(bright upbeat music) >> Welcome to theCUBE. Welcome to this CUBE Conversation. I'm Dave Nicholson and with me today, I have Jen Bennett, technical director out of the office of the CTO at Google. Jen, welcome to theCUBE. How are you? >> I'm doing great, Dave. Thanks for having me. >> It's great to have you here. Now, some estimates have 1% of global electrical production. So all of the electricity produced globally, 1% of that is being used currently for the category we call ICT or Information Communications Technology. By 2030, the estimate is that if that goes unchecked, unmeasured, unmitigated, we could be at between 8% and 20% consumption of all of the electricity generated globally, at that point. The question I have for you to start this off is, is that sustainable and if not, what are you and the folks at Google thinking about in this area? >> Yeah Dave, that is a great question. And I think one that's top of mind for many of us in the industry, and there's unfortunately not a simple answer, but for sure, I think that sustainability has been something that's been a core value of ours from the very beginning at Google. And we've really focused on the impact that we have throughout, you know, our computation on climate and on other aspects of the environment. And so I think this is something that's a really important topic that we need to discuss. And so, you know, I want to give you a little bit of a history from a Google perspective. You know, in 2007, we were the first major company to become carbon neutral. So today, you hear a lot about companies talking about carbon neutrality and how important that is to reach, you know, some of these targets that we've been talking about from the Paris Climate Agreement, et cetera. In 2017, you know, as we were looking at this target of how do we become more carbon free, we became the first company, first major company to match a hundred percent of our electricity use associated with our computation that you talked about earlier with renewable energy. So this was a really big push for us. So with the question around, is it sustainable, has to kind of go back to, well, where does the energy come from and how is it generated? And renewable energy is one of those big pieces of the puzzle here. And so we invested very heavily in what was called Power Purchase Agreements. This is, you know, creating the mechanisms, the financial instruments to, you know, enable energy companies to transition from maybe things that are less carbon friendly to more renewable energy. So that was a big emphasis that we put on that first decade of climate action. Since then, and you may be familiar that in 2020, we announced probably our most ambitious goal yet, which is to operate on carbon free 24 by seven. So one of the things that you'll appreciate is that renewable energy, the sun doesn't shine 24/7, wind doesn't blow 24/7. And so we need more to become carbon free, 24/7 around the clock. We need storage techniques. We need a lot of other potential, you know, technologies that are evolving. And so we're very committed to investing in making sure that we advance those technologies just like we did with renewable energy. And so this is a really ambitious goal. We don't know how to get there yet. And that's why we often referred to is as a moonshot. >> Yeah, so yeah, that is a moonshot. And it's interesting because it sounds like you've decided to use a metric that is more difficult than the metric you could use, which is aggregate neutrality. You mentioned the sun doesn't shine 24 hours a day. Well, of course it does somewhere on the planet, but not on an individual facility. So if you're... If you're measuring carbon neutral at a certain physical location, that's a higher bar. So you talk about storage with batteries and things like that. So it's admirable that you're not taking the sort of easy way out, and looking at it just from an aggregate perspective. >> Yeah. And I guess I would say, you know, I think it's a journey. So you referred to it as sort of easy way out. And, I know, I certainly... I think this is one of the challenges is that oftentimes, action... Climate action can seem overwhelming and we have to find ways to advance and to get to impact. And so I think, you for us and our journey, you know, we started with how can we best make the use of the energy that we have. And so we applied machine learning in our data centers to optimize the energy utilization so that we could really bring down, you know, or optimize how we used energy in the first place. Of course the most renewable energy is the energy you don't use. But then I think the next point was where do we go next and how do we continue to advance to a place where we do get to a carbon free future, at least for us with our energy use as well. And I think, just as importantly, the next step is, "And how do we enable others?" And that's where we're at today. And so, you know, we've made some really exciting announcements and we're hoping that that's just the start of many more to come. >> Well, let's talk about that. So how do you enable others on this journey? >> Yeah, it's great. So when we talked a little bit about carbon free and that idea of energy in 24 x 7, one of the things that we started to focus on was how do we look at energy on an hourly basis, not on an annual basis or on a monthly basis. And so, as we started to pull the, you know, gather that data instrument up, we realized that this information is extremely valuable to our customers as well, who leverage our cloud platform. And so one of the big announcements that we made as part of our suite of sustainability offerings is really... It's called carbon footprint. And it's really providing that information to customers that use our cloud technology to give them a sense of where, how carbon friendly, the energy that they're using. Of course, we are carbon neutral and so effectively the neutrality is zero. But for those who really want to make sure that they're selecting locations where there's clean energy, making sure that their digital infrastructure is running as efficiently as possible. This new tool carbon footprint will give them the opportunity to see that just like you might see on your billing, you know, how much you spend, how much carbon emission there is associated with that as well. So that's one of our big announcements and we've worked very closely with customers like HSBC and Twitter and Salesforce as we've developed that capability so that we know that it helps meet their needs as well. >> So right now the part of your carbon footprint that is managed by this capability would be say GCP consumption or the Google part of a organization's footprint. Any plans in the future to integrate other data sources into a centralized capability? >> Yeah. So I think we have a lot of other exciting news we shared as well. You know, we recognize that our customer's digital footprint is a growing part of their business. As we've seen, sort of the pandemic has accelerated this shift to digital, and we want to make sure that we're bringing the most climate friendly cloud platform, to enable that. But a lot of our customers also have other parts of their business that aren't digital. And so being able to bring sustainability offerings to them, to help them with data, as you mentioned, but also with other tooling is really important. And so the other exciting announcement that we made is that, we are bringing Google Earth Engine to Google Cloud for our customers and Google Earth Engine has been around for over a decade used by, climate researchers, NGOs, It's really a trusted platform for earth observation. And so we're really excited to bring this to our Google Cloud customers as a mechanism, to look at the impacts of climate on their business, as well as see how their business is impacting the environment. And so this is an amazing platform that we're really excited and have partnered very closely with customers like Unilever, to leverage this platform in their business. >> So what are some practical examples of data sources that come out of the Google Earth platform and how they integrate from a sustainability perspective? What are some insights that organizations can gain by leveraging this? >> Super question. So, within Earth Engine, there is, over 50 petabytes of data and over 700 curated data sets. It's the world's largest for earth observation. Things like satellite imagery to look at land cover and deforestation for example, soil moisture, water, and availability of water in flood zones. There's just a... It's a really rich catalog and has been, you know, really curated over a matter of time. It brings also not just historical data, but is constantly refreshed. And so for someone who wants to look at changes over a period of time, it's really the perfect platform for that. And so for example, with Unilever, we have been looking at deforestation and the association with their supply chain because of course, many companies like Unilever have committed to a zero deforestation, And pit land conversion as part of their commitments to climate action. And so this is a really powerful capability. It's sort been kept within the research community and the NGOs for such a long time, that we're really excited to start to now evolve that into applications where we can make a real difference. I think this is part of our mission is to say, "How do we extend our reach in our climate action to not only the billions of users that use our platform, but also our customers who also reach many, many users through their products." So we're really excited about that opportunity. >> So imagine five, 10 years in the future. How does this play out? Give us a view into the enterprise of the future and the kind of information they're going to have at their fingertips. You gave a great example of deforestation within a supply chain, and we're not talking software supply chain here, we're physical supply chains, >> Physical supply chain, yeah. >> If think about a modern enterprise having hundreds or thousands of connections with other enterprises when you're trying to understand what the impact is that you're having on the earth, you have to take all of those things into account. So, can you kind of walk us through a scenario of what that might look like in the not too distant future, the kinds of things they'll have access to? >> You know, I wish I had that, the vision of five to 10 years from now, but if I look at what has evolved even over the last few years, you know, we've been... As consumers, we have so much information at our fingertips, that's both a blessing and a curse in many ways. And I think our job as technology providers is to make sure that the technology that we bring enhances the ability to make decisions quickly and that's... And effectively. And I think that's really what the power of these tools is enabling. So if I look into the future and I look at a little bit of the past and say, you know, "How do we navigate?" We often navigate no longer by paper maps. We navigate by using digital maps. And that same capability is what I'm talking about with some of this geospatial capability is being able to see that information in a way that's very meaningful, very powerful, but also helps you make decisions right now. And so I think that capability is going to be really critical and it's going to bring the complexity of information in a way that I believe is going to help us make better decisions. So today, if we take that... if we continue to take that example that we talked about, you know, often we talk about a lot of decision-making in terms of, you know, pricing, I think in the future, you're going to start to see the complexity of decision making, you know, sort of, you know, becomes more complex, but with the capability and the tools and some of the AI capabilities, we're going to be able to make that very tangible for people so that the decision making is fairly straightforward, but enables us to take into consideration the complexity of the ecosystem. Really excited about the future. I also believe that the future involves a lot more collaboration than maybe we see today, because I think one of the challenges with climate change is, we need to come to an agreed, you know, a shared truth, a shared sense of truth. And that really means we have to break down some of these silos we've created, you know, whether it be in business, whether it be in collaboration with the ecosystem but we're going to start to see a lot more collaboration around data. >> Yeah. Well, Those other things, those other costs that are sometimes hidden and hard to fair it through, in economics terms we refer to those as externalities and may have always been a challenge, how do you account for those real costs of your activity? Jen, I have to say that your passion for this comes through, despite the technology dividing us, it must be a delight working in an environment where you have the support of your organization. And you've got a clear mission that you can wake up every day and pursue that. Talk a little bit about that from a personal perspective. >> Yeah, I wake up every day, well, wake up, I think about this all the time, and I'm reminded of it, everything that I do, you know, whether it be turning off the lights, whether it be running the water too long. I mean, we're now experiencing droughts like we never have you know, historically. And so I think all these things impact us on a personal level. It's really... I feel so fortunate to be able to be thinking about this, to be able to take on something that really is one of the most pressing issues of our time. But I will say I'm also truly inspired by the customers I get to work with and their passion for the same topic. And I really feel like we've crossed this point of really moving to action. So it's no longer just about debating, you know, the science, if you will, we've really started to move into and what action can we take? And I think that is really what is going to both help move the technology and the ability to leverage technology and push us to continue to develop solutions that help make those actions real. But I also am very inspired by the fact that we have these large commitments across organizations to really make change. And I think this is what we all need. >> Well, Jen, thanks for joining us today in this CUBE Conversation and sharing some of the insights that you have along with Google and your customers. We appreciate the work that you do, it's critically important. With that, this is Dave Nicholson. Thanks for joining us for this CUBE Conversation. Until next time, see you then. (bright upbeat music)

>> Narrator: From around the globe, It's theCUBE! Covering the Upgrade 2020, the NTT Research Summit presented by NTT Research. >> Hey, welcome back everybody, Jeff Frick here with theCUBE. Welcome back to our ongoing coverage of Upgrade 2020. It's the NTT Research Summit covering a lot of really deep topics around a lot of the basic core research that NTT is sponsoring. Kind of like the old days of Mobell or some of the other kind of core research. And we're excited to have our next guest to go. A little bit beyond the core research and actually talk about working with people today. So we'd like to welcome in Alex Bennett. He is the global senior vice president of the intelligent workplace for NTT. Alex, good morning? >> Good morning, Jeff. How are you doing. >> Terrific. So I think for a lot of people, you know, probably know the NTT name, certainly in the States, but are not familiar with, I think, you know, the degree of which you guys have this huge business around services and workplace collaboration, I wonder if you can give us kind of a high level summary of the services angle at NTT, you know, beyond just putting in communications infrastructure equipment. >> Yeah, definitely. I mean, the NTT, as you said, is it's a huge organization, Very well known in Japan and growing in last year that we brought together about 32 different brands under the entity limited brand and we have NTT data services as well. So our role is really to look at the client requirements, the business needs that they have and be able to provide end to end solutions and wrap them with our services to make sure they've got, you know, efficiency gains, but also improving employee experience and experience around, you know, improving how they connect to their customers as well. >> Right, right. So obviously COVID-19, what was, you know, kind of a light switch moment back in March has now turned into, you know, kind of an ongoing, a new normal here we are six months plus into this, into this thing, really no end in sight in the immediate term. So, you know, people were thrown into the situation where work from home, work from anywhere had to happen with no prep. You've been in the business for a long time working on solutions. So there's the obvious things like security and access, but what are some of the less obvious things that people should be thinking about when they think about supporting their employees that are not now coming into the office? >> Well, I mean, it's been interesting, right. I said I have been in the sector for a long time and a lot of the themes have been the same for the last 10, 15 years, you know, how do we improve employee experience? How do we start to look at things like wellbeing? You know, how does it have an impact on productivity? And how do you make sure that we make it simple for people to carry out their tasks? Now, something I get asked a lot is this idea of how do we make it frictionless? A lot of the time, people don't really care about the brand or the technology. They just want to be able to carry out their role from whatever industry sector they aren't doing it efficiently and do it well, but also to be able to interact. I think it's been really important. And this pandemic has brought about this view, that people haven't been able to socialize in the same way they have in the past and work is really about people, you know, the workplace is also about people and how you connect those people into customers and provide efficiencies in that area. So the conversations I've been having in the last, you know, six to seven months, it's been quite interesting that the programs they were taking 18 months, 24 months, 36 months over, have had to be accelerated and really deployed in about three months. And then that's brought about the lows of operation on policy concerns. So as you mentioned, as you start to have this new, what we're calling, you know, distributed workforce, especially those organizations which have been perhaps more enterprise specific, you know, which are going into carpeted office environments, they've been requested by governments to only work from home. And that's brought about a huge impact to how people work, but also socialize. So from a technology standpoint, you've asked people, right, you're going to work from home, actually, do you have network connectivity? Can you actually connect with a technology tool? Like, you know, collaboration to be able to speak to your customers, to speak to your GOPs. Now what device are you actually working on? So we saw this real drive around what is this sort of immediate business continuity requirement for a secure remote worker. >> Right. And that brought about other concerns as well. >> Right. So there's so many layers to this conversation. I'm psyched to dig into it. But one of the ones I want to dig in is kind of tools overload, you know, this idea of collaboration and, you know, trying to get your work done and trying to get bears removed. At the same time though, it just can't seems like we just keep getting more tools added to the palette that we have to interact with every day, whether it's lack or a sauna or Salesforce or box, or you know, the list goes on and on and on. And the other thing that just seems strange to me is that right, all of these things have a notification component. So it's almost like the noise is increasing. I don't hear a lot of people ripping out old tooling or ripping out old systems. So how do you help guide people to say, that there's all these great collaboration tools, there's all these great communication tools, but you can't have all of them firing all the time and expect people to actually have time to get work done. >> Yeah. And it's also, you know, some people are used to that, you might have a digital native who's used to using multiple tools, but you don't have others that actually haven't been taught or a learning program about how to use different tools for different applications. And that becomes that person becomes frustrated and their productivity levels can go down. I think that what we'd really try and do is understand what are the business requirements by the persona? And also if you think of that distributed worker, that's now having to work from home and go into the office for specific tasks that are allowed, are they a sales person? No. Are they actually working in HR? What do they need and what are the tasks they need? And that start to provide the right types of tools and technology specifically for them and make sure they have a learning path that's driven around how they actually enable that technology. But you're right though. I think one of the thing that COVID founder's that doesn't happen overnight, you know, that's an engagement process. COVID hit and everyone was at home straight away. So we did see this huge transition from what may have been a legacy on premise application to starting to use more cloud based applications. And almost everyone was thrown in at the deep pant. Right? Well, here you go, just get on and use it. And at the same time they had WeChat or they had no other types of applications like WhatsApp and there were all these channels were happening. And they always had an impact on things like compliance and security, because all of a sudden, you're not using a corporately approved platform and solution. And you're starting to talk about perhaps confidential information. That's not in a way that is actually retained inside of a corporate network for the compliance and regulatory components. Right. So it's been a really interesting time in the last few months. >> Right. Well, so let's just touch on security for a minute. 'Cause obviously security is a huge concern. As you said, there's a whole bunch of security. You get kind of new security issues. One is just, everybody's working from home, whether they've got to VPN or not, or they're on their... You know, whatever their cable provider. You don't know what devices they're on, right? There's so many different devices and too as these apps have proliferated all over all these devices, whether access Salesforce on my phone or on my laptop or on my computer at work. Right. All very different. So when you look at the kind of security challenge that has come from distributed workforce with this super acceleration, you know, how many customers are ready for it, it's just caused a complete, you know, kind of fire, a hair on fire reaction to get up to speed, or, you know, are a lot of the systems of the monitored system relatively well locked down. So it wasn't a giant, you know, kind of adjustment back in March. >> Really. It depends on the type of company culture it was before. You know, what we've actually seen from some research we've done very recently across 1500 different companies, those organizations that have really invested become more digital disruptors. Now that they've embedded an idea of agility, they've actually already got a distributed workforce. They've already started to move a lot of their platforms and applications to the cloud. They've started to think about these IT policies and security. Previously, they've been very successful in how they've been able to pivot and drive this business continuity. I think for others that have been, no have large installed base of employees, no have set policies in place it's been harder for them to transition. And what we've seen is that they're the organizations that have really tried to integrate some of the new technologies into the old and that that's quite difficult sometimes. So, you know, around security, out of those 1500 organizations, nearly 70% of them said that they have a higher level of risk and concern about this. You're already in compliance today than they had prior to the pandemic. >> Right. >> What also is brought about is this idea of moving from a sort of perimeter security now where you'd come into an office and you have this perimeter where the network's secure, the physical location, and security, containerize the applications. And you've got to empower employees more now because you know, people are going to be mobile. They're going to be using multiple devices in different locations, all around the world. So we're seeing this transition as people move to cloud based platform, security is starting to get embedded into the application and it goes back to that persona aspect. So you can start to initiate things like you know, data loss protection and rights management about the content an individual has based on their location or the confidentiality of that document or piece of information. So that's where we're seeing this move is sort of really accelerating to the group, take the stress away from the employee embedded into an actual system and an application. And that has the intelligence to work out the security and the compliance on behalf of the individual. >> Right. You know, where I was going to go is, you know, there's a lot of conversations now about certain companies announcing that people can just work from home for the foreseeable future, especially here in Silicon Valley. And you mentioned that, you know, for some people that were already kind of down at digital transformation path, they're in good shape. Other people, you know, weren't that far, and of course all the means on social media are, you know, what drove your digital transformation, the CEO, the CMO, or COVID. And we all know the answer to the question. So I just want to get, you know, kind of a longterm perspective. You've been in this space for a long time. I think there's going to be, you know, a significantly increased percentage of people that are working from home. A significantly increased percentage of the time, if not a hundred percent of the time. How do you see this kind of, you know, extending out and how will it impact the way that people motivate? 'Cause at the end of the day, you've written a ton of blog posts on this, you know, motivation equals profitability. And a motivated engaged people do better work and do get better results on the bottom line. How do you see this as this as (indistinct) rules for six months, 12 months, 24 months, when there's some mishmash of combination of work from home and work from the office? >> I think probably the first thing to say is that from the research we've done, we think that's going to differ by different geographies. I mean, it's interesting when you look at areas like India and perhaps South Africa where the network connectivity home is actually not as good as in Northern Europe or North America, and actually it becomes quite hard to carry out your role and task at home. And it can become really frustrating. There's also sort of health and safety components to also working at home. Now we've had a lot of people, especially the younger generation who are in shared home, shared facilities. Now who's going to pay for the internet, the bundles, you know, and actually you only have your bedroom and is it healthy to work at your bedroom all day? So when you really sort of peel back the layers of this, this is a really complex environment, and it's also dependent on the industry sector you are. You're actually driving. But at a high level, one thing we're really seeing is most people still want to have a level of human interaction. That we as humans like to like to work together and engage together. And in fact, about 80% of the respondents of our report actually said, they want to come back to the office. Now this, this speaks to this idea of choice and flexibility. 'Cause it's not just about coming back for five days a week, eight to five, it's about going actually I've got a task to carry out. It'd be really helpful if I was with my team face to face. >> Right. >> And I can come in for four hours, book my time in that physical space, carry that out, and then I can go home and do that sort of really the research based work which I can do in the safety of my own environment. So that's what we're seeing across the industry whereas before. Now, I think everyone's trying to build these really nice big offices that looked fantastic, more huge and talked about your brand. Most organizations now are repurposing space 'cause they're not going to have as many people inside of those physical locations, but they're motivating for them to come in for creative work, you know, to be social, to think about how they do cross agile team development. >> Jeff: Right. >> And that's what we're starting to see today. >> Yeah. It's really interesting you think of some young engineer that just graduated from school, gets a job at Google and you know, you get all your food there and they'll do your dry cleaning and they'll change the oil in your car and they'll, you know, take care of everything. And, and so there's this little growth in these little micro houses. Well guess what, now you don't have any of that stuff anymore. The micro house with no kitchen or kitchen that does look so attractive. And I want to shift gears a little bit more detail on NTT. You know, we've talked to lots of people about new ways to work. IBM, Citrix, you know, VM-ware has a solution and you work with big company. So how does NTT fit in, you know, kind of a transformation process big and that on the big scale, but more kind of an employee engagement and a work from anywhere type of engagement. How do you guys fit within, you know, big system integrators, like a center that are driving organizational change and, you know, kind of all this other suite of technology that they might already have in place. >> Yeah. I mean, we sort of sit in that role of a service delivery organization as well as systems integrator. So our role is to actually go into those clients and sit down with them, which is now virtually, rather than in person a lot of the time. And really understand what are those business KPIs they have and help them shape that strategy. And to do that, you've got to understand what they have today, that view of the assets. And that goes across multiple components as you said, from, you know, desktop application, security, inclusive of culture, property assets, network. And what we do is really take a holistic view of those areas that go for you to reach that business goal, that KPI, you know, this is the project that you're going to have to do. And anything around employee engagement ultimately is fed also by how good your network is and how secure that network is to deliver those applications efficiently for that employee to carry out their task in that frictionless way. So we have a very holistic view about how we then deliver Upgrade. That the core infrastructure, we do that secure by design is our sort of policy and everything we do, you know, security is embedded into what we do, and then we deliver that outcome. But then we erupt things like adoption services. I think one thing in the past, you know, people say here's a technology, go on and do it. Especially nowadays, you've got quite complex platforms. You've got to really understand how do you give information to people to self serve them, that sort of nudge technology, so they can understand how to carry it out on that idea of adoption training. Change of management is becoming ever increasingly important for our clients. >> Right. So I wan shift gears again, Alex, and talk about the show Upgrade 2020. Lot of (laughs) a lot of really heavy science going on here in healthcare, in IT, in a whole bunch of areas. Pretty exciting stuff, you know, we've talked to some other guests about some of the real details and I'm definitely going to attend some sessions and have my brain exploded I'm sure. But I'm just curious of how it fits with what your doing, you know, you've been involved, as you said, not necessarily the NTT, but you've been involved in kind of workplace collaboration tools for a long, long time. You know, how do you see, you know, kind of basic research and some of this really fundamental research, you know, kind of helping you and your customers and your solutions, you know, as we kind of moved down the road. >> Alright, hold at that. The main conversation we're having with executives today is this idea of employee wellbeing and experience is fundamental to the success of their business. 'Cause it drives customer centricity productivity gains. You've got to think about how technology can underpin that and deliver insights to you. So, you know, the new currency is data. And what I find really interesting around and what we're talking about with Upgrade 2020 is this ideas of digital twins. So when you think of this concept of a digital twin, it all is based on this idea of extensibility. So all your decisions that you're making right to today, you know, these short term decisions you having to do for business continuity, you've got to think about the longterm impact of how you're going to be able to ingest that data from all those systems into a central area, to give you insight. Now, from that insight, you've then got the, you know, the power of machine learning and artificial intelligence to actually say right, for this component how many of my employees really are? Then well, are they doing well in the productivity gains? And from my property estate, you know, how many of my properties are actually reducing the energy consumption? And are we adhering to our sustainability goals? Are they well? So the actual physical environment is safe for those employees. So all of those disparate platforms have to come together into that one area and give you insight. So that the marrying of physical space with the how humans interact all into a digital twin, I think is really interesting and something I'm speaking to clients about day in, day out. >> I love that, that is awesome. You know, we're first exposed to the digital twin concept years ago, doing some work with general electric, because they were doing a lot of digital twin work around, you know, engines on airplanes and, you know, simulate an airplane engine that's running on a plane in the Middle East, it's going to act very different than a plane that's running in Alaska. And then, you know, I love the concept of digital twin around the context of people in medicine, right. And modeling a heart or modeling a behavior system or cardiovascular system. How are you talking about digital twins? 'Cause it sounds like you're talking about kind of a combination between, you know, kind of individual people and how they're doing versus some group of people as a unit or organization. And then you even mentioned, you know, sustainability goals and buildings. So when you're talking about digital twin in this context, what are the boundaries? How are you organizing that thing that you can then do, you know, kind of tests and kind of predictive exercises to see how the real thing is going to do relative to what the digital twin did. >> Yeah. But it goes back to defining those business outcomes. And most of the discussions we're having is, yeah, obviously increased productivity, but it's also a reducing costs. A big one we've seen in my area is attraction retention of talent. You know, intellectual property is going to differentiate organizations in the future as technology sort of standardizes. But sustainability again from the research we've done is really high up on the executives agenda. You know, the idea that we, as NTT as well, we have a duty to society to actually start giving back a view of how technology can improve the sustainability goal. And in fact, we've just become the business Avenger for the UN sustainability goal, number 11, around the idea of communities and smart cities. So the clients that I'm speaking to when they're looking at those business objectives are no 10, 15% of my, my actual costs associated to my property. We've now got a new distributed workforce, but I've got a huge amount of energy going into those properties. Now we can actually connect now building management systems into now that digital twin. We can also start to look at the other platforms such as lifts, you know, also all the heating and air ventilation. And start to get the data that allows us to model and predict when certain issues may occur. So, you know, as less people start coming in, you'll have occupancy data. You'll be able to say, you're actually, this location has only been used 30% capacity. We could reduce the amount of space we have, or in fact, we don't need that space at all. And in that space, we know that we're running an HVAC system and air conditioning a hundred percent of the time. You start to actually reduce that and you can reduce energy consumption by 30%. Now goes back to this whole idea of extensibility on one building that can have a big impact, but across 500 buildings that we're NTT have, that's a significant amount of energy that we can change. >> Jeff: Right. >> And also you can then start to think about the idea of, you know, more different type of power purchasing agreement with sustainable energy going into those environments. >> So many, you know, kind of so many interesting twists and turns on this journey since, you know, that COVID hit. And it is going to be really fascinating to see kind of what sticks and, you know, and the longterm ramifications. 'Cause we're not going back to the way that it was. I think that's not even a question. Just the last thing on kind of the data, you know, we saw some really, I think not such great things early on in this thing where, you know, you get put us basically a sniffer on and you know, our people sitting in front of their computer all day. I saw some nasty thing on Twitter the other day. My boss wants me to be on Zoom calls all day long. I mean, do people get it that, you know, there's an opportunity to increase motivation, not decrease motivation by, you know, a responsible use and a good use of this data versus, you know, a potential perception of, well now they're just big brothering me to death. >> It's such a hot topic, right? I mean, even before COVID we had, you know, the GDPR compliance in Europe. But that ultimately is a global compliance and the West coast America also got a similar one now about what data you're actually keeping about me as an individual. And I should have access to that and I can not speak to my company about it. And is it big brother or actually using that data to help inform me as an individual ways of improving the way I work or working in a way that has a better balance for me as an individual. And we're having these conversations with our clients right now about how we do this, because they having to work with workers counselors in countries like Germany. Because track and trace does have that view of that sort of big brother. What, where are you? What are you doing? And how long have you been on your computer? I think it's down to the culture of your business and the purpose that you have and how you engage with your employees, that you show that data to be about all benefiting them as an individual. Now, I'm going back to that digital twin, that the view of ingesting data, then from perhaps platforms like, you know, Cisco WebEx or Office 365, and you can see how long they are actually in front of their screen. You can then start to predict and see where you may have burnout or in fact affect change where you say RHR policy should dictate, you shouldn't be working 14 hours a day. That's not good for you. It's not good for us. And actually nudge them and teach them about taking no time away from the desk and actually having a better work balance. And that's important because it all goes back to increases the productivity longterm, but it's great brand association and it's good for attraction and retention of talent. >> Right, Right. Well, I think the retention and attraction is a huge thing. You keep talking about productivity and obviously in your blog post talking about engagement, right. And engagement is such a direct tie to that. And then at the bottom line (giggles) it's kind of like diversity of opinion. It actually makes good business sense. And you actually put more money in the bank at the end of the day, when you do some of these more progressive, you know, kind of approaches to how you manage the people. 'Cause they're not machines, they're people. >> Yeah. And you should allow them to make decisions. You know, that again, distributed working, you've got to think of how to empower them with the tools that gives them the choice to make decisions. And you know, that that decision making is more democratized inside of organizations that are successful. But if you don't have the technology that allows them to do that, it goes back to a hierarchical decision making. And that takes time, it's slower to market, and then you know, you're not as successful as your competition. So we're really trying to prove that this idea of thinking about people first using the data that backs it up you know, with empirical data to show the benefits, is the way forward for organizations today. >> Yeah. Alex, great conversation. Certainly nothing but opportunity (laughs) I had for you and what you do in this really fast evolving and transformative space, which is so important. Which is how do people work? How do they feel good? How are they engaged? How are they productive and really contribute? And at the end of the day, it is good business. So exciting times, good luck on the show and some of this crazy research coming out of it on the digital twin, and we look forward to continuing to watch the story unfold. >> Thank you very much, Jeff. >> Alright. He's Alex. I'm Jeff. You're watching Upgrade 2020. The continuous coverage from theCUBE. Thanks for watching. We'll see you next time.

(energetic electronic music) >> Hey, welcome back everybody. Jeff Frick here with The Cube. We're in a very special place. We're in Austin, Texas at the Dell EMC HPC and AI Innovation Lab. High performance computing, artificial intelligence. This is really where it all happens. Where the engineers at Dell EMC are putting together these ready-made solutions for the customers. They got every type of application stack in here, and we're really excited to have our next guest. He's right in the middle of it, he's Michael Bennett, Senior Principal Engineer for Dell EMC. Mike, great to see you. >> Great to see you too. >> So you're working on one particular flavor of the AI solutions, and that's really machine learning with Hadoop. So tell us a little bit about that. >> Sure yeah, the product that I work on is called the Ready Solution for AI Machine Learning with Hadoop, and that product is a Cloudera Hadoop distribution on top of our Dell powered servers. And we've partnered with Intel, who has released a deep learning library, called Big DL, to bring both the traditional machine learning capabilities as well as deep learning capabilities to the product. Product also adds a data science workbench that's released by Cloudera. And this tool allows the customer's data scientists to collaborate together, provides them secure access to the Hadoop cluster, and we think all-around makes a great product to allow customers to gain the power of machine learning and deep learning in their environment, while also kind of reducing some of those overhead complexities that IT often faces with managing multiple environments, providing secure access, things like that. >> Right, cause the big knock always on Hadoop is that it's just hard. It's hard to put in, there aren't enough people, there aren't enough experts. So you guys are really offering a pre-bundled solution that's ready to go? >> Correct, yeah. We've built seven or eight different environments going in the lab at any time to validate different hardware permutations that we may offer of the product as well as, we've been doing this since 2009, so there's a lot of institutional knowledge here at Dell to draw on when building and validating these Hadoop products. Our Dell services team has also been going out installing and setting these up, and our consulting services has been helping customers fit the Hadoop infrastructure into their IT model. >> Right, so is there one basic configuration that you guys have? Or have you found there's two or three different standard-use cases that call for two or three different kinds of standardized solutions? >> We find that most customers are preferring the R7-40XC series. This platform can hold 12 3 1/2" form-factor drives in the front, along with four in the mid-plane, while still providing four SSDs in the back. So customers get a lot of versatility with this. It's also won several Hadoop benchmarking awards. >> And do you find, when you're talking to customers or you're putting this together, that they've tried themselves and they've tried to kind of stitch together and cobble together the open-source proprietary stuff all the way down to network cards and all this other stuff to actually make the solution come together? And it's just really hard, right? >> Yeah, right exactly. What we hear over and over from our product management team is that their interactions with customers, come back with customers saying it's just too hard. They get something that's stable and they come back and they don't know why it's no longer working. They have customized environments that each developer wants for their big data analytics jobs. Things like that. So yeah, overall we're hearing that customers are finding it very complex. >> Right, so we hear time and time again that same thing. And even though we've been going to Hadoop Summit and Hadoop World and Stratus, since 2010. The momentum seems to be a little slower in terms of the hype, but now we're really moving into heavy-duty real time production and that's what you guys are enabling with this ready-made solution. >> So with this product, yeah, we focused on enabling Apache Spark on the Hadoop environment. And that Apache Spark distributed computing has really changed the game as far as what it allows customers to do with their analytics jobs. No longer are we writing things to disc, but multiple transformations are being performed in memory, and that's also a big part of what enables the big DL library that Intel released for the platform to train these deep-learning models. >> Right, cause the Sparks enables the real-time analytics, right? Now you've got streaming data coming into this thing, versus the batch which was kind of the classic play of Hadoop. >> Right and not only do you have streaming data coming in, but Spark also enables you to load your data in memory and perform multiple operations on it. And draw insights that maybe you couldn't before with traditional map-reduce jobs. >> Right, right. So what gets you excited to come to work every day? You've been playing with these big machines. You're in the middle of nerd nirvana I think-- >> Yeah exactly. >> With all of the servers and spin-discs. What gets you up in the morning? What are you excited about, as you see AI get more pervasive within the customers and the solutions that you guys are enabling? >> You know, for me, what's always exciting is trying new things. We've got this huge lab environment with all kinds of lab equipment. So if you want to test a new iteration, let's say tiered HGFS storage with SSDs and traditional hard drives, throw it together in a couple of hours and see what the results are. If we wanted to add new PCIE devices like FPGAs for the inference portion the deep-learning development we can put those in our servers and try them out. So I enjoy that, on top of the validated, thoroughly-worked-through solutions that we offer customers, we can also experiment, play around, and work towards that next generation of technology. >> Right, 'cause any combination of hardware that you basically have at your disposal to try together and test and see what happens? >> Right, exactly. And this is my first time actually working at a OEM, and so I was surprised, not only do we have access to anything that you can see out in the market, but we often receive test and development equipment from partners and vendors, that we can work with and collaborate with to ensure that once the product reaches market it has the features that customers need. >> Right, what's the one thing that trips people up the most? Just some simple little switch configuration that you think is like a minor piece of something, that always seems to get in the way? >> Right, or switches in general. I think that people focus on the application because the switch is so abstracted from what the developer or even somebody troubleshooting the system sees, that oftentimes some misconfiguration or some typo that was entered during the switch configuration process that throws customers off or has somebody scratching their head, wondering why they're not getting the kind of performance that they thought. >> Right, well that's why we need more automation, right? That's what you guys are working on. >> Right yeah exactly. >> Keep the fat-finger typos out of the config settings. >> Right, consistent reproducible. None of that, I did it yesterday and it worked I don't know what changed. >> Right, alright Mike. Well thanks for taking a few minutes out of your day, and don't have too much fun playing with all this gear. >> Awesome, thanks for having me. >> Alright, he's Mike Bennett and I'm Jeff Frick. You're watching The Cube, from Austin Texas at the Dell EMC High Performance Computing and AI Labs. Thanks for watching. (energetic electronic music)

>>Hello and welcome to the cubes coverage of dr khan 2021. I'm john Kerry, host of the cube agent I own L. C. Ceo and co founder chairman of Morantes cube alumni Adrian Great to see you. Thanks for coming on the cube here for dr khan coverage. Good to see you. Hey >>john nice to see. You gotta do. >>So obviously open source innovation continues. You guys are at the forefront of it. Great to see you what's new Miranda's, give us the update on what's happening. >>Well, I mean what's, what's interesting is we've had one of the best years ever last year and it's very much more continuous, you know, into this year. It's pretty fantastic. We wanted about 160 new customers. Kubernetes is definitely on a tear. We see customers doing bigger and bigger and more exciting things, which is absolutely great to say lens is getting tremendous destruction and I think we have a five fold increase in user base within a year. So it's a lot of fun Right now, customers are definitely pushing the boundaries of what benefits can do. And they want to get the cloud native infrastructure and they want to get there faster and they want to be big and exciting things. And we are so happy to be part of the right. >>You guys are investing in brand new open source solutions for customers. Give us an update on on why and why do they matter for your customer? >>Well, there are, let me unpack this a little bit and there are really two elements to this. One is wide. Open Source and what's new. What matters. So the open source is not new, but open source is being embraced more and more heavily. Bye bye companies everywhere because just a very flexible and cost efficient and highly innovative way to to use innovation and to continue software and a lot of innovation these days is happening the open source communities, which is why it's super exciting for many, many users now. What's new with us? I think there are two really terrific things that we brought the market that we see, get a lot of interest and attention from our customers and create value. One is this idea of delivering, including the infrastructure that's been in space as a service for some of the largest news cases out there. Very large enterprises. We want to have a cloud experience on prime just like they have it in public clouds. That is absolutely fantastic. And that's new and different and very, very exciting. Customs. The second thing that's new and compelling and exciting is the is lands which is this kubernetes, i. e. that has empowered in the meantime, close to 180,000 communities, developers around the world to make it much much easier to take advantage of genetics. So you can think of it as a I. D. And a D. Bugger for anybody who is using genetics on public clouds or on on private infrastructure. That is getting tremendous traction and adoption. >>The interest in kubernetes has been unbelievable. I mean in coop con we saw kubernetes almost become boring in the sense of like it's everyone's using it and there's still now it's enabling a lot more cloud native development. Why does that lens matter what is the benefit? Because that's that's a killer opportunity because kubernetes is actively being adopted. The general consensus is it's delivering the value. >>Yeah. So let me unpack this in two aspects why Wise Bennett is important, why people adopting it and then how it lands adding value on top of it for people who want to use humanity's common. It is tremendously important is because it solves some very, very fundamental problems for developers and operators when building cloud native applications. These are problems that are very essential to actually operating in production but are really unpleasant people to solve, like availability, scalability, reusability of services. So all of that with amenities comes right out of the box and developers no longer have to worry about it. And at the same time, the benefits gives you a standard where you can build apps on public clouds and then move them on prem or build them on trend with them on public clouds and anywhere in between. So it gives a kind of this universal cloud native standard that you as a developer can rely on. And that's extremely valuable for developers. We all remember from the java times when java came online, people really value this idea of white ones run anywhere and that's exactly what benefits does for you in a clown in the world. So it's extremely screaming valuable for people. Um now how does let's add value in this context is also very exciting. So what's happening when you build these applications on a minute? This is that you have many, many services which interact with each other in fairly complex and sometimes unpredictable ways and they're also very much interact with the infrastructure. So you have you can you can imagine kind of this jungle this label building of many different cloud native services working together to build your app, run your app well, how are you going to navigate that and debug that as a developer as you build and optimize your code. So what lengths does it gives you kind of like a real time poppet of pounds of console. You can imagine like you're a fighter pilot in this jet and you have all these instruments kind of coming out here and gives you like this fantastic real time situational awareness. So you can very quickly figure out what is it that you need to do? Either fixing a bug in your application or optimize the performance of the code of making more your rival fixing security issues. And it makes it extremely easy for developers to use. Right? But this tradition has been hard to use complicated, this makes it super fast, easy, have a lot of fun. >>You know, that is really the great theme about this conference this year and your point exactly is developer experience making it simpler and easier. Okay. And innovative is really hits the mark on productivity. I mean and that's really been a key part. So I think that's why I think people are so excited about kubernetes because it's not like some other technologies that had all the setup requirement and making things easier to get stood up and manage. Its huge. So congratulations. A great point, great call out there, great insight. The next question to ask you is you guys have coined the term software factory. Um, yeah, this kind of plays into this. If you have all the services, you can roll them up together with lens and those tools, it's gonna be easier, more productive. So that means it's more software, open source is the software factory to what does that term mean? And how >>it is leverage. Yeah, So here's what it means to us. And so, as you know, today, Soft is being produced by two groups working together to build software, uh, certainly the poor people are the developments, these are the people who create the core functionality. Imagine all the software should be architected and ultimately ship the code right? And maintain the code, but the developers today don't operate just by themselves. They have their psychics, they have their friends for often platform engineering and platform engineers. These are the people who are helping developers, you know, make some of the most important choices as to which platform states we should use, which services they should use, how they should think about governance. How should they think about cloud infrastructure they should use, which open source libraries they should use. How often they should be fresh those libraries and support. So this platform engineers create if you want the factory, the substrate and the automation, which allows these developers to be highly productive. And the analogy want to make is the chip design, right. If you imagine ship design today, you take advantage of a lot of software, a lot of tooling and a lot of free package libraries. You get your job done, you're not doing it by yourself. Uh just wiring transistors together or logical elements. You do it using a massive amount of automation and software, like recent polls. So that's that's what we aim to provide you to customers because what we discovered is that customers, I don't want to be in the business of buildings off the factories, They don't want to be in the business or building platform engineering teams. If they can avoid it, they just do it because they have no choice. But it's difficult for them to do. It's cumbersome, it's expensive. It's a one off. It really doesn't create any unique business value because the platform engineering for a bank is very similar to the platform engineering for, let's say, an oil gas company or the insurance company. Um So we do it for them turnkey as a service. So they can be focusing on what Madison's for that. >>That's a great inside. I love that platform engineering, enabling software developers because, you know, look at sas throwing features together. Being a feature developer is cool. And and and the old days of platform was the full stack developer. And now you have this notion of platform as a service in a way, in this kind of new way. What's different agents? You've seen these waves of innovation? Certainly an open source that we've been covering your career for over a decade uh with more Anderson and open stick and others. This idea of a platform that enables software. What's changed now about this new substrate, you mentioned what's different than the old platform model? >>Uh That's a wonderful question. Uh a couple of things are different. So the first thing that's different is the openness and uh, and that everything is based on open source frameworks as opposed to platforms that we that are highly opinionated and, and I lock in. So I think that's that's a very, very fundamental difference. If you're looking at the initial kind of platform as a service approaches, there were there were extremely opinionated and very rigid and not always open source or just a combination between open source and proprietary. So that's one very big difference. The second very big difference is the emphasis on, and it goes along with the first one, the emphasis on um, multi cloud and infrastructure independence, where a platform is not wedded to a particular stack, where it's a AWS stack or a uh, an Azure stack or the EMR stack. And, and but it's truly a layer above. That's completely open source center. >>Yeah. >>And the third thing that is different is the idea that it's not just the software, the software alone will not do the job, you need the software and the content and the support and the expertise. If you're looking at how platform engineering is done at the large company like Apple, for example, facebook, it's really always the combination of those three things. It's the automation framework, the software, It's the content, the open source libraries or any other libraries that you create. And then it's the expertise that goes all this together and it's being offered to developers to be able to take advantage of this like soft factory. So I think these are the major differences in terms of where we are today was five years ago, 10 years ago. >>Thank you for unpacking that for I think that's a great uh great captures the shift and value. This brings up my next uh question for you because you know, you take that to the next level. DeVOps is now also graduating to a whole another level. The future of devops uh and software engineering more and more around kubernetes and your tools like lens and others managing the point. What is the new role of devops? Obviously Deb see cops but devops is now changing to What's the future of devops in your opinion? >>Well, I believe that there is going to become more and more integrated where our option is going to become uh something like Zero Arts, where are you going to be fully automated And something that's being delivered entirely through software and developers will be able to focus entirely, on, on creating and shipping code. I think that's the major, that's a major change that's happening. The problem is still yet I think to be solved like 100% correctly is the challenge of the last mile. like deploying that code on on on the infrastructure and making sure that he's performing correctly to the sls and optimizing everything. I also believe that the complexity veneta is very powerful by the same time offers a lot of room for complexity. There are many knobs and dials that you can turn in these microservices based architecture. And what we're discovering now is that this complexity kind of exceeds the ability of the individual developer or even a group of developers who constantly optimize things. So I believe what we will see is a I machine learning, taking charge of optimizing a lot of parameters, operating parameters around the applications and that unemployment benefits to ensure those applications perform to the expectations of the illness. And that might mean performing to a very high standard security. Or it might mean performing to a very a low latency in certain geography. Might mean performing too a very low cost structure that you can expect and those things can change over time. Right? So this challenge of operating an application introduction Burnett is substrate is I think dramatically higher than on just additional cloud infrastructure or virtualization. Because you have so many services inter operating with each other and so many different parameters you can set for machine learning and Ai >>I love the machine learning. Ai and I'd love to just get your thoughts on because I love the Zero ops narrative Because that's day one zero ops now that you're here day to being discussed and people are also hyping up, you know, ai Ops and other things. But you know this notion of day to, okay, I'm shipping stuff in the cloud and I have to have zero ops on day 234 et cetera. Uh, what's your take on that? Because that seems to be a hot air that customers and enterprises are getting in and understanding the new wave, writing it and then going, wait a minute pushing new code that's breaking something over there I built months ago. So this is just notion of day to obstacle. But again, if you want to be zero ops, it's gonna be every day. >>Oh, I think you hit the nail on the head. I don't think there's going to be a difference between they want the zero they want and today chair, I think every day is going to be the zero. And the reason for that is because people will be shipping all the time. So your application will change all the time. So the application will always be fresh, so it will always be there zero. So zero ops has to be there all the time. Not just in the birthday. >>Great slogan! Every day is day zero, which means it's going well. I mean there's no no problems. So I gotta ask you the question was one of the big things that's coming up as well as this idea of an SRE not new to devops world, but as enterprises start to get into an SRE role where with hybrid and now edge becoming people not just industrial, um there's been a lot of activity going on a distributed basis. So you're gonna need to have this kind of notion of large scale and 00 ops, which essentially means automation, all those things you mentioned, >>not everyone can >>afford that. Um Not every company can afford to have you know hardcore devops groups to manage and their release process, all that stuff. So how are you helping customers and how do you see this problem being solved? Because this is the accelerant people want, they want the the easy button, they want the zero ops but they just they don't they can't pipeline people fast enough to do this role. >>Yeah. What you're describing is the central differentiator we bring to customers is this idea of as a service experience with guaranteed outcomes. So that's what makes us different versus the traditional enterprise infrastructure software model where people just consume software vendors and system integrate themselves and then are in charge of operations themselves and carrying the technical risks themselves. We deliver everything as a service with guaranteed outcomes through the through cloud native experience. That means guaranteed as L. A. Is predictable outcomes, continuous updates, continuous upgrades. Your on prem infrastructure or your edge infrastructure is going to look and feel and behave exactly like a public cloud experience where you're not going to have to worry about sRS or maintaining the underlying being delivered to you as a service. That's a big part, that's a central part of what makes us different in this space. >>That's great value proposition. Can you just expand give an example of a use case where you guys are doing that? Because this is something that I'm seeing a lot of people looking to go faster. You know speed is good but also it could kill right? So you can break things if you go to a. >>Yeah absolutely. I can give you several examples where we're doing this um very exciting company. So one companies booking dot com booking dot com as a massive on from infrastructure but they also massive public cloud consumer. And they decided they want to bring their own infrastructure to the cloud level of automation, cloud level Sophistication, in other words, they want to have their Aws on brand, they wanted to the old, so eccentric and we're delivering this to them with very high in the cell is exactly as a service turnkey Where there is nothing for them to system in grade or to tune and optimize and operate is being really operating 24/7 guaranteed sls and outcomes by us. Well, combination of soft film expertise that we have at massive scale and to the standards of booking dot com. This is one example, another example and this is a very large company um is the opposite side of the spectrum. You know, because they're not called Mexico super successful. Soft as a service company in the security space, growing in leaps and bounds in very high technical demands and security demands. And they want to have an on prem and cloud infrastructure to complement public clouds. Why? Because security is very important to them. Latency is very important to them. Control the customer experience is very important to them. Cost is very important to them. So for that reason they want that in a network of data centers around the globe And we provide that for them. Turnkey as a service than before seven, which enables them to focus 100% on building their own sense on their the functionality which matters to their customers and not have to worry about the underlying cloud infrastructure in their data centers. All of that gets provided to them has guaranteed about experience to their end users. So this would be the examples where we're doing a >>great service. People are looking for a great job. Adrian, Great to see you. Thank you for coming on the cube here, doc are gone 2021. Um, take a minute to put a plug in for the company. What are you guys up to? What you're looking for hiring? I'll see. You got great tracks with customers, congratulates on lens. Um give a quick update on what's going >>on. Happy happy to give it up in the company. So he, here are the highlights. It was super excited about about what we achieved last year and then what we're up to this year. So last year, what we're proud of is despite Covid, we haven't laid off a single person. We kept all the staff and we hired staff. We have gained 160 new customers, many of them, some of the world's largest and best companies and 300 of all existing customers have expanded their business with us last year, which is fantastic. We also had a very strong financial physical cash flow positive. It was a tremendous, tremendous here for us. Uh, this year is very much growth here for us and we would incredible focus on customer outcomes and customer experience. So what we are really, really digging in super hard on is to give the customers the technology and the services that enable them to get to ship software faster and easier to dramatically increase the productivity of dissolved the development efforts on any cloud infrastructure on crime and public clouds using containers and is and to do that as scale. So we're extremely focused on customer outcomes, custom experience and then the innovation is required to make that happen. So you will continue to see a lot of innovation around lens. So the last better release of lens that we brought about has now a cloud service and have a lot of feature where you can share all your cloud automation with your bodies, in, in uh, in uh, in your development team. So the lens used to be a single user product. Now it's a multi user and team based product, which is fantastic, continues to grow very quickly. And then container cloud as a service. Uh, it's a very big part that we're meeting on the infrastructure side. Are you get quite >>the open source cloud company. Adrian. Congratulations. We've been again following even on the many waves of innovation. Open stack, large scale open source software. Congratulations. >>Uh chris >>Thank you very much for coming on the cube. >>Yeah. >>Okay. Dr khan 2021 cube coverage. I'm john furrier here where the Gi Enel Ceo, co founder and chairman of Miranda's sharing his perspective on the open source innovation with their process and also key trends in the industry that is changing the game in accelerating cloud value cloud scales. Cloud native applications. Thanks for watching. Mhm.

>>from around the globe. It's the Cube presenting adaptive data governance brought to you by Iota Ho. >>And we're back with the data automation. Siri's. In this episode, we're gonna learn more about what I owe Tahoe is doing in the field of adaptive data governance how it can help achieve business outcomes and mitigate data security risks. I'm Lisa Martin, and I'm joined by a J. Bihar on the CEO of Iot Tahoe and Lester Waters, the CEO of Bio Tahoe. Gentlemen, it's great to have you on the program. >>Thank you. Lisa is good to be back. >>Great. Staley's >>likewise very socially distant. Of course as we are. Listen, we're gonna start with you. What's going on? And I am Tahoe. What's name? Well, >>I've been with Iot Tahoe for a little over the year, and one thing I've learned is every customer needs air just a bit different. So we've been working on our next major release of the I O. Tahoe product. But to really try to address these customer concerns because, you know, we wanna we wanna be flexible enough in order to come in and not just profile the date and not just understand data quality and lineage, but also to address the unique needs of each and every customer that we have. And so that required a platform rewrite of our product so that we could, uh, extend the product without building a new version of the product. We wanted to be able to have plausible modules. We also focused a lot on performance. That's very important with the bulk of data that we deal with that we're able to pass through that data in a single pass and do the analytics that are needed, whether it's, uh, lineage, data quality or just identifying the underlying data. And we're incorporating all that we've learned. We're tuning up our machine learning we're analyzing on MAWR dimensions than we've ever done before. We're able to do data quality without doing a Nen initial rejects for, for example, just out of the box. So I think it's all of these things were coming together to form our next version of our product. We're really excited by it, >>So it's exciting a J from the CEO's level. What's going on? >>Wow, I think just building on that. But let's still just mentioned there. It's were growing pretty quickly with our partners. And today, here with Oracle are excited. Thio explain how that shaping up lots of collaboration already with Oracle in government, in insurance, on in banking and we're excited because we get to have an impact. It's real satisfying to see how we're able. Thio. Help businesses transform, Redefine what's possible with their data on bond. Having I recall there is a partner, uh, to lean in with is definitely helping. >>Excellent. We're gonna dig into that a little bit later. Let's let's go back over to you. Explain adaptive data governance. Help us understand that >>really adaptive data governance is about achieving business outcomes through automation. It's really also about establishing a data driven culture and pushing what's traditionally managed in I t out to the business. And to do that, you've got to you've got Thio. You've got to enable an environment where people can actually access and look at the information about the data, not necessarily access the underlying data because we've got privacy concerns itself. But they need to understand what kind of data they have, what shape it's in what's dependent on it upstream and downstream, and so that they could make their educated decisions on on what they need to do to achieve those business outcomes. >>Ah, >>lot of a lot of frameworks these days are hardwired, so you can set up a set of business rules, and that set of business rules works for a very specific database and a specific schema. But imagine a world where you could just >>say, you >>know, the start date of alone must always be before the end date of alone and having that generic rule, regardless of the underlying database and applying it even when a new database comes online and having those rules applied. That's what adaptive data governance about I like to think of. It is the intersection of three circles, Really. It's the technical metadata coming together with policies and rules and coming together with the business ontology ease that are that are unique to that particular business. And this all of this. Bringing this all together allows you to enable rapid change in your environment. So it's a mouthful, adaptive data governance. But that's what it kind of comes down to. >>So, Angie, help me understand this. Is this book enterprise companies are doing now? Are they not quite there yet. >>Well, you know, Lisa, I think every organization is is going at its pace. But, you know, markets are changing the economy and the speed at which, um, some of the changes in the economy happening is is compelling more businesses to look at being more digital in how they serve their own customers. Eh? So what we're seeing is a number of trends here from heads of data Chief Data Officers, CEO, stepping back from, ah, one size fits all approach because they've tried that before, and it it just hasn't worked. They've spent millions of dollars on I T programs China Dr Value from that data on Bennett. And they've ended up with large teams of manual processing around data to try and hardwire these policies to fit with the context and each line of business and on that hasn't worked. So the trends that we're seeing emerge really relate. Thio, How do I There's a chief data officer as a CEO. Inject more automation into a lot of these common tax. Andi, you know, we've been able toc that impact. I think the news here is you know, if you're trying to create a knowledge graph a data catalog or Ah, business glossary. And you're trying to do that manually will stop you. You don't have to do that manually anymore. I think best example I can give is Lester and I We we like Chinese food and Japanese food on. If you were sitting there with your chopsticks, you wouldn't eat the bowl of rice with the chopsticks, one grain at a time. What you'd want to do is to find a more productive way to to enjoy that meal before it gets cold. Andi, that's similar to how we're able to help the organizations to digest their data is to get through it faster, enjoy the benefits of putting that data to work. >>And if it was me eating that food with you guys, I would be not using chopsticks. I would be using a fork and probably a spoon. So eso Lester, how then does iota who go about doing this and enabling customers to achieve this? >>Let me, uh, let me show you a little story have here. So if you take a look at the challenges the most customers have, they're very similar, but every customers on a different data journey, so but it all starts with what data do I have? What questions or what shape is that data in? Uh, how is it structured? What's dependent on it? Upstream and downstream. Um, what insights can I derive from that data? And how can I answer all of those questions automatically? So if you look at the challenges for these data professionals, you know, they're either on a journey to the cloud. Maybe they're doing a migration oracle. Maybe they're doing some data governance changes on bits about enabling this. So if you look at these challenges and I'm gonna take you through a >>story here, E, >>I want to introduce Amanda. Man does not live like, uh, anyone in any large organization. She's looking around and she just sees stacks of data. I mean, different databases, the one she knows about, the one she doesn't know about what should know about various different kinds of databases. And a man is just tasking with understanding all of this so that they can embark on her data journey program. So So a man who goes through and she's great. I've got some handy tools. I can start looking at these databases and getting an idea of what we've got. Well, as she digs into the databases, she starts to see that not everything is as clear as she might have hoped it would be. You know, property names or column names, or have ambiguous names like Attribute one and attribute to or maybe date one and date to s Oh, man is starting to struggle, even though she's get tools to visualize. And look what look at these databases. She still No, she's got a long road ahead. And with 2000 databases in her large enterprise, yes, it's gonna be a long turkey but Amanda Smart. So she pulls out her trusty spreadsheet to track all of her findings on what she doesn't know about. She raises a ticket or maybe tries to track down the owner to find what the data means. And she's tracking all this information. Clearly, this doesn't scale that well for Amanda, you know? So maybe organization will get 10 Amanda's to sort of divide and conquer that work. But even that doesn't work that well because they're still ambiguities in the data with Iota ho. What we do is we actually profile the underlying data. By looking at the underlying data, we can quickly see that attribute. One looks very much like a U. S. Social Security number and attribute to looks like a I c D 10 medical code. And we do this by using anthologies and dictionaries and algorithms to help identify the underlying data and then tag it. Key Thio Doing, uh, this automation is really being able to normalize things across different databases, so that where there's differences in column names, I know that in fact, they contain contain the same data. And by going through this exercise with a Tahoe, not only can we identify the data, but we also could gain insights about the data. So, for example, we can see that 97% of that time that column named Attribute one that's got us Social Security numbers has something that looks like a Social Security number. But 3% of the time, it doesn't quite look right. Maybe there's a dash missing. Maybe there's a digit dropped. Or maybe there's even characters embedded in it. So there may be that may be indicative of a data quality issues, so we try to find those kind of things going a step further. We also try to identify data quality relationships. So, for example, we have two columns, one date, one date to through Ah, observation. We can see that date 1 99% of the time is less than date, too. 1% of the time. It's not probably indicative of a data quality issue, but going a step further, we can also build a business rule that says Day one is less than date to. And so then when it pops up again, we can quickly identify and re mediate that problem. So these are the kinds of things that we could do with with iota going even a step further. You could take your your favorite data science solution production ISAT and incorporated into our next version a zey what we call a worker process to do your own bespoke analytics. >>We spoke analytics. Excellent, Lester. Thank you. So a J talk us through some examples of where you're putting this to use. And also what is some of the feedback from >>some customers? But I think it helped do this Bring it to life a little bit. Lisa is just to talk through a case study way. Pull something together. I know it's available for download, but in ah, well known telecommunications media company, they had a lot of the issues that lasted. You spoke about lots of teams of Amanda's, um, super bright data practitioners, um, on baby looking to to get more productivity out of their day on, deliver a good result for their own customers for cell phone subscribers, Um, on broadband users. So you know that some of the examples that we can see here is how we went about auto generating a lot of that understanding off that data within hours. So Amanda had her data catalog populated automatically. A business class three built up on it. Really? Then start to see. Okay, where do I want Thio? Apply some policies to the data to to set in place some controls where they want to adapt, how different lines of business, maybe tax versus customer operations have different access or permissions to that data on What we've been able to do there is, is to build up that picture to see how does data move across the entire organization across the state. Andi on monitor that overtime for improvement, so have taken it from being a reactive. Let's do something Thio. Fix something. Thio, Now more proactive. We can see what's happening with our data. Who's using it? Who's accessing it, how it's being used, how it's being combined. Um, on from there. Taking a proactive approach is a real smart use of of the talents in in that telco organization Onda folks that worked there with data. >>Okay, Jason, dig into that a little bit deeper. And one of the things I was thinking when you were talking through some of those outcomes that you're helping customers achieve is our ally. How do customers measure are? Why? What are they seeing with iota host >>solution? Yeah, right now that the big ticket item is time to value on. And I think in data, a lot of the upfront investment cause quite expensive. They have been today with a lot of the larger vendors and technologies. So what a CEO and economic bio really needs to be certain of is how quickly can I get that are away. I think we've got something we can show. Just pull up a before and after, and it really comes down to hours, days and weeks. Um, where we've been able Thio have that impact on in this playbook that we pulled together before and after picture really shows. You know, those savings that committed a bit through providing data into some actionable form within hours and days to to drive agility, but at the same time being out and forced the controls to protect the use of that data who has access to it. So these are the number one thing I'd have to say. It's time on. We can see that on the the graphic that we've just pulled up here. >>We talk about achieving adaptive data governance. Lester, you guys talk about automation. You talk about machine learning. How are you seeing those technologies being a facilitator of organizations adopting adaptive data governance? Well, >>Azaz, we see Mitt Emmanuel day. The days of manual effort are so I think you know this >>is a >>multi step process. But the very first step is understanding what you have in normalizing that across your data estate. So you couple this with the ontology, that air unique to your business. There is no algorithms, and you basically go across and you identify and tag tag that data that allows for the next steps toe happen. So now I can write business rules not in terms of columns named columns, but I could write him in terms of the tags being able to automate. That is a huge time saver and the fact that we can suggest that as a rule, rather than waiting for a person to come along and say, Oh, wow. Okay, I need this rule. I need this will thes air steps that increased that are, I should say, decrease that time to value that A. J talked about and then, lastly, a couple of machine learning because even with even with great automation and being able to profile all of your data and getting a good understanding, that brings you to a certain point. But there's still ambiguities in the data. So, for example, I might have to columns date one and date to. I may have even observed the date. One should be less than day two, but I don't really know what date one and date to our other than a date. So this is where it comes in, and I might ask the user said, >>Can >>you help me identify what date? One and date You are in this in this table. Turns out they're a start date and an end date for alone That gets remembered, cycled into the machine learning. So if I start to see this pattern of date one day to elsewhere, I'm going to say, Is it start dating and date? And these Bringing all these things together with this all this automation is really what's key to enabling this This'll data governance. Yeah, >>great. Thanks. Lester and a j wanna wrap things up with something that you mentioned in the beginning about what you guys were doing with Oracle. Take us out by telling us what you're doing there. How are you guys working together? >>Yeah, I think those of us who worked in i t for many years we've We've learned Thio trust articles technology that they're shifting now to ah, hybrid on Prohm Cloud Generation to platform, which is exciting. Andi on their existing customers and new customers moving to article on a journey. So? So Oracle came to us and said, you know, we can see how quickly you're able to help us change mindsets Ondas mindsets are locked in a way of thinking around operating models of I t. That there may be no agile and what siloed on day wanting to break free of that and adopt a more agile A p I at driven approach. A lot of the work that we're doing with our recall no is around, uh, accelerating what customers conduce with understanding their data and to build digital APS by identifying the the underlying data that has value. Onda at the time were able to do that in in in hours, days and weeks. Rather many months. Is opening up the eyes to Chief Data Officers CEO to say, Well, maybe we can do this whole digital transformation this year. Maybe we can bring that forward and and transform who we are as a company on that's driving innovation, which we're excited about it. I know Oracle, a keen Thio to drive through and >>helping businesses transformed digitally is so incredibly important in this time as we look Thio things changing in 2021 a. J. Lester thank you so much for joining me on this segment explaining adaptive data governance, how organizations can use it benefit from it and achieve our Oi. Thanks so much, guys. >>Thank you. Thanks again, Lisa. >>In a moment, we'll look a adaptive data governance in banking. This is the Cube, your global leader in high tech coverage. >>Innovation, impact influence. Welcome to the Cube. Disruptors. Developers and practitioners learn from the voices of leaders who share their personal insights from the hottest digital events around the globe. Enjoy the best this community has to offer on the Cube, your global leader in high tech digital coverage. >>Our next segment here is an interesting panel you're gonna hear from three gentlemen about adaptive data. Governments want to talk a lot about that. Please welcome Yusuf Khan, the global director of data services for Iot Tahoe. We also have Santiago Castor, the chief data officer at the First Bank of Nigeria, and good John Vander Wal, Oracle's senior manager of digital transformation and industries. Gentlemen, it's great to have you joining us in this in this panel. Great >>to be >>tried for me. >>Alright, Santiago, we're going to start with you. Can you talk to the audience a little bit about the first Bank of Nigeria and its scale? This is beyond Nigeria. Talk to us about that. >>Yes, eso First Bank of Nigeria was created 125 years ago. One of the oldest ignored the old in Africa because of the history he grew everywhere in the region on beyond the region. I am calling based in London, where it's kind of the headquarters and it really promotes trade, finance, institutional banking, corporate banking, private banking around the world in particular, in relationship to Africa. We are also in Asia in in the Middle East. >>So, Sanjay, go talk to me about what adaptive data governance means to you. And how does it help the first Bank of Nigeria to be able to innovate faster with the data that you have? >>Yes, I like that concept off adaptive data governor, because it's kind of Ah, I would say an approach that can really happen today with the new technologies before it was much more difficult to implement. So just to give you a little bit of context, I I used to work in consulting for 16, 17 years before joining the president of Nigeria, and I saw many organizations trying to apply different type of approaches in the governance on by the beginning early days was really kind of a year. A Chicago A. A top down approach where data governance was seeing as implement a set of rules, policies and procedures. But really, from the top down on is important. It's important to have the battle off your sea level of your of your director. Whatever I saw, just the way it fails, you really need to have a complimentary approach. You can say bottom are actually as a CEO are really trying to decentralize the governor's. Really, Instead of imposing a framework that some people in the business don't understand or don't care about it, it really needs to come from them. So what I'm trying to say is that data basically support business objectives on what you need to do is every business area needs information on the detector decisions toe actually be able to be more efficient or create value etcetera. Now, depending on the business questions they have to solve, they will need certain data set. So they need actually to be ableto have data quality for their own. For us now, when they understand that they become the stores naturally on their own data sets. And that is where my bottom line is meeting my top down. You can guide them from the top, but they need themselves to be also empower and be actually, in a way flexible to adapt the different questions that they have in orderto be able to respond to the business needs. Now I cannot impose at the finish for everyone. I need them to adapt and to bring their answers toe their own business questions. That is adaptive data governor and all That is possible because we have. And I was saying at the very beginning just to finalize the point, we have new technologies that allow you to do this method data classifications, uh, in a very sophisticated way that you can actually create analitico of your metadata. You can understand your different data sources in order to be able to create those classifications like nationalities, a way of classifying your customers, your products, etcetera. >>So one of the things that you just said Santa kind of struck me to enable the users to be adaptive. They probably don't want to be logging in support ticket. So how do you support that sort of self service to meet the demand of the users so that they can be adaptive. >>More and more business users wants autonomy, and they want to basically be ableto grab the data and answer their own question. Now when you have, that is great, because then you have demand of businesses asking for data. They're asking for the insight. Eso How do you actually support that? I would say there is a changing culture that is happening more and more. I would say even the current pandemic has helped a lot into that because you have had, in a way, off course, technology is one of the biggest winners without technology. We couldn't have been working remotely without these technologies where people can actually looking from their homes and still have a market data marketplaces where they self serve their their information. But even beyond that data is a big winner. Data because the pandemic has shown us that crisis happened, that we cannot predict everything and that we are actually facing a new kind of situation out of our comfort zone, where we need to explore that we need to adapt and we need to be flexible. How do we do that with data. Every single company either saw the revenue going down or the revenue going very up For those companies that are very digital already. Now it changed the reality, so they needed to adapt. But for that they needed information. In order to think on innovate, try toe, create responses So that type of, uh, self service off data Haider for data in order to be able to understand what's happening when the prospect is changing is something that is becoming more, uh, the topic today because off the condemning because of the new abilities, the technologies that allow that and then you then are allowed to basically help your data. Citizens that call them in the organization people that no other business and can actually start playing and an answer their own questions. Eso so these technologies that gives more accessibility to the data that is some cataloging so they can understand where to go or what to find lineage and relationships. All this is is basically the new type of platforms and tools that allow you to create what are called a data marketplace. I think these new tools are really strong because they are now allowing for people that are not technology or I t people to be able to play with data because it comes in the digital world There. Used to a given example without your who You have a very interesting search functionality. Where if you want to find your data you want to sell, Sir, you go there in that search and you actually go on book for your data. Everybody knows how to search in Google, everybody's searching Internet. So this is part of the data culture, the digital culture. They know how to use those schools. Now, similarly, that data marketplace is, uh, in you can, for example, see which data sources they're mostly used >>and enabling that speed that we're all demanding today during these unprecedented times. Goodwin, I wanted to go to you as we talk about in the spirit of evolution, technology is changing. Talk to us a little bit about Oracle Digital. What are you guys doing there? >>Yeah, Thank you. Um, well, Oracle Digital is a business unit that Oracle EMEA on. We focus on emerging countries as well as low and enterprises in the mid market, in more developed countries and four years ago. This started with the idea to engage digital with our customers. Fear Central helps across EMEA. That means engaging with video, having conference calls, having a wall, a green wall where we stand in front and engage with our customers. No one at that time could have foreseen how this is the situation today, and this helps us to engage with our customers in the way we were already doing and then about my team. The focus of my team is to have early stage conversations with our with our customers on digital transformation and innovation. And we also have a team off industry experts who engaged with our customers and share expertise across EMEA, and we inspire our customers. The outcome of these conversations for Oracle is a deep understanding of our customer needs, which is very important so we can help the customer and for the customer means that we will help them with our technology and our resource is to achieve their goals. >>It's all about outcomes, right? Good Ron. So in terms of automation, what are some of the things Oracle's doing there to help your clients leverage automation to improve agility? So that they can innovate faster, which in these interesting times it's demanded. >>Yeah, thank you. Well, traditionally, Oracle is known for their databases, which have bean innovated year over year. So here's the first lunch on the latest innovation is the autonomous database and autonomous data warehouse. For our customers, this means a reduction in operational costs by 90% with a multi medal converts, database and machine learning based automation for full life cycle management. Our databases self driving. This means we automate database provisioning, tuning and scaling. The database is self securing. This means ultimate data protection and security, and it's self repairing the automates failure, detection fail over and repair. And then the question is for our customers, What does it mean? It means they can focus on their on their business instead off maintaining their infrastructure and their operations. >>That's absolutely critical use if I want to go over to you now. Some of the things that we've talked about, just the massive progression and technology, the evolution of that. But we know that whether we're talking about beta management or digital transformation, a one size fits all approach doesn't work to address the challenges that the business has, um that the i t folks have, as you're looking through the industry with what Santiago told us about first Bank of Nigeria. What are some of the changes that you're seeing that I owe Tahoe seeing throughout the industry? >>Uh, well, Lisa, I think the first way I'd characterize it is to say, the traditional kind of top down approach to data where you have almost a data Policeman who tells you what you can and can't do, just doesn't work anymore. It's too slow. It's too resource intensive. Uh, data management data, governments, digital transformation itself. It has to be collaborative on. There has to be in a personalization to data users. Um, in the environment we find ourselves in. Now, it has to be about enabling self service as well. Um, a one size fits all model when it comes to those things around. Data doesn't work. As Santiago was saying, it needs to be adapted toe how the data is used. Andi, who is using it on in order to do this cos enterprises organizations really need to know their data. They need to understand what data they hold, where it is on what the sensitivity of it is they can then any more agile way apply appropriate controls on access so that people themselves are and groups within businesses are our job and could innovate. Otherwise, everything grinds to a halt, and you risk falling behind your competitors. >>Yeah, that one size fits all term just doesn't apply when you're talking about adaptive and agility. So we heard from Santiago about some of the impact that they're making with First Bank of Nigeria. Used to talk to us about some of the business outcomes that you're seeing other customers make leveraging automation that they could not do >>before it's it's automatically being able to classify terabytes, terabytes of data or even petabytes of data across different sources to find duplicates, which you can then re mediate on. Deletes now, with the capabilities that iota offers on the Oracle offers, you can do things not just where the five times or 10 times improvement, but it actually enables you to do projects for Stop that otherwise would fail or you would just not be able to dio I mean, uh, classifying multi terrible and multi petabytes states across different sources, formats very large volumes of data in many scenarios. You just can't do that manually. I mean, we've worked with government departments on the issues there is expect are the result of fragmented data. There's a lot of different sources. There's lot of different formats and without these newer technologies to address it with automation on machine learning, the project isn't durable. But now it is on that that could lead to a revolution in some of these businesses organizations >>to enable that revolution that there's got to be the right cultural mindset. And one of the when Santiago was talking about folks really kind of adapted that. The thing I always call that getting comfortably uncomfortable. But that's hard for organizations to. The technology is here to enable that. But well, you're talking with customers use. How do you help them build the trust in the confidence that the new technologies and a new approaches can deliver what they need? How do you help drive the kind of a tech in the culture? >>It's really good question is because it can be quite scary. I think the first thing we'd start with is to say, Look, the technology is here with businesses like I Tahoe. Unlike Oracle, it's already arrived. What you need to be comfortable doing is experimenting being agile around it, Andi trying new ways of doing things. Uh, if you don't wanna get less behind that Santiago on the team that fbn are a great example off embracing it, testing it on a small scale on, then scaling up a Toyota, we offer what we call a data health check, which can actually be done very quickly in a matter of a few weeks. So we'll work with a customer. Picky use case, install the application, uh, analyzed data. Drive out Cem Cem quick winds. So we worked in the last few weeks of a large entity energy supplier, and in about 20 days, we were able to give them an accurate understanding of their critical data. Elements apply. Helping apply data protection policies. Minimize copies of the data on work out what data they needed to delete to reduce their infrastructure. Spend eso. It's about experimenting on that small scale, being agile on, then scaling up in a kind of very modern way. >>Great advice. Uh, Santiago, I'd like to go back to Is we kind of look at again that that topic of culture and the need to get that mindset there to facilitate these rapid changes, I want to understand kind of last question for you about how you're doing that from a digital transformation perspective. We know everything is accelerating in 2020. So how are you building resilience into your data architecture and also driving that cultural change that can help everyone in this shift to remote working and a lot of the the digital challenges and changes that we're all going through? >>The new technologies allowed us to discover the dating anyway. Toe flawed and see very quickly Information toe. Have new models off over in the data on giving autonomy to our different data units. Now, from that autonomy, they can then compose an innovator own ways. So for me now, we're talking about resilience because in a way, autonomy and flexibility in a organization in a data structure with platform gives you resilience. The organizations and the business units that I have experienced in the pandemic are working well. Are those that actually because they're not physically present during more in the office, you need to give them their autonomy and let them actually engaged on their own side that do their own job and trust them in a way on as you give them, that they start innovating and they start having a really interesting ideas. So autonomy and flexibility. I think this is a key component off the new infrastructure. But even the new reality that on then it show us that, yes, we used to be very kind off structure, policies, procedures as very important. But now we learn flexibility and adaptability of the same side. Now, when you have that a key, other components of resiliency speed, because people want, you know, to access the data and access it fast and on the site fast, especially changes are changing so quickly nowadays that you need to be ableto do you know, interact. Reiterate with your information to answer your questions. Pretty, um, so technology that allows you toe be flexible iterating on in a very fast job way continue will allow you toe actually be resilient in that way, because you are flexible, you adapt your job and you continue answering questions as they come without having everything, setting a structure that is too hard. We also are a partner off Oracle and Oracle. Embodies is great. They have embedded within the transactional system many algorithms that are allowing us to calculate as the transactions happened. What happened there is that when our customers engaged with algorithms and again without your powers, well, the machine learning that is there for for speeding the automation of how you find your data allows you to create a new alliance with the machine. The machine is their toe, actually, in a way to your best friend to actually have more volume of data calculated faster. In a way, it's cover more variety. I mean, we couldn't hope without being connected to this algorithm on >>that engagement is absolutely critical. Santiago. Thank you for sharing that. I do wanna rap really quickly. Good On one last question for you, Santiago talked about Oracle. You've talked about a little bit. As we look at digital resilience, talk to us a little bit in the last minute about the evolution of Oracle. What you guys were doing there to help your customers get the resilience that they have toe have to be not just survive but thrive. >>Yeah. Oracle has a cloud offering for infrastructure, database, platform service and a complete solutions offered a South on Daz. As Santiago also mentioned, We are using AI across our entire portfolio and by this will help our customers to focus on their business innovation and capitalize on data by enabling new business models. Um, and Oracle has a global conference with our cloud regions. It's massively investing and innovating and expanding their clouds. And by offering clouds as public cloud in our data centers and also as private cloud with clouded customer, we can meet every sovereignty and security requirements. And in this way we help people to see data in new ways. We discover insights and unlock endless possibilities. And and maybe 11 of my takeaways is if I If I speak with customers, I always tell them you better start collecting your data. Now we enable this partners like Iota help us as well. If you collect your data now, you are ready for tomorrow. You can never collect your data backwards, So that is my take away for today. >>You can't collect your data backwards. Excellently, John. Gentlemen, thank you for sharing all of your insights. Very informative conversation in a moment, we'll address the question. Do you know your data? >>Are you interested in test driving the iota Ho platform kick Start the benefits of data automation for your business through the Iota Ho Data Health check program. Ah, flexible, scalable sandbox environment on the cloud of your choice with set up service and support provided by Iota ho. Look time with a data engineer to learn more and see Io Tahoe in action from around the globe. It's the Cube presenting adaptive data governance brought to you by Iota Ho. >>In this next segment, we're gonna be talking to you about getting to know your data. And specifically you're gonna hear from two folks at Io Tahoe. We've got enterprise account execs to be to Davis here, as well as Enterprise Data engineer Patrick Simon. They're gonna be sharing insights and tips and tricks for how you could get to know your data and quickly on. We also want to encourage you to engage with the media and Patrick, use the chat feature to the right, send comments, questions or feedback so you can participate. All right, Patrick Savita, take it away. Alright. >>Thankfully saw great to be here as Lisa mentioned guys, I'm the enterprise account executive here in Ohio. Tahoe you Pat? >>Yeah. Hey, everyone so great to be here. I said my name is Patrick Samit. I'm the enterprise data engineer here in Ohio Tahoe. And we're so excited to be here and talk about this topic as one thing we're really trying to perpetuate is that data is everyone's business. >>So, guys, what patent I got? I've actually had multiple discussions with clients from different organizations with different roles. So we spoke with both your technical and your non technical audience. So while they were interested in different aspects of our platform, we found that what they had in common was they wanted to make data easy to understand and usable. So that comes back. The pats point off to being everybody's business because no matter your role, we're all dependent on data. So what Pan I wanted to do today was wanted to walk you guys through some of those client questions, slash pain points that we're hearing from different industries and different rules and demo how our platform here, like Tahoe, is used for automating Dozier related tasks. So with that said are you ready for the first one, Pat? >>Yeah, Let's do it. >>Great. So I'm gonna put my technical hat on for this one. So I'm a data practitioner. I just started my job. ABC Bank. I have, like, over 100 different data sources. So I have data kept in Data Lakes, legacy data, sources, even the cloud. So my issue is I don't know what those data sources hold. I don't know what data sensitive, and I don't even understand how that data is connected. So how can I saw who help? >>Yeah, I think that's a very common experience many are facing and definitely something I've encountered in my past. Typically, the first step is to catalog the data and then start mapping the relationships between your various data stores. Now, more often than not, this has tackled through numerous meetings and a combination of excel and something similar to video which are too great tools in their own part. But they're very difficult to maintain. Just due to the rate that we are creating data in the modern world. It starts to beg for an idea that can scale with your business needs. And this is where a platform like Io Tahoe becomes so appealing, you can see here visualization of the data relationships created by the I. O. Tahoe service. Now, what is fantastic about this is it's not only laid out in a very human and digestible format in the same action of creating this view, the data catalog was constructed. >>Um so is the data catalog automatically populated? Correct. Okay, so So what I'm using Iota hope at what I'm getting is this complete, unified automated platform without the added cost? Of course. >>Exactly. And that's at the heart of Iota Ho. A great feature with that data catalog is that Iota Ho will also profile your data as it creates the catalog, assigning some meaning to those pesky column underscore ones and custom variable underscore tents. They're always such a joy to deal with. Now, by leveraging this interface, we can start to answer the first part of your question and understand where the core relationships within our data exists. Uh, personally, I'm a big fan of this view, as it really just helps the i b naturally John to these focal points that coincide with these key columns following that train of thought, Let's examine the customer I D column that seems to be at the center of a lot of these relationships. We can see that it's a fairly important column as it's maintaining the relationship between at least three other tables. >>Now you >>notice all the connectors are in this blue color. This means that their system defined relationships. But I hope Tahoe goes that extra mile and actually creates thes orange colored connectors as well. These air ones that are machine learning algorithms have predicted to be relationships on. You can leverage to try and make new and powerful relationships within your data. >>Eso So this is really cool, and I can see how this could be leverage quickly now. What if I added new data sources or your multiple data sources and need toe identify what data sensitive can iota who detect that? >>Yeah, definitely. Within the hotel platform. There, already over 300 pre defined policies such as hip for C, C, P. A and the like one can choose which of these policies to run against their data along for flexibility and efficiency and running the policies that affect organization. >>Okay, so so 300 is an exceptional number. I'll give you that. But what about internal policies that apply to my organization? Is there any ability for me to write custom policies? >>Yeah, that's no issue. And it's something that clients leverage fairly often to utilize this function when simply has to write a rejects that our team has helped many deploy. After that, the custom policy is stored for future use to profile sensitive data. One then selects the data sources they're interested in and select the policies that meet your particular needs. The interface will automatically take your data according to the policies of detects, after which you can review the discoveries confirming or rejecting the tagging. All of these insights are easily exported through the interface. Someone can work these into the action items within your project management systems, and I think this lends to the collaboration as a team can work through the discovery simultaneously, and as each item is confirmed or rejected, they can see it ni instantaneously. All this translates to a confidence that with iota hope, you can be sure you're in compliance. >>So I'm glad you mentioned compliance because that's extremely important to my organization. So what you're saying when I use the eye a Tahoe automated platform, we'd be 90% more compliant that before were other than if you were going to be using a human. >>Yeah, definitely the collaboration and documentation that the Iot Tahoe interface lends itself to really help you build that confidence that your compliance is sound. >>So we're planning a migration. Andi, I have a set of reports I need to migrate. But what I need to know is, uh well, what what data sources? Those report those reports are dependent on. And what's feeding those tables? >>Yeah, it's a fantastic questions to be toe identifying critical data elements, and the interdependencies within the various databases could be a time consuming but vital process and the migration initiative. Luckily, Iota Ho does have an answer, and again, it's presented in a very visual format. >>Eso So what I'm looking at here is my entire day landscape. >>Yes, exactly. >>Let's say I add another data source. I can still see that unified 3 60 view. >>Yeah, One future that is particularly helpful is the ability to add data sources after the data lineage. Discovery has finished alone for the flexibility and scope necessary for any data migration project. If you only need need to select a few databases or your entirety, this service will provide the answers. You're looking for things. Visual representation of the connectivity makes the identification of critical data elements a simple matter. The connections air driven by both system defined flows as well as those predicted by our algorithms, the confidence of which, uh, can actually be customized to make sure that they're meeting the needs of the initiative that you have in place. This also provides tabular output in case you needed for your own internal documentation or for your action items, which we can see right here. Uh, in this interface, you can actually also confirm or deny the pair rejection the pair directions, allowing to make sure that the data is as accurate as possible. Does that help with your data lineage needs? >>Definitely. So So, Pat, My next big question here is So now I know a little bit about my data. How do I know I can trust >>it? So >>what I'm interested in knowing, really is is it in a fit state for me to use it? Is it accurate? Does it conform to the right format? >>Yeah, that's a great question. And I think that is a pain point felt across the board, be it by data practitioners or data consumers alike. Another service that I owe Tahoe provides is the ability to write custom data quality rules and understand how well the data pertains to these rules. This dashboard gives a unified view of the strength of these rules, and your dad is overall quality. >>Okay, so Pat s o on on the accuracy scores there. So if my marketing team needs to run, a campaign can read dependent those accuracy scores to know what what tables have quality data to use for our marketing campaign. >>Yeah, this view would allow you to understand your overall accuracy as well as dive into the minutia to see which data elements are of the highest quality. So for that marketing campaign, if you need everything in a strong form, you'll be able to see very quickly with these high level numbers. But if you're only dependent on a few columns to get that information out the door, you can find that within this view, eso >>you >>no longer have to rely on reports about reports, but instead just come to this one platform to help drive conversations between stakeholders and data practitioners. >>So I get now the value of IATA who brings by automatically capturing all those technical metadata from sources. But how do we match that with the business glossary? >>Yeah, within the same data quality service that we just reviewed, one can actually add business rules detailing the definitions and the business domains that these fall into. What's more is that the data quality rules were just looking at can then be tied into these definitions. Allowing insight into the strength of these business rules is this service that empowers stakeholders across the business to be involved with the data life cycle and take ownership over the rules that fall within their domain. >>Okay, >>so those custom rules can I apply that across data sources? >>Yeah, you could bring in as many data sources as you need, so long as you could tie them to that unified definition. >>Okay, great. Thanks so much bad. And we just want to quickly say to everyone working in data, we understand your pain, so please feel free to reach out to us. we are Website the chapel. Oh, Arlington. And let's get a conversation started on how iota Who can help you guys automate all those manual task to help save you time and money. Thank you. Thank >>you. Your Honor, >>if I could ask you one quick question, how do you advise customers? You just walk in this great example this banking example that you instantly to talk through. How do you advise customers get started? >>Yeah, I think the number one thing that customers could do to get started with our platform is to just run the tag discovery and build up that data catalog. It lends itself very quickly to the other needs you might have, such as thes quality rules. A swell is identifying those kind of tricky columns that might exist in your data. Those custom variable underscore tens I mentioned before >>last questions to be to anything to add to what Pat just described as a starting place. >>I'm no, I think actually passed something that pretty well, I mean, just just by automating all those manual task. I mean, it definitely can save your company a lot of time and money, so we we encourage you just reach out to us. Let's get that conversation >>started. Excellent. So, Pete and Pat, thank you so much. We hope you have learned a lot from these folks about how to get to know your data. Make sure that it's quality, something you can maximize the value of it. Thanks >>for watching. Thanks again, Lisa, for that very insightful and useful deep dive into the world of adaptive data governance with Iota Ho Oracle First Bank of Nigeria This is Dave a lot You won't wanna mess Iota, whose fifth episode in the data automation Siri's in that we'll talk to experts from Red Hat and Happiest Minds about their best practices for managing data across hybrid cloud Inter Cloud multi Cloud I T environment So market calendar for Wednesday, January 27th That's Episode five. You're watching the Cube Global Leader digital event technique

>>from around the globe. It's the Cube with digital coverage of Dell Technologies. World Digital experience Brought to you by Dell Technologies. Hello, everyone. And welcome back to the cubes Coverage of Del Tech World 2020. With me is Jeff Boudreau, the president general manager of Infrastructure Solutions group Deltek. Jeff, always good to see you, my friend. How you doing? >>Good. Good to see you. >>I wish we were hanging out a Sox game or a pat's game, but, uh, I guess this will dio But, you know, it was about a year ago when you took over leadership of I s G. I actually had way had that sort of brief conversation. You were in the room with Jeff Clark. I thought it was a great, great choice. How you doing? How you feeling Any sort of key moments the past 12 months that you you feel like sharing? >>Sure. So I first I want to say, I do remember that about a year ago. So thank you for reminding me. Yeah, it's, uh it's been a very interesting year, right? It's been it's been one year. It was in September was one year since I took over I s G. But I'm feeling great. So thank you for asking. I hope you're doing the same. And I'm really optimistic about where we are and where we're heading. Aziz, you know, it's been an extremely challenging year in a very unpredictable year, as we've all experienced. And I'd say for the, you know, the first part of the year, especially starting in March on I've been really focused on the health and safety of our, you know, the families, our customers and our team members of the team on a lot of it's been shifting, you know, in regards to helping our customers around, you know, work from home or education and learn from home. And, you know, during all this time, though, I'll tell you, as a team, we've accomplished a lot. There's a handful of things that I'm very proud of, you know, first and foremost, that states around the customer experience we have delivered on our best quality in our product. NPS scores in our entire history. So something I'm extremely proud of during this time around our innovation and innovation engine, we part of the entire portfolio which you're well aware of. We had nine launches in nine weeks back in that May in June. Timeframe. So something I'm really proud of the team on, uh, on. Then last, I'd say it's around the team and right, we shifted about 90% of our workforce from the office tow home, you know, from an engineering team. That could be, you know, 85% of my team is engineers and writing code. And so, you know, people were concerned about that. But we didn't skip a beat, so, you know, pretty impressed by the team and what they've done there. So, you know, the strategy remains unchanged. Uh, you know, we're focused on our customers integrating across the entire portfolio and the businesses like VM ware and really focused on getting share. So despite all the uncertainty in the market, I'm pretty pleased with the team and everything that's been going on. So uh, yeah, it's it's been it's been an interesting year, but it's really great. I'm really optimistic about what we have in front of us. >>Yeah, I mean, there's not much you could do a control about the macro condition on it, you know it. Z dealt to us and we have to deal with it. I mean, in your space. It's the sort of countervailing things here one is. Look, you're not selling laptops and endpoint security. That's not your business right in the data center. Eso. But the flip side of that is you mentioned your portfolio refresh. You know, things like Power Store. You got product cycles now kicking in. So that could be, you know, a buffer. What are you seeing with Power Store and what's the uptake look like? They're >>sure. Well, specifically, let me take a step back and the regards the portfolio. So first, you know, the portfolio itself is a direct reflection in the feedback from all our partners and our customers over the last couple of years on Day two, ramp up that innovation. I spent a lot of time in the last few years simplifying under the power brands, which you're well aware of, right? So we had a lot of for a legacy EMC and Legacy dollars. Really? How do we simplify under a set of brands really over delivering innovation on a fewer set of products that really accelerating in exceeding customer needs? And we did that across the board. So from power edge servers, you know, power Max, the high end storage, the Powerball, all that we didn't hear one. And just most recently. And, you know, it's part of the big launches. We had power scale. We have power flex for software to find. And, of course, the new flagship offer for the mid range, which is power store. Um, Specifically, the policy of the momentum has been building since our launch back in May. And the feedback from our partners and our customers has been fantastic. And we've had a lot of big wins against, you know, a lot of a lot of our core competitors. A couple examples one is Arrow Electronics SAA, Fortune 500 Global Elektronik supplier. They leverage power Store to provide, you know, basically both, you know, enterprise computing and storage needs for their for their broader bases around the world on there, really taking advantage of the 41 data reduction, really helping them simplify their capacity planning and really improve operational efficiencies specifically without impacting performance. So it's it's one. We're given the data reductions, but there's no impact on performance, which is a huge value proffer for arrow another big customers tickets and write a global law firm on their reporting to us that over 90 they've had a 90% reduction in their rack space, and they've had over five times two performance over a core competitors storage systems azi. They've deployed power store around the world, really, and it's really been helping them. Thio easily migrate workloads across, so the feedback from the customers and partners has been extremely positive. Um, there really citing benefits around the architecture, the flexibility architecture around the micro services, the containers they're loving, the D M or integration. They're loving the height of the predictable data reduction capabilities in line with in line performance or no performance penalties with data efficiencies, the workload support, I'd say the other big things around the anytime upgrades is another big thing that customers we're really talking about so very excited and optimistic in regards as we continue to re empower store the second half of the year into next year really is the full full year for power store. >>So can I ask you about that? That in line data reduction with no performance hit is that new ipe? I mean, you're not doing some kind of batch data reduction, right? >>No, it's It's new, I p. It's all patented. We've actually done a lot of work in regards to our technologies. There's some of the things we talk about GPS and deep use and smart Knicks and things like that. We've used some offload engines to help with that. So between the software and the hardware, we've had leverage new I. P. So we can actually provide that predictable data reduction. But right with the performance customers need, So we're not gonna have a trade off in regards. You get more efficiencies and less performance or more performance and less efficiency. >>That's interesting. Yeah, when I talked to the chip guys, they talk about this sort of the storage offloads and other offloads we're seeing. These alternative processors really start to hit the market videos. The obvious one. But you're seeing others. Aziz. Well, you're really it sounds like you're taking advantage of that. >>Yeah, it's a huge benefit. I mean, we should, you know, with our partners, if it's Intel's and in videos and folks like that broad comes, it's really leveraging the great innovation that they do, plus our innovation. So if you know the sum of the parts, can you know equal Mauritz a benefit to our customers in the other day? That's what it's all about. >>So it sounds like Cove. It hasn't changed your strategy. I was talking toe Dennis Hoffman and he was saying, Look, you know, fundamentally, we're executing on the same strategy. You know, tactically, there's things that we do differently. But what's your summarize your strategy coming in tow 2021. You know, we're still early in this decade. What are you seeing is the trends that you're trying to take advantage of? What do you excited about? Maybe some things that keep you up at night? >>Yeah, so I'd say, you know, I'll stay with what Dennis said. You know, it's our strategy is not changing its a company. You probably got that from Michael and from job, obviously, Dennis just recently. But for me, it's a two pronged approach. One's all about winning the consolidation in the core infrastructure markets that we could just paid in today. So I think Service Storage Network, we're already clear leader across all those segments that we serve in our you know, we'll continue to innovate within our existing product categories. And you saw that with the nine launches in the nine weeks in my point on that one is we're gonna always make sure that we have best debris offers. If it's a three tier, two tier or converge or hyper converged offer, we wanna make sure that we serve that and have the best innovation possible. In addition to that, though, the secondary piece of the strategy really is around. How do we differentiate value across or innovating across I S G? You know, Dell Technologies and even the broader ecosystems and some of the examples I'll give you right now that we're doing is if you think about innovating across icy, that's all about providing improved customer experience, a set of solutions and offers that really helped simplify customer operations, right? And really give them better T CEOs or better. S L. A. An example of something like that's cloud like it's a SAS based off of that we have. That really helps provide great insights and telemetry to our customers. That helps them simplify their I T operations, and it's a major step forward towards, you know, autonomous infrastructure which is really what they're asking for. Customers of a very happy with the work we've done around Day one, you know, faster, time to value. But now it's like Day two and beyond. How do you really helped me Kinda accelerate the operations and really take that away from a three other big pieces innovating across all technologies. And you know, we do this with VM Ware now live today, and that's just writing. So things like VX rail is an example where we work together and where the clear leader in H C I. Things like Delta Cloud Uh, when we built in V M V C F A, B, M or cloud foundation in Tan Xue delivering an industry leading hybrid cloud platform just recently a VM world. I'm sure you heard about it, but Project Monterey was just announced, and that's an effort we're doing with VM Ware and some other partners. They're really about the next generation of infrastructure. Um, you know, I guess taking it up a notch out of the infrastructure and I've g phase, you know, some of the areas that we're gonna be looking at the end to end solutions to help our customers around six key areas. I'm sure John Rose talking about the past, but things like cloud Edge five g A i m l data management security. So those will be the big things. You'll see us lean into a Z strategies consistent. Some big themes that you'll see us lean into going into next year. >>Yeah, I mean, it is consistent, right? You guys have always tried to ride the waves, vector your portfolio into those waves and add value. I'm particularly impressed with your focus on customer experience, and I think that's a huge deal. You know, in the past, a lot of companies yours included your predecessor. You see, Hey, throwing so many products at me, I can't I don't understand the portfolio. So I mean, focusing on that I think is huge right now because people want that experience, you know, to be mawr cloudlike. And that's that's what you got to deliver. What about any news from from Dell Tech world? Any any announcements that you you wanna highlight that we could talk about? >>Sure. And actually, just touching back on the point you had no about the simplification that is a major 10 of my in regards the organization. So there's three key components that I drive once around customer focus, and that's keeping customers first and foremost. And everything we do to is around axillary that innovation. Engine three is really bringing everything together as one team. So we provide a better outcome to our customers. You know, in that simplification after that you talk about is court toe what we're driving. So I want to do less things, I guess better in the notion of how we do that. What that means to me is, as I make decisions that want to move away from other technologies and really leverage our best of breed type shared type, that's technology. I p people I p I can, you know, e can exceed customer needs in those markets that were serving. So it's actually allows me to x Sorry, my innovation engine, because I shift more and more resource is onto the newer stock now for Del Tech world. Yes, We got some cool stuff coming. You probably heard about a few of them. Uh, we're gonna be announcing a project project Apex. Hopefully you've been briefed on that already. This isn't new news or I'll be in trouble. But that's really around. Our strategy about delivering, simple, consistent as a service experiences for our customers bringing together are dealt technology as a service offering and our cloud strategy together. Onda also our technology offerings in our go to market all under a single unified effort, which Ellison do would be leading. Um, you know, on behalf of our executive leadership team s, that's one big area. And there is also another big one that I'll talk about a sui expand our as a service offers. And we think there's a big power to that in regards to our Dell Technologies. Cloud console solving will be launching a new cloud console that will provide uniformed experience across all the resources and give users and ability toe instantly managed every aspect of their cloud journey with just a few clicks. So going back to your broader point, it's all about simplicity. >>Yeah, we definitely all over Apex. That's something I wanted to ask you about this notion of as a service, really requiring it could have a new mindset, certainly from a pricing and how you talk about the customer experience that it's a whole new customer experience. Your you're basically giving them access. Thio What I would consider more of a platform on giving them some greater flexibility. Yeah, there's some constraints in there, but of course, you know the physical only put so much capacity and before him. But the idea of being ableto dial up, dial down within certain commitments is, I think, a powerful one. How does it change the way in which you you think about how you go about developing products just in terms of you know, this AP economy Infrastructure is code. How how you converse about those products internally and externally. How would you see that shaking >>out Dave? That's an awesome question. And it's actually for its front center. For everything we do, obviously, customers one choice and flexibility what they do. And to your point as we evolved warm or as a service, no specific product and product brands and logos on probably the way of the future. It's the services. It's the experience that you provide in regards to how we do that. So if you think about me, you know, in in infrastructure making infrastructure as a service, you really want to define what that customer experiences. That s L. A. That they're trying toe realize. And then how do we make sure that we build the right solutions? Products feature functions to enable that a law that goes back to the core engineering stuff that we need to dio right now, a lot of that stuff is about making sure that we have the right things around. If it's around developer community. If it's around AP rich, it's around. SdK is it's all about how do we leverage if it's internal source or external open source, if you will. It's regards to How do we do that? No. A thing that I think we all you know what you're well aware but we ought to keep in mind is that the cloud native applications are really relevant. Toe both the on premises, wealthy off premise. So think about things around portability reusability. You know, those are some great examples of just kind of how we think about this as we go forward. But those modern applications were required modern infrastructure, and regardless of how that infrastructure is abstracted now, just think about things like this. Aggregation or compose ability or Internet based computing. It's just it's a huge trend that we have to make sure we're thinking of. So is we. We just aggregate between the physical layers to the software layers and how we provide that to a service that could be think of a modern container based asset that could be repurposed. Either could be on a purpose built thing. It could be deployed in a converge or hyper converged. Or it could be two points a software feature in a cloud. Now, that's really how we're thinking about that, regards that we go forward. So we're talking about building modern assets or components That could be you right once we used many type model, and we can deploy that wherever you want because of some of the abstraction of desegregation that we're gonna do. >>E could see customers in the in the near term saying, I don't care so much about the product. I want the fast one all right with the cheaper one e. >>It's kind of what you talking about, that I talked about the ways. If you think about that regards, you know, maybe it's on a specific brand or portfolio. You look into and you say, Hey, what's the service level that I'd wanted to your point like Hey, for compute or for storage, it's really gonna end up being the specific S l A. And that's around performance or Leighton see, or cost or resiliency they want. They want that experience in that that you know, And that's why they're gonna be looking for the end of the end state. That's what we have to deliver is an engineering. >>So there's an opportunity here for you guys that I wonder if you could comment on. And that's the storage admin E. M. C essentially created. You know, you get this army of people that you know pretty good of provisioning lungs, although that's not really that's a great career path for folks. But program ability is, and this notion of infrastructure is code as you as you make your systems more programmable. Is there a skill set opportunity to take that army of constituents that you guys helped train and grow and over their careers and bring them along into sort of the next decade? This new era? >>I think the the easy answer is yes, I obviously that's a hard thing to do and you go forward. But I think embracing the change in the evolution of change, I think is a great opportunity. And I think there is e mean if you look step back and you think about data management, right? And you think about all the you know all data is not created equal and you know, and it has a life cycle, if you will. And so if it's on edge to Korda, Cloward, depending think about data vaults and data mobility and all that stuff. There's gonna be a bunch of different personas and people touching data along the way. I think the I T advance and the storage admin. They're just one of those personas that we have to help serve and way talk about How do we make them heroes, if you will, in regards to their broader environment. So if they're providing, if they evolve and really helped provide a modern infrastructure that really enables, you know infrastructure is a code or infrastructure as a service, they become a nightie hero, if you will for the rest of team. So I think there's a huge opportunity for them to evolve as the technology evolves. >>Yeah, you talked about you know, your families, your employees, your team s o. You obviously focused on them. You got your products going hitting all the marks. How are you spending your time these days? >>Thes days right now? Well, we're in. We're in our cycle for fiscal 22 planning. Right? And right now, a lot of that's above the specific markets were serving. It's gonna be about the strategy and making sure that we have people focused on those things. So it really comes back to some of the strategy tents were driving for next year. Now, as I said, our focus big time. Well, I guess for the for this year is one is consolidation of the core markets. Major focus for May 2 is going to be around winning in storage, and I want to be very specific. It's winning midrange storage. And that was one of the big reasons why Power Store came. That's gonna be a big focus on Bennett's really making sure that we're delivering on the as a service stuff that we just talked about in regards to all the technology innovation that's required to really provide the customer experience. And then, lastly, it's making sure that we take advantage of some of these growth factors. So you're going to see a dentist. Probably talked a lot about Telco, but telco on edge and as a service and cloud those things, they're just gonna be key to everything I do. So if you think about from poor infrastructure to some of these emerging opportunities Z, I'm spending all my time. >>Well, it's a It's a big business and a really important one for Fidel. Jeff Boudreau. Thanks so much for coming back in the Cube. Really a pleasure seeing you. I hope we can see each other face to face soon. >>You too. Thank you for having me. >>You're very welcome. And thank you for watching everybody keep it right there. This is Dave Volonte for the Cube. 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from around the globe it's thecube with digital coverage of exascale day made possible by hewlett packard enterprise welcome everyone to the cube celebration of exascale day ben bennett is here he's an hpc strategist and evangelist at hewlett-packard enterprise ben welcome good to see you good to see you too son hey well let's evangelize exascale a little bit you know what's exciting you uh in regards to the coming of exoskilled computing um well there's a couple of things really uh for me historically i've worked in super computing for many years and i have seen the coming of several milestones from you know actually i'm old enough to remember gigaflops uh coming through and teraflops and petaflops exascale is has been harder than many of us anticipated many years ago the sheer amount of technology that has been required to deliver machines of this performance has been has been us utterly staggering but the exascale era brings with it real solutions it gives us opportunities to do things that we've not been able to do before if you look at some of the the most powerful computers around today they've they've really helped with um the pandemic kovid but we're still you know orders of magnitude away from being able to design drugs in situ test them in memory and release them to the public you know we still have lots and lots of lab work to do and exascale machines are going to help with that we are going to be able to to do more um which ultimately will will aid humanity and they used to be called the grand challenges and i still think of them as that i still think of these challenges for scientists that exascale class machines will be able to help but also i'm a realist is that in 10 20 30 years time you know i should be able to look back at this hopefully touch wood look back at it and look at much faster machines and say do you remember the days when we thought exascale was faster yeah well you mentioned the pandemic and you know the present united states was tweeting this morning that he was upset that you know the the fda in the u.s is not allowing the the vaccine to proceed as fast as you'd like it in fact it the fda is loosening some of its uh restrictions and i wonder if you know high performance computing in part is helping with the simulations and maybe predicting because a lot of this is about probabilities um and concerns is is is that work that is going on today or are you saying that that exascale actually you know would be what we need to accelerate that what's the role of hpc that you see today in regards to sort of solving for that vaccine and any other sort of pandemic related drugs so so first a disclaimer i am not a geneticist i am not a biochemist um my son is he tries to explain it to me and it tends to go in one ear and out the other um um i just merely build the machines he uses so we're sort of even on that front um if you read if you had read the press there was a lot of people offering up systems and computational resources for scientists a lot of the work that has been done understanding the mechanisms of covid19 um have been you know uncovered by the use of very very powerful computers would exascale have helped well clearly the faster the computers the more simulations we can do i think if you look back historically no vaccine has come to fruition as fast ever under modern rules okay admittedly the first vaccine was you know edward jenner sat quietly um you know smearing a few people and hoping it worked um i think we're slightly beyond that the fda has rules and regulations for a reason and we you don't have to go back far in our history to understand the nature of uh drugs that work for 99 of the population you know and i think exascale widely available exoscale and much faster computers are going to assist with that imagine having a genetic map of very large numbers of people on the earth and being able to test your drug against that breadth of person and you know that 99 of the time it works fine under fda rules you could never sell it you could never do that but if you're confident in your testing if you can demonstrate that you can keep the one percent away for whom that drug doesn't work bingo you now have a drug for the majority of the people and so many drugs that have so many benefits are not released and drugs are expensive because they fail at the last few moments you know the more testing you can do the more testing in memory the better it's going to be for everybody uh personally are we at a point where we still need human trials yes do we still need due diligence yes um we're not there yet exascale is you know it's coming it's not there yet yeah well to your point the faster the computer the more simulations and the higher the the chance that we're actually going to going to going to get it right and maybe compress that time to market but talk about some of the problems that you're working on uh and and the challenges for you know for example with the uk government and maybe maybe others that you can you can share with us help us understand kind of what you're hoping to accomplish so um within the united kingdom there was a report published um for the um for the uk research institute i think it's the uk research institute it might be epsrc however it's the body of people responsible for funding um science and there was a case a science case done for exascale i'm not a scientist um a lot of the work that was in this documentation said that a number of things that can be done today aren't good enough that we need to look further out we need to look at machines that will do much more there's been a program funded called asimov and this is a sort of a commercial problem that the uk government is working with rolls royce and they're trying to research how you build a full engine model and by full engine model i mean one that takes into account both the flow of gases through it and how those flow of gases and temperatures change the physical dynamics of the engine and of course as you change the physical dynamics of the engine you change the flow so you need a closely coupled model as air travel becomes more and more under the microscope we need to make sure that the air travel we do is as efficient as possible and currently there aren't supercomputers that have the performance one of the things i'm going to be doing as part of this sequence of conversations is i'm going to be having an in detailed uh sorry an in-depth but it will be very detailed an in-depth conversation with professor mark parsons from the edinburgh parallel computing center he's the director there and the dean of research at edinburgh university and i'm going to be talking to him about the azimoth program and and mark's experience as the person responsible for looking at exascale within the uk to try and determine what are the sort of science problems that we can solve as we move into the exoscale era and what that means for humanity what are the benefits for humans yeah and that's what i wanted to ask you about the the rolls-royce example that you gave it wasn't i if i understood it wasn't so much safety as it was you said efficiency and so that's that's what fuel consumption um it's it's partly fuel consumption it is of course safety there is a um there is a very specific test called an extreme event or the fan blade off what happens is they build an engine and they put it in a cowling and then they run the engine at full speed and then they literally explode uh they fire off a little explosive and they fire a fan belt uh a fan blade off to make sure that it doesn't go through the cowling and the reason they do that is there has been in the past uh a uh a failure of a fan blade and it came through the cowling and came into the aircraft depressurized the aircraft i think somebody was killed as a result of that and the aircraft went down i don't think it was a total loss one death being one too many but as a result you now have to build a jet engine instrument it balance the blades put an explosive in it and then blow the fan blade off now you only really want to do that once it's like car crash testing you want to build a model of the car you want to demonstrate with the dummy that it is safe you don't want to have to build lots of cars and keep going back to the drawing board so you do it in computers memory right we're okay with cars we have computational power to resolve to the level to determine whether or not the accident would hurt a human being still a long way to go to make them more efficient uh new materials how you can get away with lighter structures but we haven't got there with aircraft yet i mean we can build a simulation and we can do that and we can be pretty sure we're right um we still need to build an engine which costs in excess of 10 million dollars and blow the fan blade off it so okay so you're talking about some pretty complex simulations obviously what are some of the the barriers and and the breakthroughs that are kind of required you know to to do some of these things that you're talking about that exascale is going to enable i mean presumably there are obviously technical barriers but maybe you can shed some light on that well some of them are very prosaic so for example power exoscale machines consume a lot of power um so you have to be able to design systems that consume less power and that goes into making sure they're cooled efficiently if you use water can you reuse the water i mean the if you take a laptop and sit it on your lap and you type away for four hours you'll notice it gets quite warm um an exascale computer is going to generate a lot more heat several megawatts actually um and it sounds prosaic but it's actually very important to people you've got to make sure that the systems can be cooled and that we can power them yeah so there's that another issue is the software the software models how do you take a software model and distribute the data over many tens of thousands of nodes how do you do that efficiently if you look at you know gigaflop machines they had hundreds of nodes and each node had effectively a processor a core a thread of application we're looking at many many tens of thousands of nodes cores parallel threads running how do you make that efficient so is the software ready i think the majority of people will tell you that it's the software that's the problem not the hardware of course my friends in hardware would tell you ah software is easy it's the hardware that's the problem i think for the universities and the users the challenge is going to be the software i think um it's going to have to evolve you you're just you want to look at your machine and you just want to be able to dump work onto it easily we're not there yet not by a long stretch of the imagination yeah consequently you know we one of the things that we're doing is that we have a lot of centers of excellence is we will provide well i hate say the word provide we we sell super computers and once the machine has gone in we work very closely with the establishments create centers of excellence to get the best out of the machines to improve the software um and if a machine's expensive you want to get the most out of it that you can you don't just want to run a synthetic benchmark and say look i'm the fastest supercomputer on the planet you know your users who want access to it are the people that really decide how useful it is and the work they get out of it yeah the economics is definitely a factor in fact the fastest supercomputer in the planet but you can't if you can't afford to use it what good is it uh you mentioned power uh and then the flip side of that coin is of course cooling you can reduce the power consumption but but how challenging is it to cool these systems um it's an engineering problem yeah we we have you know uh data centers in iceland where it gets um you know it doesn't get too warm we have a big air cooled data center in in the united kingdom where it never gets above 30 degrees centigrade so if you put in water at 40 degrees centigrade and it comes out at 50 degrees centigrade you can cool it by just pumping it round the air you know just putting it outside the building because the building will you know never gets above 30 so it'll easily drop it back to 40 to enable you to put it back into the machine um right other ways to do it um you know is to take the heat and use it commercially there's a there's a lovely story of they take the hot water out of the supercomputer in the nordics um and then they pump it into a brewery to keep the mash tuns warm you know that's that's the sort of engineering i can get behind yeah indeed that's a great application talk a little bit more about your conversation with professor parsons maybe we could double click into that what are some of the things that you're going to you're going to probe there what are you hoping to learn so i think some of the things that that are going to be interesting to uncover is just the breadth of science that can be uh that could take advantage of exascale you know there are there are many things going on that uh that people hear about you know we people are interested in um you know the nobel prize they might have no idea what it means but the nobel prize for physics was awarded um to do with research into black holes you know fascinating and truly insightful physics um could it benefit from exascale i have no idea uh i i really don't um you know one of the most profound pieces of knowledge in in the last few hundred years has been the theory of relativity you know an austrian patent clerk wrote e equals m c squared on the back of an envelope and and voila i i don't believe any form of exascale computing would have helped him get there any faster right that's maybe flippant but i think the point is is that there are areas in terms of weather prediction climate prediction drug discovery um material knowledge engineering uh problems that are going to be unlocked with the use of exascale class systems we are going to be able to provide more tools more insight [Music] and that's the purpose of computing you know it's not that it's not the data that that comes out and it's the insight we get from it yeah i often say data is plentiful insights are not um ben you're a bit of an industry historian so i've got to ask you you mentioned you mentioned mentioned gigaflop gigaflops before which i think goes back to the early 1970s uh but the history actually the 80s is it the 80s okay well the history of computing goes back even before that you know yes i thought i thought seymour cray was you know kind of father of super computing but perhaps you have another point of view as to the origination of high performance computing [Music] oh yes this is um this is this is one for all my colleagues globally um you know arguably he says getting ready to be attacked from all sides arguably you know um computing uh the parallel work and the research done during the war by alan turing is the father of high performance computing i think one of the problems we have is that so much of that work was classified so much of that work was kept away from commercial people that commercial computing evolved without that knowledge i uh i have done in in in a previous life i have done some work for the british science museum and i have had the great pleasure in walking through the the british science museum archives um to look at how computing has evolved from things like the the pascaline from blaise pascal you know napier's bones the babbage's machines uh to to look all the way through the analog machines you know what conrad zeus was doing on a desktop um i think i think what's important is it doesn't matter where you are is that it is the problem that drives the technology and it's having the problems that requires the you know the human race to look at solutions and be these kicks started by you know the terrible problem that the us has with its nuclear stockpile stewardship now you've invented them how do you keep them safe originally done through the ascii program that's driven a lot of computational advances ultimately it's our quest for knowledge that drives these machines and i think as long as we are interested as long as we want to find things out there will always be advances in computing to meet that need yeah and you know it was a great conversation uh you're a brilliant guest i i love this this this talk and uh and of course as the saying goes success has many fathers so there's probably a few polish mathematicians that would stake a claim in the uh the original enigma project as well i think i think they drove the algorithm i think the problem is is that the work of tommy flowers is the person who took the algorithms and the work that um that was being done and actually had to build the poor machine he's the guy that actually had to sit there and go how do i turn this into a machine that does that and and so you know people always remember touring very few people remember tommy flowers who actually had to turn the great work um into a working machine yeah super computer team sport well ben it's great to have you on thanks so much for your perspectives best of luck with your conversation with professor parsons we'll be looking forward to that and uh and thanks so much for coming on thecube a complete pleasure thank you and thank you everybody for watching this is dave vellante we're celebrating exascale day you're watching the cube [Music]

>>welcome. My name is Ben Bennett. I am the director of HPC Strategic programs here at Hewlett Packard Enterprise. It is my great pleasure and honor to be talking to Professor Mark Parsons from the Edinburgh Parallel Computing Center. And we're gonna talk a little about exa scale. What? It means we're gonna talk less about the technology on Maura about the science, the requirements on the need for exa scale. Uh, rather than a deep dive into the enabling technologies. Mark. Welcome. >>I then thanks very much for inviting me to tell me >>complete pleasure. Um, so I'd like to kick off with, I suppose. Quite an interesting look back. You and I are both of a certain age 25 plus, Onda. We've seen these milestones. Uh, I suppose that the S I milestones of high performance computing's come and go, you know, from a gig a flop back in 1987 teraflop in 97 a petaflop in 2000 and eight. But we seem to be taking longer in getting to an ex a flop. Um, so I'd like your thoughts. Why is why is an extra flop taking so long? >>So I think that's a very interesting question because I started my career in parallel computing in 1989. I'm gonna join in. IPCC was set up then. You know, we're 30 years old this year in 1990 on Do you know the fastest computer we have them is 800 mega flops just under a getting flogged. So in my career, we've gone already. When we reached the better scale, we'd already gone pretty much a million times faster on, you know, the step from a tariff block to a block scale system really didn't feel particularly difficult. Um, on yet the step from A from a petaflop PETA scale system. To an extent, block is a really, really big challenge. And I think it's really actually related to what's happened with computer processes over the last decade, where, individually, you know, approached the core, Like on your laptop. Whoever hasn't got much faster, we've just got more often So the perception of more speed, but actually just being delivered by more course. And as you go down that approach, you know what happens in the supercomputing world as well. We've gone, uh, in 2010 I think we had systems that were, you know, a few 1000 cores. Our main national service in the UK for the last eight years has had 118,000 cores. But looking at the X scale we're looking at, you know, four or five million cores on taming that level of parallelism is the real challenge. And that's why it's taking an enormous and time to, uh, deliver these systems. That is not just on the hardware front. You know, vendors like HP have to deliver world beating technology and it's hard, hard. But then there's also the challenge to the users. How do they get the codes to work in the face of that much parallelism? >>If you look at what the the complexity is delivering an annex a flop. Andi, you could have bought an extra flop three or four years ago. You couldn't have housed it. You couldn't have powered it. You couldn't have afforded it on, do you? Couldn't program it. But you still you could have You could have bought one. We should have been so lucky to be unable to supply it. Um, the software, um I think from our standpoint, is is looking like where we're doing mawr enabling with our customers. You sell them a machine on, then the the need then to do collaboration specifically seems mawr and Maura around the software. Um, so it's It's gonna be relatively easy to get one x a flop using limb pack, but but that's not extra scale. So what do you think? On exa scale machine versus an X? A flop machine means to the people like yourself to your users, the scientists and industry. What is an ex? A flop versus >>an exa scale? So I think, you know, supercomputing moves forward by setting itself challenges. And when you when you look at all of the excess scale programs worldwide that are trying to deliver systems that can do an X a lot form or it's actually very arbitrary challenge. You know, we set ourselves a PETA scale challenge delivering a petaflop somebody manage that, Andi. But you know, the world moves forward by setting itself challenges e think you know, we use quite arbitrary definition of what we mean is well by an exit block. So, you know, in your in my world, um, we either way, first of all, see ah flop is a computation, so multiply or it's an ad or whatever on we tend. Thio, look at that is using very high precision numbers or 64 bit numbers on Do you know, we then say, Well, you've got to do the next block. You've got to do a billion billion of those calculations every second. No, a some of the last arbitrary target Now you know today from HPD Aiken by my assistant and will do a billion billion calculations per second. And they will either do that as a theoretical peak, which would be almost unattainable, or using benchmarks that stressed the system on demonstrate a relaxing law. But again, those benchmarks themselves attuned Thio. Just do those calculations and deliver and explore been a steady I'll way if you like. So, you know, way kind of set ourselves this this this big challenge You know, the big fence on the race course, which were clambering over. But the challenge in itself actually should be. I'm much more interesting. The water we're going to use these devices for having built um, eso. Getting into the extra scale era is not so much about doing an extra block. It's a new generation off capability that allows us to do better scientific and industrial research. And that's the interesting bit in this whole story. >>I would tend to agree with you. I think the the focus around exa scale is to look at, you know, new technologies, new ways of doing things, new ways of looking at data and to get new results. So eventually you will get yourself a nexus scale machine. Um, one hopes, sooner rather >>than later. Well, I'm sure you don't tell me one, Ben. >>It's got nothing to do with may. I can't sell you anything, Mark. But there are people outside the door over there who would love to sell you one. Yes. However, if we if you look at your you know your your exa scale machine, Um, how do you believe the workloads are going to be different on an extra scale machine versus your current PETA scale machine? >>So I think there's always a slight conceit when you buy a new national supercomputer. On that conceit is that you're buying a capability that you know on. But many people will run on the whole system. Known truth. We do have people that run on the whole of our archer system. Today's A 118,000 cores, but I would say, and I'm looking at the system. People that run over say, half of that can be counted on Europe on a single hand in a year, and they're doing very specific things. It's very costly simulation they're running on. So, you know, if you look at these systems today, two things show no one is. It's very difficult to get time on them. The Baroque application procedures All of the requirements have to be assessed by your peers and your given quite limited amount of time that you have to eke out to do science. Andi people tend to run their applications in the sweet spot where their application delivers the best performance on You know, we try to push our users over time. Thio use reasonably sized jobs. I think our average job says about 20,000 course, she's not bad, but that does mean that as we move to the exits, kill two things have to happen. One is actually I think we've got to be more relaxed about giving people access to the system, So let's give more people access, let people play, let people try out ideas they've never tried out before. And I think that will lead to a lot more innovation and computational science. But at the same time, I think we also need to be less precious. You know, we to accept these systems will have a variety of sizes of job on them. You know, we're still gonna have people that want to run four million cores or two million cores. That's absolutely fine. Absolutely. Salute those people for trying really, really difficult. But then we're gonna have a huge spectrum of views all the way down to people that want to run on 500 cores or whatever. So I think we need Thio broaden the user base in Alexa Skill system. And I know this is what's happening, for example, in Japan with the new Japanese system. >>So, Mark, if you cast your mind back to almost exactly a year ago after the HPC user forum, you were interviewed for Premier Magazine on Do you alluded in that article to the needs off scientific industrial users requiring, you know, uh on X a flop or an exa scale machine it's clear in your in your previous answer regarding, you know, the workloads. Some would say that the majority of people would be happier with, say, 10 100 petaflop machines. You know, democratization. More people access. But can you provide us examples at the type of science? The needs of industrial users that actually do require those resources to be put >>together as an exa scale machine? So I think you know, it's a very interesting area. At the end of the day, these systems air bought because they are capability systems on. I absolutely take the argument. Why shouldn't we buy 10 100 pattern block systems? But there are a number of scientific areas even today that would benefit from a nexus school system and on these the sort of scientific areas that will use as much access onto a system as much time and as much scale of the system as they can, as you can give them eso on immediate example. People doing chroma dynamics calculations in particle physics, theoretical calculations, they would just use whatever you give them. But you know, I think one of the areas that is very interesting is actually the engineering space where, you know, many people worry the engineering applications over the last decade haven't really kept up with this sort of supercomputers that we have. I'm leading a project called Asimov, funded by M. P S O. C in the UK, which is jointly with Rolls Royce, jointly funded by Rolls Royce and also working with the University of Cambridge, Oxford, Bristol, Warrick. We're trying to do the whole engine gas turbine simulation for the first time. So that's looking at the structure of the gas turbine, the airplane engine, the structure of it, how it's all built it together, looking at the fluid dynamics off the air and the hot gasses, the flu threat, looking at the combustion of the engine looking how fuel is spread into the combustion chamber. Looking at the electrics around, looking at the way the engine two forms is, it heats up and cools down all of that. Now Rolls Royce wants to do that for 20 years. Andi, Uh, whenever they certify, a new engine has to go through a number of physical tests, and every time they do on those tests, it could cost them as much as 25 to $30 million. These are very expensive tests, particularly when they do what's called a blade off test, which would be, you know, blade failure. They could prove that the engine contains the fragments of the blade. Sort of think, continue face really important test and all engines and pass it. What we want to do is do is use an exa scale computer to properly model a blade off test for the first time, so that in future, some simulations can become virtual rather than having thio expend all of the money that Rolls Royce would normally spend on. You know, it's a fascinating project is a really hard project to do. One of the things that I do is I am deaf to share this year. Gordon Bell Price on bond I've really enjoyed to do. That's one of the major prizes in our area, you know, gets announced supercomputing every year. So I have the pleasure of reading all the submissions each year. I what's been really interesting thing? This is my third year doing being on the committee on what's really interesting is the way that big systems like Summit, for example, in the US have pushed the user communities to try and do simulations Nowhere. Nobody's done before, you know. And we've seen this as well, with papers coming after the first use of the for Goku system in Japan, for example, people you know, these are very, very broad. So, you know, earthquake simulation, a large Eddie simulations of boats. You know, a number of things around Genome Wide Association studies, for example. So the use of these computers spans of last area off computational science. I think the really really important thing about these systems is their challenging people that do calculations they've never done before. That's what's important. >>Okay, Thank you. You talked about challenges when I nearly said when you and I had lots of hair, but that's probably much more true of May. Um, we used to talk about grand challenges we talked about, especially around the teraflop era, the ski red program driving, you know, the grand challenges of science, possibly to hide the fact that it was a bomb designing computer eso they talked about the grand challenges. Um, we don't seem to talk about that much. We talk about excess girl. We talk about data. Um Where are the grand challenges that you see that an exa scale computer can you know it can help us. Okay, >>so I think grand challenges didn't go away. Just the phrase went out of fashion. Um, that's like my hair. I think it's interesting. The I do feel the science moves forward by setting itself grand challenges and always had has done, you know, my original backgrounds in particle physics. I was very lucky to spend four years at CERN working in the early stage of the left accelerator when it first came online on. Do you know the scientists there? I think they worked on left 15 years before I came in and did my little ph d on it. Andi, I think that way of organizing science hasn't changed. We just talked less about grand challenges. I think you know what I've seen over the last few years is a renaissance in computational science, looking at things that have previously, you know, people have said have been impossible. So a couple of years ago, for example, one of the key Gordon Bell price papers was on Genome Wide Association studies on some of it. If I may be one of the winner of its, if I remember right on. But that was really, really interesting because first of all, you know, the sort of the Genome Wide Association Studies had gone out of favor in the bioinformatics by a scientist community because people thought they weren't possible to compute. But that particular paper should Yes, you could do these really, really big Continental little problems in a reasonable amount of time if you had a big enough computer. And one thing I felt all the way through my career actually is we've probably discarded Mawr simulations because they were impossible at the time that we've actually decided to do. And I sometimes think we to challenge ourselves by looking at the things we've discovered in the past and say, Oh, look, you know, we could actually do that now, Andi, I think part of the the challenge of bringing an extra service toe life is to get people to think about what they would use it for. That's a key thing. Otherwise, I always say, a computer that is unused to just be turned off. There's no point in having underutilized supercomputer. Everybody loses from that. >>So Let's let's bring ourselves slightly more up to date. We're in the middle of a global pandemic. Uh, on board one of the things in our industry has bean that I've been particularly proud about is I've seen the vendors, all the vendors, you know, offering up machine's onboard, uh, making resources available for people to fight things current disease. Um, how do you see supercomputers now and in the future? Speeding up things like vaccine discovery on help when helping doctors generally. >>So I think you're quite right that, you know, the supercomputer community around the world actually did a really good job of responding to over 19. Inasmuch as you know, speaking for the UK, we put in place a rapid access program. So anybody wanted to do covert research on the various national services we have done to the to two services Could get really quick access. Um, on that, that has worked really well in the UK You know, we didn't have an archer is an old system, Aziz. You know, we didn't have the world's largest supercomputer, but it is happily bean running lots off covert 19 simulations largely for the biomedical community. Looking at Druk modeling and molecular modeling. Largely that's just been going the US They've been doing really large uh, combinatorial parameter search problems on on Summit, for example, looking to see whether or not old drugs could be reused to solve a new problem on DSO, I think, I think actually, in some respects Kobe, 19 is being the sounds wrong. But it's actually been good for supercomputing. Inasmuch is pointed out to governments that supercomputers are important parts off any scientific, the active countries research infrastructure. >>So, um, I'll finish up and tap into your inner geek. Um, there's a lot of technologies that are being banded around to currently enable, you know, the first exa scale machine, wherever that's going to be from whomever, what are the current technologies or emerging technologies that you are interested in excited about looking forward to getting your hands on. >>So in the business case I've written for the U. K's exa scale computer, I actually characterized this is a choice between the American model in the Japanese model. Okay, both of frozen, both of condoms. Eso in America, they're very much gone down the chorus plus GPU or GPU fruit. Um, so you might have, you know, an Intel Xeon or an M D process er center or unarmed process or, for that matter on you might have, you know, 24 g. P. U s. I think the most interesting thing that I've seen is definitely this move to a single address space. So the data that you have will be accessible, but the G p u on the CPU, I think you know, that's really bean. One of the key things that stopped the uptake of GPS today and that that that one single change is going Thio, I think, uh, make things very, very interesting. But I'm not entirely convinced that the CPU GPU model because I think that it's very difficult to get all the all the performance set of the GPU. You know, it will do well in H p l, for example, high performance impact benchmark we're discussing at the beginning of this interview. But in riel scientific workloads, you know, you still find it difficult to find all the performance that has promised. So, you know, the Japanese approach, which is the core, is only approach. E think it's very attractive, inasmuch as you know They're using very high bandwidth memory, very interesting process of which they are going to have to, you know, which they could develop together over 10 year period. And this is one thing that people don't realize the Japanese program and the American Mexico program has been working for 10 years on these systems. I think the Japanese process really interesting because, um, it when you look at the performance, it really does work for their scientific work clothes, and that's that does interest me a lot. This this combination of a A process are designed to do good science, high bandwidth memory and a real understanding of how data flows around the supercomputer. I think those are the things are exciting me at the moment. Obviously, you know, there's new networking technologies, I think, in the fullness of time, not necessarily for the first systems. You know, over the next decade we're going to see much, much more activity on silicon photonics. I think that's really, really fascinating all of these things. I think in some respects the last decade has just bean quite incremental improvements. But I think we're supercomputing is going in the moment. We're a very very disruptive moment again. That goes back to start this discussion. Why is extra skill been difficult to get? Thio? Actually, because the disruptive moment in technology. >>Professor Parsons, thank you very much for your time and your insights. Thank you. Pleasure and folks. Thank you for watching. I hope you've learned something, or at least enjoyed it. With that, I would ask you to stay safe and goodbye.

>> Hi everyone, this is Dave Vellante and I want to welcome you to our celebration of Exascale Day. A community event with support from Hewlett Packard Enterprise. Now, Exascale Day is October 18th, that's 10, 18 as in 10 to the power of 18. And on that day we celebrate the scientists, and researchers, who make breakthrough discoveries, with the assistance, of some of the most sophisticated supercomputers in the world. Ones that can run and Exascale. Now in this program, we're going to kick off the weekend and discuss the significance of Exascale computing, how we got here, why it's so challenging to get to the point where we're at now where we can perform almost, 10 to the 18th floating point operations per second. Or an exaFLOP. We should be there by 2021. And importantly, what innovations and possibilities Exascale computing will unlock. So today, we got a great program for you. We're not only going to dig into a bit of the history of supercomputing, we're going to talk with experts, folks like Dr. Ben Bennett, who's doing and some work with the UK government. And he's going to talk about some of the breakthroughs that we can expect with Exascale. You'll also hear from experts like, Professor Mark Parsons of the University of Edinburgh, who cut his teeth at CERN, in Geneva. And Dr. Brian Pigeon Nuskey of Purdue University, who's studying buyer diversity. We're going to also hear about supercomputers in space as we get as a great action going on with supercomputers up at the International Space Station. Let me think about that, powerful high performance water-cooled supercomputers, running on solar, and mounted overhead, that's right. Even though at the altitude at the International Space Station, there's 90% of the Earth's gravity. Objects, including humans they're essentially in a state of free fall. At 400 kilometers above earth, there no air. You're in a vacuum. Like have you ever been on the Tower of Terror at Disney? In that free fall ride, or a nosedive in an airplane, I have. And if you have binoculars around your neck, they would float. So the supercomputers can actually go into the ceiling, crazy right? And that's not all. We're going to hear from experts on what the exascale era. will usher in for not only space exploration, but things like weather forecasting, life sciences, complex modeling, and all types of scientific endeavors. So stay right there for all the great content. You can use the #ExascaleDay on Twitter, and, enjoy the program. Thanks everybody for watching.

>>live from San Francisco. It's the Q covering Micron Insight 2019 to You by Micron. >>Welcome back to San Francisco, everybody. We're here. Appear 27 covering the micron inside. 2019. I'm David Lot day with my co host, David Flores. Sanjay Moreau chose here. He's the president and CEO of Micron. Sanjay, great to see you again. Good to >>see you, too. >>I love the show because you guys are a highly technical company. You get you get down in the weeds and talk about nano meters and cycles and things like that. But we're here talking about technology, how it's changing people's lives. I mean, you see, that is your mission. So tell us a year on what's new from this >>event. You new at this event is so many new products that we announced today. You know, you see, Micron, I'd like to say that my clone 41 year old, but it has a new heart beat and you see that new heartbeat coming through New innovations, new products, you know, several new solid state drive solutions. Breakthrough speeds portable. SSD renounced our tent, a security solution as well as demonstrated test platform a platform for deep learning algorithms to be applied, enabling influence at EJ. So you see my front continuing to focus on driving high value solutions, engaging deeper with the ecosystem, engaging with our customers to understand what are the main points off the future and bringing innovative technology solutions. And, of course, during the course of last year in a while it was a year where memory pricing went down substantially because of some excess supply forces demand. But my crone, actually in that year produced the second best year in the history of the company. And during the year, he also had the second best year for free class cash flow for the company, >>about $13 billion in nearly 12 months. I want to get into some of that. But before I do our industry and you know this well has marched to the cadence of Moore's law for decades and decades of that has been the innovation engine. It no longer is. You talked about that on your panel today, the innovation engine is now data applying machine intelligence and a I and machine learning to that data scaling with cloud. Now the edge comes into a whole new innovation cocktail. I wonder if you could comment on that in terms of what it means for your business. >>So, yes, in terms of innovation, challenges you to Morse law scaling, but also the workloads that are there today, driving the eye of the future required, nor the solutions of yesterday. They require new, innovative architectures. I like to call them be spoke processors like you have a new custom suit for that fits a particular individual. Now baseball processors that actually meet needs off the specific workloads. But what is important is that as these new innovations, new architectures proliferate through the ecosystem. Memory and storage is key because the trends off the eye, after all, are about drawing deeper insight and creating greater value from all the data that billions of devices the coyote devices are creating around the world. So tremendous support unity for memory and storage. We are focused on bringing solutions with Denham with flash that meet the needs of our customers in terms of performance be announced today. You know the highest clocking speed for our ballistics. Dylan. You know, a delight to the gamers, but we also announce new SS D's for enterprise application, we announce a three d cross point x 100 solid state drive with the fastest in the industry kind of performance. So these are the kind of things were focused on. Yes, technology is getting more complex in and, you know, event from 64 layers to 96 layers. And next year, going 228 layers on Dedham side were the first ones to introduce the ones e technology Norden the industry and actually forced ones to start shipping already in production in the industry. This one z dina nor has the smallest feature size. So these are new, exciting things that are happening at my crime. >>I mean, Sanjay's right. They see all these alternative processes going on. You see, cos tech companies building their own custom silicon, right? I mean, >>so yes, way we're seeing these hybrid solutions being put together on dhe, seeing that the rise of new players completely like arm, for example, now taking a more and more important part in that. So But I'd like to just ask one question on three D cross point that that was an interesting one. How you gonna get volume in that is there going to be through working with some of these other vendors? Because Intel have just combined it with The process is essentially, Are you looking at new ways that you can use that at the lower end to get volume up? >>Where's that fit in your overall strategy? >>Certainly, as we said in the memory hierarchy, TV Cross Point fits in very nicely between NAND on one end and Di Dam on the other end city cost point as a persistent memory gives the benefit that it has capacities, ship densities that can be higher than Denham. Yet it has performance that is close to Denham and much faster than then. And it has the persistence, the non volatility that man has as well. So you can imagine with those kind of attributes, it will have exciting new opportunities in the future. But these new technologies do take several years before they become mainstream technologies. This is still early innings for three D plus point technology. Be engaged with the ecosystem partners with the customers in terms of understanding how this will fit in best in terms of their data center applications. This is what we have started working on This is what we announced the product. And we'll, of course, continue to evolve. The road map are pretty cross point technology. I just also want to touch upon what you said that Yes, you do see that compute now is not being just done like yesterday with CP use. Yes, you do have that idea of solutions. Si pues gp used tp use a six and f bjs. And here's some of the social media giants and those tech giants that are driving innovation in various industry segments, including transportation. They have their own silicon, you know, to address their deep learning requirements, and that creates new. Unfortunately, for us, this is what we call putting silicon back into Silicon Valley. Silicon is driving the innovations today that are coming out of Silicon Valley, regardless off world and market segment and Member Lee and storage is very much at the heart is at the center of yours. >>So you know I want to retire about vertical integration and one of my business early business heroes was Al Shugart. When I was a young puppet, i d. C. And he educated me on the importance of vertical integration and his market, which of course, was spinning destroys heads, media, etcetera. That was a game changer for that that you know, emerging company at the time. See gate. How is vertical? What your philosophy and vertical integration And how does it affect your business and your customers? >>So vertical indication enables us to bring value to our customers, bring greater value to our customers. We have a massive scale off manufacturing that is built on a very comprehensive and actually the world's most unique technology platform. Now taking this vertical integration of the technology platform manufacturing scales off, you know, more than six million acres a year and extending it to controller and form their expertise deep packaging expertise to bring high bandwidth Delia memory solutions for data center applications as well. A solid state drives and multi chip manage nan solutions for smartphone and automotive applications. These are just examples off how we're leveraging our vertical integration, extending it into controllers and firmware and packaging and assembly capability to really bring a diverse and expanded product for a full year to the market, do air just the markets needs from cloud to the edge. And now we're extending our work till indication capabilities even deeper from we have gone from silicon two solutions and now going from also silicon. Two solutions to system and software working with customers to understand water, the hardware and software intricacies involved that can help bring out even more power from the memory as they look at, you know, driving their deep learning algorithms for training as well as for influence. I think this is the vertical integration on build on the most unique platform of technologies. Nobody else in the world has Denham, NAND and CD cross point now building vertical indication on this gives us tremendous defense created opportunity to bring value to our cast. >>A troll more value or being able leveraged. I wanna ask you about your business. You mentioned a record number two free cash flow year for you guys. Two summers ago, I listened to you at the analyst, the Wall Street analyst meetings in you. We're very much aware of the demand and supply pending imbalance that was coming. And you said at the time we're gonna manage through that much better than we have. Historically, it reminds me of airlines when you go when you fly now there's no empty seats, so and you've done a masterful job. The stock prices stayed up. Now, granted, you know you've reduced share count. But how have you done that? What is the new discipline that has allowed you to navigate through those icebergs? >>Soviet Full focus on accelerating our technology development. We're focused on making, for example, technology development exploration. I mentioned earlier being the forest to the market with one C D M, which has the smallest feature size, right. That ability to accelerate technology development and deploy it in production to meet our CUSTOMERSNEEDS gives us ability to manage cost reduction ability as well. So Micron has actually in terms of cost reductions on a year over year basis has led the industry both on the same side as well as the NAN site. This has contributed to stronger financial performance of the company. During 2019 there, prices came down due to some excess supply in the industry, and yet Micron, due to its healthy cost position, was able to produce better financial results as well. Another very important element is increasing the mics off high value solutions, infusing the mics off SS D's and Managed man for mobile applications, as well as bringing more high performance memory to the Bennett, did a memory to the benefit of our customers. High value solutions, Cost positioning, technology acceleration. These have bean the elements that will give us long lasting advantage in terms of continuing to weather the potential ups and downs in our industry. And, you know, yes, we're proud of the fact that, unlike in the past, in an environment like will be experienced in 2019 with respect to price declines, Micron would have had media challenges Micron actually delivered, as I said, the second best year in terms of revenues profits as well as free cash flow. And I want to highlight that we narrowed the gap with our competitors. We narrow the gap with our competitors in terms of orbital margin again as a result, off a stronger, more diversified high value product portfolio and our cost reduction capabilities. >>Yeah, and you've also done a great job communicating to Wall Street. So my last question I know you gotta go is around tech for good. Mark Betty of's been really front center on this. You said that are really jar job is to make lives better, because I still say your job is to increase shareholder value as well. Whoa is the CEO who Mrs 4/4 in a row, but then points to tech for good at the same time. New new workers millennials expect checked for good. How do you see those fitting together? What's your philosophy? There >>were very passionate at Micron that it is important for us that we support the communities that be working where our team members 11. Yes, we want to dry for betterment of humanity through bringing the benefit off our technologies and products, you know? And of course, you see your life's changed with the benefit of more memory and storage, whether it is in your smart home or your smartphone or all of the benefits that you're getting from advances in technology today. But we also absolutely have a social responsibility going from customers to communities, really making sure that we had a good corporate citizen. So at my crone, philanthropy is important doing this year. A microloan foundation is matching our team members contributions, and through that through our team member contributions as well as our match, we have given two and 1/2 $1,000,000 to the communities during the course of the year. We have also supported several new initiatives related to stem education as well as basic human needs again in all the countries and sites where micro nasty members. And actually Micron Foundation has given $11 million during our fiscal year 2019 for supporting various causes toward basic human needs, as well as advancement of science, technology and generating and map. And, most importantly, microphone team members more white have contributed 165,000 hours in community giving, volunteering activities, and we are trying to continue to take our engagement with the community to the next level. I considered it very, very important that and our responsibility that is not only about producing best business results, but we need to help our communities and people in need get to the next level off betterment as well. And it's part of there's diversity and inclusion and equality is a very, very important initiative at the company as well. And we're making tremendous progress in that respect as well. That's >>a great story, Sanjay. Thanks. Um I know you're super busy. Get a bunch of customers to see you're an awesome CEO and doing a great job. Really appreciate you taking the time to come on the Cube. Thank you. Thank you >>for being here with you today. >>Fantastic. All right. Thank you for watching. Everybody will be right back with our next guest. Live from Micron inside. 2019. You're watching the Cube?

>> Live, from Las Vegas, it's theCUBE covering UiPath Forward Americas 2019. Brought to you by UiPath. >> Welcome back to the Bellagio in Las Vegas, everybody. You're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante. Day one of UiPath Forward III, hashtag UiPathForward. Elena Christopher is here. She's the senior vice president at HFS Research, and Elena, I'm going to recruit you to be my co-host here. >> Co-host! >> On this power panel. Jean Youngers here, CUBE alum, VP, a Six Sigma Leader at Security Benefit. Great to see you again. >> Thank you. >> Dave: And Amy Chandler, who is the Assistant Vice President and Director of Internal Controls, also from Security Benefit. >> Hello. >> Dave: Thanks for coming on theCUBE. >> Thank you. >> Alright Elena, let's start off with you. You follow this market, you have for some time, you know HFS is sort of anointed as formulating this market place, right? >> Elena: We like to think of ourselves as the voice-- >> You guys were early on. >> The voice of the automation industry. >> So, what are you seeing? I mean, process automation has been around forever, RPA is a hot recent trend, but what are you seeing the last year or two? What are the big trends and rip currents that you see in the market place? >> I mean, I think one of the big trends that's out there, I mean, RPA's come on to the scene. I like how you phrase it Dave, because you refer to it as, rightly so, automation is not new, and so we sort of say the big question out there is, "Is RPA just flavor of the month?" RPA is definitely not, and I come from a firm, we put out a blog earlier this year called "RPA is dead. Long live automation." And that's because, when we look at RPA, and when we think about what it's impact is in the market place, to us the whole point of automation in any form, regardless of whether it's RPA, whether it be good old old school BPM, whatever it may be, it's mission is to drive transformation, and so the HFS perspective, and what all of our research shows and sort of justifies that the goal is, what everyone is striving towards, is to get to that transformation. And so, the reason we put out that piece, the "RPA is dead. Long live integrated automation platforms" is to make the point that if you're not- 'cause what does RPA allow? It affords an opportunity for change to drive transformation so, if you're not actually looking at your processes within your company and taking this opportunity to say, "What can I change, what processes are just bad, "and we've been doing them, I'm not even sure why, "for so long. What can we transform, "what can we optimize, what can we invent?" If you're not taking that opportunity as an enterprise to truly embrace the change and move towards transformation, that's a missed opportunity. So I always say, RPA, you can kind of couch it as one of many technologies, but what RPA has really done for the market place today, it's given business users and business leaders the realization that they can have a role in their own transformation. And that's one of the reasons why it's actually become very important, but a single tool in it's own right will never be the holistic answer. >> So Jean, Elena's bringing up a point about transformation. We, Stew Bennett and I interviewed you last year and we've played those clips a number of times, where you sort of were explaining to us that it didn't make sense before RPA to try to drive Six Sigma into business processes; you couldn't get the return. >> Jean: Right. >> Now you can do it very cheaply. And for Six Sigma or better, is what you use for airplane engines, right? >> Right. >> So, now you're bringing up the business process. So, you're a year in, how's it going? What kind of results are you seeing? Is it meeting your expectations? >> It's been wonderful. It has been the best, it's been probably the most fun I've had in the last fifteen years of work. I have enjoyed, partly because I get to work with this great person here, and she's my COE, and helps stand up the whole RPA solution, but you know, we have gone from finance into investment operations, into operations, you know we've got one sitting right now that we're going to be looking at statements that it's going to be fourteen thousand hours out of both time out as well as staff hours saved, and it's going to touch our customer directly, that they're not going to get a bad statement anymore. And so, you know, it has just been an incredible journey for us over the past year, it really has. >> And so okay Amy, your role is, you're the hardcore practitioner here right? >> Amy: That's right. >> You run the COE. Tell us more about your role, and I'm really interested in how you're bringing it out, RPA to the organization. Is that led by your team, or is it kind of this top-down approach? >> Yeah, this last year, we spent a lot of time trying to educate the lower levels and go from a bottom-up perspective. Pretty much, we implemented our infrastructure, we had a nice solid change management process, we built in logical access, we built in good processes around that so that we'd be able to scale easily over this last year, which kind of sets us up for next year, and everything that we want to accomplish then. >> So Elena, we were talking earlier on theCUBE about you know, RPA, in many ways, I called it cleaning up the crime scene, where stuff is kind of really sort of a mass and huge opportunities to improve. So, my question to you is, it seems like RPA is, in some regards, successful because you can drop it into existing processes, you're not changing things, but in a way, this concerns that, oh well, I'm just kind of paving the cow path. So how much process reinvention should have to occur in order to take advantage of RPA? >> I love that you use that phrase, "paving the cow path." As a New Englander, as you know the roads in Boston are in fact paved cow paths, so we know that can lead to some dodgy roads, and that's part of, and I say it because that's part of what the answer is, because the reinvention, and honestly the optimization has to be part of what the answer is. I said it just a little bit earlier in my comments, you're missing an opportunity with RPA and broader automation if you don't take that step to actually look at your processes and figure out if there's just essentially deadwood that you need to get rid of, things that need to be improved. One of the sort of guidelines, because not all processes are created equal, because you don't want to spend the time and effort, and you guys should chime in on this, you don't want to spend the time and effort to optimize a process if it's not critical to your business, if you're not going to get lift from it, or from some ROI. It's a bit of a continuum, so one of the things that I always encourage enterprises to think about, is this idea of, well what's the, obviously, what business problem are you trying to solve? But as you're going through the process optimization, what kind of user experience do you want out of this? And your users, by the way, you tend to think of your user as, it could be your end customer, it could be your employee, it could even be your partner, but trying to figure out what the experience is that you actually want to have, and then you can actually then look at the process and figure out, do we need to do something different? Do we need to do something completely new to actually optimize that? And then again, line it with what you're trying to solve and what kind of lift you want to get from it. But I'd love to, I mean, hopping over to you guys, you live and breathe this, right? And so I think you have a slightly different opinion than me, but-- >> We do live and breathe it, and every process we look at, we take into consideration. But you've also got to, you have a continuum right? If it's a simple process and we can put it up very quickly, we do, but we've also got ones where one process'll come into us, and a perfect example is our rate changes. >> Amy: Rate changes. >> It came in and there was one process at the very end and they ended up, we did a wing to wing of the whole thing, followed the data all the way back through the process, and I think it hit, what, seven or eight-- >> Yeah. >> Different areas-- >> Areas. >> Of the business, and once we got done with that whole wing to wing to see what we could optimize, it turned into what, sixty? >> Amy: Yeah, sixty plus. Yeah. >> Dave: Sixty plus what? >> Bot processes from one entry. >> Yeah. >> And so, right now, we've got 189 to 200 processes in the back log. And so if you take that, and exponentially increase it, we know that there's probably actually 1,000 to 2,000 more processes, at minimum, that we can hit for the company, and we need to look at those. >> Yeah, and I will say, the wing to wing approach is very important because you're following the data as it's moving along. So if you don't do that, if you only focus on a small little piece of it, you don't what's happening to the data before it gets to you and you don't know what's going to happen to it when it leaves you, so you really do have to take that wing to wing approach. >> So, internal controls is in your title, so talking about scale, it's a big theme here at UiPath, and these days, things scale really fast, and boo-boos can happen really fast. So how are you ensuring, you know that the edicts of the organization are met, whether it's security, compliance, governance? Is that part of your role? >> Yeah, we've actually kept internal audit and internal controls, and in fact, our external auditors, EY. We've kept them all at the table when we've gone through processes, when we've built out our change management process, our logical access. When we built our whole process from beginning to end they kind of sat at the table with us and kind of went over everything to make sure that we were hitting all the controls that we needed to do. >> And actually, I'd like to piggyback on that comment, because just that inclusion of the various roles, that's what we found as an emerging best practice, and in all of our research and all of the qualitative conversations that we have with enterprises and service providers, is because if you do things, I mean it applies on multiple levels, because if you do things in a silo, you'll have siloed impact. If you bring the appropriate constituents to the table, you're going to understand their perspective, but it's going to have broader reach. So it helps alleviate the silos but it also supports the point that you just made Amy, about looking at the processes end to end, because you've got the necessary constituents involved so you know the context, and then, I believe, I mean I think you guys shared this with me, that particularly when audit's involved, you're perhaps helping cultivate an understanding of how even their processes can improve as well. >> Right. >> That is true, and from an overall standpoint with controls, I think a lot of people don't realize that a huge benefit is your controls, cause if you're automating your controls, from an internal standpoint, you're not going to have to test as much, just from an associate process owner paying attention to their process to the internal auditors, they're not going to have to test as much either, and then your external auditors, which that's revenue. I mean, that's savings. >> You lower your auditing bill? >> Yeah. Yeah. >> Well we'll see right? >> Yeah. (laughter) >> That's always the hope. >> Don't tell EY. (laughter) So I got to ask you, so you're in a little over a year So I don't know if you golf, but you know a mulligan in golf. If you had a mulligan, a do over, what would you do over? >> The first process we put in place. At least for me, it breaks a lot, and we did it because at the time, we were going through decoupling and trying to just get something up to make sure that what we stood up was going to work and everything, and so we kind of slammed it in, and we pay for that every quarter, and so actually it's on our list to redo. >> Yeah, we automated a bad process. >> Yeah, we automated a bad process. >> That's a really good point. >> So we pay for it in maintenance every quarter, we pay for it, cause it breaks inevitably. >> Yes. >> Okay so what has to happen? You have to reinvent the process, to Elena's? >> Yes, you know, we relied on a process that somebody else had put in place, and in looking at it, it was kind of a up and down and through the hoop and around this way to get what they needed, and you know there's much easier ways to get the data now. And that's what we're doing. In fact, we've built our own, we call it a bot mart. That's where all our data goes, they won't let us touch the other data marts and so forth so they created us a bot mart, and anything that we need data for, they dump in there for us and then that's where our bot can hit, and our bot can hit it at anytime of the day or night when we need the data, and so it's worked out really well for us, and so the bot mart kind of came out of that project of there's got to be a better way. How can we do this better instead of relying on these systems that change and upgrade and then we run the bot and its working one day and the next day, somebody has gone in and tweaked something, and when all's I really need out of that system is data, that's all I need. I don't need, you know, a report. I don't need anything like that, cause the reports change and they get messed up. I just want the raw data, and so that's what we're starting to do. >> How do you ensure that the data is synchronized with your other marts and warehouses, is that a problem? >> Not yet. >> No not yet! (laughter) >> I'm wondering cause I was thinking the exact same question Dave, because on one hand its a nice I think step from a governance standpoint. You have what you need, perhaps IT or whomever your data curators are, they're not going to have a heart attack that you're touching stuff that they don't want you to, but then there is that potential for synchronization issues, cause that whole concept of golden source implies one copy if you will. >> Well, and it is. It's all coming through, we have a central data repository that the data's going to come through, and it's all sitting there, and then it'll move over, and to me, what I most worry about, like I mentioned on the statement once, okay, I get my data in, is it the same data that got used to create those statements? And as we're doing the testing and as we're looking at going live, that's one of our huge test cases. We need to understand what time that data comes in, when will it be into our bot mart, so when can I run those bots? You know, cause they're all going to be unattended on those, so you know, the timing is critical, and so that's why I said not yet. >> Dave: (chuckle) >> But you want to know what, we can build the bot to do that compare of the data for us. >> Haha all right. I love that. >> I saw a stat the other day. I don't know where it was, on Twitter or maybe it was your data, that more money by whatever, 2023 is going to be spent on chat bots than mobile development. >> Jean: I can imagine, yes. >> What are you doing with chat bots? And how are you using them? >> Do you want to answer that one or do you want me to? >> Go ahead. >> Okay so, part of the reason I'm so enthralled by the chat bot or personal assistant or anything, is because the unattended robots that we have, we have problems making sure that people are doing what they're supposed to be doing in prep. We have some in finance, and you know, finance you have a very fine line of what you can automate and what you need the user to still understand what they're doing, right? And so we felt like we had a really good, you know, combination of that, but in some instances, they forget to do things, so things aren't there and we get the phone call the bot broke, right? So part of the thing I'd like to do is I'd like to move that back to an unattended bot, and I'm going to put a chat bot in front of it, and then all's they have to do is type in "run my bot" and it'll come up if they have more than one bot, it'll say "which one do you want to run?" They'll click it and it'll go. Instead of having to go out on their machine, figure out where to go, figure out which button to do, and in the chat I can also send them a little message, "Did you run your other reports? Did you do this?" You know, so, I can use it for the end user, to make that experience for them better. And plus, we've got a lot of IT, we've got a lot of HR stuff that can fold into that, and then RPA all in behind it, kind of the engine on a lot of it. >> I mean you've child proofed the bot. >> Exactly! There you go. There you go. >> Exactly. Exactly. And it also provides a means to be able to answer those commonly asked questions for HR for example. You know, how much vacation time do I have? When can I change my benefits? Examples of those that they answer frequently every day. So that provides another avenue for utilization of the chat bot. >> And if I may, Dave, it supports a concept that I know we were talking about yesterday. At HFS it's our "Triple-A Trifecta", but it's taking the baseline of automation, it intersects with components of AI, and then potentially with analytics. This is starting to touch on some of the opportunities to look at other technologies. You say chat bots. At HFS we don't use the term chat bot, just because we like to focus and emphasize the cognitive capability if you will. But in any case, you guys essentially are saying, well RPA is doing great for what we're using RPA for, but we need a little bit of extension of functionality, so we're layering in the chat bot or cognitive assistant. So it's a nice example of some of that extension of really seeing how it's, I always call it the power of and if you will. Are you going to layer these things in to get what you need out of it? What best solves your business problems? Just a very practical approach I think. >> So Elena, Guy has a session tomorrow on predictions. So we're going to end with some predictions. So our RPA is dead, (chuckle) will it be resuscitated? What's the future of RPA look like? Will it live up to the hype? I mean so many initiatives in our industry haven't. I always criticize enterprise data warehousing and ETL and big data is not living up to the hype. Will RPA? >> It's got a hell of a lot of hype to live up to, I'll tell you that. So, back to some of our causality about why we even said it's dead. As a discrete software category, RPA is clearly not dead at all. But unless it's helping to drive forward with transformation, and even some of the strategies that these fine ladies from Security Benefit are utilizing, which is layering in additional technology. That's part of the path there. But honestly, the biggest challenge that you have to go through to get there and cannot be underestimated, is the change that your organization has to go through. Cause think about it, if we look at the grand big vision of where RPA and broader intelligent automation takes us, the concept of creating a hybrid workforce, right? So what's a hybrid workforce? It's literally our humans complemented by digital workers. So it still sounds like science fiction. To think that any enterprise could try and achieve some version of that and that it would be A, fast or B, not take a lot of change management, is absolutely ludicrous. So it's just a very practical approach to be eyes wide open, recognize that you're solving problems but you have to want to drive change. So to me, and sort of the HFS perspective, continues to be that if RPA is not going to die a terrible death, it needs to really support that vision of transformation. And I mean honestly, we're here at a UiPath event, they had many announcements today that they're doing a couple of things. Supporting core functionality of RPA, literally adding in process discovery and mining capabilities, adding in analytics to help enterprises actually track what your benefit is. >> Jean: Yes. >> These are very practical cases that help RPA live another day. But they're also extending functionality, adding in their whole announcement around AI fabric, adding in some of the cognitive capability to extend the functionality. And so prediction-wise, RPA as we know it three years from now is not going to look like RPA at all. I'm not going to call it AI, but it's going to become a hybrid, and it's honestly going to look a lot like that Triple-A Trifecta I mentioned. >> Well, and UiPath, and I presume other suppliers as well, are expanding their markets. They're reaching, you hear about citizens developers and 100% of the workforce. Obviously you guys are excited and you see a long-run way for RPA. >> Jean: Yeah, we do. >> I'll give you the last word. >> It's been a wonderful journey thus far. After this morning's event where they showed us everything, I saw a sneak peek yesterday during the CAB, and I had a list of things I wanted to talk to her about already when I came out of there. And then she saw more of 'em today, and I've got a pocketful of notes of stuff that we're going to take back and do. I really, truly believe this is the future and we can do so much. Six Sigma has kind of gotten a rebirth. You go in and look at your processes and we can get those to perfect. I mean, that's what's so cool. It is so cool that you can actually tell somebody, I can do something perfect for you. And how many people get to do that? >> It's back to the user experience, right? We can make this wildly functional to meet the need. >> Right, right. And I don't think RPA is the end all solution, I think it's just a great tool to add to your toolkit and utilize moving forward. >> Right. All right we'll have to leave it there. Thanks ladies for coming on, it was a great segment. Really appreciate your time. >> Thanks. >> Thank you. >> Thank you for watching, everybody. This is Dave Vellante with theCUBE. We'll be right back from UiPath Forward III from Las Vegas, right after this short break. (technical music)

>> Live, from Las Vegas, it's theCUBE covering UiPath Forward Americas 2019. Brought to you by UiPath. >> Welcome back to the Bellagio in Las Vegas, everybody. You're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante. Day one of UiPath Forward III, hashtag UiPathForward. Elena Christopher is here. She's the senior vice president at HFS Research, and Elena, I'm going to recruit you to be my co-host here. >> Co-host! >> On this power panel. Jean Youngers here, CUBE alum, VP, a Six Sigma Leader at Security Benefit. Great to see you again. >> Thank you. >> Dave: And Amy Chandler, who is the Assistant Vice President and Director of Internal Controls, also from Security Benefit. >> Hello. >> Dave: Thanks for coming on theCUBE. >> Thank you. >> Alright Elena, let's start off with you. You follow this market, you have for some time, you know HFS is sort of anointed as formulating this market place, right? >> Elena: We like to think of ourselves as the voice-- >> You guys were early on. >> The voice of the automation industry. >> So, what are you seeing? I mean, process automation has been around forever, RPA is a hot recent trend, but what are you seeing the last year or two? What are the big trends and rip currents that you see in the market place? >> I mean, I think one of the big trends that's out there, I mean, RPA's come on to the scene. I like how you phrase it Dave, because you refer to it as, rightly so, automation is not new, and so we sort of say the big question out there is, "Is RPA just flavor of the month?" RPA is definitely not, and I come from a firm, we put out a blog earlier this year called "RPA is dead. Long live automation." And that's because, when we look at RPA, and when we think about what it's impact is in the market place, to us the whole point of automation in any form, regardless of whether it's RPA, whether it be good old old school BPM, whatever it may be, it's mission is to drive transformation, and so the HFS perspective, and what all of our research shows and sort of justifies that the goal is, what everyone is striving towards, is to get to that transformation. And so, the reason we put out that piece, the "RPA is dead. Long live integrated automation platforms" is to make the point that if you're not- 'cause what does RPA allow? It affords an opportunity for change to drive transformation so, if you're not actually looking at your processes within your company and taking this opportunity to say, "What can I change, what processes are just bad, "and we've been doing them, I'm not even sure why, "for so long. What can we transform, "what can we optimize, what can we invent?" If you're not taking that opportunity as an enterprise to truly embrace the change and move towards transformation, that's a missed opportunity. So I always say, RPA, you can kind of couch it as one of many technologies, but what RPA has really done for the market place today, it's given business users and business leaders the realization that they can have a role in their own transformation. And that's one of the reasons why it's actually become very important, but a single tool in it's own right will never be the holistic answer. >> So Jean, Elena's bringing up a point about transformation. We, Stew Bennett and I interviewed you last year and we've played those clips a number of times, where you sort of were explaining to us that it didn't make sense before RPA to try to drive Six Sigma into business processes; you couldn't get the return. >> Jean: Right. >> Now you can do it very cheaply. And for Six Sigma or better, is what you use for airplane engines, right? >> Right. >> So, now you're bringing up the business process. So, you're a year in, how's it going? What kind of results are you seeing? Is it meeting your expectations? >> It's been wonderful. It has been the best, it's been probably the most fun I've had in the last fifteen years of work. I have enjoyed, partly because I get to work with this great person here, and she's my COE, and helps stand up the whole RPA solution, but you know, we have gone from finance into investment operations, into operations, you know we've got one sitting right now that we're going to be looking at statements that it's going to be fourteen thousand hours out of both time out as well as staff hours saved, and it's going to touch our customer directly, that they're not going to get a bad statement anymore. And so, you know, it has just been an incredible journey for us over the past year, it really has. >> And so okay Amy, your role is, you're the hardcore practitioner here right? >> Amy: That's right. >> You run the COE. Tell us more about your role, and I'm really interested in how you're bringing it out, RPA to the organization. Is that led by your team, or is it kind of this top-down approach? >> Yeah, this last year, we spent a lot of time trying to educate the lower levels and go from a bottom-up perspective. Pretty much, we implemented our infrastructure, we had a nice solid change management process, we built in logical access, we built in good processes around that so that we'd be able to scale easily over this last year, which kind of sets us up for next year, and everything that we want to accomplish then. >> So Elena, we were talking earlier on theCUBE about you know, RPA, in many ways, I called it cleaning up the crime scene, where stuff is kind of really sort of a mass and huge opportunities to improve. So, my question to you is, it seems like RPA is, in some regards, successful because you can drop it into existing processes, you're not changing things, but in a way, this concerns that, oh well, I'm just kind of paving the cow path. So how much process reinvention should have to occur in order to take advantage of RPA? >> I love that you use that phrase, "paving the cow path." As a New Englander, as you know the roads in Boston are in fact paved cow paths, so we know that can lead to some dodgy roads, and that's part of, and I say it because that's part of what the answer is, because the reinvention, and honestly the optimization has to be part of what the answer is. I said it just a little bit earlier in my comments, you're missing an opportunity with RPA and broader automation if you don't take that step to actually look at your processes and figure out if there's just essentially deadwood that you need to get rid of, things that need to be improved. One of the sort of guidelines, because not all processes are created equal, because you don't want to spend the time and effort, and you guys should chime in on this, you don't want to spend the time and effort to optimize a process if it's not critical to your business, if you're not going to get lift from it, or from some ROI. It's a bit of a continuum, so one of the things that I always encourage enterprises to think about, is this idea of, well what's the, obviously, what business problem are you trying to solve? But as you're going through the process optimization, what kind of user experience do you want out of this? And your users, by the way, you tend to think of your user as, it could be your end customer, it could be your employee, it could even be your partner, but trying to figure out what the experience is that you actually want to have, and then you can actually then look at the process and figure out, do we need to do something different? Do we need to do something completely new to actually optimize that? And then again, line it with what you're trying to solve and what kind of lift you want to get from it. But I'd love to, I mean, hopping over to you guys, you live and breathe this, right? And so I think you have a slightly different opinion than me, but-- >> We do live and breathe it, and every process we look at, we take into consideration. But you've also got to, you have a continuum right? If it's a simple process and we can put it up very quickly, we do, but we've also got ones where one process'll come into us, and a perfect example is our rate changes. >> Amy: Rate changes. >> It came in and there was one process at the very end and they ended up, we did a wing to wing of the whole thing, followed the data all the way back through the process, and I think it hit, what, seven or eight-- >> Yeah. >> Different areas-- >> Areas. >> Of the business, and once we got done with that whole wing to wing to see what we could optimize, it turned into what, sixty? >> Amy: Yeah, sixty plus. Yeah. >> Dave: Sixty plus what? >> Bot processes from one entry. >> Yeah. >> And so, right now, we've got 189 to 200 processes in the back log. And so if you take that, and exponentially increase it, we know that there's probably actually 1,000 to 2,000 more processes, at minimum, that we can hit for the company, and we need to look at those. >> Yeah, and I will say, the wing to wing approach is very important because you're following the data as it's moving along. So if you don't do that, if you only focus on a small little piece of it, you don't what's happening to the data before it gets to you and you don't know what's going to happen to it when it leaves you, so you really do have to take that wing to wing approach. >> So, internal controls is in your title, so talking about scale, it's a big theme here at UiPath, and these days, things scale really fast, and boo-boos can happen really fast. So how are you ensuring, you know that the edicts of the organization are met, whether it's security, compliance, governance? Is that part of your role? >> Yeah, we've actually kept internal audit and internal controls, and in fact, our external auditors, EY. We've kept them all at the table when we've gone through processes, when we've built out our change management process, our logical access. When we built our whole process from beginning to end they kind of sat at the table with us and kind of went over everything to make sure that we were hitting all the controls that we needed to do. >> And actually, I'd like to piggyback on that comment, because just that inclusion of the various roles, that's what we found as an emerging best practice, and in all of our research and all of the qualitative conversations that we have with enterprises and service providers, is because if you do things, I mean it applies on multiple levels, because if you do things in a silo, you'll have siloed impact. If you bring the appropriate constituents to the table, you're going to understand their perspective, but it's going to have broader reach. So it helps alleviate the silos but it also supports the point that you just made Amy, about looking at the processes end to end, because you've got the necessary constituents involved so you know the context, and then, I believe, I mean I think you guys shared this with me, that particularly when audit's involved, you're perhaps helping cultivate an understanding of how even their processes can improve as well. >> Right. >> That is true, and from an overall standpoint with controls, I think a lot of people don't realize that a huge benefit is your controls, cause if you're automating your controls, from an internal standpoint, you're not going to have to test as much, just from an associate process owner paying attention to their process to the internal auditors, they're not going to have to test as much either, and then your external auditors, which that's revenue. I mean, that's savings. >> You lower your auditing bill? >> Yeah. Yeah. >> Well we'll see right? >> Yeah. (laughter) >> That's always the hope. >> Don't tell EY. (laughter) So I got to ask you, so you're in a little over a year So I don't know if you golf, but you know a mulligan in golf. If you had a mulligan, a do over, what would you do over? >> The first process we put in place. At least for me, it breaks a lot, and we did it because at the time, we were going through decoupling and trying to just get something up to make sure that what we stood up was going to work and everything, and so we kind of slammed it in, and we pay for that every quarter, and so actually it's on our list to redo. >> Yeah, we automated a bad process. >> Yeah, we automated a bad process. >> That's a really good point. >> So we pay for it in maintenance every quarter, we pay for it, cause it breaks inevitably. >> Yes. >> Okay so what has to happen? You have to reinvent the process, to Elena's? >> Yes, you know, we relied on a process that somebody else had put in place, and in looking at it, it was kind of a up and down and through the hoop and around this way to get what they needed, and you know there's much easier ways to get the data now. And that's what we're doing. In fact, we've built our own, we call it a bot mart. That's where all our data goes, they won't let us touch the other data marts and so forth so they created us a bot mart, and anything that we need data for, they dump in there for us and then that's where our bot can hit, and our bot can hit it at anytime of the day or night when we need the data, and so it's worked out really well for us, and so the bot mart kind of came out of that project of there's got to be a better way. How can we do this better instead of relying on these systems that change and upgrade and then we run the bot and its working one day and the next day, somebody has gone in and tweaked something, and when all's I really need out of that system is data, that's all I need. I don't need, you know, a report. I don't need anything like that, cause the reports change and they get messed up. I just want the raw data, and so that's what we're starting to do. >> How do you ensure that the data is synchronized with your other marts and warehouses, is that a problem? >> Not yet. >> No not yet! (laughter) >> I'm wondering cause I was thinking the exact same question Dave, because on one hand its a nice I think step from a governance standpoint. You have what you need, perhaps IT or whomever your data curators are, they're not going to have a heart attack that you're touching stuff that they don't want you to, but then there is that potential for synchronization issues, cause that whole concept of golden source implies one copy if you will. >> Well, and it is. It's all coming through, we have a central data repository that the data's going to come through, and it's all sitting there, and then it'll move over, and to me, what I most worry about, like I mentioned on the statement once, okay, I get my data in, is it the same data that got used to create those statements? And as we're doing the testing and as we're looking at going live, that's one of our huge test cases. We need to understand what time that data comes in, when will it be into our bot mart, so when can I run those bots? You know, cause they're all going to be unattended on those, so you know, the timing is critical, and so that's why I said not yet. >> Dave: (chuckle) >> But you want to know what, we can build the bot to do that compare of the data for us. >> Haha all right. I love that. >> I saw a stat the other day. I don't know where it was, on Twitter or maybe it was your data, that more money by whatever, 2023 is going to be spent on chat bots than mobile development. >> Jean: I can imagine, yes. >> What are you doing with chat bots? And how are you using them? >> Do you want to answer that one or do you want me to? >> Go ahead. >> Okay so, part of the reason I'm so enthralled by the chat bot or personal assistant or anything, is because the unattended robots that we have, we have problems making sure that people are doing what they're supposed to be doing in prep. We have some in finance, and you know, finance you have a very fine line of what you can automate and what you need the user to still understand what they're doing, right? And so we felt like we had a really good, you know, combination of that, but in some instances, they forget to do things, so things aren't there and we get the phone call the bot broke, right? So part of the thing I'd like to do is I'd like to move that back to an unattended bot, and I'm going to put a chat bot in front of it, and then all's they have to do is type in "run my bot" and it'll come up if they have more than one bot, it'll say "which one do you want to run?" They'll click it and it'll go. Instead of having to go out on their machine, figure out where to go, figure out which button to do, and in the chat I can also send them a little message, "Did you run your other reports? Did you do this?" You know, so, I can use it for the end user, to make that experience for them better. And plus, we've got a lot of IT, we've got a lot of HR stuff that can fold into that, and then RPA all in behind it, kind of the engine on a lot of it. >> I mean you've child proofed the bot. >> Exactly! There you go. There you go. >> Exactly. Exactly. And it also provides a means to be able to answer those commonly asked questions for HR for example. You know, how much vacation time do I have? When can I change my benefits? Examples of those that they answer frequently every day. So that provides another avenue for utilization of the chat bot. >> And if I may, Dave, it supports a concept that I know we were talking about yesterday. At HFS it's our "Triple-A Trifecta", but it's taking the baseline of automation, it intersects with components of AI, and then potentially with analytics. This is starting to touch on some of the opportunities to look at other technologies. You say chat bots. At HFS we don't use the term chat bot, just because we like to focus and emphasize the cognitive capability if you will. But in any case, you guys essentially are saying, well RPA is doing great for what we're using RPA for, but we need a little bit of extension of functionality, so we're layering in the chat bot or cognitive assistant. So it's a nice example of some of that extension of really seeing how it's, I always call it the power of and if you will. Are you going to layer these things in to get what you need out of it? What best solves your business problems? Just a very practical approach I think. >> So Elena, Guy has a session tomorrow on predictions. So we're going to end with some predictions. So our RPA is dead, (chuckle) will it be resuscitated? What's the future of RPA look like? Will it live up to the hype? I mean so many initiatives in our industry haven't. I always criticize enterprise data warehousing and ETL and big data is not living up to the hype. Will RPA? >> It's got a hell of a lot of hype to live up to, I'll tell you that. So, back to some of our causality about why we even said it's dead. As a discrete software category, RPA is clearly not dead at all. But unless it's helping to drive forward with transformation, and even some of the strategies that these fine ladies from Security Benefit are utilizing, which is layering in additional technology. That's part of the path there. But honestly, the biggest challenge that you have to go through to get there and cannot be underestimated, is the change that your organization has to go through. Cause think about it, if we look at the grand big vision of where RPA and broader intelligent automation takes us, the concept of creating a hybrid workforce, right? So what's a hybrid workforce? It's literally our humans complemented by digital workers. So it still sounds like science fiction. To think that any enterprise could try and achieve some version of that and that it would be A, fast or B, not take a lot of change management, is absolutely ludicrous. So it's just a very practical approach to be eyes wide open, recognize that you're solving problems but you have to want to drive change. So to me, and sort of the HFS perspective, continues to be that if RPA is not going to die a terrible death, it needs to really support that vision of transformation. And I mean honestly, we're here at a UiPath event, they had many announcements today that they're doing a couple of things. Supporting core functionality of RPA, literally adding in process discovery and mining capabilities, adding in analytics to help enterprises actually track what your benefit is. >> Jean: Yes. >> These are very practical cases that help RPA live another day. But they're also extending functionality, adding in their whole announcement around AI fabric, adding in some of the cognitive capability to extend the functionality. And so prediction-wise, RPA as we know it three years from now is not going to look like RPA at all. I'm not going to call it AI, but it's going to become a hybrid, and it's honestly going to look a lot like that Triple-A Trifecta I mentioned. >> Well, and UiPath, and I presume other suppliers as well, are expanding their markets. They're reaching, you hear about citizens developers and 100% of the workforce. Obviously you guys are excited and you see a long-run way for RPA. >> Jean: Yeah, we do. >> I'll give you the last word. >> It's been a wonderful journey thus far. After this morning's event where they showed us everything, I saw a sneak peek yesterday during the CAB, and I had a list of things I wanted to talk to her about already when I came out of there. And then she saw more of 'em today, and I've got a pocketful of notes of stuff that we're going to take back and do. I really, truly believe this is the future and we can do so much. Six Sigma has kind of gotten a rebirth. You go in and look at your processes and we can get those to perfect. I mean, that's what's so cool. It is so cool that you can actually tell somebody, I can do something perfect for you. And how many people get to do that? >> It's back to the user experience, right? We can make this wildly functional to meet the need. >> Right, right. And I don't think RPA is the end all solution, I think it's just a great tool to add to your toolkit and utilize moving forward. >> Right. All right we'll have to leave it there. Thanks ladies for coming on, it was a great segment. Really appreciate your time. >> Thanks. >> Thank you. >> Thank you for watching, everybody. This is Dave Vellante with theCUBE. We'll be right back from UiPath Forward III from Las Vegas, right after this short break. (technical music)

>> Live from San Diego, California It's the queue covering Sisqo live US 2019 Tio by Cisco and its ecosystem. Barker's >> Welcome Back. We're here at the San Diego Convention Center for Sisqo Live 2019 30th year The show. 28,000 in attendance. I'm stupid, and we're actually at the midpoint of three days of life water wall coverage here and happy to bring back to the program to Cube alumni first. To my right is Ben I, who is the CEO of Turban on Mick. And sitting next to him is Thomas wide, who's the chief marketing and strategy officer of AP Dynamics or APD? Ia's everybody calls them here at the show. Gentlemen, thanks so much for joining us. Thank you. All right, So, Thomas, first of all, we had you on it, reinvent like soon after the acquisition of AP ti Bisys. Go. It's been about two years, and I believe it's been about two years that turban Onyx been partnering with Cisco. So let's start with you. And you know what? What changed in those two years? >> Yeah, it's been amazing. Two years ago, we were on the doorstep of an I P O and it's been a rocketship ride ever since. You know AP Dynamics. After the last two years, the businesses more than doubled team sizes more than doubled, and today we're really happy to be the largest and fastest growing provider of application for miss monitoring in the market. But the reason why, that is, is because our customers are embarking on the sigil transformation, and the application has really become the foundation of their modern day business. That's the way brands are engaging with their users. And but now more than ever, and then the application landscape has gotten way more complex, with micro services and multiple clouds and all of the threats that go on in the infrastructure. And so what Hap Dynamics has been doing is just really providing that really time business and application performance that our customers need to ensure business outcomes. We think of ourselves as Thie Marie for the application or the infrastructure. >> That's awesome. So then, you know it's been interesting to watch in the networking space the last few years. For the most part, applications used to be That's just this thing that ran through the pipes every once in a while, I need to, you know, think about performance. I need to make sure I got buffer credits or, you know, it's now going East West rather the north south and the like. But it was solutions like turban on IQ that sat on top of it and helped understand and help people manage their application. Of course. AP ti pulling that story together even tighter. So, you know, give us the latest we've talked to you. It's just go live before an important partnership. What was the latest in your world, >> boy? The well, so one of the things we're doing is we're building an actual bundle together without D. And if you think about a PM, you're getting the application topology as well as response time and use a response time, which is critical to maintaining the brand and the digital economy that we're talking about. What when you look at every one of those hops and the application of there's a entire application stack that sits underneath a resource ing stack and what we're doing is we're bringing in a R M, which is application re sourcing management with a I so that they're automatically adjusting the resource is in all times continuously in order to support the performance needs that Abdi is showing us when you put together a PM plus a r m. You have total application performance and that customers air really, uh, queuing to so much so that we've actually decided to put this bundle officially together in the marketplace. We just became the first ap TI re sell software product, and now we're taking not to market as C one plus happy. >> Well, congratulations on that is harder ship, Thomas. Bring us inside the customers a little bit. What does this mean for them? You know what that journey we talk about, you know, for, you know, last 10 15 years, you gotta break down those silos. It's not just the networking team, you know, tossing over some band within Leighton, see and write them coming back. And I need some more. No, no, we're not going. You know, we're not going to give you any service level agreement or anything like that, because that's not our job. To what? We'll just set this up and you use what you got. So what would happen in >> trend that we're seeing is a move toward this concept of a iob, which is the really the consolidation of bringing and user application network and infrastructure monitoring closer together and tying that together with a base insights to Dr Automation and Action and very similar to what turbo gnomic specializes in here. And so what we're seeing is, you know, the combination of Cisco plus APP Dynamics. Plus, companies like Turbo is beginning to build that self healing, self learning environment where developers and environments need to be able to drive automation on that. Automation ultimately gets tomb or innovation when you can reduce the mundane tasks, really take a lot of our developers time. And so we're really excited about some of the work we're doing together when you think about the ability to take really time business insights from the application and reprogrammed the network based on the needs of the AP or change out the workloads and move them around on different servers, depending on the needs of the AP, these are all things that combination of Turbo, Cisco and epidemics are doing together. >> Yeah, actually, I did a whole show down in D. C a couple months ago, Cisco Partner. We're focused on a I ops. And, you know, we understand customers had a lot of tools that they have to deal with. We need to simplify this environment, allow them tow, you know, focus on their business, not managing this complex environment of all these tools. How does that whole concept of II ops and, you know, automating this environment managing my workloads? You know what? What do you sing with your customers? >> I think all the customers are saying, Look, there's too many tools today. They don't need another resource monitor, et cetera. What they need is they need to understand, through the lens of the application, all the resource dependencies. So instead of looking at a field of servers and saying, I have five nines availability or storage or whatever, what they really want to see is whatever the servers and storage and resource is dependent on this specific up that runs the bank or the CPD company of the manufacturer. And can I make sure that those re sources are supporting performance of the application? And that's is this total application performance concept, much more so than than whether I have five nines availability and all my other host accents? >> Yeah, absolutely. Did you have a comment on other Guy's >> gonna say We're seeing so many different customers in different verticals, Whether it's retail, hospitality, automakers, they're all benefitting from the cloud migration. And now that they have the cloud migration, the ability to have that elasticity of their workloads, they're scaling in and out based on the application demands. This is becoming critical. This is no longer a luxury for the most cloud eight of companies in the world. Enterprises with mission critical systems are all becoming dependent on these more modern technologies. And I think they need partners like ours more than ever. >> Yeah, One of the questions we've had is you talk to customers today and they are multi cloud. But that multiyear hybrid cloud is a bunch of pieces and one of our premises. We ask, from a research standpoint, how can this some of those pieces be more valuable than just the independent pieces alone, you know, kind of one plus one with, you know, an extra factor talk a little bit about the customers. And also, you know, what does this combination do that I couldn't just, you know, grab these pieces together and kind of make it work in my portfolio of those, you know, dozens of tools that I have. >> What glad. But I think the customers one of things this needed. We literally announced his partnership publicly two weeks ago and already have closed the 1st 2 just out of momentum that that folks are realizing the need to be able to say, Look, I can host my applications on Prem with a number of different vendors, I can host my applications off Prem with a number of different vendors. But the real question is, where am I going to get the most performance? Where can I do it in a compliant way with all my policies and how can I make sure that I'm doing it cost effectively? And when there's a multiplicity of tradeoffs where I can choose, then it's incumbent upon each of those vendors, strategic as they are to be able to offer the best service, the best performance, the best compliance and resource ing, and that's what we're bringing to him. And I think that's why you're seeing that a pipeline is built to several double digit millions and already deals are closing everything I'd >> add to that Is that, you know, going back to the point around a ops in the evolution of a lot of these modern ing and automation technologies. >> A lot of our >> customers have a hybrid environment of different tools and providers that they leverage. And so one of the things that were really focused on is an open ecosystem where you'd be able to ingest data sources from various different players. Some of them can be Cisco, Turman, Onyx and Abdi. But some of them can be other providers that are also have very good products in very specific domains. I think the key is that being ableto be ableto bring that data together, Dr Cross domain correlation in a more automated way than ever before, leveraging some of the more modern AI ai capabilities, which drives the action ing that people really need. And that is really the automation step is where customers start to see the benefits. But I think the better and more valuable the data that you have, the better automation you could do because your predictability of your algorithms are much better at that >> point. All right, been your customers that have rolled out that this solution I know the joint solutions brand new. But what? What is then the key metrics? Howto they define success how today they know you know that they they've reached that success. >> So first and foremost, the line of business. Who's the customer to central it? Whether it's hosted or not, they care the most. That performance does not degrade and is always improving. Okay, But when they do that and they can show that, then a ll the decision that the rest of central takes down in fromthe container layer to the pods that a virtual to the cloud I asked on Prem in off those become acceptable choices for central i t. To make because fundamentally, Lina businesses saying, Yep, we're good, right? So that's where we're seeing the value of being able to see the response time and bridging the application performance to the application resource ing that frankly hasn't ever been solved in five decades of it. And I think it goes back to a Thomas was just saying It's the quality of the analytics that comes from a iob. I don't think people need more tools to capture more data. There's a lot of data out there. The question is, can you make it actionable? And are your analytics correct? And, frankly, are they the best? And I think we see that that's been a big parcel of what we've done during the two years Cisco's told us on multiple occasions it's the fastest software O AM they've had by bringing it through, starting with the data center team and growing up through traditional Cisco and then with their purchase of Abdi two years ago. That combination makes a ton of sense, and now you've got the top all the way to the bottom. And that's a pretty special spot, I think un replicated by any other strategic today. Yeah, the other thing, >> I just added, That is the importance of being able to monitor the business in real time as well. And so a lot of what we've talked about are the technology analytics, the operational analytics that we run our business on, but being able to correlate the business transactions running through the application, so users what their journey looks like, they're, you know, abandonment, rates, revenues, you know, the ability to engage with the users, tying that back to the specific infrastructure in a way that's used to be a bit of black box before. Now that all comes a life by the combination of these technologies. >> So Thomas big trends we see at this show. So a Cisco's transformation towards a software company and the world of multi cloud abdi plays a pretty important piece of that. You know, discussion. Talk a little bit about kind of where you are and you know where do you see Cisco moving along that journey and then, you know, help tie in where turban Ah, Mick Fitz. >> Yeah. So I think it really goes back to the fact that as our customers are making this digital transformation, they're really looking at a variety of infrastructures. You know, cloud providers to be able to offer these applications. And what Appdynamics has done is really created this monitoring fabric that sits across any infrastructure and it tightly ties to the business value of the application. So if you combine that with a lot of what Cisco's doing around connectivity securing the clouds, securing the infrastructure around it and tying that Teo where we're strong and networking and bringing all that together, I think fundamentally, we've got a lot of the pieces of the puzzle to truly enable a i ops, but we don't have them all. And I think that's what's important, that we partner with people like Ben because it brings together a set of automation capability around application resource ing that we don't have and our customers are better suited working with with Ben and team on that. So how do we integrate those things in a frictionless way and make that part of our sales process? That's really what this partnerships all about. >> All right, then where do we see the partnership going down the road? >> I think it's going to get more exciting. So right now we're pulling unit Election Lee from Abdi. I think we're going to go right back the other way. That Thomas referred to, which is one of my favorite parts of Abdi. Is the business like you? Yeah, it's where you say, What is the cost of the late and see in anyone? Hop and where do the Bandon rates? Abandonment rates happen from consumers on that application right now, we can price for the first time what's the cost of the late sea in that one tear and across the across the application overall. And then, more importantly, what do we do about it? Well, that's the resource ing and the digestion is being resolved in real time. And so I think, the ability look att, the resiliency of applications both across and up and down the a p m plus the a r m and being able to guarantee or assure performance, total application performance. That's a big message. >> All right, what would I give you both? Just fun. A word here, you know, about halfway through the conference here in San Diego. Thomas, >> I would just say that the energy that we're seeing, the feedback we're getting from customers in the business insights part of the world of solutions been phenomenal. I think there's so many more developer oriented, application developer oriented individuals that's just go live than ever before. And I think that serves both of our business is quite well. >> Look, I think this has been a great show, but one of the things you're going to see is all of these vendors who have had global presence for in this case, 30 years. Sisqo live 30 years long But now being able to think through how do I become that much more application relevant? You know, if you think about transformation of application is going to come top down, not bottom up. And so, while we have all the evolution and, frankly disruption happening, digital disruption happening across it, the way to know which of the ones that are going to stick, they're going to come top down. And I think the moves that they're making all the way through buying happy all the way through partnering with C warmer turban Ah, Mick has been emblematic of what that opportunity is in the marketplace on the realization that customers care about their applications, their applications run their business. And you've got to look at the topology and you gotta look and response time and you gotta look at the resource ing. But that's a really fun spot for us to be in together. >> Bennett Thomas Congratulations on the expanded partnership and thanks again for joining us on the Cube. Thanks to you. All right, we're here in the Definite zone. Three days, Walter Wall coverage. Arms to Minuteman, David Long days in the house. Lisa Martin's here to we'll be back with lots more coverage. Thanks for watching the Cube

>> Announcer: Live from Orlando, Florida it's theCUBE covering Pentaho World 2017. Brought to you by Hitachi Vantara. (upbeat techno music) (coughs) >> Welcome to Day Two of theCUBE's live coverage of Pentaho World, brought to you by Hitachi Vantara. I'm your host Rebecca Knight along with my co-host Dave Vellante. We're joined by Robert Walsh. He is the Technical Director Enterprise Business Intelligence at ZeniMax. Thanks so much for coming on the show. >> Thank you, good morning. >> Good to see ya. >> I should say congratulations is in order (laughs) because you're company, ZeniMax, has been awarded the Pentaho Excellence Award for the Big Data category. I want to talk about the award, but first tell us a little bit about ZeniMax. >> Sure, so the company itself, so most people know us by the games versus the company corporate name. We make a lot of games. We're the third biggest company for gaming in America. And we make a lot of games such as Quake, Fallout, Skyrim, Doom. We have game launching this week called Wolfenstein. And so, most people know us by the games versus the corporate entity which is ZeniMax Media. >> Okay, okay. And as you said, you're the third largest gaming company in the country. So, tell us what you do there. >> So, myself and my team, we are primarily responsible for the ingestion and the evaluation of all the data from the organization. That includes really two main buckets. So, very simplistically we have the business world. So, the traditional money, users, then the graphics, people, sales. And on the other side we have the game. That's where a lot of people see the fun in what we do, such as what people are doing in the game, where in the game they're doing it, and why they're doing it. So, get a lot of data on gameplay behavior based on our playerbase. And we try and fuse those two together for the single viewer or customer. >> And that data comes from is it the console? Does it come from the ... What's the data flow? >> Yeah, so we actually support many different platforms. So, we have games on the console. So, Microsoft, Sony, PlayStation, Xbox, as well as the PC platform. Mac's for example, Android, and iOS. We support all platforms. So, the big challenge that we have is trying to unify that ingestion of data across all these different platforms in a unified way to facilitate downstream the reporting that we do as a company. >> Okay, so who ... When it says you're playing the game on a Microsoft console, whose data is that? Is it the user's data? Is it Microsoft's data? Is it ZeniMax's data? >> I see. So, many games that we actually release have a service act component. Most of our games are actually an online world. So, if you disconnect today people are still playing in that world. It never ends. So, in that situation, we have all the servers that people connect to from their desktop, from their console. Not all but most data we generate for the game comes from the servers that people connect to. We own those. >> Dave: Oh, okay. >> Which simplifies greatly getting that data from the people. >> Dave: So, it's your data? >> Exactly. >> What is the data telling you these days? >> Oh, wow, depends on the game. I think people realize what people do in games, what games have become. So, we have one game right now called Elder Scrolls Online, and this year we released the ability to buy in-game homes. And you can buy furniture for your in-game homes. So, you can furnish them. People can come and visit. And you can buy items, and weapons, and pets, and skins. And what's really interesting is part of the reason why we exist is to look at patterns and trends based on people interact with that environment. So for example, we'll see America playerbase buy very different items compared to say the European playerbase, based on social differences. And so, that helps immensely for the people who continuously develop the game to add items and features that people want to see and want to leverage. >> That is fascinating that Americans and Europeans are buying different furniture for their online homes. So, just give us some examples of the difference that you're seeing between these two groups. >> So, it's not just the homes, it applies to everything that they purchase as well. It's quite interesting. So, when it comes to the Americans versus Europeans for example what we find is that Europeans prefer much more cosmetic, passive experiences. Whereas the Americans are much things that stand out, things that are ... I'm trying to avoid stereotypes right now. >> Right exactly. >> It is what it is. >> Americans like ostentatious stuff. >> Robert: Exactly. >> We get it. >> Europeans are a bit more passive in that regard. And so, we do see that. >> Rebecca: Understated maybe. >> Thank you, that's a much better way of putting it. But games often have to be tweaked based on the environment. A different way of looking at it is a lot of companies in career in Asia all of these games in the West and they will have to tweak the game completely before it releases in these environments. Because players will behave differently and expect different things. And these games have become global. We have people playing all over the world all at the same time. So, how do you facilitate it? How do you support these different users with different needs in this one environment? Again, that's why BI has grown substantially in the gaming industry in the past five, ten years. >> Can you talk about the evolution of how you've been able to interact and essentially affect the user behavior or response to that behavior. You mentioned BI. So, you know, go back ten years it was very reactive. Not a lot of real time stuff going on. Are you now in the position to effect the behavior in real time, in a positive way? >> We're very close to that. We're not quite there yet. So yes, that's a very good point. So, five, ten years ago most games were traditional boxes. You makes a game, you get a box, Walmart or Gamestop, and then you're finished. The relationship with the customer ends. Now, we have this concept that's used often is games as a service. We provide an online environment, a service around a game, and people will play those games for weeks, months, if not years. And so, the shift as well as from a BI tech standpoint is one item where we've been able to streamline the ingest process. So, we're not real time but we can be hourly. Which is pretty responsive. But also, the fact that these games have become these online environments has enabled us to get this information. Five years ago, when the game was in a box, on the shelf, there was no connective tissue between us and them to interact and facilitate. With the games now being online, we can leverage BI. We can be more real time. We can respond quicker. But it's also due to the fact that now games themselves have changed to facilitate that interaction. >> Can you, Robert, paint a picture of the data pipeline? We started there with sort of the different devices. And you're bringing those in as sort of a blender. But take us through the data pipeline and how you're ultimately embedding or operationalizing those analytics. >> Sure. So, the game theater, the game and the business information, game theater is most likely 90, 95% of our total data footprint. We generate a lot more game information than we do business information. It's just due to how much we can track. We can do so. And so, a lot of these games will generate various game events, game logs that we can ingest into a single data lake. And we can use Amazon S3 for that. But it's not just a game theater. So, we have databases for financial information, account users, and so we will ingest the game events as well as the databases into one single location. At that point, however, it's still very raw. It's still very basic. We enable the analysts to actually interact with that. And they can go in there and get their feet wet but it's still very raw. The next step is really taking that raw information that is disjointed and separated, and unifying that into a single model that they can use in a much more performant way. In that first step, the analysts have the burden of a lot of the ETL work, to manipulate the data, to transform it, to make it useful. Which they can do. They should be doing the analysis, not the ingesting the data. And so, the progression from there into our warehouse is the next step of that pipeline. And so in there, we create these models and structures. And they're often born out of what the analysts are seeing and using in that initial data lake stage. So, they're repeating analysis, if they're doing this on a regular basis, the company wants something that's automated and auditable and productionized, then that's a great use case for promotion into our warehouse. You've got this initial staging layer. We have a warehouse where it's structured information. And we allow the analysts into both of those environments. So, they can pick their poison in respects. Structured data over here, raw and vast over here based on their use case. >> And what are the roles ... Just one more follow up, >> Yeah. >> if I may? Who are the people that are actually doing this work? Building the models, cleaning the data, and shoring data. You've got data scientists. You've got quality engineers. You got data engineers. You got application developers. Can you describe the collaboration between those roles? >> Sure. Yeah, so we as a BI organization we have two main groups. We have our engineering team. That's the one I drive. Then we have reporting, and that's a team. Now, we are really one single unit. We work as a team but we separate those two functions. And so, in my organization we have two main groups. We have our big data team which is doing that initial ingestion. Now, we ingest billions of troves of data a day. Terabytes a data a day. And so, we have a team just dedicated to ingestion, standardization, and exposing that first stage. Then we have our second team who are the warehouse engineers, who are actually here today somewhere. And they're the ones who are doing the modeling, the structuring. I mean the data modeling, making the data usable and promoting that into the warehouse. On the reporting team, basically we are there to support them. We provide these tool sets to engage and let them do their work. And so, in that team they have a very split of people do a lot of report development, visualization, data science. A lot of the individuals there will do all those three, two of the three, one of the three. But they do also have segmentation across your day to day reporting which has to function as well as the more deep analysis for data science or predictive analysis. >> And that data warehouse is on-prem? Is it in the cloud? >> Good question. Everything that I talked about is all in the cloud. About a year and a half, two years ago, we made the leap into the cloud. We drunk the Kool-Aid. As of Q2 next year at the very latest, we'll be 100% cloud. >> And the database infrastructure is Amazon? >> Correct. We use Amazon for all the BI platforms. >> Redshift or is it... >> Robert: Yes. >> Yeah, okay. >> That's where actually I want to go because you were talking about the architecture. So, I know you've mentioned Amazon Redshift. Cloudera is another one of your solutions provider. And of course, we're here in Pentaho World, Pentaho. You've described Pentaho as the glue. Can you expand on that a little bit? >> Absolutely. So, I've been talking about these two environments, these two worlds data lake to data warehouse. They're both are different in how they're developed, but it's really a single pipeline, as you said. And so, how do we get data from this raw form into this modeled structure? And that's where Pentaho comes into play. That's the glue. If the glue between these two environments, while they're conceptually very different they provide a singular purpose. But we need a way to unify that pipeline. And so, Pentaho we use very heavily to take this raw information, to transform it, ingest it, and model it into Redshift. And we can automate, we can schedule, we can provide error handling. And so it gives us the framework. And it's self-documenting to be able to track and understand from A to B, from raw to structured how we do that. And again, Pentaho is allowing us to make that transition. >> Pentaho 8.0 just came out yesterday. >> Hmm, it did? >> What are you most excited about there? Do you see any changes? We keep hearing a lot about the ability to scale with Pentaho World. >> Exactly. So, there's three things that really appeal to me actually on 8.0. So, things that we're missing that they've actually filled in with this release. So firstly, we on the streaming component from earlier the real time piece we were missing, we're looking at using Kafka and queuing for a lot of our ingestion purposes. And Pentaho in releasing this new version the mechanism to connect to that environment. That was good timing. We need that. Also too, get into more critical detail, the logs that we ingest, the data that we handle we use Avro and Parquet. When we can. We use JSON, Avro, and Parquet. Pentaho can handle JSON today. Avro, Parquet are coming in 8.0. And then lastly, to your point you made as well is where they're going with their system, they want to go into streaming, into all this information. It's very large and it has to go big. And so, they're adding, again, the ability to add worker nodes and scale horizontally their environment. And that's really a requirement before these other things can come into play. So, those are the things we're looking for. Our data lake can scale on demand. Our Redshift environment can scale on demand. Pentaho has not been able to but with this release they should be able to. And that was something that we've been hoping for for quite some time. >> I wonder if I can get your opinion on something. A little futures-oriented. You have a choice as an organization. You could just take roll your own opensource, best of breed opensource tools, and slog through that. And if you're an internet giant or a huge bank, you can do that. >> Robert: Right. >> You can take tooling like Pentaho which is end to end data pipeline, and this dramatically simplifies things. A lot of the cloud guys, Amazon, Microsoft, I guess to a certain extent Google, they're sort of picking off pieces of the value chain. And they're trying to come up with as a service fully-integrated pipeline. Maybe not best of breed but convenient. How do you see that shaking out generally? And then specifically, is that a challenge for Pentaho from your standpoint? >> So, you're right. That why they're trying to fill these gaps in their environment. To what Pentaho does and what they're offering, there's no comparison right now. They're not there yet. They're a long way away. >> Dave: You're saying the cloud guys are not there. >> No way. >> Pentaho is just so much more functional. >> Robert: They're not close. >> Okay. >> So, that's the first step. However, though what I've been finding in the cloud, there's lots of benefits from the ease of deployment, the scaling. You use a lot of dev ops support, DBA support. But the tools that they offer right now feel pretty bare bones. They're very generic. They have a place but they're not designed for singular purpose. Redshift is the only real piece of the pipeline that is a true Amazon product, but that came from a company called Power Excel ten years ago. They licensed that from a separate company. >> Dave: What a deal that was for Amazon! (Rebecca and Dave laugh) >> Exactly. And so, we like it because of the functionality Power Excel put in many year ago. Now, they've developed upon that. And it made it easier to deploy. But that's the core reason behind it. Now, we use for our big data environment, we use Data Breaks. Data Breaks is a cloud solution. They deploy into Amazon. And so, what I've been finding more and more is companies that are specialized in application or function who have their product support cloud deployment, is to me where it's a sweet middle ground. So, Pentaho is also talking about next year looking at Amazon deployment solutioning for their tool set. So, to me it's not really about going all Amazon. Oh, let's use all Amazon products. They're cheap and cheerful. We can make it work. We can hire ten engineers and hack out a solution. I think what's more applicable is people like Pentaho, whatever people in the industry who have the expertise and are specialized in that function who can allow their products to be deployed in that environment and leverage the Amazon advantages, the Elastic Compute, storage model, the deployment methodology. That is where I see the sweet spot. So, if Pentaho can get to that point, for me that's much more appealing than looking at Amazon trying to build out some things to replace Pentaho x years down the line. >> So, their challenge, if I can summarize, they've got to stay functionally ahead. Which they're way ahead now. They got to maintain that lead. They have to curate best of breed like Spark, for example, from Databricks. >> Right. >> Whatever's next and curate that in a way that is easy to integrate. And then look at the cloud's infrastructure. >> Right. Over the years, these companies that have been looking at ways to deploy into a data center easily and efficiently. Now, the cloud is the next option. How do they support and implement into the cloud in a way where we can leverage their tool set but in a way where we can leverage the cloud ecosystem. And that's the gap. And I think that's what we look for in companies today. And Pentaho is moving towards that. >> And so, that's a lot of good advice for Pentaho? >> I think so. I hope so. Yeah. If they do that, we'll be happy. So, we'll definitely take that. >> Is it Pen-ta-ho or Pent-a-ho? >> You've been saying Pent-a-ho with your British accent! But it is Pen-ta-ho. (laughter) Thank you. >> Dave: Cheap and cheerful, I love it. >> Rebecca: I know -- >> Bless your cotton socks! >> Yes. >> I've had it-- >> Dave: Cord and Bennett. >> Rebecca: Man, okay. Well, thank you so much, Robert. It's been a lot of fun talking to you. >> You're very welcome. >> We will have more from Pen-ta-ho World (laughter) brought to you by Hitachi Vantara just after this. (upbeat techno music)