it was so manual takes tons of effort to get it right, right. So what inform Attica is done, which is absolute breakthrough? This embed a i into their enterprise data can log into which kind of accelerates the whole data. Cataloging on basically gives them gives climbs. The value in terms of cutting down on there are packed in terms of how many people, how many data students you need to put together >>So they modernize that. Basically, they exactly all the manual stuff put automation around and put some software to find around at machine learning. Is that kind of the secret to their success? >>Absolutely. And Down Delight has been partnering with Informatica for quite a while. In fact, we are one of the few companies that have a seat on the product advice report s o what we see from the marketplace we cannot feed into in from Attica to say, Hey, here's what you need to build into your products, right? So we be doing that with their MDM solution. For example, we have what we have. Articles indium, elevate. So we build machine learning into their MP and platform and offer. That's a solution similarly, and for America has built the clear platform into their E. D. C s. Oh, that's absolutely driving Valley for clients. And we have a lot of clients that are already leveraging >>a lot of risk and platforms tools, right? I see a lot of data stuff out there that's like like a feature, not a platform, that these guys got a platform, right? So But now the world's changing the cloud. How do you guys take that data advantage program or go to a CDO and saying, Look, you gotta think differently around the data, protect you explain your view on that. >>For us, data is now the center of everything, right? So any business who want to remain competitive in the future needs to get into entire end twin management of the data, getting the value of off data and also understanding what is the data coming from and what is the day they're going to write off course is studded with all the regulations. And now GDP are coming on Friday. It is a big, you know, pusher for companies to realize that over. If >>you have a big party on Friday, a big party or is this what you Katie was a big part. Nothing happened. So you're never mean GDP. Are you guys have a lot going on there? I mean, this is the center of the conversation. >>Yeah. I mean, we do have a lot of clients who need to be compliant on GDP are on informatica is one of the tools that have already pre established the policies, so you can quickly determine where is the data that GPR is gonna be monitoring and looking for compliance on So rather than doing it from a scratch, right? So it takes a lot of it >>for Let's build on this a little bit. So when we talk about different as John was saying, different generations of data management technology, we're coming out of a generation was focused on extract, transform and load where every single application or every single new analytics application wasn't you identify the source is uniquely you build extractions unique. You'd build transformations, you build load scripts. Uniquely all that stuff was done uniquely. Now what we're saying is catalog allows us to think to move into a re use world. We've been reusing code fragments and gets and all these other things for years. In many respects, what we're talking about is the ability to bring a reuse orientation inside the enterprise to data. Have I got that right? You got it >>right. Two minutes. But the most important parties how to get value out of that, right? Because they did >>manage to get value out of using >>it more exactly And understanding, You know, how can improve your operations or you know, the bottom line, or reduce the risk that you have in your data, which is basically CPR is about, >>and one other Salin point is on very scene for America bringing values their completeness of mission. Right. So when you talk about gdp are you need different aspects, right? You need your data integration. Whether it be through cloud around. Promise you need get a governor on top of what you're cataloging, right? You need security data security. Right? So it all comes together in the hole in dramatic solutions. And I think that's very see value is supposed to like pocket pockets >>of guys. I gotta ask you a question. We've seen many ways. I think it's a big way this whole date away. But you guys, you have a term called industry four point. Oh, is what is industry but the Deloitte term. But what is that? What is industry four point? Oh, me. Can you define that? >>You wanna take that door? >>Yeah, sure. So we've seen, you know, revolutions in terms off technology and data on. We've seen people going from kind of the industrial revolution to the dark. Amira, What? Three terms in the street? Four point off where data is annoying, right? So data is an acid that needs to be completely leverage. Not just you look a reactively and retrospectively like How did we do? Right? And not even just for predictive analytics. We've seen that for a few years now. It's also about using data to drive. This is value, right? So are there new ways to monetize data? Are there new ways to leverage data and grow your business? Right? So that's what Industry four. No, no is about. >>That's awesome. Well, we got a lot of things going on here. Thanks for coming on. The Cube had a couple of questions. Got a lot of dishes going on. That preparing for the big opening of the Solutions Expo Hall. We're in the middle of all the action. You're out in the open, accused. What we do. We go out in the open final question, eyes around the CDO. Who should the chief date officer report to the C I O board? What >>do you >>guys seeing? Because the CDO now picking a strategic role if Davis the new oil. That data is the fourth wave of innovation that we've seen over centuries. What does that mean? For the chief Data Officer? More power? Why'd you report to the C i o? Why is the CEO reported the Chief Data officer? What's your take? >>Traditionally our clients in the past, where the mandate for the studios were more in the data governess, right? As of today, it is going more into enablement the data, right? So more than Analytics case. Still, service is so well seen clients going from the studio moving from under the CEO in tow, the CEO and into the CMO in some cases, more about marketing. However, at the lawyer, our proposition is that companies should do a big shift and funded the new data function as a totally new vertical next to H. R next to finance right, which have his own funding and the CDO being the leader of that function, reporting directly to the CEO or >>enablement side CEO handling much of three things engineering, governance and enablement correct. So the CEO will handle Engineering Dept. Which not just its engineering, full stack developers, possibly our cloud native developers. Governance could come into policy, normal stuff. We've seen enablement more tooling, democratization of things. >>Yeah, yeah, >>yeah. I mean, what we've been seeing right in the real world, Liss, you have, for example, finance transformation that CIA full heads, right? So there's a lot of traction at that point to kind of bring the company together. But then that soon fizzles out. Sometimes you have, ah, the CMO bringing on and marketing campaign and, you know, analytics initiative, right? There's a lot of traction. Then it fizzes out. So you need somebody at the chief data officer of the C suite level to maintain that traction that moment, Um, in order freed value. >>But it seems the key issue is someone who is focused on data as an asset generating competitive returns on data as an asset because and the reason why it could be the CEO, it could be somebody else. Historically, an i t. The asset was the hardware on the argument here is that the asset is no longer the hardware now the data data. So whoever whatever you call it, someone and a group who's focused on generating returns out of data, >>Yes. But it has to have that executive level and that new talent mortal that we're proposing right where everybody knows a little bit of data in a sense. >>And the other thing is that I mean, think about this role that's dedicated to creating value of data, right? So you can understand you know how you create value in one function. Take it to the other function and tell them Hey, here's have helped finance right, get more value and then use the same thing marketing our sales. So it's also the cross pollination of ideas across different functions in an organization. S O n roll like that is helpful in terms of >>just to say, the data could very well become the next shared service's organization. That's because you don't want your salespeople to be great with data and your marketing people to be lousy with data. >>Correct. You're totally right on that. That's what we're proposing, right? So data being another vertical in entire business, >>the Lloyd bring all the action here on the Q. With all the data they're sharing here to you. It's the Cuban John for With Peter Burst, more live cover. Stay with us. We're here in Las Vegas. Live for in from Attica, World 2018 day. One of two days of wall to wall comes here out in the open. Bringing you all the data is Thank you. Stay with us.

>> Announcer: Live from Madrid, Spain, it's the Cube, covering HPE Discover Madrid 2017, brought to you by Hewlett Packard Enterprise. >> Welcome back to Madrid, Spain everybody, this is the Cube, the leader in live tech coverage. We're here covering HPE Discover 2017. I'm Dave Vellante with my cohost for the week, Peter Burris, Randy Meyer is back, he's the vice president and general manager Synergy and Mission Critical Solutions at Hewlett Packard Enterprise and Paul Shellerd is here, the director of the Center for Theoretical Cosmology at Cambridge University, thank you very much for coming on the Cube. >> It's a pleasure. >> Good to see you again. >> Yeah good to be back for the second time this week. I think that's, day stay outlets play too. >> Talking about computing meets the cosmos. >> Well it's exciting, yesterday we talked about Superdome Flex that we announced, we talked about it in the commercial space, where it's taking HANA and Orcale databases to the next level but there's a whole different side to what you can do with in memory compute. It's all in this high performance computing space. You think about the problems people want to solve in fluid dynamics, in forecasting, in all sorts of analytics problems, high performance compute, one of the things it does is it generates massive amounts of data that people then want to do things with. They want to compare that data to what their model said, okay can I run that against, they want to take that data and visualize it, okay how do I go do that. The more you can do that in memory, it means it's just faster to deal with because you're not going and writing this stuff off the disk, you're not moving it to another cluster back and forth, so we're seeing this burgeoning, the HPC guys would call it fat nodes, where you want to put lots of memory and eliminate the IO to go make their jobs easier and Professor Shallard will talk about a lot of that in terms of what they're doing at the Cosmos Institute, but this is a trend, you don't have to be a university. We're seeing this inside of oil and gas companies, aerospace engineering companies, anybody that's solving these complex computational problems that have an analytical element to whether it's comparative model, visualize, do something with that once you've done that. >> Paul, explain more about what it is you do. >> Well in the Cosmos Group, of which I'm the head, we're interested in two things, cosmology, which is trying to understand where the universe comes from, the whole big bang and then we're interested in black holes, particularly their collisions which produce gravitational waves, so they're the two main areas, relativity and cosmology. >> That's a big topic. I don't even know where to start, I just want to know okay what have you learned and can you summarize it for a lay person, where are you today, what can you share with us that we can understand? >> What we do is we take our mathematical models and we make predictions about the real universe and so we try and compare those to the latest observational data. We're in a particularly exciting period of time at the moment because of a flood of new data about the universe and about black holes and in the last two years, gravitational waves were discovered, there's a Nobel prize this year so lots of things are happening. It's a very data driven science so we have to try and keep up with this flood of new data which is getting larger and larger and also with new types of data, because suddenly gravitational waves are the latest thing to look at. >> What are the sources of data and new sources of data that you're tapping? >> Well, in cosmology we're mainly interested in the cosmic microwave background. >> Peter: Yeah the sources of data are the cosmos. >> Yeah right, so this is relic radiation left over from the big bang fireball, it's like a photograph of the universe, a blueprint and then also in the distribution of galaxies, so 3D maps of the universe and we've only, we're in a new age of exploration, we've only got a tiny fraction of the universe mapped so far and we're trying to extract new information about the origin of the universe from that data. In relativity, we've got these gravitational waves, these ripples in space time, they're traversing across the universe, they're essentially earthquakes in the universe and they're sound waves or seismic waves that propagate to us from these very violent events. >> I want to take you to the gravitational waves because in many respects, it's an example of a lot of what's here in action. Here's what I mean, that the experiment and correct me if I'm wrong, but it's basically, you create a, have two lasers perpendicular to each other, shooting a signal about two or three miles in that direction and it is the most precise experiment ever undertaken because what you're doing is you're measuring the time it takes for one laser versus another laser and that time is a function of the slight stretching that comes from the gravitational rays. That is an unbelievable example of edge computing, where you have just the tolerances to do that, that's not something you can send back to the cloud, you gotta do a lot of the compute right there, right? >> That's right, yes so a gravitational wave comes by and you shrink one way and you stretch the other. >> Peter: It distorts the space time. >> Yeah you become thinner and these tiny, tiny changes are what's measured and nobody expected gravitational waves to be discovered in 2015, we all thought, oh another five years, another five years, they've always been saying, we'll discover them, we'll discover them, but it happened. >> And since then, it's been used two or three times to discover new types of things and there's now a whole, I'm sure this is very centric to what you're doing, there's now a whole concept of gravitational information, can in fact becomes an entirely new branch of cosmology, have I got that right? >> Yeah you have, it's called multimessenger astronomy now because you don't just see the universe in electromagnetic waves, in light, you hear the universe. This is qualitatively different, it's sound waves coming across the universe and so combining these two, the latest event was where they heard the event first, then they turned their telescope and they saw it. So much information came out of that, even information about cosmology, because these signals are traveling hundreds of billions of light years across to us, we're getting a picture of the whole universe as they propagate all that way, so we're able to measure the expansion rate of the universe from that point. >> The techniques for the observational, the technology for observation, what is that, how has that evolved? >> Well you've got the wrong guy here. I'm from the theory group, we're doing the predictions and these guys with their incredible technology, are seeing the data, seeing and it's imagined, the whole point is you've gotta get the predictions and then you've gotta look in the data for a needle in the haystack which is this signature of these black holes colliding. >> You think about that, I have a model, I'm looking for the needle in the haystack, that's a different way to describe an in memory analytic search pattern recognition problem, that's really what it is. This is the world's largest pattern recognition problem. >> Most precise, and literally. >> And that's an observation that confirms your theory right? >> Confirms the theory, maybe it was your theory. >> I'm actually a cosmologist, so in my group we have relativists who are actively working on the black hole collisions and making predictions about this stuff. >> But they're dampening vibration from passing trucks and these things and correcting it, it's unbelievable. But coming back to the technology, the technology is, one of the reasons why this becomes so exciting and becomes practical is because for the first time, the technology has gotten to the point where you can assume that the problem you're trying to solve, that you're focused on and you don't have to translate it in technology terms, so talk a little bit about, because in many respects, that's where business is. Business wants to be able to focus on the problem and how to think the problem differently and have the technology to just respond. They don't want to have to start with the technology and then imagine what they can do with it. >> I think from our point of view, it's a very fast moving field, things are changing, new data's coming in. The data's getting bigger and bigger because instruments are getting packed tighter and tighter, there's more information, so we've got a computational problem as well, so we've got to get more computational power but there's new types of data, like suddenly there's gravitational waves. There's new types of analysis that we want to do so we want to be able to look at this data in a very flexible way and ingest it and explore new ideas more quickly because things are happening so fast, so that's why we've adopted this in memory paradigm for a number of years now and the latest incarnation of this is the HP Superdome flex and that's a shared memory system, so you can just pull in all your data and explore it without carefully programming how the memory is distributed around. We find this is very easy for our users to develop data analytic pipelines to develop their new theoretical models and to compare the two on the single system. It's also very easy for new users to use. You don't have to be an advanced programmer to get going, you can just stay with the science in a sense. >> You gotta have a PhD in Physics to do great in Physics, you don't have to have a PhD in Physics and technology. >> That's right, yeah it's a very flexible program. A flexible architecture with which to program so you can more or less take your laptop pipeline, develop your pipeline on a laptop, take it to the Superdome and then scale it up to these huge memory problems. >> And get it done fast and you can iterate. >> You know these are the most brilliant scientists in the world, bar none, I made the analogy the other day. >> Oh, thanks. >> You're supposed to say aw, chucks. >> Peter: Aw, chucks. >> Present company excepted. >> Oh yeah, that's right. >> I made the analogy of, imagine I.M. Pei or Frank Lloyd Wright or someone had to be their own general contractor, right? No, they're brilliant at designing architectures and imagining things that no one else could imagine and then they had people to go do that. This allows the people to focus on the brilliance of the science without having to go become the expert programmer, we see that in business too. Parallel programming techniques are difficult, spoken like an old tandem guy, parallelism is hard but to the extent that you can free yourself up and focus on the problem and not have to mess around with that, it makes life easier. Some problems parallelize well, but a lot of them don't need to be and you can allow the data to shine, you can allow the science to shine. >> Is it correct that the barrier in your ability to reach a conclusion or make a discovery is the ability to find that needle in a haystack or maybe there are many, but. >> Well, if you're talking about obstacles to progress, I would say computational power isn't the obstacle, it's developing the software pipelines and it's the human personnel, the smart people writing the codes that can look for the needle in the haystack who have the efficient algorithms to do that and if they're cobbled by having to think very hard about the hardware and the architecture they're working with and how they've parallelized the problem, our philosophy is much more that you solve the problem, you validate it, it can be quite inefficient if you like, but as long as it's a working program that gets you to where you want, then your second stage you worry about making it efficient, putting it on accelerators, putting it on GPUs, making it go really fast and that's, for many years now we've bought these very flexible shared memory or in memory is the new word for it, in memory architectures which allow new users, graduate students to come straight in without a Master's degree in high performance computing, they can start to tackle problems straight away. >> It's interesting, we hear the same, you talk about it at the outer reaches of the universe, I hear it at the inner reaches of the universe from the life sciences companies, we want to map the genome and we want to understand the interaction of various drug combinations with that genetic structure to say can I tune exactly a vaccine or a drug or something else for that patient's genetic makeup to improve medical outcomes? The same kind of problem, I want to have all this data that I have to run against a complex genome sequence to find the one that gets me to the answer. From the macro to the micro, we hear this problem in all different sorts of languages. >> One of the things we have our clients, mainly in business asking us all the time, is with each, let me step back, as analysts, not the smartest people in the world, as you'll attest I'm sure for real, as analysts, we like to talk about change and we always talked about mainframe being replaced by minicomputer being replaced by this or that. I like to talk in terms of the problems that computing's been able to take on, it's been able to take on increasingly complex, challenging, more difficult problems as a consequence of the advance of technology, very much like you're saying, the advance of technology allows us to focus increasingly on the problem. What kinds of problems do you think physicists are gonna be able to attack in the next five years or so as we think about the combination of increasingly powerful computing and an increasingly simple approach to use it? >> I think the simplification you're indicating here is really going to more memory. Holding your whole workload in memory, so that you, one of the biggest bottlenecks we find is ingesting the data and then writing it out, but if you can do everything at once, then that's the key element, so one of the things we've been working on a great deal is in situ visualization for example, so that you see the black holes coming together and you see that you've set the right parameters, they haven't missed each other or something's gone wrong with your simulation, so that you do the post-processing at the same time, you never need the intermediate data products, so larger and larger memory and the computational power that balances with that large memory. It's all very well to get a fat node, but you don't have the computational power to use all those terrabytes, so that's why this in memory architecture of the Superdome Flex is much more balanced between the two. What are the problems that we're looking forward to in terms of physics? Well, in cosmology we're looking for these hints about the origin of the universe and we've made a lot of progress analyzing the Plank satellite data about the cosmic microwave background. We're honing in on theories of inflation, which is where all the structure in the universe comes from, from Heisenberg's uncertainty principle, rapid period of expansion just like inflation in the financial markets in the very early universe, okay and so we're trying to identify can we distinguish between different types and are they gonna tell us whether the universe comes from a higher dimensional theory, ten dimensions, gets reduced to three plus one or lots of clues like that, we're looking for statistical fingerprints of these different models. In gravitational waves of course, this whole new area, we think of the cosmic microwave background as a photograph of the early universe, well in fact gravitational waves look right back to the earliest moment, fractions of a nanosecond after the big bang and so it may be that the answers, the clues that we're looking for come from gravitational waves and of course there's so much in astrophysics that we'll learn about compact objects, about neutron stars, about the most energetic events there are in the whole universe. >> I never thought about the idea, because cosmic radiation background goes back what, about 300,000 years if that's right. >> Yeah that's right, you're very well informed, 400,000 years because 300 is. >> Not that well informed. >> 370,000. >> I never thought about the idea of gravitational waves as being noise from the big bang and you make sense with that. >> Well with the cosmic microwave background, we're actually looking for a primordial signal from the big bang, from inflation, so it's yeah. Well anyway, what were you gonna say Randy? >> No, I just, it's amazing the frontiers we're heading down, it's kind of an honor to be able to enable some of these things, I've spent 30 years in the technology business and heard customers tell me you transformed by business or you helped me save costs, you helped me enter a new market. Never before in 30 plus years of being in this business have I had somebody tell me the things that you're providing are helping me understand the origins of the universe. It's an honor to be affiliated with you guys. >> Oh no, the honor's mine Randy, you're producing the hardware, the tools that allow us to do this work. >> Well now the honor's ours for coming onto the Cube. >> That's right, how do we learn more about your work and your discoveries, inclusions. >> In terms of looking at. >> Are there popular authors we could read other than Stephen Hawking? >> Well, read Stephen's books, they're very good, he's got a new one called A Briefer History of Time so it's more accessible than the Brief History of Time. >> So your website is. >> Yeah our website is ctc.cam.ac.uk, the center for theoretical cosmology and we've got some popular pages there, we've got some news stories about the latest things that have happened like the HP partnership that we're developing and some nice videos about the work that we're doing actually, very nice videos of that. >> Certainly, there were several videos run here this week that if people haven't seen them, go out, they're available on Youtube, they're available at your website, they're on Stephen's Facebook page also I think. >> Can you share that website again? >> Well, actually you can get the beautiful videos of Stephen and the rest of his group on the Discover website, is that right? >> I believe so. >> So that's at HP Discover website, but your website is? >> Is ctc.cam.ac.uk and we're just about to upload those videos ourselves. >> Can I make a marketing suggestion. >> Yeah. >> Simplify that. >> Ctc.cam.ac.uk. >> Yeah right, thank you. >> We gotta get the Cube at one of these conferences, one of these physics conferences and talk about gravitational waves. >> Bone up a little bit, you're kind of embarrassing us here, 100,000 years off. >> He's better informed than you are. >> You didn't need to remind me sir. Thanks very much for coming on the Cube, great pleasure having you today. >> Thank you. >> Keep it right there everybody, Mr. Universe and I will be back after this short break. (upbeat techno music)

>>live from Madrid, Spain. It's the Q covering HP Discover Madrid 2017 Brought to you by Hewlett Packard Enterprise >>Welcome back to Madrid, Spain. Everybody, this is cute. The leader in live tech coverage And we have a day to HP discover Madrid. My name is Dave Volonte with my co host for the week Peter Verse. Jeff, Carla is here. He's the senior director of solutions. Go to market system integrators at Hewlett Packard Enterprise and Kalyan Gara Mela. Who is the i o t manager? Deloitte. Yes, Gentlemen, welcome to the Cube. Thanks for coming on, You bad love too deep here. It's always a great time. Yes. So you know, when you come on with Deloitte, we always sort of mentioned you guys. One of the top system integrators in the planet. You got deep expertise and vertical industries. You guys bring the technology expertise. Last time we were talking about manufacturing. This time we're gonna talk about retail. Yes. Why? Retail, You know, retails in turmoil. Everybody's got numbers on war room. But you guys are going after that, helping some of your customers so to take advantage of their physical presence, bringing in an online presence move into digital. Is there hope there's hope, their retail dead? You know, >>I hear all the time about this retail apocalypse retail is dead, and in reality, it's not dead at all. Still, 85 to 90% of purchases were being going through a brick and mortar store problem here, and the apocalypse will happen to those brick and mortar retailers that don't change. They don't digitize and change to the changing demands of a consumer and the way they want purchase something, give you an example, my son or even myself. Now I increasingly want to do things through an experience. My computer, my mobile phone. I do research. I I want to understand. I want recommendations. I want personalization. I want to be catered to. I don't want to go stand in line. Well, that experience can be done but are unique. Ability. Is taking that experience in a planet into a brick and mortar environment? >>Well, I got to say I love going Cabela's with my kid with my wife. I mean, I could spend all day. Hey, get that on Callie and tell us about your you're rolling. The Lloyd, obviously specializing in the retail practice. What, Your background? >>Yes, my name's Kalyan. Gotta Mila being a coyote manager from the >>delight you >>practice based out of San Francisco, and we have been working with our partners and friends. Hitch be Aruba over the past year or so, helping them dollar, I ot go to market projects, products that can be that we can take to market on Dhe. Recently. We're just working with manufacturing and retail industry. >>So what's the conversation >>like with your customers? As I said, everybody's got an Amazon war room they're trying to figure out. Okay, >>how do we leverage our physical presence as an advantage? What were the conversations like with clients with >>our clients? Mostly that talking about How did the mimic our online channels? Right. If I go to an online retailer, you know, if I go open, say amazon dot com, they know exactly what item I am for chasing where I'm going next. What? How much time I'm spending. So in order to differentiate the brick and mortars in order to differentiate themselves from the fellow retailers, they have to offer that customized shopping experience in order to get given a reason for the customers to come in store and make that purchase. So they're trying to look at what new technologies that we can can we can help with. What are some of the new processes that we can help with? And that's where most of our conversations have been going on, >>Really experience. Problem >>it is. And you talk about the bells and I moved into a new house, ready to buy my big >>lazy boy chair and watch Sunday >>football, and I'm not gonna go online just by here. I want to touch and feel that I was late and I want to understand. Well, that is a perfect opportunity of providing an experience. Allows me to do the research, get suggestions, go into a brick and mortar store. Try it out, then guess what? I'm getting personalized. Hey, you know what? There's a nice beer stand that I could put right next to that table. Be calm, perfectly complemented. Hey, there's a light that can look over So we have that ability of actually tying together and experience, actually predicting in advance what the customer really doesn't know they want next. But they really do want example. We just walked out of a client engagement. Beautiful example. Plan Engagement sells high end women's fashions, right dresses and shoes and accessories. Everything. And he's He basically said, We're dabbling around with R F I. D tags, um, inventory management, but we don't know what to do, right? Bingo. We now have a proven, referenced architecture called the Connected Consumer. This is a preview to be announced to be soon, but that can allow, actually that client to integrate and optimized and digitized the solution for a number of different use cases that spans a unique customer experience in store operations and efficiencies, and then providing insights through analytics in store analytics to make decisions quickly. So you've got by using this architecture building of solutions based with Deloitte Competence season capabilities in HPD Aruba technology. We can deliver that to increase top line revenue, increase basket side, decrease inventory costs, lost inventory and provide much greater brand loyalty to those customers by having a nice, personalized teachers. They know me by name. They know what I'm looking for in advance. Beautiful solution. >>So the online retail world did two crucial things. One is provided new way of customer to buy something and number two, it provided a new way for the retailer to learn something about the customer. Very, very powerful. But as you said, we're still last time. I checked physical things that move through space that used physical senses, too. Make decisions, Tactile. Do I like the color? You know the experience. I mean, I remember having arguments with people about whether the Apple stores are ever gonna have any impact in the world. And, boy, did they prove that experience of physically being there matters. So in many respects we're talking about, We're talking about creating spaces, the correspond to the experience that a customer wants in a way that doesn't force them into another channel. >>I think that is excellent. Thank you will hear security and character talks about who these are Aruba team. And they are renowned for taking a space and providing using technology and I, t and software and security to provide a total experience, an immersive experience for those that are occupying that. >>But that's not how retailers used to think. What they used to think was this is the space where I put my inventory where I show my product and then I'll put the catch register over here. What you guys I presume we're trying to do is show how. Show them how they could turn that physical space into a place that can bring in the online digital elements, complimented in a way that makes that door a source of different jack >>experience in the brick and mortar store and allows the comfort of Yeah, you know >>that makes it differentiated so that someone wants to go there, because that is a valuable experience in and of itself. >>And sadly, retailers of the past 40 years have always relied on big brand names to attract customers. If I have the best brands in the world, customers will come to me back. That scenario doesn't hold true anymore. You need to give them a reason. A personalized, curated experience for them to come in >>well, not least of which is the digital technology allows us to spin up new brands like overnight and so also so there's a there's it's having an erosion of effect on the other side of the inventory. So tell us a little bit about where you think over the next few years that differentiated in store experience is gonna be what is going to constitute great retail. >>I'll start enough shit. >>First and foremost, the expectations of millennials and other generations is more of that online experience. So I think I think retailers of the future have to be able to provide that customized experience. To be able to provide predicted people are not waiting in line is not an option in the future, right? I mean, even you. You look a waiting in line is not an option. I think that ability of you have to have more instantaneous gratification but allowing, if you will, the personalization being covered. I think that one expectation for those that want to sustain a business in retail in the future >>and add on to that right. I mean, the marketing managers are the store managers of the past have always relied on opinions rather than data and insights to make this better business stations. Where do I place my product? Where are my customer spending most of my time? It's just guess it's most of it was guessing. Now there is a technology out there where we can actually monitor what's happening inside your retail store and dead. While you can make better business nations to help you with your customer journeys, >>traffic, foot traffic, you know through video analytics and the data someone's hanging around the Nike booth or whatever you know financially, and you can purposely point them and give them suggestions of 20% off. And so you can personalize that experience. >>So wait. See Io client on DDE that's in the retail space on the way he described it is, you gotta break the whole thing down. Let me test you guys. You have a period of I want the experience of shopping on. The example that he gave me was a bike company a number of years ago who used flexible manufacturing to collapse the time high end bike to collapse, a time from order in the bike, getting the bike down to a few days. And they failed because the customer like waiting the process of buying, reducing time. Simple, straightforward, but also what they said. And this is the kind of flexibility we're talking about is some people don't wanna walk out of the store with the product they want to deliver to their home, so the store is again, not the place where the inventory is. It's the place where you experience the product and that they create an option. How would you like that? I like to be delivered to my house. No problem. There you go. Is that the kind of thing that we're talking about in the future? >>Absolutely. We call it the unified commerce of the Arm and channel shopping experience. You want to give the customer all the options available. Like you said, I could buy online shipping in store O. R. I can buy in store get into my house all the different options that a customer is looking for. A non online channel, which is easy and convenient. We want to do that in a brick and mortar as well, and our solution can help you do that as well. So you >>guys encounter a client that is, you know, declining same store sales management is concerned about, You know, the future. It seems like it's a tired sort of experience and, you know, that's sort of the end of the spectrum. And you want, you know, the to be his future. Stefano, the talk about where do you start so >>who brings what experts is. >>Actually, I'm gonna repeat what I said last time. Our mantra is First off, you gotta think big. Then you start small and then you scale fast. And what I mean, that what we mean by that is with the Lloyds capability. It's been a week and jointly come in and help a retailer. Let's think it through. Let's think you have how many branches looking to wear? What are your problems? What your inventory leak age. You know what your current experience, but you're in store WiFi. We can build a plan on what we can do. But the next big problem that we see is not about the technology is about the people in the process. How do you convince its How do you commit? Some who invest to change well, this through our proof of concept capabilities, we have the ability of starting small. Let's just go in and we can do through this architect modular proven architecture. We could do a starting Well, let's just start with some R F I. D tags and tags and start small. We can deliver the business value and calculate that and extrapolate that out if we apply that to your all your stores and scale fast. So we're making it. This be an on ramp for those retailers because they're saying what I do. I know I need to change, but what do I >>So you do like a test store model, right? Okay. And then what? That's your POC is actual. >>Yeah, And then So I wanna go back a little bit on this whole coyote offering. It's a composite offering, right? It takes a lot of technologies coming together and a lot of SMEs subject matter experts to come in and help you to build a whole solution. And that's where I think our solution is where it's ready to go, where all the pieces have been put together and can be easy from day one. The time to market has been drastically reduced because of this. Right? So we see a lot of value in that. >>So So you're able to say Okay, what kind of target customer? What kind of inventory? What's the cost of it? What's the turn? Take all those business attributes and then say we can map that back into a set of physical and system components that you can scale fast >>really comes around you. Three buckets were doing this to optimize an increase revenue, basket size conversions, everything timed revenue, decrease costs, efficiencies and inventory logistics people, uh, labor. And then providing a much greater experience of brand loyalty, which will also affect both costs and >>capture and capture additional data. So, for example, returns means two things costs, but also, somebody had a problem. >>So, uh, we're out of time, but so summarize kind of where you guys were at, >>uh, your solutions when it's gonna be available, you go to market, give us the >>tickets. That right now we're here at HP discovered we're previewing this connected consumer architecture. We're will deploy it. Calendar quarter one of next year will be the full announcement. We have contact information. We would love to engage in clients and start that discussion now around doing proof of concepts on dhe. We're going to be not only driving this collective retail solution that could be extrapolated into different use. Cases in markets were also continued to drive the Moorman industrial Internet of things and manufacturing offering around predicting maintenance, asset monitoring, maintenance that we talked about in Vegas. >>Great. Well, I hope next next Vegas come back with some examples and some a customer, and we could go through so that one of impact you've had, maybe you'll be through a POC. At that point. I'd >>love to get the cube into one of their poc >>a well loved. All right, guys. Thanks very much for coming on the Cube. All right. Good >>to see you. See? All right. Thanks. Keep it right there, >>buddy. We'll be back with our next guest day. Volonte for Peter Burke alive from Madrid 17.

>>live from San Francisco. It's the Q covering in dramatic. A World 2017 brought to you by Inform Attica. Welcome >>back, everyone. We live here in San Francisco at the Mosconi West with In From Attica. World 2017. This is Cubes Exclusive coverage. I'm John Furry with the Cube and Peter Barris with vicky bond dot com General manager we have on research. Our next guest is Tracy Ring, specialist leader at Deloitte Consulting in the trenches. Put it all together. Welcome to the Cube. Thanks for joining us today. Appreciate it. >>Thank you for having me. I'm excited to be here. >>So your specialist, But in the system global system, integrated world, that means you basically globally look at the solutions. And And what's interesting is why I'm excited. Conversation with you is that, you know, point solutions can come and go. But now we're in this compose herbal world of cloud data, etcetera, where ah, holistic view has to be looked at. So what? I want to get your thoughts on in from Attica and what you guys are doing because we've heard it's the heartbeat. But yet there's also a hygiene issue. So you got this heart surgeon and the hygienist, and you have all kinds of specialty rolls of and data. It's pretty broad, but yet supercritical. How do you look at the holistic big picture? >>Absolutely. I mean, we're seeing the view of ecosystems being so much more important. Were so Maney technology disruptors. I mean, three years ago, we weren't even hearing about Kafka, and Duke was really new, and and so I think demystifying, simplifying, helping customers understand the art of the possible what can be done? What are leading practice organizations doing and then really making it real? How do you so this complex story together, how do you best leverage and get your investment out of technologies like in from Attica in their complimentary tools >>is interesting. IBM has Watson in from Attica. Has Claire ASAP has Leonardo s A P has Einstein. >>It would be >>great to get them all together >>and have dinner, right? So I mean, but this speaks >>well, You got Alexa and Amazon and Google. I mean, this is an interface issues you're talking about. Ah, cognitive. A real time new user interface and machine interface into data that is completely out of the possible. It's what's happening in the world is changing. Developers is changing. Practitioners, architects. Everyone's impacted your reaction to all this. >>You know, I think it's probably the most exciting time that we've seen in so long, and I think you so well articulated all of the players that air there. I think when you add in I, O. T. And Device Management, you know it's really an exciting time. And I think it's really driving some amazing things with regard to how organizations are literally transforming themselves. And in both our clients as well as the ecosystem of technologies, companies air are literally shifting their entire business model. It's it's very exciting. >>So one of the things that the typified system integrator types behavior like to elect a lawyer big consulting firm was big application. Let's deploy the big application for accounting for finance for HR whatever. Also culminating in New York, which was the Grand pa of everything. Right now we're talking about analytics where we have to focus on the outcome's not just a big package for a function, but really a complex, ideally strategic differentiating outcome. Yeah, typically using a whole bunch of smaller tools that have to be bought together similar. What John was talking about as a specialist who looks at these tools take us through kind of a new thought process, outcome, capability to tool in the entire journey to get there. >>Absolutely. I think one of the things that delight does that is really, really unique is having conversations that start with art of the possible, what could be done? What are leading practice organizations doing Help me set a strategy? Yeah, and I think the real answer is there's less about sort of benchmarking what everyone else is doing and more about >>really, You got it, You got >>it. It's really about revolutionizing, you know, and and going into a new angle of what is truly, truly possible. And I think, ah, lot of the things that were sort of table stakes and in the way that we would look at success totally turned on its head. And we're looking at organizations monetizing their data and, you know, creating new business ventures because of the insights that they're deriving and a lot of times will use. Delight has an insight studio and a greenhouse, and a couple of really highly collaborative spaces that we take clients to. Ah, well, you know, plan 123 day workshops, depending on how difficult of problem they're trying to solve and help them charter road map. And take that road map, which is in many cases, business oriented business results driven and help them so in and layer in the technologies that are gonna make that reality possible. What's >>the opportunities for cognitive? I mean, you guys talk a lot of Deloitte about a Friday different things, but specifically there's some key opportunity around. Call the cognitive or you guys call the cognitive. IBM also used that word cognition, but really a I artificial augmented intelligence are signs of a new kind of opportunity landscape. Whether you see for customer opportunities out there, >>absolutely, we talk a lot about what we consider the inside driven advantage. And that's really about using all of the tools in the toolkit to make that insight driven, data driven, better decisions around what organizations conduce. Oh, and kind of. It is a huge component of that, you know, it's we've been hearing stories for years about companies sort of predicting the next best offer and you know, we're seeing this move so much further, removing into robotics process automation. You know, the space is getting, I think, even more complex. But I think what's interesting is when we talk to organizations about, you know, they're not hiring tons of people to go out and do data integration through wonderful organizations. Confirm Attica. That's really been solved. So companies were able to both take their technical resource is and shift them into solving Maur difficult problems, hairier technology opportunities and use that to help shape their business. >>That's like compose abilities. So in dramatic, a world's got a set of solutions and technologies. Some sass ified someone fram. But here it is. But you're deluded you. That's just one element to your mix of things composed for clients. You mention those three years opportunities. Digital transformation is kind of the categorical wave >>Iran, but the end of >>the day it's business transformation. You mentioned changing the business model. >>How do >>customers take advantage of those business opportunities in whether it's robotics or industrial i ot or insights and analytics? What What is the customer impact and how did they get those business benefits? >>Yeah, I mean, I think again like I said, a lot of times it starts with, you know, what is their goal? What do they want to be known for in the marketplace and that value branding of Of what is it that they see themselves differentiating amongst their competitors and using a pretty solid process and rigorous approach to that strategy? Tea set? You know, what are the pillars to achieve? That is, I think, a big piece of it. I think the other component is we see a lot of organizations sort of challenging themselves to do more. And we'll have organizations say I believe that I can doom or what? What could I do? And I think that's interesting that >>we'll just fall upon that because Pete and I were talking earlier before we came on about what gets customers excited when the iPad came out. That was the first kind of visual of >>I gotta have my analytics on the dashboard. Let's start. I >>call the dashboard wave now with bots and aye aye. You're seeing another reaction. >>Yeah, I gotta have that. Automated. Do you see it the same way? And how does that >>translate to the custom when they see these this eye candy and the visualization stuff. How does that impact your world and the impact of the customer? Your customer? >>Absolutely. I mean, we used to live in a world where if I needed to have my data extracted, I would, you know, submit a request. And it was this very long, lengthy process. And, you know, when you think about the robotic single and and process automation, you know, automated data pools are are there. And I think the interesting part is is that it's not about just cost out of i t. It's not about, you know, getting off of on premise hardware. It's about driving better customer satisfaction, driving better business outcomes. You know, the implications. I think whether you're in life sciences or you're in retail, you can touch your customer in a way that is. You know what I would say? Sort of delighting them versus just giving them what they asked for. >>So I wanna I wanna test of theory on you and see how live and see how this seals lines up with thinking and where you see your customers going. So we have this notion that wicked bond, our research of what we call systems of agency. And by that we mean effectively that historically we did we create systems that recorded action big t p e r p. More recently, as you said, we're now creating systems that suggest action predictive analytics, those types of things. And now we're moving in the world were actually going to have systems that take action. Yeah, where authority and data have to move together so that the system is acting as an agent on behalf of the brand now in from Attica has done some really interesting things here with some of their new tooling, some of the metadata tooling to ensure that that type of meeting can move with the data. So if you think about where Deloitte and customers are going, are they starting to move into this new realm where we're building systems, take action on behalf of the brand and what does that mean for the types of tooling? But we're gonna have to find for customers so they can make it, you >>know? I mean, this morning we were delighted to hear the latest announcement around how metadata is really such a core component, and and I think of it is metadata is in many cases where most organizations do see the monetization of their data payoff. Right? We're not only do I have highest golden record like we talked about 10 years ago, I have data lineage. I have data traceability. I have the whole entire story. So it's really much more cost justified. Uh, you know, hearing the announcement today of Claire, and you know how we now have the Aye Aye of our clairvoyance is really exciting. And, you know, I I don't know that we're completely there. And I think we'll continue to innovate as in from Attica. Always does. But we certainly are a whole lot closer. And I would say, you know, your concept is you know, certainly we're all going to the park for >>good. My final question. Let's get your thoughts on because you have a global perspective. You work with the ecosystem partners. You heard all the stories. You've heard all the raps and all the Kool Aid injectors from the different suppliers. But there's two things going on that that's interesting. One is we're kind of going back to the end to end solution. Absolutely. I'm seeing five g with Intel Smart cities I ot So everyone wants to get back to that end to an accountability with data and packets moving. All that could step with applications over the top. But yet there's not one single vendor owning it, so it's kind of a multi vendor world, yet it's gotta be in tow end and bulletproof secure. I mean, >>that's your world. It's not derailed. I mean, you got to be busy, your reaction to that. And what's that? What's that >>mean to the industry? And how should customers? I'd look at that Say okay, Want to get some stability? I want great SL ways, but I want a flexibility for compose ability I want and empower my app developers Dr Top Line Revenue. This is the Holy Grail. We're kind of in the wheelhouse right now. >>Yeah, 100%. I think it's a very exciting time and the like, I said, the fabric of what organizations need to sew together two really achieve their analytic insights and, uh, you know, leveraging their data. I think data is just becoming more and more important, and it's a phenomenal place toe to be in both for where I sit on the consulting side helping all of our customers and certainly where globally we're seeing our client's going >>and your and your message to the client is what we got your back on. This >>has to look, that's what you guys do. You sew it together. It's got to be more than that. It's got ideas for you could see. I think it's a >>lot. I think it's that it's not just about bolting in a technology or 10 technologies. It's about solving the most difficulty technology problems with, you know, with data helping. >>You gotta be savvy to, as they say in the swim lanes of the different firms and got to bring your expertise to the table with some of your own tech. >>Absolutely. And and I think for us we never sort of a ra missed that there is a huge business, and if you if you don't take the business aspect of it, what business problem are we solving? What value are regenerating? How are we ultimately impacting our customers customers, you know? Then you know you're sort of missing the what we consider the most important piece of the pie. >>Tracey Ring with the Lloyd. Great to have you on. Thanks for your insight. Very insightful. That all the data's right there. We're gonna make sense of it here in the Cube. Thanks for sharing, Dee Lloyd. Really put it all together. Composing the future Cloud Data Mobile. It's all here. Social is the que bringing all the live action from San Francisco. I'm John for Peter Burst more after this short break.