(upbeat music) >> Hello, welcome to theCUBE's presentation of Women in Tech, Global Event, celebrating International Women's Day. I'm John Furrier, your host of theCUBE here in Palo Alto, California. We got two great guests. Cindy Polin, head of Solution Architects for Public Sector in Mexico for AWS. And Fernanda Spinardi, who's also the head of Solution Architects for Public Sector in Brazil, both with AWS. Thanks for coming, appreciate your time. >> Thanks for the invitation. >> Thank you, John. >> So we're celebrating International Women's Day this week, and this month, and pretty much every day, I think we're going to be doing a lot of good stuff. But today's a special day. And talking about people's careers, their roles, the gender gap, is a big theme this year. These are all the topics that are going on and being discussed. So, it's a been a lot of fun when learning a lot, I have to ask you guys with AWS, Cindy we'll start with you. How is AWS addressing the gender gap in its technical teams? Because solution architects, they're technical. And we need more women in there. How is AWS addressing the gender gap with its technical teams? >> Yes, for sure, thank you very much. Let me start with a quick note about what is the situation in Mexico. Let me go first into a report published by IMCO, and this is talking about this gender gaps in a STEM career. So let me tell you that three out of 10 professionals who choose careers related with the STEM, with the science technology, engineering and mathematics, are women. So, can you imagine this difference, It's really critical because for sure, we have few women. And in the moment that you try to reach people, to be part of the company, it's difficult. So it's important for AWS to be very very supportive in this initiative and also to be supporting diverse teams. So, that's why we are very supportive in bringing diverse talent in the company. >> There's a lot of focus on getting people early into the pipe lining. Is that some another big area? Did the study show anything there? >> Well, basically it's that we are studying to push harder, to bring more information to the ladies, to the women in general. And also to start developing the technical skills. Because it's really difficult and in the moment that you try to do this, it start like seeing these behaviors or stigmas about this is only for men, it's not for women. So we are trying to start breaking this point in general. >> Fernanda, we had a great chat about Latin America reinvent on theCUBE with your leader over there and, we were talking about the broader community and how you guys are partnering with external organizations and customers. How is Amazon Web Services, AWS, aiming to foster better balance and gender balance and technology partnerships in Latin America? >> Sure, so while the situation in Brazil is not different from the situation that Cindy was mentioning in Mexico right? Our research shows that women only represent around 37% of the workforce where in the country we have over 51-52% of women as part of our population. While we can take this from a gap perspective, also, we can take it from an opportunity perspective. There is such a huge unexplored workforce that we can bring to be part of AWS in the technology world, right? So for us on AWS and Amazon, it's part part of our day one culture. So we are still learning, right? And we are still trying, experimenting to see how we can bring more women to the tech world. One of the things that we are investing in Brazil and in Latin America, are the early in career talent programs. This is something that we have the opportunity to work with the students. And in LATAM, it's a little bit different from the US. We have the opportunity to work with them for one year sometimes for two years in a role while they work they are still in the university and we prepare that talent really early in their career and bring them to be part of Amazon. So yeah, I'm super excited with those programs, I can, talk more about it, but this is one of the initiatives that we are betting that will maybe be a game changer for us in the technology. >> Yeah, those are very interesting stats, 37% of the workers in country where women represent over half of the population. So definitely a lot of work to be done. I got to ask both of you. Amazon has a leadership principle that says that they want to strive to be the world's, or earth's best employer earth being, Earth Day and all that sustainability as well. Diversity, inclusion and equity is a big part of that mission more. And also Amazon's also known for high performing work environment. So, so having the best diversity and inclusion you know, is a, is a, as some say and many are saying is a force multiplier in performance. How is that going in your areas? Can you talk about how the culture that you're in, the countries that you're in and the Amazonian leadership principles tie together? Can you share your thoughts and experiences? >> Sure. I can, I can get started maybe with that one. So, although we have a new leadership principle from my perspective, we have we have always had leadership principles that foster diversity and, and inclusion, right. Pick up, earn trust as an example like it says, listen carefully, right. And speak candidly, this is for me it's the baseline for any, any inclusion conversation. Right. And also you have things like have backbone, disagree and commit. Like you are empowering people to actually have an opinion and bring back that opinion and be heard. Right. So it was already there. I think the thing now is that we have a very specific leadership principle so that there is no, no room for interpretation. Right. It's right there saying that there is a mission a mission to, to be the best employer. Right. And, and I'm, I'm very excited about it. >> John: Cindy, share your thoughts too. I like that comment because you know, Amazon culture's known for, you know, debate then align. Okay. And now you got that cultural factor. Now it's in the leadership principle. What's your reaction? >> Yes. And, and let me add a comment on that about Fernanda's point is that this LP is giving us like the empower to give this environment to prepare, to to give this space to the team and also to be more creative. And also to be more diverse is really important for us to have this space with a lot of empathy, with the in the space to have a lot of fun. And it's important to keep all the time in mind that are we doing the right thing for our employees? Are we are empowering them to be the best of, of the world? So, that is something that is critical for us and, and well that is something that we are right now working on it. >> Okay. So first of all I'm very impressed by both of you. You're inspiring. And I can also tell you that being a solution architect is not an easy job. But it's also in high demand. A lot of people want to, they need solution architects. It's one of the most coveted positions in the industry right now. So how do we get more women in that role? What ideas do you guys have besides being great role models, yourselves? How do we get more solution architects? Because it's super valuable and everyone wants to hire them. >> Fernanda, did you want to start? >> It's you guys. >> You touched a very important point, John. It's about having, having good examples. Like, I mean, it's about you seeing yourself in the role right? You, you believing that it's, it's possible. It's for everyone. If you have a spirit where you, you want to build things if you have this spirit of exploring new possibilities if you like to experiment, well, then you have all that we need in a solution architect, right? It's just then a matter of, you know, know learning technical, learning technology, technical stuff. But this is, this is about having fun on your journey as as a solution architect as well. >> And, and let me tell you something that we are also investing in trainings. Training is online for the for the women that they are, that has this interest that they want to learn more about the technology. They want to have a deeper knowledge about the technical stuff. So we are supporting these initiatives and that is something that they can do background and in their own pace. >> And this is an important role because they need the leadership as head of solution architects. It's a good thing. Is, is there any ways that you found that's a best practice for identifying or advice for people to know if they have what it takes or they have an affinity towards technology? Sometimes it's math. Because cloud is great levels it out. I mean, cloud is new, is more jobs open now that didn't exist years ago, couple years ago. So anyone can rise to the top. >> Yeah. I think that's the beauty of the cloud. There is so much space when we say technology I think this is such a, a broad word, right? It means so much, right. It can be someone that likes to develop code. It can be someone that likes to work with infrastructure. It can be someone that likes machine learning or databases or someone that is inspired about applications for the education world or to research genomes or cure cancer. So, yeah, I don't think that there is like any more like a specific profile. I think it's very open for everyone to explore what they love doing. And even from a technology perspective AWS is working to simplify access to the technology. If we take our services on machine learning. For instance, they are for people, for business people like you don't have to know much about algorithms, right. To use some of the AWS services. So I think we're experiencing the democratization of the technology, and with that more opportunity for people to join us. >> A lot of people are changing careers into cloud. So Cindy, I want to ask you guys also if you can share how the mentoring process works there. Is there mentoring? How does that work? Do you match people? Have you found a nice formula for providing some mentoring and some pathways as people come in? >> Yes, we have many ways but one is very important, is that we have user groups. That is a way that we have like a community with internal and external people, and we share advices, guidance, best practices for the people that is interested in this matter. So for one side as I already mentioned, we have training online that you can reach. We have a lot of free courses. Maybe you can start jumping into artificial intelligence. IUT whatever you want to, to, to want that given them. But in the other hand, we have this option to have this kind of support. We have AWS Girl Chile user groups. We have AWS women, Colombian user groups girls in Argentina, we have many of them. We have four hundreds of user communities. So, that is the way that we can keep in touch. >> Any other programs? I mean, Amazon Web Service and Amazon has very strong representation of women. There's a lot of pockets of women groups in all over the world. How does it come together? Because you also have customers in the user groups. You have partners in the partner network. You have technologists learning. So you have this ecosystem of people. It's not just AWS. How are you guys extending that gap into those areas? >> Exactly. And those conversations are getting more and more constant with our customers, right? So we used to talk about technology, we used to talk about business problems, now we talk about diversity. We talk about improving representation and improving the sentiment of inclusion within our customers as well. And one of the things that I can bring, we have been working with a number of our customers in Brazil just to mention New Bank, one of our customers there in building programs. between AWS and the customer, where we train people, and we expose that people to the market, even if it's inside AWS, inside New Bank or any other partner in that ecosystem. So we are building talent not only for us, but for for the entire ecosystem to benefit from. >> Okay, so I have to ask you guys How did you guys get into the tech, Cindy? What was your way? Did it just jump at you? Did it grab you? Did you kind of discover it early? When did you kind of get into the tech? >> That's a good question. I was remembering this moment that when I was seven years old I just started like working with cars and also with that kind of companies, literally companies. And in that moment say, "I want to be part of this technology work." And after that in high school, I have the opportunity to touch a computer. In that moment I said, "This is the thing that I want to do in the rest of my life." >> Yeah. that's it right there. You got the diction, you taste it. Fernanda, what about you? What's your story? How did you get into it? What was the moment? Was there an exact moment or did it just surround you? >> Yeah, I think I was always curious about how things work. I was not thinking about a career in tech honestly. I was thinking about becoming a lawyer, but at some point in time just clicked, right? And I had actually to fight my way into the technical world literally because, I had this very important university close to my house, like maybe 15 minutes from my house. But at that point in time in Brazil, that particular institution was not accepting women. And believe me, it was not like a hundred years ago. Like it was.... (laughing) >> Yeah, you're young, it's just recently. >> Yeah, so I had to move out out of my hometown, back to the city, to Sao Paulo, which is our biggest city in Brazil to find a place for me on an university that would take women. So yeah, I had to fight my way into technology, but I am very proud of that I was able to. >> Yeah, you know what's great now is you have YouTube, you have all these resources, these videos are going to be going everywhere. We're going to put this out there. There's communities where people can learn and see people like themselves out in positions of leadership and technology. So more and more contents being out there. And I think hopefully no one will have to fight to get into tech. If they like it, they're in it. One of the leaders at AWS she said, "We're in a nerd native environment now, the young generation is natively technical." And, I believe that, I see that. I think that's going to be a really exciting trend and seeing leaders like yourselves out there is really wonderful, so thank you for spending the time with us here on theCUBE. Final question I'll ask you, what's next for you Cindy and Fernanda? What's next in your journey? >> Okay, I think the next for me is to keep pushing the women in Mexico to keep installing and also to start thinking into what is the next step in my career? Where should I go? So I think that is the point that I want to do. >> Cindy, what's next for you? >> I feel I'm just starting. (laughing) So much to do, so much to do. I mean, there is a big business for us to make happen in Brazil right now, and we are looking for talent. So, if the video's going to go on YouTube, I would like everybody there to know that yeah, we are looking for talents in Brazil with opportunities all over the world actually. And yeah, that's building, building and building. >> And there's some rig twitch channels by the way too on some developer programmings, tons of programming, it's all out there. Congratulations, and we're looking forward to following up with you both in the future to get an update and thank you for spending the time and sharing your your stories here on theCUBE I really appreciate, thank you. >> Thank you too. >> Thank you so much. >> Okay, theCUBE presentation of Women in Tech, Global Events celebrating International Women's Day. This is the beginning of more programming. We're going to see more episodes from theCUBE, I'm John Furrier, your host. Thanks for watching. (upbeat music)

>> Okay, we continue now with the theme of turning ideas into insights so ultimately you can take action. We heard earlier that public cloud first doesn't mean public cloud only. And a winning strategy comprises data, irrespective of physical location on prem, across multiple clouds at the edge where real-time inference is going to drive a lot of incremental value. Data is going to help the world come back to normal we heard, or at least semi normal as we begin to better understand and forecast demand and supply imbalances and economic forces. AI is becoming embedded into every aspect of our business, our people, our processings, and applications. And now we're going to get into some of the foundational principles that support the data and insights centric processes, which are fundamental to digital transformation initiatives. And it's my pleasure to welcome two great guests, Michelle Goetz, who's a Cube alum and VP and principal analyst at Forrester, and doin' some groundbreaking work in this area. And Cindy Maike who is the vice president of industry solutions and value management at Cloudera. Welcome to both of you. >> Welcome, thank you. >> Thanks Dave. >> All right Michelle, let's get into it. Maybe you could talk about your foundational core principles. You start with data. What are the important aspects of this first principle that are achievable today? >> It's really about democratization. If you can't make your data accessible, it's not usable. Nobody's able to understand what's happening in the business and they don't understand what insights can be gained or what are the signals that are occurring that are going to help them with decisions, create stronger value or create deeper relationships with their customers due to their experiences. So it really begins with how do you make data available and bring it to where the consumer of the data is rather than trying to hunt and peck around within your ecosystem to find what it is that's important. >> Great thank you for that. So, Cindy, I wonder in hearing what Michelle just said, what are your thoughts on this? And when you work with customers at Cloudera, are there any that stand out that perhaps embody the fundamentals that Michelle just shared? >> Yeah, there's quite a few. And especially as we look across all the industries that were actually working with customers in. A few that stand out in top of mind for me is one is IQVIA. And what they're doing with real-world evidence and bringing together data across the entire healthcare and life sciences ecosystems, bringing it together in different shapes and formats, making it accessible by both internally, as well as for the entire extended ecosystem. And then for SIA, who's working to solve some predictive maintenance issues within, they're are a European car manufacturer and how do they make sure that they have efficient and effective processes when it comes to fixing equipment and so forth. And then also there's an Indonesian based telecommunications company, Techsomel, who's bringing together over the last five years, all their data about their customers and how do they enhance a customer experience, how do they make information accessible, especially in these pandemic and post pandemic times. Just getting better insights into what customers need and when do they need it? >> Cindy, platform is another core principle. How should we be thinking about data platforms in this day and age? Where do things like hybrid fit in? What's Cloudera's point of view here? >> Platforms are truly an enabler. And data needs to be accessible in many different fashions, and also what's right for the business. When I want it in a cost and efficient and effective manner. So, data resides everywhere, data is developed and it's brought together. So you need to be able to balance both real time, our batch, historical information. It all depends upon what your analytical workloads are and what types of analytical methods you're going to use to drive those business insights. So putting in placing data, landing it, making it accessible, analyzing it, needs to be done in any accessible platform, whether it be a public cloud doing it on-prem or a hybrid of the two is typically what we're seeing being the most successful. >> Great, thank you. Michelle let's move on a little bit and talk about practices and processes, the next core principles. Maybe you could provide some insight as to how you think about balancing practices and processes while at the same time managing agility. >> Yeah, it's a really great question 'cause it's pretty complex when you have to start to connect your data to your business. The first thing to really gravitate towards is what are you trying to do. And what Cindy was describing with those customer examples is that they're all based off of business goals, off of very specific use cases. That helps kind of set the agenda about what is the data and what are the data domains that are important to really understanding and recognizing what's happening within that business activity and the way that you can affect that either in near time or real time, or later on, as you're doing your strategic planning. What that's balancing against is also being able to not only see how that business is evolving, but also be able to go back and say, "Well, can I also measure the outcomes from those processes and using data and using insight? Can I also get intelligence about the data to know that it's actually satisfying my objectives to influence my customers in my market? Or is there some sort of data drift or detraction in my analytic capabilities that are allowing me to be effective in those environments?" But everything else revolves around that and really thinking succinctly about a strategy that isn't just data aware, what data do I have and how do I use it? But coming in more from that business perspective, to then start to be data driven, recognizing that every activity you do from a business perspective leads to thinking about information that supports that and supports your decisions. And ultimately getting to the point of being insight driven, where you're able to both describe what you want your business to be with your data, using analytics to then execute on that fluidly and in real time. And then ultimately bringing that back with linking to business outcomes and doing that in a continuous cycle where you can test and you can learn, you can improve, you can optimize and you can innovate. Because you can see your business as it's happening. And you have the right signals and intelligence that allow you to make great decisions. >> I like how you said near time or real time, because it is a spectrum. And at one end of the spectrum, autonomous vehicles. You've got to make a decision in real time but near real-time, or real-time, it's in the eyes of the beholder if you will. It might be before you lose the customer or before the market changes. So it's really defined on a case by case basis. I wonder Michelle, if you could talk about in working with a number of organizations I see folks, they sometimes get twisted up in understanding the dependencies that technology generally, and the technologies around data specifically can sometimes have on critical business processes. Can you maybe give some guidance as to where customers should start? Where can we find some of the quick wins and high returns? >> It comes first down to how does your business operate? So you're going yo take a look at the business processes and value stream itself. And if you can understand how people, and customers, partners, and automation are driving that step by step approach to your business activities, to realize those business outcomes, it's way easier to start thinking about what is the information necessary to see that particular step in the process, and then take the next step of saying what information is necessary to make a decision at that current point in the process? Or are you collecting information, asking for information that is going to help satisfy a downstream process step or a downstream decision? So constantly making sure that you are mapping out your business processes and activities, aligning your data process to that helps you now rationalize do you need that real time, near real time, or do you want to start creating greater consistency by bringing all of those signals together in a centralized area to eventually oversee the entire operations and outcomes as they happen? It's the process, and the decision points, and acting on those decision points for the best outcome that really determines are you going to move in more of a real-time streaming capacity, or are you going to push back into more of a batch oriented approach? Because it depends on the amount of information and the aggregate of which provides the best insight from that. >> Got it. Let's, bring Cindy back into the conversation here. Cindy, we often talk about people, process, and technology and the roles they play in creating a data strategy that's logical and sound. Can you speak to the broader ecosystem and the importance of creating both internal and external partners within an organization? >> Yeah. And that's kind of building upon what Michelle was talking about. If you think about datas and I hate to use the phrase almost, but the fuel behind the process and how do you actually become insight-driven. And you look at the capabilities that you're needing to enable from that business process, that insight process. Your extended ecosystem on how do I make that happen? Partners and picking the right partner is important because a partner is one that actually helps you implement what your decisions are. So looking for a partner that has the capability that believes in being insight-driven and making sure that when you're leveraging data within your process that if you need to do it in a real-time fashion, that they can actually meet those needs of the business. And enabling on those process activities. So the ecosystem looking at how you look at your vendors, and fundamentally they need to be that trusted partner. Do they bring those same principles of value, of being insight driven? So they have to have those core values themselves in order to help you as a business person enable those capabilities. >> So Cindy I'm cool with fuel, but it's like super fuel when you talk about data. 'Cause it's not scarce, right? You're never going to run out. (Dave chuckling) So Michelle, let's talk about leadership. Who leads? What does so-called leadership look like in an organization that's insight driven? >> So I think the really interesting thing that is starting to evolve as late is that organizations, enterprises are really recognizing that not just that data is an asset and data has value, but exactly what we're talking about here, data really does drive what your business outcomes are going to be. Data driving into the insight or the raw data itself has the ability to set in motion what's going to happen in your business processes and your customer experiences. And so, as you kind of think about that, you're now starting to see your CEO, your CMO, your CRO coming back and saying, I need better data. I need information that's representative of what's happening in my business. I need to be better adaptive to what's going on with my customers. And ultimately that means I need to be smarter and have clearer forecasting into what's about ready to come. Not just one month, two months, three months, or a year from now, but in a week or tomorrow. And so that is having a trickle down effect to then looking at two other types of roles that are elevating from technical capacity to more business capacity. You have your chief data officer that is shaping the experiences with data and with insight and reconciling what type of information is necessary with it within the context of answering these questions and creating a future fit organization that is adaptive and resilient to things that are happening. And you also have a chief digital officer who is participating because they're providing the experience and shaping the information and the way that you're going to interact and execute on those business activities. And either running that autonomously or as part of an assistance for your employees and for your customers. So really to go from not just data aware to data-driven, but ultimately to be insight driven, you're seeing way more participation and leadership at that C-suite level and just underneath, because that's where the subject matter expertise is coming in to know how to create a data strategy that is tightly connected to your business strategy. >> Great, thank you. Let's wrap, and I've got a question for both of you, maybe Cindy, you could start and then Michelle bring us home. A lot of customers, they want to understand what's achievable. So it's helpful to paint a picture of a maturity model. I'd love to go there, but I'm not going to get there anytime soon, but I want to take some baby steps. So when you're performing an analysis on an insight driven organization, Cindy what do you see as the major characteristics that define the differences between sort of the early beginners sort of fat middle, if you will, and then the more advanced constituents? >> Yeah, I'm going to build upon what Michelle was talking about is data as an asset. And I think also being data aware and trying to actually become insight driven. Companies can also have data, and they can have data as a liability. And so when you're data aware, sometimes data can still be a liability to your organization. If you're not making business decisions on the most recent and relevant data, you're not going to be insight-driven. So you've got to move beyond that data awareness, where you're looking at data just from an operational reporting. But data's fundamentally driving the decisions that you make as a business. You're using data in real time. You're leveraging data to actually help you make and drive those decisions. So when we use the term you're data-driven, you can't just use the term tongue-in-cheek. It actually means that I'm using the recent, the relevant, and the accuracy of data to actually make the decisions for me, because we're all advancing upon, we're talking about artificial intelligence and so forth being able to do that. If you're just data aware, I would not be embracing on leveraging artificial intelligence. Because that means I probably haven't embedded data into my processes. Yes, data could very well still be a liability in your organization, so how do you actually make it an asset? >> Yeah I think data aware it's like cable ready. (Dave chuckling) So Michelle, maybe you could add to what Cindy just said and maybe add as well any advice that you have around creating and defining a data strategy. >> So every data strategy has a component of being data aware. This is like building the data museum. How do you capture everything that's available to you? How do you maintain that memory of your business? Bringing in data from your applications, your partners, third parties, wherever that information is available, you want to ensure that you're capturing it and you're managing and you're maintaining it. And this is really where you're starting to think about the fact that it is an asset, it has value. But you may not necessarily know what that value is yet. If you move into a category of data driven, what starts to shift and change there is you're starting to classify label, organize the information in context of how you're making decisions and how you do business. It could start from being more proficient from an analytic purpose. You also might start to introduce some early stages of data science in there. So you can do some predictions and some data mining to start to weed out some of those signals. And you might have some simple types of algorithms that you're deploying to do a next next best action, for example. And that's what data-driven is really about. You're starting to get value out of it. The data itself is starting to make sense in context of your business, but what you haven't done quite yet, which is what insight driven businesses are, is really starting to take away the gap between when you see it, know it, and then get the most value and really exploit what that is at the time when it's right, so in the moment. We talk about this in terms of perishable insights, data and insights are ephemeral. And we want to ensure that the way that we're managing that and delivering on that data and insights is in time with our decisions and the highest value outcome we're going to have, that that insight can provide us. So are we just introducing it as data-driven organizations where we could see spreadsheets and PowerPoint presentations and lots of mapping to help make longer strategic decisions, or are those insights coming up and being activated in an automated fashion within our business processes that are either assisting those human decisions at the point when they're needed, or an automated decisions for the types of digital experiences and capabilities that we're driving in our organization. So it's going from, I'm a data hoarder if I'm data aware to I'm interested in what's happening as a data-driven organization and understanding my data. And then lastly being insight driven is really where light between business, data and insight, there is none, it's all coming together for the best outcomes. >> Right, it's like people are acting on perfect or near perfect information. Or machines are doing so with a high degree of confidence. Great advice and insights, and thank you both for sharing your thoughts with our audience today, it was great to have you. >> Thank you. >> Thank you. >> Okay, now we're going to go into our industry deep dives. There are six industry breakouts. Financial services, insurance, manufacturing, retail communications, and public sector. Now each breakout is going to cover two distinct use cases for a total of essentially 12 really detailed segments. Now each of these is going to be available on demand, but you can scan the calendar on the homepage and navigate to your breakout session of choice. Or for more information, click on the agenda page and take a look to see which session is the best fit for you and then dive in. Join the chat and feel free to ask questions or contribute your knowledge, opinions, and data. Thanks so much for being part of the community, and enjoy the rest of the day. (upbeat music)

>>Hi, this is Cindy Mikey, vice president of industry solutions at Cloudera. Joining me today is chef is Molly, our solution engineer for the public sector. Today. We're going to talk about speed to insight. Why using machine learning in the public sector, specifically around fraud, waste and abuse. So topic for today, we'll discuss machine learning, why the public sector uses it to target fraud, waste, and abuse, the challenges. How do we enhance your data and analytical approaches the data landscape analytical methods and Shev we'll go over reference architecture and a case study. So by definition, fraud, waste and abuse per the government accountability office is fraud is an attempt to obtain something about a value through unwelcomed. Misrepresentation waste is about squandering money or resources and abuse is about behaving improperly or unreasonably to actually obtain something of value for your personal benefit. So as we look at fraud and across all industries, it's a top of mind, um, area within the public sector. >>Um, the types of fraud that we see is specifically around cyber crime, uh, looking at accounting fraud, whether it be from an individual perspective to also, uh, within organizations, looking at financial statement fraud, to also looking at bribery and corruption, as we look at fraud, it really hits us from all angles, whether it be from external perpetrators or internal perpetrators, and specifically from the research by PWC, the key focus area is we also see over half of fraud is actually through some form of internal or external are perpetrators again, key topics. So as we also look at a report recently by the association of certified fraud examiners, um, within the public sector, the us government, um, in 2017, it was identified roughly $148 billion was attributable to fraud, waste and abuse. Specifically of that 57 billion was focused on reported monetary losses and another 91 billion on areas where that opportunity or the monetary basis had not yet been measured. >>As we look at breaking those areas down again, we look at several different topics from an out payment perspective. So breaking it down within the health system, over $65 billion within social services, over $51 billion to procurement fraud to also, um, uh, fraud, waste and abuse that's happening in the grants and the loan process to payroll fraud, and then other aspects, again, quite a few different topical areas. So as we look at those areas, what are the areas that we see additional type of focus, there's broad stroke areas? What are the actual use cases that our agencies are using the data landscape? What data, what analytical methods can we use to actually help curtail and prevent some of the, uh, the fraud waste and abuse. So, as we look at some of the analytical processes and analytical use crate, uh, use cases in the public sector, whether it's from, uh, you know, the taxation areas to looking at social services, uh, to public safety, to also the, um, our, um, uh, additional agency methods, we're going to focus specifically on some of the use cases around, um, you know, fraud within the tax area. >>Uh, we'll briefly look at some of the aspects of unemployment insurance fraud, uh, benefit fraud, as well as payment and integrity. So fraud has its, um, uh, underpinnings in quite a few different on government agencies and difficult, different analytical methods and I usage of different data. So I think one of the key elements is, you know, you can look at your, your data landscape on specific data sources that you need, but it's really about bringing together different data sources across a different variety, a different velocity. So, uh, data has different dimensions. So we'll look at on structured types of data of semi-structured data, behavioral data, as well as when we look at, um, you know, predictive models, we're typically looking at historical type information, but if we're actually trying to lock at preventing fraud before it actually happens, or when a case may be in flight, which is specifically a use case, that shadow is going to talk about later it's how do I look at more of that? >>Real-time that streaming information? How do I take advantage of data, whether it be, uh, you know, uh, financial transactions we're looking at, um, asset verification, we're looking at tax records, we're looking at corporate filings. Um, and we can also look at more, uh, advanced data sources where as we're looking at, um, investigation type information. So we're maybe going out and we're looking at, uh, deep learning type models around, uh, you know, semi or that behavioral, uh, that's unstructured data, whether it be camera analysis and so forth. So quite a different variety of data and the, the breadth, um, and the opportunity really comes about when you can integrate and look at data across all different data sources. So in a sense, looking at a more extensive on data landscape. So specifically I want to focus on some of the methods, some of the data sources and some of the analytical techniques that we're seeing, uh, being used, um, in the government agencies, as well as opportunities, uh, to look at new methods. >>So as we're looking at, you know, from a, um, an audit planning or looking at, uh, the opportunity for the likelihood of non-compliance, um, specifically we'll see data sources where we're maybe looking at a constituents profile, we might actually be, um, investigating the forms that they've provided. We might be comparing that data, um, or leveraging internal data sources, possibly looking at net worth, comparing it against other financial data, and also comparison across other constituents groups. Some of the techniques that we use are some of the basic natural language processing, maybe we're going to do some text mining. We might be doing some probabilistic modeling, uh, where we're actually looking at, um, information within the agency to also comparing that against possibly tax forms. A lot of times it's information historically has been done on a batch perspective, both structured and semi-structured type information. And typically the data volumes can be low, but we're also seeing those data volumes increase exponentially based upon the types of events that we're dealing with, the number of transactions. >>Um, so getting the throughput, um, and chef's going to specifically talk about that in a moment. The other aspect is, as we look at other areas of opportunity is when we're building upon, how do I actually do compliance? How do I actually look at conducting audits, uh, or potential fraud to also looking at areas of under reported tax information? So there you might be pulling in some of our other types of data sources, whether it's being property records, it could be data that's being supplied by the actual constituents or by vendors to also pulling in social media information to geographical information, to leveraging photos on techniques that we're seeing used is possibly some sentiment analysis, link analysis. Um, how do we actually blend those data sources together from a natural language processing? But I think what's important here is also the method and the looking at the data velocity, whether it be batch, whether it be near real time, again, looking at all types of data, whether it's structured semi-structured or unstructured and the key and the value behind this is, um, how do we actually look at increasing the potential revenue or the, um, under reported revenue? >>Uh, how do we actually look at stopping fraudulent payments before they actually occur? Um, also looking at increasing the amount of, uh, the level of compliance, um, and also looking at the potential of prosecution of fraud cases. And additionally, other areas of opportunity could be looking at, um, economic planning. How do we actually perform some link analysis? How do we bring some more of those things that we saw in the data landscape on customer, or, you know, constituent interaction, bringing in social media, bringing in, uh, potentially police records, property records, um, other tax department, database information. Um, and then also looking at comparing one individual to other individuals, looking at people like a specific, like, uh, constituent, are there areas where we're seeing, uh, um, other aspects of, of fraud potentially being, uh, occurring. Um, and also as we move forward, some of the more advanced techniques that we're seeing around deep learning is looking at computer vision, um, leveraging geospatial information, looking at social network entity analysis, uh, also looking at, um, agent-based modeling techniques, where we're looking at simulation, Monte Carlo type techniques that we typically see in the financial services industry, actually applying that to fraud, waste, and abuse within the, the public sector. >>Um, and again, that really, uh, lends itself to a new opportunities. And on that, I'm going to turn it over to Chevy to talk about, uh, the reference architecture for doing these buckets. >>Sure. Yeah. Thanks, Cindy. Um, so I'm going to walk you through an example, reference architecture for fraud detection, using Cloudera as underlying technology. Um, and you know, before I get into the technical details, uh, I want to talk about how this would be implemented at a much higher level. So with fraud detection, what we're trying to do is identify anomalies or anomalous behavior within our datasets. Um, now in order to understand what aspects of our incoming data represents anomalous behavior, we first need to understand what normal behavior is. So in essence, once we understand normal behavior, anything that deviates from it can be thought of as an anomaly, right? So in order to understand what normal behavior is, we're going to need to be able to collect store and process a very large amount of historical data. And so incomes, clutters platform, and this reference architecture that needs to be for you. >>So, uh, let's start on the left-hand side of this reference architecture with the collect phase. So fraud detection will always begin with data collection. Uh, we need to collect large amounts of information from systems that could be in the cloud. It could be in the data center or even on edge devices, and this data needs to be collected so we can create from normal behavior profiles and these normal behavioral profiles would then in turn, be used to create our predictive models for fraudulent activity. Now, uh, uh, to the data collection side, one of the main challenges that many organizations face, uh, in this phase, uh, involves using a single technology that can handle, uh, data that's coming in all different types of formats and protocols and standards with different velocities and velocities. Um, let me give you an example. Uh, we could be collecting data from a database that gets updated daily, uh, and maybe that data is being collected in Agra format. >>At the same time, we can be collecting data from an edge device that's streaming in every second, and that data may be coming in Jace on or a binary format, right? So this is a data collection challenge that can be solved with cluttered data flow, which is a suite of technologies built on Apache NIFA and mini five, allowing us to ingest all of this data, do a drag and drop interface. So now we're collecting all of this data, that's required to map out normal behavior. The next thing that we need to do is enrich it, transform it and distribute it to know downstream systems for further process. Uh, so let's, let's walk through how that would work first. Let's taking Richmond for, uh, for enrichment, think of adding additional information to your incoming data, right? Let's take, uh, financial transactions, for example, uh, because Cindy mentioned it earlier, right? >>You can store known locations of an individual in an operational database, uh, with Cloudera that would be HBase. And as an individual makes a new transaction, their geo location that's in that transaction data, it can be enriched with previously known locations of that very same individual and all of that enriched data. It can be later used downstream for predictive analysis, predictable. So the data has been enrich. Uh, now it needs to be transformed. We want the data that's coming in, uh, you know, Avro and Jason and binary and whatever other format to be transformed into a single common format. So it can be used downstream for stream processing. Uh, again, this is going to be done through clutter and data flow, which is backed by NIFA, right? So the transformed semantic data is then going to be stimulated to Kafka and coffin is going to serve as that central repository of syndicated services or a buffer zone, right? >>So cough is, you know, pretty much provides you with, uh, extremely fast resilient and fault tolerance storage. And it's also going to give you the consumer API APIs that you need that are going to enable a wide variety of applications to leverage that enriched and transform data within your buffer zone. Uh, I'll add that, you know, 17, so you can store that data, uh, in a distributed file system, give you that historical context that you're going to need later on from machine learning, right? So the next step in the architecture is to leverage, uh, clutter SQL stream builder, which enables us to write, uh, streaming sequel jobs on top of Apache Flink. So we can, uh, filter, analyze and, uh, understand the data that's in the Kafka buffer zone in real-time. Uh, I'll, you know, I'll also add like, you know, if you have time series data, or if you need a lab type of cubing, you can leverage Q2, uh, while EDA or, you know, exploratory data analysis and visualization, uh, can all be enabled through clever visualization technology. >>All right, so we've filtered, we've analyzed, and we've our incoming data. We can now proceed to train our machine learning models, uh, which will detect anomalous behavior in our historically collected data set, uh, to do this, we can use a combination of supervised unsupervised, even deep learning techniques with neural networks. Uh, and these models can be tested on new incoming streaming data. And once we've gone ahead and obtain the accuracy of the performance, the X one, uh, scores that we want, we can then take these models and deploy them into production. And once the models are productionalized or operationalized, they can be leveraged within our streaming pipeline. So as new data is ingested in real time knife, I can query these models to detect if the activity is anomalous or fraudulent. And if it is, they can alert downstream users and systems, right? So this in essence is how fraudulent activity detection works. Uh, and this entire pipeline is powered by clutters technology. Uh, Cindy, next slide please. >>Right. And so, uh, the IRS is one of, uh, clutter as customers. That's leveraging our platform today and implementing a very similar architecture, uh, to detect fraud, waste, and abuse across a very large set of, uh, historical facts, data. Um, and one of the neat things with the IRS is that they've actually recently leveraged the partnership between Cloudera and Nvidia to accelerate their Spark-based analytics and their machine learning. Uh, and the results have been nothing short of amazing, right? And in fact, we have a quote here from Joe and salty who's, uh, you know, the technical branch chief for the research analytics and statistics division group within the IRS with zero changes to our fraud detection workflow, we're able to obtain eight times to performance simply by adding GPS to our mainstream big data servers. This improvement translates to half the cost of ownership for the same workloads, right? So embedding GPU's into the reference architecture I covered earlier has enabled the IRS to improve their time to insights by as much as eight X while simultaneously reducing their underlying infrastructure costs by half, uh, Cindy back to you >>Chef. Thank you. Um, and I hope that you found, uh, some of the, the analysis, the information that Sheva and I have provided, uh, to give you some insights on how cloud era is actually helping, uh, with the fraud waste and abuse challenges within the, uh, the public sector, um, specifically looking at any and all types of data, how the clutter a platform is bringing together and analyzing information, whether it be you're structured you're semi-structured to unstructured data, both in a fast or in a real-time perspective, looking at anomalies, being able to do some of those on detection methods, uh, looking at neural network analysis, time series information. So next steps we'd love to have an additional conversation with you. You can also find on some additional information around how called areas working in federal government, by going to cloudera.com solutions slash public sector. And we welcome scheduling a meeting with you again, thank you for joining us today. Uh, we greatly appreciate your time and look forward to future conversations. Thank you.

>>Live from Boston, Massachusetts. It's the cube covering ifs world conference 2019 brought to you by ifs. >>Well going back to Boston and everybody, this is the cube, the leader in live tech coverage. We're here day one at the ifs world conference at the Hynes convention center in Boston. Cindy shutdown is here. She's the president of America's at ifs and she's joined by to my right, K in Blackwell, who's a controller at PPC partners, one of the divisions of PPC Metro power. And rod is the CIO of PPC partners. Welcome folks. Good to see you. I said, let me start with you. So you were on last year in the cube down at Atlanta. You still kind of set some, set some goals, you're a little competitive with your other brethren within then ifs. We love it. You know, we're Americans. Okay. So how's it going in North America? >>Um, well it's, it's growing well. We've had fantastic growth and it's been, you know, a little bit of competition within ifs, but you know, certainly we were very proud. We were named region of the year last year. So we won the coveted cup, which, uh, means, uh, we, uh, we want to keep that cup. So that's some of the, some of the competition that we've got going, right? >>Yeah. Well, of course, most of us based companies, they'll do, they'll start up 79, you know, 90% of their businesses, U S if not 100%, and then they'll slowly go overseas as some of the opposite. Right? >>Very much. I mean, ifs is a European based company. We've been in the, in the U S for quite awhile and, but we've really been investing in our growth and we've had fantastic growth over the last few years. And I think, you know, one of the reasons for that growth is our customer satisfaction in the fact that we really want to listen to our customers. You know, I, um, I, I travel quite a lot as you can imagine. And when I travel, I always try to make sure I can visit customers and hear what they have to say, you know, and of course we love to hear the good things, but I also like to hear when they can give us some ideas for improvement and um, you know, then that gives us something to work on and to, you know, to keep moving forward. Um, I also think that, you know, the good thing about that is, um, it gives us a chance to listen and um, you know, I heard something really great from one of our customers, they went live two weeks ago and they called up and said, Hey, can we do a customer story? I love things like that. Yeah. >>I always love that. Uh, let me think about it. I'll get back to you. Okay. What's your relationship between ifs and PPC part? >>Well, PPC partners is one of our newer customers in there in the middle of an implementation and they're doing some great things around digital transformation. And when I had this opportunity to be here on the cube, I thought it would be great to invite rod and can with me and to, you know, tell some of the things that they're doing. >>Cool. So I kind of recruited Cindy as my cohost, your, they're going to be the defective coho. So welcome to the queue and then we're going to show you right to the fire. Okay. So, uh, can you describe your, your role, your when one of the divisions of PPC partners, right? So maybe maybe set up sort of PPC partners and then your role. >>Right. Okay. So PPC is a specialty contracting company and we have four subsidiary companies that operate in the upper Midwest and then also the Southeastern United States. And we provide, um, um, customers within a base innovative, innovative solutions in the electrical and mechanical contracting. So there are those four companies. I was one of the controllers, um, of those four companies for a lot of years. And now I'm on the core team. There's four of us, five of us now, um, that are involved in the implement. >>Okay. So you got all the numbers in your head. And then rod, you're the CIO and you guys are a service organization for all the divisions. Is that correct? That is correct. >>We sit at the holding company and we're responsible for technology across all four of those specialty contractor companies that can just mention. >>So I love these segments, Cindy, because you know, we, here you go, we go to a lot of conferences in the cube and um, you hear a lot about digital transformation, but, so I'd like to ask the practitioners, what does that mean for you guys? We've got somebody who's very close to the line of business, like I say, knows the numbers, but at the end of the day you've got to deliver the technology services. So what does digital transformation mean to you? What's the company doing in that regard? So a great question actually. >>Um, you'll find companies like ours that have been on the same platform for quite a while, uh, 50 plus years, uh, five zero five, six, zero, uh, probably North of five zero, but we'll go with five zero. Uh, and what happens over time is just, you know, with the system can't grow with the organizations, you resort to a lot of manual paper pushing a lot of file flinging, lots of Excel. And so there's just a ton of duplication of effort and those types of things going on. So from a technology standpoint, that's really the stuff that I come in and see and go, you know. Um, but overall I think that getting to the ifs platform, getting a lot of those redundant processes, a lot of the file flinging out of there, it's just going to be beneficial for all of them. >>Okay. So you guys have had to make the business, you're in the middle of the implementation, right? Is that correct? So she had to go through the business case. Um, it sounds like the business case was, you know, we're, we're basically struggling with running our business because, you know, data's all over the place. We don't have a single view of our business, our customers, et cetera. So we have to come to grips with that. But, but, so what was the business case like? I presume that you were involved as well. >>Right. So I've was really involved in building the software that we've used for that 40 plus years though I haven't used it all of them two years. Um, and, and it was really. It was built by accountants. We, you know, intended for it to meet the needs of the whole, the whole organization. But really it was built by accountants. So, um, we've found that we just really weren't able to keep up with meeting the needs of all of the users. Um, so when we started looking at that, we also had, we were running on a couple of different, um, I'm going to call them boxes. We run it on IBM. So, um, we were not able to look across the entire organization and see a consolidated view of the whole organization. So that was one of the things that we were looking to do, was to really bring all four companies under one umbrella and be able to get a picture of the whole mainframe or, yes, we had a couple of mainframes and all of that software was internally written. Um, and it was good. It was, it was good, but it met, you know, just the needs that those of us within the company saw. Um, so I think we were missing a whole lot of opportunity, um, to really, you know, see what else was out there and see new things and really get outside of our sphere of understanding, you know, >>so PPC, >>no, I was going to say as SKM pointed out and the sort of running joke within the companies is the system we have today does numbers really well. Words not so much because it was designed by accountants for accounting, tracking the financials primarily. Yeah. >>In PPC you do construction of course, or construction club, but you also do some service as well, right? You've got people out in the field that are, that are doing, doing service. So when you were looking, um, I'm assuming that you were trying to find a system that could do both, both solutions. Yeah. Did. >>Absolutely. Uh, one of the things that's been concerning to the entire core team is it's great to go out and find a system and there's plenty of them that can handle your back office. Most systems do that fairly well. But what about you feel services, uh, any in our particular industry, electrical contracting, you might have residential, you know, we could very well be working on the buck stadium or a military installation or even the school, you know, those folks have to be able to process invoices, do all sorts of things from a handheld, et cetera, et cetera. That was a big, big driving factor for us. So has a lot of COBOL code running? Is that, is there right here? So you said 50 years, I mean, um, so now I'm interested in the, in the, in the migration and, and you know what that looks like. >>Yeah, I'll bet. So do you, do you have to freeze the existing sort of systems and then sort of bring the other ones up to speed? Is this cloud-based? What does that all look like? That great question. So, uh, we are, uh, we subscribe to the managed cloud solution. Um, you know, for most construction companies, electrical contracting companies like ours, you know, technology is important, but it is not what really makes our wheels turn. It's a con. It's a competitive advantage if you use it wisely. And so, um, you know, for us it was very important to think about this holistically and try to figure out if we're gonna bring in a solution, what does that solution need to look like and will it work for all of our companies, not just one, not just residential, commercial, et cetera. Okay. All right. So, so w w what's that journey look like? I mean, um, when did, when did it start? What's your >>sort of timeline? So about two and a half years ago, we really started looking at what we had in on hand now and what we had in place and thinking about did we really want to make a move? And so, um, we had a team that came together about 15 people across the organization from operations and also the back office to really evaluate what we had evaluated our needs. Um, we decided, yes, we needed something new. And then we actually brought in a second team, um, that started looking at what that new thing would be. We had a consultant assisting us with that and uh, we kinda narrowed it down to two players if you will. And ifs was one of those. Um, and we, even though, um, one of the things that we liked was the fact that that ifs had, um, a broad reach over different types of industries and we felt like that would give us, um, something in addition to a construct and centric view know domain expertise. Yeah, >>exactly. You know, and you know, with our core industries, you know, construction is a big part of that. But one of the things that we're seeing in the construction industry today is the trend to go to what we call prefabrication. The fact that you know, you can really speed up a project if you aren't trying to build everything on site and you can also do it much more cheaper. McKinsey has a study out and they believe that over time if, if of comp of construction company will engage with prefabrication, they can reduce the project timelines 20 to 50% and lower the cost up to 20% and with ifs is heritage in manufacturing. It's really a perfect marriage for construction companies because construction companies need the project management, the installation, you know, the change management that goes along with some of those back-office things. They also a lot of time have to do service. But if you really want to get that competitive advantage, if you can take advantage of the prefab, which is really manufacturing high, if this is heritage, he could really have a, a full, complete S, you know, solution from one supplier. >>There's a huge trend in home-building actually. You would, you see, you know, modular homes and kind of the future of it. But uh, so how does that affect you guys? I mean you, you prefab something that resonates with you, is that sort of more of a generic statement across the customer base or >>it's certainly an area where we're focusing on more. Um, we also have an automation, uh, division that really focuses on, um, automation for industries. And that's an area that it's kind of a manufacturing type of thing. They build panels and those sorts of things. So we're definitely seeing it >>well. So, okay. So I got to ask you, so when you pulled out the Gartner magic quadrant, I said, okay, it always is. Ifs isn't the leader that, that, that, that might've helped. Right. Okay. So you don't get fired now, but choose the leader, but then you started peeling the onion. He had to do due diligence. So what kinds of things did you look at? What kind of tires did you kick? Piers, did you talk to and be, I'm interested in what your, what you learned. Well, I'll touch on one key element and >>we can get in as many sub elements as you like. The selection process for us took several months. Um, I think initially we really pared it down to about eight packages that we were seriously considering. Then down to four and then eventually down to two. And what really, really intrigued us about ifs was the fact that they are not construction centric. So we really had a big decision to make internally, which was do we want to just get on the bandwagon and do what everyone else in construction is doing or do we really wanna you know, risk versus reward and go after something special. So ifs, they are in, you name it, manufacturing is obviously key. Aerospace engineering, race cars I saw today, I didn't know that. So that was a big selling point for us. And the plan is to retire your mainframe and go into the cloud. >>Yes, yes, yes. So IBM got you in a headlock. >>We've been friends for a long time. Good company. Um, w what's that been like just to sort of, uh, that the thought of, you know, going to the cloud. W how, how is, you know, the it folks you know, responded to that. Um, how has that changed their sort of role brokers versus all? Again, I think in construction organizations, technology is important, but it is not what makes the wheels turn. So I'm trying to bring in all of that iron and infrastructure and build it out and configure it ourselves and then maintain it for the long haul. Just not something that was value added for us. In addition, um, if you've ever worked with Oracle, which is a close partner of ifs, but there is a lot of licensing caveats and a lot of things you've got to worry about if you're going to go it alone by going with the managed cloud solution, we're sort of partnering and trusting ifs to take that on for us so we can focus on taking care of our companies, our customers, and doing what we do best. Right? So, okay, so you're still going to be an Oracle. You just won't be, it won't be as visible. We use Oracle too. We're a Salesforce customer, so Hey, Oracle is behind there, but no offense. >>Ah, I know you guys did >>for the distinction as well, right? Because even if you are going to have portions of Oracle that are running your system, you've got to have some Oracle experts on staff. You know, if you're going to have all of the infrastructure, you gotta have infrastructure folks who understand how it all ties together. So on the surface it could seem like a simple decision to do it in house or go to the cloud. Far from it. >>Yeah. You know, I think certainly one of the things that we see in a lot of different industries, but certainly in construction, the plant had always been that you bring together different, different solutions and you try to both and together and then some of that becomes a lot more concerning. You know, some of the technology behind it. But one of the things that with the ifs solution is the fact that from one provider you can do, you know, do the whole life cycle. So then some of the have it in the managed cloud where we take care of it for you. So then that takes away some of those technology issues and then you can focus on your core competencies. So Rhonda would agree generally >>with what you're saying. I mean some probably say that for most companies that you know, the technology is not the core differentiator. Obviously this for Google, sure. For Amazon, for Facebook, but for CIO is I talked to, they go people process, technology, technology is the least of my problems. It's like I was going to come and go, it's going to change. I can deal with that. It's the, if the people in the process issues. So having said that, I'm still interested in how concerned you were about peeling the onion on the cloud, what's behind it, the security model, all that stuff in terms of your due diligence, you know, with any cloud based solution, there's some concern obviously. But, but in working with ifs, we, we asked a ton of questions and they gave us a ton of answers. So the comfort level was there. Um, the industry's been going to the cloud now for quite some time. And to be brutally honest, if you're not going there, um, you need to be strongly considered >>in Microsoft is our partner with the cloud. We're on, you know, using Microsoft Azure. So it's not like, you know, it's one of the largest cloud provider. So it's not like, you know, it's, it's something that you have to worry about. You've got the, you know, the backstop of Microsoft behind you as well. You know, I'm sorry, go, go, go. I was going to say, I think one of the things that's interesting is you talk about all your different divisions and you're really trying to bring a lot of different companies together on one system. And one of the things that I, you know, as I've seen the things that's change management becomes really something that you really have to consider. I mean, how have you seen that part of the implementation going? Has there been stepping in the easy piece for you? It's not been an easy piece and that's one of the pieces that we're still working on. >>Um, I don't know if any organization that says that they're really, really good at change. Um, but we've recognized that really the, our organization is a group of entrepreneurs and we've encouraged people to have their own business, but we're really trying to streamline and get some consistency across the organization. That's a little bit of a culture shift for us. So that change management piece is a piece that we're really trying to get our arms around now and prepare, um, the organization for that team. Just trying to get my head around your software still. You guys do change management? I TSM. Well, you'll change management is really some of the, um, consulting that goes along with it and certainly ifs and AR, we've got many partners who can, you know, help our customers go through that. Because when you're going through a digital transformation, you know, you're taking people who have been using something for 50 years, being out, especially out in the field doing those things. And now you're trying to figure out what are the right processes to put in place to get what the business needs. And in some cases they might have to do things differently. So you really have to think that through and how you're going to roll those out. >>So now, is this your first ifs world? Yes, it is. It is. What final thoughts, you know, things you've, you've taken away or you're going to bring back to your teams? >>Well, yeah, Boston is a favorite city of mine. I was just glad to be here just for that. But, and we've just been here a little bit. I've already picked up some things on leadership. I was involved the um, >>Oh, the women's leadership breakfast this morning. So there's already been some things that I think we can take back to users and share with them, particularly around change management and trying to get people comfortable and understanding why they're uncomfortable with change. You know? So it, rod, you're next on the line. So I'm sure you were taking notes, pretty attentive in the sessions and just getting started, right? >>No, you know, I have, and one of the things for me that was most, I guess rewarding is, is the partner network. All of the vendors. There's a number of things with our implementation that we're still trying to sort out OCR for example, being one of them. Are we going to go there or are we gonna wait until later? Just different technologies and maybe add ons that we may want to take advantage of. All you've got to do is walk down the hallways and there's, there's people ready to talk to you about it. So that's, that's been kind of intriguing. >>Okay. Excellent. Well yeah, I said earlier I was, I was surprised and impressed at the sort of size of the ecosystem and its great. Well good luck to you guys. Really wish you the best and thanks so much for coming on the cube and sharing your story Cindy. Great to see you. Always pleasure. All right, take care. Thank you for watching everybody. We're back with our next guest right after this short break. You're watching the cube from Boston ifs world 2019 right back.

(electronic upbeat theme music) >> Announcer: Live from Las Vegas, it's theCUBE covering NetApp Insight 2018. Brought to you by NetApp. >> Welcome back to theCUBE. We are live at NetApp Insight 2018. I'm Lisa Martin with Stu Miniman, and we're please to welcome for the first time to theCUBE, Cindy Warner, SVP of Worldwide Service and Support at NetApp. Cindy, it's great to have you here. >> Thank you. I'm thrilled to be here. >> So this morning's keynote, talked a lot about transformation. Transformation of NetApp. Transformation that your customers need to execute to be competitive, to be successful. Tell us about customer transformation that you're seeing as the leader of service and support. >> Sure, our customers want outcome plain and simple. They are buying solutions that lead to outcome. So in the service and support area, the conversations we're having with our customers now is all about outcome. What can we do to ensure their outcome. To ensure their transformation. To ensure they can provide the services to their customers that they're looking to provide, or new revenue streams, or what have you. But it's really all about outcome and that's awesome because they don't care what's behind the curtain. They don't care if it's this box or that box. They care about outcomes. So that's a really big transformation for us. >> Yeah Cindy, one of the big challenges that used to be, okay, I got a box. I know exactly where it is. I know exactly, you know, who set it up and all the configuration. Now it's like wait. It's a multi-hybrid cloud world. >> Cindy: Right. >> And I got software spanning all of these environments and my data is all over the place. That has to have a huge ripple effect on the services and support. Walk us through a little bit about what that looks like. >> Yeah, I would tell you the number one thing in our world, if you really think about it, is data sovereignty. Because where's my data, you know. If I were a CTO or CIO, I'd wake up in the morning and go, where's my data. Right because, and we're managing that data for a lot of clients. And so it's really all about where's my data, and making sure that the sovereignty of the data is suppose to be in a certain place. It's suppose to be protected in a certain way. We work with a lot of regulated environments. So think healthcare, right. Think, you know, even automotive to some extend. All that IOT data, who's touching that data? That's personal data. So as, you know, the futurist talked about this morning, the ethical side of data for services and support is really intriguing to us actually. >> What's the conversation like, Cindy, with your enterprise legacy customers who weren't born in the cloud? How are you helping them kind of embrace the change that they have to go through? >> Yeah, I think the number one thing is to not be persuade into thinking it's all cloud, right. It's not everything is not made for the cloud. Especially if it wasn't born on the cloud. The pathway to the cloud could be very difficult, and maybe not even prudent. So we're doing a lot of assessments for our clients to decide what workloads belong in the cloud, and helping them to understand, it isn't all cloud. It's some cloud and it's hybrid cloud, and so it's this wonderful Lego cube that we build for them. >> NetApp has done quite a few acquisitions, you know, in the last couple of years. How does that impact what you're doing? Think about everything from the Gubernatis pieces and what's happening in AI. Talk about some of those challenges and opportunities. >> Sure, I mean, I would tell you something like Green Cloud that we did last year. When we look at managing those workloads, and helping to build up that Rubik's cube, right. Of piece parts and what that overall orchestration and architectural looks like in the future. Something like a Green Cloud helps us to orchestrate that. It helps us to manage that and really, that management plane for our clients is really where the heartburn is. It's taking look and seeing that entire data landscape and managing and orchestrating that. And the movement of all that data. That's the biggie. >> You know, follow up question. When I think about NetApp, NetApp was heavily involved in helping to really fix storage in a virtualized environment. >> Cindy: Sure. >> Lots of us have, you know, the wounds, the memories of, you know, over a decade of kind of fighting through that. What is FCS's role in kind of the cloud native this next wave? >> Yeah I, you know, I think it's the overall integration. Our team now is really fixated on where do we go with the overall integration of legacy and the cloud native stuff that clients are building. And grand it, the cloud native stuff gives competitive differentiation. Gives speed, gives scale. Really great stuff. But you can't leave the other stuff behind, right So for us, integration and how that integration is going to work through APIs or otherwise, is really a huge fixation in services and support. >> So NetApp has grown a lot. Done a lot of transformation. Talk about some of the changes to your customers' segmentation and how you're using that information and that segmentation to really deliver differentiated services. 'Cause let's face it, customers have a lot of choice. >> Right, and that's a key word for us actually. We say that the tag line, and for services and support we're looking for value based differentiated services that deliver outcomes. Big mouth. All I know, and I have no marketing chops, as you can tell, but the truth be told, when we look at our Global 1000 customer, they want high touch. And in some cases, no touch. And they want to get the information, solve problems really quickly without having to go, L1, L2 all through the tiers. And so we're piloting programs that are proactive predictive. And that are very high touch to ensure that they can solve their problems quite quickly. Either on their own or through the right person instead of going through some of that typical pathways to support. >> Alright, Cindy, I love you. You're going to help us decode some of this marketing discussion. So, hashtag data driven is something we're seeing at the show. >> Cindy: Right. >> Help connect for us, you know, how are customers being data driven as they look at their future in the cloud and beyond. >> Well, when I think of data driven, I think of new services. That to me means new services. And looking at the correlation, if you may say. Give you, you know, a start here. So the gentleman that had the DNA and Gene-Up data, right, in the keynote. If we can take that data and correlated to somebody's overall health history and see the transition, right. See as your blood pressure is going up. Or see as, you know, certain changes and doubts are happening in your health profile. Overall holistically, you can I think see the train before it hits you. Right, you can see a stroke coming. And that would be the most beautiful thing. Is to see stuff before it hits you. Same with the car manufacturer. If they see a pattern of brakes that are going out, Marry Barra probably never wants to sit in front of the Senate again, right. So we can see those patterns before a massive recall has to happen. So that's data driven to me. It's either new goods and services or seeing a train before it hits you. >> Cindy, I know this is a short segment, but we want to thank you so much for stopping by. I'm going to give you a CUBE sticker because you are now officially an alumni. >> I'll feel CUBED forever more. >> Excellent. CUBED forever more. That's a new hashtag. We want to thank you for sharing your perspective from a services and support standpoint because those are critical services >> Thank you. >> For customers needs. >> And we want to thank you for watching this segment. I'm Lisa Martin with Stu Miniman. You're watching theCUBE live from NetApp Insight 2018. (electronic upbeat theme music)

>> Live from San Jose in the heart of Silicon Valley, it's theCUBE, covering Data Works Summit 2018, brought to you by Hortonworks. >> Welcome back to theCUBE's live coverage of Dataworks here in San Jose, California. I'm your host, Rebecca Knight, along with my co-host, James Kobielus. We're joined by Cindy Maike. She is the VP Industry Solutions and GM Insurance and Healthcare at Hortonworks. Thanks so much for coming on theCUBE, Cindy. >> Thank you, thank you, look forward to it. >> So, before the cameras were rolling we were talking about the business case for data, for data analytics. Walk our viewers through how you, how you think about the business case and your approach to sort of selling it. >> So, when you think about data and analytics, I mean, as industries we've been very good sometimes at doing kind of like the operational reporting. To me that's looking in the rearview mirror, something's already happened, but when you think about data and analytics, especially big data it's about what questions haven't I been able to answer. And, a lot of companies when they embark on it they're like, let's do it for technology's sake, but from a business perspective when we, as our industry GMs we are out there working with our customers it's like, what questions can't you answer today and how can I look at existing data on new data sources to actually help me answer questions. I mean, we were talking a little bit about the usage of sensors and so forth around telematics and the insurance industry, connected homes, connective lives, connected cars, those are some types of concepts. In other industries we're looking at industrial internet of things, so how do I actually make the operations more efficient? How do I actually deploy time series analysis to actually help us become more profitable? And, that's really where companies are about. You know, I think in our keynote this morning we were talking about new communities and it's what does that mean? How do we actually leverage data to either monetize new data sources or make us more profitable? >> You're a former insurance CFO, so let's delve into that use case a little bit and talk about the questions that I haven't asked yet. What are some of those and how are companies putting this thing to work? >> Yeah so, the insurance industry you know, it's kind of frustrating sometimes where as an insurance company you sit there and you always monitor what your combined ratio is, especially if you're a property casualty company and you go, yeah, but that tells me information like once a month, you know, but I was actually with a chief marketing officer recently and she's like, she came from the retail industry and she goes, I need to understand what's going on in my business on any given day. And so, how can we leverage better real time information to say, what customers are we interacting with? You know, what customers should we not be interacting with? And then you know, the last thing insurance companies want to do is go out and say, we want you as a customer and then you decline their business because they're not risk worthy. So, that's where we're seeing the insurance industry and I'll focus a lot on insurance here, but it's how do we leverage data to change that customer engagement process, look at connected ecosystems and it's a good time to be well fundamentally in the insurance industry, we're seeing a lot of use cases, but also in the retail industry, new data opportunities that are out there. We talked a little bit before the interview started on shrinkage and you know, the retail industry's especially in the food, any type of consumer type packages, we're starting to see the usage of sensors to actually help companies move fresh food around to reduce their shrinkage. You know, we've got. >> Sorry, just define shrinkage, 'cause I'm not even sure I understand, it's not that your gapple is getting smaller. It refers to perishable goods, you explain it. >> Right, so you're actually looking at, how do we make sure that my produce or items that are perishable, you know, I want to minimize the amount of inventory write offs that I have to do, so that would be the shrinkage and this one major retail chain is, they have a lot of consumer goods that they're actually saying, you know what, their shrinkage was pretty high, so they're now using sensors to help them monitor should we, do we need to move certain types of produce? Do we need to look at food before it expires you know, to make sure that we're not doing an inventory write off. >> You say sensors and it's kind of, are you referring to cameras taking photos of the produce or are you referring to other types of chemical analysis or whatever it might be, I don't know. >> Yeah, so it's actually a little bit of both. It's how do I actually you know, looking at certain types of products, so we all know when you walk into a grocery store or some type of department store, there's cameras all over the place, so it's not just looking at security, but it's also looking at you know, are those goods moving? And so, you can't move people around a store, but I can actually use the visualization and now with deep machine learning you can actually look at that and say, you know what, those bananas are getting a little ripe. We need to like move those or we need to help turn the inventory. And then, there's also things with bar coding you know, when you think of things that are on the shelves. So, how do I look at those bar codes because in the past you would've taken somebody down the isle. They would've like checked that, but no, now we're actually looking up the bar codes and say, do we need to move this? Do we need to put these things on sale? >> At this conference we're hearing just so much excitement and talk about data as the new oil and it is an incredible strategic asset, but you were also saying that it could become a liability. Talk about the point at which it becomes a liability. >> It becomes a liability when one, we don't know what to do with it, or we make decisions off of data data, so you think about you know, I'll give you an example, in the healthcare industry. You know, medical procedures have changed so immensely. The advancement in technology, precision medicine, but if we're making healthcare decisions on medical procedures from 10 years ago, so you really need to say how do I leverage you know, newer data stats, so over time if you make your algorithms based on data that's 10, 20 years old, it's good in certain things, but you know, you can make some bad business decisions if the data is not recent. So, that's when I talk about the liability aspect. >> Okay, okay, and then, thinking about how you talk with, collaborate with customers, what is your approach in the sense of how you help them think through their concerns, their anxieties? >> So, a lot of times it's really kind of understanding what's their business strategy. What are their financial, what are their operational goals? And you say, what can we look at from a data perspective, both data that we have today or data that we can acquire from new data sources to help them actually achieve their business goals and you know, specifically in the insurance industry we focus on top line growth with growing your premium or decreasing your combined ratio. So, what are the types of data sources and the analytical use cases that we can actually you know, use? See the exact same thing in manufacturing, so. >> And, have customer attitudes evolved over time since you've been in the industry? How would you describe their mindsets right now? >> I think we still have some industries that we struggle with, but it's actually you know, I mentioned healthcare, the way we're seeing data being used in the healthcare industry, I mean, it's about precision medicine. You look at gnomics research. It says that if people like 58 percent of the world's population would actually do a gnomics test if they could actually use that information. So, it's interesting to see. >> So, the struggle is with people's concern about privacy encroachment, is that the primary struggle? >> There's a little bit of that and companies are saying, you know, I want to make sure that it's not being used against me, but there was actually a recent article in Best Review, which is an insurance trade magazine, that says, you know, if I have, actually have a gnomic test can the insurance industry use that against me? So, I mean, there's still a little bit of concern. >> Which is a legitimate concern. >> It is, it is, absolutely and then also you know, we see globally with just you know, the General Data Protection act, the GDPR, you know, how are companies using my information and data? So you know, consumers have to be comfortable with the type of data, but outside of the consumer side there's so much data in the industry and you made the comment about you know, data's the new oil. I have a thing, against, with that is, but we don't use oil straight in a car, we don't use crude putting in a car, so once we do something with it which is the analytical side, then that's where we get the business end side. So, data for data's sake is just data. It's the business end sites is what's really important. >> Looking ahead at Hortonworks five, 10 years from now I mean, how much, how much will your business account for the total business of Hortonworks do you think, in the sense of as you've said, this is healthcare and insurance represents such huge potential possibilities and opportunities for the company? Where do you see the trajectory? >> The trajectory I believe is really in those analytical apps, so we were working with a lot of partners that are like you know, how do I accelerate those business value because like I said, it's like we're not just into data management, we're in the data age and what does that mean? It's like turning those things into business value and I've got to be able to I think from an industry perspective, you know be working with the right partners and then also customers because they lack some of the skillsets. So, who can actually accelerate the time to value of using data for profitability? >> Is your primary focus area at helping regulated industries with their data analytics challenges and using IOT or does it also cover unregulated? >> Unregulated as well. >> Are the analytics requirements different between regulated and unregulated in terms of the underlying capabilities they require in terms of predictive modeling, of governance and so forth and how does Hortonworks differentiate their response to those needs? >> Yeah, so it varies a little bit based upon their regulations. I mean, even if you look at life sciences, life sciences is very, very regulated on how long do I have to keep the data? How can I actually use the data? So, if you look at those industries that maybe aren't regulated as much, so we'll get away from financial services, highly regulated across all different areas, but I'll also look at say business insurance, not as much regulated as like you and I as consumers, because insurance companies can use any type of data to actually do the pricing and doing the underwriting and the actual claims. So, still regulated based upon the solvency, but not regulated on how we use it to evaluate risk. Manufacturing, definitely some regulation there from a work safety perspective, but you can use the data to optimize your yields you know, however you see fit. So, we see a mixture of everything, but I think from a Hortonworks perspective it's being able to share data across multiple industries 'cause we talk about connected ecosystems and connected ecosystems are really going to change business of the future. >> So, how so? I mean, especially in bringing it back to this conference, to Data Works, and the main stage this morning we heard so much about these connected communities and really it's all about the ecosystem, what do you see as the biggest change going forward? >> So, you look at, and I'll give you the context of the insurance industry. You look at companies like Arity, which is a division of All State, what they're doing actually working with the car manufacturers, so at some point in time you know, the automotive industry, General Motors tried this 20 years ago, they didn't quite get it with On Star and GMAC Insurance. Now, you actually have the opportunity with you know, maybe on the front man for the insurance industry. So, I can now start to collect the data from the vehicle. I'm using that for driving of the vehicle, but I can also use it to help a driver make safer driving. >> And upsize their experience of actually driving, making it more pleasant as well as safer. There's many layers of what can be done now with the same data. Some of those uses impinge or relate to regulated concern or mandatory concerns, then some are purely for competitive differentiation of the whole issue of experience. >> Right, and you think about certain aspects that the insurance industry just has you know, a negative connotation and we have an image challenge on what data can and cannot be used, so, but a lot of people opt in to an automotive manufacturer and share that type of data, so moving forward who's to say with the connected ecosystem I still have the insurance company in the background doing all the underwriting, but my distribution channel is now the car dealer. >> I love it, great. That's a great note to end on. Thanks so much for coming on theCUBE. Thank you Cindy. I'm Rebecca Knight for James Kobielus. We will have more from theCUBE's live coverage of Data Works in just a little bit. (upbeat music)

>> Narrator: Live from Atlanta, Georgia, it's theCUBE. Covering IFS World Conference 2018. Brought to you by IFS. >> Welcome back to theCUBE's coverage of IFS World here at Georgia here at World Congress Center I'm your host Rebecca Knight and along with my cohost Jeff Frick. We are joined by Cindy Jowden, she is the CEO North America a position she has held since 2004. Thanks so much for joining us Cindy. >> Good morning, how are you? >> Good, I'm good. >> Great. >> Good. >> It's our first IFS World, it's quite a show you guys have. >> Yeah, we're very excited, you know it's such a great opportunity for us to, you know, connect with so many of our great customers. >> So, tell us a little bit about the theme of this year's conference which is Connect to What's Next. What, what is that all about? >> Well, it's about connecting to what's going on next in technology, and in business, and in the economy. You know, we've got many, you know great customers who are, you know, medium to large size industries and they're having you know, all different kinds of things come toward them around business transformations, you know, their customers are becoming more demanding, consumers are becoming more demanding, and so this conference really helps them see not only what they're facing today but what they're facing for the future. You know, we've got many levels of people that come to this conference you know, we've got CFO's, CIO's to power users and so there's something here really for everyone. So, you know if you want to talk about trends in the industry, you want to talk about what's going on with our new versions of products, that's available. If you are a power user and you're in finance and you just want to go connect with a industry expert to find out how you can do your job easier, it's all here. >> So, it's not only what is next in the technology, it's also connecting human to human. >> Oh. >> I mean that's really what the congress is about. >> Oh, most definitely, you know it's really fun because you'll see you know, customers that maybe haven't seen each other in person since the last world conference. But, they connect and they talk all the time you know via the phone or Skype or whatever, but they see each other and they run and they hug each other and they say, "oh it's so good "to be able to see what's going on" and you know our customers share so much and so that's really just a great opportunity and also for our customers to connect with our experts and you know, the people that they work with, you know from day to day as well. >> Man: So you're CEO of North America. >> I'm the president of the Americas. >> President of the Americas. >> Yes. >> Which includes the southern hemisphere, right? >> Yes, you don't want to forget our friends in Latin America. >> That's right. So it's a Swedish, founded in Sweden, so how are things going in North America or South America, excuse me the Americas, and what kind of values and things that you take from a Swedish based company that you're applying here in the Americas that's maybe a little bit different than a company that was founded in Silicone Valley or someplace like that. >> That's a great question, you know at IFS we've got you know strong you know, Swedish roots and Swedish heritage which says, you know, do what's right, work hard, stay close to your customer and you know, say what you can do and if you can't do something, make sure you say that as well. So, it's setting that right expectations, and we've taken that and that's really pervasive through all that we do. And, you know, we want to make sure that we, you know, can do, you know, say what we do, deliver on what we do, and then, you know, our employees love working with our customers and I think our customers feel you know, feel that we're partners and it's not something that you know, we're not just saying something to get the next deal. It's not unusual for us to say well, I'm sorry, you know, we shouldn't work together because what you want to do Mr. Prospect, is something different and it's not really in our focus and you know and sometimes it's hard to do especially if you're in sales is to walk away from somebody who's ready to buy business, right? >> Right, right. >> But, we want to make sure that you know, the customers that we work with are really good fits for where we're going because these are really long term relationships. >> Right, and how about that, it probably increases your probability of customer success pretty dramatically if you can actually deliver you know, what they want. >> Oh, most definitely, most definitely and you know certainly we also, I don't have the largest marketing budget depending on you know, my competitors that I deal with and so I really depend on great customer satisfaction and great customer references to help, you know, bring the next prospect on as part of the IFS family. And, you know, and our customers I think are some of our best sales people out there. It's really, it's really great. >> One of the things that the CEO talked about in the key note was really about building trust and you were just talking about your marketing budget. He also said, we're not going to market nonsense. Can you talk a little bit about how you build that trust, being honest with customers, obviously, sorry we can't do that, we can't deliver that, but we can deliver this. How, what else, what other kinds of ways do you make sure that you are building the kind of trusting, collaborative relationship with customers that you want? >> Well, it starts with listening. I mean, when you meet with a customer you got to step back, you have to listen, you have to be willing to listen to what you're doing well, and what you're, you know, what you need to improve on. And then you need to be able to take that in and then you know, synthesize it and then, you know, figure out how you're going to improve, you know and at IFS we're always striving to improve, not just with our products and you can see you know, we just released Applications 10 and that's exciting and many many things that are in Apps 10 came from feedback from our customers and from the user group. But, it's also listening with how we do service or how we work with our partners or do we need more partners? You know, we, you know, we have to just, you know be very open and communicative with our customers and I think everybody says that, you know, but you know, you don't say and say oh, I'm not going to listen to my customer. But, you really have to listen and then put it into action. >> Right, right. And, it's not easy to be maniacally focused on your customers, a lot of people say they are but when you peel back just a little bit they're more focused on their products, they're more focused on the competition, they're more focused on a lot of stuff so it is hard to be really singularly focused but you guys are kind of in services management management business so you work with those types of businesses that they themselves are really active in managing that client relationship. >> Oh, most definitely and when they're involved in that business they have very high expectations of what they expect, you know, on the other side when they're the customer as well. And I think we've learned some things from them, too and you know and how they, their service levels and things that they expect from that particular area. I also think it has something to do with the fact that, when we, you know IFS has been in the U.S. for, 20 some years now. But, we didn't come as the biggest player and so we really had to listen. We really had to work directly with those customers and you know we really needed to make sure that every one of those implementations was successful because we needed to you know have that customer ground swell of you know this is the greatest you know greatest software out there to help us continue to grow. >> Right. >> Really prove yourself. >> Exactly, exactly because I can. >> We're number two, we try harder right? >> Exactly, yeah. >> I mean it's a great its a great person to get together with versus we're number one and we're cocky and arrogant and don't care what you say. >> Exactly, exactly, exactly, yeah. >> So, so what is next, I mean we've seen the introduction of IFS 10 and I know we have some early adopters that it's already live with. You've got great scores, your NPS score, your Gartner insight scores are very high. What are some of your ambitions for growth? >> Well, certainly we want, you know, I would look to have the Americas be the largest region for IFS. I mean, that's, I think that you know we've got a great opportunity here. We've got a large market, we've got a great product and you know certainly we just want to continue to grow and so you know right now we are a large percentage of the IFS revenue but we want that to be even larger here in North America and in the Americas, so I think that's certainly very important to us. And we want to grow not only with what we're doing with IFS applications in its core, but also as we're adding new pieces with IFS, new add on products, new technologies to be able to make sure that our customers understand what we're doing there and how that can help their business. You know, I think it was interesting Dan's keynote today was talking about cloud which was a few years ago and now it's mainstream for us. Last time it was talking about IOT and now we've got more and more customers doing that, and so certainly we're looking about artificial intelligence and everybody is talking about that but at IFS we don't just want to say these buzz words. We want to really figure out as a customer what you need, how can you use this technology and monetize it, right because no one implements technology just to implement it. You want to have it help your business. And, so you know those are the kinds of things we're working on what's next and then there's going to be the next thing after you know, artificial intelligence and the next thing and that's why we depend on labs so we're always ahead of the curve and we can be bringing what our customers need. >> I thought it was interesting on Darren's keynote the other thing really is function versus experience, which he talked about time and time again and then with the Arena demonstration, kind of getting to a unified UI experience across all the different platforms. Looks like in nine you had kind of a different hodge podge of five and then you showed how Arena slowly replacing all of them so you'll have this unified experience. But, that's an interesting point of view, really to focus on the experience ahead of really the function and that seemed to be a pretty clear message in his keynote. >> Well, we've been focusing on user experience, that's been one of our you know, core things for the product road map for many years and I think Dan talked about that as well. Certainly it's a balance because if you don't have the feature and function it doesn't matter what your user experience is, you're not going to use it. But, IFS is a very feature rich product and then you need to make sure that you can make it easier to use and so certainly it is focusing on that user experience but continuing to add the functionality that we need to support that as well. And you know, millennials today, they expect to be able just to sit down, they don't want to go to days of training, they don't want to have to. It just should be intuitive and that's our, you know, really what we're trying to do is just to make sure that it's as intuitive to use as a consumer product but really has the depth that you need to get your job done because you know, our customers they have complex businesses and complex business problems that they need to solve and so we need to make sure that we can develop, you know use both and have both of them for our customers to use. >> But, historically in the ERP space was always function over experience and a lot of the historical companies had a pretty bad rap for the user experience so you know, to really prioritize that and then to add some of the automation and the AI to hide certain levels of that detail that you just don't need to see under the UI. I thought that was pretty impressive. >> Yeah, I think it is, I think it is and I think it's very special for where we're going and if you don't, people never really get to implement all the features and functions underneath it. And what my hope is, is that with a good user experience people will use more of the product and then they'll be able to use more of the features and functions that are there today and that we're adding for the future, and they can use that to make their businesses even better. >> So are you working with the customers in the labs, too? I mean, how, how, at what point, 'cause you said that's why you have the labs so you can experiment and iterate and then, but then how do you know what the customer, what is intuitive to the customer and then what the customer needs, how closely? >> Well we'll bring customers into the labs. We will do a labs tour, we did last year that we did that and you let some customers see that. Then our customers know that everything that we do in the labs doesn't necessarily mean that it's going to come out, right? Because you know, we want, we don't want them to fail, but they have the right to fail in the labs because you learn a lot about, you know, what didn't work as well. So, it's making sure that when we have events like this, you know, there's the innovation center over there and making sure that, you know, getting feedback on what they're doing there and letting customers see there and get their input. It's all, once again, about we've got ideas, we need to bring those ideas to the customer, listen to them, get their feedback, listen, and then take it back, synthesize it and go to the next step. >> Deliver it. >> You talked about growth, being a big objective. Are there any particular market segments that you're, that you're looking at? >> Well IFS has had an industry focus for quite some time and we don't expect to change that industry focus. You know, we're very focused on customers who make products and who can, you know, maintain and service assets and so you know right now we're very strong in aerospace and defense, we're extremely strong in service. You know we're ranked highest on those. We've got a great customer base in industrial manufacturing and process and in those particular industries and so we're going to continue to focus on those. I don't see that we're going to go outside those industries because there is more than enough market here in the Americas for us to focus on those and to be very good at it and we need to focus and be extremely good at what we do. Therefore, we can keep the good customer satisfaction. >> All right, and then we just had Tobias on too talking about IOT and really starting to integrate multiple data sources you know a lot more stuff into your existing application to expand on your capabilities. >> Cindy: Oh, most definitely, that's certainly the point. >> You don't need to build a bunch of new stuff necessarily. >> Cindy: Yeah, yeah exactly. >> Great, well Cindy thank you so much for coming on theCUBE. We've had a great time talking to you. >> Cindy: Great, it was a pleasure, thank you. >> Thanks. >> I'm Rebecca Knight for Jeff Frick, we will have more from IFS World, theCUBE's live coverage just after this. (techno music)

>>Yeah. Hello. Welcome to the Cubes International Women's Showcase, featuring International Women's Day. I'm John for your host of the queue here in Palo Alto, California. And we have three great guests videoing in from Kuala Lumpur as well as Bangkok. Johann Kwa, group CEO of K s K Group. It's just a Christina Equal, co founder and head of K s, K C Labs and Cindy, co founder of Sunday Insurance in Bangkok. Ladies. Thanks for coming on the cue. Appreciate you coming on. Thanks for Thanks for joining me on this special day. >>Thank you. Thank you so much. You >>guys are three sisters, trailblazing and the insurance and real estate through digital transformation in the cloud taking a three decade old family business to the next level raising the bar, as they say in the cloud business. Congratulations. Tell us how it all started. What's going on now? What does it look like? Where did it come from? Tell the Storey. >>Okay, so maybe I'll start, Uh, you know, since I'm at the group CEO level. So, um, as a quick introduction, you know? Okay. SK group, uh, were about 30 years old now, as a group three decades. Um, we started off as an insurance, uh, nonlife insurance company. Um, and then over the years, um, you know, we we operate in in South East Asia, So we are based in the US and markets. That message is also sitting in, um, and very quickly over the years, you know, we decided to actually venture into property development as well. Um, and really across the journey. Um, you know, we we've always been very, um, obsessed over the customers. You know, uh, and, you know, during this time and age, you know, all the customers are really digital natives now, and and, you know, the tech is very, very interesting. And so So starting in the year of 2017, we decided, um, to actually venture. Cindy and I at least we decided to start up our own, uh, tech, uh, called Sunday. Uh, Cindy is now the full time CEO and co founders. Um, and, you know, uh, it's an exciting journey from then on, uh, where now The first full stack ensure attack in in the whole of of the Asian market, uh, starting off in Thailand. Um, And then when Christine came back, to join the business. You know, since we were already in real estate, we decided, taking on from the inspiration of what we did with Sunday, how about we do the same in in in property? Because we obviously saw, you know, there was super loads of opportunities that we could we could we could do. And and a year ago, we gave birth to cast a city lapse. Um, now a prop tech company based in Malaysia. >>Christine and Cindy tell the storey here because this is actually fascinating. Storey, your sisters, your entrepreneurial. So you know each other? You're related and you've got ups and downs with the startups and growing companies changing landscape. A lot of challenges. You all gotta get along all the time. How's it going? What's it like? Mm. >>Maybe I'll start. I think I think for me I'm probably the newest addition to the trio in the, you know, working together kind of space. So for me, I think it's all about really learning how to, you know, separate your professional and personal life. And like you mentioned, you know, we live together. But we also work together. So for me, I think I took a >>lot of advice >>and direction. Um, both from Johann and, >>uh, help >>me a lot. Um, so So I think that's been my experience. Been great So far, Um, they've been really, really supportive. And I think going through this journey of, you know, like, founding a company together, it's obviously very challenging. And so I feel very fortunate to have two sisters who have already gone through it once, you know? >>So for the other guests is trying to get on the cube here. Over there. Um, sounds like fun. Uh, Christine. So on the city labs, you gotta cheque side of it there in the in the property tech. That's exciting. How's it going over there? >>Uh, super, Super cool. Super fun. Uh, has been one heck of a journey building a company from scratch, let alone in tech. I think you know, we created K s K C d lives because we really wanted to modernise the real estate industry, uh, and create, like, super transformative solutions, uh, many for two reasons. You know, one is to improve the quality of life, um, of the community around us. Uh, and secondly, really to harness all the technology and this unused data right in the real estate industry. And try and say, how can we use that to make more intelligent business decisions? Yeah, so So really, Um, I guess for us, it's been really exciting because we've launched two products. Uh, you know, one of which is Ai driven, dynamic pricing engine. And we realised that actually, the way that homes are priced today, uh, in real estate is super RK right? You only use a few basic variables. Like, how big is your house? What views do you have? But then we realised that, actually hey, with a I where you suddenly can use, like, hundreds of variables, um, and even, you know, consisting of wellness variables, for example. Um, and you can really customise pricing all the way down to a single unit level. Uh, and we realise that by doing this, we could actually unlock, um, ferret prices for our customers while also constantly kind of tracking the financial health of the company. >>Awesome. Cindy, I wanna get you in here. A co founder, Sunday Insurance. That was the origination. But a lot of change data drives everything machine learning. You gotta have the state of the art. What's going on with you? >>Yeah, I think for us, essentially, uh, we're operating in a very old industry. Um, it's one of the oldest industries globally. And if you look at the entire insurance value chain, um, every part of the process can actually, it's all about data. You can. It can be disrupted. Um, but yet every inch of the value chain is also regulated. So I think essentially what we're trying to do is, um, we're trying to really innovate the customer journey. So imagine if, um, even in the States now and even coming back to Asia, a lot of how people buy insurance is still very face to face agency. But I think in the future is going to be remote online on your app, through any partners as well. So I think, uh, we're trying to adopt any machine learning to really scale and automate, uh, the journey of anyone who's trying to buy insurance. But at the same time for insurance companies were also trying to help them automate that function itself. So imagine if banks are trying to dish out loans and you're trying to predict. What's the credit risk of every, um, single customer? That's exactly what insurance company needs to do as well. Um, And I guess insurance is all about buying a service as well. >>It's unlike you >>know, I'm gonna buy an apple. It comes to the hardware, >>right? So we're >>selling a service. So essentially you're service has to also dramatically changed. And I think these days, especially when we're operating in, uh, Thailand, Indonesia is one of the highest adoption rates for mobile these days. Everyone does. Everything lives on on the apps. So, um, insurance companies also needs to really on board their journey on that as well as increased engagement. So I don't just want to be an insurance company where, um, I speak to you and I have an issue with my claim. I want to really build a relationship with you and engage you differently. So I think it's actually that's the mission for a Sunday. So I think Imagine if imagine an insurance company 50 years in the future. How would it be? Uh, that's our mission. >>This is a great example. You guys, First of all, you're very dynamic. Thanks for sharing your storey. But when you get into the tech here, if industries that are transforming because of the digital transformation, the consumers expect the apps. You guys, as co founders and entrepreneurs now running this big business have to meet the demands and leverage the technology. How have you done that? How are you guys manage that? What kinds of decisions have you made? And you share some either experiences or observations of how to navigate and how you're riding that wave. >>Yeah. So I think if you hear from what Cindy and Christine has just mentioned, I mean, uh, we were playing in, you know, two of the oldest and largest industries in the world. Real estate and insurance. And, uh, you know, in both industries, as I said earlier, you know, it's really all about the customers, right? Um you know, in in the past, we used to think of of businesses as you know, what's your vertical and the horizontal today? Um, at least four k s k and and and all the all these, um, you know, tech ventures that we are now venture building. We're really thinking about it from the customer land. So really thinking about it from a customer ecosystem perspective. So instead of, you know, creating products and and having that push out to the customers, you know, we use tech and data and and especially data today and the right amount of data and what type of data that we want understanding that and really, um, building that product and really the services, uh, for the customers. So once you know the customer enters our ecosystem, whether you know, in your real estate, um, ecosystem or whether it's in your insurance ecosystem, we want you to to continue to stay with us, um, and to trust us. Um, and so it's not just about selling you a product, but really, you know, like, what Cindy says building a relationship with you because we think that, you know, obviously you know when insurance is something you really need when when when things go wrong in your life, we don't only want to be there. When things go wrong in your life and for real estate, you know everybody needs a shelter. So so so that's why we think that building relationships are very important and from really true, that lands is when you really think about the ecosystem and you think about data. I think Cindy Increasing gave some examples of how we're approaching it. Um, a lot of people start from from from a, you know, from a traditional business and from within. But for us, um, we decided to actually take it outside. Um, and, you know, take the approach of venture building from a startup, um, but really have, on the back end, really have that Connexion to the core businesses. Because what the core businesses understand is, you know, lifetime and experience of how customers feel and and, you know, um, in insurance, it's really about how to run a financial institution in real estate is really how to build buildings, and that is something that we can't take away. But, you know, you use technology to enable and to power. But what venture and start ups do extremely well is really the way we are extremely nimble and the way you use tech and data to navigate the quick changes of customer demands. And and you know, one thing an app and it's all about quick iterations. Right? When you build a super app, how do you incorporate all the features that are coming in, you have to keep on, you know, iterating changing, innovating, um, and innovating small with quick wins and then taking on a larger scale. And so the way we position ourselves is when you have to start up and you combine that with the core. Um, and putting the two together is how, how, how we look at things and that four minutes, the whole ecosystem >>that's awesome and being agile as fast and speed is key if you want to be there. Startup. But at the core business, that's going kind of slow. You got to kind of make everything go faster. That's a great, great insight. Let's talk about the disruption of the property industry again. That's real estate now with the Internet of things, technologies and also people expect technology. They wanna have access. I don't wanna have all these passwords and, you know they want to have easy in and out. They want good efficiency, save money. What's the disruption angle on? Um, the property neck. Christine, what's your How do you see that? The big disruption going? >>Yeah. So I think as Johann already mentioned before, you know um I think our customers we know are becoming, um, digital natives. Right? And they expect very convenient lifestyles. And we're all about our customers. So, actually, that's why we launched also another product, right where we're taking all of these things that you just mentioned, you know, about Iot into account. So what we found is, um, that actually, today, um, you know, the village about real estate is that we all live through that life as well, so we can experience that. Uh, we found that residents today, um, they find it quite challenging to request, you know, basic services like housekeeping managing, um, their defects, their tenants. Um, you know, even the financial planning and even getting into the building, right, they want more convenience. Um, but we realised that actually, all these services in the real estate industry right now and even in the prop tech space, they are very, very segmented. They're all discussed across multiple different apps. So what we really try to do is hey, let's try and consolidate all of this into one single app, which we have done, which is really cool, And it helps our residents really stay engaged and connected with our property. Um, what we did also was on the Iot front. We we were actually the first developer in Malaysia to also integrate, You know, future proof solutions like remote lift calling as well, um, into the mobile app. And that's to really go like, push on the Iot front. For us as well. >>Must be great for retention. It's all the gadgets are built into the of course. You have good WiFi fibre in their everyone's got good band with >>for sure >>It's like water and plumbing. Uh, I'd like to get everyone everyone loves that. I gotta ask Now, on the on the on the on The disruption is great. Now you've got the clouds, the clouds here for actually Amazon. You guys are big customer because you guys can move fast and they do all the heavy lifting. How are you guys seeing that helped modernise in the industry of insurance? Because that's a big vertical for a W s and you guys are doing is Cindy. What is the What is the modernisation? Um, half that you guys have taken with a W s. >>Yeah, sure. So I think essentially, for insurance, it's a product development. And when we talk about product development means, um how do you price, um, every certain individual or company very differently, right, Because everyone has very different risks surrounding them. Uh, currently, what we face is that it's a flat pricing fixed pricing. Um, and it's not really personalised to you. If you are a very good behaviour and safe kind of customer, it doesn't translate to any premium savings for you. Um, so I think, uh, part of insurance is to give, for example, affordable access to health care. But if your premiums isn't sustainable for health insurance, then it doesn't really need the point. So, uh, for Sunday, like, how we're trying to trying to do it differently is, for example, we use some AWS cloud solutions and AWS Lambda too, really power our machine learning Savalas and Cloud infrastructure. So, for example, uh, Sunday we are a serious bee companies sober and the growth stage. So at any point in time, we need to ensure that our infrastructure is able to support a huge spike in transaction volume, and we're working with large scale partners like telcos, e commerce companies, or even within our organic channels. So our AI machine learning risk prediction model, which is basically, um, powering our premium pricing engines whenever there's any requests coming in front of the Web for foreign quotation. For example, if someone wants to buy health insurance, um, it can go up and spike. But also, the data model is actually pricing, uh, processing billions of calculations, ingesting a lot of data points. Uh, it needs to do that within seconds, so yeah, I think a w s. We've been using it from day one since we launched. It's been, uh, helping us on >>that and make it go faster. That's the big thing. I gotta ask you when you guys have this family business now, three decades, you got a lot going on extending that legacy and sustaining the family legacy. I love the Storey. So who decides whether to do the startup and you guys draw straws? Is that you guys flip a coin? You gotta who runs the big business? How do you guys decide that? Mm. >>Um, maybe I'll >>I >>would say maybe it came very naturally to us. Really? I guess Here we don't have to disclose. Our age is a little bit, so I mean, I mean, we all actually the background and really all three of us. Before we came into the family business, we were all working professionals in very different fields. I was a I was in banking. Cindy was a lawyer, and Christine was a a doctor, actually, Um um, but, you know, I came back first. I'm the eldest, so after, you know, walking outside and looking into the family business. So I came back first, and and And from there, I took over the insurance business and looking at it, it was a very lonely place to be. So, um, you know, after a couple of years of Cindy being a professional life, you know, we said, Hey, would you like to come back? And let's, uh, take a different journey with insurance and see how we can build something different? Uh, since we know a lot about insurance, but let's make make make a difference and and and, you know, be sustainable, but also evolve over time and show the world that insurance is actually pretty sexy, actually. Um, and then, you know, Christine saw the fund that the two of us were having, uh, already started building a real estate on on my end. Uh, and then, uh, she came back. And, you know, we have a conversation, and we said, Look, looking at you know what we're doing in Sunday? You know, building pricing engines and being able to price to a single customer level. Um, we saw that opportunity in real estate, and, uh so I asked her. I said, Look, would you like to do this? You know, because I think there is something cool. Um, the three of us can band together and still inspire each other share ideas across each other. That's an opportunity that a lot of people don't get right. I mean, to all these industries in the world being able to cross share ideas. Uh, and sometimes inspirations and ideas don't come from the same industry. Uh, and so I think. And that's how we started. Really, John, it's not. Maybe we're lucky, and we should be grateful for >>that. You're all power women. I love the storey, and it is good that you come together, and I think the entrepreneurial kind of twist makes it more fun. But not everyone is cut out with the entrepreneurship, but it also gives you more risk management. You can. You can go after opportunities I love. I love the strategy there. You guys are great leaders. Any advice for other aspiring women leaders and entrepreneurs out there who want to make a difference? Make an impact? The world is. Change is getting better for everyone. And and again, entrepreneurial could be in big companies and also big companies doing startups. There's a whole new world. What advice would you guys give other aspiring women leaders? Okay, >>I'll keep it short from my end. I think for me it's about really following your passion following your ambition. And lastly, I think not to try and not feel like you need to conform to any gender stereotypes because I think in male dominated industries such as real estate, our are attack. I think people might have some ideas about you know what a what a tech leader or what a real estate leader might have to look like. But you don't have to conform to that. So that's probably my advice. Uh, >>yeah, I I fully agree with Chris right there. I think, um, gender isn't an issue here. If you have a passion and you identify, there is a market opportunity that you can, you know, you can really do something about it. Just just pursue it. I think most importantly, if you ever want to be an entrepreneur and start your own business or your own, start up. Uh, so long as you have the confidence, I think you're you're good to go. Um, there's a lot of talk out that that or, you know, um, women led start ups are not >>attracting >>funds, but we haven't faced that anyway. In this part of Asia, I think there's a lot of, um, I think it attracts even more attention. If you're a woman in a male dominated that industry like, hey, then you know it's it's quite unique. So I think you have a strength there, and I think there's a lot of diverse talent out there. Um, post pandemic. A lot of people are looking for changes as well, so I think it is a lot of a lot of opportunity out there. >>Yeah, Joanne, you know, you know, the thing is with cloud computing, it's a level centre. It really because if you can come together, whether it's sisters like you guys, powerful sisters and professional experience coming together leverage technology to re factor old industries. It's all about the numbers and the performance. At the end of the day, you know, you move faster and you take territory and beat the competition. >>Ultimate >>the ultimate uh, leveller. Well, congratulations. You guys are great. Thanks for coming on The Cube Sisters. You guys are amazing. Great Storey Love it. Thanks for coming out and celebrating International Women's Day feature today as part of our international women's showcase here in the Cube. Thank you so much. >>Thank you. Thank you for having us. >>Okay. The Cubes International Women's showcase Going on all year, this time featuring International Women's Day The big celebration. I'm John Ferrier, host of the Cube here in Palo Alto, California. Thanks for watching. Mm mm

>> Good day, everyone. Thank you for joining me. I'm Cindy Maike, joined by Rick Taylor of Cloudera. We're here to talk about predictive maintenance for the public sector and how to increase asset service reliability. On today's agenda, we'll talk specifically around how to optimize your equipment maintenance, how to reduce costs, asset failure with data and analytics. We'll go into a little more depth on what type of data, the analytical methods that we're typically seeing used, the associated- Brooke will go over a case study as well as a reference architecture. So by basic definition, predictive maintenance is about determining when an asset should be maintained and what specific maintenance activities need to be performed either based upon an assets actual condition or state. It's also about predicting and preventing failures and performing maintenance on your time on your schedule to avoid costly unplanned downtime. McKenzie has looked at analyzing predictive maintenance costs across multiple industries and has identified that there's the opportunity to reduce overall predictive maintenance costs by roughly 50% with different types of analytical methods. So let's look at those three types of models. First, we've got our traditional type of method for maintenance, and that's really about uncorrective maintenance, and that's when we're performing maintenance on an asset after the equipment fails. The challenges with that is we end up with unplanned downtime. We end up with disruptions in our schedules, as well as reduce quality around the performance of the asset. And then we started looking at preventive maintenance and preventative maintenance is really when we're performing maintenance on a set schedule. The challenges with that is we're typically doing it regardless of the actual condition of the asset, which has resulted in unnecessary downtime and expense. And specifically we're really now focused on condition-based maintenance, which is looking at leveraging predictive maintenance techniques based upon actual conditions and real time events and processes. Within that, we've seen organizations and again, source from McKenzie, have a 50% reduction in downtime, as well as overall 40% reduction in maintenance costs. Again, this is really looking at things across multiple industries, but let's look at it in the context of the public sector and based upon some activity by the department of energy several years ago, they really looked at what does predictive maintenance mean to the public sector? What is the benefit of looking at increasing return on investment of assets, reducing, you know, reduction in downtime as well as overall maintenance costs. So corrective or reactive based maintenance is really about performing once there's been a failure and then the movement towards preventative, which is based upon a set schedule. We're looking at predictive where we're monitoring real-time conditions. And most importantly is now actually leveraging IOT and data and analytics to further reduce those overall down times. And there's a research report by the department of energy that goes into more specifics on the opportunity within the public sector. So Rick, let's talk a little bit about what are some of the challenges regarding data, regarding predictive maintenance? >> Some of the challenges include having data silos, historically our government organizations and organizations in the commercial space as well, have multiple data silos. They've spun up over time. There are multiple business units and note, there's no single view of assets. And oftentimes there's redundant information stored in these silos of information. Couple that with huge increases in data volume, data growing exponentially, along with new types of data that we can ingest there's social media, there's semi and unstructured data sources and the real time data that we can now collect from the internet of things. And so the challenge is to collect all these assets together and begin to extract intelligence from them and additional insights and and that in turn, then fuels machine learning and what we call artificial intelligence, which enables predictive maintenance. Next slide. >> Cindy: So let's look specifically at, you know, the types of use cases and I'm going to- Rick and I are going to focus on those use cases, where do we see predictive maintenance coming in to the procurement facility, supply chain, operations and logistics? We've got various level of maturity. So, you know, we're talking about predictive maintenance. We're also talking about using information, whether it be on a connected asset or a vehicle doing monitoring to also leveraging predictive maintenance on how do we bring about looking at data from connected warehouses facilities and buildings? I'll bring an opportunity to both increase the quality and effectiveness of the missions within the agencies to also looking at looking at cost efficiency, as well as looking at risk and safety. And the types of data, you know, that Rick mentioned around, you know, the new types of information. Some of those data elements that we typically have seen is looking at failure history. So when has an asset or a machine or a component within a machine failed in the past? We've also looking at bringing together a maintenance history, looking at a specific machine. Are we getting error codes off of a machine or assets looking at when we've replaced certain components to looking at how are we actually leveraging the assets? What were the operating conditions? Pulling up data from a sensor on that asset? Also looking at the features of an asset, whether it's, you know, engine size it's make and model, where's the asset located? To also looking at who's operated the asset, you know, whether it be their certifications, what's their experience, how are they leveraging the assets? And then also bringing in together some of the pattern analysis that we've seen. So what are the operating limits? Are we getting service reliability? Are we getting a product recall information from the actual manufacturer? So Rick, I know the data landscape has really changed. Let's, let's go over looking at some of those components. >> Rick: Sure. So this slide depicts sort of the, some of the inputs that inform a predictive maintenance program. So we've talked a little bit about the silos of information, the ERP system of record, perhaps the spares and the service history. So we want, what we want to do is combine that information with sensor data, whether it's a facility and equipment sensors, or temperature and humidity, for example. All this stuff is then combined together and then used to develop machine learning models that better inform predictive maintenance, because we do need to take into account the environmental factors that may cause additional wear and tear on the asset that we're monitoring. So here are some examples of private sector maintenance use cases that also have broad applicability across the government. For example, one of the busiest airports in Europe is running Cloudera on Azure to capture secure and correlate sensor data collected from equipment within the airport. The people moving equipment more specifically, the escalators, the elevators, and the baggage carousels. The objective here is to prevent breakdowns and improve airport efficiency and passenger safety. Another example is a container shipping port. In this case, we use IOT data and machine learning to help customers recognize how their cargo handling equipment is performing in different weather conditions to understand how usage relates to failure rates and to detect anomalies in transport systems. These all improve port efficiency. Another example is Navistar. Navistar is a leading manufacturer of commercial trucks, buses, and military vehicles. Typically vehicle maintenance, as Cindy mentioned, is based on miles traveled or based on a schedule or a time since the last service. But these are only two of the thousands of data points that can signal the need for maintenance. And as it turns out, unscheduled maintenance and vehicle breakdowns account for a large share of the total cost for vehicle owners. So to help fleet owners move from a reactive approach to a more predictive model, Navistar built an IOT enabled remote diagnostics platform called On Command. The platform brings in over 70 sensor data feeds for more than 375,000 connected vehicles. These include engine performance, trucks speed, acceleration, coolant temperature and break ware. This data is then correlated with other Navistar and third-party data sources, including weather, geolocation, vehicle usage, traffic, warranty, and parts inventory information. So the platform then uses machine learning and advanced analytics to automatically detect problems early and predict maintenance requirements. So how does the fleet operator use this information? They can monitor truck health and performance from smartphones or tablets and prioritize needed repairs. Also, they can identify that the nearest service location that has the relevant parts, the train technicians and the available service space. So sort of wrapping up the benefits. Navistar's helped fleet owners reduce maintenance costs by more than 30%. This same platform has also used to help school buses run safely and on time. For example, one school district with 110 buses that travel over a million miles annually reduce the number of tows needed year over year, thanks to predictive insights, delivered by this platform. So I'd like to take a moment and walk through the data life cycle as depicted in this diagram. So data ingest from the edge may include feeds from the factory floor or things like connected vehicles, whether they're trucks, aircraft, heavy equipment, cargo vessels, et cetera. Next, the data lands on a secure and governed data platform where it is combined with data from existing systems of record to provide additional insights. And this platform supports multiple analytic functions working together on the same data while maintaining strict security, governance and control measures. Once processed the data is used to train machine learning models, which are then deployed into production, monitored and retrained as needed to maintain accuracy. The process data is also typically placed in a data warehouse and use to support business intelligence analytics and dashboards. And in fact, this data life cycle is representative of one of our government customers doing condition-based maintenance across a variety of aircraft. And the benefits they've discovered include; less unscheduled maintenance and a reduction in mean man hours to repair, increased maintenance efficiencies, improved aircraft availability, and the ability to avoid cascading component failures, which typically costs more in repair cost and downtime. Also, they're able to better forecast the requirements for replacement parts and consumables and last, and certainly very importantly, this leads to enhanced safety. This chart overlays the secure open source Cloudera platform used in support of the data life cycle we've been discussing. Cloudera data flow, provides the data ingest, data movement and real time streaming data query capabilities. So data flow gives us the capability to bring data in from the asset of interest, from the internet of things. While the data platform provides a secure governed data lake and visibility across the full machine learning life cycle eliminates silos and streamlines workflows across teams. The platform includes a integrated suite of secure analytic applications. And two that we're specifically calling out here are Cloudera machine learning, which supports the collaborative data science and machine learning environment, which facilitates machine learning and AI and the Cloudera data warehouse, which supports the analytics and business intelligence, including those dashboards for leadership Cindy, over to you. >> Cindy: Rick, Thank you. And I hope that Rick and I provided you some insights on how predictive maintenance condition-based maintenance is being used and can be used within your respective agency, bringing together data sources that maybe you're having challenges with today, bringing that more real-time information in from a streaming perspective, blending that industrial IOT, as well as historical information together to help actually optimize maintenance and produce costs within each of your agencies. To learn a little bit more about Cloudera and our, what we're doing from a predictive maintenance, please visit us at Cloudera.com/Solutions/PublicSector And we look forward to scheduling a meeting with you. And on that, we appreciate your time today and thank you very much.

>>Hi, this is Cindy Mikey, vice president of industry solutions at caldera. Joining me today is chef is Molly, our solution engineer for the public sector. Today. We're going to talk about speed to insight. Why using machine learning in the public sector, specifically around fraud, waste and abuse. So topic for today, we'll discuss machine learning, why the public sector uses it to target fraud, waste, and abuse, the challenges. How do we enhance your data and analytical approaches the data landscape analytical methods and shad we'll go over reference architecture and a case study. So by definition at fraud waste and abuse per the government accountability office is broad as an attempt to obtain something about a value through unwelcomed misrepresentation waste is about squandering money or resources and abuse is about behaving improperly or unreasonably to actually obtain something of value for your personal, uh, benefit. So as we look at fraud, um, and across all industries, it's a top of mind, um, area within the public sector. >>Um, the types of fraud that we see is specifically around cyber crime, uh, looking at accounting fraud, whether it be from an individual perspective to also, uh, within organizations, looking at financial statement fraud, to also looking at bribery and corruption, as we look at fraud, it really hits us from all angles, whether it be from external perpetrators or internal perpetrators, and specifically for the research by PWC, the key focus area is we also see over half of fraud is actually through some form of internal or external perpetrators, again, key topics. So as we also look at a report recently by the association of certified fraud examiners, um, within the public sector, the us government, um, in 2017, it was identified roughly $148 billion was attributable to fraud, waste and abuse. Specifically of that 57 billion was focused on reported monetary losses and another 91 billion on areas where that opportunity or the monetary basis had not yet been measured. >>As we look at breaking those areas down again, we look at several different topics from an out payment perspective. So breaking it down within the health system, over $65 billion within social services, over $51 billion to procurement fraud to also, uh, uh, fraud, waste and abuse that's happening in the grants and the loan process to payroll fraud, and then other aspects, again, quite a few different topical areas. So as we look at those areas, what are the areas that we see additional type of focus, those are broad stroke areas. What are the actual use cases that, um, agencies are using the data landscape? What data, what analytical methods can we use to actually help curtail and prevent some of the, uh, the fraud waste and abuse. So, as we look at some of the analytical processes and analytical use great, uh, use cases in the public sector, whether it's from, uh, you know, the taxation areas to looking at, you know, social services, uh, to public safety, to also the, um, our, um, additional agency methods, we're going to focus specifically on some of the use cases around, um, you know, fraud within the tax area. >>Uh, we'll briefly look at some of the aspects of unemployment insurance fraud, uh, benefit fraud, as well as payment integrity. So fraud has its, um, uh, underpinnings in quite a few different government agencies and difficult, different analytical methods and I usage of different data. So I think one of the key elements is, you know, you can look at your, your data landscape on specific data sources that you need, but it's really about bringing together different data sources across a different variety, a different velocity. So, uh, data has different dimensions. So we'll look at on structured types of data of semi-structured data, behavioral data, as well as when we look at, um, you know, predictive models, we're typically looking at historical type information, but if we're actually trying to look at preventing fraud before it actually happens, or when a case may be in flight, which is specifically a use case that Chev is going to talk about later it's how do I look at more, that real, that streaming information? >>How do I take advantage of data, whether it be, uh, you know, uh, financial transactions we're looking at, um, asset verification, we're looking at tax records, we're looking at corporate filings. Um, and we can also look at more, uh, advanced data sources where as we're looking at, um, investigation type information. So we're maybe going out and we're looking at, uh, deep learning type models around, uh, you know, semi or that, uh, behavioral that's unstructured data, whether it be camera analysis and so forth. So for quite a different variety of data and the breadth and the opportunity really comes about when you can integrate and look at data across all different data sources. So in essence, looking at a more extensive, uh, data landscape. So specifically I want to focus on some of the methods, some of the data sources and some of the analytical techniques that we're seeing, uh, being used, um, in the government agencies, as well as opportunities to look at new methods. >>So as we're looking at, you know, from a, um, an audit planning or looking at, uh, the opportunity for the likelihood of non-compliance, um, specifically we'll see data sources where we're maybe looking at a constituents profile, we might actually be investigating the forms that they provided. We might be comparing that data, um, or leveraging internal data sources, possibly looking at net worth, comparing it against other financial data, and also comparison across other constituents groups. Some of the techniques that we use are some of the basic natural language processing, maybe we're going to do some text mining. We might be doing some probabilistic modeling, uh, where we're actually looking at, um, information within the agency to also comparing that against possibly tax forms. A lot of times it's information historically has been done on a batch perspective, both structured and semi-structured type information. And typically the data volumes can be low, but we're also seeing those data volumes on increase exponentially based upon the types of events that we're dealing with, the number of transactions. >>Um, so getting the throughput, um, and chef's going to specifically talk about that in a moment. The other aspect is, as we look at other areas of opportunity is when we're building upon, how do I actually do compliance? How do I actually look at conducting audits or potential fraud to also looking at areas of under-reported tax information? So there you might be pulling in, um, some of our other types of data sources, whether it's being property records, it could be data that's being supplied by the actual constituents or by vendors to also pulling in social media information to geographical information, to leveraging photos on techniques that we're seeing used is possibly some sentiment analysis, link analysis. Um, how do we actually blend those data sources together from a natural language processing? But I think what's important here is also the method and the looking at the data velocity, whether it be batch, whether it be near real time, again, looking at all types of data, whether it's structured semi-structured or unstructured and the key and the value behind this is, um, how do we actually look at increasing the potential revenue or the, uh, under reported revenue? >>Uh, how do we actually look at stopping fraudulent payments before they actually occur? Um, also looking at increasing the amount of, uh, the level of compliance, um, and also looking at the potential of prosecution of fraud cases. And additionally, other areas of opportunity could be looking at, um, economic planning. How do we actually perform some link analysis? How do we bring some more of those things that we saw in the data landscape on customer, or, you know, constituent interaction, bringing in social media, bringing in, uh, potentially police records, property records, um, other tax department, database information. Um, and then also looking at comparing one individual to other individuals, looking at people like a specific constituent, are there areas where we're seeing, uh, um, other aspects of a fraud potentially being occurring. Um, and also as we move forward, some of the more advanced techniques that we're seeing around deep learning is looking at computer vision, um, leveraging geospatial information, looking at social network entity analysis, uh, also looking at, um, agent-based modeling techniques, where we're looking at, uh, simulation Monte Carlo type techniques that we typically see in the financial services industry, actually applying that to fraud, waste, and abuse within the, uh, the public sector. >>Um, and again, that really lends itself to a new opportunities. And on that, I'm going to turn it over to Shev to talk about, uh, the reference architecture for, uh, doing these baskets. >>Thanks, Cindy. Um, so I'm going to walk you through an example, reference architecture for fraud detection using, uh, Cloudera underlying technology. Um, and you know, before I get into the technical details, uh, I want to talk about how this would be implemented at a much higher level. So with fraud detection, what we're trying to do is identify anomalies or novelists behavior within our data sets. Um, now in order to understand what aspects of our incoming data represents anomalous behavior, we first need to understand what normal behavior is. So in essence, once we understand normal behavior, anything that deviates from it can be thought of as an anomaly, right? So in order to understand what normal behavior is, we're going to need to be able to collect store and process a very large amount of historical data. And so then comes clutter's platform and this reference architecture that needs to before you, so, uh, let's start on the left-hand side of this reference architecture with the collect phase. >>So fraud detection will always begin with data collection. Uh, we need to collect large amounts of information from systems that could be in the cloud. It could be in the data center or even on edge devices, and this data needs to be collected so we can create our normal behavior profiles. And these normal behavioral profiles would then in turn, be used to create our predictive models for fraudulent activity. Now, uh, uh, to the data collection side, one of the main challenges that many organizations face, uh, in this phase, uh, involves using a single technology that can handle, uh, data that's coming in all different types of formats and protocols and standards with different porosities and velocities. Um, let me give you an example. Uh, we could be collecting data from a database that gets updated daily, uh, and maybe that data is being collected in Agra format. >>At the same time, we can be collecting data from an edge device that's streaming in every second, and that data may be coming in Jason or a binary format, right? So this is a data collection challenge that can be solved with clutter data flow, which is a suite of technologies built on Apache NIFA and mini five, allowing us to ingest all of this data, do a drag and drop interface. So now we're collecting all of this data, that's required to map out normal behavior. The next thing that we need to do is enrich it, transform it and distribute it to, uh, you know, downstream systems for further process. Uh, so let's, let's walk through how that would work first. Let's taking Richmond for, uh, for enrichment, think of adding additional information to your incoming data, right? Let's take, uh, financial transactions, for example, uh, because Cindy mentioned it earlier, right? >>You can store known locations of an individual in an operational database, uh, with Cloudera that would be HBase. And as an individual makes a new transaction, their geo location that's in that transaction data, it can be enriched with previously known locations of that very same individual and all of that enriched data. It can be later used downstream for predictive analysis, predictable. So the data has been enrich. Uh, now it needs to be transformed. We want the data that's coming in, uh, you know, Avro and Jason and binary and whatever other format to be transformed into a single common format. So it can be used downstream for stream processing. Uh, again, this is going to be done through clutter and data flow, which is backed by NIFA, right? So the transformed semantic data is then going to be stimulated to Kafka and coffin. It's going to serve as that central repository of syndicated services or a buffer zone, right? >>So cough is, you know, pretty much provides you with, uh, extremely fast resilient and fault tolerance storage. And it's also going to give you the consumer APIs that you need that are going to enable a wide variety of applications to leverage that enriched and transformed data within your buffer zone. Uh, I'll add that, you know, 17, so you can store that data, uh, in a distributed file system, give you that historical context that you're going to need later on for machine learning, right? So the next step in the architecture is to leverage a cluttered SQL string builder, which enables us to write, uh, streaming sequel jobs on top of Apache Flink. So we can, uh, filter, analyze and, uh, understand the data that's in the Kafka buffer zone in real time. Uh I'll you know, I'll also add like, you know, if you have time series data, or if you need a lab type of cubing, you can leverage kudu, uh, while EDA or exploratory data analysis and visualization, uh, can all be enabled through clever visual patient technology. >>All right, so we've filtered, we've analyzed and we've explored our incoming data. We can now proceed to train our machine learning models, uh, which will detect anomalous behavior in our historically collected data set, uh, to do this, we can use a combination of supervised unsupervised, uh, even deep learning techniques with neural networks and these models can be tested on new incoming streaming data. And once we've gone ahead and obtain the accuracy of the performance, the scores that we want, we can then take these models and deploy them into production. And once the models are productionalized or operationalized, they can be leveraged within our streaming pipeline. So as new data is ingested in real-time knife, I can query these models to detect if the activity is anomalous or fraudulent. And if it is, they can alert downstream users and systems, right? So this in essence is how fraudulent activity detection works. >>Uh, and this entire pipeline is powered by clutter's technology, right? And so, uh, the IRS is one of, uh, clutters customers. That's leveraging our platform today and implementing, uh, a very similar architecture, uh, to detect fraud, waste, and abuse across a very large set of, uh, historical facts, data. Um, and one of the neat things with the IRS is that they've actually, uh, recently leveraged the partnership between Cloudera and Nvidia to accelerate their Spark-based analytics and their machine learning. Uh, and the results have been nothing short of amazing, right? And in fact, we have a quote here from Joe and salty who's, uh, you know, the technical branch chief for the research analytics and statistics division group within the IRS with zero changes to our fraud detection workflow, we're able to obtain eight times to performance simply by adding GPS to our mainstream big data servers. This improvement translates to half the cost of ownership for the same workloads, right? So embedding GPU's into the reference architecture I covered earlier has enabled the IRS to improve their time to insights by as much as eight X while simultaneously reducing their underlying infrastructure costs by half, uh, Cindy back to you >>Chef. Thank you. Um, and I hope that you found, uh, some of the, the analysis, the information that Sheva and I have provided, um, to give you some insights on how cloud era is actually helping, uh, with the fraud waste and abuse challenges within the, uh, the public sector, um, specifically looking at any and all types of data, how the clutter a platform is bringing together and analyzing information, whether it be you're structured you're semi-structured to unstructured data, both in a fast or in a real time perspective, looking at anomalies, being able to do some of those on detection methods, uh, looking at neural network analysis, time series information. So next steps we'd love to have an additional conversation with you. You can also find on some additional information around, uh, how quad areas working in the federal government by going to cloudera.com solutions slash public sector. And we welcome scheduling a meeting with you again, thank you for joining Chevy and I today, we greatly appreciate your time and look forward to future >>Conversation..

>>Hi, this is Cindy Mikey, vice president of industry solutions at caldera. Joining me today is chef is Molly, our solution engineer for the public sector. Today. We're going to talk about speed to insight. Why using machine learning in the public sector, specifically around fraud, waste and abuse. So topic for today, we'll discuss machine learning, why the public sector uses it to target fraud, waste, and abuse, the challenges. How do we enhance your data and analytical approaches the data landscape analytical methods and shad we'll go over reference architecture and a case study. So by definition, fraud, waste and abuse per the government accountability office is fraud. Isn't an attempt to obtain something about value through unwelcome misrepresentation waste is about squandering money or resources and abuse is about behaving improperly or unreasonably to actually obtain something of value for your personal benefit. So as we look at fraud, um, and across all industries, it's a top of mind, um, area within the public sector. >>Um, the types of fraud that we see is specifically around cyber crime, uh, looking at accounting fraud, whether it be from an individual perspective to also, uh, within organizations, looking at financial statement fraud, to also looking at bribery and corruption, as we look at fraud, it really hits us from all angles, whether it be from external perpetrators or internal perpetrators, and specifically from the research by PWC, the key focus area is we also see over half of fraud is actually through some form of internal or external, uh, perpetrators again, key topics. So as we also look at a report recently by the association of certified fraud examiners, um, within the public sector, the us government, um, in 2017, it was identified roughly $148 billion was attributable to fraud, waste and abuse. Specifically about 57 billion was focused on reported monetary losses and another 91 billion on areas where that opportunity or the monetary basis had not yet been measured. >>As we look at breaking those areas down again, we look at several different topics from permit out payment perspective. So breaking it down within the health system, over $65 billion within social services, over $51 billion to procurement fraud to also, um, uh, fraud, waste and abuse that's happening in the grants and the loan process to payroll fraud, and then other aspects, again, quite a few different topical areas. So as we look at those areas, what are the areas that we see additional type of focus, there's a broad stroke areas. What are the actual use cases that our agencies are using the data landscape? What data, what analytical methods can we use to actually help curtail and prevent some of the, uh, the fraud waste and abuse. So, as we look at some of the analytical processes and analytical use crate, uh, use cases in the public sector, whether it's from, uh, you know, the taxation areas to looking at, you know, social services, uh, to public safety, to also the, um, our, um, uh, additional agency methods, we're gonna use focused specifically on some of the use cases around, um, you know, fraud within the tax area. >>Uh, we'll briefly look at some of the aspects of, um, unemployment insurance fraud, uh, benefit fraud, as well as payment and integrity. So fraud has it it's, um, uh, underpinnings inquiry, like you different on government agencies and difficult, different analytical methods, and I usage of different data. So I think one of the key elements is, you know, you can look at your, your data landscape on specific data sources that you need, but it's really about bringing together different data sources across a different variety, a different velocity. So, uh, data has different dimensions. So we'll look at structured types of data of semi-structured data, behavioral data, as well as when we look at, um, you know, predictive models. We're typically looking at historical type information, but if we're actually trying to look at preventing fraud before it actually happens, or when a case may be in flight, which is specifically a use case that shad is going to talk about later is how do I look at more of that? >>Real-time that streaming information? How do I take advantage of data, whether it be, uh, you know, uh, financial transactions we're looking at, um, asset verification, we're looking at tax records, we're looking at corporate filings. Um, and we can also look at more, uh, advanced data sources where as we're looking at, um, investigation type information. So we're maybe going out and we're looking at, uh, deep learning type models around, uh, you know, semi or that, uh, behavioral, uh, that's unstructured data, whether it be camera analysis and so forth. So for quite a different variety of data and the, the breadth and the opportunity really comes about when you can integrate and look at data across all different data sources. So in a looking at a more extensive, uh, data landscape. So specifically I want to focus on some of the methods, some of the data sources and some of the analytical techniques that we're seeing, uh, being used, um, in the government agencies, as well as opportunities, uh, to look at new methods. >>So as we're looking at, you know, from a, um, an audit planning or looking at, uh, the opportunity for the likelihood of non-compliance, um, specifically we'll see data sources where we're maybe looking at a constituents profile, we might actually be investigating the forms that they've provided. We might be comparing that data, um, or leveraging internal data sources, possibly looking at net worth, comparing it against other financial data, and also comparison across other constituents groups. Some of the techniques that we use are some of the basic natural language processing, maybe we're going to do some text mining. We might be doing some probabilistic modeling, uh, where we're actually looking at, um, information within the agency to also comparing that against possibly tax forms. A lot of times it's information historically has been done on a batch perspective, both structured and semi-structured type information. And typically the data volumes can be low, but we're also seeing those data volumes on increase exponentially based upon the types of events that we're dealing with, the number of transactions. >>Um, so getting the throughput, um, and chef's going to specifically talk about that in a moment. The other aspect is, as we look at other areas of opportunity is when we're building upon, how do I actually do compliance? How do I actually look at conducting audits, uh, or potential fraud to also looking at areas of under-reported tax information? So there you might be pulling in some of our other types of data sources, whether it's being property records, it could be data that's being supplied by the actual constituents or by vendors to also pulling in social media information to geographical information, to leveraging photos on techniques that we're seeing used is possibly some sentiment analysis, link analysis. Um, how do we actually blend those data sources together from a natural language processing? But I think what's important here is also the method and the looking at the data velocity, whether it be batch, whether it be near real time, again, looking at all types of data, whether it's structured semi-structured or unstructured and the key and the value behind this is, um, how do we actually look at increasing the potential revenue or the, um, under reported revenue? >>Uh, how do we actually look at stopping fraudulent payments before they actually occur? Um, also looking at increasing the amount of, uh, the level of compliance, um, and also looking at the potential of prosecution of fraud cases. And additionally, other areas of opportunity could be looking at, um, economic planning. How do we actually perform some link analysis? How do we bring some more of those things that we saw in the data landscape on customer, or, you know, constituent interaction, bringing in social media, bringing in, uh, potentially police records, property records, um, other tax department, database information. Um, and then also looking at comparing one individual to other individuals, looking at people like a specific, like a constituent, are there areas where we're seeing, uh, >>Um, other >>Aspects of, of fraud potentially being occurring. Um, and also as we move forward, some of the more advanced techniques that we're seeing around deep learning is looking at computer vision, um, leveraging geospatial information, looking at social network entity analysis, uh, also looking at, uh, agent-based modeling techniques, where we're looking at simulation Monte Carlo type techniques that we typically see in the financial services industry, actually applying that to fraud, waste, and abuse within the, uh, the public sector. Um, and again, that really, uh, lends itself to a new opportunities. And on that, I'm going to turn it over to chef to talk about, uh, the reference architecture for, uh, doing these buckets. >>Thanks, Cindy. Um, so I'm gonna walk you through an example, reference architecture for fraud detection using, uh, Cloudera's underlying technology. Um, and you know, before I get into the technical details, uh, I want to talk about how this would be implemented at a much higher level. So with fraud detection, what we're trying to do is identify anomalies or novelists behavior within our datasets. Um, now in order to understand what aspects of our incoming data represents anomalous behavior, we first need to understand what normal behavior is. So in essence, once we understand normal behavior, anything that deviates from it can be thought of as an anomaly, right? So in order to understand what normal behavior is, we're going to need to be able to collect store and process a very large amount of historical data. And so incomes, clutters platform, and this reference architecture that needs to be for you. >>So, uh, let's start on the left-hand side of this reference architecture with the collect phase. So fraud detection will always begin with data collection. We need to collect large amounts of information from systems that could be in the cloud. It could be in the data center or even on edge devices, and this data needs to be collected so we can create our normal behavior profiles. And these normal behavioral profiles would then in turn, be used to create our predictive models for fraudulent activity. Now, uh, thinking, uh, to the data collection side, one of the main challenges that many organizations face, uh, in this phase, uh, involves using a single technology that can handle, uh, data that's coming in all different types of formats and protocols and standards with different velocities and velocities. Um, let me give you an example. Uh, we could be collecting data from a database that gets updated daily, uh, and maybe that data is being collected in Agra format. >>At the same time, we can be collecting data from an edge device that's streaming in every second, and that data may be coming in Jason or a binary format, right? So this is a data collection challenge that can be solved with cluttered data flow, which is a suite of technologies built on a patch NIFA in mini five, allowing us to ingest all of this data, do a drag and drop interface. So now we're collecting all of this data, that's required to map out normal behavior. The next thing that we need to do is enrich it, transform it and distribute it to, uh, you know, downstream systems for further process. Uh, so let's, let's walk through how that would work first. Let's taking Richmond for, uh, for enrichment, think of adding additional information to your incoming data, right? Let's take, uh, financial transactions, for example, uh, because Cindy mentioned it earlier, right? >>You can store known locations of an individual in an operational database, uh, with Cloudera that would be HBase. And as an individual makes a new transaction, their geolocation that's in that transaction data can be enriched with previously known locations of that very same individual. And all of that enriched data can be later used downstream for predictive analysis, predictable. So the data has been enrich. Uh, now it needs to be transformed. We want the data that's coming in, uh, you know, Avro and Jason and binary and whatever other format to be transformed into a single common format. So it can be used downstream for stream processing. Uh, again, this is going to be done through clutter and data flow, which is backed by NIFA, right? So the transformed semantic data is then going to be stricted to Kafka and coffin. It's going to serve as that central repository of syndicated services or a buffer zone, right? >>So coffee is going to pretty much provide you with, uh, extremely fast resilient and fault tolerance storage. And it's also gonna give you the consumer APIs that you need that are going to enable a wide variety of applications to leverage that enriched and transformed data within your buffer zone, uh, allowed that, you know, 17. So you can store that data in a distributed file system, give you that historical context that you're going to need later on for machine learning, right? So the next step in the architecture is to leverage a cluttered SQL stream builder, which enables us to write, uh, streaming SQL jobs on top of Apache Flink. So we can, uh, filter, analyze and, uh, understand the data that's in the Kafka buffer in real time. Uh I'll you know, I'll also add like, you know, if you have time series data, or if you need a lab type of cubing, you can leverage kudu, uh, while EDA or, you know, exploratory data analysis and visualization, uh, can all be enabled through clever visualization technology. >>All right, so we've filtered, we've analyzed and we've explored our incoming data. We can now proceed to train our machine learning models, uh, which will detect anomalous behavior in our historically collected data set, uh, to do this, we can use a combination of supervised unsupervised, uh, even deep learning techniques with neural networks. And these models can be tested on new incoming streaming data. And once we've gone ahead and obtain the accuracy of the performance, the scores that we want, we can then take these models and deploy them into production. And once the models are productionalized or operationalized, they can be leveraged within our streaming pipeline. So as new data is ingested in real-time knife, I can query these models to detect if the activity is anomalous or fraudulent. And if it is, they can alert downstream users and systems, right? So this in essence is how fraudulent activity detection works. >>Uh, and this entire pipeline is powered by clutters technology, right? And so, uh, the IRS is one of, uh, clutter's customers. That's leveraging our platform today and implementing, uh, a very similar architecture, uh, to detect fraud, waste, and abuse across a very large set of historical facts, data. Um, and one of the neat things with the IRS is that they've actually recently leveraged the partnership between Cloudera and Nvidia to accelerate their spark based analytics and their machine learning, uh, and the results have been nothing short of amazing, right? And in fact, we have a quote here from Joe and salty who's, uh, you know, the technical branch chief for the research analytics and statistics division group within the IRS with zero changes to our fraud detection workflow, we're able to obtain eight times to performance simply by adding GPS to our mainstream big data servers. This improvement translates to half the cost of ownership for the same workloads, right? So embedding GPU's into the reference architecture I covered earlier has enabled the IRS to improve their time to insights by as much as eight X while simultaneously reducing their underlying infrastructure costs by half, uh, Cindy back to you >>Chef. Thank you. Um, and I hope that you found, uh, some of the, the analysis, the information that Sheva and I have provided, um, to give you some insights on how cloud era is actually helping, uh, with the fraud waste and abuse challenges within the, uh, the public sector, um, specifically looking at any and all types of data, how the clutter platform is bringing together and analyzing information, whether it be you're structured you're semi-structured to unstructured data, both in a fast or in a real-time perspective, looking at anomalies, being able to do some of those on detection, uh, looking at neural network analysis, time series information. So next steps we'd love to have additional conversation with you. You can also find on some additional information around, I have caught areas working in the, the federal government by going to cloudera.com solutions slash public sector. And we welcome scheduling a meeting with you again, thank you for joining us Sheva and I today. We greatly appreciate your time and look forward to future progress. >>Good day, everyone. Thank you for joining me. I'm Sydney. Mike joined by Rick Taylor of Cloudera. Uh, we're here to talk about predictive maintenance for the public sector and how to increase assets, service, reliability on today's agenda. We'll talk specifically around how to optimize your equipment maintenance, how to reduce costs, asset failure with data and analytics. We'll go into a little more depth on, um, what type of data, the analytical methods that we're typically seeing used, um, the associated, uh, Brooke, we'll go over a case study as well as a reference architecture. So by basic definition, uh, predictive maintenance is about determining when an asset should be maintained and what specific maintenance activities need to be performed either based upon an assets of actual condition or state. It's also about predicting and preventing failures and performing maintenance on your time on your schedule to avoid costly unplanned downtime. >>McKinsey has looked at analyzing predictive maintenance costs across multiple industries and has identified that there's the opportunity to reduce overall predictive maintenance costs by roughly 50% with different types of analytical methods. So let's look at those three types of models. First, we've got our traditional type of method for maintenance, and that's really about our corrective maintenance, and that's when we're performing maintenance on an asset, um, after the equipment fails. But the challenges with that is we end up with unplanned. We end up with disruptions in our schedules, um, as well as reduced quality, um, around the performance of the asset. And then we started looking at preventive maintenance and preventative maintenance is really when we're performing maintenance on a set schedule. Um, the challenges with that is we're typically doing it regardless of the actual condition of the asset, um, which has resulted in unnecessary downtime and expense. Um, and specifically we're really now focused on pre uh, condition-based maintenance, which is looking at leveraging predictive maintenance techniques based upon actual conditions and real time events and processes. Um, within that we've seen organizations, um, and again, source from McKenzie have a 50% reduction in downtime, as well as an overall 40% reduction in maintenance costs. Again, this is really looking at things across multiple industries, but let's look at it in the context of the public sector and based upon some activity by the department of energy, um, several years ago, >>Um, they've really >>Looked at what does predictive maintenance mean to the public sector? What is the benefit, uh, looking at increasing return on investment of assets, reducing, uh, you know, reduction in downtime, um, as well as overall maintenance costs. So corrective or reactive based maintenance is really about performing once there's been a failure. Um, and then the movement towards, uh, preventative, which is based upon a set schedule or looking at predictive where we're monitoring real-time conditions. Um, and most importantly is now actually leveraging IOT and data and analytics to further reduce those overall downtimes. And there's a research report by the, uh, department of energy that goes into more specifics, um, on the opportunity within the public sector. So, Rick, let's talk a little bit about what are some of the challenges, uh, regarding data, uh, regarding predictive maintenance. >>Some of the challenges include having data silos, historically our government organizations and organizations in the commercial space as well, have multiple data silos. They've spun up over time. There are multiple business units and note, there's no single view of assets. And oftentimes there's redundant information stored in, in these silos of information. Uh, couple that with huge increases in data volume data growing exponentially, along with new types of data that we can ingest there's social media, there's semi and unstructured data sources and the real time data that we can now collect from the internet of things. And so the challenge is to collect all these assets together and begin to extract intelligence from them and insights and, and that in turn then fuels, uh, machine learning and, um, and, and what we call artificial intelligence, which enables predictive maintenance. Next slide. So >>Let's look specifically at, you know, the, the types of use cases and I'm going to Rick and I are going to focus on those use cases, where do we see predictive maintenance coming into the procurement facility, supply chain, operations and logistics. Um, we've got various level of maturity. So, you know, we're talking about predictive maintenance. We're also talking about, uh, using, uh, information, whether it be on a, um, a connected asset or a vehicle doing monitoring, uh, to also leveraging predictive maintenance on how do we bring about, uh, looking at data from connected warehouses facilities and buildings all bring on an opportunity to both increase the quality and effectiveness of the missions within the agencies to also looking at re uh, looking at cost efficiency, as well as looking at risk and safety and the types of data, um, you know, that Rick mentioned around, you know, the new types of information, some of those data elements that we typically have seen is looking at failure history. >>So when has that an asset or a machine or a component within a machine failed in the past? Uh, we've also looking at bringing together a maintenance history, looking at a specific machine. Are we getting error codes off of a machine or assets, uh, looking at when we've replaced certain components to looking at, um, how are we actually leveraging the assets? What were the operating conditions, uh, um, pulling off data from a sensor on that asset? Um, also looking at the, um, the features of an asset, whether it's, you know, engine size it's make and model, um, where's the asset located on to also looking at who's operated the asset, uh, you know, whether it be their certifications, what's their experience, um, how are they leveraging the assets and then also bringing in together, um, some of the, the pattern analysis that we've seen. So what are the operating limits? Um, are we getting service reliability? Are we getting a product recall information from the actual manufacturer? So, Rick, I know the data landscape has really changed. Let's, let's go over looking at some of those components. Sure. >>So this slide depicts sort of the, some of the inputs that inform a predictive maintenance program. So, as we've talked a little bit about the silos of information, the ERP system of record, perhaps the spares and the service history. So we want, what we want to do is combine that information with sensor data, whether it's a facility and equipment sensors, um, uh, or temperature and humidity, for example, all this stuff is then combined together, uh, and then use to develop machine learning models that better inform, uh, predictive maintenance, because we'll do need to keep, uh, to take into account the environmental factors that may cause additional wear and tear on the asset that we're monitoring. So here's some examples of private sector, uh, maintenance use cases that also have broad applicability across the government. For example, one of the busiest airports in Europe is running cloud era on Azure to capture secure and correlate sensor data collected from equipment within the airport, the people moving equipment more specifically, the escalators, the elevators, and the baggage carousels. >>The objective here is to prevent breakdowns and improve airport efficiency and passenger safety. Another example is a container shipping port. In this case, we use IOT data and machine learning, help customers recognize how their cargo handling equipment is performing in different weather conditions to understand how usage relates to failure rates and to detect anomalies and transport systems. These all improve for another example is Navistar Navistar, leading manufacturer of commercial trucks, buses, and military vehicles. Typically vehicle maintenance, as Cindy mentioned, is based on miles traveled or based on a schedule or a time since the last service. But these are only two of the thousands of data points that can signal the need for maintenance. And as it turns out, unscheduled maintenance and vehicle breakdowns account for a large share of the total cost for vehicle owner. So to help fleet owners move from a reactive approach to a more predictive model, Navistar built an IOT enabled remote diagnostics platform called on command. >>The platform brings in over 70 sensor data feeds for more than 375,000 connected vehicles. These include engine performance, trucks, speed, acceleration, cooling temperature, and break where this data is then correlated with other Navistar and third-party data sources, including weather geo location, vehicle usage, traffic warranty, and parts inventory information. So the platform then uses machine learning and advanced analytics to automatically detect problems early and predict maintenance requirements. So how does the fleet operator use this information? They can monitor truck health and performance from smartphones or tablets and prioritize needed repairs. Also, they can identify that the nearest service location that has the relevant parts, the train technicians and the available service space. So sort of wrapping up the, the benefits Navistar's helped fleet owners reduce maintenance by more than 30%. The same platform is also used to help school buses run safely. And on time, for example, one school district with 110 buses that travel over a million miles annually reduce the number of PTOs needed year over year, thanks to predictive insights delivered by this platform. >>So I'd like to take a moment and walk through the data. Life cycle is depicted in this diagram. So data ingest from the edge may include feeds from the factory floor or things like connected vehicles, whether they're trucks, aircraft, heavy equipment, cargo vessels, et cetera. Next, the data lands on a secure and governed data platform. Whereas combined with data from existing systems of record to provide additional insights, and this platform supports multiple analytic functions working together on the same data while maintaining strict security governance and control measures once processed the data is used to train machine learning models, which are then deployed into production, monitored, and retrained as needed to maintain accuracy. The process data is also typically placed in a data warehouse and use to support business intelligence, analytics, and dashboards. And in fact, this data lifecycle is representative of one of our government customers doing condition-based maintenance across a variety of aircraft. >>And the benefits they've discovered include less unscheduled maintenance and a reduction in mean man hours to repair increased maintenance efficiencies, improved aircraft availability, and the ability to avoid cascading component failures, which typically cost more in repair cost and downtime. Also, they're able to better forecast the requirements for replacement parts and consumables and last, and certainly very importantly, this leads to enhanced safety. This chart overlays the secure open source Cloudera platform used in support of the data life cycle. We've been discussing Cloudera data flow, the data ingest data movement and real time streaming data query capabilities. So data flow gives us the capability to bring data in from the asset of interest from the internet of things. While the data platform provides a secure governed data lake and visibility across the full machine learning life cycle eliminates silos and streamlines workflows across teams. The platform includes an integrated suite of secure analytic applications. And two that we're specifically calling out here are Cloudera machine learning, which supports the collaborative data science and machine learning environment, which facilitates machine learning and AI and the cloud era data warehouse, which supports the analytics and business intelligence, including those dashboards for leadership Cindy, over to you, Rick, >>Thank you. And I hope that, uh, Rick and I provided you some insights on how predictive maintenance condition-based maintenance is being used and can be used within your respective agency, bringing together, um, data sources that maybe you're having challenges with today. Uh, bringing that, uh, more real-time information in from a streaming perspective, blending that industrial IOT, as well as historical information together to help actually, uh, optimize maintenance and reduce costs within the, uh, each of your agencies, uh, to learn a little bit more about Cloudera, um, and our, what we're doing from a predictive maintenance please, uh, business@cloudera.com solutions slash public sector. And we look forward to scheduling a meeting with you, and on that, we appreciate your time today and thank you very much.

>>Have you ever wondered how we sequence the human genome, how your smartphone is so well smart, how we will ever analyze all the patient data for the new vaccines or even how we plan to send humans to Mars? Well, at Cloudera, we believe that data can make what is impossible today possible tomorrow we are the enterprise data cloud company. In fact, we provide analytics and machine learning technology that does everything from making your smartphone smarter, to helping scientists ensure that new vaccines are both safe and effective, big data, no problem out era, the enterprise data cloud company. >>So I think for a long time in this country, we've known that there's a great disparity between minority populations and the majority of population in terms of disease burden. And depending on where you live, your zip code has more to do with your health than almost anything else. But there are a lot of smaller, um, safety net facilities, as well as small academic medical colleges within the United States. And those in those smaller environments don't have the access, you know, to the technologies that the larger ones have. And, you know, I call that, uh, digital disparity. So I'm, Harry's in academic scientist center and our mission is to train diverse health care providers and researchers, but also provide services to underserved populations. As part of the reason that I think is so important for me hearing medical college, to do data science. One of the things that, you know, both Cloudera and Claire sensor very passionate about is bringing those height in technologies to, um, to the smaller organizations. >>It's very expensive to go to the cloud for these small organizations. So now with the partnership with Cloudera and Claire sets a clear sense, clients now enjoy those same technologies and really honestly have a technological advantage over some of the larger organizations. The reason being is they can move fast. So we were able to do this on our own without having to, um, hire data scientists. Uh, we probably cut three to five years off of our studies. I grew up in a small town in Arkansas and is one of those towns where the railroad tracks divided the blacks and the whites. My father died without getting much healthcare at all. And as an 11 year old, I did not understand why my father could not get medical attention because he was very sick. >>Since we come at my Harry are looking to serve populations that reflect themselves or affect the population. He came from. A lot of the data you find or research you find health is usually based on white men. And obviously not everybody who needs a medical provider is going to be a white male. >>One of the things that we're concerned about in healthcare is that there's bias in treatment already. We want to make sure those same biases do not enter into the algorithms. >>The issue is how do we get ahead of them to try to prevent these disparities? >>One of the great things about our dataset is that it contains a very diverse group of patients. >>Instead of just saying, everyone will have these results. You can break it down by race, class, cholesterol, level, other kinds of factors that play a role. So you can make the treatments in the long run. More specifically, >>Researchers are now able to use these technologies and really take those hypotheses from, from bench to bedside. >>We're able to overall improve the health of not just the person in front of you, but the population that, yeah, >>Well, the future is now. I love a quote by William Gibson who said the future is already here. It's just not evenly distributed. If we think hard enough and we apply things properly, uh, we can again take these technologies to, you know, underserved environments, um, in healthcare. Nobody should be technologically disadvantage. >>When is a car not just a car when it's a connected data driven ecosystem, dozens of sensors and edge devices gathering up data from just about anything road, infrastructure, other vehicles, and even pedestrians to create safer vehicles, smarter logistics, and more actionable insights. All the data from the connected car supports an entire ecosystem from manufacturers, building safer vehicles and fleet managers, tracking assets to insurers monitoring, driving behaviors to make roads safer. Now you can control the data journey from edge to AI. With Cloudera in the connected car, data is captured, consolidated and enriched with Cloudera data flow cloud Dara's data engineering, operational database and data warehouse provide the foundation to develop service center applications, sales reports, and engineering dashboards. With data science workbench data scientists can continuously train AI models and use data flow to push the models back to the edge, to enhance the car's performance as the industry's first enterprise data cloud Cloudera supports on-premise public and multi-cloud deployments delivering multifunction analytics on data anywhere with common security governance and metadata management powered by Cloudera SDX, an open platform built on open source, working with open compute architectures and open data stores all the way from edge to AI powering the connected car. >>The future has arrived. >>The Dawn of a retail Renaissance is here and shopping will never be the same again. Today's connected. Consumers are always on and didn't control. It's the era of smart retail, smart shelves, digital signage, and smart mirrors offer an immersive customer experience while delivering product information, personalized offers and recommendations, video analytics, capture customer emotions and gestures to better understand and respond to in-store shopping experiences. Beacons sensors, and streaming video provide valuable data into in-store traffic patterns, hotspots and dwell times. This helps retailers build visual heat maps to better understand custom journeys, conversion rates, and promotional effectiveness in our robots automate routine tasks like capturing inventory levels, identifying out of stocks and alerting in store personnel to replenish shelves. When it comes to checking out automated e-commerce pickup stations and frictionless checkouts will soon be the norm making standing in line. A thing of the past data and analytics are truly reshaping. >>The everyday shopping experience outside the store, smart trucks connect the supply chain, providing new levels of inventory visibility, not just into the precise location, but also the condition of those goods. All in real time, convenience is key and customers today have the power to get their goods delivered at the curbside to their doorstep, or even to their refrigerators. Smart retail is indeed here. And Cloudera makes all of this possible using Cloudera data can be captured from a variety of sources, then stored, processed, and analyzed to drive insights and action. In real time, data scientists can continuously build and train new machine learning models and put these models back to the edge for delivering those moment of truth customer experiences. This is the enterprise data cloud powered by Cloudera enabling smart retail from the edge to AI. The future has arrived >>For is a global automotive supplier. We have three business groups, automotive seating in studios, and then emission control technologies or biggest automotive customers are Volkswagen for the NPSA. And we have, uh, more than 300 sites. And in 75 countries >>Today, we are generating tons of data, more and more data on the manufacturing intelligence. We are trying to reduce the, the defective parts or anticipate the detection of the, of the defective part. And this is where we can get savings. I would say our goal in manufacturing is zero defects. The cost of downtime in a plant could be around the a hundred thousand euros. So with predictive maintenance, we are identifying correlations and patterns and try to anticipate, and maybe to replace a component before the machine is broken. We are in the range of about 2000 machines and we can have up to 300 different variables from pressure from vibration and temperatures. And the real-time data collection is key, and this is something we cannot achieve in a classical data warehouse approach. So with the be data and with clouded approach, what we are able to use really to put all the data, all the sources together in the classical way of working with that at our house, we need to spend weeks or months to set up the model with the Cloudera data lake. We can start working on from days to weeks. We think that predictive or machine learning could also improve on the estimation or NTC patient forecasting of what we'll need to brilliance with all this knowledge around internet of things and data collection. We are applying into the predictive convene and the cockpit of the future. So we can work in the self driving car and provide a better experience for the driver in the car. >>The Cloudera data platform makes it easy to say yes to any analytic workload from the edge to AI, yes. To enterprise grade security and governance, yes. To the analytics your people want to use yes. To operating on any cloud. Your business requires yes to the future with a cloud native platform that flexes to meet your needs today and tomorrow say yes to CDP and say goodbye to shadow it, take a tour of CDP and see how it's an easier, faster and safer enterprise analytics and data management platform with a new approach to data. Finally, a data platform that lets you say yes, >>Welcome to transforming ideas into insights, presented with the cube and made possible by cloud era. My name is Dave Volante from the cube, and I'll be your host for today. And the next hundred minutes, you're going to hear how to turn your best ideas into action using data. And we're going to share the real world examples and 12 industry use cases that apply modern data techniques to improve customer experience, reduce fraud, drive manufacturing, efficiencies, better forecast, retail demand, transform analytics, improve public sector service, and so much more how we use data is rapidly evolving as is the language that we use to describe data. I mean, for example, we don't really use the term big data as often as we used to rather we use terms like digital transformation and digital business, but you think about it. What is a digital business? How is that different from just a business? >>Well, digital business is a data business and it differentiates itself by the way, it uses data to compete. So whether we call it data, big data or digital, our belief is we're entering the next decade of a world that puts data at the core of our organizations. And as such the way we use insights is also rapidly evolving. You know, of course we get value from enabling humans to act with confidence on let's call it near perfect information or capitalize on non-intuitive findings. But increasingly insights are leading to the development of data, products and services that can be monetized, or as you'll hear in our industry, examples, data is enabling machines to take cognitive actions on our behalf. Examples are everywhere in the forms of apps and products and services, all built on data. Think about a real-time fraud detection, know your customer and finance, personal health apps that monitor our heart rates. >>Self-service investing, filing insurance claims and our smart phones. And so many examples, IOT systems that communicate and act machine and machine real-time pricing actions. These are all examples of products and services that drive revenue cut costs or create other value. And they all rely on data. Now while many business leaders sometimes express frustration that their investments in data, people, and process and technologies haven't delivered the full results they desire. The truth is that the investments that they've made over the past several years should be thought of as a step on the data journey. Key learnings and expertise from these efforts are now part of the organizational DNA that can catapult us into this next era of data, transformation and leadership. One thing is certain the next 10 years of data and digital transformation, won't be like the last 10. So let's get into it. Please join us in the chat. >>You can ask questions. You can share your comments, hit us up on Twitter right now. It's my pleasure to welcome Mick Holliston in he's the president of Cloudera mic. Great to see you. Great to see you as well, Dave, Hey, so I call it the new abnormal, right? The world is kind of out of whack offices are reopening again. We're seeing travel coming back. There's all this pent up demand for cars and vacations line cooks at restaurants. Everything that we consumers have missed, but here's the one thing. It seems like the algorithms are off. Whether it's retail's fulfillment capabilities, airline scheduling their pricing algorithms, you know, commodity prices we don't know is inflation. Transitory. Is it a long-term threat trying to forecast GDP? It's just seems like we have to reset all of our assumptions and make a feel a quality data is going to be a key here. How do you see the current state of the industry and the role data plays to get us into a more predictable and stable future? Well, I >>Can sure tell you this, Dave, uh, out of whack is definitely right. I don't know if you know or not, but I happen to be coming to you live today from Atlanta and, uh, as a native of Atlanta, I can, I can tell you there's a lot to be known about the airport here. It's often said that, uh, whether you're going to heaven or hell, you got to change planes in Atlanta and, uh, after 40 minutes waiting on algorithm to be right for baggage claim when I was not, I finally managed to get some bag and to be able to show up dressed appropriately for you today. Um, here's one thing that I know for sure though, Dave, clean, consistent, and safe data will be essential to getting the world and businesses as we know it back on track again, um, without well-managed data, we're certain to get very inconsistent outcomes, quality data will the normalizing factor because one thing really hasn't changed about computing since the Dawn of time. Back when I was taking computer classes at Georgia tech here in Atlanta, and that's what we used to refer to as garbage in garbage out. In other words, you'll never get quality data-driven insights from a poor data set. This is especially important today for machine learning and AI, you can build the most amazing models and algorithms, but none of it will matter if the underlying data isn't rock solid as AI is increasingly used in every business app, you must build a solid data foundation mic. Let's >>Talk about hybrid. Every CXO that I talked to, they're trying to get hybrid, right? Whether it's hybrid work hybrid events, which is our business hybrid cloud, how are you thinking about the hybrid? Everything, what's your point of view with >>All those descriptions of hybrid? Everything there, one item you might not have quite hit on Dave and that's hybrid data. >>Oh yeah, you're right. Mick. I did miss that. What, what do you mean by hybrid data? Well, >>David in cloud era, we think hybrid data is all about the juxtaposition of two things, freedom and security. Now every business wants to be more agile. They want the freedom to work with their data, wherever it happens to work best for them, whether that's on premises in a private cloud and public cloud, or perhaps even in a new open data exchange. Now this matters to businesses because not all data applications are created equal. Some apps are best suited to be run in the cloud because of their transitory nature. Others may be more economical if they're running a private cloud, but either way security, regulatory compliance and increasingly data sovereignty are playing a bigger and more important role in every industry. If you don't believe me, just watch her read a recent news story. Data breaches are at an all time high. And the ethics of AI applications are being called into question every day and understanding the lineage of machine learning algorithms is now paramount for every business. So how in the heck do you get both the freedom and security that you're looking for? Well, the answer is actually pretty straightforward. The key is developing a hybrid data strategy. And what do you know Dave? That's the business cloud era? Is it on a serious note from cloud era's perspective? Adopting a hybrid data strategy is central to every business's digital transformation. It will enable rapid adoption of new technologies and optimize economic models while ensuring the security and privacy of every bit of data. What can >>Make, I'm glad you brought in that notion of hybrid data, because when you think about things, especially remote work, it really changes a lot of the assumptions. You talked about security, the data flows are going to change. You've got the economics, the physics, the local laws come into play. So what about the rest of hybrid? Yeah, >>It's a great question, Dave and certainly cloud era itself as a business and all of our customers are feeling this in a big way. We now have the overwhelming majority of our workforce working from home. And in other words, we've got a much larger surface area from a security perspective to keep in mind the rate and pace of data, just generating a report that might've happened very quickly and rapidly on the office. Uh, ether net may not be happening quite so fast in somebody's rural home in, uh, in, in the middle of Nebraska somewhere. Right? So it doesn't really matter whether you're talking about the speed of business or securing data, any way you look at it. Uh, hybrid I think is going to play a more important role in how work is conducted and what percentage of people are working in the office and are not, I know our plans, Dave, uh, involve us kind of slowly coming back to work, begin in this fall. And we're looking forward to being able to shake hands and see one another again for the first time in many cases for more than a year and a half, but, uh, yes, hybrid work, uh, and hybrid data are playing an increasingly important role for every kind of business. >>Thanks for that. I wonder if we could talk about industry transformation for a moment because it's a major theme of course, of this event. So, and the case. Here's how I think about it. It makes, I mean, some industries have transformed. You think about retail, for example, it's pretty clear, although although every physical retail brand I know has, you know, not only peaked up its online presence, but they also have an Amazon war room strategy because they're trying to take greater advantage of that physical presence, uh, and ended up reverse. We see Amazon building out physical assets so that there's more hybrid going on. But when you look at healthcare, for example, it's just starting, you know, with such highly regulated industry. It seems that there's some hurdles there. Financial services is always been data savvy, but you're seeing the emergence of FinTech and some other challenges there in terms of control, mint control of payment systems in manufacturing, you know, the pandemic highlighted America's reliance on China as a manufacturing partner and, and supply chain. Uh it's so my point is it seems that different industries they're in different stages of transformation, but two things look really clear. One, you've got to put data at the core of the business model that's compulsory. It seems like embedding AI into the applications, the data, the business process that's going to become increasingly important. So how do you see that? >>Wow, there's a lot packed into that question there, Dave, but, uh, yeah, we, we, uh, you know, at Cloudera I happened to be leading our own digital transformation as a technology company and what I would, what I would tell you there that's been arresting for us is the shift from being largely a subscription-based, uh, model to a consumption-based model requires a completely different level of instrumentation and our products and data collection that takes place in real, both for billing, for our, uh, for our customers. And to be able to check on the health and wellness, if you will, of their cloud era implementations. But it's clearly not just impacting the technology industry. You mentioned healthcare and we've been helping a number of different organizations in the life sciences realm, either speed, the rate and pace of getting vaccines, uh, to market, uh, or we've been assisting with testing process. >>That's taken place because you can imagine the quantity of data that's been generated as we've tried to study the efficacy of these vaccines on millions of people and try to ensure that they were going to deliver great outcomes and, and healthy and safe outcomes for everyone. And cloud era has been underneath a great deal of that type of work and the financial services industry you pointed out. Uh, we continue to be central to the large banks, meeting their compliance and regulatory requirements around the globe. And in many parts of the world, those are becoming more stringent than ever. And Cloudera solutions are really helping those kinds of organizations get through those difficult challenges. You, you also happened to mention, uh, you know, public sector and in public sector. We're also playing a key role in working with government entities around the world and applying AI to some of the most challenging missions that those organizations face. >>Um, and while I've made the kind of pivot between the industry conversation and the AI conversation, what I'll share with you about AI, I touched upon a little bit earlier. You can't build great AI, can't grow, build great ML apps, unless you've got a strong data foundation underneath is back to that garbage in garbage out comment that I made previously. And so in order to do that, you've got to have a great hybrid dated management platform at your disposal to ensure that your data is clean and organized and up to date. Uh, just as importantly from that, that's kind of the freedom side of things on the security side of things. You've got to ensure that you can see who just touched, not just the data itself, Dave, but actually the machine learning models and organizations around the globe are now being challenged. It's kind of on the topic of the ethics of AI to produce model lineage. >>In addition to data lineage. In other words, who's had access to the machine learning models when and where, and at what time and what decisions were made perhaps by the humans, perhaps by the machines that may have led to a particular outcome. So every kind of business that is deploying AI applications should be thinking long and hard about whether or not they can track the full lineage of those machine learning models just as they can track the lineage of data. So lots going on there across industries, lots going on as those various industries think about how AI can be applied to their businesses. Pretty >>Interesting concepts. You bring it into the discussion, the hybrid data, uh, sort of new, I think, new to a lot of people. And th this idea of model lineage is a great point because people want to talk about AI, ethics, transparency of AI. When you start putting those models into, into machines to do real time inferencing at the edge, it starts to get really complicated. I wonder if we could talk about you still on that theme of industry transformation? I felt like coming into the pandemic pre pandemic, there was just a lot of complacency. Yeah. Digital transformation and a lot of buzz words. And then we had this forced March to digital, um, and it's, but, but people are now being more planful, but there's still a lot of sort of POC limbo going on. How do you see that? Can you help accelerate that and get people out of that state? It definitely >>Is a lot of a POC limbo or a, I think some of us internally have referred to as POC purgatory, just getting stuck in that phase, not being able to get from point a to point B in digital transformation and, um, you know, for every industry transformation, uh, change in general is difficult and it takes time and money and thoughtfulness, but like with all things, what we found is small wins work best and done quickly. So trying to get to quick, easy successes where you can identify a clear goal and a clear objective and then accomplish it in rapid fashion is sort of the way to build your way towards those larger transformative efforts set. Another way, Dave, it's not wise to try to boil the ocean with your digital transformation efforts as it relates to the underlying technology here. And to bring it home a little bit more practically, I guess I would say at cloud era, we tend to recommend that companies begin to adopt cloud infrastructure, for example, containerization. >>And they begin to deploy that on-prem and then they start to look at how they may move those containerized workloads into the public cloud. That'll give them an opportunity to work with the data and the underlying applications themselves, uh, right close to home in place. They can kind of experiment a little bit more safely and economically, and then determine which workloads are best suited for the public cloud and which ones should remain on prem. That's a way in which a hybrid data strategy can help get a digital transformation accomplish, but kind of starting small and then drawing fast from there on customer's journey to the we'll make we've >>Covered a lot of ground. Uh, last question. Uh, w what, what do you want people to leave this event, the session with, and thinking about sort of the next era of data that we're entering? >>Well, it's a great question, but, uh, you know, I think it could be summed up in, uh, in two words. I want them to think about a hybrid data, uh, strategy. So, uh, you know, really hybrid data is a concept that we're bringing forward on this show really for the, for the first time, arguably, and we really do think that it enables customers to experience what we refer to Dave as the power of, and that is freedom, uh, and security, and in a world where we're all still trying to decide whether each day when we walk out each building, we walk into, uh, whether we're free to come in and out with a mask without a mask, that sort of thing, we all want freedom, but we also also want to be safe and feel safe, uh, for ourselves and for others. And the same is true of organizations. It strategies. They want the freedom to choose, to run workloads and applications and the best and most economical place possible. But they also want to do that with certainty, that they're going to be able to deploy those applications in a safe and secure way that meets the regulatory requirements of their particular industry. So hybrid data we think is key to accomplishing both freedom and security for your data and for your business as a whole, >>Nick, thanks so much great conversation and really appreciate the insights that you're bringing to this event into the industry. Really thank you for your time. >>You bet Dave pleasure being with you. Okay. >>We want to pick up on a couple of themes that Mick discussed, you know, supercharging your business with AI, for example, and this notion of getting hybrid, right? So right now we're going to turn the program over to Rob Bearden, the CEO of Cloudera and Manny veer, DAS. Who's the head of enterprise computing at Nvidia. And before I hand it off to Robin, I just want to say for those of you who follow me at the cube, we've extensively covered the transformation of the semiconductor industry. We are entering an entirely new era of computing in the enterprise, and it's being driven by the emergence of data, intensive applications and workloads no longer will conventional methods of processing data suffice to handle this work. Rather, we need new thinking around architectures and ecosystems. And one of the keys to success in this new era is collaboration between software companies like Cloudera and semiconductor designers like Nvidia. So let's learn more about this collaboration and what it means to your data business. Rob, thanks, >>Mick and Dave, that was a great conversation on how speed and agility is everything in a hyper competitive hybrid world. You touched on AI as essential to a data first strategy and accelerating the path to value and hybrid environments. And I want to drill down on this aspect today. Every business is facing accelerating everything from face-to-face meetings to buying groceries has gone digital. As a result, businesses are generating more data than ever. There are more digital transactions to track and monitor. Now, every engagement with coworkers, customers and partners is virtual from website metrics to customer service records, and even onsite sensors. Enterprises are accumulating tremendous amounts of data and unlocking insights from it is key to our enterprises success. And with data flooding every enterprise, what should the businesses do? A cloud era? We believe this onslaught of data offers an opportunity to make better business decisions faster. >>And we want to make that easier for everyone, whether it's fraud, detection, demand, forecasting, preventative maintenance, or customer churn, whether the goal is to save money or produce income every day that companies don't gain deep insight from their data is money they've lost. And the reason we're talking about speed and why speed is everything in a hybrid world and in a hyper competitive climate, is that the faster we get insights from all of our data, the faster we grow and the more competitive we are. So those faster insights are also combined with the scalability and cost benefit they cloud provides and with security and edge to AI data intimacy. That's why the partnership between cloud air and Nvidia together means so much. And it starts with the shared vision making data-driven, decision-making a reality for every business and our customers will now be able to leverage virtually unlimited quantities of varieties, of data, to power, an order of magnitude faster decision-making and together we turbo charge the enterprise data cloud to enable our customers to work faster and better, and to make integration of AI approaches a reality for companies of all sizes in the cloud. >>We're joined today by NVIDIA's Mandy veer dos, and to talk more about how our technologies will deliver the speed companies need for innovation in our hyper competitive environment. Okay, man, you're veer. Thank you for joining us over the unit. >>Thank you, Rob, for having me. It's a pleasure to be here on behalf of Nvidia. We are so excited about this partnership with Cloudera. Uh, you know, when, when, uh, when Nvidia started many years ago, we started as a chip company focused on graphics, but as you know, over the last decade, we've really become a full stack accelerated computing company where we've been using the power of GPU hardware and software to accelerate a variety of workloads, uh, AI being a prime example. And when we think about Cloudera, uh, and your company, a great company, there's three things we see Rob. Uh, the first one is that for the companies that will already transforming themselves by the use of data, Cloudera has been a trusted partner for them. The second thing seen is that when it comes to using your data, you want to use it in a variety of ways with a powerful platform, which of course you have built over time. >>And finally, as we've heard already, you believe in the power of hybrid, that data exists in different places and the compute needs to follow the data. Now, if you think about in various mission, going forward to democratize accelerated computing for all companies, our mission actually aligns very well with exactly those three things. Firstly, you know, we've really worked with a variety of companies today who have been the early adopters, uh, using the power acceleration by changing the technology in their stacks. But more and more, we see the opportunity of meeting customers, where they are with tools that they're familiar with with partners that they trust. And of course, Cloudera being a great example of that. Uh, the second, uh, part of NVIDIA's mission is we focused a lot in the beginning on deep learning where the power of GPU is really shown through, but as we've gone forward, we found that GPU's can accelerate a variety of different workloads from machine learning to inference. >>And so again, the power of your platform, uh, is very appealing. And finally, we know that AI is all about data, more and more data. We believe very strongly in the idea that customers put their data, where they need to put it. And the compute, the AI compute the machine learning compute needs to meet the customer where their data is. And so that matches really well with your philosophy, right? And Rob, that's why we were so excited to do this partnership with you. It's come to fruition. We have a great combined stack now for the customer and we already see people using it. I think the IRS is a fantastic example where literally they took the workflow. They had, they took the servers, they had, they added GPS into those servers. They did not change anything. And they got an eight times performance improvement for their fraud detection workflows, right? And that's the kind of success we're looking forward to with all customers. So the team has actually put together a great video to show us what the IRS is doing with this technology. Let's take a look. >>My name's Joanne salty. I'm the branch chief of the technical branch and RAs. It's actually the research division research and statistical division of the IRS. Basically the mission that RAs has is we do statistical and research on all things related to taxes, compliance issues, uh, fraud issues, you know, anything that you can think of. Basically we do research on that. We're running into issues now that we have a lot of ideas to actually do data mining on our big troves of data, but we don't necessarily have the infrastructure or horsepower to do it. So it's our biggest challenge is definitely the, the infrastructure to support all the ideas that the subject matter experts are coming up with in terms of all the algorithms they would like to create. And the diving deeper within the algorithm space, the actual training of those Agra algorithms, the of parameters each of those algorithms have. >>So that's, that's really been our challenge. Now the expectation was that with Nvidia in cloud, there is help. And with the cluster, we actually build out the test this on the actual fraud, a fraud detection algorithm on our expectation was we were definitely going to see some speed up in prom, computational processing times. And just to give you context, the size of the data set that we were, uh, the SMI was actually working, um, the algorithm against Liz around four terabytes. If I recall correctly, we'd had a 22 to 48 times speed up after we started tweaking the original algorithm. My expectations, quite honestly, in that sphere, in terms of the timeframe to get results, was it that you guys actually exceeded them? It was really, really quick. Uh, the definite now term short term what's next is going to be the subject matter expert is actually going to take our algorithm run with that. >>So that's definitely the now term thing we want to do going down, go looking forward, maybe out a couple of months, we're also looking at curing some, a 100 cards to actually test those out. As you guys can guess our datasets are just getting bigger and bigger and bigger, and it demands, um, to actually do something when we get more value added out of those data sets is just putting more and more demands on our infrastructure. So, you know, with the pilot, now we have an idea with the infrastructure, the infrastructure we need going forward. And then also just our in terms of thinking of the algorithms and how we can approach these problems to actually code out solutions to them. Now we're kind of like the shackles are off and we can just run them, you know, come onto our art's desire, wherever imagination takes our skis to actually develop solutions, know how the platforms to run them on just kind of the close out. >>I rarely would be very missed. I've worked with a lot of, you know, companies through the year and most of them been spectacular. And, uh, you guys are definitely in that category. The, the whole partnership, as I said, a little bit early, it was really, really well, very responsive. I would be remiss if I didn't. Thank you guys. So thank you for the opportunity to, and fantastic. And I'd have to also, I want to thank my guys. My, uh, my staff, David worked on this Richie worked on this Lex and Tony just, they did a fantastic job and I want to publicly thank him for all the work they did with you guys and Chev, obviously also. Who's fantastic. So thank you everyone. >>Okay. That's a real great example of speed and action. Now let's get into some follow up questions guys, if I may, Rob, can you talk about the specific nature of the relationship between Cloudera and Nvidia? Is it primarily go to market or you do an engineering work? What's the story there? >>It's really both. It's both go to market and engineering and engineering focus is to optimize and take advantage of invidious platform to drive better price performance, lower cost, faster speeds, and better support for today's emerging data intensive applications. So it's really both >>Great. Thank you. Many of Eric, maybe you could talk a little bit more about why can't we just existing general purpose platforms that are, that are running all this ERP and CRM and HCM and you know, all the, all the Microsoft apps that are out there. What, what do Nvidia and cloud era bring to the table that goes beyond the conventional systems that we've known for many years? >>Yeah. I think Dave, as we've talked about the asset that the customer has is really the data, right? And the same data can be utilized in many different ways. Some machine learning, some AI, some traditional data analytics. So the first step here was really to take a general platform for data processing, Cloudera data platform, and integrate with that. Now Nvidia has a software stack called rapids, which has all of the primitives that make different kinds of data processing go fast on GPU's. And so the integration here has really been taking rapids and integrating it into a Cloudera data platform. So that regardless of the technique, the customer's using to get insight from that data, the acceleration will apply in all cases. And that's why it was important to start with a platform like Cloudera rather than a specific application. >>So I think this is really important because if you think about, you know, the software defined data center brought in, you know, some great efficiencies, but at the same time, a lot of the compute power is now going toward doing things like networking and storage and security offloads. So the good news, the reason this is important is because when you think about these data intensive workloads, we can now put more processing power to work for those, you know, AI intensive, uh, things. And so that's what I want to talk about a little bit, maybe a question for both of you, maybe Rob, you could start, you think about the AI that's done today in the enterprise. A lot of it is modeling in the cloud, but when we look at a lot of the exciting use cases, bringing real-time systems together, transaction systems and analytics systems and real time, AI inference, at least even at the edge, huge potential for business value and a consumer, you're seeing a lot of applications with AI biometrics and voice recognition and autonomous vehicles and the like, and so you're putting AI into these data intensive apps within the enterprise. >>The potential there is enormous. So what can we learn from sort of where we've come from, maybe these consumer examples and Rob, how are you thinking about enterprise AI in the coming years? >>Yeah, you're right. The opportunity is huge here, but you know, 90% of the cost of AI applications is the inference. And it's been a blocker in terms of adoption because it's just been too expensive and difficult from a performance standpoint and new platforms like these being developed by cloud air and Nvidia will dramatically lower the cost, uh, of enabling this type of workload to be done. Um, and what we're going to see the most improvements will be in the speed and accuracy for existing enterprise AI apps like fraud detection, recommendation, engine chain management, drug province, and increasingly the consumer led technologies will be bleeding into the enterprise in the form of autonomous factory operations. An example of that would be robots that AR VR and manufacturing. So driving quality, better quality in the power grid management, automated retail IOT, you know, the intelligent call centers, all of these will be powered by AI, but really the list of potential use cases now are going to be virtually endless. >>I mean, this is like your wheelhouse. Maybe you could add something to that. >>Yeah. I mean, I agree with Rob. I mean he listed some really good use cases. You know, the way we see this at Nvidia, this journey is in three phases or three steps, right? The first phase was for the early adopters. You know, the builders who assembled, uh, use cases, particular use cases like a chat bot, uh, uh, from the ground up with the hardware and the software almost like going to your local hardware store and buying piece parts and constructing a table yourself right now. I think we are in the first phase of the democratization, uh, for example, the work we did with Cloudera, which is, uh, for a broader base of customers, still building for a particular use case, but starting from a much higher baseline. So think about, for example, going to Ikea now and buying a table in a box, right. >>And you still come home and assemble it, but all the parts are there. The instructions are there, there's a recipe you just follow and it's easy to do, right? So that's sort of the phase we're in now. And then going forward, the opportunity we really look forward to for the democratization, you talked about applications like CRM, et cetera. I think the next wave of democratization is when customers just adopt and deploy the next version of an application they already have. And what's happening is that under the covers, the application is infused by AI and it's become more intelligent because of AI and the customer just thinks they went to the store and bought, bought a table and it showed up and somebody placed it in the right spot. Right. And they didn't really have to learn, uh, how to do AI. So these are the phases. And I think they're very excited to be going there. Yeah. You know, >>Rob, the great thing about for, for your customers is they don't have to build out the AI. They can, they can buy it. And, and just in thinking about this, it seems like there are a lot of really great and even sometimes narrow use cases. So I want to ask you, you know, staying with AI for a minute, one of the frustrations and Mick and I talked about this, the guy go problem that we've all studied in college, uh, you know, garbage in, garbage out. Uh, but, but the frustrations that users have had is really getting fast access to quality data that they can use to drive business results. So do you see, and how do you see AI maybe changing the game in that regard, Rob over the next several years? >>So yeah, the combination of massive amounts of data that have been gathered across the enterprise in the past 10 years with an open API APIs are dramatically lowering the processing costs that perform at much greater speed and efficiency, you know, and that's allowing us as an industry to democratize the data access while at the same time, delivering the federated governance and security models and hybrid technologies are playing a key role in making this a reality and enabling data access to be hybridized, meaning access and treated in a substantially similar way, your respect to the physical location of where that data actually resides. >>That's great. That is really the value layer that you guys are building out on top of that, all this great infrastructure that the hyperscalers have have given us, I mean, a hundred billion dollars a year that you can build value on top of, for your customers. Last question, and maybe Rob, you could, you can go first and then manufacture. You could bring us home. Where do you guys want to see the relationship go between cloud era and Nvidia? In other words, how should we, as outside observers be, be thinking about and measuring your project specifically and in the industry's progress generally? >>Yeah, I think we're very aligned on this and for cloud era, it's all about helping companies move forward, leverage every bit of their data and all the places that it may, uh, be hosted and partnering with our customers, working closely with our technology ecosystem of partners means innovation in every industry and that's inspiring for us. And that's what keeps us moving forward. >>Yeah. And I agree with Robin and for us at Nvidia, you know, we, this partnership started, uh, with data analytics, um, as you know, a spark is a very powerful technology for data analytics, uh, people who use spark rely on Cloudera for that. And the first thing we did together was to really accelerate spark in a seamless manner, but we're accelerating machine learning. We accelerating artificial intelligence together. And I think for Nvidia it's about democratization. We've seen what machine learning and AI have done for the early adopters and help them make their businesses, their products, their customer experience better. And we'd like every company to have the same opportunity. >>Okay. Now we're going to dig into the data landscape and cloud of course. And talk a little bit more about that with drew Allen. He's a managing director at Accenture drew. Welcome. Great to see you. Thank you. So let's talk a little bit about, you know, you've been in this game for a number of years. Uh, you've got particular expertise in, in data and finance and insurance. I mean, you know, you think about it within the data and analytics world, even our language is changing. You know, we don't say talk about big data so much anymore. We talk more about digital, you know, or, or, or data driven when you think about sort of where we've come from and where we're going. What are the puts and takes that you have with regard to what's going on in the business today? >>Well, thanks for having me. Um, you know, I think some of the trends we're seeing in terms of challenges and puts some takes are that a lot of companies are already on this digital journey. Um, they focused on customer experience is kind of table stakes. Everyone wants to focus on that and kind of digitizing their channels. But a lot of them are seeing that, you know, a lot of them don't even own their, their channels necessarily. So like we're working with a big cruise line, right. And yes, they've invested in digitizing what they own, but a lot of the channels that they sell through, they don't even own, right. It's the travel agencies or third party, real sellers. So having the data to know where, you know, where those agencies are, that that's something that they've discovered. And so there's a lot of big focus on not just digitizing, but also really understanding your customers and going across products because a lot of the data has built, been built up in individual channels and in digital products. >>And so bringing that data together is something that customers that have really figured out in the last few years is a big differentiator. And what we're seeing too, is that a big trend that the data rich are getting richer. So companies that have really invested in data, um, are having, uh, an outside market share and outside earnings per share and outside revenue growth. And it's really being a big differentiator. And I think for companies just getting started in this, the thing to think about is one of the missteps is to not try to capture all the data at once. The average company has, you know, 10,000, 20,000 data elements individually, when you want to start out, you know, 500, 300 critical data elements, about 5% of the data of a company drives 90% of the business value. So focusing on those key critical data elements is really what you need to govern first and really invest in first. And so that's something we, we tell companies at the beginning of their data strategy is first focus on those critical data elements, really get a handle on governing that data, organizing that data and building data products around >>That day. You can't boil the ocean. Right. And so, and I, I feel like pre pandemic, there was a lot of complacency. Oh yeah, we'll get to that. You know, not on my watch, I'll be retired before that, you know, is it becomes a minute. And then of course the pandemic was, I call it sometimes a forced March to digital. So in many respects, it wasn't planned. It just ha you know, you had to do it. And so now I feel like people are stepping back and saying, okay, let's now really rethink this and do it right. But is there, is there a sense of urgency, do you think? Absolutely. >>I think with COVID, you know, we were working with, um, a retailer where they had 12,000 stores across the U S and they had didn't have the insights where they could drill down and understand, you know, with the riots and with COVID was the store operational, you know, with the supply chain of the, having multiple distributors, what did they have in stock? So there are millions of data points that you need to drill down at the cell level, at the store level to really understand how's my business performing. And we like to think about it for like a CEO and his leadership team of it, like, think of it as a digital cockpit, right? You think about a pilot, they have a cockpit with all these dials and, um, dashboards, essentially understanding the performance of their business. And they should be able to drill down and understand for each individual, you know, unit of their work, how are they performing? That's really what we want to see for businesses. Can they get down to that individual performance to really understand how their business >>Is performing good, the ability to connect those dots and traverse those data points and not have to go in and come back out and go into a new system and come back out. And that's really been a lot of the frustration. W where does machine intelligence and AI fit in? Is that sort of a dot connector, if you will, and an enabler, I mean, we saw, you know, decades of the, the AI winter, and then, you know, there's been a lot of talk about it, but it feels like with the amount of data that we've collected over the last decade and the, the, the low costs of processing that data now, it feels like it's, it's real. Where do you see AI fitting? Yeah, >>I mean, I think there's been a lot of innovation in the last 10 years with, um, the low cost of storage and computing and these algorithms in non-linear, um, you know, knowledge graphs, and, um, um, a whole bunch of opportunities in cloud where what I think the, the big opportunity is, you know, you can apply AI in areas where a human just couldn't have the scale to do that alone. So back to the example of a cruise lines, you know, you may have a ship being built that has 4,000 cabins on the single cruise line, and it's going to multiple deaths that destinations over its 30 year life cycle. Each one of those cabins is being priced individually for each individual destination. It's physically impossible for a human to calculate the dynamic pricing across all those destinations. You need a machine to actually do that pricing. And so really what a machine is leveraging is all that data to really calculate and assist the human, essentially with all these opportunities where you wouldn't have a human being able to scale up to that amount of data >>Alone. You know, it's interesting. One of the things we talked to Nicolson about earlier was just the everybody's algorithms are out of whack. You know, you look at the airline pricing, you look at hotels it's as a consumer, you would be able to kind of game the system and predict that they can't even predict these days. And I feel as though that the data and AI are actually going to bring us back into some kind of normalcy and predictability, uh, what do you see in that regard? Yeah, I think it's, >>I mean, we're definitely not at a point where, when I talked to, you know, the top AI engineers and data scientists, we're not at a point where we have what they call broad AI, right? You can get machines to solve general knowledge problems, where they can solve one problem and then a distinctly different problem, right? That's still many years away, but narrow why AI, there's still tons of use cases out there that can really drive tons of business performance challenges, tons of accuracy challenges. So for example, in the insurance industry, commercial lines, where I work a lot of the time, the biggest leakage of loss experience in pricing for commercial insurers is, um, people will go in as an agent and they'll select an industry to say, you know what, I'm a restaurant business. Um, I'll select this industry code to quote out a policy, but there's, let's say, you know, 12 dozen permutations, you could be an outdoor restaurant. >>You could be a bar, you could be a caterer and all of that leads to different loss experience. So what this does is they built a machine learning algorithm. We've helped them do this, that actually at the time that they're putting in their name and address, it's crawling across the web and predicting in real time, you know, is this a address actually, you know, a business that's a restaurant with indoor dining, does it have a bar? Is it outdoor dining? And it's that that's able to accurately more price the policy and reduce the loss experience. So there's a lot of that you can do even with narrow AI that can really drive top line of business results. >>Yeah. I liked that term, narrow AI, because getting things done is important. Let's talk about cloud a little bit because people talk about cloud first public cloud first doesn't necessarily mean public cloud only, of course. So where do you see things like what's the right operating model, the right regime hybrid cloud. We talked earlier about hybrid data help us squint through the cloud landscape. Yeah. I mean, I think for most right, most >>Fortune 500 companies, they can't just snap their fingers and say, let's move all of our data centers to the cloud. They've got to move, you know, gradually. And it's usually a journey that's taking more than two to three plus years, even more than that in some cases. So they're have, they have to move their data, uh, incrementally to the cloud. And what that means is that, that they have to move to a hybrid perspective where some of their data is on premise and some of it is publicly on the cloud. And so that's the term hybrid cloud essentially. And so what they've had to think about is from an intelligence perspective, the privacy of that data, where is it being moved? Can they reduce the replication of that data? Because ultimately you like, uh, replicating the data from on-premise to the cloud that introduces, you know, errors and data quality issues. So thinking about how do you manage, uh, you know, uh on-premise and, um, public as a transition is something that Accenture thinks, thinks, and helps our clients do quite a bit. And how do you move them in a manner that's well-organized and well thought of? >>Yeah. So I've been a big proponent of sort of line of business lines of business becoming much more involved in, in the data pipeline, if you will, the data process, if you think about our major operational systems, they all have sort of line of business context in them. And then the salespeople, they know the CRM data and, you know, logistics folks there they're very much in tune with ERP, almost feel like for the past decade, the lines of business have been somewhat removed from the, the data team, if you will. And that, that seems to be changing. What are you seeing in terms of the line of line of business being much more involved in sort of end to end ownership, if you will, if I can use that term of, uh, of the data and sort of determining things like helping determine anyway, the data quality and things of that nature. Yeah. I >>Mean, I think this is where thinking about your data operating model and thinking about ideas of a chief data officer and having data on the CEO agenda, that's really important to get the lines of business, to really think about data sharing and reuse, and really getting them to, you know, kind of unlock the data because they do think about their data as a fiefdom data has value, but you've got to really get organizations in their silos to open it up and bring that data together because that's where the value is. You know, data doesn't operate. When you think about a customer, they don't operate in their journey across the business in silo channels. They don't think about, you know, I use only the web and then I use the call center, right? They think about that as just one experience and that data is a single journey. >>So we like to think about data as a product. You know, you should think about a data in the same way. You think about your products as, as products, you know, data as a product, you should have the idea of like every two weeks you have releases to it. You have an operational resiliency to it. So thinking about that, where you can have a very product mindset to delivering your data, I think is very important for the success. And that's where kind of, there's not just the things about critical data elements and having the right platform architecture, but there's a soft stuff as well, like a, a product mindset to data, having the right data, culture, and business adoption and having the right value set mindset for, for data, I think is really >>Important. I think data as a product is a very powerful concept and I think it maybe is uncomfortable to some people sometimes. And I think in the early days of big data, if you will, people thought, okay, data is a product going to sell my data and that's not necessarily what you mean, thinking about products or data that can fuel products that you can then monetize maybe as a product or as a, as, as a service. And I like to think about a new metric in the industry, which is how long does it take me to get from idea I'm a business person. I have an idea for a data product. How long does it take me to get from idea to monetization? And that's going to be something that ultimately as a business person, I'm going to use to determine the success of my data team and my data architecture. Is that kind of thinking starting to really hit the marketplace? Absolutely. >>I mean, I insurers now are working, partnering with, you know, auto manufacturers to monetize, um, driver usage data, you know, on telematics to see, you know, driver behavior on how, you know, how auto manufacturers are using that data. That's very important to insurers, you know, so how an auto manufacturer can monetize that data is very important and also an insurance, you know, cyber insurance, um, are there news new ways we can look at how companies are being attacked with viruses and malware. And is there a way we can somehow monetize that information? So companies that are able to agily, you know, think about how can we collect this data, bring it together, think about it as a product, and then potentially, you know, sell it as a service is something that, um, company, successful companies, you're doing great examples >>Of data products, and it might be revenue generating, or it might be in the case of, you know, cyber, maybe it reduces my expected loss and exactly. Then it drops right to my bottom line. What's the relationship between Accenture and cloud era? Do you, I presume you guys meet at the customer, but maybe you could give us some insight. >>Yeah. So, um, I, I'm in the executive sponsor for, um, the Accenture Cloudera partnership on the Accenture side. Uh, we do quite a lot of business together and, um, you know, Cloudera has been a great partner for us. Um, and they've got a great product in terms of the Cloudera data platform where, you know, what we do is as a big systems integrator for them, we help, um, you know, configure and we have a number of engineers across the world that come in and help in terms of, um, engineer architects and install, uh, cloud errors, data platform, and think about what are some of those, you know, value cases where you can really think about organizing data and bringing it together for all these different types of use cases. And really just as the examples we thought about. So the telematics, you know, um, in order to realize something like that, you're bringing in petabytes and huge scales of data that, you know, you just couldn't bring on a normal, uh, platform. You need to think about cloud. You need to think about speed of, of data and real-time insights and cloud era is the right data platform for that. So, um, >>Having a cloud Cloudera ushered in the modern big data era, we kind of all know that, and it was, which of course early on, it was very services intensive. You guys were right there helping people think through there weren't enough data scientists. We've sort of all, all been through that. And of course in your wheelhouse industries, you know, financial services and insurance, they were some of the early adopters, weren't they? Yeah, absolutely. >>Um, so, you know, an insurance, you've got huge amounts of data with loss history and, um, a lot with IOT. So in insurance, there's a whole thing of like sensorized thing in, uh, you know, taking the physical world and digitizing it. So, um, there's a big thing in insurance where, um, it's not just about, um, pricing out the risk of a loss experience, but actual reducing the loss before it even happens. So it's called risk control or loss control, you know, can we actually put sensors on oil pipelines or on elevators and, you know, reduce, um, you know, accidents before they happen. So we're, you know, working with an insurer to actually, um, listen to elevators as they move up and down and are there signals in just listening to the audio of an elevator over time that says, you know what, this elevator is going to need maintenance, you know, before a critical accident could happen. So there's huge applications, not just in structured data, but in unstructured data like voice and audio and video where a partner like Cloudera has a huge role to play. >>Great example of it. So again, narrow sort of use case for machine intelligence, but, but real value. True. We'll leave it like that. Thanks so much for taking some time. Yes. Thank you so much. Okay. We continue now with the theme of turning ideas into insights. So ultimately you can take action. We heard earlier that public cloud first doesn't mean public cloud only, and a winning strategy comprises data, irrespective of physical location on prem, across multiple clouds at the edge where real time inference is going to drive a lot of incremental value. Data is going to help the world come back to normal. We heard, or at least semi normal as we begin to better understand and forecast demand and supply and balances and economic forces. AI is becoming embedded into every aspect of our business, our people, our processes, and applications. And now we're going to get into some of the foundational principles that support the data and insights centric processes, which are fundamental to digital transformation initiatives. And it's my pleasure to welcome two great guests, Michelle Goetz. Who's a Kuba woman, VP and principal analyst at Forrester, and doing some groundbreaking work in this area. And Cindy, Mikey, who is the vice president of industry solutions and value management at Cloudera. Welcome to both of >>You. Welcome. Thank you. Thanks Dave. >>All right, Michelle, let's get into it. Maybe you could talk about your foundational core principles. You start with data. What are the important aspects of this first principle that are achievable today? >>It's really about democratization. If you can't make your data accessible, um, it's not usable. Nobody's able to understand what's happening in the business and they don't understand, um, what insights can be gained or what are the signals that are occurring that are going to help them with decisions, create stronger value or create deeper relationships, their customers, um, due to their experiences. So it really begins with how do you make data available and bring it to where the consumer of the data is rather than trying to hunt and Peck around within your ecosystem to find what it is that's important. Great. >>Thank you for that. So, Cindy, I wonder in hearing what Michelle just said, what are your thoughts on this? And when you work with customers at Cloudera, does, are there any that stand out that perhaps embody the fundamentals that Michelle just shared? >>Yeah, there's, there's quite a few. And especially as we look across, um, all the industries that we're actually working with customers in, you know, a few that stand out in top of mind for me is one is IQ via and what they're doing with real-world evidence and bringing together data across the entire, um, healthcare and life sciences ecosystems, bringing it together in different shapes and formats, making the ed accessible by both internally, as well as for their, um, the entire extended ecosystem. And then for SIA, who's working to solve some predictive maintenance issues within, there are a European car manufacturer and how do they make sure that they have, you know, efficient and effective processes when it comes to, uh, fixing equipment and so forth. And then also, um, there's, uh, an Indonesian based, um, uh, telecommunications company tech, the smell, um, who's bringing together, um, over the last five years, all their data about their customers and how do they enhance our customer experience? How do they make information accessible, especially in these pandemic and post pandemic times, um, uh, you know, just getting better insights into what customers need and when do they need it? >>Cindy platform is another core principle. How should we be thinking about data platforms in this day and age? I mean, where does, where do things like hybrid fit in? Um, what's cloud era's point >>Of view platforms are truly an enabler, um, and data needs to be accessible in many different fashions. Um, and also what's right for the business. When, you know, I want it in a cost and efficient and effective manner. So, you know, data needs to be, um, data resides everywhere. Data is developed and it's brought together. So you need to be able to balance both real time, you know, our batch historical information. It all depends upon what your analytical workloads are. Um, and what types of analytical methods you're going to use to drive those business insights. So putting and placing data, um, landing it, making it accessible, analyzing it needs to be done in any accessible platform, whether it be, you know, a public cloud doing it on-prem or a hybrid of the two is typically what we're seeing, being the most successful. >>Great. Thank you, Michelle. Let's move on a little bit and talk about practices and practices and processes as the next core principles. Maybe you could provide some insight as to how you think about balancing practices and processes while at the same time managing agility. >>Yeah, it's a really great question because it's pretty complex. When you have to start to connect your data to your business, the first thing to really gravitate towards is what are you trying to do? And what Cindy was describing with those customer examples is that they're all based off of business goals off of very specific use cases that helps kind of set the agenda about what is the data and what are the data domains that are important to really understanding and recognizing what's happening within that business activity and the way that you can affect that either in, you know, near time or real time, or later on, as you're doing your strategic planning, what that's balancing against is also being able to not only see how that business is evolving, but also be able to go back and say, well, can I also measure the outcomes from those processes and using data and using insight? >>Can I also get intelligence about the data to know that it's actually satisfying my objectives to influence my customers in my market? Or is there some sort of data drift or detraction in my, um, analytic capabilities that are allowing me to be effective in those environments, but everything else revolves around that and really thinking succinctly about a strategy that isn't just data aware, what data do I have and how do I use it, but coming in more from that business perspective to then start to be, data-driven recognizing that every activity you do from a business perspective leads to thinking about information that supports that and supports your decisions, and ultimately getting to the point of being insight driven, where you're able to both, uh, describe what you want your business to be with your data, using analytics, to then execute on that fluidly and in real time. And then ultimately bringing that back with linking to business outcomes and doing that in a continuous cycle where you can test and you can learn, you can improve, you can optimize, and you can innovate because you can see your business as it's happening. And you have the right signals and intelligence that allow you to make great decisions. >>I like how you said near time or real time, because it is a spectrum. And you know, one of the spectrum, autonomous vehicles, you've got to make a decision in real time, but, but, but near real-time, or real-time, it's, it's in the eyes of the holder, if you will, it's it might be before you lose the customer before the market changes. So it's really defined on a case by case basis. Um, I wonder Michelle, if you could talk about in working with a number of organizations, I see folks, they sometimes get twisted up and understanding the dependencies that technology generally, and the technologies around data specifically can have on critical business processes. Can you maybe give some guidance as to where customers should start, where, you know, where can we find some of the quick wins and high return, it >>Comes first down to how does your business operate? So you're going to take a look at the business processes and value stream itself. And if you can understand how people and customers, partners, and automation are driving that step by step approach to your business activities, to realize those business outcomes, it's way easier to start thinking about what is the information necessary to see that particular step in the process, and then take the next step of saying what information is necessary to make a decision at that current point in the process, or are you collecting information asking for information that is going to help satisfy a downstream process step or a downstream decision. So constantly making sure that you are mapping out your business processes and activities, aligning your data process to that helps you now rationalize. Do you need that real time near real time, or do you want to start grading greater consistency by bringing all of those signals together, um, in a centralized area to eventually oversee the entire operations and outcomes as they happen? It's the process and the decision points and acting on those decision points for the best outcome that really determines are you going to move in more of a real-time, uh, streaming capacity, or are you going to push back into more of a batch oriented approach? Because it depends on the amount of information and the aggregate of which provides the best insight from that. >>Got it. Let's, let's bring Cindy back into the conversation in your city. We often talk about people process and technology and the roles they play in creating a data strategy. That's that's logical and sound. Can you speak to the broader ecosystem and the importance of creating both internal and external partners within an organization? Yeah. >>And that's, uh, you know, kind of building upon what Michelle was talking about. If you think about datas and I hate to use the phrase almost, but you know, the fuel behind the process, um, and how do you actually become insight-driven? And, you know, you look at the capabilities that you're needing to enable from that business process, that insight process, um, you're extended ecosystem on, on how do I make that happen? You know, partners, um, and, and picking the right partner is important because a partner is one that actually helps under or helps you implement what your decisions are. Um, so, um, looking for a partner that has the capability that believes in being insight-driven and making sure that when you're leveraging data, um, you know, for within process on that, if you need to do it in a time fashion, that they can actually meet those needs of the business, um, and enabling on those, those process activities. So the ecosystem looking at how you, um, look at, you know, your vendors are, and fundamentally they need to be that trusted partner. Um, do they bring those same principles of value of being insight driven? So they have to have those core values themselves in order to help you as a, um, an end of business person enable those capabilities. So, so yeah, I'm >>Cool with fuel, but it's like super fuel when you talk about data, cause it's not scarce, right? You're never going to run out. So Michelle, let's talk about leadership. W w who leads, what does so-called leadership look like in an organization that's insight driven? >>So I think the really interesting thing that is starting to evolve as late is that organizations enterprises are really recognizing that not just that data is an asset and data has value, but exactly what we're talking about here, data really does drive what your business outcomes are going to be data driving into the insight or the raw data itself has the ability to set in motion. What's going to happen in your business processes and your customer experiences. And so, as you kind of think about that, you're now starting to see your CEO, your CMO, um, your CRO coming back and saying, I need better data. I need information. That's representative of what's happening in my business. I need to be better adaptive to what's going on with my customers. And ultimately that means I need to be smarter and have clearer forecasting into what's about ready to come, not just, you know, one month, two months, three months or a year from now, but in a week or tomorrow. >>And so that's, how is having a trickle down effect to then looking at two other types of roles that are elevating from technical capacity to more business capacity, you have your chief data officer that is shaping the exp the experiences, uh, with data and with insight and reconciling, what type of information is necessary with it within the context of answering these questions and creating a future fit organization that is adaptive and resilient to things that are happening. And you also have a chief digital officer who is participating because they're providing the experience and shaping the information and the way that you're going to interact and execute on those business activities, and either running that autonomously or as part of an assistance for your employees and for your customers. So really to go from not just data aware to data driven, but ultimately to be insight driven, you're seeing way more, um, participation, uh, and leadership at that C-suite level. And just underneath, because that's where the subject matter expertise is coming in to know how to create a data strategy that is tightly connected to your business strategy. >>Right. Thank you. Let's wrap. And I've got a question for both of you, maybe Cindy, you could start and then Michelle bring us home. You know, a lot of customers, they want to understand what's achievable. So it's helpful to paint a picture of a, of a maturity model. Uh, you know, I'd love to go there, but I'm not going to get there anytime soon, but I want to take some baby steps. So when you're performing an analysis on, on insight driven organization, city, what do you see as the major characteristics that define the differences between sort of the, the early, you know, beginners, the sort of fat middle, if you will, and then the more advanced, uh, constituents. >>Yeah, I'm going to build upon, you know, what Michelle was talking about as data as an asset. And I think, you know, also being data where, and, you know, trying to actually become, you know, insight driven, um, companies can also have data and they can have data as a liability. And so when you're data aware, sometimes data can still be a liability to your organization. If you're not making business decisions on the most recent and relevant data, um, you know, you're not going to be insight driven. So you've got to move beyond that, that data awareness, where you're looking at data just from an operational reporting, but data's fundamentally driving the decisions that you make. Um, as a business, you're using data in real time. You're, um, you're, you know, leveraging data to actually help you make and drive those decisions. So when we use the term you're, data-driven, you can't just use the term, you know, tongue in cheek. It actually means that I'm using the recent, the relevant and the accuracy of data to actually make the decisions for me, because we're all advancing upon. We're talking about, you know, artificial intelligence and so forth. Being able to do that, if you're just data where I would not be embracing on leveraging artificial intelligence, because that means I probably haven't embedded data into my processes. It's data could very well still be a liability in your organization. So how do you actually make it an asset? Yeah, I think data >>Where it's like cable ready. So, so Michelle, maybe you could, you could, you could, uh, add to what Cindy just said and maybe add as well, any advice that you have around creating and defining a data strategy. >>So every data strategy has a component of being data aware. This is like building the data museum. How do you capture everything that's available to you? How do you maintain that memory of your business? You know, bringing in data from your applications, your partners, third parties, wherever that information is available, you want to ensure that you're capturing and you're managing and you're maintaining it. And this is really where you're starting to think about the fact that it is an asset. It has value, but you may not necessarily know what that value is. Yet. If you move into a category of data driven, what starts to shift and change there is you're starting to classify label, organize the information in context of how you're making decisions and how you do business. It could start from being more, um, proficient from an analytic purpose. You also might start to introduce some early stages of data science in there. >>So you can do some predictions and some data mining to start to weed out some of those signals. And you might have some simple types of algorithms that you're deploying to do a next next best action for example. And that's what data-driven is really about. You're starting to get value out of it. The data itself is starting to make sense in context of your business, but what you haven't done quite yet, which is what insight driven businesses are, is really starting to take away. Um, the gap between when you see it, know it and then get the most value and really exploit what that insight is at the time when it's right. So in the moment we talk about this in terms of perishable insights, data and insights are ephemeral. And we want to ensure that the way that we're managing that and delivering on that data and insights is in time with our decisions and the highest value outcome we're going to have, that that insight can provide us. >>So are we just introducing it as data-driven organizations where we could see, you know, spreadsheets and PowerPoint presentations and lots of mapping to help make sort of longer strategic decisions, or are those insights coming up and being activated in an automated fashion within our business processes that are either assisting those human decisions at the point when they're needed, or an automated decisions for the types of digital experiences and capabilities that we're driving in our organization. So it's going from, I'm a data hoarder. If I'm data aware to I'm interested in what's happening as a data-driven organization and understanding my data. And then lastly being insight driven is really where light between business, data and insight. There is none it's all coming together for the best outcomes, >>Right? So people are acting on perfect or near perfect information or machines or, or, uh, doing so with a high degree of confidence, great advice and insights. And thank you both for sharing your thoughts with our audience today. It's great to have you. Thank you. Thank you. Okay. Now we're going to go into our industry. Deep dives. There are six industry breakouts, financial services, insurance, manufacturing, retail communications, and public sector. Now each breakout is going to cover two distinct use cases for a total of essentially 12 really detailed segments that each of these is going to be available on demand, but you can scan the calendar on the homepage and navigate to your breakout session for choice of choice or for more information, click on the agenda page and take a look to see which session is the best fit for you. And then dive in, join the chat and feel free to ask questions or contribute your knowledge, opinions, and data. Thanks so much for being part of the community and enjoy the rest of the day.

>>Welcome back to HPD discovered 2021. The virtual edition. My name is Dave a lot and you're watching the cube. We're here with Omar assad is the vice president, GM of H P S H C I and primary storage and data management business. And Sandeep Singh was the vice president of marketing for HP storage division. Welcome gents. Great to see you. >>Great to be here. Dave, >>it's a pleasure to be here today. >>Hey, so uh, last month you guys, you made a big announcement and and now you're, you know, shining the spotlight on that here at discover Cindy. Maybe you can give us a quick recap, what do we need to know? >>Yeah, Dave. We announced that we're expanding HB Green Lake by transforming HB storage to a cloud native software defined data services business. We unveiled a new vision for data that accelerates data dream of transformation for our customers. Uh and it introduced a and we introduced the data services platform that consists of two game changing innovations are first announcement was data services cloud console. It's a SAS based console that delivers the cut operational agility and it's designed to unify data operations through a suite of cloud data services. Our second announcement is H P E electra. It's cloud native data infrastructure to power your data edge to cloud. And it's managed natively with data services cloud console to bring that cloud operational model to our customers wherever their data lives. Together with the data services >>platform. >>Hp Green Green Lake brings that cloud experience to our customers data across edge and on premises environment and lays the foundation for our customers to shift from managing storage to managing data. >>Well, I think it lays the foundation for the next decade. You know, when we entered this past decade, we we we we keep we use terms like software led that that sort of morphed into. So the software defined data center containers with kubernetes, let's zoom out for a minute. If we can homer, maybe you could describe the problems that you're trying to address with this announcement. >>Thanks dave. It's always a pleasure talking to you on these topics. So in my role as general manager for primary storage, I speak with the hundreds of customers across the board and I consistently hear that data is at the heart of what our customers are doing and they're looking for a data driven transformative approach to their business. But as they engage on these things, there are two challenges that they consistently faced. The first one is that managing storage at scale Is rife with complexity. So while storage has gotten faster in the last 20 years, managing a single array or maybe two or three arrays has gotten simpler over time. But managing storage at scale when you deploy fleet, so storage as customers continue to gather, store and life cycle of that data. This process is extremely frustrating for customers. Still I. T. Administrators are firefighting, they're unable to innovate for their business because now data spans all the way from edge to corridor cloud. And then with the advent of public cloud there's another dimension of multi cloud that has been added to their data sprawl. And then secondly what what we what we consistently hear is that idea administrators need to shift from managing storage to managing data. What this basically means is that I. T. Has a desire to mobilize, protect and provision data seamlessly across its lifecycle and across the locations that it is stored at. This ensures that I. D. Leaders uh and also people within the organization understand the context of the data that they store and they operate upon. Yet data management is an extremely big challenge and it is a web of fragmented data silos across processes across infrastructure all the way from test and dev to administration uh to production uh to back up to lifecycle data advantage. Uh And so up till now data management was tied up with storage management and this needs to change for our customers especially with the diversity of the application workloads as they're growing and as customers are expanding their footprint across a multi cloud environment, >>just had to almost um response there. We recently conducted a survey that was actually done by E. S. She. Um and that was a survey of IT. decision makers. And it's interesting what it showcased, 93% of the respondents indicated that storage and data management complexity is impeding their digital transformation. 95% of the respondents indicated that solving storage and data management complexity is a top 10 business initiative for them And 94% want to bring the cloud experience on premises. >>You know, I'll chime in. I think as you guys move to the sort of software world and container world affinity to developers homer. You talked about, you know, things like data protection and we talk about security being bolted on all the time. Now. It's designed in it's it's done at sort of the point of creation, not as an afterthought and that's a big change that we see coming. Uh Let's talk about, you know what also needs to change as customers make the move from this idea of managing storage to to managing data or maybe you can take that one. >>That's a that's a very interesting problem. Right. What are the things that have to be true in order for us to move into this new data management model? So, dave one of the things that the public cloud got right is the cloud operational model which sets the standard for agility and a fast pace for our customers in a classic I. T. On prime model. If you ever wanted to stand up an application or if you were thinking about standing up a particular workload, uh you're going to file a series of I. T. Tickets uh And then you are at the mercy of whatever complex processes exist within organization and and depending on what the level of approvals are within a particular organization, standing up a workload can take days, weeks or even months in certain cases. So what cloud did was a rock that level of simplicity for someone that wanted to instead she ate an app. This means that the provision of underlying infrastructure that makes that workload possible needs to be reduced to minutes from days and weeks. But so what we are intending to do over here is to bring the best of both worlds together so that the cloud experience can be experienced everywhere with ease and simplicity and the customers don't need to change their operating model. So it's blending the two together. And that's what we are trying to usher in into this new era where we start to differentiate between data management and storage management as two independent. Yes, >>Great. Thank you for that. Omer. So deep. I wonder if you could share with the audience, you know, the vision that you guys unveiled, What does it look like? How are you making it actually substantive and and real? >>Yeah. David, That's also great question. Um across the board it's time to reimagine data management. Everything that homer shared. Those challenges are leading to customers needing to break down the silos and complexity that plagues these distributed data environments. And our vision is to deliver a new data experience that helps customers unleash the power of data. We call this vision unified data obs Unified Data Ops integrates data centric policies to streamline data management cloud native control to bring the cloud operational model to where customers data labs and a I driven insights to make the infrastructure invisible. It delivers a new data experience to simplify and bring that agility of cloud to data infrastructure. Streamline data management and help customers innovate faster than ever before. We're making the promise of unified Data Ops Real by transforming H P E storage to a cloud native software defined data services business and introducing a data services platform that expands Hve Green Lake. >>I mean, you know, you talk about the complexity, I see, I look at it as you kind of almost embracing the complexity saying, look, it's gonna keep getting more complex as the cloud expands to the edge on prem Cross cloud, it gets more complex underneath. What you're doing is you're almost embracing that complexity, putting a layer over it and hiding that complexity from from the end customer that and so they can spend their time doing other things over. I wonder if you can maybe talk a little bit more about the data services console, is it sort of another, you know, software layer to manage infrastructure? What exactly is it? >>It's a lot more than that dave and you're you're 100% right. It's basically we're attempting in this release to attack that complexity. Head on. So simply put data services. Cloud console is a SAS based console that delivers cloud operational model and cloud operational agility uh to our customers, it unifies data operations through a series of cloud data services that are delivered on top of this console to our customers in a continuous innovation stream. Uh And what we have done is going back to the point that I made earlier separating storage and data management and putting the strong suites of each of those together into the SAS delivered console for our customers. So what we have done is we have separated data and infrastructure management away from physical hardware to provide a comprehensive and a unified approach to managing data and infrastructure wherever it lives from a customer's perspective, it could be at the edge, it could be in a coal. Oh, it could be in their data center or it could be a bunch of data services that are deployed within the public cloud. So now our customers with data services, cloud console can manage the entire life cycle of their data from all the way from deployment, upgrading and optimizing it uh from a single console from anywhere in the world. Uh This console is designed to streamline data management with cloud data services that enable access to data, It allows for policy-based data protection, it allows for an organizational wide search on top of your storage assets. And we deliver basically a 360° visibility to all your data from a single console that the customer can experience from anywhere. So, so if you look at the journey, the way we're deciding to deliver this. So the first in its first incarnation, uh data services, cloud console gives you infrastructure and cloud data services to start to do data management along with that. But this is that foundation that we are placing in front of our customers, the SAS console through which we get touch our customers on a daily basis. And now as our customers get access to the SAAS platform on the back end, we will continue to roll in additional services throughout the years on a true SAS based innovation base for our customers. And and these services can will be will be ranging all the way from data protection to multiple out data management, all the way to visibility all the way to understanding the context of your data as it's stored across your enterprise. And in addition to that, we're offering a consistent, revised, unified API which allows for our customers to build automation against their storage infrastructure without ever worrying about that. As infrastructure changes. Uh the A P I proof points are going to break for them. That is never going to happen because they are going to be programming to a single SAS based aPI interface from now on. >>Right. And that brings in this idea of infrastructures coding because you talk about as a service to talk about Green Lake and and my question is always okay. Tell me what's behind that. And if and if and if and if you're talking about boxes and and widgets, that's a it's a problem. And you're not you're talking about services and A P. I. S and microservices and that's really the future model. And infrastructure is code and ultimately data as code is really part of that. So, All right. So you guys, I know some of your branding folks, you guys give deep thought uh, to this. So the second part of the announcement is the new product brands and deep maybe you can talk about that a little bit. >>Sure. Ultimately delivering the cloud operational model requires cognitive data infrastructure and that has been engineered to be natively managed from the cloud. And that's why we have also introduced H. P. E. Electra. Omar. Can you perhaps described HB electro even more? >>Absolutely. Thank you. Sandy. Uh, so with with HB Electoral we're launching a new brand of cloud native hardware infrastructure to power our customers data all the way from edge to the core to the cloud. The releases are smaller models for the edge then at the same time having models for the data center and then expanding those services into the public cloud as well. Right. All these hardware devices, Electoral hardware devices are cloud native. Empowered by our Data services. Cloud Council. We're announcing two models with this launch H. P. E. Electra 9000. Uh, this is for our mission critical workloads. It has its history and bases in H P E primera. It comes with 100% availability guarantee. Uh It's the first of its type in the industry. It comes with standard support contract, No special verb is required. And then we're also launching HB electoral 6000. Uh These are based in our history of uh nimble storage systems. Uh These these are for business critical applications, especially for that mid range of the storage market, optimizing price, performance and efficiency. Both of these systems are full envy, any storage powered by our timeless capabilities with data in place upgrades. And then they both deliver a unified infrastructure and data management experience through the data services, cloud console. Uh and and and at the back end, unified ai Ops experience with H P E info site is seamlessly blended in along with the offering for our customers. >>So this is what I was talking about before. It's sort of not your grandfather's storage business anymore. Is this is this is this is something that is part of that, that unified vision, that layer that I talked about. The AP is the program ability. So you're you're reaching into new territory here. Maybe you can give us an example of how the customers experience what that looks like. >>Excellent, loved her Dave. So essentially what we're doing is we're changing the storage experience to a true cloud operational model for our customers. These recent announcements that we just went through along with, indeed they expand the cloud experience that our customers get with storage as a service with HPD Green Lake. So a couple of examples to make this real. So the first of all is simplified deployment. Uh, so I t no longer has to go through complex startup and deployment processes. Now, all you need to do is these systems shipped and delivered to the customer's data center. Operational staff just need to rack and stack and then leave, connect the power cable, connect the network cable. And the job is done from that point onwards, data services console takes over where you can onboard these systems, you can provision these systems if you have a pre existing organization wide security as well as standard profile setup in data services console, we can automatically apply those on your behalf and bring these systems online. From a customer's perspective, they can be anywhere in the world to onboard these systems, they could be driving in a car, they could be sitting on a beach uh And and you know, these systems are automatically on boarded through this cloud operational model which is delivered through the SAAS application for our customers. Another big example. All that I'd like to shed light on is intent based provisioning. Uh So Dave typically provisioning a workload within a data center is an extremely spreadsheet driven trial and error kind of a task. Which system do I land it on? Uh Is my existing sl is going to be affected which systems that loaded, which systems are loaded enough that I put this additional workload on it and the performance doesn't take. All of these decisions are trial and error on a constant basis with cloud data services console along with the electron new systems that are constantly in a loop back information feeding uh Typical analytics to the console. All you need to do is to describe the type of the workload and the intent of the workload in terms of block size S. L. A. That you would like to experience at that point. Data services console consults with intra site at the back end. We run through thousands of data points that are constantly being given to us by your fleet and we come back with a few recommendations. You can accept the recommendation and at that time we go ahead and fully deploy this workload on your behalf or you can specify a particular system and then we will try to enforce the S. L. A. On that system. So it completely eliminates the guesswork and the planning that you have to do in this regard. Uh And last but not the least. Uh you know, one of the most important things is, you know, upgrades has been a huge problem for our customers. Uh And typically oftentimes when you're not in this constant, you know, loop back communication with your customers. It often is a big challenge to identify which release or which bug fix or which update goes on to which particular machine. All of that has been completely taken away from our customers and fully automated. Uh we run thousands of signatures across are installed base. We identify which upgrades need to be curated for which machines in a fleet for a particular customer. And then if it applies to that customer we presented, and if the customer accepts it, we automatically go ahead and upgrade the system and and and last, but not the least from a global management perspective. Now, a customer has an independent data view of their data estate, independent from a storage estate. And data services. Council can blend the two to give a consistent view or you can just look at the fleet view or the data view. >>It's kind of the Holy Grail. I mean I've been in this business a long time and I think I t. People have dreamt about you know this kind of capability for for a long long time. I wonder if we could sort of stay on the customers for a moment here and and talk about what's enabled. Now everybody's talking digital transformation that I joke about the joke. Not funny. The force marched to digital with Covid uh and we really wasn't planned for but the customers really want to drive now that digital transfer some of them are on the back burner and now they're moving to the front burner. What are the outcomes that are that are enabled here? Omar. >>Excellent. So so on on a typical basis for a traditional I. T. Customer, this cloud operational model means that you know information technology staff can move a lot faster and they can be a lot more productive on the things that are directly relevant to their business. They can get up to 99% of the savings back to spend more time on strategic projects or best of all spend time with their families rather than managing and upgrading infrastructure and fleets of infrastructure. Right. For line of business owners, the new experience means that their data infrastructure can be presented can be provision where the self service on demand type of capability. Uh They necessarily don't have to be in the data center to be able to make those decisions. Capacity management, performance management, all of that is died in and presented to them wherever they are easy to consume SAS based models and especially for data innovators, whether it's D B A s, uh whether it's data analysts, they can start to consume infrastructure and ultimately data as a code to speed up their app development because again, the context that we're bringing forward is the context of data decoupling it from. Actually, storage management, storage management and data management are now two separate domains that can be presented through a single console to tie the end to end picture for a customer. But at the end of the day, what we have felt is that customers really really want to rely and move forward with the data management and leave infrastructure management to machine oriented task, which we have completely automated on their behalf. >>So I'm sure you've heard you got the memo about, you know, H H P going all in on as a service. Uh it's clear that the companies all in. How does this announcement fit in to that overall mission, Sandeep >>Dave. We believe the future is edge to cloud and our mission is to be the edge to cloud platform as a service company and as as HB transforms HP Green Lake is our unified cloud platform. Hp Green Link is how we deliver cloud services and agile cloud experiences to customers, applications and data across the edge to cloud. With the storage announcement that we made recently, we announced that we're expanding HB Green Lake with as a service transformation of the HPV storage business to a cloud native software defined data services business. And this expands storage as a service delivering full cloud experience to our customers data across edge and on prem environment across the board were committed to being a strategic partner for every one of our customers and helping them accelerate their digital transformation. >>Yeah, that's where the puck is going guys. Hey as always great conversation with with our friends from HP storage. Thanks so much for the collaboration and congratulations on the announcements and I know you're not done yet. >>Thanks. Dave. Thanks. Dave. All right. Dave. It's a pleasure to be here. >>You're very welcome. And thank you for being with us for hp. You discovered 2021. You're watching the cube, the leader digital check coverage. Keep it right there, but right back. >>Mhm. Mhm.

>>Welcome back to HPD discovered 2021. The virtual edition. My name is Dave Volonte and you're watching the cube. We're here with Omar assad is the vice president GM of H P S H C I and primary storage and data management business. And Sandeep Singh was the vice president of marketing for HP storage division. Welcome gents. Great to see you. >>Great to be here. Dave, >>It's a pleasure to be here today. >>Hey, so uh, last month you guys, you made a big announcement and and now you're, you know, shining the spotlight on that here at discover Cindy. Maybe you can give us a quick recap, what do we need to know? >>Yeah, Dave. We announced that we're expanding HB Green Lake by transforming HB storage to a cloud native software defined data services business. We unveiled a new vision for data that accelerates data, dream of transformation for our customers. Uh and it introduced a and we introduced the data services platform that consists of two game changing innovations are first announcement was Data services cloud console. It's a SAS based console that delivers the cut operational agility and it's designed to unify data operations through a suite of cloud data services. Our 2nd announcement is HPE. Electra. It's cloud native data infrastructure to power your data edge to cloud. And it's managed natively with data services cloud console to bring that cloud operational model to our customers wherever their data lives together with the data services platform. Hp Green Green Lake brings that cloud experience to our customers data across edge and on premises environment and lays the foundation for our customers to shift from managing storage to managing data. >>Well, I think it lays the foundation for the next decade. You know, when we entered this past decade, we we were Ricky bobby's terms like software led that that sort of morphed into. So the software defined data center containers with kubernetes, Let's zoom out for a minute. If we can homer maybe you could describe the problems that you're trying to address with this announcement. >>Thanks dave. It's always a pleasure talking to you on these topics. So in my role as general manager for primary storage, I speak with the hundreds of customers across the board and I consistently hear that data is at the heart of what our customers are doing and they're looking for a data driven transformative approach to their business. But as they engage on these things, there are two challenges that they consistently faced. The first one is that managing storage at scale Is rife with complexity. So while storage has gotten faster in the last 20 years, managing a single array or maybe two or three arrays has gotten simpler over time. But managing storage at scale when you deploy fleet. So storage as customers continue to gather, store and lifecycle that data. This process is extremely frustrating for customers. Still I. T. Administrators are firefighting, they're unable to innovate for their business because now data spans all the way from edge to corridor cloud. And then with the advent of public cloud there's another dimension of multi cloud that has been added to their data sprawl. And then secondly what what we what we consistently hear is that idea administrators need to shift from managing storage to managing data. What this basically means is that I. D. Has a desire to mobilize, protect and provision data seamlessly across its lifecycle and across the locations that it is stored at. Uh This ensures that I. D. Leaders uh and also people within the organization understand the context of the data that they store and they operate upon. Yet data management is an extremely big challenge and it is a web of fragmented data silos across processes across infrastructure all the way from test and dev to administration uh to production uh to back up to lifecycle data management. Uh And so up till now data management was tied up with storage management and this needs to change for our customers especially with the diversity of the application workloads as they're growing and as customers are expanding their footprint across a multi cloud environment >>just to add to almost uh response there. We recently conducted a survey that was actually done by E. S. She. Um and that was a survey of IT. decision makers. And it's interesting what it showcased, 93% of the respondents indicated that storage and data management complexity is impeding their digital transformation. 95% of the respondents indicated that solving storage and data management complexity is a top 10 business initiative for them and 94% want to bring the cloud experience on premises, >>you know, al china. And I think as you guys move to the sort of software world and container world affinity to developers homer, you talked about, you know, things like data protection and we talk about security being bolted on all the time. Now. It's designed in it's it's done at sort of the point of creation, not as an afterthought. And that's a big change that we see coming. Uh But let's talk about, you know, what also needs to change as customers make the move from this idea of managing storage to to managing data or maybe you can take that one. >>That's a that's a that's a very interesting problem. Right. What are the things that have to be true in order for us to move into this new data management model? So, dave one of the things that the public cloud got right is the cloud operational model uh which sets the standard for agility and a fast pace for our customers in a classic I. T. On prime model, if you ever wanted to stand up an application or if you were thinking about standing up a particular workload, uh you're going to file a series of I. T. Tickets and then you're at the mercy of whatever complex processes exist within organization and and depending on what the level of approvals are within a particular organization, standing up a workload can take days, weeks or even months in certain cases. So what cloud did was they brought that level of simplicity for someone that wanted to instead she ate an app. This means that the provisioning of underlying infrastructure that makes that workload possible needs to be reduced to minutes from days and weeks. But so what we are intending to do over here is to bring the best of both worlds together so that the cloud experience can be experienced everywhere with ease and simplicity and the customers don't need to change their operating model. So it's blending the two together. And that's what we are trying to usher in into this new era where we start to differentiate between data management and storage management as two independent things. >>Great, thank you for that. Omer sometimes I wonder if you could share with the audience, you know, the vision that you guys unveiled, What does it look like? How are you making it actually substantive and and real? >>Yeah. Dave. That's also great question. Um across the board it's time to reimagine data management. Everything that homer shared. Those challenges are leading to customers needing to break down the silos and complexity that plagues these distributed data environments. And our vision is to deliver a new data experience that helps customers unleash the power of data. We call this vision unified data jobs, Unified Data Ops integrates data centric policies to streamline data management, cloud native control to bring the cloud operational model to where customers data labs and a I driven insights to make the infrastructure invisible. It delivers a new data experience to simplify and bring that agility of cloud to data infrastructure. Streamline data management and help customers innovate faster than ever before. We're making the promise of Unified Data Ops Real by transforming Hve storage to a cloud native software defined data services business and introducing a data services platform that expands Hve Green Lake. >>I mean, you know, you talk about the complexity, I see, I look at it as you kind of almost embracing the complexity saying, look, it's gonna keep getting more complex as the cloud expands to the edge on prem Cross cloud, it gets more complex underneath. What you're doing is you're almost embracing that complexity and putting a layer over it and hiding that complexity from from the end customer that and so they can spend their time doing other things over. I wonder if you can maybe talk a little bit more about the data services console, Is it sort of another software layer to manage infrastructure? What exactly is it? >>It's a lot more than that, Dave and you're you're 100% right. It's basically we're attempting in this release to attack that complexity head on. So simply put data services. Cloud console is a SAS based console that delivers cloud operational model and cloud operational agility uh to our customers. It unifies data operations through a series of cloud data services that are delivered on top of this console to our customers in a continuous innovation stream. Uh And what we have done is going back to the point that I made earlier separating storage and data management and putting the strong suites of each of those together into the SAS delivered console for our customers. So what we have done is we have separated data and infrastructure management away from physical hardware to provide a comprehensive and a unified approach to managing data and infrastructure wherever it lives. From a customer's perspective, it could be at the edge, it could be in a coal. Oh, it could be in their data center or it could be a bunch of data services that are deployed within the public cloud. So now our customers with data services. Cloud console can manage the entire life cycle of their data from all the way from deployment, upgrading and optimizing it uh from a single console from anywhere in the world. Uh This console is designed to streamline data management with cloud data services that enable access to data. It allows for policy-based data protection, it allows for an organizational wide search on top of your storage assets. And we deliver basically a 360° visibility to all your data from a single console that the customer can experience from anywhere. So, so if you look at the journey the way we're deciding to deliver this. So the first, in its first incarnation, uh Data services, Cloud console gives you infrastructure and cloud data services to start to do data management along with that. But this is that foundation that we are placing in front of our customers, the SAS console, through which we get touch our customers on a daily basis. And now as our customers get access to the SAAS platform on the back end, we will continue to roll in additional services throughout the years on a true SAS based innovation base for our customers. And and these services can will be will be ranging all the way from data protection to multiple out data management, all the way to visibility all the way to understanding the context of your data as it's stored across your enterprise. And in addition to that, we're offering a consistent revised unified Api which allows for our customers to build automation against their storage infrastructure. Without ever worrying about that. As infrastructure changes, uh, the A. P I proof points are going to break for them. That is never going to happen because they are going to be programming to a single SAS based aPI interface from now on. >>Right. And that brings in this idea of infrastructure as code because you talk about as a service to talk about Green Lake and and my question is always okay. Tell me what's behind that. And if and if and if and if you're talking about boxes and and widgets, that's a it's a problem. And you're not, you're talking about services and A P. I. S and microservices and that's really the future model and infrastructure is code and ultimately data as code is really part of that. So, All right. So you guys, I know some of your branding folks, you guys give deep thought to this. So the second part of the announcement is the new product brands and deep maybe you can talk about that a little bit. >>Sure. Ultimately delivering the cloud operational model requires cognitive data infrastructure and that has been engineered to be natively managed from the cloud. And that's why we have also introduced H. P. E. Electra. Omar, Can you perhaps described HB electro even more. >>Absolutely. Thank you. Sandy. Uh, so with with HB Electoral we're launching a new brand of cloud native hardware infrastructure to power our customers data all the way from edge to the core to the cloud. The releases are smaller models for the edge then at the same time having models for the data center and then expanding those services into the public cloud as well. Right. All these hardware devices, Electoral hardware devices are cloud native and powered by our data services. Cloud Council, we're announcing two models with this launch H. P. E Electoral 9000. Uh, this is for our mission critical workloads. It has its history and bases in H P E. Primera. It comes with 100% availability guarantee. Uh It's the first of its type in the industry. It comes with standard support contract, no special verb is required. And then we're also launching HB Electoral 6000. Uh These are based in our history of uh nimble storage systems. Uh These these are for business critical applications, especially for that mid range of the storage market, optimizing price, performance and efficiency. Both of these systems are full envy any storage powered by our timeless capabilities with data in place upgrades. And then they both deliver a unified infrastructure and data management experience through the data services, cloud console. Uh And and and at the back end unified Ai Ops experience with H P. E. Info site is seamlessly blended in along with the offering for our >>customers. So this is what I was talking about before. It's sort of not your grandfather's storage business anymore. This is this is this is something that is part of that, that unified vision, that layer that I talked about, the A. P. I. Is the program ability. So you're you're reaching into new territory here. Maybe you can give us an example of how the customers experience what that looks like. >>Excellent. Love to Dave. So essentially what we're doing is we're changing the storage experience to a true cloud operational model for our customers. These recent announcements that we just went through along with, indeed they expand the cloud experience that our customers get with storage as a service with HP Green Lake. So a couple of examples to make this real. So the first of all is simplified deployment. Uh So I t no longer has to go through complex startup and deployment processes. Now all you need to do is these systems shipped and delivered to the customer's data center. Operational staff just need to rack and stack and then leave connect the power cable, connect the network cable. And the job is done. From that point onwards, data services console takes over where you can onboard these systems, you can provision these systems if you have a pre existing organization wide security as well as standard profile setup in data services console, we can automatically apply those on your behalf and bring these systems online. From a customer's perspective, they can be anywhere in the world to onboard these systems, they could be driving in a car, they could be sitting on a beach. Uh And and you know, these systems are automatically on boarded through this cloud operational model which is delivered through the SAAS application for our customers. Another big example. All that I'd like to shed light on is intent based provisioning. Uh So Dave typically provisioning a workload within a data center is an extremely spreadsheet driven trial and error kind of a task. Which system do I land it on? Uh Is my existing sl is going to be affected which systems that loaded which systems are loaded enough that I put this additional workload on it and the performance doesn't take. All of these decisions are trial and error on a constant basis with cloud Data services console along with the electron new systems that are constantly in a loop back information feeding uh Typical analytics to the console. All you need to do is to describe the type of the workload and the intent of the workload in terms of block size S. L. A. That you would like to experience at that point. Data services console consults with intra site at the back end. We run through thousands of data points that are constantly being given to us by your fleet and we come back with a few recommendations. You can accept the recommendation and at that time we go ahead and fully deploy this workload on your behalf or you can specify a particular system and then people try to enforce the S. L. A. On that system. So it completely eliminates the guesswork and the planning that you have to do in this regard. Uh And last but not the least. Uh You know, one of the most important things is, you know, upgrades has been a huge problem for our customers. Uh And typically oftentimes when you're not in this constant, you know, loop back communication with your customers. It often is a big challenge to identify which release or which bug fix or which update goes on to which particular machine, all of that has been completely taken away from our customers and fully automated. Uh We run thousands of signatures across are installed base. We identify which upgrades need to be curated for which machines in a fleet for a particular customer. And then if it applies to that customer we presented, and if the customer accepts it, we automatically go ahead and upgrade the system and and and last, but not the least from a global management perspective. Now, a customer has an independent data view of their data estate, independent from a storage estate and data services. Council can blend the two to give a consistent view or you can just look at the fleet view or the data view. >>It's kind of the holy Grail. I mean I've been in this business a long time and I think I. T. People have dreamt about you know this kind of capability for for a long long time. I wonder if we could sort of stay on the customers for a moment here and and talk about what's enabled. Now. Everybody's talking digital transformation. I joke about the joke. Not funny. The force marched to digital with Covid. Uh and we really wasn't planned for but the customers really want to drive now that digital transfer some of them are on the back burner and now they're moving to the front burner. What are the outcomes that are that are enabled here? Omar. >>Excellent. So so on on a typical basis for a traditional I. T. Customer this cloud operational model means that you know information technology staff can move a lot faster and they can be a lot more productive on the things that are directly relevant to their business. They can get up to 99% of the savings back to spend more time on strategic projects or best of all spend time with their families rather than managing and upgrading infrastructure and fleets of infrastructure. Right for line of business owners, the new experience means that their data infrastructure can be presented can be provision where the self service on demand type of capability. Uh They necessarily don't have to be in the data center to be able to make those decisions. Capacity management, performance management, all of that is died in and presented to them wherever they are easy to consume. SaS based models and especially for data innovators, whether it's D B A s, whether it's data analysts, they can start to consume infrastructure and ultimately data as a code to speed up their app development because again, the context that we're bringing forward is the context of data decoupling it from. Actually, storage management, storage management and data management are now two separate domains that can be presented through a single console to tie the end to end picture for a customer. But at the end of the day, what we have felt is that customers really, really want to rely and move forward with the data management and leave infrastructure management to machine oriented task, which we have completely automated on their behalf. >>So I'm sure you've heard you got the memo about, you know, H H p going all in on as a service. Uh it is clear that the companies all in. How does this announcement fit in to that overall mission? Cindy >>dave We believe the future is edge to cloud and our mission is to be the edge to cloud platform as a service company and as as HB transforms HP Green Lake is our unified cloud platform. Hp Green Link is how we deliver cloud services and agile cloud experiences to customers applications and data across the edge to cloud. With the storage announcement that we made recently, we announced that we're expanding HB Green Lake with as a service transformation of the HPV storage business to a cloud native software defined data services business. And this expands storage as a service, delivering full cloud experience to our customers data across edge and on prem environment across the board were committed to being a strategic partner for every one of our customers and helping them accelerate their digital transformation. >>Yeah, that's where the puck is going guys. Hey as always great conversation with with our friends from HP storage. Thanks so much for the collaboration and congratulations on the announcements and and I know you're not done yet. >>Thanks. Dave. Thanks. Dave. >>Thanks. Dave. It's a pleasure to be here. >>You're very welcome. And thank you for being with us for hp. You discovered 2021 you're watching the cube, the leader digital check coverage. Keep it right there, but right back. >>Yeah. Yeah.

>>Yeah, yeah. >>Welcome back to our final session in cultivating a data fluent culture track earlier today, we heard from experts like Valerie from the Data Lodge who shared best practices that you can apply to build that data flew into culture in your organization and tips on how to become the next analyst of the future from Yasmin at Comcast and Steve at all Terex. Then we heard from a captivating session with Cindy Hausen and Ruhollah Benjamin, professor at Princeton, on how now is our chance to change the patterns of injustice that we see have been woven into the fabric of society. If you do not have a chance to see today's content, I highly recommend that you check it out on demand. There's a lot of great information that you could start applying today. Now I'm excited to introduce our next session, which will take a look at how the democratization of data is powering digital transformation in the insurance industry. We have two prestigious guests joining us today. First Jim Bramblett, managing director of North America insurance practice, lead at its center. Throughout Jim's career, he's been focused on large scale transformation from large to midsize insurance carriers. His direct experience with clients has traditionally been in the intersection of technology, platform transformation and operating remodel redesign. We also have Michael cast Onus, executive VP and chief operating officer at DNA. He's responsible for all information technology, analytics and operating functions across the organization. Michael has led major initiatives to launch digital programs and incorporating modern AP I architectures ER, which was primarily deployed in the cloud. Jim, please take it away. >>Great. Thanks, Paula E thought we'd cover a few things today around around data. This is some of the trends we see in data within the insurance sector. And then I'll hand it over to Michael Teoh, take you through his story. You know, I think at the macro level, as we think about data and we think about data in the context of the insurance sector, it's interesting because the entire history of the insurance sector has been built on data and yet, at the same time, the entire future of it relies on that same data or similar similar themes for data. But but different. Right? So we think about the history, what has existed in an insurance companies. Four walls was often very enough, very enough to compete, right? So if you think about your customer data, claims, data, CRM, data, digital data, all all the data that was yeah, contained within the four walls of your company was enough to compete on. And you're able to do that for hundreds of years. But as we we think about now as we think about the future and the ability to kind of compete on data, this data comes from many more places just than inside your four walls. It comes from every device, every human, every vehicle, every property, every every digital interaction. Um in upon this data is what we believe insurers need to pivot to. To compete right. They need to be able to consume this data at scale. They need to be able to turn through this data to drive analytics, and they serve up insights based on those analytics really at the desktop of insurance professionals. And by the way, that has to be in the natural transition of national transaction. Of that employees work day. So an underwriter at a desktop claim him on the desktop, the sales associate of desktop. Those insights need to be served up at that point in time when most relevant. And you know. So if we think about how insurance companies are leveraging data, we see this really on kind of three horizons and starting from the left hand side of the page here, this is really brilliant basics. So how my leveraging core core data and core applied intelligence to monetize your existing strategy? And I think this brilliant based, brilliant basics concept is where most of most of my clients, at least within insurance are are today. You know, how are we leveraging data in the most effective way and putting it in the hands of business decision makers to make decisions largely through reporting and some applied intelligence? Um, Horizon two. We see, you know, definitely other industries blazing a trail here, and this is really about How do we integrate ecosystems and partners Now? I think within insurance, you know, we've had data providers forever, right? Whether it's NPR data, credit data risk data, you know, data aggregators and data providers have been a critical part of the insurance sector for for decades. I think what's different about this this ecosystem and partnership model is that it's much more Oneto one and it's much more, you know, kind of. How do we integrate more tightly and how do we become more embedded in each other's transactions? I think that we see some emergence of this, um, in insurance with automotive manufacturers with building management systems. But I think in the grand scheme of things, this is really very, very nascent for us as a sector. And I think the third horizon is is, you know, how do we fundamentally think about data differently to drive new business models? And I, you know, I don't know that we haven't ensure here in North America that's really doing this at any sort of scale. We certainly see pilots and proofs of concepts. We see some carriers in Europe farther down this path, but it's really it's really very new for us. A Z Think about these three horizons for insurance. So you know what's what's behind all this and what's behind. You know, the next powering of digital transformation and and we think at the end of the exercise, its data data will be the next engine that powers digital transformation. So in this exhibit, you know we see the three horizons across the top. You know, data is activated and activating digital transformation. And this, you know, this purple 3rd, 3rd road here is we think some of the foundational building blocks required to kind of get this right. But I think what's most important about about this this purple third bar here is the far right box, which is business adoption. Because you can build this infrastructure, you can have. You know, this great scalable cloud capability. Um, you can create a bunch of applications and intelligence, but unless it's adopted by the business, unless it's democratized, unless those insights and decisions air served up in the natural course of business, you're gonna have trouble really driving value. So that way, I think this is a really interesting time for data. We think this is kind of the next horizon to power the next age of digital transformation for insurance companies. With that brief prelude, I am, I'm honored. Thio, turn it over to Michael Stone Is the Cielo at CNN Insurance? >>Thanks, Jim, for that intro and very exciting Thio be here is part of part of beyond when I think a digital transformation within the context of insurance, actually look at it through the lens of competing in an era of near perfect information. So in order to be able to deliver all of the potential value that we talked about with regard to data and changing ecosystem and changing demands, the question becomes, How do you actually harness the information that's available to everybody to fundamentally change the business? So if you'll indulge me a bit here, let me tell you just a little bit more for those that don't know about insurance, what it really is. And I use a very long run on sentence to do that. It's a business model where capital is placed against risk in the form of products and associated services sold the customers through channels two companies to generate a return. Now, this sounds like a lot of other businesses in across multiple industries that were there watching today. But the difference within insurance is that every major word in that long run on sentence is changing sources of capital that we could draw on to be able to underwrite risk of going away. The nature of risk itself is changing from the perspective of policies that live six months to a year, the policies that could last six minutes. The products that we're creating are changing every day for our ability to actually put a satellite up in the air or ensure against the next pandemic. Our customers are not just companies or individuals, but they could be governments completely different entities than we would have been in sharing in the past and channels were changing. We sell direct, we sell through brokers and products are actually being embedded in other products. So you may buy something and not even know that insurance is a part of it. And what's most interesting here is the last word which is around return In the old world. Insurance was a cash flow business in which we could bring the premium in and get a level of interest income and being able to use that money to be able thio buffer the underwriting results that we would have. But those returns or dramatically reduced because of the interest income scenario, So we have to generate a higher rate of return. So what do we need to do? Is an insurance company in through this digital transformation to be able to get there? Well, fundamentally, we need to rethink how we're using information, and this is where thought spot and the cloud coming for us. We have two basic problems that we're looking to solve with information. The first one is information veracity. Do we believe it? When we get it? Can we actually trust it? Do we know what it means when we say that this is a policy in force or this is a new customer where this is the amount of attention or rate that we're going to get? Do we actually believe in that piece of data? The second is information velocity. Can we get it fast enough to be able to capitalize upon it? So in other words, we're We're working in a situation where the feedback loop is closing quickly and it's operating at a speed that we've never worked in before. So if we can't solve veracity and velocity, then we're never going to be able to get to where we need to go. So when we think of something like hot spot, what do we use it for? We use it to be able to put it in the hands of our business years so that they could ask the key questions about how the business is running. How much profit of my generating this month? What brokers do I need to talk? Thio. What is my rate retention? Look like what? The trends that I'm seeing. And we're using that mechanism not just to present nice visualizations, but to enable that really quick, dynamic question and answer and social, socially enabled search, which completely puts us in a different position of being able to respond to the market conditions. In addition, we're using it for pattern recognition. Were using it for artificial intelligence. We're gonna be capitalizing on the social aspect of of search that's that's enabled through thought spot and also connecting it into our advanced machine learning models and other capabilities that we currently have. But without it solving the two fundamental problems of veracity and velocity, we would be handicapped. So let me give you some advice about if I were in your position and you don't need to be in sleepy old industry like insurance to be able to do this, I'll leave you with three things. The first one is picking water holes so What are the things that you really want to be good at? What are the pieces of information that you really need to know more about? I mean, in insurance, its customers, it's businesses, locations, it's behavior. There are only a few water also really understand and pick those water holes that you're going to be really good at. The second is stand on the shoulders of giants. You know, in the world of technology, there's often a philosophy that says, Well, I can build it something better than somebody else create if I have it in house. But I'm happy to stand on the shoulders of giants like Thought Spot and Google and others to be able to create this capability because guess what? They're gonna out innovate any of the internal shops all day and every day. So don't be afraid. Thio. Stand side by side on the shoulders of giants as part of your journey. Unless you've got to build these organizations not just the technology for rapid experimentation and learning, because guess what? The moment you deliver insight, it begs another question, which also could change the business process, which could change the business model and If your organization the broader organization of business technology, analytics, customer service operations, etcetera is not built in a way that could be dynamic and flexible based on where the market is or is going, then you're gonna miss out on the opportunity. So again, I'm proud to be part of the fast black community. Really love the technology. And if if you look too, have the same kind of issues with your given industry about how you can actually speed up decision making, deliver insights and deliver this kind of search and recommended to use it. And with that, let's go to some questions. >>Awesome. Thank you so much, Michael and Jim for that in depth perspective and those tangible takeaways for our audience. We have a few minutes left and would love to ask a few questions. So here's the first one for Michael Michael. What are some of the most important things that you know now that you didn't know before you started this process? I think one of >>the things that's a great question. I think one of the things that really struck me is that, you know, traditional thinking would be very use case centric or pain point centric Show me, uh, this particular model or a particular question you want me to answer that can build your own analytics to do that or show me a deficiency in the system and I can go and develop a quick head that will do well, then you know, wallpaper over that particular issue. But what we've really learned is the foundation matters. So when we think about building things is building the things that are below the waterline, the pipes and plumbing about how you move data around how the engines work and how it all connects together gives you the above the waterline features that you could deliver to. You know, your employees into your customers much faster chasing use cases across the top above the waterline and ignoring what's below the water line to me. Is it really, uh, easy recipe too quick? Get your way to nothing. So again, focus on the foundation bill below the water line and then iterated above the water line that z what the lessons we've learned. It has been very effective for us. >>I think that's a very great advice for all those watching today on. But Here's one for Jim. Jim. What skills would you say are required for teams to truly adopt this digital transformation process? >>Yeah, well, I think that's a really good question, and I think I'd start with it's It's never one. Well, our experience has shown us number a one person show, right? So So we think to kind of drive some of the value that that that Michael spoke about. We really looked across disciplinary teams, which is a an amalgamation of skills and and team members, right? So if you think about the data science skills required, just kinda under under understand how toe toe work with data and drive insights, Sometimes that's high end analytic skills. Um, where you gonna find value? So some value architectural skills Thio really articulate, you know, Is this gonna move the needle for my business? I think there's a couple of critical critical components of this team. One is, you know, the operation. Whatever. That operation maybe has to be embedded, right, because they designed this is gonna look at a piece of data that seems interesting in the business Leader is going to say that that actually means nothing to me in my operation. So and then I think the last the last type of skill would be would be a data translator. Um, sitting between sometimes the technology in the business so that this amalgamation of skills is important. You know, something that Michael talked about briefly that I think is critical is You know, once you deliver insight, it leads to 10 more questions. So just in a intellectual curiosity and an understanding of, you know, if I find something here, here, the implications downstream from my business are really important. So in an environment of experimenting and learning thes thes cross discipline teams, we have found to be most effective. And I think we thought spot, you know, the platform is wired to support that type of analysis and wired to support that type of teaming. >>Definitely. I think that's though there's some really great skills. That's for people to keep in mind while they are going through this process. Okay, Michael, we have another question for you. What are some of the key changes you've had to make in your environment to make this digital transformation happen? >>That's a great question. I think if you look at our environment. We've got a mixture of, you know, space agent Stone age. We've got old legacy systems. We have all sorts of different storage. We have, you know, smatterings of things that were in cloud. The first thing that we needed to do was make a strong commitment to the cloud. So Google is our partner for for the cloud platform on unabashedly. The second thing that we needed to dio was really rethink the interplay between analytics systems in operational systems. So traditionally, you've got a large data warehouses that sit out over here that, you know, we've got some kind of extract and low that occurs, and we've got transactional operational systems that run the business, and we're thinking about them very differently from the perspective of bringing them together. How Doe I actually take advantage of data emotion that's in the cloud. So then I can actually serve up analytics, and I can also change business process as it's happening for the people that are transacting business. And in the meantime, I can also serve the multiple masters of total cost and consumption. So again, I didn't applications are two ships that pass in the night and never be in the world of Sienna. When you look at them is very much interrelated, especially as we want to get our analytics right. We want to get our A i m all right, and we want to get operational systems right By capturing that dated motion force across that architecture er that was an important point. Commit to the cloud, rethink the way we think analytics systems, work and operational systems work and then move them in tandem, as opposed to doing one without the other one in the vacuum. >>That's that's great advice, Michael. I think it's very important those key elements you just hit one question that we have final question we have for Jim. Jim, how do you see your client sustain the benefits that they've gained through this process? >>Yeah, it's a really good question. Um, you know, I think about some of the major themes around around beyond right, data fluency is one of them, right? And as I think about fluency, you only attain fluency through using the language every single day. They were day, week, over week, month over month. So you know, I think that applies to this. This problem too. You know, we see a lot of clients have to change probably two things at the same time. Number one is mindset, and number two is is structure. So if you want to turn these data projects from projects into processes, right, so so move away from spinning up teams, getting getting results and winding down. You wanna move away from that Teoh process, which is this is just the way working for these teams. Um, you have to change the mindset and often times you have to marry that with orb structure change. So So I'm gonna spin up these teams, but this team is going to deliver a set of insights on day. Then we're gonna be continuous improvement teams that that persist over time. So I think this shifting from project teams to persistent teams coupled with mindset coupled with with or structure changed, you know, a lot of times has to be in place for a period of time to get to get the fluency and achieve the fluency that that most organizations need. >>Thanks, Jim, for that well thought out answer. It really goes to show that the transformation process really varies when it comes to organizations, but I think this is a great way to close out today's track. I like to think Jim, Michael, as well as all the experts that you heard earlier today for sharing. There's best practice as to how you all can start transforming your organization's by building a data fluent culture, Um, and really empowering your employees to understand what data means and how to take actions with it. As we wrap up and get ready for the next session, I'd like to leave you all with just a couple of things. Number one if you miss anything or would like to watch any of the other tracks. Don't worry. We have everything available after this event on demand number two. If you want to ask more questions from the experts that you heard earlier today, you have a chance to do so. At the Meet The Experts Roundtable, make sure to attend the one for track four in cultivating a data fluent culture. Now, as we get ready for the product roadmap, go take a sip of water. This is something you do not want to miss. If you love what you heard yesterday, you're gonna like what you hear today. I hear there's some type of Indiana Jones theme to it all, so I won't say anything else, but I'll see you there.

>>Yeah, yeah. >>Welcome back. I'm so excited to introduce our next session empowerment through inclusion, reimagining society and technology. This is a topic that's personally very near and dear to my heart. Did you know that there's only 2% of Latinas in technology as a Latina? I know that there's so much more we could do collectively to improve these gaps and diversity. I thought spot diversity is considered a critical element across all levels of the organization. The data shows countless times. A diverse and inclusive workforce ultimately drives innovation better performance and keeps your employees happier. That's why we're passionate about contributing to this conversation and also partnering with organizations that share our mission of improving diversity across our communities. Last beyond, we hosted the session during a breakfast and we packed the whole room. This year, we're bringing the conversation to the forefront to emphasize the importance of diversity and data and share the positive ramifications that it has for your organization. Joining us for this session are thought spots Chief Data Strategy Officer Cindy Housing and Ruhollah Benjamin, associate professor of African American Studies at Princeton University. Thank you, Paola. So many >>of you have journeyed with me for years now on our efforts to improve diversity and inclusion in the data and analytic space. And >>I would say >>over time we cautiously started commiserating, eventually sharing best practices to make ourselves and our companies better. And I do consider it a milestone. Last year, as Paola mentioned that half the room was filled with our male allies. But I remember one of our Panelists, Natalie Longhurst from Vodafone, suggesting that we move it from a side hallway conversation, early morning breakfast to the main stage. And I >>think it was >>Bill Zang from a I G in Japan. Who said Yes, please. Everyone else agreed, but more than a main stage topic, I want to ask you to think about inclusion beyond your role beyond your company toe. How Data and analytics can be used to impact inclusion and equity for the society as a whole. Are we using data to reveal patterns or to perpetuate problems leading Tobias at scale? You are the experts, the change agents, the leaders that can prevent this. I am thrilled to introduce you to the leading authority on this topic, Rou Ha Benjamin, associate professor of African studies at Princeton University and author of Multiple Books. The Latest Race After Technology. Rou ha Welcome. >>Thank you. Thank you so much for having me. I'm thrilled to be in conversation with you today, and I thought I would just kick things off with some opening reflections on this really important session theme. And then we could jump into discussion. So I'd like us to as a starting point, um, wrestle with these buzzwords, empowerment and inclusion so that we can have them be more than kind of big platitudes and really have them reflected in our workplace cultures and the things that we design in the technologies that we put out into the world. And so to do that, I think we have to move beyond techno determinism, and I'll explain what that means in just a minute. Techno determinism comes in two forms. The first, on your left is the idea that technology automation, um, all of these emerging trends are going to harm us, are going to necessarily harm humanity. They're going to take all the jobs they're going to remove human agency. This is what we might call the techno dystopian version of the story and this is what Hollywood loves to sell us in the form of movies like The Matrix or Terminator. The other version on your right is the techno utopian story that technologies automation. The robots as a shorthand, are going to save humanity. They're gonna make everything more efficient, more equitable. And in this case, on the surface, he seemed like opposing narratives right there, telling us different stories. At least they have different endpoints. But when you pull back the screen and look a little bit more closely, you see that they share an underlying logic that technology is in the driver's seat and that human beings that social society can just respond to what's happening. But we don't really have a say in what technologies air designed and so to move beyond techno determinism the notion that technology is in the driver's seat. We have to put the human agents and agencies back into the story, the protagonists, and think carefully about what the human desires worldviews, values, assumptions are that animate the production of technology. And so we have to put the humans behind the screen back into view. And so that's a very first step and when we do that, we see, as was already mentioned, that it's a very homogeneous group right now in terms of who gets the power and the resource is to produce the digital and physical infrastructure that everyone else has to live with. And so, as a first step, we need to think about how to create more participation of those who are working behind the scenes to design technology now to dig a little more a deeper into this, I want to offer a kind of low tech example before we get to the more hi tech ones. So what you see in front of you here is a simple park bench public bench. It's located in Berkeley, California, which is where I went to graduate school and on this particular visit I was living in Boston, and so I was back in California. It was February. It was freezing where I was coming from, and so I wanted to take a few minutes in between meetings to just lay out in the sun and soak in some vitamin D, and I quickly realized, actually, I couldn't lay down on this bench because of the way it had been designed with these arm rests at intermittent intervals. And so here I thought. Okay, the the armrest have, ah functional reason why they're there. I mean, you could literally rest your elbows there or, um, you know, it can create a little bit of privacy of someone sitting there that you don't know. When I was nine months pregnant, it could help me get up and down or for the elderly, the same thing. So it has a lot of functional reasons, but I also thought about the fact that it prevents people who are homeless from sleeping on the bench. And this is the Bay area that we were talking about where, in fact, the tech boom has gone hand in hand with a housing crisis. Those things have grown in tandem. So innovation has grown within equity because we haven't thought carefully about how to address the social context in which technology grows and blossoms. And so I thought, Okay, this crisis is growing in this area, and so perhaps this is a deliberate attempt to make sure that people don't sleep on the benches by the way that they're designed and where the where they're implemented and So this is what we might call structural inequity. By the way something is designed. It has certain effects that exclude or harm different people. And so it may not necessarily be the intense, but that's the effect. And I did a little digging, and I found, in fact, it's a global phenomenon, this thing that architects called hostile architecture. Er, I found single occupancy benches in Helsinki, so only one booty at a time no laying down there. I found caged benches in France. And in this particular town. What's interesting here is that the mayor put these benches out in this little shopping plaza, and within 24 hours the people in the town rallied together and had them removed. So we see here that just because we have, uh, discriminatory design in our public space doesn't mean we have to live with it. We can actually work together to ensure that our public space reflects our better values. But I think my favorite example of all is the meter bench. In this case, this bench is designed with spikes in them, and to get the spikes to retreat into the bench, you have to feed the meter you have to put some coins in, and I think it buys you about 15 or 20 minutes. Then the spikes come back up. And so you'll be happy to know that in this case, this was designed by a German artists to get people to think critically about issues of design, not just the design of physical space but the design of all kinds of things, public policies. And so we can think about how our public life in general is metered, that it serves those that can pay the price and others are excluded or harm, whether we're talking about education or health care. And the meter bench also presents something interesting. For those of us who care about technology, it creates a technical fix for a social problem. In fact, it started out his art. But some municipalities in different parts of the world have actually adopted this in their public spaces in their parks in order to deter so called lawyers from using that space. And so, by a technical fix, we mean something that creates a short term effect, right. It gets people who may want to sleep on it out of sight. They're unable to use it, but it doesn't address the underlying problems that create that need to sleep outside in the first place. And so, in addition to techno determinism, we have to think critically about technical fixes that don't address the underlying issues that technology is meant to solve. And so this is part of a broader issue of discriminatory design, and we can apply the bench metaphor to all kinds of things that we work with or that we create. And the question we really have to continuously ask ourselves is, What values are we building in to the physical and digital infrastructures around us? What are the spikes that we may unwittingly put into place? Or perhaps we didn't create the spikes. Perhaps we started a new job or a new position, and someone hands us something. This is the way things have always been done. So we inherit the spike bench. What is our responsibility when we noticed that it's creating these kinds of harms or exclusions or technical fixes that are bypassing the underlying problem? What is our responsibility? All of this came to a head in the context of financial technologies. I don't know how many of you remember these high profile cases of tech insiders and CEOs who applied for Apple, the Apple card and, in one case, a husband and wife applied and the husband, the husband received a much higher limit almost 20 times the limit as his wife, even though they shared bank accounts, they lived in Common Law State. And so the question. There was not only the fact that the husband was receiving a much better interest rate and the limit, but also that there was no mechanism for the individuals involved to dispute what was happening. They didn't even know what the factors were that they were being judged that was creating this form of discrimination. So in terms of financial technologies, it's not simply the outcome that's the issue. Or that could be discriminatory, but the process that black boxes, all of the decision making that makes it so that consumers and the general public have no way to question it. No way to understand how they're being judged adversely, and so it's the process not only the product that we have to care a lot about. And so the case of the apple cart is part of a much broader phenomenon of, um, racist and sexist robots. This is how the headlines framed it a few years ago, and I was so interested in this framing because there was a first wave of stories that seemed to be shocked at the prospect that technology is not neutral. Then there was a second wave of stories that seemed less surprised. Well, of course, technology inherits its creator's biases. And now I think we've entered a phase of attempts to override and address the default settings of so called racist and sexist robots, for better or worse. And here robots is just a kind of shorthand, that the way people are talking about automation and emerging technologies more broadly. And so as I was encountering these headlines, I was thinking about how these air, not problems simply brought on by machine learning or AI. They're not all brand new, and so I wanted to contribute to the conversation, a kind of larger context and a longer history for us to think carefully about the social dimensions of technology. And so I developed a concept called the New Jim Code, which plays on the phrase Jim Crow, which is the way that the regime of white supremacy and inequality in this country was defined in a previous era, and I wanted us to think about how that legacy continues to haunt the present, how we might be coding bias into emerging technologies and the danger being that we imagine those technologies to be objective. And so this gives us a language to be able to name this phenomenon so that we can address it and change it under this larger umbrella of the new Jim Code are four distinct ways that this phenomenon takes shape from the more obvious engineered inequity. Those were the kinds of inequalities tech mediated inequalities that we can generally see coming. They're kind of obvious. But then we go down the line and we see it becomes harder to detect. It's happening in our own backyards. It's happening around us, and we don't really have a view into the black box, and so it becomes more insidious. And so in the remaining couple minutes, I'm just just going to give you a taste of the last three of these, and then a move towards conclusion that we can start chatting. So when it comes to default discrimination. This is the way that social inequalities become embedded in emerging technologies because designers of these technologies aren't thinking carefully about history and sociology. Ah, great example of this came Thio headlines last fall when it was found that widely used healthcare algorithm affecting millions of patients, um, was discriminating against black patients. And so what's especially important to note here is that this algorithm healthcare algorithm does not explicitly take note of race. That is to say, it is race neutral by using cost to predict healthcare needs. This digital triaging system unwittingly reproduces health disparities because, on average, black people have incurred fewer costs for a variety of reasons, including structural inequality. So in my review of this study by Obermeyer and colleagues, I want to draw attention to how indifference to social reality can be even more harmful than malicious intent. It doesn't have to be the intent of the designers to create this effect, and so we have to look carefully at how indifference is operating and how race neutrality can be a deadly force. When we move on to the next iteration of the new Jim code coded exposure, there's attention because on the one hand, you see this image where the darker skin individual is not being detected by the facial recognition system, right on the camera or on the computer. And so coated exposure names this tension between wanting to be seen and included and recognized, whether it's in facial recognition or in recommendation systems or in tailored advertising. But the opposite of that, the tension is with when you're over included. When you're surveiled when you're to centered. And so we should note that it's not simply in being left out, that's the problem. But it's in being included in harmful ways. And so I want us to think carefully about the rhetoric of inclusion and understand that inclusion is not simply an end point. It's a process, and it is possible to include people in harmful processes. And so we want to ensure that the process is not harmful for it to really be effective. The last iteration of the new Jim Code. That means the the most insidious, let's say, is technologies that are touted as helping US address bias, so they're not simply including people, but they're actively working to address bias. And so in this case, There are a lot of different companies that are using AI to hire, create hiring software and hiring algorithms, including this one higher view. And the idea is that there there's a lot that AI can keep track of that human beings might miss. And so so the software can make data driven talent decisions. After all, the problem of employment discrimination is widespread and well documented. So the logic goes, Wouldn't this be even more reason to outsource decisions to AI? Well, let's think about this carefully. And this is the look of the idea of techno benevolence trying to do good without fully reckoning with what? How technology can reproduce inequalities. So some colleagues of mine at Princeton, um, tested a natural learning processing algorithm and was looking to see whether it exhibited the same, um, tendencies that psychologists have documented among humans. E. And what they found was that in fact, the algorithm associating black names with negative words and white names with pleasant sounding words. And so this particular audit builds on a classic study done around 2003, before all of the emerging technologies were on the scene where two University of Chicago economists sent out thousands of resumes to employers in Boston and Chicago, and all they did was change the names on those resumes. All of the other work history education were the same, and then they waited to see who would get called back. And the applicants, the fictional applicants with white sounding names received 50% more callbacks than the black applicants. So if you're presented with that study, you might be tempted to say, Well, let's let technology handle it since humans are so biased. But my colleagues here in computer science found that this natural language processing algorithm actually reproduced those same associations with black and white names. So, too, with gender coded words and names Amazon learned a couple years ago when its own hiring algorithm was found discriminating against women. Nevertheless, it should be clear by now why technical fixes that claim to bypass human biases are so desirable. If Onley there was a way to slay centuries of racist and sexist demons with a social justice box beyond desirable, more like magical, magical for employers, perhaps looking to streamline the grueling work of recruitment but a curse from any jobseekers, as this headline puts it, your next interview could be with a racist spot, bringing us back to that problem space we started with just a few minutes ago. So it's worth noting that job seekers are already developing ways to subvert the system by trading answers to employers test and creating fake applications as informal audits of their own. In terms of a more collective response, there's a federation of European Trade unions call you and I Global that's developed a charter of digital rights for work, others that touches on automated and a I based decisions to be included in bargaining agreements. And so this is one of many efforts to change their ecosystem to change the context in which technology is being deployed to ensure more protections and more rights for everyday people in the US There's the algorithmic accountability bill that's been presented, and it's one effort to create some more protections around this ubiquity of automated decisions, and I think we should all be calling from more public accountability when it comes to the widespread use of automated decisions. Another development that keeps me somewhat hopeful is that tech workers themselves are increasingly speaking out against the most egregious forms of corporate collusion with state sanctioned racism. And to get a taste of that, I encourage you to check out the hashtag Tech won't build it. Among other statements that they have made and walking out and petitioning their companies. Who one group said, as the people who build the technologies that Microsoft profits from, we refuse to be complicit in terms of education, which is my own ground zero. Um, it's a place where we can we can grow a more historically and socially literate approach to tech design. And this is just one, um, resource that you all can download, Um, by developed by some wonderful colleagues at the Data and Society Research Institute in New York and the goal of this interventionist threefold to develop an intellectual understanding of how structural racism operates and algorithms, social media platforms and technologies, not yet developed and emotional intelligence concerning how to resolve racially stressful situations within organizations, and a commitment to take action to reduce harms to communities of color. And so as a final way to think about why these things are so important, I want to offer a couple last provocations. The first is for us to think a new about what actually is deep learning when it comes to computation. I want to suggest that computational depth when it comes to a I systems without historical or social depth, is actually superficial learning. And so we need to have a much more interdisciplinary, integrated approach to knowledge production and to observing and understanding patterns that don't simply rely on one discipline in order to map reality. The last provocation is this. If, as I suggested at the start, inequity is woven into the very fabric of our society, it's built into the design of our. Our policies are physical infrastructures and now even our digital infrastructures. That means that each twist, coil and code is a chance for us toe. We've new patterns, practices and politics. The vastness of the problems that we're up against will be their undoing. Once we accept that we're pattern makers. So what does that look like? It looks like refusing color blindness as an anecdote to tech media discrimination rather than refusing to see difference. Let's take stock of how the training data and the models that we're creating have these built in decisions from the past that have often been discriminatory. It means actually thinking about the underside of inclusion, which can be targeting. And how do we create a more participatory rather than predatory form of inclusion? And ultimately, it also means owning our own power in these systems so that we can change the patterns of the past. If we're if we inherit a spiked bench, that doesn't mean that we need to continue using it. We can work together to design more just and equitable technologies. So with that, I look forward to our conversation. >>Thank you, Ruth. Ha. That was I expected it to be amazing, as I have been devouring your book in the last few weeks. So I knew that would be impactful. I know we will never think about park benches again. How it's art. And you laid down the gauntlet. Oh, my goodness. That tech won't build it. Well, I would say if the thoughts about team has any saying that we absolutely will build it and will continue toe educate ourselves. So you made a few points that it doesn't matter if it was intentional or not. So unintentional has as big an impact. Um, how do we address that does it just start with awareness building or how do we address that? >>Yeah, so it's important. I mean, it's important. I have good intentions. And so, by saying that intentions are not the end, all be all. It doesn't mean that we're throwing intentions out. But it is saying that there's so many things that happened in the world, happened unwittingly without someone sitting down to to make it good or bad. And so this goes on both ends. The analogy that I often use is if I'm parked outside and I see someone, you know breaking into my car, I don't run out there and say Now, do you feel Do you feel in your heart that you're a thief? Do you intend to be a thief? I don't go and grill their identity or their intention. Thio harm me, but I look at the effect of their actions, and so in terms of art, the teams that we work on, I think one of the things that we can do again is to have a range of perspectives around the table that can think ahead like chess, about how things might play out, but also once we've sort of created something and it's, you know, it's entered into, you know, the world. We need to have, ah, regular audits and check ins to see when it's going off track just because we intended to do good and set it out when it goes sideways, we need mechanisms, formal mechanisms that actually are built into the process that can get it back on track or even remove it entirely if we find And we see that with different products, right that get re called. And so we need that to be formalized rather than putting the burden on the people that are using these things toe have to raise the awareness or have to come to us like with the apple card, Right? To say this thing is not fair. Why don't we have that built into the process to begin with? >>Yeah, so a couple things. So my dad used to say the road to hell is paved with good intentions, so that's >>yes on. In fact, in the book, I say the road to hell is paved with technical fixes. So they're me and your dad are on the same page, >>and I I love your point about bringing different perspectives. And I often say this is why diversity is not just about business benefits. It's your best recipe for for identifying the early biases in the data sets in the way we build things. And yet it's such a thorny problem to address bringing new people in from tech. So in the absence of that, what do we do? Is it the outside review boards? Or do you think regulation is the best bet as you mentioned a >>few? Yeah, yeah, we need really need a combination of things. I mean, we need So on the one hand, we need something like a do no harm, um, ethos. So with that we see in medicine so that it becomes part of the fabric and the culture of organizations that that those values, the social values, have equal or more weight than the other kinds of economic imperatives. Right. So we have toe have a reckoning in house, but we can't leave it to people who are designing and have a vested interest in getting things to market to regulate themselves. We also need independent accountability. So we need a combination of this and going back just to your point about just thinking about like, the diversity on teams. One really cautionary example comes to mind from last fall, when Google's New Pixel four phone was about to come out and it had a kind of facial recognition component to it that you could open the phone and they had been following this research that shows that facial recognition systems don't work as well on darker skin individuals, right? And so they wanted Thio get a head start. They wanted to prevent that, right? So they had good intentions. They didn't want their phone toe block out darker skin, you know, users from from using it. And so what they did was they were trying to diversify their training data so that the system would work better and they hired contract workers, and they told these contract workers to engage black people, tell them to use the phone play with, you know, some kind of app, take a selfie so that their faces would populate that the training set, But they didn't. They did not tell the people what their faces were gonna be used for, so they withheld some information. They didn't tell them. It was being used for the spatial recognition system, and the contract workers went to the media and said Something's not right. Why are we being told? Withhold information? And in fact, they told them, going back to the park bench example. To give people who are homeless $5 gift cards to play with the phone and get their images in this. And so this all came to light and Google withdrew this research and this process because it was so in line with a long history of using marginalized, most vulnerable people and populations to make technologies better when those technologies are likely going toe, harm them in terms of surveillance and other things. And so I think I bring this up here to go back to our question of how the composition of teams might help address this. I think often about who is in that room making that decision about sending, creating this process of the contract workers and who the selfies and so on. Perhaps it was a racially homogeneous group where people didn't want really sensitive to how this could be experienced or seen, but maybe it was a diverse, racially diverse group and perhaps the history of harm when it comes to science and technology. Maybe they didn't have that disciplinary knowledge. And so it could also be a function of what people knew in the room, how they could do that chest in their head and think how this is gonna play out. It's not gonna play out very well. And the last thing is that maybe there was disciplinary diversity. Maybe there was racial ethnic diversity, but maybe the workplace culture made it to those people. Didn't feel like they could speak up right so you could have all the diversity in the world. But if you don't create a context in which people who have those insights feel like they can speak up and be respected and heard, then you're basically sitting on a reservoir of resource is and you're not tapping into it to ensure T to do right by your company. And so it's one of those cautionary tales I think that we can all learn from to try to create an environment where we can elicit those insights from our team and our and our coworkers, >>your point about the culture. This is really inclusion very different from just diversity and thought. Eso I like to end on a hopeful note. A prescriptive note. You have some of the most influential data and analytics leaders and experts attending virtually here. So if you imagine the way we use data and housing is a great example, mortgage lending has not been equitable for African Americans in particular. But if you imagine the right way to use data, what is the future hold when we've gotten better at this? More aware >>of this? Thank you for that question on DSO. You know, there's a few things that come to mind for me one. And I think mortgage environment is really the perfect sort of context in which to think through the the both. The problem where the solutions may lie. One of the most powerful ways I see data being used by different organizations and groups is to shine a light on the past and ongoing inequities. And so oftentimes, when people see the bias, let's say when it came to like the the hiring algorithm or the language out, they see the names associated with negative or positive words that tends toe have, ah, bigger impact because they think well, Wow, The technology is reflecting these biases. It really must be true. Never mind that people might have been raising the issues in other ways before. But I think one of the most powerful ways we can use data and technology is as a mirror onto existing forms of inequality That then can motivate us to try to address those things. The caution is that we cannot just address those once we come to grips with the problem, the solution is not simply going to be a technical solution. And so we have to understand both the promise of data and the limits of data. So when it comes to, let's say, a software program, let's say Ah, hiring algorithm that now is trained toe look for diversity as opposed to homogeneity and say I get hired through one of those algorithms in a new workplace. I can get through the door and be hired. But if nothing else about that workplace has changed and on a day to day basis I'm still experiencing microaggressions. I'm still experiencing all kinds of issues. Then that technology just gave me access to ah harmful environment, you see, and so this is the idea that we can't simply expect the technology to solve all of our problems. We have to do the hard work. And so I would encourage everyone listening to both except the promise of these tools, but really crucially, um, Thio, understand that the rial kinds of changes that we need to make are gonna be messy. They're not gonna be quick fixes. If you think about how long it took our society to create the kinds of inequities that that we now it lived with, we should expect to do our part, do the work and pass the baton. We're not going to magically like Fairy does create a wonderful algorithm that's gonna help us bypass these issues. It can expose them. But then it's up to us to actually do the hard work of changing our social relations are changing the culture of not just our workplaces but our schools. Our healthcare systems are neighborhoods so that they reflect our better values. >>Yeah. Ha. So beautifully said I think all of us are willing to do the hard work. And I like your point about using it is a mirror and thought spot. We like to say a fact driven world is a better world. It can give us that transparency. So on behalf of everyone, thank you so much for your passion for your hard work and for talking to us. >>Thank you, Cindy. Thank you so much for inviting me. Hey, I live back to you. >>Thank you, Cindy and rou ha. For this fascinating exploration of our society and technology, we're just about ready to move on to our final session of the day. So make sure to tune in for this customer case study session with executives from Sienna and Accenture on driving digital transformation with certain AI.