>>live from Stanford University. It's the queue covering Stanford women in data science 2020. Brought to you by Silicon Angle Media. >>Yeah, yeah. Hi. And welcome to the Cube. I'm your host, Sonia Atari. And we're live at Stanford University, covering the fifth annual Woods Women in Data Science Conference. Joining us today is Lillian Kearse. Keo, who's the Insights manager at Spotify. Slowly and welcome to the Cube. Thank you so much for having me. So tell us a little bit about your role at a Spotify. >>Yeah, So I'm actually one of the few insights managers in the personalization team. Um, and within my little group, we think about data and algorithms that help power the larger personalization experiences throughout Spotify. So, from your limits to discover weekly to your year and wrap stories to your experience on home and the search results, that's >>awesome. Can you tell us a little bit more about the personalization? Um, team? >>Yes. We actually have a variety of different product areas that come together to form the personalization mission, which is the mission is like the term that we use for a big department at Spotify, and we collaborate across different product areas to understand what are the foundational data sets and the foundational machine learning tools that are needed to be able to create features that a user can actually experience in the app? >>Great. Um, and so you're going to be on the career panel today? How do you feel about that? I'm >>really excited. Yeah, Yeah, the would seem is in a great job of bringing together Diverse is very, uh, it's overused term. Sometimes they're a very diverse group of people with lots of different types of experiences, which I think is core. So how I think about data science, it's a wide definition. And so I think it's great to show younger and mid career women all of the different career paths that we can all take. >>And what advice would you would you give to? Women were coming out of college right now about data science. >>Yeah, so my my big advice is to follow your interests. So there's so many different types of data science problems. You don't have to just go into a title that says data scientists or a team that says Data scientist, You can follow your interest into your data science. Use your data science skills in ways that might require a lot of collaboration or mixed methods, or work within a team where there are different types of different different types of expertise coming together to work on problems. >>And speaking of mixed methods, insights is a team that's a mixed methods research groups. So tell us more about that. Yes, I >>personally manage a data scientist, Um, user researcher and the three of us collaborate highly together across their disciplines. We also collaborate across research science, the research science team right into the product and engineering teams that are actually delivering the different products that users get to see. So it's highly collaborative, and the idea is to understand the problem. Space deeply together, be able to understand. What is it that we're trying to even just form in our head is like the need that a user work and human and user human has, um, in bringing in research from research scientists and the product side to be able to understand those needs and then actually have insights that another human, you know, a product owner you can really think through and understand the current space and like the product opportunities >>and to understand that user insight do use a B testing. >>We use a lot of >>a B testing, so that's core to how we think about our users at Spotify. So we use a lot of a B testing. We do a lot of offline experiments to understand the potential consequences or impact that certain interventions can have. But I think a B testing, you know, there's so much to learn about best practices there and where you're talking about a team that does foundational data and foundational features. You also have to think about unintended or second order effects of algorithmic a B test. So it's been just like a huge area of learning in a huge area of just very interesting outcomes. And like every test that we run, we learn a lot about not just the individual thing. We're testing with just the process overall. >>And, um, what are some features of Spotify that customers really love anything? Anything >>that's like we know use a daily mix people absolutely love every time that I make a new friend and I saw them what they work on there like I was just listening to my daily makes this morning discover weekly for people who really want >>to stay, >>you know, open to new music is also very popular. But I think the one that really takes it is any of the end of year wrapped campaigns that we have just the nostalgia that people have, even just for the last year. But in 2019 we were actually able to do 10 years, and that amount of nostalgia just went through the roof like people were just like, Oh my goodness, you captured the time that I broke up with that, you >>know, the 1st 5 years ago, or just like when I discovered that I love Taylor Swift, even though I didn't think I like their or something like that, you know? >>Are there any surprises or interesting stories that you have about, um, interesting user experiences? Yeah. >>I mean, I could give I >>can give you an example from my experience. So recently, A few a few months ago, I was scrolling through my home feed, and I noticed that one of the highly rated things for me was women in >>country, and I was like, Oh, that's kind of weird. I don't consider >>myself a country fan, right? And I was like having this moment where I went through this path of Wait, That's weird. Why would Why would this recommend? Why would the home screen recommend women in country, country music to me? And then when I click through it, um, it would show you a little bit of information about it because it had, you know, Dolly Parton. It had Margo Price and it had the high women and those were all artistes. And I've been listening to a lot, but I just had not formed an identity as a country music. And then I click through It was like, Oh, this is a great play list and I listen to it and it got me to the point where I was realizing I really actually do like country music when the stories were centered around women, that it was really fun to discover other artists that I wouldn't have otherwise jumped into as well. Based on the fact that I love the story writing and the song, writing these other country acts that >>so quickly discovered that so you have a degree in industrial mathematics, went to a liberal arts college on purpose because you want to try out different classes. So how is that diversity of education really helped >>you in your Yes, in my undergrad is from Smith College, which is a liberal arts school, very strong liberal arts foundation. And when I went to visit, one of the math professors that I met told me that he, you know, he considers studying math, not just to make you better at math, but that it makes you a better thinker. And you can take in much more information and sort of question assumptions and try to build a foundation for what? The problem that you're trying to think through is. And I just found that extremely interesting. And I also, you know, I haven't undeclared major in Latin American studies, and I studied like neuroscience and quantum physics for non experts and film class and all of these other things that I don't know if I would have had the same opportunity at a more technical school, and I just found it really challenging and satisfying to be able to push myself to think in different ways. I even took a poetry writing class I did not write good poetry, but the experience really stuck with me because it was about pushing myself outside of my own boundaries. >>And would you recommend having this kind of like diverse education to young women now who are looking >>and I absolutely love it? I mean, I think, you know, there's some people believe that instead of thinking about steam, we should be talking instead of thinking about stem. Rather, we should be talking about steam, which adds the arts education in there, and liberal arts is one of them. And I think that now, in these conversations that we have about biases in data and ML and AI and understanding, fairness and accountability, accountability bitterly, it's a hardware. Apparently, I think that a strong, uh, cross disciplinary collaborative and even on an individual level, cross disciplinary education is really the only way that we're gonna be able to make those connections to understand what kind of second order effects for having based on the decisions of parameters for a model. In a local sense, we're optimizing and doing a great job. But what are the global consequences of those decisions? And I think that that kind of interdisciplinary approach to education as an individual and collaboration as a team is really the only way. >>And speaking about bias. Earlier, we heard that diversity is great because it brings out new perspectives, and it also helps to reduce that unfair bias. So how it Spotify have you managed? Or has Spotify managed to create a more diverse team? >>Yeah, so I mean, it starts with recruiting. It starts with what kind of messaging we put out there, and there's a great team that thinks about that exclusively. And they're really pushing all of us as managers. As I seizes leaders to really think about the decisions in the way that we talk about things and all of these micro decisions that we make and how that creates an inclusive environments, it's not just about diversity. It's also about making people feel like this is where they should be. On a personal level, you know, I talk a lot with younger folks and people who are trying to just figure out what their place is in technology, whether it be because they come from a different culture, >>there are, >>you know, they might be gender, non binary. They might be women who feel like there is in a place for them. It's really about, You know, the things that I think about is because you're different. Your voice is needed even more. You know, like your voice matters and we need to figure out. And I always ask, How can I highlight your voice more? You know, how can I help? I have a tiny, tiny bit of power and influence. You know, more than some other folks. How can I help other people acquire that as well? >>Lilian, thank you so much for your insight. Thank you for being on the Cube. Thank you. I'm your host, Sonia today. Ari. Thank you for watching and stay tuned for more. Yeah, yeah.

>>From around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Welcome to cube three 60 fives coverage of the Accenture executive summit. Part of AWS reinvent. I'm your host Rebecca Knight. Today we are joined by a cube alum, Karthik, Lorraine. He is Accenture senior managing director and lead Accenture cloud. First, welcome back to the show Karthik. >>Thank you. Thanks for having me here. >>Always a pleasure. So I want to talk to you. You are an industry veteran, you've been in Silicon Valley for decades. Um, I want to hear from your perspective what the impact of the COVID-19 pandemic has been, what are you hearing from clients? What are they struggling with? What are their challenges that they're facing day to day? >>I think, um, COVID-19 is being a eye-opener from, you know, various facets, you know, um, first and foremost, it's a, it's a hell, um, situation that everybody's facing, which is not just, uh, highest economic bearings to it. It has enterprise, um, an organization with bedding to it. And most importantly, it's very personal to people, um, because they themselves and their friends, family near and dear ones are going through this challenge, uh, from various different dimension. But putting that aside, when you come to it from an organization enterprise standpoint, it has changed everything well, the behavior of organizations coming together, working in their campuses, working with each other as friends, family, and, uh, um, near and dear colleagues, all of them are operating differently. So that's what big change to get things done in a completely different way, from how they used to get things done. >>Number two, a lot of things that were planned for normal scenarios, like their global supply chain, how they interact with their client customers, how they go innovate with their partners on how that employees contribute to the success of an organization at all changed. And there are no data models that give them a hint of something like this for them to be prepared for this. So we are seeing organizations, um, that have adapted to this reasonably okay, and are, you know, launching to innovate faster in this. And there are organizations that have started with struggling, but are continuing to struggle. And the gap between the leaders and legs are widening. So this is creating opportunities in a different way for the leaders, um, with a lot of pivot their business, but it's also creating significant challenge for the lag guides, uh, as we defined in our future systems research that we did a year ago, uh, and those organizations are struggling further. So the gap is actually widening. >>So you just talked about the widening gap. I've talked about the tremendous uncertainty that so many companies, even the ones who have adapted reasonably well, uh, in this, in this time, talk a little bit about Accenture cloud first and why, why now? >>I think it's a great question. Um, we believe that for many of our clients COVID-19 has turned, uh, cloud from an experimentation aspiration to an origin mandate. What I mean by that is everybody has been doing something on the other end cloud. There's no company that says we don't believe in cloud are, we don't want to do cloud. It was how much they did in cloud. And they were experimenting. They were doing the new things in cloud, but they were operating a lot of their core business outside the cloud or not in the cloud. Those organizations have struggled to operate in this new normal, in a remote fashion, as well as, uh, their ability to pivot to all the changes the pandemic has brought to them. But on the other hand, the organizations that had a solid foundation in cloud were able to collect faster and not actually gone into the stage of innovating faster and driving a new behavior in the market, new behavior within their organization. >>So we are seeing that spend to make is actually fast-forwarded something that we always believed was going to happen. This, uh, uh, moving to cloud over the next decade is fast forward it to happen in the next three to five years. And it's created this moment where it's a once in an era, really replatforming of businesses in the cloud that we are going to see. And we see this moment as a cloud first moment where organizations will use cloud as the, the, the canvas and the foundation with which they're going to reimagine their business after they were born in the cloud. Uh, and this requires a whole new strategy. Uh, and as Accenture, we are getting a lot in cloud, but we thought that this is the moment where we bring all of that, gave him a piece together because we need a strategy for addressing, moving to cloud are embracing cloud in a holistic fashion. And that's what Accenture cloud first brings together a holistic strategy, a team that's 70,000 plus people that's coming together with rich cloud skills, but investing to tie in all the various capabilities of cloud to Delaware, that holistic strategy to our clients. So I want you to >>Delve into a little bit more about what this strategy actually entails. I mean, it's clearly about embracing change and being willing to experiment and having capabilities to innovate. Can you tell us a little bit more about what this strategy entails? >>Yeah. The reason why we say that as a need for strategy is like I said, cloud is not new. There's almost every customer client is doing something with the cloud, but all of them have taken different approaches to cloud and different boundaries to cloud. Some organizations say, I just need to consolidate my multiple data centers to a small data center footprint and move the nest to cloud. Certain other organizations say that well, I'm going to move certain workloads to cloud. Certain other organizations said, well, I'm going to build this Greenfield application or workload in cloud. Certain other said, um, I'm going to use the power of AI ML in the cloud to analyze my data and drive insights. But a cloud first strategy is all of this tied with the corporate strategy of the organization with an industry specific cloud journey to say, if in this current industry, if I were to be reborn in the cloud, would I do it in the exact same passion that I did in the past, which means that the products and services that they offer need to be the matching, how they interact with that customers and partners need to be revisited, how they bird and operate their IP systems need to be the, imagine how they unearthed the data from all of the systems under which they attract need to be liberated so that you could drive insights of cloud. >>First strategy hands is a corporate wide strategy, and it's a C-suite responsibility. It doesn't take the ownership away from the CIO or CIO, but the CIO is, and CDI was felt that it was just their problem and they were to solve it. And everyone as being a customer, now, the center of gravity is elevated to it becoming a C-suite agenda on everybody's agenda, where probably the CDI is the instrument to execute that that's a holistic cloud-first strategy >>And it, and it's a strategy, but the way you're describing it, it sounds like it's also a mindset and an approach, as you were saying, this idea of being reborn in the cloud. So now how do I think about things? How do I communicate? How do I collaborate? How do I get done? What I need to get done. Talk a little bit about how this has changed, the way you support your clients and how Accenture cloud first is changing your approach to cloud services. >>Wonderful. Um, you know, I did not color one very important aspect in my previous question, but that's exactly what you just asked me now, which is to do all of this. I talked about all of the variables, uh, an organization or an enterprise is going to go through, but the good part is they have one constant. And what is that? That is their employees, uh, because you do, the employees are able to embrace this change. If they are able to, uh, change them, says, pivot them says retool and train themselves to be able to operate in this new cloud. First one, the ability to reimagine every function of the business would be happening at speed. And cloud first approach is to do all of this at speed, because innovation is deadly proposed there, do the rate of probability on experimentation. You need to experiment a lot for any kind of experimentation. >>There's a probability of success. Organizations need to have an ability and a mechanism for them to be able to innovate faster for which they need to experiment a lot, the more the experiment and the lower cost at which they experiment is going to help them experiment a lot. And they experiment demic speed, fail fast, succeed more. And hence, they're going to be able to operate this at speed. So the cloud-first mindset is all about speed. I'm helping the clients fast track that innovation journey, and this is going to happen. Like I said, across the enterprise and every function across every department, I'm the agent of this change is going to be the employees or weapon, race, this change through new skills and new grueling and new mindset that they need to adapt to. >>So Karthik what you're describing it, it sounds so exciting. And yet for a pandemic wary workforce, that's been working remotely that may be dealing with uncertainty if for their kid's school and for so many other aspects of their life, it sounds hard. So how are you helping your clients, employees get onboard with this? And because the change management is, is often the hardest part. >>Yeah, I think it's, again, a great question. A bottle has only so much capacity. Something got to come off for something else to go in. That's what you're saying is absolutely right. And that is again, the power of cloud. The reason why cloud is such a fundamental breakthrough technology and capability for us to succeed in this era, because it helps in various forms. What we talked so far is the power of innovation that can create, but cloud can also simplify the life of the employees in an enterprise. There are several activities and tasks that people do in managing that complex infrastructure, complex ID landscape. They used to do certain jobs and activities in a very difficult underground about with cloud has simplified. And democratised a lot of these activities. So that things which had to be done in the past, like managing the complexity of the infrastructure, keeping them up all the time, managing the, um, the obsolescence of the capabilities and technologies and infrastructure, all of that could be offloaded to the cloud. >>So that the time that is available for all of these employees can be used to further innovate. Every organization is going to spend almost the same amount of money, but rather than spending activities, by looking at the rear view mirror on keeping the lights on, they're going to spend more money, more time, more energy, and spend their skills on things that are going to add value to their organization. Because you, every innovation that an enterprise can give to their end customer need not come from that enterprise. The word of platform economy is about democratising innovation. And the power of cloud is to get all of these capabilities from outside the four walls of the enterprise, >>It will add value to the organization, but I would imagine also add value to that employee's life because that employee, the employee will be more engaged in his or her job and therefore bring more excitement and energy into her, his or her day-to-day activities too. >>Absolutely. Absolutely. And this is, this is a normal evolution we would have seen everybody would have seen in their lives, that they keep moving up the value chain of what activities that, uh, gets performed buying by those individuals. And this is, um, you know, no more true than how the United States, uh, as an economy has operated where, um, this is the power of a powerhouse of innovation, where the work that's done inside the country keeps moving up to value chain. And, um, us leverage is the global economy for a lot of things that is required to power the United States and that global economic, uh, phenomenon is very proof for an enterprise as well. There are things that an enterprise needs to do them soon. There are things an employee needs to do themselves. Um, but there are things that they could leverage from the external innovation and the power of innovation that is coming from technologies like cloud. >>So at Accenture, you have long, long, deep Stan, sorry, you have deep and long-standing relationships with many cloud service providers, including AWS. How does the Accenture cloud first strategy, how does it affect your relationships with those providers? >>Yeah, we have great relationships with cloud providers like AWS. And in fact, in the cloud world, it was one of the first, um, capability that we started about years ago, uh, when we started developing these capabilities. But five years ago, we hit a very important milestone where the two organizations came together and said that we are forging a pharma partnership with joint investments to build this partnership. And we named that as a Accenture, AWS business group ABG, uh, where we co-invest and brought skills together and develop solutions. And we will continue to do that. And through that investment, we've also made several acquisitions that you would have seen in the recent times, like, uh, an invoice and gecko that we made acquisitions in in Europe. But now we're taking this to the next level. What we are saying is two cloud first and the $3 billion investment that we are bringing in, uh, through cloud-first. >>We are going to make specific investment to create unique joint solution and landing zones foundation, um, cloud packs with which clients can accelerate their innovation or their journey to cloud first. And one great example is what we are doing with Takeda, uh, billable, pharmaceutical giant, um, between we've signed a five-year partnership. And it was out in the media just a month ago or so, where we are, the two organizations are coming together. We have created a partnership as a power of three partnership, where the three organizations are jointly hoarding hats and taking responsibility for the innovation and the leadership position that Takeda wants to get to with this. We are going to simplify their operating model and organization by providing and flexibility. We're going to provide a lot more insights. Tequila has a 230 year old organization. Imagine the amount of trapped data and intelligence that is there. >>How about bringing all of that together with the power of AWS and Accenture and Takeda to drive more customer insights, um, come up with breakthrough R and D uh, accelerate clinical trials and improve the patient experience using AI ML and edge technologies. So all of these things that we will do through this partnership with joined investment from Accenture cloud first, as well as partner like AWS, so that Takeda can realize their gain. And, uh, their senior actually made a statement that five years from now, every ticket an employee will have an AI assistant. That's going to make that beginner employee move up the value chain on how they contribute and add value to the future of tequila with the AI assistant, making them even more equipped and smarter than what they could be otherwise. >>So, one last question to close this out here. What is your future vision for, for Accenture cloud first? What are we going to be talking about at next year's Accenture executive summit? Yeah, the future >>Is going to be, um, evolving, but the part that is exciting to me, and this is, uh, uh, a fundamental belief that we are entering a new era of industrial revolution from industry first, second, and third industry. The third happened probably 20 years ago with the advent of Silicon and computers and all of that stuff that happened here in the Silicon Valley. I think the fourth industrial revolution is going to be in the cross section of, uh, physical, digital and biological boundaries. And there's a great article, um, in one economic forum that people, uh, your audience can Google and read about it. Uh, but the reason why this is very, very important is we are seeing a disturbing phenomenon that over the last 10 years are seeing a Blackwing of the, um, labor productivity and innovation, which has dropped to about 2.1%. When you see that kind of phenomenon over that longer period of time, there has to be breakthrough innovation that needs to happen to come out of this barrier and get to the next, you know, base camp, as I would call it to further this productivity, um, lack that we are seeing, and that is going to happen in the intersection of the physical, digital and biological boundaries. >>And I think cloud is going to be the connective tissue between all of these three, to be able to provide that where it's the edge, especially is good to come closer to the human lives. It's going to come from cloud. Yeah. Pick totally in your mind, you can think about cloud as central, either in a private cloud, in a data center or in a public cloud, you know, everywhere. But when you think about edge, it's going to be far reaching and coming close to where we live and maybe work and very, um, get entertained and so on and so forth. And there's good to be, uh, intervention in a positive way in the field of medicine, in the field of entertainment, in the field of, um, manufacturing in the field of, um, you know, mobility. When I say mobility, human mobility, people, transportation, and so on and so forth with all of this stuff, cloud is going to be the connective tissue and the vision of cloud first is going to be, uh, you know, blowing through this big change that is going to happen. And the evolution that is going to happen where, you know, the human grace of mankind, um, our person kind of being very gender neutral in today's world. Um, go first needs to be that beacon of, uh, creating the next generation vision for enterprises to take advantage of that kind of an exciting future. And that's why it, Accenture, are we saying that there'll be change as our, as our purpose? >>I genuinely believe that cloud first is going to be in the forefront of that change agenda, both for Accenture as well as for the rest of the work. Excellent. Let there be change, indeed. Thank you so much for joining us Karthik. A pleasure I'm Rebecca nights stay tuned for more of Q3 60 fives coverage of the Accenture executive summit >>From around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS >>Welcome everyone to the Q virtual and our coverage of the Accenture executive summit, which is part of AWS reinvent 2020. I'm your host Rebecca Knight. Today, we are talking about the green cloud and joining me is Kishor Dirk. He is Accenture senior managing director cloud first global services lead. Thank you so much for coming on the show. Kishor nice to meet you. So I want to start by asking you what it is that we mean when we say green cloud, we know the sustainability is a business imperative. So many organizations around the world are committing to responsible innovation, lowering carbon emissions. But what is this? What is it? What does it mean when they talk about cloud from a sustainability perspective? I think it's about responsible innovation being cloud is a cloud first approach that has benefit the clients by helping reduce carbon emissions. Think about it this way. >>You have a large number of data centers. Each of these data centers are increasing by 14% every year. And this double digit growth. What you're seeing is these data centers and the consumption is nearly coolant to the kind of them should have a country like Spain. So the magnitude of the problem that is out there and how do we pursue a green approach. If you look at this, our Accenture analysis, in terms of the migration to public cloud, we've seen that we can reduce that by 59 million tons of CO2 per year with just the 5.9% reduction in total emissions and equates this to 22 million cars off the road. And the magnitude of reduction can go a long way in meeting climate change commitments, particularly for data sensitive. Wow, that's incredible. The numbers that you're putting forward are, are absolutely mind blowing. So how does it work? Is it a simple cloud migration? So, you know, when companies begin their cloud journey and then they confront, uh, with >>Them a lot of questions, the decision to make, uh, this particular, uh, element sustainable in the solution and benefits they drive and they have to make wise choices, and then they will gain unprecedented level of innovation leading to both a greener planet, as well as, uh, a greener balance sheet, I would say, uh, so effectively it's all about ambition, data ambition, greater the reduction in carbon emissions. So from a cloud migration perspective, we look at it as a, as a simple solution with approaches and sustainability benefits, uh, that vary based on things it's about selecting the right cloud provider, a very carbon thoughtful provider and the first step towards a sustainable cloud journey. And here we're looking at cloud operators know, obviously they have different corporate commitments towards sustainability, and that determines how they plan, how they build, uh, their, uh, uh, the data centers, how they are consumed and assumptions that operate there and how they, or they retire their data centers. >>Then, uh, the next element that you want to do is how do you build it ambition, you know, for some of the companies, uh, and average on-prem, uh, drives about 65% energy reduction and the carbon emission reduction number was 84%, which is kind of good, I would say. But then if you could go up to 98% by configuring applications to the cloud, that is significant benefit for, uh, for the board. And obviously it's a, a greener cloud that we're talking about. And then the question is, how far can you go? And, uh, you know, the, obviously the companies have to unlock greater financial societal environmental benefits, and Accenture has this cloud based circular operations and sustainable products and services that we bring into play. So it's a, it's a very thoughtful, broader approach that w bringing in, in terms of, uh, just a simple concept of cloud migration. >>So we know that in the COVID era, shifting to the cloud has really become a business imperative. How is Accenture working with its clients at a time when all of this movement has been accelerated? How do you partner and what is your approach in terms of helping them with their migrations? >>Yeah, I mean, let, let me talk a little bit about the pandemic and the crisis that is that today. And if you really look at that in terms of how we partnered with a lot of our clients in terms of the cloud first approach, I'll give you a couple of examples. We worked with rolls, Royce, MacLaren, DHL, and others, as part of the ventilator, a UK challenge consortium, again, to, uh, coordinate production of medical ventilator surgically needed for the UK health service. Many of these farms I've taken similar initiatives in, in terms of, uh, you know, from a few manufacturers hand sanitizers, and to answer it as us and again, leading passionate labels, making PPE, and again, at the UN general assembly, we launched the end-to-end integration guide that helps company is essentially to have a sustainable development goals. And that's how we are parking at a very large scale. >>Uh, and, and if you really look at how we work with our clients and what is Accenture's role there, uh, you know, from, in terms of our clients, you know, there are multiple steps that we look at. One is about planning, building, deploying, and managing an optimal green cloud solution. And Accenture has this concept of, uh, helping clients with a platform to kind of achieve that goal. And here we are having, we are having a platform or a mine app, which has a module called BGR advisor. And this is a capability that helps you provide optimal green cloud, uh, you know, a business case, and obviously a blueprint for each of our clients and right from the start in terms of how do we complete cloud migration recommendation to an improved solution, accurate accuracy to obviously bringing in the end to end perspective, uh, you know, with this green card advisor capability, we're helping our clients capture what we call as a carbon footprint for existing data centers and provide, uh, I would say the current cloud CO2 emission score that, you know, obviously helps them, uh, with carbon credits that can further that green agenda. >>So essentially this is about recommending a green index score, reducing carbon footprint for migration migrating for green cloud. And if we look at how Accenture itself is practicing what we preach, 95% of our applications are in the cloud. And this migration has helped us, uh, to lead to about $14.5 million in benefit. And in the third year and another 3 million analytics costs that are saved through right-sizing a service consumption. So it's a very broad umbrella and a footprint in terms of how we engage societaly with the UN or our clients. And what is it that we exactly bring to our clients in solving a specific problem? >>Accenture isn't is walking the walk, as you say, >>Instead of it, we practice what we preach, and that is something that we take it to heart. We want to have a responsible business and we want to practice it. And we want to advise our clients around that >>You are your own use case. And so they can, they know they can take your advice. So talk a little bit about, um, the global, the cooperation that's needed. We know that conquering this pandemic is going to take a coordinated global effort and talk a little bit about the great reset initiative. First of all, what is that? Why don't we, why don't we start there and then we can delve into it a little bit more. >>Okay. So before we get to how we are cooperating, the great reset, uh, initiative is about improving the state of the world. And it's about a group of global stakeholders cooperating to simultaneously manage the direct consequences of their COVID-19 crisis. Uh, and in spirit of this cooperation that we're seeing during COVID-19, uh, which will obviously either to post pandemic, to tackle the world's pressing issues. As I say, uh, we are increasing companies to realize a combined potential of technology and sustainable impact to use enterprise solutions, to address with urgency and scale, and, um, obviously, uh, multiple challenges that are facing our world. One of the ways that are increasing, uh, companies to reach their readiness cloud with Accenture's cloud strategy is to build a solid foundation that is resilient and will be able to faster to the current, as well as future times. Now, when you think of cloud as the foundation, uh, that drives the digital transformation, it's about scale speed, streamlining your operations, and obviously reducing costs. >>And as these businesses seize the construct of cloud first, they must remain obviously responsible and trusted. Now think about this, right, as part of our analysis, uh, that profitability can co-exist with responsible and sustainable practices. Let's say that all the data centers, uh, migrated from on-prem to cloud based, we estimate that would reduce carbon emissions globally by 60 million tons per year. Uh, and think about it this way, right? Easier metric would be taking out 22 million cars off the road. Um, the other examples that you've seen, right, in terms of the NHS work that they're doing, uh, in, in UK to build, uh, uh, you know, uh, Microsoft teams in based integration. And, uh, the platform rolled out for 1.2 million users, uh, and got 16,000 users that we were able to secure, uh, instant messages, obviously complete audio video calls and host virtual meetings across India. So, uh, this, this work that we did with NHS is, is something that we have, we are collaborating with a lot of tools and powering businesses. >>Well, you're vividly describing the business case for sustainability. What do you see as the future of cloud when thinking about it from this lens of sustainability, and also going back to what you were talking about in terms of how you are helping your, your fostering cooperation within these organizations. >>Yeah, that's a very good question. So if you look at today, right, businesses are obviously environmentally aware and they are expanding efforts to decrease power consumption, carbon emissions, and they want to run a sustainable operational efficiency across all elements of their business. And this is an increasing trend, and there is that option of energy efficient infrastructure in the global market. And this trend is the cloud first thinking. And with the right cloud migration that we've been discussing is about unlocking new opportunity, like clean energy foundations enable enabled by cloud based geographic analysis, material, waste reductions, and better data insights. And this is something that, uh, uh, will drive, uh, with obviously faster analytics platform that is out there. Now, the sustainability is actually the future of business, which is companies that are historically different, the financial security or agility benefits to cloud. Now sustainability becomes an imperative for them. And I would own experience Accenture's experience with cloud migrations. We have seen 30 to 40% total cost of ownership savings, and it's driving a greater workload, flexibility, better service, your obligation, and obviously more energy efficient, uh, public clouds that cost, uh, we'll see that, that drive a lot of these enterprise own data centers. So in our view, what we are seeing is that this, this, uh, sustainable cloud position helps, uh, helps companies to, uh, drive a lot of the goals in addition to their financial and other goods. >>So what should organizations who are, who are watching this interview and saying, Hey, I need to know more, what, what do you recommend to them? And what, where should they go to get more information on Greenplum? >>Yeah. If you wanna, if you are a business leader and you're thinking about which cloud provider is good, or how, how should applications be modernized to meet our day-to-day needs, which cloud driven innovations should be priorities. Uh, you know, that's why Accenture, uh, formed up the cloud first organization and essentially to provide the full stack of cloud services to help our clients become a cloud first business. Um, you know, it's all about excavation, uh, the digital transformation innovating faster, creating differentiated, uh, and sustainable value for our clients. And we are powering it up at 70,000 cloud professionals, $3 billion investment, and, uh, bringing together and services for our clients in terms of cloud solutions. And obviously the ecosystem partnership that we have that we are seeing today, uh, and, and the assets that help our clients realize their goals. Um, and again, to do reach out to us, uh, we can help them determine obviously, an optimal, sustainable cloud for solution that meets the business needs and being unprecedented levels of innovation. Our experience, uh, will be our advantage. And, uh, now more than ever Rebecca, >>Just closing us out here. Do you have any advice for these companies who are navigating a great deal of uncertainty? We, what, what do you think the next 12 to 24 months? What do you think that should be on the minds of CEOs as they go through? >>So, as CEO's are thinking about rapidly leveraging cloud, migrating to cloud, uh, one of the elements that we want them to be thoughtful about is can they do that, uh, with unprecedent level of innovation, but also build a greener planet and a greener balance sheet, if we can achieve this balance and kind of, uh, have a, have a world which is greener, I think the world will win. And we all along with Accenture clients will win. That's what I would say, uh, >>Optimistic outlook, and I will take it. Thank you so much. Kishor for coming on the show >>That was >>Accenture's Kishor Dirk, I'm Rebecca Knight stay tuned for more of the cube virtuals coverage of the Accenture executive summit >>Around the globe. >>It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Welcome everyone to the cube virtual and our coverage of the Accenture executive summit. Part of AWS reinvent 2020. I'm your host Rebecca Knight. Today, we are talking about the power of three. And what happens when you bring together the scientific know-how of a global bias biopharmaceutical powerhouse in Takeda, a leading cloud services provider in AWS, and Accenture's ability to innovate, execute, and deliver innovation. Joining me to talk about these things. We have Aaron, sorry, Arjun, baby. He is the senior managing director and chairman of Accenture's diamond leadership council. Welcome Arjun, Karl hick. He is the chief digital and information officer at Takeda. What is your bigger, thank you, Rebecca and Brian bowhead, global director, and head of the Accenture AWS business group at Amazon web services. Thanks so much for coming up. So, as I said, we're talking today about this relationship between, uh, your three organizations. Carl, I want to talk with you. I know you're at the beginning of your cloud journey. What was the compelling reason? What w why, why move to the cloud and why now? >>Yeah, no, thank you for the question. So, you know, as a biopharmaceutical leader, we're committed to bringing better health and a brighter future to our patients. We're doing that by translating science into some really innovative and life transporting therapies, but throughout, you know, we believe that there's a responsible use of technology, of data and of innovation. And those three ingredients are really key to helping us deliver on that promise. And so, you know, while I think, uh, I'll call it, this cloud journey is already always been a part of our strategy. Um, and we've made some pretty steady progress over the last years with a number of I'll call it diverse approaches to the digital and AI. We just weren't seeing the impact at scale that we wanted to see. Um, and I think that, you know, there's a, there's a need ultimately to, you know, accelerate and, uh, broaden that shift. >>And, you know, we were commenting on this earlier, but there's, you know, it's been highlighted by a number of factors. One of those has been certainly a number of the large acquisitions we've made Shire, uh, being the most pressing example, uh, but also the global pandemic, both of those highlight the need for us to move faster, um, at the speed of cloud, ultimately. Uh, and so we started thinking outside of the box because it was taking us too long and we decided to leverage the strategic partner model. Uh, and it's giving us a chance to think about our challenges very differently. We call this the power of three, uh, and ultimately our focus is singularly on our patients. I mean, they're waiting for us. We need to get there faster. It can take years. And so I think that there is a focus on innovation, um, at a rapid speed, so we can move ultimately from treating conditions to keeping people healthy. >>So, as you are embarking on this journey, what are some of the insights you want to share about, about what you're seeing so far? >>Yeah, no, it's a great question. So, I mean, look, maybe right before I highlight some of the key insights, uh, I would say that, you know, with cloud now as the, as the launchpad for innovation, you know, our vision all along has been that in less than 10 years, we want every single to kid, uh, associate we're employed to be empowered by an AI assistant. And I think that, you know, that's going to help us make faster, better decisions. It'll help us, uh, fundamentally deliver transformative therapies and better experiences to, to that ecosystem, to our patients, to physicians, to payers, et cetera, much faster than we previously thought possible. Um, and I think that technologies like cloud and edge computing together with a very powerful I'll call it data fabric is going to help us to create this, this real-time, uh, I'll call it the digital ecosystem. >>The data has to flow ultimately seamlessly between our patients and providers or partners or researchers, et cetera. Uh, and so we've been thinking about this, uh, I'll call it, we call it sort of this pyramid, um, that helps us describe our vision. Uh, and a lot of it has to do with ultimately modernizing the foundation, modernizing and rearchitecting, the platforms that drive the company, uh, heightening our focus on data, which means that there's an accelerated shift towards, uh, enterprise data platforms and digital products. And then ultimately, uh, uh, P you know, really an engine for innovation sitting at the very top. Um, and so I think with that, you know, there's a few different, I'll call it insights that, you know, are quickly kind of come zooming into focus. I would say one is this need to collaborate very differently. Um, you know, not only internally, but you know, how do we define ultimately, and build a connected digital ecosystem with the right partners and technologies externally? >>I think the second component that maybe people don't think as much about, but, you know, I find critically important is for us to find ways of really transforming our culture. We have to unlock talent and shift the culture certainly as a large biopharmaceutical very differently. And then lastly, you've touched on it already, which is, you know, innovation at the speed of cloud. How do we re-imagine that, you know, how do ideas go from getting tested and months to kind of getting tested in days? You know, how do we collaborate very differently? Uh, and so I think those are three, uh, perhaps of the larger I'll call it, uh, insights that, you know, the three of us are spending a lot of time thinking about right now. >>So Arjun, I want to bring you into this conversation a little bit, let let's delve into those a bit. Talk first about the collaboration, uh, that Carl was referencing there. How, how have you seen that? It is enabling, uh, colleagues and teams to communicate differently and interact in new and different ways? Uh, both internally and externally, as Carl said, >>No, th thank you for that. And, um, I've got to give call a lot of credit, because as we started to think about this journey, it was clear, it was a bold ambition. It was, uh, something that, you know, we had all to do differently. And so the, the concept of the power of three that Carl has constructed has become a label for us as a way to think about what are we going to do to collectively drive this journey forward. And to me, the unique ways of collaboration means three things. The first one is that, um, what is expected is that the three parties are going to come together and it's more than just the sum of our resources. And by that, I mean that we have to bring all of ourselves, all of our collective capabilities, as an example, Amazon has amazing supply chain capabilities. >>They're one of the best at supply chain. So in addition to resources, when we have supply chain innovations, uh, that's something that they're bringing in addition to just, uh, talent and assets, similarly for Accenture, right? We do a lot, uh, in the talent space. So how do we bring our thinking as to how we apply best practices for talent to this partnership? So, um, as we think about this, so that's, that's the first one, the second one is about shared success very early on in this partnership, we started to build some foundations and actually develop seven principles that all of us would look at as the basis for this success shared success model. And we continue to hold that sort of in the forefront, as we think about this collaboration. And maybe the third thing I would say is this one team mindset. So whether it's the three of our CEOs that get together every couple of months to think about, uh, this partnership, or it is the governance model that Carl has put together, which has all three parties in the governance and every level of leadership. We always think about this as a collective group, so that we can keep that front and center. And what I think ultimately has enabled us to do is it allowed us to move at speed, be more flexible. And ultimately all we're looking at the target the same way, the North side, the same way. >>Brian, what about you? What have you observed? And are you thinking about in terms of how this is helping teams collaborate differently, >>Lillian and Arjun made some, some great points there. And I think if you really think about what he's talking about, it's that, that diversity of talent, diversity of scale and viewpoint and even culture, right? And so we see that in the power of three. And then I think if we drill down into what we see at Takeda, and frankly, Takeda was, was really, I think, pretty visionary and on their way here, right? And taking this kind of cross functional approach and applying it to how they operate day to day. So moving from a more functional view of the world to more of a product oriented view of the world, right? So when you think about we're going to be organized around a product or a service or a capability that we're going to provide to our customers or our patients or donors in this case, it implies a different structure, although altogether, and a different way of thinking, right? >>Because now you've got technical people and business experts and marketing experts, all working together in this is sort of cross collaboration. And what's great about that is it's really the only way to succeed with cloud, right? Because the old ways of thinking where you've got application people and infrastructure, people in business, people is suboptimal, right? Because we can all access this tool as these capabilities and the best way to do that. Isn't across kind of a cross-collaborative way. And so this is product oriented mindset. It's a keto was already on. I think it's allowed us to move faster in those areas. >>Carl, I want to go back to this idea of unlocking talent and culture. And this is something that both Brian and Arjun have talked about too. People are an essential part of their, at the heart of your organization. How will their experience of work change and how are you helping re-imagine and reinforce a strong organizational culture, particularly at this time when so many people are working remotely. >>Yeah. It's a great question. And it's something that, you know, I think we all have to think a lot about, I mean, I think, um, you know, driving this, this call it, this, this digital and data kind of capability building, uh, takes a lot of, a lot of thinking. So, I mean, there's a few different elements in terms of how we're tackling this one is we're recognizing, and it's not just for the technology organization or for those actors that, that we're innovating with, but it's really across all of the Cato where we're working through ways of raising what I'll call the overall digital leaders literacy of the organization, you know, what are the, you know, what are the skills that are needed almost at a baseline level, even for a global bio-pharmaceutical company and how do we deploy, I'll call it those learning resources very broadly. >>And then secondly, I think that, you know, we're, we're very clear that there's a number of areas where there are very specialized skills that are needed. Uh, my organization is one of those. And so, you know, we're fostering ways in which, you know, we're very kind of quickly kind of creating, uh, avenues excitement for, for associates in that space. So one example specifically, as we use, you know, during these very much sort of remote, uh, sort of days, we, we use what we call global it meet days, and we set a day aside every single month and this last Friday, um, you know, we, we create during that time, it's time for personal development. Um, and we provide active seminars and training on things like, you know, robotic process automation, data analytics cloud, uh, in this last month we've been doing this for months and months now, but in his last month, more than 50% of my organization participated, and there's this huge positive shift, both in terms of access and excitement about really harnessing those new skills and being able to apply them. >>Uh, and so I think that that's, you know, one, one element that, uh, can be considered. And then thirdly, um, of course, every organization to work on, how do you prioritize talent, acquisition and management and competencies that you can't rescale? I mean, there are just some new capabilities that we don't have. And so there's a large focus that I have with our executive team and our CEO and thinking through those critical roles that we need to activate in order to kind of, to, to build on this, uh, this business led cloud transformation. And lastly, probably the hardest one, but the one that I'm most jazzed about is really this focus on changing the mindsets and behaviors. Um, and I think there, you know, this is where the power of three is, is really, uh, kind of coming together nicely. I mean, we're working on things like, you know, how do we create this patient obsessed curiosity, um, and really kind of unlock innovation with a real, kind of a growth mindset. >>Uh, and the level of curiosity that's needed, not to just continue to do the same things, but to really challenge the status quo. So that's one big area of focus we're having the agility to act just faster. I mean, to worry less, I guess I would say about kind of the standard chain of command, but how do you make more speedy, more courageous decisions? And this is places where we can emulate the way that a partner like AWS works, or how do we collaborate across the number of boundaries, you know, and I think, uh, Arjun spoke eloquently to a number of partnerships that we can build. So we can break down some of these barriers and use these networks, um, whether it's within our own internal ecosystem or externally to help, to create value faster. So a lot of energy around ways of working and we'll have to check back in, but I mean, we're early in on this mindset and behavioral shift, um, but a lot of good early momentum. >>Carl you've given me a good segue to talk to Brian about innovation, because you said a lot of the things that I was the customer obsession and this idea of innovating much more quickly. Obviously now the world has its eyes on drug development, and we've all learned a lot about it, uh, in the past few months and accelerating drug development is all, uh, is of great interest to all of us. Brian, how does a transformation like this help a company's, uh, ability to become more agile and more innovative and add a quicker speed to, >>Yeah, no, absolutely. And I think some of the things that Carl talked about just now are critical to that, right? I think where sometimes folks fall short is they think, you know, we're going to roll out the technology and the technology is going to be the silver bullet where in fact it is the culture, it is, is the talent. And it's the focus on that. That's going to be, you know, the determinant of success. And I will say, you know, in this power of three arrangement and Carl talked a little bit about the pyramid, um, talent and culture and that change, and that kind of thinking about that has been a first-class citizen since the very beginning, right. That absolutely is critical for, for being there. Um, and, and so that's been, that's been key. And so we think about innovation at Amazon and AWS, and Carl mentioned some of the things that, you know, partner like AWS can bring to the table is we talk a lot about builders, right? >>So kind of obsessive about builders. Um, and, and we meet what we mean by that is we at Amazon, we hire for builders, we cultivate builders and we like to talk to our customers about it as well. And it also implies a different mindset, right? When you're a builder, you have that, that curiosity, you have that ownership, you have that stake and whatever I'm creating, I'm going to be a co-owner of this product or this service, right. Getting back to that kind of product oriented mindset. And it's not just the technical people or the it people who are builders. It is also the business people as, as Carl talked about. Right. So when we start thinking about, um, innovation again, where we see folks kind of get into a little bit of a innovation pilot paralysis, is that you can focus on the technology, but if you're not focusing on the talent and the culture and the processes and the mechanisms, you're going to be putting out technology, but you're not going to have an organization that's ready to take it and scale it and accelerate it. >>Right. And so that's, that's been absolutely critical. So just a couple of things we've been doing with, with Takeda and Decatur has really been leading the way is, think about a mechanism and a process. And it's really been working backward from the customer, right? In this case, again, the patient and the donor. And that was an easy one because the key value of Decatur is to be a patient focused bio-pharmaceutical right. So that was embedded in their DNA. So that working back from that, that patient, that donor was a key part of that process. And that's really deep in our DNA as well. And Accenture's, and so we were able to bring that together. The other one is, is, is getting used to experimenting and even perhaps failing, right. And being able to iterate and fail fast and experiment and understanding that, you know, some decisions, what we call it at Amazon are two two-way doors, meaning you can go through that door, not like what you see and turn around and go back. And cloud really helps there because the costs of experimenting and the cost of failure is so much lower than it's ever been. You can do it much faster and the implications are so much less. So just a couple of things that we've been really driving, uh, with the cadence around innovation, that's been really critical. Carl, where are you already seeing signs of success? >>Yeah, no, it's a great question. And so we chose, you know, uh, with our focus on innovation to try to unleash maybe the power of data digital in, uh, in focusing on what I call sort of a nave. And so we chose our, our, our plasma derived therapy business, um, and you know, the plasma-derived therapy business unit, it develops critical life-saving therapies for patients with rare and complex diseases. Um, but what we're doing is by bringing kind of our energy together, we're focusing on creating, I'll call it state of the art digitally connected donation centers. And we're really modernizing, you know, the, the, the donor experience right now, we're trying to, uh, improve also I'll call it the overall plasma collection process. And so we've, uh, selected a number of alcohol at a very high speed pilots that we're working through right now, specifically in this, in this area. And we're seeing >>Really great results already. Um, and so that's, that's one specific area of focus are Jen, I want you to close this out here. Any ideas, any best practices advice you would have for other pharmaceutical companies that are, that are at the early stage of their cloud journey? Sorry. Was that for me? Yes. Sorry. Origin. Yeah, no, I was breaking up a bit. No, I think they, um, the key is what's sort of been great for me to see is that when people think about cloud, you know, you always think about infrastructure technology. The reality is that the cloud is really the true enabler for innovation and innovating at scale. And, and if you think about that, right, and all the components that you need, ultimately, that's where the value is for the company, right? Because yes, you're going to get some cost synergies and that's great, but the true value is in how do we transform the organization in the case of the Qaeda and our life sciences clients, right. >>We're trying to take a 14 year process of research and development that takes billions of dollars and compress that right. Tremendous amounts of innovation opportunity. You think about the commercial aspect, lots of innovation can come there. The plasma derived therapy is a great example of how we're going to really innovate to change the trajectory of that business. So I think innovation is at the heart of what most organizations need to do. And the formula, the cocktail that the Qaeda has constructed with this footie program really has all the ingredients, um, that are required for that success. Great. Well, thank you so much. Arjun, Brian and Carl was really an enlightening conversation. Thank you. It's been a lot of, thank you. Yeah, it's been fun. Thanks Rebecca. And thank you for tuning into the cube. Virtual has coverage of the Accenture executive summit >>From around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Welcome everyone to the cubes coverage of Accenture executive summit here at AWS reinvent. I'm your host Rebecca Knight for this segment? We have two guests. First. We have Helen Davis. She is the senior director of cloud platform services, assistant director for it and digital for the West Midlands police. Thanks so much for coming on the show, Helen, and we also have Matthew pound. He is Accenture health and public service associate director and West Midlands police account lead. Thanks so much for coming on the show. Matthew, thank you for having us. So we are going to be talking about delivering data-driven insights to the West Midlands police force. Helen, I want to start with >>You. Can you tell us a little bit about the West Midlands police force? How big is the force and also what were some of the challenges that you were grappling with prior to this initiative? >>Yeah, certainly. So Westerners police is the second largest police force in the UK, outside of the metropolitan police in London. Um, we have an excessive, um, 11,000 people work at Westman ins police serving communities, um, through, across the Midlands region. So geographically, we're quite a big area as well, as well as, um, being population, um, density, having that as a, at a high level. Um, so the reason we sort of embarked on the data-driven insights platform and it, which was a huge change for us was for a number of reasons. Um, namely we had a lot of disparate data, um, which was spread across a range of legacy systems that were many, many years old, um, with some duplication of what was being captured and no single view for offices or, um, support staff. Um, some of the access was limited. You have to be in a, in an actual police building on a desktop computer to access it. Um, other information could only reach the offices on the frontline through a telephone call back to one of our enabling services where they would do a manual checkup, um, look at the information, then call the offices back, um, and tell them what they needed to know. So it was a very long laborious, um, process and not very efficient. Um, and we certainly weren't exploiting the data that we had in a very productive way. >>So it sounds like as you're describing and an old clunky system that needed a technological, uh, reimagination, so what was the main motivation for, for doing, for making this shift? >>It was really, um, about making us more efficient and more effective in how we do how we do business. So, um, you know, certainly as a, as an it leader and sort of my operational colleagues, we recognize the benefits, um, that data and analytics could bring in, uh, in a policing environment, not something that was, um, really done in the UK at the time. You know, we have a lot of data, so we're very data rich and the information that we have, but we needed to turn it into information that was actionable. So that's where we started looking for, um, technology partners and suppliers to help us and sort of help us really with what's the art of the possible, you know, this hasn't been done before. So what could we do in this space that's appropriate for policing? >>I love that idea. What is the art of the possible, can you tell us a little bit about why you chose AWS? >>I think really, you know, as with all things and when we're procuring a partner in the public sector that, you know, there are many rules and regulations, uh, quite rightly as you would expect that to be because we're spending public money. So we have to be very, very careful and, um, it's, it's a long process and we have to be open to public scrutiny. So, um, we sort of look to everything, everything that was available as part of that process, but we recognize the benefits that Clyde would provide in this space because, you know, without moving to a cloud environment, we would literally be replacing something that was legacy with something that was a bit more modern. Um, that's not what we wanted to do. Our ambition was far greater than that. So I think, um, in terms of AWS, really, it was around the scalability, interoperability, you know, disaster things like the disaster recovery service, the fact that we can scale up and down quickly, we call it dialing up and dialing back. Um, you know, it's it's page go. So it just sort of ticked all the boxes for us. And then we went through the full procurement process, fortunately, um, it came out on top for us. So we were, we were able to move forward, but it just sort of had everything that we were looking for in that space. >>Matthew, I want to bring you into the conversation a little bit here. How are you working with a wet with the West Midlands police, sorry. And helping them implement this cloud-first journey? >>Yeah, so I guess, um, by January the West Midlands police started, um, favorite five years ago now. So, um, we set up a partnership with the force. I wanted to operate in a way that it was very different to a traditional supplier relationship. Um, secretary that the data difference insights program is, is one of many that we've been working with last nights on, um, over the last five years. Um, as having said already, um, cloud gave a number of, uh, advantages certainly from a big data perspective and the things that that enabled us today, um, I'm from an Accenture perspective that allowed us to bring in a number of the different themes that we have say, cloud teams, security teams, um, and drafted from an insurance perspective, as well as more traditional services that people would associate with the country. >>I mean, so much of this is about embracing comprehensive change to experiment and innovate and try different things. Matthew, how, how do you help, uh, an entity like West Midlands police think differently when they are, there are these ways of doing things that people are used to, how do you help them think about what is the art of the possible, as Helen said, >>There's a few things to that enable those being critical is trying to co-create solutions together. Yeah. There's no point just turning up with, um, what we think is the right answer, try and say, um, collectively work three, um, the issues that the fullest is seeing and the outcomes they're looking to achieve rather than simply focusing on a long list of requirements, I think was critical and then being really open to working together to create the right solution. Um, rather than just, you know, trying to pick something off the shelf that maybe doesn't fit the forces requirements in the way that it should too, >>Right. It's not always a one size fits all. >>Absolutely not. You know, what we believe is critical is making sure that we're creating something that met the forces needs, um, in terms of the outcomes they're looking to achieve the financial envelopes that were available, um, and how we can deliver those in a, uh, iterative agile way, um, rather than spending years and years, um, working towards an outcome, um, that is gonna update before you even get that. >>So Helen, how, how are things different? What kinds of business functions and processes have been re-imagined in, in light of this change and this shift >>It's, it's actually unrecognizable now, um, in certain areas of the business as it was before. So to give you a little bit of, of context, when we, um, started working with essentially in AWS on the data driven insights program, it was very much around providing, um, what was called locally, a wizzy tool for our intelligence analysts to interrogate data, look at data, you know, decide whether they could do anything predictive with it. And it was very much sort of a back office function to sort of tidy things up for us and make us a bit better in that, in that area or a lot better in that area. And it was rolled out to a number of offices, a small number on the front line. Um, I'm really, it was, um, in line with a mobility strategy that we, hardware officers were getting new smartphones for the first time, um, to do sort of a lot of things on, on, um, policing apps and things like that to again, to avoid them, having to keep driving back to police stations, et cetera. >>And the pilot was so successful. Every officer now has access to this data, um, on their mobile devices. So it literally went from a handful of people in an office somewhere using it to do sort of clever bang things to, um, every officer in the force, being able to access that level of data at their fingertips. Literally. So what they were touched with done before is if they needed to check and address or check details of an individual, um, just as one example, they would either have to, in many cases, go back to a police station to look it up themselves on a desktop computer. Well, they would have to make a call back to a centralized function and speak to an operator, relay the questions, either, wait for the answer or wait for a call back with the answer when those people are doing the data interrogation manually. >>So the biggest change for us is the self-service nature of the data we now have available. So officers can do it themselves on their phone, wherever they might be. So the efficiency savings from that point of view are immense. And I think just parallel to that is the quality of our, because we had a lot of data, but just because you've got a lot of data and a lot of information doesn't mean it's big data and it's valuable necessarily. Um, so again, it was having the single source of truth as we, as we call it. So you know that when you are completing those safe searches and getting the responses back, that it is the most accurate information we hold. And also you're getting it back within minutes, as opposed to, you know, half an hour, an hour or a drive back to a station. So it's making officers more efficient and it's also making them safer. The more efficient they are, the more time they have to spend out with the public doing what they, you know, we all should be doing >>That kind of return on investment because what you were just describing with all the steps that we needed to be taken in prior to this, to verify an address say, and those are precious seconds when someone's life is on the line in, in sort of in the course of everyday police work. >>Absolutely. Yeah, absolutely. It's difficult to put a price on it. It's difficult to quantify. Um, but all the, you know, the minutes here and there certainly add up to a significant amount of efficiency savings, and we've certainly been able to demonstrate the officers are spending less time up police stations as a result or more time out on the front line. Also they're safer because they can get information about what may or may not be and address what may or may not have occurred in an area before very, very quickly without having to wait. >>I do, I want to hear your observations of working so closely with this West Midlands police. Have you noticed anything about changes in its culture and its operating model in how police officers interact with one another? Have you seen any changes since this technology change? >>What's unique about the Western displaces, the buy-in from the top down, the chief and his exact team and Helen as the leader from an IOT perspective, um, the entire force is bought in. So what is a significant change program? Uh, I'm not trickles three. Um, everyone in the organization, um, change is difficult. Um, and there's a lot of time effort that's been put in to bake the technical delivery and the business change and adoption aspects around each of the projects. Um, but you can see the step change that is making in each aspect to the organization, uh, and where that's putting West Midlands police as a leader in, um, technology I'm policing in the UK. And I think globally, >>And this is a question for both of you because Matthew, as you said, change is difficult and there is always a certain intransigence in workplaces about this is just the way we've always done things and we're used to this and don't try us to get us. Don't try to get us to do anything new here. It works. How do you get the buy-in that you need to do this kind of digital transformation? >>I think it would be wrong to say it was easy. Um, um, we also have to bear in mind that this was one program in a five-year program. So there was a lot of change going on, um, both internally for some of our back office functions, as well as front tie, uh, frontline offices. So with DDI in particular, I think the stack change occurred when people could see what it could do for them. You know, we had lots of workshops and seminars where we all talk about, you know, big data and it's going to be great and it's data analytics and it's transformational, you know, and quite rightly people that are very busy doing a day job, but not necessarily technologists in the main and, you know, are particularly interested quite rightly so in what we are not dealing with the cloud, you know? And it was like, yeah, okay. >>It's one more thing. And then when they started to see on that, on their phones and what teams could do, that's when it started to sell itself. And I think that's when we started to see, you know, to see the stat change, you know, and, and if we, if we have any issues now it's literally, you know, our help desks in meltdown. Cause everyone's like, well, we call it manage without this anymore. And I think that speaks for itself. So it doesn't happen overnight. It's sort of incremental changes and then that's a step change in attitude. And when they see it working and they see the benefits, they want to use it more. And that's how it's become fundamental to all policing by itself, really, without much selling >>You, Helen just made a compelling case for how to get buy in. Have you discovered any other best practices when you are trying to get everyone on board for this kind of thing? >>We've um, we've used a lot of the traditional techniques, things around comms and engagement. We've also used things like, um, the 30 day challenge and nudge theory around how can we gradually encourage people to use things? Um, I think there's a point where all of this around, how do we just keep it simple and keep it user centric from an end user perspective? I think DDI is a great example of where the, the technology is incredibly complex. The solution itself is, um, you know, extremely large and, um, has been very difficult to, um, get delivered. But at the heart of it is a very simple front end for the user to encourage it and take that complexity away from them. Uh, I think that's been critical through the whole piece of DDR. >>One final word from Helen. I want to hear, where do you go from here? What is the longterm vision? I know that this has made productivity, um, productivity savings equivalent to 154 full-time officers. Uh, what's next, >>I think really it's around, um, exploiting what we've got. Um, I use the phrase quite a lot, dialing it up, which drives my technical architects crazy, but because it's apparently not that simple, but, um, you know, we've, we've been through significant change in the last five years and we are still continuing to batch all of those changes into everyday, um, operational policing. But what we need to see is we need to exploit and build on the investments that we've made in terms of data and claims specifically, the next step really is about expanding our pool of data and our functions. Um, so that, you know, we keep getting better and better at this. Um, the more we do, the more data we have, the more refined we can be, the more precise we are with all of our actions. Um, you know, we're always being expected to, again, look after the public purse and do more for less. And I think this is certainly an and our cloud journey and cloud first by design, which is where we are now, um, is helping us to be future-proofed. So for us, it's very much an investment. And I see now that we have good at embedded in operational policing for me, this is the start of our journey, not the end. So it's really exciting to see where we can go from here. >>Exciting times. Indeed. Thank you so much. Lily, Helen and Matthew for joining us. I really appreciate it. Thank you. And you are watching the cube stay tuned for more of the cubes coverage of the AWS reinvent Accenture executive summit. I'm Rebecca Knight from around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Hi, everyone. Welcome to the cube virtual coverage of the executive summit at AWS reinvent 2020 virtual. This is the cube virtual. We can't be there in person like we are every year we have to be remote. This executive summit is with special programming supported by Accenture where the cube virtual I'm your host John for a year, we had a great panel here called uncloud first digital transformation from some experts, Stuart driver, the director of it and infrastructure and operates at lion Australia, Douglas Regan, managing director, client account lead at lion for Accenture as a deep Islam associate director application development lead for Accenture gentlemen, thanks for coming on the cube virtual that's a mouthful, all that digital, but the bottom line it's cloud transformation. This is a journey that you guys have been on together for over 10 years to be really a digital company. Now, some things have happened in the past year that kind of brings all this together. This is about the next generation organization. So I want to ask Stuart you first, if you can talk about this transformation at lion has undertaken some of the challenges and opportunities and how this year in particular has brought it together because you know, COVID has been the accelerant of digital transformation. Well, if you're 10 years in, I'm sure you're there. You're in the, uh, on that wave right now. Take a minute to explain this transformation journey. >>Yeah, sure. So number of years back, we looked at kind of our infrastructure and our landscape trying to figure out where we >>Wanted to go next. And we were very analog based and stuck in the old it groove of, you know, Capitol reef rash, um, struggling to transform, struggling to get to a digital platform and we needed to change it up so that we could become very different business to the one that we were back then obviously cloud is an accelerant to that. And we had a number of initiatives that needed a platform to build on. And a cloud infrastructure was the way that we started to do that. So we went through a number of transformation programs that we didn't want to do that in the old world. We wanted to do it in a new world. So for us, it was partnering up with a dried organizations that can take you on the journey and, uh, you know, start to deliver bit by bit incremental progress, uh, to get to the, uh, I guess the promise land. >>Um, we're not, not all the way there, but to where we're on the way along. And then when you get to some of the challenges like we've had this year, um, it makes all of the hard work worthwhile because you can actually change pretty quickly, um, provide capacity and, uh, and increase your environments and, you know, do the things that you need to do in a much more dynamic way than we would have been able to previously where we might've been waiting for the hardware vendors, et cetera, to deliver capacity. So for us this year, it's been a pretty strong year from an it perspective and delivering for the business needs >>Before I hit the Douglas. I want to just real quick, a redirect to you and say, you know, if all the people said, Oh yeah, you got to jump on cloud, get in early, you know, a lot of naysayers like, well, wait till to mature a little bit, really, if you got in early and you, you know, paying your dues, if you will taking that medicine with the cloud, you're really kind of peaking at the right time. Is that true? Is that one of the benefits that comes out of this getting in the cloud? Yeah, >>John, this has been an unprecedented year, right. And, um, you know, Australia, we had to live through Bush fires and then we had covert and, and then we actually had to deliver a, um, a project on very large transformational project, completely remote. And then we also had had some, some cyber challenges, which is public as well. And I don't think if we weren't moved into and enabled through the cloud, we would have been able to achieve that this year. It would have been much different, would have been very difficult to do the backing. We're able to work and partner with Amazon through this year, which is unprecedented and actually come out the other end. Then we've delivered a brand new digital capability across the entire business. Um, in many, you know, wouldn't have been impossible if we could, I guess, state in the old world, the fact that we were moved into the new Naval by the new allowed us to work in this unprecedented year. >>Just quick, what's your personal view on this? Because I've been saying on the Cuban reporting necessity is the mother of all invention and the word agility has been kicked around as kind of a cliche, Oh, it'd be agile. You know, we're going to get the city, you get a minute on specifically, but from your perspective, uh, Douglas, what does that mean to you? Because there is benefits there for being agile. And >>I mean, I think as Stuart mentioned, right, in a lot of these things we try to do and, you know, typically, you know, hardware and of the last >>To be told and, and, and always on the critical path to be done, we really didn't have that in this case, what we were doing with our projects in our deployments, right. We were able to move quickly able to make decisions in line with the business and really get things going. Right. So you see a lot of times in a traditional world, you have these inhibitors, you have these critical path, it takes weeks and months to get things done as opposed to hours and days, and truly allowed us to, we had to, you know, VJ things, move things. And, you know, we were able to do that in this environment with AWS support and the fact that we can kind of turn things off and on as quickly as we need it. >>Yeah. Cloud-scale is great for speed. So DECA, Gardez get your thoughts on this cloud first mission, you know, it, you know, the dev ops world, they saw this early that jumping in there, they saw the, the, the agility. Now the theme this year is modern applications with the COVID pandemic pressure, there's real business pressure to make that happen. How did you guys learn to get there fast? And what specifically did you guys do at Accenture and how did it all come together? Can you take us inside kind of how it played out? >>Oh, right. So yeah, we started off with, as we do in most cases with a much more bigger group, and we worked with lions functional experts and, uh, the lost knowledge that allowed the infrastructure being had. Um, we then applied our journey to cloud strategy, which basically revolves around the seminars and, and, uh, you know, the deep three steps from our perspective, uh, assessing the current environment, setting up the new cloud environment. And as we go modernizing and, and migrating these applications to the cloud now, you know, one of the key things that, uh, you know, we learned along this journey was that, you know, you can have the best plans, but bottom line that we were dealing with, we often than not have to make changes. Uh, what a lot of agility and also work with a lot of collaboration with the, uh, Lyon team, as well as, uh, uh, AWS. I think the key thing for me was being able to really bring it all together. It's not just, uh, you know, essentially mobilize it's all of us working together to make this happen. >>What were some of the learnings real quick journeys? >>So I think so the perspective of the key learnings that, you know, uh, you know, when you look back at, uh, the, the infrastructure that was that we were trying to migrate over to the cloud, a lot of the documentation, et cetera, was not available. We were having to, uh, figure out a lot of things on the fly. Now that really required us to have, uh, uh, people with deep expertise who could go into those environments and, and work out, uh, you know, the best ways to, to migrate the workloads to the cloud. Uh, I think, you know, the, the biggest thing for me was making sure all the had on that real SMEs across the board globally, that we could leverage across the various technologies, uh, uh, and, and, and, you know, that would really work in our collaborative and agile environment with line. >>Let's do what I got to ask you. How did you address your approach to the cloud and what was your experience? >>Yeah, for me, it's around getting the foundations right. To start with and then building on them. Um, so, you know, you've gotta have your, your, your process and you've got to have your, your kind of your infrastructure there and your blueprints ready. Um, AWS do a great job of that, right. Getting the foundations right. And then building upon it, and then, you know, partnering with Accenture allows you to do that very successfully. Um, I think, um, you know, the one thing that was probably surprising to us when we started down this journey and kind of after we got a long way down the track and looking backwards is actually how much you can just turn off. Right? So a lot of stuff that you, uh, you get left with a legacy in your environment, and when you start to work through it with the types of people that civic just mentioned, you know, the technical expertise working with the business, um, you can really rationalize your environment and, uh, you know, cloud is a good opportunity to do that, to drive that legacy out. >>Um, so you know, a few things there, the other thing is, um, you've got to try and figure out the benefits that you're going to get out of moving here. So there's no point just taking something that is not delivering a huge amount of value in the traditional world, moving it into the cloud, and guess what is going to deliver the same limited amount of value. So you've got to transform it, and you've got to make sure that you build it for the future and understand exactly what you're trying to gain out of it. So again, you need a strong collaboration. You need a good partners to work with, and you need good engagement from the business as well, because the kind of, uh, you know, digital transformation, cloud transformation, isn't really an it project, I guess, fundamentally it is at the core, but it's a business project that you've got to get the whole business aligned on. You've got to make sure that your investment streams are appropriate and that you're able to understand the benefits and the value that, so you're going to drive back towards the business. >>Let's do it. If you don't mind me asking, what was some of the obstacles you encountered or learnings, um, that might different from the expectation we all been there, Hey, you know, we're going to change the world. Here's the sales pitch, here's the outcome. And then obviously things happen, you know, you learn legacy, okay. Let's put some containerization around that cloud native, um, all that rational. You're talking about what are, and you're going to have obstacles. That's how you learn. That's how perfection has developed. How, what obstacles did you come up with and how are they different from your expectations going in? >>Yeah, they're probably no different from other people that have gone down the same journey. If I'm totally honest, the, you know, 70 or 80% of what you do is relatively easy of the known quantity. It's relatively modern architectures and infrastructures, and you can upgrade, migrate, move them into the cloud, whatever it is, rehost, replatform, rearchitect, whatever it is you want to do, it's the other stuff, right? It's the stuff that always gets left behind. And that's the challenge. It's, it's getting that last bit over the line and making sure that you haven't invested in the future while still carrying all of your legacy costs and complexity within your environment. So, um, to be quite honest, that's probably taken longer and has been more of a challenge than we thought it would be. Um, the other piece I touched on earlier on in terms of what was surprising was actually how much of, uh, your environment is actually not needed anymore. >>When you start to put a critical eye across it and understand, um, uh, ask the tough questions and start to understand exactly what, what it is you're trying to achieve. So if you ask a part of a business, do they still need this application or this service a hundred percent of the time, they will say yes until you start to lay out to them, okay, now I'm going to cost you this to migrate it or this, to run it in the future. And, you know, here's your ongoing costs and, you know, et cetera, et cetera. And then, uh, for a significant amount of those answers, you get a different response when you start to layer on the true value of it. So you start to flush out those hidden costs within the business, and you start to make some critical decisions as a company based on, uh, based on that. So that was a little tougher than we first thought and probably broader than we thought there was more of that than we anticipated, um, which actually results in a much cleaner environment post and post migration. >>You know, the old expression, if it moves automated, you know, it's kind of a joke on government, how they want to tax everything, you know, you want to automate, that's a key thing in cloud, and you've got to discover those opportunities to create value Stuart and Sadiq. Mainly if you can weigh in on this love to know the percentage of total cloud that you have now, versus when you started, because as you start to uncover whether it's by design for purpose, or you discover opportunities to innovate, like you guys have, I'm sure it kind of, you took on some territory inside Lyon, what percentage of cloud now versus stark? >>Yeah. At the start, it was minimal, right. You know, close to zero, right. Single and single digits. Right. It was mainly SAS environments that we had, uh, sitting in clouds when we, uh, when we started, um, Doug mentioned earlier on a really significant transformation project, um, that we've undertaken and recently gone live on a multi-year one. Um, you know, that's all stood up on AWS and is a significant portion of our environment, um, in terms of what we can move to cloud. Uh, we're probably at about 80 or 90% now. And the balanced bit is, um, legacy infrastructure that is just gonna retire as we go through the cycle rather than migrate to the cloud. Um, so we are significantly cloud-based and, uh, you know, we're reaping the benefits of it. I know you like 20, 20, I'm actually glad that you did all the hard yards in the previous years when you started that business challenges thrown out as, >>So do you any common reaction to the cloud percentage penetration? >>I mean, guys don't, but I was going to say was, I think it's like the 80 20 rule, right? We, we, we worked really hard in the, you know, I think 2018, 19 to get any person off, uh, after getting a loan, the cloud and, or the last year is the 20% that we have been migrating. And Stuart said like, uh, not that is also, that's going to be a good diet. And I think our next big step is going to be obviously, you know, the icing on the tape, which is to decommission all these apps as well. Right. So, you know, to get the real benefits out of, uh, the whole conservation program from a, uh, from a >>Douglas and Stewart, can you guys talk about the decision around the cloud because you guys have had success with AWS, why AWS how's that decision made? Can you guys give some insight into some of those thoughts? >>I can stop, start off. I think back when the decision was made and it was, it was a while back, um, you know, there's some clear advantages of moving relay, Ws, a lot of alignment with some of the significant projects and, uh, the trend, that particular one big transformation project that we've alluded to as well. Um, you know, we needed some, uh, some very robust and, um, just future proof and, um, proven technology. And they Ws gave that to us. We needed a lot of those blueprints to help us move down the path. We didn't want to reinvent everything. So, um, you know, having a lot of that legwork done for us and AWS gives you that, right. And, and particularly when you partner up with, uh, with a company like Accenture as well, you get combinations of the technology and the skills and the knowledge to, to move you forward in that direction. >>So, um, you know, for us, it was a, uh, uh, it was a decision based on, you know, best of breed, um, you know, looking forward and, and trying to predict the future needs and, and, and kind of the environmental that we might need. Um, and, you know, partnering up with organizations that can then take you on the journey. Yeah. And just to build on it. So obviously, you know, lion's like an AWS, but, you know, we knew it was a very good choice given that, um, uh, the skills and the capability that we had, as well as the assets and tools we had to get the most out of, um, AWS and obviously our, our CEO globally, you know, announcement about a huge investment that we're making in cloud. Um, but you know, we've, we've worked very well DWS, we've done some joint workshops and joint investments, um, some joint POC. So yeah, w we have a very good working relationship, AWS, and I think, um, one incident to reflect upon whether it's cyber it's and again, where we actually jointly, you know, dove in with, um, with Amazon and some of their security experts and our experts. And we're able to actually work through that with mine quite successfully. So, um, you know, really good behaviors as an organization, but also really good capabilities. >>Yeah. As you guys, you're essential cloud outcomes, research shown, it's the cycle of innovation with the cloud. That's creating a lot of benefits, knowing what you guys know now, looking back certainly COVID is impacted a lot of people kind of going through the same process, knowing what you guys know now, would you advocate people to jump on this transformation journey? If so, how, and what tweaks they make, which changes, what would you advise? >>Uh, I might take that one to start with. Um, I hate to think where we would have been when, uh, COVID kicked off here in Australia and, you know, we were all sent home, literally were at work on the Friday, and then over the weekend. And then Monday, we were told not to come back into the office and all of a sudden, um, our capacity in terms of remote access and I quadrupled, or more four, five X, uh, what we had on the Friday we needed on the Monday. And we were able to stand that up during the day Monday and into Tuesday, because we were cloud-based. And, uh, you know, we just found up your instances and, uh, you know, sort of our licensing, et cetera. And we had all of our people working remotely, um, within, uh, you know, effectively one business day. >>Um, I know peers of mine in other organizations and industries that are relying on kind of a traditional wise and getting hardware, et cetera, that were weeks and months before they could get their, the right hardware to be able to deliver to their user base. So, um, you know, one example where you're able to scale and, uh, uh, get, uh, get value out of this platform beyond probably what was anticipated at the time you talk about, um, you know, less the, in all of these kinds of things. And you can also think of a few scenarios, but real world ones where you're getting your business back up and running in that period of time is, is just phenomenal. There's other stuff, right? There's these programs that we've rolled out, you do your sizing, um, and in the traditional world, you would just go out and buy more servers than you need. >>And, you know, probably never realize the full value of those, you know, the capability of those servers over the life cycle of them. Whereas you're in a cloud world, you put in what you think is right. And if it's not right, you pump it up a little bit when, when all of your metrics and so on, tell you that you need to bump it up. And conversely you scale it down at the same rate. So for us, with the types of challenges and programs and, uh, uh, and just business need, that's come at as this year, uh, we wouldn't have been able to do it without a strong cloud base, uh, to, uh, to move forward >>Know Douglas. One of the things that I talked to, a lot of people on the right side of history who have been on the right wave with cloud, with the pandemic, and they're happy, they're like, and they're humble. Like, well, we're just lucky, you know, luck is preparation meets opportunity. And this is really about you guys getting in early and being prepared and readiness. This is kind of important as people realize, then you gotta be ready. I mean, it's not just, you don't get lucky by being in the right place, the right time. And there were a lot of companies were on the wrong side of history here who might get washed away. This is a super important, I think, >>To echo and kind of build on what Stewart said. I think that the reason that we've had success and I guess the momentum is we, we didn't just do it in isolation within it and technology. It was actually linked to broader business changes, you know, creating basically a digital platform for the entire business, moving the business, where are they going to be able to come back stronger after COVID, when they're actually set up for growth, um, and actually allows, you know, lying to achievements growth objectives, and also its ambitions as far as what it wants to do, uh, with growth in whatever they make, do with acquiring other companies and moving into different markets and launching new products. So we've actually done it in a way that is, you know, real and direct business benefit, uh, that actually enables line to grow >>General. I really appreciate you coming. I have one final question. If you can wrap up here, uh, Stuart and Douglas, you don't mind weighing in what's the priorities for the future. What's next for lion in a century >>Christmas holidays, I'll start Christmas holidays been a big deal and then a, and then a reset, obviously, right? So, um, you know, it's, it's figuring out, uh, transform what we've already transformed, if that makes sense. So God, a huge proportion of our services sitting in the cloud. Um, but we know we're not done even with the stuff that is in there. We need to take those next steps. We need more and more automation and orchestration. We need to, um, our environment, there's more future growth. We need to be able to work with the business and understand what's coming at them so that we can, um, you know, build that into, into our environment. So again, it's really transformation on top of transformation is the way that I'll describe it. And it's really an open book, right? Once you get it in and you've got the capabilities and the evolving tool sets that, uh, AWS continue to bring to the market, um, you know, working with the partners to, to figure out how we unlock that value, um, you know, drive our costs down efficiency, uh, all of those kind of, you know, standard metrics. >>Um, but you know, we're looking for the next things to transform and show value back out to our customer base, um, that, uh, that we continue to, you know, sell our products to and work with and understand how we can better meet their needs. Yeah, I think just to echo that, I think it's really leveraging this and then did you capability they have and getting the most out of that investment. And then I think it's also moving to, uh, and adopting more new ways of working as far as, you know, the speed of the business, um, is getting up the speed of the market is changing. So being able to launch and do things quickly and also, um, competitive and efficient operating costs, uh, now that they're in the cloud, right? So I think it's really leveraging the most out of the platform and then, you know, being efficient in launching things. So putting them with the business, >>Any word from you on your priorities by you see this year in folding, >>There's got to say like e-learning squares, right, for me around, you know, just journey. This is a journey to the cloud, right. >>And, uh, you know, as well, the sort of Saturday, it's getting all, you know, different parts of the organization along the journey business to it, to your, uh, product lenders, et cetera. Right. And it takes time. It is tough, but, uh, uh, you know, you got to get started on it. And, you know, once we, once we finish off, uh, it's the realization of the benefits now that, you know, looking forward, I think for, from Alliance perspective, it is, uh, you know, once we migrate all the workloads to the cloud, it is leveraging, uh, all staff, right. And as I think students said earlier, uh, with, uh, you know, the latest and greatest stuff that AWS is basically working to see how we can really, uh, achieve more better operational excellence, uh, from a, uh, from a cloud perspective. >>Well, Stewart, thanks for coming on with a and sharing your environment and what's going on and your journey you're on the right wave. Did the work you're in, it's all coming together with faster, congratulations for your success, and, uh, really appreciate Douglas with Steve for coming on as well from Accenture. Thank you for coming on. Thanks, John. Okay. Just the cubes coverage of executive summit at AWS reinvent. This is where all the thought leaders share their best practices, their journeys, and of course, special programming with Accenture and the cube. I'm Sean ferry, your host, thanks for watching from around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Welcome everyone to the cube virtuals coverage of the Accenture executive summit. Part of AWS reinvent 2020. I'm your host Rebecca Knight. We are talking today about reinventing the energy data platform. We have two guests joining us. First. We have Johan Krebbers. He is the GM digital emerging technologies and VP of it. Innovation at shell. Thank you so much for coming on the show, Johan you're welcome. And next we have Liz Dennett. She is the lead solution architect for O S D U on AWS. Thank you so much Liz to be here. So I want to start our conversation by talking about OSD. You like so many great innovations. It started with a problem Johan. What was the problem you were trying to solve at shell? >>Yeah, the ethical back a couple of years, we started shoving 2017 where we had a meeting with the deg, the gas exploration in shell, and the main problem they had. Of course, they got lots of lots of data, but are unable to find the right data. They need to work from all over the place. And totally >>Went to real, probably tried to solve is how that person working exploration could find their proper date, not just a day, but also the date you really needed that we did probably talked about his summer 2017. And we said, okay, they don't maybe see this moving forward is to start pulling that data into a single data platform. And that, that was at the time that we called it as the, you, the subsurface data universe in there was about the shell name was so in, in January, 2018, we started a project with Amazon to start grating a co fricking that building, that Stu environment that subserve the universe, so that single data level to put all your exploration and Wells data into that single environment that was intent. And every cent, um, already in March of that same year, we said, well, from Michelle point of view, we will be far better off if we could make this an industry solution and not just a shelf sluice, because Shelby, Shelby, if you can make an industry solution where people are developing applications for it, it also is far better than for shell to say we haven't shell special solution because we don't make money out of how we start a day that we can make money out of it. >>We have access to the data, we can explore the data. So storing the data we should do as efficiently possibly can. So we monitor, we reach out to about eight or nine other large, uh, or I guess operators like the economics, like the tutorials, like the chefs of this world and say, Hey, we inshallah doing this. Do you want to join this effort? And to our surprise, they all said, yes. And then in September, 2018, we had our kickoff meeting with your open group where we said, we said, okay, if you want to work together with lots of other companies, we also need to look at okay, how, how we organize that. Or if you started working with lots of large companies, you need to have some legal framework around some framework around it. So that's why we went to the open group and say, okay, let's, let's form the old forum as we call it at the time. So it's September, 2080, where I did a Galleria in Houston, but the kickoff meeting for the OT four with about 10 members at the time. So there's just over two years ago, we started an exercise for me called ODU, uh, kicked it off. Uh, and so that's really them will be coming from and how we've got there. Also >>The origin story. Um, what, so what digging a little deeper there? What were some of the things you were trying to achieve with the OSU? >>Well, a couple of things we've tried to achieve with you, um, first is really separating data from applications for what is, what is the biggest problem we have in the subsurface space that the data and applications are all interlinked tied together. And if, if you have them and a new company coming along and say, I have this new application and is access to the data that is not possible because the data often interlinked with the application. So the first thing we did is really breaking the link between the application, the data out as those levels, the first thing we did, secondly, put all the data to a single data platform, take the silos out what was happening in the sub-service space and know they got all the data in what we call silos in small little islands out there. So what we're trying to do is first break the link to great, great. >>They put the data single day, the bathroom, and the third part, put a standard layer on top of that, it's an API layer on top to create a platform. So we could create an ecosystem out of companies to start a valving shop application on top of dev data platform across you might have a data platform, but you're only successful. If you have a rich ecosystem of people start developing applications on top of that. And then you can export the data like small companies, last company, university, you name it, we're getting after create an ecosystem out there. So the three things were as was first break, the link between application data, just break it and put data at the center and also make sure that data, this data structure would not be managed by one company. It would only be met. It will be managed the data structures by the ODI forum. Secondly, then put a data, a single data platform certainly then has an API layer on top and then create an ecosystem. Really go for people, say, please start developing applications because now you have access to the data or the data no longer linked to somebody whose application was all freely available, but an API layer that was, that was all September, 2018, more or less >>To hear a little bit. Can you talk a little bit about some of the imperatives from the AWS standpoint in terms of what you were trying to achieve with this? Yeah, absolutely. And this whole thing is Johann said started with a challenge that was really brought out at shell. The challenges that geoscientists spend up to 70% of their time looking for data. I'm a geologist I've spent more than 70% of my time trying to find data in these silos. And from there, instead of just figuring out how we could address that one problem, we worked together to really understand the root cause of these challenges and working backwards from that use case OSU and OSU on AWS has really enabled customers to create solutions that span, not just this in particular problem, but can really scale to be inclusive of the entire energy value chain and deliver value from these use cases to the energy industry and beyond. >>Thank you, Lee, >>Uh, Johann. So talk a little bit about Accenture's cloud first approach and how it has, uh, helped shell work faster and better with it. >>Well, of course, access a cloud first approach only works together. It's been an Amazon environment, AWS environment. So we really look at, uh, at, at Accenture and others up together helping shell in this space. Now the combination of the two is where we're really looking at, uh, where access of course can be increased knowledge student to that environment operates support knowledge to do an environment. And of course, Amazon will be doing that to this environment that underpinning their services, et cetera. So, uh, we would expect a combination, a lot of goods when we started rolling out and put in production, the old you are three and four because we are anus. Then when release feed comes to the market in Q1 next year of ODU, when he started going to Audi production inside shell, but as the first release, which is ready for prime time production across an enterprise will be released just before Christmas, last year when he's still in may of this year. But really three is the first release we want to use for full scale production deployment inside shell, and also all the operators around the world. And there is one Amazon, sorry, at that one. Um, extensive can play a role in the ongoing, in the, in deployment building up, but also support environment. >>So one of the other things that we talk a lot about here on the cube is sustainability. And this is a big imperative at so many organizations around the world in particular energy companies. How does this move to OSD you, uh, help organizations become, how is this a greener solution for companies? >>Well, first he make it's a greatest solution because you start making a much more efficient use of your resources. is already an important one. The second thing we're doing is also, we started with ODU in framers, in the oil and gas space in the expert development space. We've grown, uh, OTU in our strategy, we've grown. I was, you know, also do an alternative energy sociology. We'll all start supporting next year. Things like solar farms, wind farms, uh, the, the dermatomal environment hydration. So it becomes an and, and an open energy data platform, not just what I want to get into steep that's for new industry, any type of energy industry. So our focus is to create, bring the data of all those various energy data sources to get me to a single data platform you can to use AI and other technology on top of that, to exploit the data, to beat again into a single data platform. >>Liz, I want to ask you about security because security is, is, is such a big concern when it comes to data. How secure is the data on OSD? You, um, actually, can I talk, can I do a follow up on this sustainability talking? Oh, absolutely. By all means. I mean, I want to interject though security is absolutely our top priority. I don't mean to move away from that, but with sustainability, in addition to the benefits of the OSU data platform, when a company moves from on-prem to the cloud, they're also able to leverage the benefits of scale. Now, AWS is committed to running our business in the most environmentally friendly way possible. And our scale allows us to achieve higher resource utilization and energy efficiency than a typical data center. Now, a recent study by four 51 research found that AWS is infrastructure is 3.6 times more energy efficient than the median of surveyed enterprise data centers. Two thirds of that advantage is due to higher, um, server utilization and a more energy efficient server population. But when you factor in the carbon intensity of consumed electricity and renewable energy purchases for 51 found that AWS performs the same task with an 88% lower carbon footprint. Now that's just another way that AWS and OSU are working to support our customers is they seek to better understand their workflows and make their legacy businesses less carbon intensive. >>That's that's incorrect. Those are those statistics are incredible. Do you want to talk a little bit now about security? Absolutely. Security will always be AWS is top priority. In fact, AWS has been architected to be the most flexible and secure cloud computing environment available today. Our core infrastructure is built to satisfy. There are the security requirements for the military global banks and other high sensitivity organizations. And in fact, AWS uses the same secure hardware and software to build an operate each of our regions. So that customers benefit from the only commercial cloud that's hat hits service offerings and associated supply chain vetted and deemed secure enough for top secret workloads. That's backed by a deep set of cloud security tools with more than 200 security compliance and governmental service and key features as well as an ecosystem of partners like Accenture, that can really help our customers to make sure that their environments for their data meet and or exceed their security requirements. Johann, I want you to talk a little bit about how OSD you can be used today. Does it only handle subsurface data? >>Uh, today it's Honda's subserves or Wells data. We got to add to that production around the middle of next year. That means that the whole upstate business. So we've got goes from exploration all the way to production. You've made it together into a single data platform. So production will be added around Q3 of next year. Then a principal. We have a difficult, the elder data that single environment, and we want to extend it then to other data sources or energy sources like solar farms, wind farms, uh, hydrogen, hydro, et cetera. So we're going to add a whore, a whole list of audit day energy source to them and be all the data together into a single data club. So we move from an all in guest data platform to an entity data platform. That's really what our objective is because the whole industry, if you look it over, look at our competition or moving in that same two acts of quantity of course, are very strong in oil and gas, but also increased the, got into other energy sources like, like solar, like wind, like th like highly attended, et cetera. So we would be moving exactly what it's saying, method that, that, that, that the whole OSU can't really support at home. And as a spectrum of energy sources, >>Of course, and Liz and Johan. I want you to close this out here by just giving us a look into your crystal balls and talking about the five and 10 year plan for OSD. We'll start with you, Liz, what do you, what do you see as the future holding for this platform? Um, honestly, the incredibly cool thing about working at AWS is you never know where the innovation and the journey is going to take you. I personally am looking forward to work with our customers, wherever their OSU journeys, take them, whether it's enabling new energy solutions or continuing to expand, to support use cases throughout the energy value chain and beyond, but really looking forward to continuing to partner as we innovate to slay tomorrow's challenges, Johann first, nobody can look at any more nowadays, especially 10 years, but our objective is really in the next five years, you will become the key backbone for energy companies for store your data intelligence and optimize the whole supply energy supply chain, uh, in this world Johan Krebbers Liz Dennett. Thank you so much for coming on the cube virtual. Thank you. I'm Rebecca Knight stay tuned for more of our coverage of the Accenture executive summit >>From around the globe. It's the cube with digital coverage of AWS reinvent executive summit 2020, sponsored by Accenture and AWS. >>Welcome everyone to the cubes coverage of the Accenture executive summit. Part of AWS reinvent. I'm your host Rebecca Knight today we're welcoming back to Cuba alum. We have Kishor Dirk. He is the Accenture senior managing director cloud first global services lead. Welcome back to the show Kishore. Thank you very much. Nice to meet again. And, uh, Tristan moral horse set. He is the managing director, Accenture cloud first North American growth. Welcome back to you to Tristin. Great to be back in grapes here again, Rebecca. Exactly. Even in this virtual format, it is good to see your faces. Um, today we're going to be talking about my NAB and green cloud advisor capability. Kishor I want to start with you. So my NAB is a platform that is really celebrating its first year in existence. Uh, November, 2019 is when Accenture introduced it. Uh, but it's, it has new relevance in light of this global pandemic that we are all enduring and suffering through. Tell us a little bit about the lineup platform, what it is that cloud platform to help our clients navigate the complexity of cloud and cloud decisions and to make it faster. And obviously, you know, we have in the cloud, uh, you know, with >>The increased relevance and all the, especially over the last few months with the impact of COVID crisis and exhibition of digital transformation, you know, we are seeing the transformation of the exhibition to cloud much faster. This platform that you're talking about has enabled hardened 40 clients globally across different industries. You identify the right cloud solution, navigate the complexity, provide a cloud specific solution simulate for our clients to meet that strategy business needs. And the clients are loving it. >>I want to go to you now trust and tell us a little bit about how my nav works and how it helps companies make good cloud choice. >>Yeah, so Rebecca, we we've talked about cloud is, is more than just infrastructure and that's what mine app tries to solve for it. It really looks at a variety of variables, including infrastructure operating model and fundamentally what clients' business outcomes, um, uh, our clients are, are looking for and, and identifies the optimal solution for what they need. And we assign this to accelerate. And we mentioned that the pandemic, one of the big focus now is to accelerate. And so we worked through a three-step process. The first is scanning and assessing our client's infrastructure, their data landscape, their application. Second, we use our automated artificial intelligence engine to interact with. We have a wide variety and library of, uh, collective plot expertise. And we look to recommend what is the enterprise architecture and solution. And then third, before we live with our clients, we look to simulate and test this scaled up model. And the simulation gives our clients a way to see what cloud is going to look like, feel like and how it's going to transform their business before they go there. >>Tell us a little bit about that in real life. Now as a company, so many of people are working remotely having to collaborate, uh, not in real life. How is that helping them right now? >>So, um, the, the pandemic has put a tremendous strain on systems, uh, because of the demand on those systems. And so we talk about resiliency. We also now need to collaborate across data across people. Um, I think all of us are calling from a variety of different places where our last year we were all at the VA cube itself. Um, and, and cloud technologies such as teams, zoom that we're we're leveraging now has fundamentally accelerated and clients are looking to onboard this for their capabilities. They're trying to accelerate their journey. They realize that now the cloud is what is going to become important for them to differentiate. Once we come out of the pandemic and the ability to collaborate with their employees, their partners, and their clients through these systems is becoming a true business differentiator for our clients. >>Keisha, I want to talk with you now about my navs multiple capabilities, um, and helping clients design and navigate their cloud journeys. Tell us a little bit about the green cloud advisor capability and its significance, particularly as so many companies are thinking more deeply and thoughtfully about sustainability. >>Yes. So since the launch of my NAB, we continue to enhance capabilities for our clients. One of the significant, uh, capabilities that we have enabled is the being or advisor today. You know, Rebecca, a lot of the businesses are more environmentally aware and are expanding efforts to decrease power consumption, uh, and obviously carbon emissions and, uh, and run a sustainable operations across every aspect of the enterprise. Uh, as a result, you're seeing an increasing trend in adoption of energy, efficient infrastructure in the global market. And one of the things that we did, a lot of research we found out is that there's an ability to influence our client's carbon footprint through a better cloud solution. And that's what we internalize, uh, brings to us, uh, in, in terms of a lot of the client connotation that you're seeing in Europe, North America and others. Lot of our clients are accelerating to a green cloud strategy to unlock greater financial societal and environmental benefit, uh, through obviously cloud-based circular, operational, sustainable products and services. That is something that we are enhancing my now, and we are having active client discussions at this point of time. >>So Tristan, tell us a little bit about how this capability helps clients make greener decisions. >>Yeah. Um, well, let's start about the investments from the cloud providers in renewable and sustainable energy. Um, they have most of the hyperscalers today, um, have been investing significantly on data centers that are run on renewable energy, some incredibly creative constructs on the, how, how to do that. And sustainability is there for a key, um, key item of importance for the hyperscalers and also for our clients who now are looking for sustainable energy. And it turns out this marriage is now possible. I can, we marry the, the green capabilities of the cloud providers with a sustainability agenda of our clients. And so what we look into the way the mind works is it looks at industry benchmarks and evaluates our current clients, um, capabilities and carpet footprint leveraging their existing data centers. We then look to model from an end-to-end perspective, how the, their journey to the cloud leveraging sustainable and, um, and data centers with renewable energy. We look at how their solution will look like and, and quantify carbon tax credits, um, improve a green index score and provide quantifiable, um, green cloud capabilities and measurable outcomes to our clients, shareholders, stakeholders, clients, and customers. Um, and our green plot advisers sustainability solutions already been implemented at three clients. And in many cases in two cases has helped them reduce the carbon footprint by up to 400% through migration from their existing data center to green cloud. Very, very, >>That is remarkable. Now tell us a little bit about the kinds of clients. Is this, is this more interesting to clients in Europe? Would you say that it's catching on in the United States? Where, what is the breakdown that you're seeing right now? >>Sustainability is becoming such a global agenda and we're seeing our clients, um, uh, tie this and put this at board level, um, uh, agenda and requirements across the globe. Um, Europe has specific constraints around data sovereignty, right, where they need their data in country, but from a green, a sustainability agenda, we see clients across all our markets, North America, Europe in our growth markets adopt this. And we have seen case studies and all three months, >>Kesha. I want to bring you back into the conversation. Talk a little bit about how MindUP ties into Accenture's cloud first strategy, your Accenture's CEO, Julie Sweet, um, has talked about post COVID leadership, requiring every business to become a cloud first business. Tell us a little bit about how this ethos is in Accenture and how you're sort of looking outward with it too. >>So Rebecca mine is the launch pad, uh, to a cloud first transformation for our clients. Uh, Accenture, see your jewelry suite, uh, shared the Accenture cloud first and our substantial investment demonstrate our commitment and is delivering greater value for our clients when they need it the most. And with the digital transformation requiring cloud at scale, you know, we're seeing that in the post COVID leadership, it requires that every business should become a cloud business. And my nap helps them get there by evaluating the cloud landscape, navigating the complexity, modeling architecting and simulating an optimal cloud solution for our clients. And as Justin was sharing a greener cloud. >>So Tristan, talk a little bit more about some of the real life use cases in terms of what are we, what are clients seeing? What are the results that they're having? >>Yes. Thank you, Rebecca. I would say two key things right around my notes. The first is the iterative process. Clients don't want to wait, um, until they get started, they want to get started and see what their journey is going to look like. And the second is fundamental acceleration, dependent make, as we talked about, has accelerated the need to move to cloud very quickly. And my nav is there to do that. So how do we do that? First is generating the business cases. Clients need to know in many cases that they have a business case by business case, we talk about the financial benefits, as well as the business outcomes, the green, green clot impact sustainability impacts with minus. We can build initial recommendations using a basic understanding of their environment and benchmarks in weeks versus months with indicative value savings in the millions of dollars arranges. >>So for example, very recently, we worked with a global oil and gas company, and in only two weeks, we're able to provide an indicative savings where $27 million over five years, this enabled the client to get started, knowing that there is a business case benefit and then iterate on it. And this iteration is, I would say the second point that is particularly important with my nav that we've seen in bank of clients, which is, um, any journey starts with an understanding of what is the application landscape and what are we trying to do with those, these initial assessments that used to take six to eight weeks are now taking anywhere from two to four weeks. So we're seeing a 40 to 50% reduction in the initial assessment, which gets clients started in their journey. And then finally we've had discussions with all of the hyperscalers to help partner with Accenture and leverage mine after prepared their detailed business case module as they're going to clients. And as they're accelerating the client's journey, so real results, real acceleration. And is there a journey? Do I have a business case and furthermore accelerating the journey once we are by giving the ability to work in iterative approach. >>I mean, it sounds as though that the company that clients and and employees are sort of saying, this is an amazing time savings look at what I can do here in, in so much in a condensed amount of time, but in terms of getting everyone on board, one of the things we talked about last time we met, uh, Tristin was just how much, uh, how one of the obstacles is getting people to sign on and the new technologies and new platforms. Those are often the obstacles and struggles that companies face. Have you found that at all? Or what is sort of the feedback that you're getting? >>Yeah, sorry. Yes. We clearly, there are always obstacles to a cloud journey. If there were an obstacles, all our clients would be, uh, already fully in the cloud. What man I gives the ability is to navigate through those, to start quickly. And then as we identify obstacles, we can simulate what things are going to look like. We can continue with certain parts of the journey while we deal with that obstacle. And it's a fundamental accelerator. Whereas in the past one, obstacle would prevent a class from starting. We can now start to address the obstacles one at a time while continuing and accelerating the contrary. That is the fundamental difference. >>Kishor I want to give you the final word here. Tell us a little bit about what is next for Accenture might have and what we'll be discussing next year at the Accenture executive summit, >>Rebecca, we are continuously evolving with our client needs and reinventing reinventing for the future. Well, mine has been toward advisor. Our plan is to help our clients reduce carbon footprint and again, migrate to a green cloud. Uh, and additionally, we're looking at, you know, two capabilities, uh, which include sovereign cloud advisor, uh, with clients, especially in, in Europe and others are under pressure to meet, uh, stringent data norms that Kristen was talking about. And the sovereign cloud advisor helps organization to create an architecture cloud architecture that complies with the green. Uh, I would say the data sovereignty norms that is out there. The other element is around data to cloud. We are seeing massive migration, uh, for, uh, for a lot of the data to cloud. And there's a lot of migration hurdles that come within that. Uh, we have expanded mine app to support assessment capabilities, uh, for, uh, assessing applications, infrastructure, but also covering the entire state, including data and the code level to determine the right cloud solution. So we are, we are pushing the boundaries on what mine app can do with mine. Have you created the ability to take the guesswork out of cloud, navigate the complexity? We are rolling risks costs, and we are, you know, achieving client's static business objectives while building a sustainable alerts with being cloud, >>Any platform that can take some of the guesswork out of the future. I am I'm on board with thank you so much, Tristin and Kishore. This has been a great conversation. Stay tuned for more of the cubes coverage of the Accenture executive summit. I'm Rebecca Knight.

>> Narrator: Live, from Munich, Germany, it's the CUBE. Covering IBM, Fast Track Your Data. Brought to you by IBM. >> Welcome to Munich everybody. This is a special presentation of the CUBE, Fast Track Your Data, brought to you by IBM. My name is Dave Vellante. And I'm here with my cohost, Jim Kobielus. Jim, good to see you. Really good to see you in Munich. >> Jim: I'm glad I made it. >> Thanks for being here. So last year Jim and I hosted a panel at New York City on the CUBE. And it was quite an experience. We had, I think it was nine or 10 data scientists and we felt like that was a lot of people to organize and talk about data science. Well today, we're going to do a repeat of that. With a little bit of twist on topics. And we've got five data scientists. We're here live, in Munich. And we're going to kick off the Fast Track Your Data event with this data science panel. So I'm going to now introduce some of the panelists, or all of the panelists. Then we'll get into the discussions. I'm going to start with Lillian Pierson. Lillian thanks very much for being on the panel. You are in data science. You focus on training executives, students, and you're really a coach but with a lot of data science expertise based in Thailand, so welcome. >> Thank you, thank you so much for having me. >> Dave: You're very welcome. And so, I want to start with sort of when you focus on training people, data science, where do you start? >> Well it depends on the course that I'm teaching. But I try and start at the beginning so for my Big Data course, I actually start back at the fundamental concepts and definitions they would even need to understand in order to understand the basics of what Big Data is, data engineering. So, terms like data governance. Going into the vocabulary that makes up the very introduction of the course, so that later on the students can really grasp the concepts I present to them. You know I'm teaching a deep learning course as well, so in that case I start at a lot more advanced concepts. So it just really depends on the level of the course. >> Great, and we're going to come back to this topic of women in tech. But you know, we looked at some CUBE data the other day. About 17% of the technology industry comprises women. And so we're a little bit over that on our data science panel, we're about 20% today. So we'll come back to that topic. But I don't know if there's anything you would add? >> I'm really passionate about women in tech and women who code, in particular. And I'm connected with a lot of female programmers through Instagram. And we're supporting each other. So I'd love to take any questions you have on what we're doing in that space. At least as far as what's happening across the Instagram platform. >> Great, we'll circle back to that. All right, let me introduce Chris Penn. Chris, Boston based, all right, SMI. Chris is a marketing expert. Really trying to help people understand how to get, turn data into value from a marketing perspective. It's a very important topic. Not only because we get people to buy stuff but also understanding some of the risks associated with things like GDPR, which is coming up. So Chris, tell us a little bit about your background and your practice. >> So I actually started in IT and worked at a start up. And that's where I made the transition to marketing. Because marketing has much better parties. But what's really interesting about the way data science is infiltrating marketing is the technology came in first. You know, everything went digital. And now we're at a point where there's so much data. And most marketers, they kind of got into marketing as sort of the arts and crafts field. And are realizing now, they need a very strong, mathematical, statistical background. So one of the things, Adam, the reason why we're here and IBM is helping out tremendously is, making a lot of the data more accessible to people who do not have a data science background and probably never will. >> Great, okay thank you. I'm going to introduce Ronald Van Loon. Ronald, your practice is really all about helping people extract value out of data, driving competitive advantage, business advantage, or organizational excellence. Tell us a little bit about yourself, your background, and your practice. >> Basically, I've three different backgrounds. On one hand, I'm a director at a data consultancy firm called Adversitement. Where we help companies to become data driven. Mainly large companies. I'm an advisory board member at Simply Learn, which is an e-learning platform, especially also for big data analytics. And on the other hand I'm a blogger and I host a series of webinars. >> Okay, great, now Dez, Dez Blanchfield, I met you on Twitter, you know, probably a couple of years ago. We first really started to collaborate last year. We've spend a fair amount of time together. You are a data scientist, but you're also a jack of all trades. You've got a technology background. You sit on a number of boards. You work very active with public policy. So tell us a little bit more about what you're doing these days, a little bit more about your background. >> Sure, I think my primary challenge these days is communication. Trying to join the dots between my technical background and deeply technical pedigree, to just plain English, every day language, and business speak. So bridging that technical world with what's happening in the boardroom. Toe to toe with the geeks to plain English to execs in boards. And just hand hold them and steward them through the journey of the challenges they're facing. Whether it's the enormous rapid of change and the pace of change, that's just almost exhaustive and causing them to sprint. But not just sprint in one race but in multiple lanes at the same time. As well as some of the really big things that are coming up, that we've seen like GDPR. So it's that communication challenge and just hand holding people through that journey and that mix of technical and commercial experience. >> Great, thank you, and finally Joe Caserta. Founder and president of Caserta Concepts. Joe you're a practitioner. You're in the front lines, helping organizations, similar to Ronald. Extracting value from data. Translate that into competitive advantage. Tell us a little bit about what you're doing these days in Caserta Concepts. >> Thanks Dave, thanks for having me. Yeah, so Caserta's been around. I've been doing this for 30 years now. And natural progressions have been just getting more from application development, to data warehousing, to big data analytics, to data science. Very, very organically, that's just because it's where businesses need the help the most, over the years. And right now, the big focus is governance. At least in my world. Trying to govern when you have a bunch of disparate data coming from a bunch of systems that you have no control over, right? Like social media, and third party data systems. Bringing it in and how to you organize it? How do you ingest it? How do you govern it? How do you keep it safe? And also help to define ownership of the data within an organization within an enterprise? That's also a very hot topic. Which ties back into GDPR. >> Great, okay, so we're going to be unpacking a lot of topics associated with the expertise that these individuals have. I'm going to bring in Jim Kobielus, to the conversation. Jim, the newest Wikibon analyst. And newest member of the SiliconANGLE Media Team. Jim, get us started off. >> Yeah, so we're at an event, at an IBM event where machine learning and data science are at the heart of it. There are really three core themes here. Machine learning and data science, on the one hand. Unified governance on the other. And hybrid data management. I want to circle back or focus on machine learning. Machine learning is the coin of the realm, right now in all things data. Machine learning is the heart of AI. Machine learning, everybody is going, hiring, data scientists to do machine learning. I want to get a sense from our panel, who are experts in this area, what are the chief innovations and trends right now on machine learning. Not deep learning, the core of machine learning. What's super hot? What's in terms of new techniques, new technologies, new ways of organizing teams to build and to train machine learning models? I'd like to open it up. Let's just start with Lillian. What are your thoughts about trends in machine learning? What's really hot? >> It's funny that you excluded deep learning from the response for this, because I think the hottest space in machine learning is deep learning. And deep learning is machine learning. I see a lot of collaborative platforms coming out, where people, data scientists are able to work together with other sorts of data professionals to reduce redundancies in workflows. And create more efficient data science systems. >> Is there much uptake of these crowd sourcing environments for training machine learning wells. Like CrowdFlower, or Amazon Mechanical Turk, or Mighty AI? Is that a huge trend in terms of the workflow of data science or machine learning, a lot of that? >> I don't see that crowdsourcing is like, okay maybe I've been out of the crowdsourcing space for a while. But I was working with Standby Task Force back in 2013. And we were doing a lot of crowdsourcing. And I haven't seen the industry has been increasing, but I could be wrong. I mean, because there's no, if you're building automation models, most of the, a lot of the work that's being crowdsourced could actually be automated if someone took the time to just build the scripts and build the models. And so I don't imagine that, that's going to be a trend that's increasing. >> Well, automation machine learning pipeline is fairly hot, in terms of I'm seeing more and more research. Google's doing a fair amount of automated machine learning. The panel, what do you think about automation, in terms of the core modeling tasks involved in machine learning. Is that coming along? Are data scientists in danger of automating themselves out of a job? >> I don't think there's a risk of data scientist's being put out of a job. Let's just put that on the thing. I do think we need to get a bit clearer about this meme of the mythical unicorn. But to your call point about machine learning, I think what you'll see, we saw the cloud become baked into products, just as a given. I think machine learning is already crossed this threshold. We just haven't necessarily noticed or caught up. And if we look at, we're at an IBM event, so let's just do a call out for them. The data science experience platform, for example. Machine learning's built into a whole range of things around algorithm and data classification. And there's an assisted, guided model for how you get to certain steps, where you don't actually have to understand how machine learning works. You don't have to understand how the algorithms work. It shows you the different options you've got and you can choose them. So you might choose regression. And it'll give you different options on how to do that. So I think we've already crossed this threshold of baking in machine learning and baking in the data science tools. And we've seen that with Cloud and other technologies where, you know, the Office 365 is not, you can't get a non Cloud Office 365 account, right? I think that's already happened in machine learning. What we're seeing though, is organizations even as large as the Googles still in catch up mode, in my view, on some of the shift that's taken place. So we've seen them write little games and apps where people do doodles and then it runs through the ML library and says, "Well that's a cow, or a unicorn, or a duck." And you get awards, and gold coins, and whatnot. But you know, as far as 12 years ago I was working on a project, where we had full size airplanes acting as drones. And we mapped with two and 3-D imagery. With 2-D high res imagery and LiDAR for 3-D point Clouds. We were finding poles and wires for utility companies, using ML before it even became a trend. And baking it right into the tools. And used to store on our web page and clicked and pointed on. >> To counter Lillian's point, it's not crowdsourcing but crowd sharing that's really powering a lot of the rapid leaps forward. If you look at, you know, DSX from IBM. Or you look at Node-RED, huge number of free workflows that someone has probably already done the thing that you are trying to do. Go out and find in the libraries, through Jupyter and R Notebooks, there's an ability-- >> Chris can you define before you go-- >> Chris: Sure. >> This is great, crowdsourcing versus crowd sharing. What's the distinction? >> Well, so crowdsourcing, kind of, where in the context of the question you ask is like I'm looking for stuff that other people, getting people to do stuff that, for me. It's like asking people to mine classifieds. Whereas crowd sharing, someone has done the thing already, it already exists. You're not purpose built, saying, "Jim, help me build this thing." It's like, "Oh Jim, you already "built this thing, cool. "So can I fork it and make my own from it?" >> Okay, I see what you mean, keep going. >> And then, again, going back to earlier. In terms of the advancements. Really deep learning, it probably is a good idea to just sort of define these things. Machine learning is how machines do things without being explicitly programmed to do them. Deep learning's like if you can imagine a stack of pancakes, right? Each pancake is a type of machine learning algorithm. And your data is the syrup. You pour the data on it. It goes from layer, to layer, to layer, to layer, and what you end up with at the end is breakfast. That's the easiest analogy for what deep learning is. Now imagine a stack of pancakes, 500 or 1,000 high, that's where deep learning's going now. >> Sure, multi layered machine learning models, essentially, that have the ability to do higher levels of abstraction. Like image analysis, Lillian? >> I had a comment to add about automation and data science. Because there are a lot of tools that are able to, or applications that are able to use data science algorithms and output results. But the reason that data scientists aren't in risk of losing their jobs, is because just because you can get the result, you also have to be able to interpret it. Which means you have to understand it. And that involves deep math and statistical understanding. Plus domain expertise. So, okay, great, you took out the coding element but that doesn't mean you can codify a person's ability to understand and apply that insight. >> Dave: Joe, you have something to add? >> I could just add that I see the trend. Really, the reason we're talking about it today is machine learning is not necessarily, it's not new, like Dez was saying. But what's different is the accessibility of it now. It's just so easily accessible. All of the tools that are coming out, for data, have machine learning built into it. So the machine learning algorithms, which used to be a black art, you know, years ago, now is just very easily accessible. That you can get, it's part of everyone's toolbox. And the other reason that we're talking about it more, is that data science is starting to become a core curriculum in higher education. Which is something that's new, right? That didn't exist 10 years ago? But over the past five years, I'd say, you know, it's becoming more and more easily accessible for education. So now, people understand it. And now we have it accessible in our tool sets. So now we can apply it. And I think that's, those two things coming together is really making it becoming part of the standard of doing analytics. And I guess the last part is, once we can train the machines to start doing the analytics, right? And get smarter as it ingests more data. And then we can actually take that and embed it in our applications. That's the part that you still need data scientists to create that. But once we can have standalone appliances that are intelligent, that's when we're going to start seeing, really, machine learning and artificial intelligence really start to take off even more. >> Dave: So I'd like to switch gears a little bit and bring Ronald on. >> Okay, yes. >> Here you go, there. >> Ronald, the bromide in this sort of big data world we live in is, the data is the new oil. You got to be a data driven company and many other cliches. But when you talk to organizations and you start to peel the onion. You find that most companies really don't have a good way to connect data with business impact and business value. What are you seeing with your clients and just generally in the community, with how companies are doing that? How should they do that? I mean, is that something that is a viable approach? You don't see accountants, for example, quantifying the value of data on a balance sheet. There's no standards for doing that. And so it's sort of this fuzzy concept. How are and how should organizations take advantage of data and turn it into value. >> So, I think in general, if you look how companies look at data. They have departments and within the departments they have tools specific for this department. And what you see is that there's no central, let's say, data collection. There's no central management of governance. There's no central management of quality. There's no central management of security. Each department is manages their data on their own. So if you didn't ask, on one hand, "Okay, how should they do it?" It's basically go back to the drawing table and say, "Okay, how should we do it?" We should collect centrally, the data. And we should take care for central governance. We should take care for central data quality. We should take care for centrally managing this data. And look from a company perspective and not from a department perspective what the value of data is. So, look at the perspective from your whole company. And this means that it has to be brought on one end to, whether it's from C level, where most of them still fail to understand what it really means. And what the impact can be for that company. >> It's a hard problem. Because data by its' very nature is now so decentralized. But Chris you have a-- >> The thing I want to add to that is, think about in terms of valuing data. Look at what it would cost you for data breach. Like what is the expensive of having your data compromised. If you don't have governance. If you don't have policy in place. Look at the major breaches of the last couple years. And how many billions of dollars those companies lost in market value, and trust, and all that stuff. That's one way you can value data very easily. "What will it cost us if we mess this up?" >> So a lot of CEOs will hear that and say, "Okay, I get it. "I have to spend to protect myself, "but I'd like to make a little money off of this data thing. "How do I do that?" >> Well, I like to think of it, you know, I think data's definitely an asset within an organization. And is becoming more and more of an asset as the years go by. But data is still a raw material. And that's the way I think about it. In order to actually get the value, just like if you're creating any product, you start with raw materials and then you refine it. And then it becomes a product. For data, data is a raw material. You need to refine it. And then the insight is the product. And that's really where the value is. And the insight is absolutely, you can monetize your insight. >> So data is, abundant insights are scarce. >> Well, you know, actually you could say that intermediate between insights and the data are the models themselves. The statistical, predictive, machine learning models. That are a crystallization of insights that have been gained by people called data scientists. What are your thoughts on that? Are statistical, predictive, machine learning models something, an asset, that companies, organizations, should manage governance of on a centralized basis or not? >> Well the models are essentially the refinery system, right? So as you're refining your data, you need to have process around how you exactly do that. Just like refining anything else. It needs to be controlled and it needs to be governed. And I think that data is no different from that. And I think that it's very undisciplined right now, in the market or in the industry. And I think maturing that discipline around data science, I think is something that's going to be a very high focus in this year and next. >> You were mentioning, "How do you make money from data?" Because there's all this risk associated with security breaches. But at the risk of sounding simplistic, you can generate revenue from system optimization, or from developing products and services. Using data to develop products and services that better meet the demands and requirements of your markets. So that you can sell more. So either you are using data to earn more money. Or you're using data to optimize your system so you have less cost. And that's a simple answer for how you're going to be making money from the data. But yes, there is always the counter to that, which is the security risks. >> Well, and my question really relates to, you know, when you think of talking to C level executives, they kind of think about running the business, growing the business, and transforming the business. And a lot of times they can't fund these transformations. And so I would agree, there's many, many opportunities to monetize data, cut costs, increase revenue. But organizations seem to struggle to either make a business case. And actually implement that transformation. >> Dave, I'd love to have a crack at that. I think this conversation epitomizes the type of things that are happening in board rooms and C suites already. So we've really quickly dived into the detail of data. And the detail of machine learning. And the detail of data science, without actually stopping and taking a breath and saying, "Well, we've "got lots of it, but what have we got? "Where is it? "What's the value of it? "Is there any value in it at all?" And, "How much time and money should we invest in it?" For example, we talk of being about a resource. I look at data as a utility. When I turn the tap on to get a drink of water, it's there as a utility. I counted it being there but I don't always sample the quality of the water and I probably should. It could have Giardia in it, right? But what's interesting is I trust the water at home, in Sydney. Because we have a fairly good experience with good quality water. If I were to go to some other nation. I probably wouldn't trust that water. And I think, when you think about it, what's happening in organizations. It's almost the same as what we're seeing here today. We're having a lot of fun, diving into the detail. But what we've forgotten to do is ask the question, "Well why is data even important? "What's the reasoning to the business? "Why are we in business? "What are we doing as an organization? "And where does data fit into that?" As opposed to becoming so fixated on data because it's a media hyped topic. I think once you can wind that back a bit and say, "Well, we have lot's of data, "but is it good data? "Is it quality data? "Where's it coming from? "Is it ours? "Are we allowed to have it? "What treatment are we allowed to give that data?" As you said, "Are we controlling it? "And where are we controlling it? "Who owns it?" There's so many questions to be asked. But the first question I like to ask people in plain English is, "Well is there any value "in data in the first place? "What decisions are you making that data can help drive? "What things are in your organizations, "KPIs and milestones you're trying to meet "that data might be a support?" So then instead of becoming fixated with data as a thing in itself, it becomes part of your DNA. Does that make sense? >> Think about what money means. The Economists' Rhyme, "Money is a measure for, "a systems for, a medium, a measure, and exchange." So it's a medium of exchange. A measure of value, a way to exchange something. And a way to store value. Data, good clean data, well governed, fits all four of those. So if you're trying to figure out, "How do we make money out of stuff." Figure out how money works. And then figure out how you map data to it. >> So if we approach and we start with a company, we always start with business case, which is quite clear. And defined use case, basically, start with a team on one hand, marketing people, sales people, operational people, and also the whole data science team. So start with this case. It's like, defining, basically a movie. If you want to create the movie, You know where you're going to. You know what you want to achieve to create the customer experience. And this is basically the same with a business case. Where you define, "This is the case. "And this is how we're going to derive value, "start with it and deliver value within a month." And after the month, you check, "Okay, where are we and how can we move forward? "And what's the value that we've brought?" >> Now I as well, start with business case. I've done thousands of business cases in my life, with organizations. And unless that organization was kind of a data broker, the business case rarely has a discreet component around data. Is that changing, in your experience? >> Yes, so we guide companies into be data driven. So initially, indeed, they don't like to use the data. They don't like to use the analysis. So that's why, how we help. And is it changing? Yes, they understand that they need to change. But changing people is not always easy. So, you see, it's hard if you're not involved and you're not guiding it, they fall back in doing the daily tasks. So it's changing, but it's a hard change. >> Well and that's where this common parlance comes in. And Lillian, you, sort of, this is what you do for a living, is helping people understand these things, as you've been sort of evangelizing that common parlance. But do you have anything to add? >> I wanted to add that for organizational implementations, another key component to success is to start small. Start in one small line of business. And then when you've mastered that area and made it successful, then try and deploy it in more areas of the business. And as far as initializing big data implementation, that's generally how to do it successfully. >> There's the whole issue of putting a value on data as a discreet asset. Then there's the issue, how do you put a value on a data lake? Because a data lake, is essentially an asset you build on spec. It's an exploratory archive, essentially, of all kinds of data that might yield some insights, but you have to have a team of data scientists doing exploration and modeling. But it's all on spec. How do you put a value on a data lake? And at what point does the data lake itself become a burden? Because you got to store that data and manage it. At what point do you drain that lake? At what point, do the costs of maintaining that lake outweigh the opportunity costs of not holding onto it? >> So each Hadoop note is approximately $20,000 per year cost for storage. So I think that there needs to be a test and a diagnostic, before even inputting, ingesting the data and storing it. "Is this actually going to be useful? "What value do we plan to create from this?" Because really, you can't store all the data. And it's a lot cheaper to store data in Hadoop then it was in traditional systems but it's definitely not free. So people need to be applying this test before even ingesting the data. Why do we need this? What business value? >> I think the question we need to also ask around this is, "Why are we building data lakes "in the first place? "So what's the function it's going to perform for you?" There's been a huge drive to this idea. "We need a data lake. "We need to put it all somewhere." But invariably they become data swamps. And we only half jokingly say that because I've seen 90 day projects turn from a great idea, to a really bad nightmare. And as Lillian said, it is cheaper in some ways to put it into a HDFS platform, in a technical sense. But when we look at all the fully burdened components, it's actually more expensive to find Hadoop specialists and Spark specialists to maintain that cluster. And invariably I'm finding that big data, quote unquote, is not actually so much lots of data, it's complex data. And as Lillian said, "You don't always "need to store it all." So I think if we go back to the question of, "What's the function of a data lake in the first place? "Why are we building one?" And then start to build some fully burdened cost components around that. We'll quickly find that we don't actually need a data lake, per se. We just need an interim data store. So we might take last years' data and tokenize it, and analyze it, and do some analytics on it, and just keep the meta data. So I think there is this rush, for a whole range of reasons, particularly vendor driven. To build data lakes because we think they're a necessity, when in reality they may just be an interim requirement and we don't need to keep them for a long term. >> I'm going to attempt to, the last few questions, put them all together. And I think, they all belong together because one of the reasons why there's such hesitation about progress within the data world is because there's just so much accumulated tech debt already. Where there's a new idea. We go out and we build it. And six months, three years, it really depends on how big the idea is, millions of dollars is spent. And then by the time things are built the idea is pretty much obsolete, no one really cares anymore. And I think what's exciting now is that the speed to value is just so much faster than it's ever been before. And I think that, you know, what makes that possible is this concept of, I don't think of a data lake as a thing. I think of a data lake as an ecosystem. And that ecosystem has evolved so much more, probably in the last three years than it has in the past 30 years. And it's exciting times, because now once we have this ecosystem in place, if we have a new idea, we can actually do it in minutes not years. And that's really the exciting part. And I think, you know, data lake versus a data swamp, comes back to just traditional data architecture. And if you architect your data lake right, you're going to have something that's substantial, that's you're going to be able to harness and grow. If you don't do it right. If you just throw data. If you buy Hadoop cluster or a Cloud platform and just throw your data out there and say, "We have a lake now." yeah, you're going to create a mess. And I think taking the time to really understand, you know, the new paradigm of data architecture and modern data engineering, and actually doing it in a very disciplined way. If you think about it, what we're doing is we're building laboratories. And if you have a shabby, poorly built laboratory, the best scientist in the world isn't going to be able to prove his theories. So if you have a well built laboratory and a clean room, then, you know a scientist can get what he needs done very, very, very efficiently. And that's the goal, I think, of data management today. >> I'd like to just quickly add that I totally agree with the challenge between on premise and Cloud mode. And I think one of the strong themes of today is going to be the hybrid data management challenge. And I think organizations, some organizations, have rushed to adopt Cloud. And thinking it's a really good place to dump the data and someone else has to manage the problem. And then they've ended up with a very expensive death by 1,000 cuts in some senses. And then others have been very reluctant as a result of not gotten access to rapid moving and disruptive technology. So I think there's a really big challenge to get a basic conversation going around what's the value using Cloud technology as in adopting it, versus what are the risks? And when's the right time to move? For example, should we Cloud Burst for workloads? Do we move whole data sets in there? You know, moving half a petabyte of data into a Cloud platform back is a non-trivial exercise. But moving a terabyte isn't actually that big a deal anymore. So, you know, should we keep stuff behind the firewalls? I'd be interested in seeing this week where 80% of the data, supposedly is. And just push out for Cloud tools, machine learning, data science tools, whatever they might be, cognitive analytics, et cetera. And keep the bulk of the data on premise. Or should we just move whole spools into the Cloud? There is no one size fits all. There's no silver bullet. Every organization has it's own quirks and own nuances they need to think through and make a decision themselves. >> Very often, Dez, organizations have zonal architectures so you'll have a data lake that consists of a no sequel platform that might be used for say, mobile applications. A Hadoop platform that might be used for unstructured data refinement, so forth. A streaming platform, so forth and so on. And then you'll have machine learning models that are built and optimized for those different platforms. So, you know, think of it in terms of then, your data lake, is a set of zones that-- >> It gets even more complex just playing on that theme, when you think about what Cisco started, called Folk Computing. I don't really like that term. But edge analytics, or computing at the edge. We've seen with the internet coming along where we couldn't deliver everything with a central data center. So we started creating this concept of content delivery networks, right? I think the same thing, I know the same thing has happened in data analysis and data processing. Where we've been pulling social media out of the Cloud, per se, and bringing it back to a central source. And doing analytics on it. But when you think of something like, say for example, when the Dreamliner 787 from Boeing came out, this airplane created 1/2 a terabyte of data per flight. Now let's just do some quick, back of the envelope math. There's 87,400 fights a day, just in the domestic airspace in the USA alone, per day. Now 87,400 by 1/2 a terabyte, that's 43 point five petabytes a day. You physically can't copy that from quote unquote in the Cloud, if you'll pardon the pun, back to the data center. So now we've got the challenge, a lot of our Enterprise data's behind a firewall, supposedly 80% of it. But what's out at the edge of the network. Where's the value in that data? So there are zonal challenges. Now what do I do with my Enterprise versus the open data, the mobile data, the machine data. >> Yeah, we've seen some recent data from IDC that says, "About 43% of the data "is going to stay at the edge." We think that, that's way understated, just given the examples. We think it's closer to 90% is going to stay at the edge. >> Just on the airplane topic, right? So Airbus wasn't going to be outdone. Boeing put 4,000 sensors or something in their 787 Dreamliner six years ago. Airbus just announced an 83, 81,000 with 10,000 sensors in it. Do the same math. Now the FAA in the US said that all aircraft and all carriers have to be, by early next year, I think it's like March or April next year, have to be at the same level of BIOS. Or the same capability of data collection and so forth. It's kind of like a mini GDPR for airlines. So with the 83, 81,000 with 10,000 sensors, that becomes two point five terabytes per flight. If you do the math, it's 220 petabytes of data just in one day's traffic, domestically in the US. Now, it's just so mind boggling that we're going to have to completely turn our thinking on its' head, on what do we do behind the firewall? What do we do in the Cloud versus what we might have to do in the airplane? I mean, think about edge analytics in the airplane processing data, as you said, Jim, streaming analytics in flight. >> Yeah that's a big topic within Wikibon, so, within the team. Me and David Floyer, and my other colleagues. They're talking about the whole notion of edge architecture. Not only will most of the data be persisted at the edge, most of the deep learning models like TensorFlow will be executed at the edge. To some degree, the training of those models will happen in the Cloud. But much of that will be pushed in a federated fashion to the edge, or at least I'm predicting. We're already seeing some industry moves in that direction, in terms of architectures. Google has a federated training, project or initiative. >> Chris: Look at TensorFlow Lite. >> Which is really fascinating for it's geared to IOT, I'm sorry, go ahead. >> Look at TensorFlow Lite. I mean in the announcement of having every Android device having ML capabilities, is Google's essential acknowledgment, "We can't do it all." So we need to essentially, sort of like a setting at home. Everyone's smartphone top TV box just to help with the processing. >> Now we're talking about this, this sort of leads to this IOT discussion but I want to underscore the operating model. As you were saying, "You can't just "lift and shift to the Cloud." You're not going to, CEOs aren't going to get the billion dollar hit by just doing that. So you got to change the operating model. And that leads to, this discussion of IOT. And an entirely new operating model. >> Well, there are companies that are like Sisense who have worked with Intel. And they've taken this concept. They've taken the business logic and not just putting it in the chip, but actually putting it in memory, in the chip. So as data's going through the chip it's not just actually being processed but it's actually being baked in memory. So level one, two, and three cache. Now this is a game changer. Because as Chris was saying, even if we were to get the data back to a central location, the compute load, I saw a real interesting thing from I think it was Google the other day, one of the guys was doing a talk. And he spoke about what it meant to add cognitive and voice processing into just the Android platform. And they used some number, like that had, double the amount of compute they had, just to add voice for free, to the Android platform. Now even for Google, that's a nontrivial exercise. So as Chris was saying, I think we have to again, flip it on its' head and say, "How much can we put "at the edge of the network?" Because think about these phones. I mean, even your fridge and microwave, right? We put a man on the moon with something that these days, we make for $89 at home, on the Raspberry Pie computer, right? And even that was 1,000 times more powerful. When we start looking at what's going into the chips, we've seen people build new, not even GPUs, but deep learning and stream analytics capable chips. Like Google, for example. That's going to make its' way into consumer products. So that, now the compute capacity in phones, is going to, I think transmogrify in some ways because there is some magic in there. To the point where, as Chris was saying, "We're going to have the smarts in our phone." And a lot of that workload is going to move closer to us. And only the metadata that we need to move is going to go centrally. >> Well here's the thing. The edge isn't the technology. The edge is actually the people. When you look at, for example, the MIT language Scratch. This is kids programming language. It's drag and drop. You know, kids can assemble really fun animations and make little movies. We're training them to build for IOT. Because if you look at a system like Node-RED, it's an IBM interface that is drag and drop. Your workflow is for IOT. And you can push that to a device. Scratch has a converter for doing those. So the edge is what those thousands and millions of kids who are learning how to code, learning how to think architecturally and algorithmically. What they're going to create that is beyond what any of us can possibly imagine. >> I'd like to add one other thing, as well. I think there's a topic we've got to start tabling. And that is what I refer to as the gravity of data. So when you think about how planets are formed, right? Particles of dust accrete. They form into planets. Planets develop gravity. And the reason we're not flying into space right now is that there's gravitational force. Even though it's one of the weakest forces, it keeps us on our feet. Oftentimes in organizations, I ask them to start thinking about, "Where is the center "of your universe with regard to the gravity of data." Because if you can follow the center of your universe and the gravity of your data, you can often, as Chris is saying, find where the business logic needs to be. And it could be that you got to think about a storage problem. You can think about a compute problem. You can think about a streaming analytics problem. But if you can find where the center of your universe and the center of your gravity for your data is, often you can get a really good insight into where you can start focusing on where the workloads are going to be where the smarts are going to be. Whether it's small, medium, or large. >> So this brings up the topic of data governance. One of the themes here at Fast Track Your Data is GDPR. What it means. It's one of the reasons, I think IBM selected Europe, generally, Munich specifically. So let's talk about GDPR. We had a really interesting discussion last night. So let's kind of recreate some of that. I'd like somebody in the panel to start with, what is GDPR? And why does it matter, Ronald? >> Yeah, maybe I can start. Maybe a little bit more in general unified governance. So if i talk to companies and I need to explain to them what's governance, I basically compare it with a crime scene. So in a crime scene if something happens, they start with securing all the evidence. So they start sealing the environment. And take care that all the evidence is collected. And on the other hand, you see that they need to protect this evidence. There are all kinds of policies. There are all kinds of procedures. There are all kinds of rules, that need to be followed. To take care that the whole evidence is secured well. And once you start, basically, investigating. So you have the crime scene investigators. You have the research lab. You have all different kind of people. They need to have consent before they can use all this evidence. And the whole reason why they're doing this is in order to collect the villain, the crook. To catch him and on the other hand, once he's there, to convict him. And we do this to have trust in the materials. Or trust in basically, the analytics. And on the other hand to, the public have trust in everything what's happened with the data. So if you look to a company, where data is basically the evidence, this is the value of your data. It's similar to like the evidence within a crime scene. But most companies don't treat it like this. So if we then look to GDPR, GDPR basically shifts the power and the ownership of the data from the company to the person that created it. Which is often, let's say the consumer. And there's a lot of paradox in this. Because all the companies say, "We need to have this customer data. "Because we need to improve the customer experience." So if you make it concrete and let's say it's 1st of June, so GDPR is active. And it's first of June 2018. And I go to iTunes, so I use iTunes. Let's go to iTunes said, "Okay, Apple please "give me access to my data." I want to see which kind of personal information you have stored for me. On the other end, I want to have the right to rectify all this data. I want to be able to change it and give them a different level of how they can use my data. So I ask this to iTunes. And then I say to them, okay, "I basically don't like you anymore. "I want to go to Spotify. "So please transfer all my personal data to Spotify." So that's possible once it's June 18. Then I go back to iTunes and say, "Okay, I don't like it anymore. "Please reduce my consent. "I withdraw my consent. "And I want you to remove all my "personal data for everything that you use." And I go to Spotify and I give them, let's say, consent for using my data. So this is a shift where you can, as a person be the owner of the data. And this has a lot of consequences, of course, for organizations, how to manage this. So it's quite simple for the consumer. They get the power, it's maturing the whole law system. But it's a big consequence of course for organizations. >> This is going to be a nightmare for marketers. But fill in some of the gaps there. >> Let's go back, so GDPR, the General Data Protection Regulation, was passed by the EU in 2016, in May of 2016. It is, as Ronald was saying, it's four basic things. The right to privacy. The right to be forgotten. Privacy built into systems by default. And the right to data transfer. >> Joe: It takes effect next year. >> It is already in effect. GDPR took effect in May of 2016. The enforcement penalties take place the 25th of May 2018. Now here's where, there's two things on the penalty side that are important for everyone to know. Now number one, GDPR is extra territorial. Which means that an EU citizen, anywhere on the planet has GDPR, goes with them. So say you're a pizza shop in Nebraska. And an EU citizen walks in, orders a pizza. Gives her the credit card and stuff like that. If you for some reason, store that data, GDPR now applies to you, Mr. Pizza shop, whether or not you do business in the EU. Because an EU citizen's data is with you. Two, the penalties are much stiffer then they ever have been. In the old days companies could simply write off penalties as saying, "That's the cost of doing business." With GDPR the penalties are up to 4% of your annual revenue or 20 million Euros, whichever is greater. And there may be criminal sanctions, charges, against key company executives. So there's a lot of questions about how this is going to be implemented. But one of the first impacts you'll see from a marketing perspective is all the advertising we do, targeting people by their age, by their personally identifiable information, by their demographics. Between now and May 25th 2018, a good chunk of that may have to go away because there's no way for you to say, "Well this person's an EU citizen, this person's not." People give false information all the time online. So how do you differentiate it? Every company, regardless of whether they're in the EU or not will have to adapt to it, or deal with the penalties. >> So Lillian, as a consumer this is designed to protect you. But you had a very negative perception of this regulation. >> I've looked over the GDPR and to me it actually looks like a socialist agenda. It looks like (panel laughs) no, it looks like a full assault on free enterprise and capitalism. And on its' face from a legal perspective, its' completely and wholly unenforceable. Because they're assigning jurisdictional rights to the citizen. But what are they going to do? They're going to go to Nebraska and they're going to call in the guy from the pizza shop? And call him into what court? The EU court? It's unenforceable from a legal perspective. And if you write a law that's unenforceable, you know, it's got to be enforceable in every element. It can't be just, "Oh, we're only "going to enforce it for Facebook and for Google. "But it's not enforceable for," it needs to be written so that it's a complete and actionable law. And it's not written in that way. And from a technological perspective it's not implementable. I think you said something like 652 EU regulators or political people voted for this and 10 voted against it. But what do they know about actually implementing it? Is it possible? There's all sorts of regulations out there that aren't possible to implement. I come from an environmental engineering background. And it's absolutely ridiculous because these agencies will pass laws that actually, it's not possible to implement those in practice. The cost would be too great. And it's not even needed. So I don't know, I just saw this and I thought, "You know, if the EU wants to," what they're essentially trying to do is regulate what the rest of the world does on the internet. And if they want to build their own internet like China has and police it the way that they want to. But Ronald here, made an analogy between data, and free enterprise, and a crime scene. Now to me, that's absolutely ridiculous. What does data and someone signing up for an email list have to do with a crime scene? And if EU wants to make it that way they can police their own internet. But they can't go across the world. They can't go to Singapore and tell Singapore, or go to the pizza shop in Nebraska and tell them how to run their business. >> You know, EU overreach in the post Brexit era, of what you're saying has a lot of validity. How far can the tentacles of the EU reach into other sovereign nations. >> What court are they going to call them into? >> Yeah. >> I'd like to weigh in on this. There are lots of unknowns, right? So I'd like us to focus on the things we do know. We've already dealt with similar situations before. In Australia, we introduced a goods and sales tax. Completely foreign concept. Everything you bought had 10% on it. No one knew how to deal with this. It was a completely new practice in accounting. There's a whole bunch of new software that had to be written. MYRB had to have new capability, but we coped. No one actually went to jail yet. It's decades later, for not complying with GST. So what it was, was a framework on how to shift from non sales tax related revenue collection. To sales tax related revenue collection. I agree that there are some egregious things built into this. I don't disagree with that at all. But I think if I put my slightly broader view of the world hat on, we have well and truly gone past the point in my mind, where data was respected, data was treated in a sensible way. I mean I get emails from companies I've never done business with. And when I follow it up, it's because I did business with a credit card company, that gave it to a service provider, that thought that I was going to, when I bought a holiday to come to Europe, that I might want travel insurance. Now some might say there's value in that. And other's say there's not, there's the debate. But let's just focus on what we're talking about. We're talking about a framework for governance of the treatment of data. If we remove all the emotive component, what we are talking about is a series of guidelines, backed by laws, that say, "We would like you to do this," in an ideal world. But I don't think anyone's going to go to jail, on day one. They may go to jail on day 180. If they continue to do nothing about it. So they're asking you to sort of sit up and pay attention. Do something about it. There's a whole bunch of relief around how you approach it. The big thing for me, is there's no get out of jail card, right? There is no get out of jail card for not complying. But there's plenty of support. I mean, we're going to have ambulance chasers everywhere. We're going to have class actions. We're going to have individual suits. The greatest thing to do right now is get into GDPR law. Because you seem to think data scientists are unicorn? >> What kind of life is that if there's ambulance chasers everywhere? You want to live like that? >> Well I think we've seen ad blocking. I use ad blocking as an example, right? A lot of organizations with advertising broke the internet by just throwing too much content on pages, to the point where they're just unusable. And so we had this response with ad blocking. I think in many ways, GDPR is a regional response to a situation where I don't think it's the exact right answer. But it's the next evolutional step. We'll see things evolve over time. >> It's funny you mentioned it because in the United States one of the things that has happened, is that with the change in political administrations, the regulations on what companies can do with your data have actually been laxened, to the point where, for example, your internet service provider can resell your browsing history, with or without your consent. Or your consent's probably buried in there, on page 47. And so, GDPR is kind of a response to saying, "You know what? "You guys over there across the Atlantic "are kind of doing some fairly "irresponsible things with what you allow companies to do." Now, to Lillian's point, no one's probably going to go after the pizza shop in Nebraska because they don't do business in the EU. They don't have an EU presence. And it's unlikely that an EU regulator's going to get on a plane from Brussels and fly to Topeka and say, or Omaha, sorry, "Come on Joe, let's get the pizza shop in order here." But for companies, particularly Cloud companies, that have offices and operations within the EU, they have to sit up and pay attention. So if you have any kind of EU operations, or any kind of fiscal presence in the EU, you need to get on board. >> But to Lillian's point it becomes a boondoggle for lawyers in the EU who want to go after deep pocketed companies like Facebook and Google. >> What's the value in that? It seems like regulators are just trying to create work for themselves. >> What about the things that say advertisers can do, not so much with the data that they have? With the data that they don't have. In other words, they have people called data scientists who build models that can do inferences on sparse data. And do amazing things in terms of personalization. What do you do about all those gray areas? Where you got machine learning models and so forth? >> But it applies-- >> It applies to personally identifiable information. But if you have a talented enough data scientist, you don't need the PII or even the inferred characteristics. If a certain type of behavior happens on your website, for example. And this path of 17 pages almost always leads to a conversion, it doesn't matter who you are or where you're coming from. If you're a good enough data scientist, you can build a model that will track that. >> Like you know, target, infer some young woman was pregnant. And they inferred correctly even though that was never divulged. I mean, there's all those gray areas that, how can you stop that slippery slope? >> Well I'm going to weigh in really quickly. A really interesting experiment for people to do. When people get very emotional about it I say to them, "Go to Google.com, "view source, put it in seven point Courier "font in Word and count how many pages it is." I guess you can't guess how many pages? It's 52 pages of seven point Courier font, HTML to render one logo, and a search field, and a click button. Now why do we need 52 pages of HTML source code and Java script just to take a search query. Think about what's being done in that. It's effectively a mini operating system, to figure out who you are, and what you're doing, and where you been. Now is that a good or bad thing? I don't know, I'm not going to make a judgment call. But what I'm saying is we need to stop and take a deep breath and say, "Does anybody need a 52 page, "home page to take a search query?" Because that's just the tip of the iceberg. >> To that point, I like the results that Google gives me. That's why I use Google and not Bing. Because I get better search results. So, yeah, I don't mind if you mine my personal data and give me, our Facebook ads, those are the only ads, I saw in your article that GDPR is going to take out targeted advertising. The only ads in the entire world, that I like are Facebook ads. Because I actually see products I'm interested in. And I'm happy to learn about that. I think, "Oh I want to research that. "I want to see this new line of products "and what are their competitors?" And I like the targeted advertising. I like the targeted search results because it's giving me more of the information that I'm actually interested in. >> And that's exactly what it's about. You can still decide, yourself, if you want to have this targeted advertising. If not, then you don't give consent. If you like it, you give consent. So if a company gives you value, you give consent back. So it's not that it's restricting everything. It's giving consent. And I think it's similar to what happened and the same type of response, what happened, we had the Mad Cow Disease here in Europe, where you had the whole food chain that needed to be tracked. And everybody said, "No, it's not required." But now it's implemented. Everybody in Europe does it. So it's the same, what probably going to happen over here as well. >> So what does GDPR mean for data scientists? >> I think GDPR is, I think it is needed. I think one of the things that may be slowing data science down is fear. People are afraid to share their data. Because they don't know what's going to be done with it. If there are some guidelines around it that should be enforced and I think, you know, I think it's been said but as long as a company could prove that it's doing due diligence to protect your data, I think no one is going to go to jail. I think when there's, you know, we reference a crime scene, if there's a heinous crime being committed, all right, then it's going to become obvious. And then you do go directly to jail. But I think having guidelines and even laws around privacy and protection of data is not necessarily a bad thing. You can do a lot of data, really meaningful data science, without understanding that it's Joe Caserta. All of the demographics about me. All of the characteristics about me as a human being, I think are still on the table. All that they're saying is that you can't go after Joe, himself, directly. And I think that's okay. You know, there's still a lot of things. We could still cure diseases without knowing that I'm Joe Caserta, right? As long as you know everything else about me. And I think that's really at the core, that's what we're trying to do. We're trying to protect the individual and the individual's data about themselves. But I think as far as how it affects data science, you know, a lot of our clients, they're afraid to implement things because they don't exactly understand what the guideline is. And they don't want to go to jail. So they wind up doing nothing. So now that we have something in writing that, at least, it's something that we can work towards, I think is a good thing. >> In many ways, organizations are suffering from the deer in the headlight problem. They don't understand it. And so they just end up frozen in the headlights. But I just want to go back one step if I could. We could get really excited about what it is and is not. But for me, the most critical thing there is to remember though, data breaches are happening. There are over 1,400 data breaches, on average, per day. And most of them are not trivial. And when we saw 1/2 a billion from Yahoo. And then one point one billion and then one point five billion. I mean, think about what that actually means. There were 47,500 Mongodbs breached in an 18 hour window, after an automated upgrade. And they were airlines, they were banks, they were police stations. They were hospitals. So when I think about frameworks like GDPR, I'm less worried about whether I'm going to see ads and be sold stuff. I'm more worried about, and I'll give you one example. My 12 year old son has an account at a platform called Edmodo. Now I'm not going to pick on that brand for any reason but it's a current issue. Something like, I think it was like 19 million children in the world had their username, password, email address, home address, and all this social interaction on this Facebook for kids platform called Edmodo, breached in one night. Now I got my hands on a copy. And everything about my son is there. Now I have a major issue with that. Because I can't do anything to undo that, nothing. The fact that I was able to get a copy, within hours on a dark website, for free. The fact that his first name, last name, email, mobile phone number, all these personal messages from friends. Nobody has the right to allow that to breach on my son. Or your children, or our children. For me, GDPR, is a framework for us to try and behave better about really big issues. Whether it's a socialist issue. Whether someone's got an issue with advertising. I'm actually not interested in that at all. What I'm interested in is companies need to behave much better about the treatment of data when it's the type of data that's being breached. And I get really emotional when it's my son, or someone else's child. Because I don't care if my bank account gets hacked. Because they hedge that. They underwrite and insure themselves and the money arrives back to my bank. But when it's my wife who donated blood and a blood donor website got breached and her details got lost. Even things like sexual preferences. That they ask questions on, is out there. My 12 year old son is out there. Nobody has the right to allow that to happen. For me, GDPR is the framework for us to focus on that. >> Dave: Lillian, is there a comment you have? >> Yeah, I think that, I think that security concerns are 100% and definitely a serious issue. Security needs to be addressed. And I think a lot of the stuff that's happening is due to, I think we need better security personnel. I think we need better people working in the security area where they're actually looking and securing. Because I don't think you can regulate I was just, I wanted to take the microphone back when you were talking about taking someone to jail. Okay, I have a background in law. And if you look at this, you guys are calling it a framework. But it's not a framework. What they're trying to do is take 4% of your business revenues per infraction. They want to say, "If a person signs up "on your email list and you didn't "like, necessarily give whatever "disclaimer that the EU said you need to give. "Per infraction, we're going to take "4% of your business revenue." That's a law, that they're trying to put into place. And you guys are talking about taking people to jail. What jail are you? EU is not a country. What jurisdiction do they have? Like, you're going to take pizza man Joe and put him in the EU jail? Is there an EU jail? Are you going to take them to a UN jail? I mean, it's just on its' face it doesn't hold up to legal tests. I don't understand how they could enforce this. >> I'd like to just answer the question on-- >> Security is a serious issue. I would be extremely upset if I were you. >> I personally know, people who work for companies who've had data breaches. And I respect them all. They're really smart people. They've got 25 plus years in security. And they are shocked that they've allowed a breach to take place. What they've invariably all agreed on is that a whole range of drivers have caused them to get to a bad practice. So then, for example, the donate blood website. The young person who was assist admin with all the right skills and all the right experience just made a basic mistake. They took a db dump of a mysql database before they upgraded their Wordpress website for the business. And they happened to leave it in a folder that was indexable by Google. And so somebody wrote a radio expression to search in Google to find sql backups. Now this person, I personally respect them. I think they're an amazing practitioner. They just made a mistake. So what does that bring us back to? It brings us back to the point that we need a safety net or a framework or whatever you want to call it. Where organizations have checks and balances no matter what they do. Whether it's an upgrade, a backup, a modification, you know. And they all think they do, but invariably we've seen from the hundreds of thousands of breaches, they don't. Now on the point of law, we could debate that all day. I mean the EU does have a remit. If I was caught speeding in Germany, as an Australian, I would be thrown into a German jail. If I got caught as an organization in France, breaching GDPR, I would be held accountable to the law in that region, by the organization pursuing me. So I think it's a bit of a misnomer saying I can't go to an EU jail. I don't disagree with you, totally, but I think it's regional. If I get a speeding fine and break the law of driving fast in EU, it's in the country, in the region, that I'm caught. And I think GDPR's going to be enforced in that same approach. >> All right folks, unfortunately the 60 minutes flew right by. And it does when you have great guests like yourselves. So thank you very much for joining this panel today. And we have an action packed day here. So we're going to cut over. The CUBE is going to have its' interview format starting in about 1/2 hour. And then we cut over to the main tent. Who's on the main tent? Dez, you're doing a main stage presentation today. Data Science is a Team Sport. Hillary Mason, has a breakout session. We also have a breakout session on GDPR and what it means for you. Are you ready for GDPR? Check out ibmgo.com. It's all free content, it's all open. You do have to sign in to see the Hillary Mason and the GDPR sessions. And we'll be back in about 1/2 hour with the CUBE. We'll be running replays all day on SiliconAngle.tv and also ibmgo.com. So thanks for watching everybody. Keep it right there, we'll be back in about 1/2 hour with the CUBE interviews. We're live from Munich, Germany, at Fast Track Your Data. This is Dave Vellante with Jim Kobielus, we'll see you shortly. (electronic music)