>> Hello everyone. Welcome to theCUBE's coverage of International Women's Day. I'm John Furrier, host of theCUBE been profiling the leaders in the technology world, women in technology from developers to the boardroom, everything in between. We have two great guests promoting in from Malaysia. Nancy Wang is the general manager, also CUBE alumni from AWS Data Protection, and founder and board chair of Advancing Women in Tech, awit.org. And of course Kate Watts who's the executive director of Advancing Women in Tech.org. So it's awit.org. Nancy, Kate, thanks for coming all the way across remotely from Malaysia. >> Of course, we're coming to you as fast as our internet bandwidth will allow us. And you know, I'm just thrilled today that you get to see a whole nother aspect of my life, right? Because typically we talk about AWS, and here we're talking about a topic near and dear to my heart. >> Well, Nancy, I love the fact that you're spending a lot of time taking the empowerment to go out and help the industries and helping with the advancement of women in tech. Kate, the executive director it's a 501C3, it's nonprofit, dedicating to accelerating the careers of women in groups in tech. Can you talk about the organization? >> Yes, I can. So Advancing Women in Tech was founded in 2017 in order to fix some of the pathway problems that we're seeing on the rise to leadership in the industry. And so we specifically focus on supporting mid-level women in technical roles, get into higher positions. We do that in a few different ways through mentorship programs through building technical skills and by connecting people to a supportive community. So you have your peer network and then a vertical sort of relationships to help you navigate the next steps in your career. So to date we've served about 40,000 individuals globally and we're just looking to expand our reach and impact and be able to better support women in the industry. >> Nancy, talk about the creation, the origination story. How'd this all come together? Obviously the momentum, everyone in the industry's been focused on this for a long time. Where did AWIT come from? Advancing Women in Technology, that's the acronym. Advancing Women in Technology.org, where'd it come from? What's the origination story? >> Yeah, so AWIT really originated from this desire that I had, to Kate's point around, well if you look around right and you know, don't take my word for it, right? Look at stats, look at news reports, or just frankly go on your LinkedIn and see how many women in underrepresented groups are in senior technical leadership roles right out in the companies whose names we all know. And so that was my case back in 2016. And so when I first got the idea and back then I was actually at Google, just another large tech company in the valley, right? It was about how do we get more role models, how we get more, for example, women into leadership roles so they can bring up the next generation, right? And so this is actually part of a longer speech that I'm about to give on Wednesday and part of the US State Department speaker program. In fact, that's why Kate and I are here in Malaysia right now is working with over 200 women entrepreneurs from all over in Southeast Asia, including Malaysia Philippines, Vietnam, Borneo, you know, so many countries where having more women entrepreneurs can help raise the GDP right, and that fits within our overall mission of getting more women into top leadership roles in tech. >> You know, I was talking about Teresa Carlson she came on the program as well for this year this next season we're going to do. And she mentioned the decision between the US progress and international. And she's saying as much as it's still bad numbers, it's worse than outside the United States and needs to get better. Can you comment on the global aspect? You brought that up. I think it's super important to highlight that it's just not one area, it's a global evolution. >> Absolutely, so let me start, and I'd love to actually have Kate talk about our current programs and all of the international groups that we're working with. So as Teresa aptly mentioned there is so much work to be done not just outside the US and North Americas where typically tech nonprofits will focus, but rather if you think about the one to end model, right? For example when I was doing the product market fit workshop for the US State Department I had women dialing in from rice fields, right? So let me just pause there for a moment. They were holding their cell phones up near towers near trees just so that they can get a few minutes of time with me to do a workshop and how to accelerate their business. So if you don't call that the desire to propel oneself or accelerate oneself, not sure what is, right. And so it's really that passion that drove me to spend the next week and a half here working with local entrepreneurs working with policy makers so we can take advantage and really leverage that passion that people have, right? To accelerate more business globally. And so that's why, you know Kate will be leading our contingent with the United Nations Women Group, right? That is focused on women's economic empowerment because that's super important, right? One aspect can be sure, getting more directors, you know vice presidents into companies like Google and Amazon. But another is also how do you encourage more women around the world to start businesses, right? To reach economic and freedom independence, right? To overcome some of the maybe social barriers to becoming a leader in their own country. >> Yes, and if I think about our own programs and our model of being very intentional about supporting the learning development and skills of women and members of underrepresented groups we focused very much on providing global access to a number of our programs. For instance, our product management certification on Coursera or engineering management our upcoming women founders accelerator. We provide both access that you can get from anywhere. And then also very intentional programming that connects people into the networks to be able to further their networks and what they've learned through the skills online, so. >> Yeah, and something Kate just told me recently is these courses that Kate's mentioning, right? She was instrumental in working with the American Council on Education and so that our learners can actually get up to six college credits for taking these courses on product management engineering management, on cloud product management. And most recently we had our first organic one of our very first organic testimonials was from a woman's tech bootcamp in Nigeria, right? So if you think about the worldwide impact of these upskilling courses where frankly in the US we might take for granted right around the world as I mentioned, there are women dialing in from rice patties from other, you know, for example, outside the, you know corporate buildings in order to access this content. >> Can you think about the idea of, oh sorry, go ahead. >> Go ahead, no, go ahead Kate. >> I was going to say, if you can't see it, you can't become it. And so we are very intentional about ensuring that we have we're spotlighting the expertise of women and we are broadcasting that everywhere so that anybody coming up can gain the skills and the networks to be able to succeed in this industry. >> We'll make sure we get those links so we can promote them. Obviously we feel the same way getting the word out. I think a couple things I'd like to ask you guys cause I think you hit a great point. One is the economic advantage the numbers prove that diverse teams perform better number one, that's clear. So good point there. But I want to get your thoughts on the entrepreneurial equation. You mentioned founders and startups and there's also different makeups in different countries. It's not like the big corporations sometimes it's smaller business in certain areas the different cultures have different business sizes and business types. How do you guys see that factoring in outside the United States, say the big tech companies? Okay, yeah. The easy lower the access to get in education than stay with them, in other countries is it the same or is it more diverse in terms of business? >> So what really actually got us started with the US State Department was around our work with women founders. And I love for Kate to actually share her experience working with AWS startups in that capacity. But frankly, you know, we looked at the content and the mentor programs that were providing women who wanted to be executives, you know, quickly realize a lot of those same skills such as finding customers, right? Scaling your product and building channels can also apply to women founders, not just executives. And so early supporters of our efforts from firms such as Moderna up in Seattle, Emergence Ventures, Decibel Ventures in, you know, the Bay Area and a few others that we're working with right now. Right, they believed in the mission and really helped us scale out what is now our existing platform and offerings for women founders. >> Those are great firms by the way. And they also are very founder friendly and also understand the global workforce. I mean, that's a whole nother dimension. Okay, what's your reaction to all that? >> Yes, we have been very intentional about taking the product expertise and the learnings of women and in our network, we first worked with AWS startups to support the development of the curriculum for the recent accelerator for women founders that was held last spring. And so we're able to support 25 founders and also brought in the expertise of about 20 or 30 women from Advancing Women in Tech to be able to be the lead instructors and mentors for that. And so we have really realized that with this network and this individual sort of focus on product expertise building strong teams, we can take that information and bring it to folks everywhere. And so there is very much the intentionality of allowing founders allowing individuals to take the lessons and bring it to their individual circumstances and the cultures in which they are operating. But the product sense is a skill that we can support the development of and we're proud to do so. >> That's awesome. Nancy, I want to ask you some never really talk about data storage and AWS cloud greatness and goodness, here's different and you also work full-time at AWS and you're the founder or the chairman of this great organization. How do you balance both and do you get, they're getting behind you on this, Amazon is getting behind you on this. >> Well, as I say it's always easier to negotiate on the way in. But jokes aside, I have to say the leadership has been tremendously supportive. If you think about, for example, my leaders Wayne Duso who's also been on the show multiple times, Bill Vaas who's also been on the show multiple times, you know they're both founders and also operators entrepreneurs at heart. So they understand that it is important, right? For all of us, it's really incumbent on all of us who are in positions to do so, to create a pathway for more people to be in leadership roles for more people to be successful entrepreneurs. So, no, I mean if you just looked at LinkedIn they're always uploading my vote so they reach to more audiences. And frankly they're rooting for us back home in the US while we're in Malaysia this week. >> That's awesome. And I think that's a good culture to have that empowerment and I think that's very healthy. What's next for you guys? What's on the agenda? Take us through the activities. I know that you got a ton of things happening. You got your event out there, which is why you're out there. There's a bunch of other activities. I think you guys call it the Advancing Women in Tech week. >> Yes, this week we are having a week of programming that you can check out at Advancing Women in Tech.org. That is spotlighting the expertise of a number of women in our space. So it is three days of programming Tuesday, Wednesday and Thursday if you are in the US so the seventh through the ninth, but available globally. We are also going to be in New York next week for the event at the UN and are looking to continue to support our mentorship programs and also our work supporting women founders throughout the year. >> All right. I have to ask you guys if you don't mind get a little market data so you can share with us here at theCUBE. What are you hearing this year that's different in the conversation space around the topics, the interests? Obviously I've seen massive amounts of global acceleration around conversations, more video, things like this more stories are scaling, a lot more LinkedIn activity. It just seems like it's a lot different this year. Can you guys share any kind of current trends you're seeing relative to the conversations and topics being discussed across the the community? >> Well, I think from a needle moving perspective, right? I think due to the efforts of wonderful organizations including the Q for spotlighting all of these awesome women, right? Trailblazing women and the nonprofits the government entities that we work with there's definitely more emphasis on creating access and creating pathways. So that's probably one thing that you're seeing is more women, more investors posting about their activities. Number two, from a global trend perspective, right? The rise of women in security. I noticed that on your agenda today, you had Lena Smart who's a good friend of mine chief information security officer at MongoDB, right? She and I are actually quite involved in helping founders especially early stage founders in the security space. And so globally from a pure technical perspective, right? There's right more increasing regulations around data privacy, data sovereignty, right? For example, India's in a few weeks about to get their first data protection regulation there locally. So all of that is giving rise to yet another wave of opportunity and we want women founders uniquely positioned to take advantage of that opportunity. >> I love it. Kate, reaction to that? I mean founders, more pathways it sounds like a neural network, it sounds like AI enabled. >> Yes, and speaking of AI, with the rise of that we are also hearing from many community members the importance of continuing to build their skills upskill learn to be able to keep up with the latest trends. There's a lot of people wondering what does this mean for my own career? And so they're turning to organizations like Advancing Women in Tech to find communities to both learn the latest information, but also build their networks so that they are able to move forward regardless of what the industry does. >> I love the work you guys are doing. It's so impressive. I think the economic angle is new it's more amplified this year. It's always kind of been there and continues to be. What do you guys hope for by next year this time what do you hope to see different from a needle moving perspective, to use your word Nancy, for next year? What's the visual output in your mind? >> I want to see real effort made towards 50-50 representation in all tech leadership roles. And I'd like to see that happen by 2050. >> Kate, anything on your end? >> I love that. I'm going to go a little bit more touchy-feely. I want everybody in our space to understand that the skills that they build and that the networks they have carry with them regardless of wherever they go. And so to be able to really lean in and learn and continue to develop the career that you want to have. So whether that be at a large organization or within your own business, that you've got the potential to move forward on that within you. >> Nancy, Kate, thank you so much for your contribution. I'll give you the final word. Put a plug in for the organization. What are you guys looking for? Any kind of PSA you want to share with the folks watching? >> Absolutely, so if you're in a position to be a mentor, join as a mentor, right? Help elevate and accelerate the next generation of women leaders. If you're an investor help us invest in more women started companies, right? Women founded startups and lastly, if you are women looking to accelerate your career, come join our community. We have resources, we have mentors and who we have investors who are willing to come in on the ground floor and help you accelerate your business. >> Great work. Thank you so much for participating in our International Women's Day 23 program and we'd look to keep this going quarterly. We'll see you next year, next time. Thanks for coming on. Appreciate it. >> Thanks so much John. >> Thank you. >> Okay, women leaders here. >> Nancy: Thanks for having us >> All over the world, coming together for a great celebration but really highlighting the accomplishments, the pathways the investment, the mentoring, everything in between. It's theCUBE. Bring as much as we can. I'm John Furrier, your host. Thanks for watching.

(upbeat music) >> Welcome back to Supercloud 2, which is an open industry collaboration between technologists, consultants, analysts, and of course, practitioners, to help shape the future of cloud. And at this event, one of the key areas we're exploring is the intersection of cloud and data, and how building value on top of hyperscale clouds and across clouds is evolving, a concept we call supercloud. And we're pleased to welcome Harvir Singh, who's the chief data architect and global head of data at Western Union. Harvir, it's good to see you again. Thanks for coming on the program. >> Thanks, David, it's always a pleasure to talk to you. >> So many things stand out from when we first met, and one of the most gripping for me was when you said to me, "When data moves, money moves." And that's the world we live in today, and really have for a long time. Money has moved as bits, and when it has to move, we want it to move quickly, securely, and in a governed manner. And the pressure to do so is only growing. So tell us how that trend is evolved over the past decade in the context of your industry generally, and Western Union, specifically. Look, I always say to people that we are probably the first ones to introduce digital currency around the world because, hey, somebody around the world needs money, we move data to make that happen. That trend has actually accelerated quite a bit. If you look at the last 10 years, and you look at all these payment companies, digital companies, credit card companies that have evolved, majority of them are working on the same principle. When data moves, money moves. When data is stale, the money goes away, right? I think that trend is continuing, and it's not just the trend is in this space, it's also continuing in other spaces, specifically around, you know, acquisition of customers, communication with customers. It's all becoming digital, and it's, at the end of the day, it's all data being moved from one place or another. At the end of the day, you're not seeing the customer, but you're looking at, you know, the data that he's consuming, and you're making actionable items on it, and be able to respond to what they need. So I think 10 years, it's really, really evolved. >> Hmm, you operate, Western Union operates in more than 200 countries, and you you have what I would call a pseudo federated organization. You're trying to standardize wherever possible on the infrastructure, and you're curating the tooling and doing the heavy lifting in the data stack, which of course lessens the burden on the developers and the line of business consumers, so my question is, in operating in 200 countries, how do you deal with all the diversity of laws and regulations across those regions? I know you're heavily involved in AWS, but AWS isn't everywhere, you still have some on-prem infrastructure. Can you paint a picture of, you know, what that looks like? >> Yeah, a few years ago , we were primarily mostly on-prem, and one of the biggest pain points has been managing that infrastructure around the world in those countries. Yes, we operate in 200 countries, but we don't have infrastructure in 200 countries, but we do have agent locations in 200 countries. United Nations says we only have like 183 are countries, but there are countries which, you know, declare themselves countries, and we are there as well because somebody wants to send money there, right? Somebody has an agent location down there as well. So that infrastructure is obviously very hard to manage and maintain. We have to comply by numerous laws, you know. And the last few years, specifically with GDPR, CCPA, data localization laws in different countries, it's been a challenge, right? And one of the things that we did a few years ago, we decided that we want to be in the business of helping our customers move money faster, security, and with complete trust in us. We don't want to be able to, we don't want to be in the business of managing infrastructure. And that's one of the reasons we started to, you know, migrate and move our journey to the cloud. AWS, obviously chosen first because of its, you know, first in the game, has more locations, and more data centers around the world where we operate. But we still have, you know, existing infrastructure, which is in some countries, which is still localized because AWS hasn't reached there, or we don't have a comparable provider there. We still manage those. And we have to comply by those laws. Our data privacy and our data localization tech stack is pretty good, I would say. We manage our data very well, we manage our customer data very well, but it comes with a lot of complexity. You know, we get a lot of requests from European Union, we get a lot of requests from Asia Pacific every pretty much on a weekly basis to explain, you know, how we are taking controls and putting measures in place to make sure that the data is secured and is in the right place. So it's a complex environment. We do have exposure to other clouds as well, like Google and Azure. And as much as we would love to be completely, you know, very, very hybrid kind of an organization, it's still at a stage where we are still very heavily focused on AWS yet, but at some point, you know, we would love to see a world which is not reliant on a single provider, but it's more a little bit more democratized, you know, as and when what I want to use, I should be able to use, and pay-per-use. And the concept started like that, but it's obviously it's now, again, there are like three big players in the market, and, you know, they're doing their own thing. Would love to see them come collaborate at some point. >> Yeah, wouldn't we all. I want to double-click on the whole multi-cloud strategy, but if I understand it correctly, and in a perfect world, everything on-premises would be in the cloud is, first of all, is that a correct statement? Is that nirvana for you or not necessarily? >> I would say it is nirvana for us, but I would also put a caveat, is it's very tricky because from a regulatory perspective, we are a regulated entity in many countries. The regulators would want to see some control if something happens with a relationship with AWS in one country, or with Google in another country, and it keeps happening, right? For example, Russia was a good example where we had to switch things off. We should be able to do that. But if let's say somewhere in Asia, this country decides that they don't want to partner with AWS, and majority of our stuff is on AWS, where do I go from there? So we have to have some level of confidence in our own infrastructure, so we do maintain some to be able to fail back into and move things it needs to be. So it's a tricky question. Yes, it's nirvana state that I don't have to manage infrastructure, but I think it's far less practical than it said. We will still own something that we call it our own where we have complete control, being a financial entity. >> And so do you try to, I'm sure you do, standardize between all the different on-premise, and in this case, the AWS cloud or maybe even other clouds. How do you do that? Do you work with, you know, different vendors at the various places of the stack to try to do that? Some of the vendors, you know, like a Snowflake is only in the cloud. You know, others, you know, whether it's whatever, analytics, or storage, or database, might be hybrid. What's your strategy with regard to creating as common an experience as possible between your on-prem and your clouds? >> You asked a question which I asked when I joined as well, right? Which question, this is one of the most important questions is how soon when I fail back, if I need to fail back? And how quickly can I, because not everything that is sitting on the cloud is comparable to on-prem or is backward compatible. And the reason I say backward compatible is, you know, there are, our on-prem cloud is obviously behind. We haven't taken enough time to kind of put it to a state where, because we started to migrate and now we have access to infrastructure on the cloud, most of the new things are being built there. But for critical application, I would say we have chronology that could be used to move back if need to be. So, you know, technologies like Couchbase, technologies like PostgreSQL, technologies like Db2, et cetera. We still have and maintain a fairly large portion of it on-prem where critical applications could potentially be serviced. We'll give you one example. We use Neo4j very heavily for our AML use cases. And that's an important one because if Neo4j on the cloud goes down, and it's happened in the past, again, even with three clusters, having all three clusters going down with a DR, we still need some accessibility of that because that's one of the biggest, you know, fraud and risk application it supports. So we do still maintain some comparable technology. Snowflake is an odd one. It's obviously there is none on-prem. But then, you know, Snowflake, I also feel it's more analytical based technology, not a transactional-based technology, at least in our ecosystem. So for me to replicate that, yes, it'll probably take time, but I can live with that. But my business will not stop because our transactional applications can potentially move over if need to. >> Yeah, and of course, you know, all these big market cap companies, so the Snowflake or Databricks, which is not public yet, but they've got big aspirations. And so, you know, we've seen things like Snowflake do a deal with Dell for on-prem object store. I think they do the same thing with Pure. And so over time, you see, Mongo, you know, extending its estate. And so over time all these things are coming together. I want to step out of this conversation for a second. I just ask you, given the current macroeconomic climate, what are the priorities? You know, obviously, people are, CIOs are tapping the breaks on spending, we've reported on that, but what is it? Is it security? Is it analytics? Is it modernization of the on-prem stack, which you were saying a little bit behind. Where are the priorities today given the economic headwinds? >> So the most important priority right now is growing the business, I would say. It's a different, I know this is more, this is not a very techy or a tech answer that, you know, you would expect, but it's growing the business. We want to acquire more customers and be able to service them as best needed. So the majority of our investment is going in the space where tech can support that initiative. During our earnings call, we released the new pillars of our organization where we will focus on, you know, omnichannel digital experience, and then one experience for customer, whether it's retail, whether it's digital. We want to open up our own experience stores, et cetera. So we are investing in technology where it's going to support those pillars. But the spend is in a way that we are obviously taking away from the things that do not support those. So it's, I would say it's flat for us. We are not like in heavily investing or aggressively increasing our tech budget, but it's more like, hey, switch this off because it doesn't make us money, but now switch this on because this is going to support what we can do with money, right? So that's kind of where we are heading towards. So it's not not driven by technology, but it's driven by business and how it supports our customers and our ability to compete in the market. >> You know, I think Harvir, that's consistent with what we heard in some other work that we've done, our ETR partner who does these types of surveys. We're hearing the same thing, is that, you know, we might not be spending on modernizing our on-prem stack. Yeah, we want to get to the cloud at some point and modernize that. But if it supports revenue, you know, we'll invest in that, and get the, you know, instant ROI. I want to ask you about, you know, this concept of supercloud, this abstracted layer of value on top of hyperscale infrastructure, and maybe on-prem. But we were talking about the integration, for instance, between Snowflake and Salesforce, where you got different data sources and you were explaining that you had great interest in being able to, you know, have a kind of, I'll say seamless, sorry, I know it's an overused word, but integration between the data sources and those two different platforms. Can you explain that and why that's attractive to you? >> Yeah, I'm a big supporter of action where the data is, right? Because the minute you start to move, things are already lost in translation. The time is lost, you can't get to it fast enough. So if, for example, for us, Snowflake, Salesforce, is our actionable platform where we action, we send marketing campaigns, we send customer communication via SMS, in app, as well as via email. Now, we would like to be able to interact with our customers pretty much on a, I would say near real time, but the concept of real time doesn't work well with me because I always feel that if you're observing something, it's not real time, it's already happened. But how soon can I react? That's the question. And given that I have to move that data all the way from our, let's say, engagement platforms like Adobe, and particles of the world into Snowflake first, and then do my modeling in some way, and be able to then put it back into Salesforce, it takes time. Yes, you know, I can do it in a few hours, but that few hours makes a lot of difference. Somebody sitting on my website, you know, couldn't find something, walked away, how soon do you think he will lose interest? Three hours, four hours, he'll probably gone, he will never come back. I think if I can react to that as fast as possible without too much data movement, I think that's a lot of good benefit that this kind of integration will bring. Yes, I can potentially take data directly into Salesforce, but I then now have two copies of data, which is, again, something that I'm not a big (indistinct) of. Let's keep the source of the data simple, clean, and a single source. I think this kind of integration will help a lot if the actions can be brought very close to where the data resides. >> Thank you for that. And so, you know, it's funny, we sometimes try to define real time as before you lose the customer, so that's kind of real time. But I want to come back to this idea of governed data sharing. You mentioned some other clouds, a little bit of Azure, a little bit of Google. In a world where, let's say you go more aggressively, and we know that for instance, if you want to use Google's AI tools, you got to use BigQuery. You know, today, anyway, they're not sort of so friendly with Snowflake, maybe different for the AWS, maybe Microsoft's going to be different as well. But in an ideal world, what I'm hearing is you want to keep the data in place. You don't want to move the data. Moving data is expensive, making copies is badness. It's expensive, and it's also, you know, changes the state, right? So you got governance issues. So this idea of supercloud is that you can leave the data in place and actually have a common experience across clouds. Let's just say, let's assume for a minute Google kind of wakes up, my words, not yours, and says, "Hey, maybe, you know what, partnering with a Snowflake or a Databricks is better for our business. It's better for the customers," how would that affect your business and the value that you can bring to your customers? >> Again, I would say that would be the nirvana state that, you know, we want to get to. Because I would say not everyone's perfect. They have great engineers and great products that they're developing, but that's where they compete as well, right? I would like to use the best of breed as much as possible. And I've been a person who has done this in the past as well. I've used, you know, tools to integrate. And the reason why this integration has worked is primarily because sometimes you do pick the best thing for that job. And Google's AI products are definitely doing really well, but, you know, that accessibility, if it's a problem, then I really can't depend on them, right? I would love to move some of that down there, but they have to make it possible for us. Azure is doing really, really good at investing, so I think they're a little bit more and more closer to getting to that state, and I know seeking our attention than Google at this point of time. But I think there will be a revelation moment because more and more people that I talk to like myself, they're also talking about the same thing. I'd like to be able to use Google's AdSense, I would like to be able to use Google's advertising platform, but you know what? I already have all this data, why do I need to move it? Can't they just go and access it? That question will keep haunting them (indistinct). >> You know, I think, obviously, Microsoft has always known, you know, understood ecosystems. I mean, AWS is nailing it, when you go to re:Invent, it's all about the ecosystem. And they think they realized they can make a lot more money, you know, together, than trying to have, and Google's got to figure that out. I think Google thinks, "All right, hey, we got to have the best tech." And that tech, they do have the great tech, and that's our competitive advantage. They got to wake up to the ecosystem and what's happening in the field and the go-to-market. I want to ask you about how you see data and cloud evolving in the future. You mentioned that things that are driving revenue are the priorities, and maybe you're already doing this today, but my question is, do you see a day when companies like yours are increasingly offering data and software services? You've been around for a long time as a company, you've got, you know, first party data, you've got proprietary knowledge, and maybe tooling that you've developed, and you're becoming more, you're already a technology company. Do you see someday pointing that at customers, or again, maybe you're doing it already, or is that not practical in your view? >> So data monetization has always been on the charts. The reason why it hasn't seen the light is regulatory pressure at this point of time. We are partnering up with certain agencies, again, you know, some pilots are happening to see the value of that and be able to offer that. But I think, you know, eventually, we'll get to a state where our, because we are trying to build accessible financial services, we will be in a state that we will be offering those to partners, which could then extended to their customers as well. So we are definitely exploring that. We are definitely exploring how to enrich our data with other data, and be able to complete a super set of data that can be used. Because frankly speaking, the data that we have is very interesting. We have trends of people migrating, we have trends of people migrating within the US, right? So if a new, let's say there's a new, like, I'll give you an example. Let's say New York City, I can tell you, at any given point of time, with my data, what is, you know, a dominant population in that area from migrant perspective. And if I see a change in that data, I can tell you where that is moving towards. I think it's going to be very interesting. We're a little bit, obviously, sometimes, you know, you're scared of sharing too much detail because there's too much data. So, but at the end of the day, I think at some point, we'll get to a state where we are confident that the data can be used for good. One simple example is, you know, pharmacies. They would love to get, you know, we've been talking to CVS and we are talking to Walgreens, and trying to figure out, if they would get access to this kind of data demographic information, what could they do be better? Because, you know, from a gene pool perspective, there are diseases and stuff that are very prevalent in one community versus the other. We could probably equip them with this information to be able to better, you know, let's say, staff their pharmacies or keep better inventory of products that could be used for the population in that area. Similarly, the likes of Walmarts and Krogers, they would like to have more, let's say, ethnic products in their aisles, right? How do you enable that? That data is primarily, I think we are the biggest source of that data. So we do take pride in it, but you know, with caution, we are obviously exploring that as well. >> My last question for you, Harvir, is I'm going to ask you to do a thought exercise. So in that vein, that whole monetization piece, imagine that now, Harvir, you are running a P&L that is going to monetize that data. And my question to you is a there's a business vector and a technology vector. So from a business standpoint, the more distribution channels you have, the better. So running on AWS cloud, partnering with Microsoft, partnering with Google, going to market with them, going to give you more revenue. Okay, so there's a motivation for multi-cloud or supercloud. That's indisputable. But from a technical standpoint, is there an advantage to running on multiple clouds or is that a disadvantage for you? >> It's, I would say it's a disadvantage because if my data is distributed, I have to combine it at some place. So the very first step that we had taken was obviously we brought in Snowflake. The reason, we wanted our analytical data and we want our historical data in the same place. So we are already there and ready to share. And we are actually participating in the data share, but in a private setting at the moment. So we are technically enabled to share, unless there is a significant, I would say, upside to moving that data to another cloud. I don't see any reason because I can enable anyone to come and get it from Snowflake. It's already enabled for us. >> Yeah, or if somehow, magically, several years down the road, some standard developed so you don't have to move the data. Maybe there's a new, Mogli is talking about a new data architecture, and, you know, that's probably years away, but, Harvir, you're an awesome guest. I love having you on, and really appreciate you participating in the program. >> I appreciate it. Thank you, and good luck (indistinct) >> Ah, thank you very much. This is Dave Vellante for John Furrier and the entire Cube community. Keep it right there for more great coverage from Supercloud 2. (uplifting music)

>> From theCUBE studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE in ETR. This is Breaking Analysis with Dave Vellante. >> Despite the more than $100 billion spent each year fighting Cyber-crime. When we do an end-of-the year look back and ask "How did we do?" The answer is invariably the same, "Worse than last year." Pre pandemic, the picture was disheartening, but since March of 2020 the situation has only worsened as cyber-criminals have become increasingly sophisticated, better funded and more brazen. SecOps pros continue to fight, but unlike conventional wars, this one has no end. Now the flip side of course, is that markets continue to value cybersecurity firms at significant premiums. Because this huge market will continue to grow by double digits for the foreseeable future. Hello and welcome to this week's Wikibon theCUBE Insights powered by ETR. In this Breaking Analysis, we look at the state of cybersecurity in 2021 and beyond. We'll update you with the latest survey data from enterprise technology research and share the fundamentals that have investors piling into the security space like never before. Let's start with the customer view. Cybersecurity remains the number one priority for CIOs and CSOs. This latest ETR survey, once again asked IT buyers to rank their top priorities for the next 12 months. Now the last three polling period dating back to last March. Cybersecurity has outranked every top spending category, including cloud, data analytics, productivity software, networking, AI, and automation or RPA. Now this shouldn't surprise anybody, but it underscores the challenges that organizations face. Not only are they in the midst of a non-optional digital transformation, but they have to also fund a cyber war that has no ceasefires, no truces, and no exit path. Now there's much more going on in cybersecurity than ransomware, but certainly that has the attention of executives. And it's becoming more and more lucrative for attackers. Here's a snapshot of some of the more well-documented attacks this decade many which have occurred in very recent months. CNA Financial, they got hit earlier this year and paid a $40 million ransom. The Ireland Health Service also got hit this year and refused to pay the ransom, but it's estimated that the cost to recover and the damage to the organization exceeded half a billion dollars. The request was for a $20 million ransom. The JBS meat company hack, they paid $11 million. CWT travel paid $5 million. The disruption from the Colonial Pipeline company, was widely reported they paid more than $4 million, as the Brenntag, the chemical company. The NBA got hit. Computer makers, Quanta and Acer also. More than 2,000 random attacks were reported to the FBI in the first seven months of 2021. Up more than 60% from 2020. Now, as I've said many times, you don't have to be a genius to be a ransomware as today. Anyone can go on the dark web, tap into ransomware as a service. Attackers, they have insidious names like darkside, evil, the cobalt, crime gang, wizard spider, the Lazarus gang, and numerous others. Criminals they have negotiation services is most typically the attackers, they'll demand a specific amount of money but they're willing to compromise in an exchange of cryptocurrency for decryption keys. And as mentioned, it's not just ransomware supply chain attacks like the solar winds hack hit organizations within the U.S government and companies like Mimecast this year. Now, while these attacks often do end up in a ransom situation. The attackers sometimes find it more lucrative to live off the land and stealth fashion and ex filtrates sensitive data that can be sold or in the case of many financial institution attacks they'll steal information from say a chief investment officer that signals an upcoming trading strategy and then the attackers will front run that trade in the stock market. Now, of course phishing, remains one of the most prominent threats. Only escalated by the work from home trend as users bring their own devices and of course home networks are less secure. So it's bad, worse than ever before. But you know, if there's a problem, entrepreneurs and investors, they're going to be there to solve it. So here's a LinkedIn post from one of the top investors in the business, Mike Speiser. He was a founding investor in Snowflake. He helped get pure storage to escape velocity and many, many other successes. This hit my LinkedIn feed the other day, his company Sutter Hill Ventures is co-leading a 1.3 Series D on an $8.3 billion valuation. They're putting in over $200 million. Now Lacework is a threat detection software company that looks at security as a data problem and they monitor exposures across clouds. So very timely. So watch that company. They're going to soar. Now the right hand chart shows venture investments in cybersecurity over the past several years. You can see it exploded in 2019 to $7.6 billion. And people thought the market was peaking at that time, if you recall. But then investments rose a little bit to $7.8 billion in 2020 right in the middle of lockdown. And then the hybrid work, the cloud, the new normal thesis kicked in big time. It's in full gear this year. You can see nearly $12 billion invested in cybersecurity in the first half of 2021 alone. So the money keeps coming in as the problem gets worse and the market gets more crowded. Now we'd like to show this slide from Optiv, it's their security taxonomy. It'll make your eyes cross. It's so packed with companies in different sectors. We'll put a link in our posts, so you can stare at this. We've used this truck before. It's pretty good. It's comprehensive and it's worth spending some time to see what that landscape looks like. But now let's reduce this down a bit and bring in some of the ETR data. This is survey data from October that shows net score or spending momentum on the vertical axis and market share or pervasiveness in the dataset on the horizontal axis. That's a measure of mentioned share if you will. Now this is just isolated on the information security sector within the ETR taxonomies. No filters in terms of the number of responses. So it's every company that ETR picks up in cybersecurity from its buyer surveys. Now companies above that red line, we consider them to have a highly elevated spending momentum for their products and services. And you can see, there are a lot of companies that are in this map first of all, and several above that magic mark. So you can see the momentum of Microsoft and Palo Alto. That's most impressive because of their size, their pervasiveness in the study, Cisco and Splunk are also quite prominent. They don't have as much spending momentum, but they're pretty respectable. And you can see the companies that have been real movers in this market that we've been reporting on for a while. Okta, CrowdStrike, Zscaler, CyberArk, SailPoint, Authzero, all companies that we've extensively covered in previous breaking analysis episodes as the up and comers. And isn't it interesting that Datadog is now showing up in the vertical axis. You see that in the left-hand side up high, they're becoming more and more competitive to Splunk in this space as an alternative and lines are blurring between observability, log analytics, security, and as we previously reported even backup and recovery. But now let's simplify this picture a bit more and filter down a little bit further. This chart shows the same X, Y view. Same data construct and framework, but we required more than a hundred responses to hit the chart. So the companies, they have to have a notable market presence in the ETR survey. It's perhaps a bit less crowded, but still very packed. Isn't it? You can see firms that are less prominent in the space like Datadog fell off. The big companies we mentioned, obviously still prominent Microsoft, Palo Alto, Cisco and Splunk and then those with real momentum, they stand out a little bit. There's somewhat smaller, but they're gaining traction in the market. As we felt they would Okta and Auth zero, which Okta acquired as we reported on earlier this year, both showing strength as our CrowdStrike, Zscaler, CyberArk, which does identity and competition with Okta and SentinelOne, which went public mid this year. The company SentinelOne uses AI to do threat detection and has been doing quite well. SalePoint and Proofpoint are right on that red elevated line and then there's a big pack in the middle. Look, this is not an easy market to track. It's virtually every company plays in security. Look, AWS says some of the most advanced security in the business but they're not in the chart specifically, but you see Microsoft is. Because much of AWS security is built into services. Amazon customers heavily rely on the Amazon ecosystem which is in the Amazon marketplace for security products. And often they associate their security spend with those partners and not necessarily Amazon. And you'll see networking companies you see right there, like Juniper and the bottom there and in the ETR data set and the players like VMware in the middle of the pack. They've been really acquisitive for example, with carbon black. And the, of course, you've got a lot of legacy players like McAfee and RSA and IBM. Look, virtually every company has a security story and that will only become more common in the coming years. Now here's another look at the ETR data it's in the raw form, but it'll give you a sense of two things; One is how the data from the previous chart is plotted. And two, it gives you a time series of the data. So the data lists the top companies in the ETR data sets sorted by the October net score in the right most column. Again, that measures spending momentum. So to make the cut here, you had to have more than a hundred mentions which is shown on the left-hand side of the chart that shared N, IE that's shared accounts in the dataset. And you can track the data from last October, July of this year and the most recent October, 2021 survey. So we, drew that red line just about at the 40% net score market coincidentally, there are 10 companies that are over that figure over that bar. We sometimes call out the four star companies. We give four stars to those companies that both are in the top 10 and spending momentum and the top in prominence are shared N in the dataset. So some of these 10 would fit into that profile by that methodology, specifically, Microsoft, Okta, CrowdStrike, and Palo Alto networks. They would be the four star companies. Now a couple of other things to point out here, DDoS attacks, they're still relevant, and they're real threat. So a company like CloudFlare which is just above that red line they play in that space. Now we've also shaded the companies in the fat middle. A lot of these companies like Cisco and Splunk for example, they're major players in the security space with very strong offerings and customer affinity. We sometimes give them two stars. So this is what makes this market so interesting. It's not like the high end discourage market where literally every vendor in the Gartner magic quadrant is up in the right, okay. And there's only five or four or five, six vendors there. This market is diverse with many, many segments and sub segments, and it's such a vital space. And there's so many holes to fill with an ever changing threat landscape as we've seen in the last two years. So this is in part which makes it such a good market for investors. There's a lot of room for growth and not just from stealing market share. That's certainly an opportunity there, but things like cloud, multi-cloud, shifting end points, the edge ,and so forth make this space really ripe for investments. And to underscore this, we put together this little chart of some of the pure play security firms to see how their stock performance has done recently. So you can see that here, you know, it's a little hard to read, but it's not hard to see that Okta, CrowdStrike, Zscaler on the left have been big movers. These charts where possible all show a cross here, starting at the lockdown last year. The only exception is SentinelOne which IPO mid this year. So that's the point March, 2020 when the whole world changed and security priorities really started to shift to accommodate the work from home. But it's quite obvious that since the pandemic, these six companies have been on a tear for the fundamental reason that hybrid work has created a shift in spending priorities for CSOs. No longer are organizations just spending on hardening a perimeter, that perimeter has been blown away. The network is flattening. Work is what you do, it's no longer a place. As such threats are on the rise and cloud, endpoint security, identity access tools there become increasingly vital and the vendors who provide them are on the rise. So it's no surprise that the players that we've listed here which play quite prominently in those markets are all on fire. So now in summary, I want to stress that while the picture is sometimes discouraging. The entire world is becoming more and more tuned in to the cyber threat. And that's a good thing. Money is pouring in. Look, technology got us into this problem and technology is a defensive weapon that will help us continue this fight. But it's going to take more than technology. And I want to share something. We get dozens and dozens of in bounds this time of the year because we do an annual predictions posts. So folks and they want to help us out. So now most of the in bounds and the predictions that we get, they're just kind of observations or frankly, non predictions that can't really be measured as like where you right, or where you're wrong. So for the most part I like predictions that are binary. For example, last December we predicted their IT spending in 2021 would rebound and grow at 4% relative to 2020. Well, it did rebound but that prediction really wasn't as accurate as I'd like. It was frankly wrong. We think it's actually the market's going to actually grow. Spending's going to grow more like 7% this year. Not to worry plenty of our predictions came true, but we'll leave that for another day. Anyway, I got an email from Dean Fisk of Fisk partners. It's a PR firm representing an individual named Lyndon Brown chief of strategy officer of Pondurance. Pondurance is a security consultancy. And the email had the standard, Hey, in case you're working on a predictions post this year end, blah, blah, blah. But instead of sharing with me, a bunch of non predictions, the notes said here's some trends in cybersecurity that might be worth thinking about. And there were a few predictions sprinkled in there, but I wanted to call it a couple of the comments from Linden Brown, whom I don't know, I never met the guy, but I really thought his trends were spot on. The first was a stat I'll share that the United Nations report cyber crime is up 600% due to the pandemic. If as if I couldn't feel worse already. His first point though was that the hybrid workplace will be the new frontier for cyber. Yes, we totally agree. There are permanent shifts taking place. And we actually predicted that last year, but he further cited that many companies went from zero to full digital transformation overnight and many are still on that journey. And his point is that hybrid work is going to require a complete overhaul of how we think about security. We think this is very true. Now the other point that stood out is that governments are going to crack down on this behavior. And we've seen this where criminals have had their critical infrastructure dismantled by governments. No doubt the U.S government has the capabilities to do so. And it is very much focused on this issue. But it's tricky as Robert Gates, who was the former defense secretary, told me a few years back in theCUBE. He said, well, we have the best offense. We also have the most to lose. So we have to be very careful, but Linden's key point was you are going to see a much more forward and aggressive public policy and new laws that give crime fighters more latitude . Again, it's tricky kind of like the Patriot act was tricky but it's coming. Now, another call-out from Linden shares his assertion that natural disasters will bring increased cyber risk. And I thought this was a really astute point because natural disasters they're on the rise. And when there's chaos, there's cash opportunities for criminals. And I'll add to this that the supply chain risk is far from over. This is going to be continuing theme this coming year and beyond. And one of the things that Linden Brown said in his note to me is essentially you can't take humans out of the equation. Automation alone can't solve the problem, but some companies operate as though they can. Just as bad human behavior, can tramp good security, Good human education and behavior is going to be a key weapon in this endless war. Now the last point is we're going to see continued escalation government crackdowns are going to bring retaliation and to Gates' point. The U.S has a lot at stake. So expect insurance premiums are going to go through the roof. That's assuming you can even get cyber insurance. And so we got to hope for the best, but for sure, we have to plan for the worst because it's coming. Deploy technology aggressively but people in process will ultimately be the other ingredients that allow us to live to battle for another day. Okay. That's a wrap for today. Remember these episodes they're all available as podcasts, wherever you listen just search "breaking analysis" podcast. Check out ETR his website at ETR.plus. We also publish a full report every week on Wikibond.com and siliconangle.com. You can get in touch. Email me @david.volante@tsiliconangle.com or you can DM me @dvellante. Comment on our LinkedIn posts. This is Dave Vellante for theCUBE insights powered by ETR. Have a great week. everybody stay safe, be well. And we'll see you next time. (techno music)

(up-tempo music) >> Welcome to today's session of the cube's presentation of the AWS startup showcase. The next big thing in AI security and life sciences. Today, we'll be speaking with Aizon, as part of our life sciences track and I'm pleased to welcome the co-founder as well as the chief science officer of Aizon: Toni Monzano, will be discussing how artificial intelligence is driving key processes in pharma manufacturing. Welcome to the show. Thanks so much for being with us today. >> Thank you Natalie to you and to your introduction. >> Yeah. Well, as you know industry 4.0 is revolutionizing manufacturing across many industries. Let's talk about how it's impacting biotech and pharma and as well as Aizon's contributions to this revolution. >> Well, actually pharmacogenetics is totally introducing a new concept of how to manage processes. So, nowadays the industry is considering that everything is particularly static, nothing changes and this is because they don't have the ability to manage the complexity and the variability around the biotech and the driving factor in processes. Nowadays, with pharma - technologies cloud, our computing, IOT, AI, we can get all those data. We can understand the data and we can interact in real time, with processes. This is how things are going on nowadays. >> Fascinating. Well, as you know COVID-19 really threw a wrench in a lot of activity in the world, our economies, and also people's way of life. How did it impact manufacturing in terms of scale up and scale out? And what are your observations from this year? >> You know, the main problem when you want to do a scale-up process is not only the equipment, it is also the knowledge that you have around your process. When you're doing a vaccine on a smaller scale in your lab, the only parameters you're controlling in your lab, they have to be escalated when you work from five liters to 2,500 liters. How to manage this different of a scale? Well, AI is helping nowadays in order to detect and to identify the most relevant factors involved in the process. The critical relationship between the variables and the final control of all the full process following a continued process verification. This is how we can help nowadays in using AI and cloud technologies in order to accelerate and to scale up vaccines like the COVID-19. >> And how do you anticipate pharma manufacturing to change in a post COVID world? >> This is a very good question. Nowadays, we have some assumptions that we are trying to overpass yet with human efforts. Nowadays, with the new situation, with the pandemic that we are living in, the next evolution that we are doing humans will take care about the good practices of the new knowledge that we have to generate. So AI will manage the repetitive tasks, all the human condition activity that we are doing, So that will be done by AI, and humans will never again do repetitive tasks in this way. They will manage complex problems and supervise AI output. >> So you're driving more efficiencies in the manufacturing process with AI. You recently presented at the United nations industrial development organization about the challenges brought by COVID-19 and how AI is helping with the equitable distribution of vaccines and therapies. What are some of the ways that companies like Aizon can now help with that kind of response? >> Very good point. Could you imagine you're a big company, a top pharma company, that you have an intellectual property of COVID-19 vaccine based on emergency and principle, and you are going to, or you would like to, expand this vaccination in order not to get vaccination, also to manufacture the vaccine. What if you try to manufacture these vaccines in South Africa or in Asia in India? So the secret is to transport, not only the raw material not only the equipment, also the knowledge. How to appreciate how to control the full process from the initial phase 'till their packaging and the vials filling. So, this is how we are contributing. AI is packaging all this knowledge in just AI models. This is the secret. >> Interesting. Well, what are the benefits for pharma manufacturers when considering the implementation of AI and cloud technologies. And how can they progress in their digital transformation by utilizing them? >> One of the benefits is that you are able to manage the variability the real complexity in the world. So, you can not create processes, in order to manufacture drugs, just considering that the raw material that you're using is never changing. You cannot consider that all the equipment works in the same way. You cannot consider that your recipe will work in the same way in Brazil than in Singapore. So the complexity and the variability is must be understood as part of the process. This is one of the benefits. The second benefit is that when you use cloud technologies, you have not a big care about computing's licenses, software updates, antivirals, scale up of cloud ware computing. Everything is done in the cloud. So well, this is two main benefits. There are more, but this is maybe the two main ones. >> Yeah. Well, that's really interesting how you highlight how this is really. There's a big shift how you handle this in different parts of the world. So, what role does compliance and regulation play here? And of course we see differences the way that's handled around the world as well. >> Well, I think that is the first time the human race in the pharma - let me say experience - that we have a very strong commitment from the 30 bodies, you know, to push forward using this kind of technologies actually, for example, the FDA, they are using cloud, to manage their own system. So why not use them in pharma? >> Yeah. Well, how does AWS and Aizon help manufacturers address these kinds of considerations? >> Well, we have a very great partner. AWS, for us, is simplifying a lot our life. So, we are a very, let me say different startup company, Aizon, because we have a lot of PhDs in the company. So we are not in the classical geeky company with guys all day parameter developing. So we have a lot of science inside the company. So this is our value. So everything that is provided by Amazon, why we have to aim to recreate again so we can rely on Sage Maker. we can rely on Cogito, we can rely on Landon we can rely on Esri to have encryption data with automatic backup. So, AWS is simplifying a lot of our life. And we can dedicate all our knowledge and all our efforts to the things that we know: pharma compliance. >> And how do you anticipate that pharma manufacturing will change further in the 2021 year? Well, we are participating not only with business cases. We also participate with the community because we are leading an international project in order to anticipate this kind of new breakthroughs. So, we are working with, let me say, initiatives in the - association we are collaborating in two different projects in order to apply AI in computer certification in order to create more robust process for the MRA vaccine. We are collaborating with the - university creating the standards for AI application in GXP. We collaborating with different initiatives with the pharma community in order to create the foundation to move forward during this year. >> And how do you see the competitive landscape? What do you think Aizon provides compared to its competitors? >> Well, good question. Probably, you can find a lot of AI services, platforms, programs softwares that can run in the industrial environment. But I think that it will be very difficult to find a GXP - a full GXP-compliant platform working on cloud with AI when AI is already qualified. I think that no one is doing that nowadays. And one of the demonstration for that is that we are also writing some scientific papers describing how to do that. So you will see that Aizon is the only company that is doing that nowadays. >> Yeah. And how do you anticipate that pharma manufacturing will change or excuse me how do you see that it is providing a defining contribution to the future of cloud-scale? >> Well, there is no limits in cloud. So as far as you accept that everything is varied and complex, you will need power computing. So the only way to manage this complexity is running a lot of power computation. So cloud is the only system, let me say, that allows that. Well, the thing is that, you know pharma will also have to be compliant with the cloud providers. And for that, we created a new layer around the platform that we say qualification as a service. We are creating this layer in order to qualify continuously any kind of cloud platform that wants to work on environment. This is how we are doing that. >> And in what areas are you looking to improve? How are you constantly trying to develop the product and bring it to the next level? >> Always we have, you know, in mind the patient. So Aizon is a patient-centric company. Everything that we do is to improve processes in order to improve at the end, to deliver the right medicine at the right time to the right patient. So this is how we are focusing all our efforts in order to bring this opportunity to everyone around the world. For this reason, for example, we want to work with this project where we are delivering value to create vaccines for COVID-19, for example, everywhere. Just packaging the knowledge using AI. This is how we envision and how we are acting. >> Yeah. Well, you mentioned the importance of science and compliance. What do you think are the key themes that are the foundation of your company? >> The first thing is that we enjoy the task that we are doing. This is the first thing. The other thing is that we are learning every day with our customers and for real topics. So we are serving to the patients. And everything that we do is enjoying science enjoying how to achieve new breakthroughs in order to improve life in the factory. We know that at the end will be delivered to the final patient. So enjoying making science and creating breakthroughs; being innovative. >> Right, and do you think that in the sense that we were lucky, in light of COVID, that we've already had these kinds of technologies moving in this direction for some time that we were somehow able to mitigate the tragedy and the disaster of this situation because of these technologies? >> Sure. So we are lucky because of this technology because we are breaking the distance, the physical distance, and we are putting together people that was so difficult to do that in all the different aspects. So, nowadays we are able to be closer to the patients to the people, to the customer, thanks to these technologies. Yes. >> So now that also we're moving out of, I mean, hopefully out of this kind of COVID reality, what's next for Aizon? Do you see more collaboration? You know, what's next for the company? >> The next for the company is to deliver AI models that are able to be encapsulated in the drug manufacturing for vaccines, for example. And that will be delivered with the full process not only materials, equipment, personnel, recipes also the AI models will go together as part of the recipe. >> Right, well, we'd love to hear more about your partnership with AWS. How did you get involved with them? And why them, and not another partner? >> Well, let me explain to you a secret. Seven years ago, we started with another top cloud provider, but we saw very soon, that this other cloud provider were not well aligned with the GXP requirements. For this reason, we met with AWS. We went together to some seminars, conferences with top pharma communities and pharma organizations. We went there to make speeches and talks. We felt that we fit very well together because AWS has a GXP white paper describing very well how to rely on AWS components. One by one. So this is for us, this is a very good credential, when we go to our customers. Do you know that when customers are acquiring and are establishing the Aizon platform in their systems, they are outbidding us. They are outbidding Aizon. Well we have to also outbid AWS because this is the normal chain in pharma supplier. Well, that means that we need this documentation. We need all this transparency between AWS and our partners. This is the main reason. >> Well, this has been a really fascinating conversation to hear how AI and cloud are revolutionizing pharma manufacturing at such a critical time for society all over the world. Really appreciate your insights, Toni Monzano: the chief science officer and co-founder of Aizon. I'm your host, Natalie Erlich, for the Cube's presentation of the AWS startup showcase. Thanks very much for watching. (soft upbeat music)

>>Please >>welcome back to HPD discovered 2021. The cubes virtual coverage, continuous coverage of H P. S H. P. S. Annual customer event. My name is Dave Volonte and we're going to dive into the intersection of high performance computing data and AI with DR Eng limb go who is the senior vice president and CTO for AI Hewlett Packard enterprise Doctor go great to see you again. Welcome back to the cube. >>Hello Dave, Great to talk to you again. >>You might remember last year we talked a lot about swarm intelligence and how AI is evolving. Of course you hosted the day two keynotes here at discover and you talked about thriving in the age of insights and how to craft a data centric strategy. And you addressed you know some of the biggest problems I think organizations face with data that's You got a data is plentiful but insights they're harder to come by. And you really dug into some great examples in retail banking and medicine and health care and media. But stepping back a little bit with zoom out on discovered 21, what do you make of the events so far? And some of your big takeaways? >>Mm Well you started with the insightful question, Right? Yeah, data is everywhere then. But we like the insight. Right? That's also part of the reason why that's the main reason why you know Antonio on day one focused and talked about that. The fact that we are now in the age of insight, right? Uh and uh and and how to thrive thrive in that in this new age. What I then did on the day to kino following Antonio is to talk about the challenges that we need to overcome in order in order to thrive in this new asia. >>So maybe we could talk a little bit about some of the things that you took away in terms I'm specifically interested in some of the barriers to achieving insights when customers are drowning in data. What do you hear from customers? What we take away from some of the ones you talked about today? >>Oh, very pertinent question. Dave You know the two challenges I spoke about right now that we need to overcome in order to thrive in this new age. The first one is is the current challenge and that current challenge is uh you know stated is no barriers to insight. You know when we are awash with data. So that's a statement. Right? How to overcome those barriers. What are the barriers of these two insight when we are awash in data? Um I in the data keynote I spoke about three main things. Three main areas that received from customers. The first one, the first barrier is in many with many of our customers. A data is siloed. All right. You know, like in a big corporation you've got data siloed by sales, finance, engineering, manufacturing, and so on, uh supply chain and so on. And uh there's a major effort ongoing in many corporations to build a federation layer above all those silos so that when you build applications above they can be more intelligent. They can have access to all the different silos of data to get better intelligence and more intelligent applications built. So that was the that was the first barrier. We spoke about barriers to incite when we are washed with data. The second barrier is uh that we see amongst our customers is that uh data is raw and dispersed when they are stored and and uh and you know, it's tough to get tough to to get value out of them. Right? And I in that case I I used the example of uh you know the May 6 2010 event where the stock market dropped a trillion dollars in in tens of minutes. You know, we we all know those who are financially attuned with know about this uh incident, But this is not the only incident. There are many of them out there and for for that particular May six event, uh you know, it took a long time to get insight months. Yeah, before we for months we had no insight as to what happened, why it happened, right. Um, and and there were many other incidences like this and the regulators were looking for that one rule that could, that could mitigate many of these incidences. Um, one of our customers decided to take the hard road to go with the tough data right? Because data is rolling dispersed. So they went into all the different feeds of financial transaction information, took the took the tough took the tough road and analyze that data took a long time to assemble. And they discovered that there was quote stuffing right? That uh people were sending a lot of traits in and then cancelling them almost immediately. You have to manipulate the market. Um And why why why didn't we see it immediately? Well, the reason is the process reports that everybody sees the rule in there that says all trades, less than 100 shares don't need to report in there. And so what people did was sending a lot of less than 103 100 100 shares trades uh to fly under the radar to do this manipulation. So here is here the second barrier right? Data could be raw and dispersed. Um Sometimes you just have to take the hard road and um and to get insight And this is 1 1 great example. And then the last barrier is uh is has to do with sometimes when you start a project to to get insight to get uh to get answers and insight. You you realize that all the datas around you but you don't you don't seem to find the right ones to get what you need. You don't you don't seem to get the right ones. Yeah. Um here we have three quick examples of customers. 111 was it was a great example right? Where uh they were trying to build a language translator, a machine language translator between two languages. Right? By not do that. They need to get hundreds of millions of word pairs, you know, of one language compared uh with a corresponding other hundreds of millions of them. They say, well I'm going to get all these word pairs. Someone creative thought of a willing source. And you thought it was the United Nations, you see. So sometimes you think you don't have the right data with you, but there might be another source. And the willing one that could give you that data Right? The 2nd 1 has to do with uh there was uh the uh sometimes you you may just have to generate that data, interesting one. We had an autonomous car customer that collects all these data from their cars, right? Massive amounts of data, loss of sensors, collect loss of data. And uh, you know, but sometimes they don't have the data they need even after collection. For example, they may have collected the data with a car uh in in um in fine weather and collected the car driving on this highway in rain and also in stone, but never had the opportunity to collect the car in hill because that's a rare occurrence. So instead of waiting for a time where the car can dr inhale, they build a simulation you by having the car collector in snow and simulated him. So, these are some of the examples where we have customers working to overcome barriers, right? You have barriers that is associated the fact that data silo the Federated it various associated with data. That's tough to get that. They just took the hard road, right? And, and sometimes, thirdly, you just have to be creative to get the right data. You need, >>wow, I I'll tell you, I have about 100 questions based on what you just said. Uh, there's a great example, the flash crash. In fact, Michael Lewis wrote about this in his book The Flash Boys and essentially right. It was high frequency traders trying to front run the market and sending in small block trades trying to get on the front end it. So that's and they, and they chalked it up to a glitch like you said, for months. Nobody really knew what it was. So technology got us into this problem. I guess my question is, can technology help us get out of the problem? And that maybe is where AI fits in. >>Yes, yes. Uh, in fact, a lot of analytics, we went in to go back to the raw data that is highly dispersed from different sources, right, assemble them to see if you can find a material trend, right? You can see lots of trends, right? Like, uh, you know, we if if humans look at things right, we tend to see patterns in clouds, right? So sometimes you need to apply statistical analysis, um math to to be sure that what the model is seeing is is real. Right? And and that required work. That's one area. The second area is uh you know, when um uh there are times when you you just need to to go through that uh that tough approach to to find the answer. Now, the issue comes to mind now is is that humans put in the rules to decide what goes into a report that everybody sees. And in this case uh before the change in the rules. Right? But by the way, after the discovery, uh authorities change the rules and all all shares, all traits of different any sizes. It has to be reported. No. Yeah. Right. But the rule was applied uh you know, to say earlier that shares under 100 trades under 100 shares need not be reported. So sometimes you just have to understand that reports were decided by humans and and under for understandable reasons. I mean they probably didn't want that for various reasons not to put everything in there so that people could still read it uh in a reasonable amount of time. But uh we need to understand that rules were being put in by humans for the reports we read. And as such there are times you just need to go back to the raw data. >>I want to ask, >>it's gonna be tough. >>Yeah. So I want to ask a question about AI is obviously it's in your title and it's something you know a lot about but and I want to make a statement, you tell me if it's on point or off point. So it seems that most of the Ai going on in the enterprise is modeling data science applied to troves of data but but there's also a lot of ai going on in consumer whether it's you know, fingerprint technology or facial recognition or natural language processing will a two part question will the consumer market as has so often in the enterprise sort of inform us uh the first part and then will there be a shift from sort of modeling if you will to more you mentioned autonomous vehicles more ai influencing in real time. Especially with the edge you can help us understand that better. >>Yeah, it's a great question. Right. Uh there are three stages to just simplify, I mean, you know, it's probably more sophisticated than that but let's simplify three stages. All right. To to building an Ai system that ultimately can predict, make a prediction right or to to assist you in decision making, have an outcome. So you start with the data massive amounts of data that you have to decide what to feed the machine with. So you feed the machine with this massive chunk of data and the machine uh starts to evolve a model based on all the data is seeing. It starts to evolve right to the point that using a test set of data that you have separately kept a site that you know the answer for. Then you test the model uh you know after you trained it with all that data to see whether it's prediction accuracy is high enough and once you are satisfied with it, you you then deploy the model to make the decision and that's the influence. Right? So a lot of times depend on what what we are focusing on. We we um in data science are we working hard on assembling the right data to feed the machine with, That's the data preparation organization work. And then after which you build your models, you have to pick the right models for the decisions and prediction you wanted to make. You pick the right models and then you start feeding the data with it. Sometimes you you pick one model and the prediction isn't that robust, it is good but then it is not consistent right now. What you do is uh you try another model so sometimes it's just keep trying different models until you get the right kind. Yeah, that gives you a good robust decision making and prediction after which It is tested well Q eight. You would then take that model and deploy it at the edge. Yeah. And then at the edges is essentially just looking at new data, applying it to the model that you have trained and then that model will give you a prediction decision. Right? So uh it is these three stages. Yeah, but more and more uh your question reminds me that more and more people are thinking as the edge become more and more powerful. Can you also do learning at the edge? Right. That's the reason why we spoke about swarm learning the last time, learning at the edge as a swamp, right? Because maybe individually they may not have enough power to do so. But as a swamp they made >>is that learning from the edge? You're learning at the edge? In other words? >>Yes. >>Yeah, I understand the question. Yeah. >>That's a great question. That's a great question. Right? So uh the quick answer is learning at the edge, right? Uh and and also from the edge, but the main goal, right? The goal is to learn at the edge so that you don't have to move the data that the edge sees first back to the cloud or the core to do the learning because that would be the reason. One of the main reasons why you want to learn at the edge, right? Uh So so that you don't need to have to send all that data back and assemble it back from all the different Edge devices, assemble it back to the cloud side to to do the learning right. With someone you can learn it and keep the data at the edge and learn at that point. >>And then maybe only selectively send the autonomous vehicle example you gave us great because maybe there, you know, there may be only persisting, they're not persisting data that is inclement weather or when a deer runs across the front. And then maybe they they do that and then they send that smaller data set back and maybe that's where it's modelling done. But the rest can be done at the edges. It's a new world that's coming down. Let me ask you a question, is there a limit to what data should be collected and how it should be collected? >>That's a great question again, you know uh wow today, full of these uh insightful questions that actually touches on the second challenge. Right? How do we uh in order to thrive in this new age of insight? The second challenge is are you know the is our future challenge, right? What do we do for our future? And and in there is uh the statement we make is we have to focus on collecting data strategically for the future of our enterprise. And within that I talk about what to collect right? When to organize it when you collect and where will your data be, you know, going forward that you are collecting from? So what, when and where for the what data for the what data to collect? That? That was the question you ask. Um it's it's a question that different industries have to ask themselves because it will vary, right? Um Let me give you the, you use the autonomous car example, let me use that. And We have this customer collecting massive amounts of data. You know, we're talking about 10 petabytes a day from the fleet of their cars. And these are not production autonomous cars, right? These are training autonomous cars, collecting data so they can train and eventually deploy commercial cars. Right? Um, so this data collection cars they collect as a fleet of them collect 10 petabytes a day and when it came to us uh building a storage system yeah, to store all of that data, they realized they don't want to afford to store all of it. Now here comes the dilemma, right? Should what should I after I spent so much effort building all these cars and sensors and collecting data, I've now decide what to delete. That's a dilemma right now in working with them on this process of trimming down what they collected. You know, I'm constantly reminded of the sixties and seventies, right? To remind myself 16 seventies we call a large part of our D. N. A junk DNA. Today we realize that a large part of that what we call john has function as valuable function. They are not jeans, but they regulate the function of jeans, you know? So, so what's jumped in the yesterday could be valuable today or what's junk today could be valuable tomorrow. Right? So, so there's this tension going on right between you decided not wanting to afford to store everything that you can get your hands on. But on the other hand, you you know, you worry you you you ignore the wrong ones, right? You can see this tension in our customers, right? And it depends on industry here. Right? In health care, they say I have no choice. I I want it. All right. One very insightful point brought up by one health care provider that really touched me was, you know, we are not we don't only care. Of course we care a lot. We care a lot about the people we are caring for, right? But you also care for the people were not caring for. How do we find them? Mhm. Right. And that therefore they did not just need to collect data that is uh that they have with from their patients. They also need to reach out right to outside data so that they can figure out who they are not caring for. Right? So they want it all. So I tell us them. So what do you do with funding if you want it all? They say they have no choice but to figure out a way to fund it and perhaps monetization of what they have now is the way to come around and find out. Of course they also come back to us rightfully that, you know, we have to then work out a way to help them build that system, you know, so that health care, right? And and if you go to other industries like banking, they say they can't afford to keep them on, but they are regulated. Seems like healthcare, they are regulated as to uh privacy and such. Like so many examples different industries having different needs but different approaches to how what they collect. But there is this constant tension between um you perhaps deciding not wanting to fund all of that uh all that you can stall right on the other hand, you know, if you if you kind of don't want to afford it and decide not to store some uh if he does some become highly valuable in the future right? Don't worry. >>We can make some assumptions about the future, can't we? I mean, we know there's gonna be a lot more data than than we've ever seen before. We know that we know. Well notwithstanding supply constraints on things like nand, we know the prices of storage is gonna continue to decline. We also know and not a lot of people are really talking about this but the processing power but he says moore's law is dead. Okay, it's waning. But the processing power when you combine the Cpus and N. P. U. S. And Gpus and accelerators and and so forth actually is is increasing. And so when you think about these use cases at the edge, you're going to have much more processing power, you're going to have cheaper storage and it's going to be less expensive processing. And so as an ai practitioner, what can you do with that? >>So the amount of data that's gonna come in, it's gonna we exceed right? Our drop in storage costs are increasing computer power. Right? So what's the answer? Right? So so the the answer must be knowing that we don't and and even the drop in price and increase in bandwidth, it will overwhelm the increased five G will overwhelm five G. Right? Given amount 55 billion of them collecting. Right? So the answer must be that there might need to be a balance between you needing to bring all that data from the 55 billion devices data back to a central as a bunch of central. Cause because you may not be able to afford to do that firstly band with even with five G. M and and SD when you'll still be too expensive given the number of devices out there, Were you given storage costs dropping? You'll still be too expensive to try and store them all. So the answer must be to start at least to mitigate the problem to some leave both a lot of the data out there. Right? And only send back the pertinent ones as you said before. But then if you did that, then how are we gonna do machine learning at the core and the cloud side? If you don't have all the data, you want rich data to train with. Right? Some sometimes you wanna mix of the uh positive type data and the negative type data so you can train the machine in a more balanced way. So the answer must be eventually right. As we move forward with these huge number of devices out of the edge to do machine learning at the edge today, we don't have enough power. Right? The edge typically is characterized by a lower uh energy capability and therefore lower compute power. But soon, you know, even with lower energy they can do more with compute power, improving in energy efficiency, Right? Uh So learning at the edge today we do influence at the edge. So we data model deploy and you do in France at the age, that's what we do today. But more and more I believe given a massive amount of data at the edge, you, you have to have to start doing machine learning at the edge and, and if when you don't have enough power then you aggregate multiple devices, compute power into a swamp and learn as a swan. >>Oh, interesting. So now of course, if, if I were sitting and fly, fly on the wall in hp board meeting, I said okay. HB is as a leading provider of compute how do you take advantage of that? I mean we're going, we're, I know its future, but you must be thinking about that and participating in those markets. I know today you are, you have, you know, edge line and other products. But there's, it seems to me that it's, it's not the general purpose that we've known in the past. It's a new type of specialized computing. How are you thinking about participating in that >>opportunity for the customers? The world will have to have a balance right? Where today the default? Well, the more common mode is to collect the data from the edge and train at uh at some centralized location or a number of centralized location um going forward. Given the proliferation of the edge devices, we'll need a balance. We need both. We need capability at the cloud side. Right? And it has to be hybrid and then we need capability on the edge side. Yeah. That they want to build systems that that on one hand, uh is uh edge adapted, right? Meaning the environmentally adapted because the edge different. They are on a lot of times. On the outside. Uh They need to be packaging adapted and also power adapted, right? Because typically many of these devices are battery power. Right? Um, so you have to build systems that adapt to it. But at the same time they must not be custom. That's my belief. They must be using standard processes and standard operating system so that they can run a rich set of applications. So yes. Um that's that's also the insightful for that Antonio announced in 2018 Uh the next four years from 2018, right $4 billion dollars invested to strengthen our edge portfolio. Edge product lines, Right. Edge solutions. >>I can doctor go, I could go on for hours with you. You're you're just such a great guest. Let's close. What are you most excited about in the future? Of of of it. Certainly H. P. E. But the industry in general. >>Yeah. I think the excitement is uh the customers, right? The diversity of customers and and the diversity in a way they have approached their different problems with data strategy. So the excitement is around data strategy, right? Just like you know uh you know, the the statement made was was so was profound, right? Um And Antonio said we are in the age of insight powered by data. That's the first line, right. Uh The line that comes after that is as such were becoming more and more data centric with data, the currency. Now the next step is even more profound. That is um You know, we are going as far as saying that you know um data should not be treated as cost anymore. No. Right. But instead as an investment in a new asset class called data with value on our balance sheet, this is a this is a step change right? In thinking that is going to change the way we look at data, the way we value it. So that's a statement that this is the exciting thing because because for for me, a city of Ai right uh machine is only as intelligent as the data you feed it with data is a source of the machine learning to be intelligent. So, so that's that's why when when people start to value data, right? And and and say that it is an investment when we collect it, it is very positive for AI because an AI system gets intelligent, get more intelligence because it has a huge amounts of data and the diversity of data. So it would be great if the community values values data. Well, >>you certainly see it in the valuations of many companies these days. Um and I think increasingly you see it on the income statement, you know, data products and people monetizing data services and maybe eventually you'll see it in the in the balance. You know, Doug Laney, when he was a gardener group wrote a book about this and a lot of people are thinking about it. That's a big change, isn't it? Dr >>yeah. Question is is the process and methods evaluation right. But I believe we'll get there, we need to get started and then we'll get there. Believe >>doctor goes on >>pleasure. And yeah. And then the Yeah, I will well benefit greatly from it. >>Oh yeah, no doubt people will better understand how to align you know, some of these technology investments, Doctor goes great to see you again. Thanks so much for coming back in the cube. It's been a real pleasure. >>Yes. A system. It's only as smart as the data you feed it with. >>Excellent. We'll leave it there, thank you for spending some time with us and keep it right there for more great interviews from HP discover 21 this is Dave Volonte for the cube. The leader in enterprise tech coverage right back

>>Welcome back to HPD discovered 2021 the cubes virtual coverage, continuous coverage of H P. S H. P. S. Annual customer event. My name is Dave Volonte and we're going to dive into the intersection of high performance computing data and AI with DR Eng limb go who is the senior vice president and CTO for AI at Hewlett Packard enterprise Doctor go great to see you again. Welcome back to the cube. >>Hello Dave, Great to talk to you again. >>You might remember last year we talked a lot about swarm intelligence and how AI is evolving. Of course you hosted the day two keynotes here at discover you talked about thriving in the age of insights and how to craft a data centric strategy and you addressed you know some of the biggest problems I think organizations face with data that's You got a data is plentiful but insights they're harder to come by. And you really dug into some great examples in retail banking and medicine and health care and media. But stepping back a little bit with zoom out on discovered 21, what do you make of the events so far? And some of your big takeaways? >>Mm Well you started with the insightful question, right? Yeah. Data is everywhere then. But we like the insight. Right? That's also part of the reason why that's the main reason why you know Antonio on day one focused and talked about that. The fact that we are now in the age of insight. Right? Uh and and uh and and how to thrive thrive in that in this new age. What I then did on the day to kino following Antonio is to talk about the challenges that we need to overcome in order in order to thrive in this new age. >>So maybe we could talk a little bit about some of the things that you took away in terms I'm specifically interested in some of the barriers to achieving insights when you know customers are drowning in data. What do you hear from customers? What we take away from some of the ones you talked about today? >>Oh, very pertinent question. Dave you know the two challenges I spoke about right now that we need to overcome in order to thrive in this new age. The first one is is the current challenge and that current challenge is uh you know stated is you know, barriers to insight, you know when we are awash with data. So that's a statement right? How to overcome those barriers. What are the barriers of these two insight when we are awash in data? Um I in the data keynote I spoke about three main things. Three main areas that received from customers. The first one, the first barrier is in many with many of our customers. A data is siloed. All right. You know, like in a big corporation you've got data siloed by sales, finance, engineering, manufacturing, and so on, uh supply chain and so on. And uh, there's a major effort ongoing in many corporations to build a federation layer above all those silos so that when you build applications above they can be more intelligent. They can have access to all the different silos of data to get better intelligence and more intelligent applications built. So that was the that was the first barrier we spoke about barriers to incite when we are washed with data. The second barrier is uh, that we see amongst our customers is that uh data is raw and dispersed when they are stored and and uh and you know, it's tough to get tough to to get value out of them. Right? And I in that case I I used the example of uh you know the May 6 2010 event where the stock market dropped a trillion dollars in in tens of ministerial. We we all know those who are financially attuned with know about this uh incident But this is not the only incident. There are many of them out there and for for that particular May six event uh you know, it took a long time to get insight months. Yeah before we for months we had no insight as to what happened, why it happened, right. Um and and there were many other incidences like this. And the regulators were looking for that one rule that could, that could mitigate many of these incidences. Um one of our customers decided to take the hard road go with the tough data right? Because data is rolling dispersed. So they went into all the different feeds of financial transaction information. Uh took the took the tough uh took the tough road and analyze that data took a long time to assemble and they discovered that there was court stuffing right? That uh people were sending a lot of traits in and then cancelling them almost immediately. You have to manipulate the market. Um And why why why didn't we see it immediately? Well the reason is the process reports that everybody sees uh rule in there that says all trades. Less than 100 shares don't need to report in there. And so what people did was sending a lot of less than 103 100 100 shares trades uh to fly under the radar to do this manipulation. So here is here the second barrier right? Data could be raw and dispersed. Um Sometimes you just have to take the hard road and um and to get insight And this is 1 1 great example. And then the last barrier is uh is has to do with sometimes when you start a project to to get insight to get uh to get answers and insight. You you realize that all the datas around you but you don't you don't seem to find the right ones To get what you need. You don't you don't seem to get the right ones. Yeah. Um here we have three quick examples of customers. 111 was it was a great example right? Where uh they were trying to build a language translator, a machine language translator between two languages. Right? But not do that. They need to get hundreds of millions of word pairs, you know, of one language compared uh with the corresponding other hundreds of millions of them. They say we are going to get all these word pairs. Someone creative thought of a willing source and a huge, so it was a United Nations you see. So sometimes you think you don't have the right data with you, but there might be another source and a willing one that could give you that data right. The second one has to do with uh there was uh the uh sometimes you you may just have to generate that data, interesting one. We had an autonomous car customer that collects all these data from their cars, right, massive amounts of data, loss of senses, collect loss of data. And uh you know, but sometimes they don't have the data they need even after collection. For example, they may have collected the data with a car uh in in um in fine weather and collected the car driving on this highway in rain and also in stone, but never had the opportunity to collect the car in hale because that's a rare occurrence. So instead of waiting for a time where the car can dr inhale, they build a simulation you by having the car collector in snow and simulated him. So these are some of the examples where we have customers working to overcome barriers, right? You have barriers that is associated the fact that data is silo Federated, it various associated with data. That's tough to get that. They just took the hard road, right? And sometimes, thirdly, you just have to be creative to get the right data you need, >>wow, I tell you, I have about 100 questions based on what you just said. Uh, there's a great example, the flash crash. In fact, Michael Lewis wrote about this in his book, The Flash Boys and essentially right. It was high frequency traders trying to front run the market and sending in small block trades trying to get on the front end it. So that's and they, and they chalked it up to a glitch like you said, for months, nobody really knew what it was. So technology got us into this problem. I guess my question is, can technology help us get out of the problem? And that maybe is where AI fits in. >>Yes, yes. Uh, in fact, a lot of analytics, we went in, uh, to go back to the raw data that is highly dispersed from different sources, right, assemble them to see if you can find a material trend, right? You can see lots of trends right? Like, uh, you know, we, if if humans look at things right, we tend to see patterns in clouds, right? So sometimes you need to apply statistical analysis, um math to be sure that what the model is seeing is is real. Right? And and that required work. That's one area. The second area is uh you know, when um uh there are times when you you just need to to go through that uh that tough approach to to find the answer. Now, the issue comes to mind now is is that humans put in the rules to decide what goes into a report that everybody sees in this case uh before the change in the rules. Right? But by the way, after the discovery, the authorities change the rules and all all shares, all traits of different any sizes. It has to be reported. No. Yeah. Right. But the rule was applied uh you know, to say earlier that shares under 100 trades under 100 shares need not be reported. So sometimes you just have to understand that reports were decided by humans and and under for understandable reasons. I mean they probably didn't want that for various reasons not to put everything in there so that people could still read it uh in a reasonable amount of time. But uh we need to understand that rules were being put in by humans for the reports we read. And as such, there are times you just need to go back to the raw data. >>I want to ask, >>albeit that it's gonna be tough. >>Yeah. So I want to ask a question about AI is obviously it's in your title and it's something you know a lot about but and I want to make a statement, you tell me if it's on point or off point. So it seems that most of the Ai going on in the enterprise is modeling data science applied to troves of data >>but >>but there's also a lot of ai going on in consumer whether it's you know, fingerprint technology or facial recognition or natural language processing. Will a two part question will the consumer market has so often in the enterprise sort of inform us uh the first part and then will there be a shift from sort of modeling if you will to more you mentioned autonomous vehicles more ai influencing in real time. Especially with the edge. She can help us understand that better. >>Yeah, it's a great question. Right. Uh there are three stages to just simplify, I mean, you know, it's probably more sophisticated than that but let's simplify three stages. All right. To to building an Ai system that ultimately can predict, make a prediction right or to to assist you in decision making, have an outcome. So you start with the data massive amounts data that you have to decide what to feed the machine with. So you feed the machine with this massive chunk of data and the machine uh starts to evolve a model based on all the data is seeing. It starts to evolve right to the point that using a test set of data that you have separately campus site that you know the answer for. Then you test the model uh you know after you trained it with all that data to see whether it's prediction accuracy is high enough and once you are satisfied with it, you you then deploy the model to make the decision and that's the influence. Right? So a lot of times depend on what what we are focusing on. We we um in data science are we working hard on assembling the right data to feed the machine with, That's the data preparation organization work. And then after which you build your models, you have to pick the right models for the decisions and prediction you wanted to make. You pick the right models and then you start feeding the data with it. Sometimes you you pick one model and the prediction isn't that robust, it is good but then it is not consistent right now what you do is uh you try another model so sometimes it's just keep trying different models until you get the right kind. Yeah, that gives you a good robust decision making and prediction after which It is tested well Q eight. You would then take that model and deploy it at the edge. Yeah. And then at the edges is essentially just looking at new data, applying it to the model, you're you're trained and then that model will give you a prediction decision. Right? So uh it is these three stages. Yeah, but more and more uh you know, your question reminds me that more and more people are thinking as the edge become more and more powerful. Can you also do learning at the edge? Right. That's the reason why we spoke about swarm learning the last time, learning at the edge as a swamp, right? Because maybe individually they may not have enough power to do so. But as a swampy me, >>is that learning from the edge or learning at the edge? In other words? Yes. Yeah. Question Yeah. >>That's a great question. That's a great question. Right? So uh the quick answer is learning at the edge, right? Uh and also from the edge, but the main goal, right? The goal is to learn at the edge so that you don't have to move the data that the Edge sees first back to the cloud or the core to do the learning because that would be the reason. One of the main reasons why you want to learn at the edge, right? Uh So so that you don't need to have to send all that data back and assemble it back from all the different edge devices, assemble it back to the cloud side to to do the learning right? With swampland. You can learn it and keep the data at the edge and learn at that point. >>And then maybe only selectively send the autonomous vehicle example you gave us. Great because maybe there, you know, there may be only persisting, they're not persisting data that is inclement weather or when a deer runs across the front and then maybe they they do that and then they send that smaller data set back and maybe that's where it's modelling done. But the rest can be done at the edges. It's a new world that's coming down. Let me ask you a question, is there a limit to what data should be collected and how it should be collected? >>That's a great question again. You know uh wow today, full of these uh insightful questions that actually touches on the second challenge. Right? How do we uh in order to thrive in this new age of inside? The second challenge is are you know the is our future challenge, right? What do we do for our future? And and in there is uh the statement we make is we have to focus on collecting data strategically for the future of our enterprise. And within that I talk about what to collect right? When to organize it when you collect and then where will your data be, you know going forward that you are collecting from? So what, when and where for the what data for the what data to collect? That? That was the question you ask. Um it's it's a question that different industries have to ask themselves because it will vary, right? Um let me give you the you use the autonomous car example, let me use that. And you have this customer collecting massive amounts of data. You know, we're talking about 10 petabytes a day from the fleet of their cars. And these are not production autonomous cars, right? These are training autonomous cars collecting data so they can train and eventually deploy commercial cars, right? Um so this data collection cars they collect as a fleet of them collect temporal bikes a day. And when it came to us building a storage system to store all of that data, they realized they don't want to afford to store all of it. Now, here comes the dilemma, right? What should I after I spent so much effort building all these cars and sensors and collecting data, I've now decide what to delete. That's a dilemma right now in working with them on this process of trimming down what they collected. You know, I'm constantly reminded of the sixties and seventies, right? To remind myself 60 and seventies, we call a large part of our D. N. A junk DNA. Today. We realize that a large part of that what we call john has function as valuable function. They are not jeans, but they regulate the function of jeans, you know, So, so what's jump in the yesterday could be valuable today or what's junk today could be valuable tomorrow, Right? So, so there's this tension going on right between you decided not wanting to afford to store everything that you can get your hands on. But on the other hand, you you know, you worry you you you ignore the wrong ones, right? You can see this tension in our customers, right? And it depends on industry here, right? In health care, they say I have no choice. I I want it. All right. One very insightful point brought up by one health care provider that really touched me was, you know, we are not we don't only care. Of course we care a lot. We care a lot about the people we are caring for, right? But you also care for the people were not caring for. How do we find them? Mhm. Right. And that therefore, they did not just need to collect data. That is that they have with from their patients. They also need to reach out right to outside data so that they can figure out who they are not caring for, right? So they want it all. So I tell us them, so what do you do with funding if you want it all? They say they have no choice but to figure out a way to fund it and perhaps monetization of what they have now is the way to come around and find that. Of course they also come back to us rightfully that you know, we have to then work out a way to help them build that system, you know? So that's health care, right? And and if you go to other industries like banking, they say they can't afford to keep them off, but they are regulated, seems like healthcare, they are regulated as to uh privacy and such. Like so many examples different industries having different needs, but different approaches to how what they collect. But there is this constant tension between um you perhaps deciding not wanting to fund all of that uh all that you can store, right? But on the other hand, you know, if you if you kind of don't want to afford it and decide not to store some uh if he does some become highly valuable in the future, right? Yeah. >>We can make some assumptions about the future, can't we? I mean, we know there's gonna be a lot more data than than we've ever seen before. We know that we know well notwithstanding supply constraints on things like nand. We know the prices of storage is going to continue to decline. We also know, and not a lot of people are really talking about this but the processing power but he says moore's law is dead okay. It's waning. But the processing power when you combine the Cpus and NP US and GPUS and accelerators and and so forth actually is is increasing. And so when you think about these use cases at the edge, you're going to have much more processing power, you're gonna have cheaper storage and it's going to be less expensive processing And so as an ai practitioner, what can you do with that? >>Yeah, it's highly again, another insightful questions that we touched on our keynote and that that goes up to the why I do the where? Right, When will your data be? Right. We have one estimate that says that by next year there will be 55 billion connected devices out there. Right. 55 billion. Right. What's the population of the world? Of the other? Of 10 billion? But this thing is 55 billion. Right? Uh and many of them, most of them can collect data. So what do you what do you do? Right. Um So the amount of data that's gonna come in, it's gonna weigh exceed right? Our drop in storage costs are increasing computer power. Right? So what's the answer? Right. So, so the the answer must be knowing that we don't and and even the drop in price and increase in bandwidth, it will overwhelm the increased five G will overwhelm five G. Right? Given amount 55 billion of them collecting. Right? So, the answer must be that there might need to be a balance between you needing to bring all that data from the 55 billion devices of data back to a central as a bunch of central Cause because you may not be able to afford to do that firstly band with even with five G. M and and SD when you'll still be too expensive given the number of devices out there. Were you given storage cause dropping will still be too expensive to try and store them all. So the answer must be to start at least to mitigate the problem to some leave both a lot of the data out there. Right? And only send back the pertinent ones as you said before. But then if you did that, then how are we gonna do machine learning at the core and the cloud side? If you don't have all the data you want rich data to train with. Right? Some sometimes you want a mix of the uh positive type data and the negative type data so you can train the machine in a more balanced way. So the answer must be eventually right. As we move forward with these huge number of devices out of the edge to do machine learning at the edge. Today, we don't have enough power. Right? The edge typically is characterized by a lower uh, energy capability and therefore lower compute power. But soon, you know, even with lower energy, they can do more with compute power improving in energy efficiency, Right? Uh, so learning at the edge today, we do influence at the edge. So we data model deploy and you do influence at the age, that's what we do today. But more and more, I believe, given a massive amount of data at the edge, you you have to have to start doing machine learning at the edge. And and if when you don't have enough power, then you aggregate multiple devices, compute power into a swamp and learn as a swan, >>interesting. So now, of course, if I were sitting and fly on the wall in HP board meeting, I said, okay, HP is as a leading provider of compute, how do you take advantage of that? I mean, we're going, I know it's future, but you must be thinking about that and participating in those markets. I know today you are you have, you know, edge line and other products. But there's it seems to me that it's it's not the general purpose that we've known in the past. It's a new type of specialized computing. How are you thinking about participating in that >>opportunity for your customers? Uh the world will have to have a balance right? Where today the default, Well, the more common mode is to collect the data from the edge and train at uh at some centralized location or a number of centralized location um going forward. Given the proliferation of the edge devices, we'll need a balance. We need both. We need capability at the cloud side. Right. And it has to be hybrid. And then we need capability on the edge side. Yeah. That they want to build systems that that on one hand, uh is uh edge adapted, right? Meaning the environmentally adapted because the edge different they are on a lot of times on the outside. Uh They need to be packaging adapted and also power adapted, right? Because typically many of these devices are battery powered. Right? Um so you have to build systems that adapt to it, but at the same time they must not be custom. That's my belief. They must be using standard processes and standard operating system so that they can run rich a set of applications. So yes. Um that's that's also the insightful for that Antonio announced in 2018, Uh the next four years from 2018, right, $4 billion dollars invested to strengthen our edge portfolio, edge product lines, right Edge solutions. >>I get a doctor go. I could go on for hours with you. You're you're just such a great guest. Let's close what are you most excited about in the future of of of it? Certainly H. P. E. But the industry in general. >>Yeah I think the excitement is uh the customers right? The diversity of customers and and the diversity in a way they have approached their different problems with data strategy. So the excitement is around data strategy right? Just like you know uh you know the the statement made was was so was profound. Right? Um And Antonio said we are in the age of insight powered by data. That's the first line right? The line that comes after that is as such were becoming more and more data centric with data the currency. Now the next step is even more profound. That is um you know we are going as far as saying that you know um data should not be treated as cost anymore. No right. But instead as an investment in a new asset class called data with value on our balance sheet, this is a this is a step change right in thinking that is going to change the way we look at data the way we value it. So that's a statement that this is the exciting thing because because for for me a city of AI right uh machine is only as intelligent as the data you feed it with. Data is a source of the machine learning to be intelligent. So so that's that's why when when people start to value data right? And and and say that it is an investment when we collect it. It is very positive for ai because an Ai system gets intelligent, more intelligence because it has a huge amounts of data and the diversity of data. So it'd be great if the community values values data. Well >>you certainly see it in the valuations of many companies these days. Um and I think increasingly you see it on the income statement, you know data products and people monetizing data services and maybe eventually you'll see it in the in the balance. You know Doug Laney when he was a gardener group wrote a book about this and a lot of people are thinking about it. That's a big change isn't it? Dr >>yeah. Question is is the process and methods evaluation. Right. But uh I believe we'll get there, we need to get started then we'll get their belief >>doctor goes on and >>pleasure. And yeah and then the yeah I will will will will benefit greatly from it. >>Oh yeah, no doubt people will better understand how to align you know, some of these technology investments, Doctor goes great to see you again. Thanks so much for coming back in the cube. It's been a real pleasure. >>Yes. A system. It's only as smart as the data you feed it with. >>Excellent. We'll leave it there. Thank you for spending some time with us and keep it right there for more great interviews from HP discover 21. This is dave a lot for the cube. The leader in enterprise tech coverage right back.

>>from >>around the >>globe, it's the >>cube with digital >>coverage of IBM, >>Think 2021 >>brought to you by IBM. Welcome back to the cubes coverage of IBM Think 2021 virtual, I'm john for your host of the cube, had a great line up here talking sustainability. Kary musa ph d general manager of AI applications and block chains, career great to see you and wayne both the vice president of corporate environmental affairs and chief sustainability officer, among other things involved in the products around that. Wait and korean, great to see you. Thanks for coming on. >>Thank you for having us. >>Well, I'll start with you. What's driving? IBMS investment sustainability as a corporate initiative. We know IBM has been active, we've covered this many times, but there's more drivers now as IBM has more of a larger global scope and continues to do that with hybrid cloud, it's much more of a global landscape. What's driving today's investments in sustainability, >>you know, johN what drives IBM in this area has always been a longstanding, mature and deep seated belief in corporate responsibility. That's the bedrock foundation. So, you know, IBM is 100 10 year old company. We've always strived to be socially responsible, But what's not as well known is that for the last 50 years, IBM has truly regarded environmental sustainability is a strategic imperative. Okay, It's strategic because hey, environmental problems require a strategic fix. It's long term imperative because you have to be persistent with environmental problems, you don't necessarily solve them overnight. And it's imperative because business cannot succeed in a world of environmental degradation, that really is the main tenant of sustainable development. You can't have successful economies with environmental degradation, you can't solving environmental problems without successful economies. So, and IBM's case as a long standing company, We were advantaged because 50 years ago our ceo at the time, Tom Watson put in place the company's first policy for environmental, our stewardship and we've been at it ever since. And he did that in 1971 and that was just six months after the U. S. C. P. A. Was created. It was a year before the Stockholm Conference on the Environment. So we've been added for that long. Um in essence really it's about recognizing that good environmental management makes good business sense. It's about corporate responsibility and today it's the E of E. S. G. >>You know, wayne. That's a great call out, by the way, referencing thomas Watson that IBM legend. Um people who don't may not know the history, he was really ahead of its time and that was a lot of the culture they still see around today. So great to see that focus and great, great call out there. But I will ask though, as you guys evolved in today's modern error. How is that evolved in today's focus? Because you know, we see data centers, carbon footprint, global warming, you now have uh A I and analytics can measure everything. So I mean you can you can measure everything now. So as the world gets larger in the surface area of what is contributing to the sustainable equation is larger, what's the current IBM focus? >>So, you know, these days we continually look at all of the ways in which IBM s day to day business practices intersect with any matter of the environment, whether it's materials waste water or energy and climate. And IBM actually has 21 voluntary goals that drive us towards leadership. But today john as you know, uh the headline is really climate change and so we're squarely focused like many others on that. And that's an imperative. But let me say before I just before I briefly tell you our current goals, it's also important to have context as to where we have been because that helps people understand what we're doing today. And so again, climate change is a topic that the men and women of IBM have paid attention to for a long time. Yeah, I was think about it. It was back in 1992 that the U. S. C. P. A. Created something called Energy Star. People look at that and they say, well, what's that all about? Okay, that's all about climate change. Because the most environmentally friendly energy you can get is the energy that you don't really need to consume. IBM was one of eight companies that helped the U. S. C. P. A. Launched that program 1992. Today we're all disclosing C. 02 emissions. IBM began doing that in 1994. Okay. In 2007, 13 years ago, I'd be unpublished. Its position on climate change, calling for urgent action around the world. We supported the Paris agreement 2015. We reiterated that support in 2017 for the us to remain a partner. 2019, we became a founding member of Climate Leadership Council, which calls for a carbon tax and a carbon dividend. So that's all background context. Today, we're working on our third renewable electricity goal, our fifth greenhouse gas emissions reduction goal and we set a new goal to achieve net zero greenhouse gas emissions. Each of those three compels IBM to near term >>action. That's awesome wayne as corporate environmental affairs and chief sustainable, great vision and awesome work. Karim dr Karim use if I wanna. We leave you in here, you're the general manager. You you've got to make this work because of the corporate citizenship that IBM is displaying. Obviously world world class, we know that's been been well reported and known, but now it's a business model. People realize that it's good business to have sustainability, whether it's carbon neutral footprints and or intersecting and contributing for the world and their employees who want mission driven companies ai and Blockchain, that's your wheelhouse. This is like you're in the big wave, wow, this is happening, give us your view because you're commercializing this in real time. >>Yeah, look as you've already said and it's the way well articulated, this is a business imperative, right? Is key to all companies corporate strategies. So the first step when you think about operationalized in this is what we've been doing, is to really step back and kind of break this down into what we call five key needs or focus areas that we've understood that we work with our clients. Remember in this context, Wayne is indeed my clients as well. Right. And so when you think about it, the five needs, as we like to lay them out, we talk about the sustainability strategy first of all, how are you approaching it as you saw from Wayne, identifying your key goals and approaches right against that, you begin to get into various areas and dimensions. Climate risk management is becoming increasingly important, especially in asset heavy industries electrification, energy and emissions management, another key focus area where we can bring technology to bear resilient infrastructure and operations, sustainable supply chain, all of these kind of come together to really connect with our clients business operations and allows us to bring together the technologies and the context of ai Blockchain and the key business operations. We can support to kind of begin to address specific news cases in the context of those needs. >>You know, I've covered it in the past and written about and also talked about the cube about sustainability on the supply chain side with Blockchain, whether it's your tracking, you know, um you know, transport of goods with with Blockchain and making sure that that kind of leads your kind of philosophy works because this waste involved is also disruption to business a security issues. But when you really move into the Ai side, how does a company scale that Corinne? Because now, you know, I have to one operationalize it and then scale it. Okay, so that's transformed, innovate and scale. How do I take take me through the examples of how that works >>well, I think really key to that, and this is really key to our ethos, it's enabling ai for business by integrating ai directly into business operations and decision making. So it's not really how can I put this? We try to make it so that the client isn't fixating on trying to deploy ai, they're just leveraging Ai. So as you say, let's take some practical examples. You talked about sustainable supply chains and you know, the key needs around transparency and provenance. Right? So we have helped clients like a tear with their seafood network or the shrimp sustainability network, where there's a big focus on understanding where are things being sourced and how they're moving through the supply chain. We also have a responsible sourcing business network that's being used for cobalt in batteries as an example from mine to manufacturing and here our technologies are allowing us to essentially track, trace and prove the provenance Blockchain serves as kind of that key shared ledger to pull all this information together. But we're leveraging AI to begin to quickly assess based upon the data inputs, the actual state of inventory, how to connect dots across multiple suppliers and as you onboard them and off board them off the network. So that's how we begin to put A. I in action so that the client begins to fixate on the work and the decisions they need to make. Not the AI itself. Another quick example would be in the context of civil infrastructure. One of our clients son and Belt large, maximum client of ours, he uses maximum to really focus on the maintenance and sustainable maintenance of their bridges. Think about how much money is spent setting up to do bridge inspections right. When you think about how much they have to invest the stopping of the traffic that scaffolding. We have been leveraging AI to do things like visual inspection, actually fly drones, take pictures, assess those images to identify cracks and use that to route and prioritized work. Similar examples are occurring in energy and utilities focused on vegetation management where we're leveraging ai to analyse satellite imagery, weather data and bringing it together so that work can be optimally prior authorized and deployed um for our clients. >>It's interesting. One of the themes coming out of think that I'm observing is this notion of transformation is innovation and innovation is about scale. Right? So it's not just innovation for innovating sake. You can transform from whether it's bridge inspections to managing any other previous pre existing kind of legacy condition and bring that into a modern error and then scale it with data. This is a common theme. It applies to to your examples. Kareem, that's super valuable. Um how do you how do you tie that together with partnering? Because wayne you were talking about the corporate initiative, that's just IBM we learned certainly in cybersecurity and now these other areas like sustainability, it's a team sport, you have to work on a global footprint with other industries and other leaders. How was I being working across the industry to connect and work with other, either initiatives or companies or governments. >>Sure. And there have been john over the years and at present a number of diverse collaborations that we seek out and we participate in. But before I address that, I just want to amplify something Kareem said, because it's so important, as I look back at the environmental movement over the last 50 years, frankly, since the first earth day in 1970, I, you know, with the benefit of hindsight, I observed there have really been three different hair, It's in the very beginning, global societies had to enact laws to control pollution that was occurring. That was the late 60s 1970s, into the early 1980s and around the early 1980s through to the first part of this century, that era of let's get control of this sort of transformed, oh, how can we prevent stuff from happening given the way we've always done business and that area ran for a while. But now, thanks to technology and data and things like Blockchain and ai we all have the opportunity to move into this era of innovation, which differs from control in which differs from traditional prevention. Innovation is about changing the way you get the same thing done. And the reason that's enabled is because of the tools that you just spoke about with korean. So how do we socialize these opportunities? Well to your question, we interact with a variety of diverse teams, government, different business associations, NGos and Academia. Some examples. There's an organization named the Center for Climate and Energy Solutions, which IBM is a founding member of its Business Leadership Council. Its predecessor was the Q Centre on global climate change. We've been involved with that since 1998. That is a cross section of people from all these different constituencies who are looking for solutions to climate. Many Fortune 102000s in there were part of the green grid. The green grid is an organization of companies involved with data centers and it's constantly looking at how do you measure energy efficiency and data centers and what are best practices to reduce consumption of energy at data centers where a member of the renewable energy buyers alliance? Many Fortune 100 200 Zar in that trying to apply scale to procure more renewable electricity to actually come to our facilities I mentioned earlier were part of the Climate Leadership Council calling for a carbon tax were part of the United Nations Environment programs science policy business form that gets us involved with many ministers of environment from countries around the world. We recently joined the new MITt Climate and sustainability consortium. Mitt Premier Research University. Many key leaders are part of that. Looking at how academic research can supercharge this opportunity for innovation and then the last one, I'm just wrap up call for code. You may be familiar with IBM s involvement in call for code. Okay. The current challenge under Call for Code in 2021 calls for solutions targeted the climate change. So that's that's a diverse set of different constituents, different types of people. But we try to get involved with all of them because we learn and hopefully we contribute something along the way as well. >>Awesome Wayne. Thank you very much, Karim, the last 30 seconds we got here. How do companies partner with IBM if they want to connect in with the mission and the citizenship that you guys are doing? How do they bring that to their company real quick. Give us a quick overview. >>Well, you know, it's really quite simple. Many of these clients are already clients of ours were engaging with them in the marketplace today, right, trying to make sure we understand their needs, trying to ensure that we tune what we've got to offer both in terms of product and consulting services with our GPS brethren, you know, to meet their needs, linking that in as well to IBM being in what we like to turn clients zero. We're also applying these same technologies and capabilities to support IBM efforts. And so as they engage in all these associations, what IBM is doing, that also provides a way to really get started. It's really fixate on those five imperatives or needs are laid out, picked kind of a starting point and tie it to something that matters. That changes how you're doing something today. That's really the key. As far as uh we're concerned, >>Karim, we thank you for your time on sustainability. Great initiative. Congratulations on the continued mission. Going back to the early days of IBM and the Watson generation continuing out in the modern era. Congratulations and thanks for sharing. >>Thank you john. >>Okay. It's the cubes coverage. I'm sean for your host. Thanks for watching. Mhm. Mhm. Mhm.

>>From around the globe, it's the Cube with digital coverage of IBM think 2021 brought to you by IBM. Welcome back to the cubes coverage of IBM Think 2021 virtual, I'm john for your host of the cube. We had a great line up here talking sustainability, kary musa ph d general manager of AI applications and block chains going great to see you and wayne, both the vice president of corporate environmental affairs and chief sustainability officer, among other things involved in the products around that. Wait and korean, great to see you. Thanks for coming on. >>Thank you for having us. >>Well, I'll start with you what's driving? IBMS investment in sustainability as a corporate initiative. We know IBM has been active, we've covered this many times, but there's more drivers now as IBM has more of a larger global scope and continues to do that with hybrid cloud, it's much more of a global landscape. What's driving today's investments in sustainability, >>You know, jOHn what drives IBM in this area has always been a longstanding, mature and deep seated belief in corporate responsibility. That's the bedrock foundation. So, you know, IBM 110 year old company, we've always strived to be socially responsible, But what's not as well known is that for the last 50 years, IBM has truly regarded environmental sustainability is a strategic imperative. Okay, It's strategic because hey, environmental problems require a strategic fix. It's a long term imperative because you have to be persistent with environmental problems, you don't necessarily solve them overnight. And it's imperative because business cannot succeed in a world of environmental degradation that really is the main tenant of sustainable development. You can't have successful economies with environmental degradation, you can't solving environmental problems without successful economies. So, and IBM's case as a long standing company, We were advantaged because 50 years ago our ceo at the time, Tom Watson put in place the company's first policy for environmental a stewardship and we've been at it ever since. And he did that in 1971 and that was just six months after the U. S. E. P. A. Was created. It was a year before the Stockholm Conference on the Environment. So we've been added for that long. Um in essence, really it's about recognizing that good environmental management makes good business sense, It's about corporate responsibility and today it's the E of E. S. G. >>You know, wayne. That's a great call out, by the way, referencing thomas Watson, the IBM legend. Um people who don't may not know the history, he was really ahead of its time and that was a lot of the culture they still see around today. So great to see that focus and great, great call out there. But I will ask though, as you guys evolved in today's modern error, how has that evolved in today's focus? Because, you know, we see data centers, carbon footprint, global warming, you now have a I and analytics can measure everything. So I mean you can you can measure everything now. So as the world gets larger in the surface area of what is contributing to the sustainable equation is larger, what's the current IBM focus? >>So these days we continually look at all of the ways in which IBM s day to day business practices intersect with any matter of the environment, whether it's materials, waste water or energy and climate. And IBM actually has 21 voluntary goals that drive us towards leadership. But today john as you know, uh the headline is really climate change and so we're squarely focused like many others on that and that's an imperative. But let me say before I just before I briefly tell you our current goals, it's also important to have context as to where we have been because that helps people understand what we're doing today. And so again, climate change is a topic that the men and women of IBM have paid attention to for a long time. Yeah, I was think about it. It was back in 1992 that the U. S. C. P. A. Created something called Energy Star. People look at that and they said, well, what's that all about? Okay, that's all about climate change. Because the most environmentally friendly energy you can get is the energy that you don't really need to consume. IBM was one of eight companies that helped the U. S. C. P. A. Launched that program 1992. Today we're all disclosing C. 02 emissions. IBM began doing that in 1994. Okay. In 2007, 13 years ago, I'd be unpublished. Its position on climate change, calling for urgent action around the world. He supported the Paris Agreement 2015. We reiterated that support in 2017 for the us to remain a partner. 2019, we became a founding member of Climate Leadership Council which calls for a carbon tax and a carbon dividend. So that's all background context. Today, we're working on our third renewable electricity goal, our fifth greenhouse gas emissions reduction goal and we set a new goal to achieve net zero greenhouse gas emissions. Each of those three compels IBM to near term action. >>That's awesome wayne as corporate environmental affairs and chief sustainable, great vision and awesome work. Karim dr Karim use if I wanna we leave you in here, you're the general manager. You you got to make this work because of the corporate citizenship that IBM is displaying. Obviously world world class, we know that's been been well reported and known, but now it's a business model. People realize that it's good business to have sustainability, whether it's carbon neutral footprints and or intersecting and contributing for the world and their employees who want mission driven companies ai and Blockchain, that's your wheelhouse. This is like you're on the big wave, wow, this is happening, give us your view because you're commercializing this in real time. >>Yeah, look as you've already said and it's the way well articulated, this is a business imperative, right is key to all companies corporate strategies. So the first step when you think about operationalized in this is what we've been doing, is to really step back and kind of break this down into what we call five key needs or focus areas that we've understood that we work with our clients. Remember in this context, Wayne is indeed my clients as well. Right. And so when you think about it, the five needs, as we like to lay them out, we talk about the sustainability strategy first of all, how are you approaching it as you saw from Wayne, identifying your key goals and approaches right against that, you begin to get into various areas and dimensions. Climate risk management is becoming increasingly important, especially in asset heavy industries electrification, energy and emissions management, another key focus area where we can bring technology to bear resilient infrastructure and operations, sustainable supply chain, All of these kind of come together to really connect with our clients business operations and allows us to bring together the technologies and context of ai Blockchain and the key business operations. We can support to kind of begin to address specific news cases in the context of those >>needs. You know, I've covered it in the past and written about and also talked about on the cube about sustainability on the supply chain side with Blockchain, whether it's your tracking, you know, um you know, transport of goods with with Blockchain and making sure that that kind of leads your kind of philosophy works because there's waste involved is also disruption to business, a security issues, but when you really move into the Ai side, how does a company scale that Corinne, because now, you know, I have to one operationalize it and then scale it. Okay, so that's transformed, innovate and scale. How do I take take me through the examples of how that works >>well, I think really key to that, and this is really key to our ethos, it's enabling ai for business by integrating ai directly into business operations and decision making. So it's not really how can I put this? We try to make it so that the client isn't fixating on trying to deploy ai, they're just leveraging Ai. So as you say, let's take some practical examples. You talked about sustainable supply chains and you know, the key needs around transparency and provenance. Right. So we have helped clients like a tear with their seafood network or the shrimp sustainability network where there's a big focus on understanding where are things being sourced and how they're moving through the supply chain. We also have a responsible sourcing business network that's being used for cobalt in batteries as an example from mine to manufacturing and here our technologies are allowing us to essentially track, trace and prove the provenance Blockchain serves as kind of that key shared ledger to pull all this information together. But we're leveraging AI to begin to quickly assess based upon the data inputs, the actual state of inventory, how to connect dots across multiple suppliers and as you on board in an off board them off the network. So that's how we begin to put A I in action so that the client begins to fixate on the work and the decisions they need to make. Not the AI itself. Another quick example would be in the context of civil infrastructure. One of our clients son and Belt large, maximum client of ours he uses maximum too rarely focus on the maintaining sustainable maintenance of their bridges. Think about how much money is spent setting up to do bridge inspections right. When you think about how much they have to invest the stopping of the traffic that scaffolding. We have been leveraging AI to do things like visual inspection. Actually fly drones, take pictures, assess those images to identify cracks and use that to route and prioritized work. Similar examples are occurring in energy and utilities focused on vegetation management where we're leveraging AI to analyse satellite imagery, weather data and bringing it together so that work can be optimally prior authorized and deployed for our >>clients. It's interesting. One of the themes coming out of think that I'm observing is this notion of transformation is innovation and innovation is about scale. Right? So it's not just innovation for innovating sake. You can transform from whether it's bridge inspections to managing any other previous pre existing kind of legacy condition and bring that into a modern error and then scale it with data. This is a common theme. It applies to to your examples. Kareem, that's super valuable. Um how do you how do you tie that together with partnering? Because wayne you were talking about the corporate initiative, that's just IBM we learned certainly in cybersecurity and now these other areas like sustainability, it's a team sport, you have to work on a global footprint with other industries and other leaders. How was I being working across the industry to connect and work with other, either initiatives or companies or governments. >>Sure. And there have been john over the years and at present a number of diverse collaborations that we seek out and we participate in. But before I address that, I just want to amplify something Kareem said, because it's so important, as I look back at the environmental movement over the last 50 years, frankly, since the first earth day in 1970, I, you know, with the benefit of hindsight, I observed there have really been three different hair, it's in the very beginning, global societies had to enact laws to control pollution that was occurring. That was the late 60s 1970s, into the early 1980s and around the early 1980s through to the first part of this century, that era of let's get control of this sort of transformed, oh how can we prevent stuff from happening given the way we've always done business and that area ran for a while. But now thanks to technology and data and things like Blockchain and ai we all have the opportunity to move into this era of innovation which differs from control in which differs from traditional prevention. Innovation is about changing the way you get the same thing done. And the reason that's enabled is because of the tools that you just spoke about with Korean. So how do we socialize these opportunities? Well to your question, we interact with a variety of diverse teams, government, different business associations, Ngos and Academia. Some examples, there's an organization named the Center for Climate and Energy Solutions, which IBM is a founding member of its Business Leadership Council. Its predecessor was the Q Centre on global climate change. We've been involved with that since 1998. That is a cross section of people from all these different constituencies who are looking for solutions to climate. Many Fortune 102000s in there were part of the green grid. The green grid is an organization of companies involved with data centers and it's constantly looking at how do you measure energy efficiency and data centers and what are best practices to reduce consumption of energy at data centers where a member of the renewable energy buyers alliance? Many Fortune 100 200 Zarin that trying to apply scale to procure more renewable electricity to actually come to our facilities I mentioned earlier were part of the Climate Leadership Council calling for a carbon tax were part of the United Nations Environment Programs science Policy business form that gets us involved with many ministers of Environment from countries around the world. We recently joined the new MITt Climate and sustainability consortium. Mitt Premier Research University. Many key leaders are part of that. Looking at how academic research can supercharge this opportunity for innovation and then the last one, I'll just wrap up call for code. You may be familiar with IBM s involvement in call for code. Okay. The current challenge under call for code in 2021 calls for solutions targeted the climate change. So that's, that's a diverse set of different constituents, different types of people. But we try to get involved with all of them because we learn and hopefully we contribute something along the way as well. >>Awesome Wayne. Thank you very much Karim, the last 30 seconds we got here. How do companies partner with IBM if they want to connect in with the mission and the citizenship that you guys are doing? How do they bring that to their company real quick. Give us a quick overview. >>Well, you know, it's really quite simple. Many of these clients are already clients of ours were engaging with them in the marketplace today, right, trying to make sure we understand their needs, trying to ensure that we tune what we've got to offer, both in terms of product and consulting services with our GPS brethren, you know, to meet their needs, linking that in as well to IBM being and what we like to turn client zero. We're also applying these same technologies and capabilities to support IBM efforts. And so as they engage in all these associations, what IBM is doing that also provides a way to really get started. It's really fixate on those five imperatives or needs are laid out, picked kind of a starting point and tie it to something that matters. That changes how you're doing something today. That's really the key. As far as uh we're concerned, >>Karim, we thank you for your time on sustainability. Great initiative, Congratulations on the continued mission. Going back to the early days of IBM and the Watson generation continuing out in the modern era. Congratulations and thanks for sharing. >>Thank you john. >>Okay. It's the cubes coverage. I'm sean for your host. Thanks for watching. >>Mm. Mhm.

(upbeat music) >> Narrator: From around the globe. It's The CUBE, with digital coverage of AWS public sector online, (upbeat music) brought to you by, Amazon Web Services. >> Everyone welcome back to The CUBE virtual coverage, of AWS public sector summit online. I'm John Furrier, your host of The CUBE. Normally we're in person, out on Asia-Pacific, and all the different events related to public sector. But this year we have to do it remote, and we're going to do the remote virtual CUBE, with Data Virtual Public Sector Online Summit. And we have two great guests here, about Digital Earth Africa project, Clive Charlton. Head of Solutions Architecture, Sub-Saharan Africa with AWS, Clive thanks for coming on, and Aditya Agrawal founder of D4DInsights, and also the advisor for the Digital Earth Africa project with AWS. So gentlemen, thank you for coming on. Appreciate you coming on remotely. >> Thanks for having us. >> Thank you for having us, John. >> So Clive take us through real quickly. Just take a minute to describe what is the Digital Earth Africa Project. What are the problems, that you're aiming to solve? >> Well, we're really aiming to provide, actionable data to governments, and organization around Africa, by providing satellite imagery, in an easy to use format, and doing that on the cloud, that serves countries throughout Africa. >> And just from a cloud perspective, give us a quick taste of what's going on, just with the tech, it's on Amazon. You got a little satellite action. Is there ground station involved? Give us a little bit more color around, you know, what's the scope of the project. >> Yeah, so, historically speaking you'd have to process satellite imagery down link it, and then do some heavy heavy lifting, around the processing of the data. Digital Earth Africa was built, from the experiences from Digital Earth Australia, originally developed by a Geo-sciences Australia and they use container services for Kubernetes's called Elastic Kubernetes Service to spin up virtual machines, which we are required to process the raw satellite imagery, into a format called a Cloud Optimized GeoTIFF. This format is used to store very large volumes of data in a format that's really easy to query. So, organizations can just use NHTTP get range request. Just a query part of the file, that they're interested in, which means, the results are served much, much quicker, from much, much better overall experience, under the hood, the store where the data is stored in the Amazon Simple Storage Service, which is S3, and the Metadata Index in a Relational Database Service, that runs the Open Data CUBE Library, which is allows Digital Earth Africa, to store this data in both space and time. >> It's interesting. I just did a, some interviews last week, on a symposium on space and cybersecurity, and we were talking about , the impact of satellites and GPS and just the overall infrastructure shift. And it's just another part of the edge of the network. Aditya, I want to get your thoughts on this, and your reaction to the Digital Earth, cause you're an advisor. Let's zoom out. What's the impact of people's lives? Give us a quick overview, of how you see it playing out because, explaining to someone, who doesn't know anything about the project, like, okay what is it about, and how does it actually impact people? >> Sure. So, you know, as, as Clive mentioned, I mean there's, there's definitely a, a digital infrastructure behind Digital Earth Africa, in a way that it's going to be able to serve free and open satellite data. And often the, the issue around satellite data, especially within the context of Africa, and other parts of the world is that there's a level of capacity that's required, in order to be able to use that data. But there's also all kinds of access issues, because, traditionally satellite data is heavy. There's the old model of being able to download the data and then being able to do something with it. And then often about 80% of the time, that you spend on satellite data is spent, just pre processing the data, before you can actually, do any of the fun analysis around it, that really sort of impacts the kinds of decisions and actions that you're looking for. And so that's why Digital Earth Africa. And that's why this partnership, with Amazon is a fantastic partnership, because it really allows us, to be able, to scale the approach across the entire continent, make it easy for that data to be accessed and make it easier for people to be able to use that data. The way that Digital Earth Africa is being operationalized, is that we're not just looking at it, from the perspective of, let's put another infrastructure into Africa. We want this program, and it is a program, that we want institutionalized within Africa itself. One that leverages expertise across the continent, and one that brings in organizations across the continent to really sort of take the leadership and ownership of this program as it moves forward. The idea of it is that, once you're able to have this information, being able to address issues like food security, climate change, coastal resilience, land degradation where illegal mining is, where is the water? We want to be able to do that, in a way that it's really looking at what are the national development priorities within the countries themselves, and how does it also then support regional and global frameworks like Africa's Agenda 2063 and the sustainable development goals. >> No doubt in my mind, obviously, is that huge benefits to these kinds of technologies. I want to also just ask you, as a follow up is a huge space race going on, right now, explosion of availability of satellite data. And again, more satellites going up, There's more congestion, more contention. Again, we had a big event on that cybersecurity, and the congestion issue, but, you know, satellite data was power everyone here in the United States, you want an Uber, you want Google Maps you've got your everywhere with GPS, without it, we'd be kind of like (laughing), wondering what's going on. How do we even vote these days? So certainly an impact, but there's a huge surge of availability, of the use of satellite data. How do you explain this? And what are some of the challenges, from the data side that's coming, from the Digital Earth Africa project that you guys hope to resolve? >> Sure. I mean, that's a great question. I mean, I think at one level, when you're looking at the space race right now, satellites are becoming cheaper. They're becoming more efficient. There's increased technology now, on the types of sensors that you can deploy. There's companies like Planet, that are really revolutionizing how even small countries are able to deploy their own satellites, and the constellation that they're putting forward, in terms of the frequency by which, you're able to get data, for any given part of the earth on a daily basis, coupled with that. And you know, this is really sort of in climbs per view, but the cloud computing capabilities, and overall computing power that you have today, then what you had 10 years, 15 years ago is so vastly different. What used to take weeks to do before, for any kind of analysis on satellite data, which is heavy data now takes, you know, minutes or hours to do. So when you put all that together, again, you know, I think it really speaks, to the power of this partnership with Amazon and really, what that means, for how this data is going to be delivered to Africa, because it really allows for the scalability, for anything that happens through Digital Earth Africa. And so, for example, one of the approaches, that we're taking us, we identify what the priorities, and needs are at the country level. Let's say that it's a land degradation, there's often common issues across countries. And so when we can take one particular issue, tested with additional countries, and then we can scale it across the whole continent because the infrastructure is there for the whole continent. >> Yeah. That's a great point. So many storylines here. We'll get to climb in a second on sustainability. And I want to talk about the Open Data Platform. Obviously, open data, having data is one thing, but now train data, and having more trusted data becomes a huge issue. Again, I want to dig into that for a second, but, Clive, I want to ask you, first, what region are we in? I mean, is this, you guys actually have a great, first of all, we've been covering the region expansion from Bahrain all the way, as moves around the world, probably soon in space. There'll be a region Amazon space station region probably, someday in the future but, what region are you running the project out of? Can you, and why is it important? Can you share the update on the regional piece? >> Well, we're very pleased, that Digital Earth Africa, is using the new Africa region in Cape Town, in South Africa, which was launched in April of this year. It's one of 24 regions around the world and we have another three new regions announced, what this means for users of Digital Earth Africa is, they're able to use region closest to them, which gives them the best user experience. It's the, it's the quickest connection for them. But more importantly, we also wanted to use, an African solution, for African people and using the Africa region in Cape Town, really aligned with that thinking. >> So, localization on the data, latency, all that stuff is kind of within the region, within country here. Right? >> That's right, Yeah >> And why is that important? Is there any other benefits? Why should someone care? Obviously, this failover option, I mean, in any other countries to go to, but why is having something, in that region important for this project? >> Well, it comes down to latency for the, for the users. So, being as close to the data, as possible is, is really important, for the user experience. Especially when you're looking at large data sets, and big queries. You don't want to be, you don't want to be waiting a long lag time, for that query to go backwards and forwards, between the user and the region. So, having the data, in the Africa region in Cape Town is important. >> So it's about the region, I love when these new regions rollout from Amazon, Cause obviously it's this huge buildup CapEx, in this huge data center servers and everything. Sustainability is a huge part of the story. How does the sustainability piece fit into the, the data initiative supported in Africa? Can you share some updates on that? >> Well, this, this project is also closely aligned with the, Amazon Sustainability Data Initiative, which looks to accelerate sustainability research. and innovation, really by minimizing the cost, and the time required to acquire, and analyze large sustainability datasets. So the initiative supports innovators, and researchers with the data and tools, and, and technical experience, that they need to move sustainability, to the next level. These are public datasets and publicly available to anyone. In addition, to that, the initiative provides cloud grants to those who are interested in exploring, exploring the use of AWS technology and scalable infrastructure, to serve sustainability challenges, of this nature. >> Aditya, I want to hear your thoughts, on this comment that Clive made around latency, and certainly having a region there has great benefits. You don't need to hop on that. Everyone knows I'm a big fan of the regional model, but it brings up the issue, of what's going on in the country, from an infrastructure standpoint, a lot of mobility, a lot of edge computing. I can almost imagine that. So, so how do you see that evolving, from a business standpoint, from a project standpoint data standpoint, can you comment and react to that edge, edge angle? >> Yeah, I mean, I think, I think that, the value of an open data infrastructure, is that, you want to use that infrastructure, to create a whole data ecosystem type of an approach. And so, from the perspective of being able. to make this data readily accessible, making it efficiently accessible, and really being able to bring industry, into that ecosystem, because of what we really want as we, as the program matures, is for this program, to then also instigate the development of new businesses, entrepreneurship, really get the young people across Africa, which has the largest proportion of young people, anywhere in the world, to be engaged around what you can do, with satellite data, and the types of businesses that can be developed around it. And, so, by having all of our data reside in Cape Town on the continent there's obviously technical benefits, to that in terms of, being able to apply the data, and create new businesses. There's also a, a perception in the fact that, the data that Digital Earth Africa is serving, is in Africa and residing in Africa which does have, which does go a long way. >> Yeah. And that's a huge value. And I can just imagine the creativity cloud, if you can comment on this open data platform idea, because some of the commentary that we've been having on The CUBE here, and all around the world is data's great. We all know we're living with a lot of data, you starting to see that, the commoditization and horizontal scalability of data, is one thing, but to put it into software defined environments, whether, it's an entrepreneur coding up an app, or doing something to share some transparency, around some initiatives going on within the region or on the continent, it's about trusted data. It's about sharing algorithms. AI is also a consumer of data, machines consume data. So, it's not just the technology data, is part of this new normal. What's this Open Data Platform, And how does that translate into value in your opinion? >> I, yeah. And you know, when, when data is shared on, on AWS anyone can analyze it and build services on top of it, using a broad range of compute and data to data analytics products, you know, things like Amazon EC2, or Lambda, which is all serverless compute, to things like Amazon Elastic MapReduce, for complex extract and transformation processes, but sharing data in the cloud, lets users, spend more time on the data analysis, rather than, than the data acquisition. And researchers can analyze data shared on AWS, without needing to pay to store their own copy, which is what the Open Data Platform provides. You only have to pay for the compute that you use and you don't need to purchase storage, to start a new project. So the registry of the open data on AWS, makes it easy to find those datasets, but, by making them publicly available through AWS services. And when you share, share your data on AWS, you make it available, to a large and growing community of developers, and startups, and enterprises, all around the world. And you know, and we've been talking particularly around, around Africa. >> Yeah. So it's an open source model, basically, it's free. You don't, it doesn't cost you anything probably, just started maybe down the road, if it gets heavy, maybe to charging but the most part easy for scientists to use and then you're leveraging it into the open, contributing back. Is that right? >> Yep. That's right. To me getting, getting researchers, and startups, and organizations growing quickly, without having to worry about the data acquisition, they can just get going and start building. >> I want to get back to Aditya, on this skill gap issue, because you brought up something that, I thought was really cool. People are going to start building apps. I'm going to start to see more innovation. What are the needs out there? Because we're seeing a huge onboarding of new talent, young talent, people rescaling from existing jobs, certainly COVID accelerated, people looking for more different kinds of work. I'm sure there's a lot of (laughing) demand to, to do some innovative things. The question I always get, and want to get your reaction is, what are the skills needed to, to get involved, to one contribute, but also benefit from it, whether it's the data satellite, data or just how to get involved skill-wise >> Sure. >> Yes. >> Yeah. So most recently we've created a six week training course. That's really kind of taken users from understanding, the basics of Earth Observation Data, to how to work, with Python, to how to create their own Jupyter notebooks, and their own Use cases. And so there's a, there's a wide sort of range of skill sets, that are required depending on who you are because, effectively, what we want to be able to do is get everyone from, kind of the technical user, that might have some remote sensing background to the developer, to the policy maker, and decision maker, to understand the value of this infrastructure, whether you're the one who's actually analyzing the data. If you're the one who's developing new applications, or you're taking that information from a managerial or policy level discussion to actually deliver the action and sort of impact that you're looking for. And so, you know, in, in that regard, we're working with ITC in the Netherlands and again, with institutions across Africa, that already have a mandate, and expertise in this particular area, to create a holistic capacity development program, that will address all of those different factors. >> So I guess the follow up question I want to have is, how do you ensure the priorities of Africa are addressed, as part of this program? >> Yeah, so, we are, we've created a governance model, that really is both top down, and bottom up. At the bottom up level, We have a technical advisory committee, that has over 15 institutions, many of which are based across Africa, that really have a good understanding of the needs, the priorities, and the mandate for how to work with countries. And at the top down level, we're developing a governing board, that will be inclusive, of the key continental level institutions, that really provide the political buy-in, the sustainability of the program, and really provide overall guidance. And within that, we're also creating an operational models, such that these institutions, that do have the capacity to support the program, they're actually the ones, who are also going to be supporting, the implementation of the program itself. >> And there's been some United Nations, sustained development projects all kinds of government involvement, around making sure certain things would happen, within the country. Can you just share, some of the highlights, or some of the key initiatives, that are going on, that you're supporting, to make it a better, better world? >> Yeah. So this is, this program is very closely aligned to a sustainable development agenda. And so looking after, looking developing methods, that really address, the sustainable development goals as one facet, in Africa, there's another program looking overall, overall national development priorities and sustainability called the Agenda 2063. And really like, I think what it really comes down to this, this wouldn't be happening, without the country level involvement themselves. So, this started with five countries, originally, Senegal, Ghana, Kenya, Tanzania, and the government of Kenya itself, has really been, a kind of a founding partner for, how Digital Earth Africa and it's predecessor of Africa Regional Data Cube, came to be. And so without high level support, and political buying within those governments, I mean, it's really because of that. That's why we're, we're where we are. >> I need you to thank you for coming on and sharing that insight. Clive will give you the final word, for the folks watching Digital Earth Africa, processes, petabytes of data. I mean the satellite data as well, huge, you mentioned it's a new region. You're running Kubernetes, Elastic Kubernetes Service, making containers easy to use, pay as you go. So you get cutting edge, take the one minute to, to share why this region's cutting edge. Does it have the scale of other regions? What should they know about AWS, in Cape Town, for Africa's new region? Take a minute to, to put plugin. >> Yeah, thank you for that, John. So all regions are built in the, in the same way, all around the world. So they're built for redundancy and reliability. They typically have a minimum of three, what we call Availability Zones. And each one is a contains a, a cluster of, of data centers, and all interconnected with fast fiber. So, you know, you can survive, you know, a failure with with no impact to your services. And the Cape Town region is built in exactly the same the same way, we have most of the services available in the, in the Cape Town region, like most other regions. So, as a user of AWS, you, you can have the confidence that, You can deploy your services and workloads, into AWS and run it in the same in the same way, with the same kind of speed, and the same kind of support, and infrastructure that's backing any region, anywhere else in the world. >> Well great. Thanks for that plug, Aditya, thank you for your insight. And again, innovation follows cloud computing, whether you're building on top of it as a startup a government or enterprise, or the big society better, in this case, the Digital Earth Africa project. Great. A great story. Thank you for sharing. I appreciate it. >> Thank you for having us. >> Thank you for having us, John >> I'm John Furrier with, The CUBE, virtual remote, not in person this year. I hope to see you next time in person. Thanks for watching. (upbeat music) (upbeat music decreases)

from around the globe it's the cube with digital coverage of Red Hat summit 2020 brought to you by Red Hat hi I'm Stu min a man and this is the cubes coverage of Red Hat summit 2020 of course this year the event is happening all online and that gives us an opportunity to meet with red hat executives customers partners and practitioners where they are around the globe in this segment one of our favorites ever years we're talking to the women in open source and joining me for this segment first of all we have Elissa and Alexander who is the executive vice president and chief people officer of Red Hat this award fit thunder her domain dallisa it is great to see you again thanks so much for joining us thank you so much for having us all right and we have two of the Award winners so first if you see right next bit Elissa we have an epic Sain who's a doctor and PhD candidate in clinical neuroscience at the University of Gothenburg coming to us from Sweden method great to see you thank you very much all right we also have Megan Burge Sinicki who is a manager of research and operations at the open source program office at Google Megan thank you so much for joining us off though thanks for having me all right so dallisa let me hand it off to you is give our audience a little bit if they're not familiar with whipping an open source what the initiative is the community and you know what might have changed from previous years when we've talked about this sure so we realized that the tech industry is a great industry for diverse populations but a lot of diverse populations don't realize that and so as the open source leader we wanted to shine a light on the contributions that some of our underrepresented populations are making an open source that trying to inspire more people to join communities to participate to contribute we know that more diverse populations help us to innovate more rapidly they help us to solve more problems and so it's really important especially today with what's happening in the world lots of important problems to solve that we really invite more of our other upper sort of populations to join in the communities awesome so absolutely there there are lots of people that volunteer there are lots of people that do it as their day job Megan why don't we fuck you have a roll open source first Google as a strong legacy and open source in general so tell us a little bit about you know what you were working on and what you're being recognized for here yeah well a lot of the recognition comes from my work with the Drupal Association I had been with Drupal for 8 years hoping to build that foundation in supporting that community and lots of different ways from fundraising to community events running sprints and helping with their developer tools and so that was a lot what the award was based on and now I'm at Google and I've been here for about a year and a half and I run their research and operations and so Google is an expression of open source and we have thousands of people using thousands of projects and we want to make sure they do it well they feel supported that we are good citizens in the projects that we participate in and so my group provides the operational support to make sure that happens you know you know what one of the things that's always fascinating when I go to Red Hat there's so many projects there's so many participants from various walks of life last year at the show there was a lot of discussion of you know it was a survey really and said that you know the majority of people that tribute now it's actually part of their job as opposed to when I think back you know you go back a couple of decades ago and it was like oh well in my spare time or down in my basement I'm contributing here so maybe talk a little bit about the communities and you know what what Megan is embodying CSUN she worked on project now she's working for obviously a good partner of Red Hat's that does a lot of open source yeah I love the way she described what her role is at Google and that it's fascinating and Google has been really a huge contributor in the community for in communities for years and years so I think that what we're seeing with the communities and people saying yeah now it's part of my day job is that you know 20 years ago the idea that open-source development would be kind of on par with proprietary development and on par in terms of being used in the enterprise and the data center was something that I think many people questioned proprietary software was the way that most people felt comfortable making sure that their intellectual property is protected and that users could feel comfortable using it within the parameters required so that was the way it was 20 years ago and then now you think about you know most companies there is some form of open source that is part of their infrastructure so now open source is no longer you know that disrupter but it's really a viable alternative and organizations really want to use both they want to have some propriety or they want to have some open sources so that means like every company is going to need to have some need to understand how to participate in communities how to influence communities and Red Hat's a great partner in helping enterprise customers to be able to understand what those red Nets might look like and then helping to kind of harden it make sure things that they need to have application city to have certified or certified and make it really usable in a way they're comfortable with in the enterprise that's kind of special Red Hat place but it's just a tribute to where we come in a world in terms of open source being really accepted and thriving and it helps us to innovate much more rapidly yeah and there's there's no better way to look at not only where we are but where we're going then talk about what's happening in the academic world so that gives it brings us Aneta so you are the academic award winner you're a PhD candidate so tell us a little bit about your participation and open source what it means to be part of this community my PhD project involves using virtual reality to measure the arm movements of people with stroke so we have participants coming in into our lab so they we're these 3d glasses and then they start seeing virtual objects in the 3d space and they use their hands to touch at these targets and make them disappear and we have all these movements data specially interpreters and then we write code and analyze the data and find out how much they have recovered within one year after stroke this is my PhD project but my involvement with open source happens they before like in starting from 2010 I have been editing Wikipedia and I have been writing several articles related to medicine and healthcare so that is where I started with open open knowledge and then I moved on words and after my medical studies I moved to research and worked on this awesome project and so there are multiple ways by which I have engaged with open source that's far that's awesome my understanding is also some of the roots that you had and some of the medical things that you're doing have an impact on what's happening today so obviously we're all dealing with the global pandemic in Koba 19 so I'd like to hear you know what your involvement there you know your data obviously is politically important that we have the right data getting to the right people as fast as possible definitely yes right now I'm working on writing creating content for Wikipedia writing on articles related to Kobe 19 so I mostly work on writing about its socio-economic impact writing about Kobe 19 testing and also about the disease in general mental health issues surrounding that social stigma associated began with it and so forth so I use all these high-quality references from the World Health Organization the United Nations and also from several journals and synthesize them and write articles on Wikipedia so we have a very cool project called wiki project code 19 on Wikipedia where people who are interested in writing articles creating data uploading images related to poet 19 come together and create some good content out of it so I am a very active participant there alright and making my understanding is you you also have some initiatives related to kovat 19 maybe you can tell us a little bit about those yeah well one I'm loosely affiliated with this kovat act now and that is a combination of developers data scientists epidemiologists and US state government officials and it's looking at how was the curve look like and how does that curve get flattened if governor's made decisions faster or differently than what they're making today and how does it impact the availability of ICU beds and ventilators and so that is a tool that's being used today by many decision-makers here in the US and my contribution to that was they needed some resources I reached into Google and found some smart generous volunteers that are contributing to the dataset and actually I just connected with Neda do this award program and now she's connected and is gonna start working on this as well yes oh that's fantastic yeah I mean dallisa you know we've known for a long time you want to move fast if you want to connect you know lots of diverse groups you know open sources is an important driver there what what else are you seeing in your group you know with your hat is the the people officer you know obviously this is a big impact not only on all of your customers partners but on fun Red Hatters themselves well it is a huge impact we're so fortunate that we have some experience working remotely we have about 25 percent of our population that historically works remotely so we have that as a foundation but certainly the quick move the rapid move to really thinking about our people first and having them work from home across the globe that is unprecedented and at this point we have some individuals who have been working from home for many many many week and others that are really in entering their fourth week so we're starting to have this huge appreciation for what it's like to work remotely and what we can learn about more effective inclusion so I think you know back to the idea of women and open source and diversity inclusion one of the things you may always prided ourself in is we focus on inclusion and we think about things like okay if the person is not in the room with their remote let's make sure for including them let's make sure they get to speak first etcetera well now we're learning what it's really like to be remote and for everyone to be remote and so we're creating this muscle as an organization I think most organizations are doing this right getting a muscle you didn't have before we really really having to think about inclusion in a different way and you're building a capability as an organization that you didn't have to appreciate those that are not in the room and to make sure they are included because no one's in the room you know we're really important pieces and dallisa you know one of the things that that's always great about Red Hat summit is you you bring together all these people as we just heard you know that your two Award winners here you know got connected through the awards so maybe give us a little bit of a peek as to what sort of things the community can still look forward to how they can continue to connect even though we're all going to be remote for this event yeah this event is is it going to be great event and I hope everyone joins us along our journey we are fortunate that Red Hat you know as the open source leader really wants to take a leadership position in thinking about how we can shine a light on opportunities for us to highlight the value of diversity and inclusion and so we've got a number of events not throughout the summit that we'd love people to join in and we're going to be celebrating our women and open-source again at our women's leadership community lunch is now not a lunch it is now a discussion unless you're having your lunch that you can check your desk but we're having a great conversation at that event I mean by people to join in and have a deeper conversation and also another look at our women in open source Award winners but these Award winners are just so amazing every year that applications that are submitted are just more and more inspiring and all the finalists were people that are so impressive so I love the fact that our community continues to grow and that they're more and more impressive people that are joining the community and that they're making those connections so that together we can you know really shine a light on the value that women bring to the communities and continue to inspire other underrepresented groups to join in and participate then a you know research obviously is an area where open-source is pretty well used but just give us a little bit of viewpoint from your standpoint yourself and your peers you know I would think from the outside that you know open sourced is just kind of part of the fabric of the tools that you're using is it something that people think specifically about a course or does it just come naturally that people are you know leveraging using and even contributing what what's available the tool I'm using is called cuteness it's an open source tool written in Python and so that gives me the possibility to have a look in deeper into the code and see what's actually inside for example I would like to know how what is the size of the target that is shown in the virtual space and I can fit know that correctly to the millimeters because it's available to me in open source so I think these are the advantages which researchers see when they have tools open-source tools and at the same time there's also a movement in Sweden and in most of Europe where they want the researchers are asking for publishing their articles in open access journals so they want most of their research be published as transparent as possible and there is also this movement where people want researchers want to have their data put in some open data city so that everybody can have a look at it and do analysis on the data and build up on that data if other people want to so there's a lot going from the open access side and knowledge side and also the open source side in the research community and I'm looking forward to what probably 19 will do to this movement in future and I am sure people will start using more more and more open-source tools because after the Manderly yeah making I'm curious from your standpoint when I think about a lot of these communities you know meetups are just kind of some of the regular fabric of how I get things done as well as you know just lots of events tie into things so when you're talking to your colleagues when you're talking to your peers out there how much is kind of the state of reality today having an impact in any any learnings that you can share with gaudí yeah that is definitely a challenge that we're going to figure out together and I am part of a group called Foss responders we are reaching out to projects and listening to their needs and amplifying their needs and helping to get them connected with resources and one of the top three areas of need include how do I run an online community event how do I replace these meetups and what is wonderful is that groups have been moving in this direction already and so who would release a guide of how they run online events and they provide some tooling as well but so has WordPress put out a guide and other projects that have gone down this path and so in the spirit of open source everyone is sharing their knowledge and Foss responders is trying to aggregate that so that you can go to their site find it and take advantage of it yeah definitely something I've seen one of the silver linings is you know these communities typically have been a lot of sharing but even more so everybody's responding everybody's kind of rallying to the cause don't want to give you the final word obviously you know this is a nice segment piece that we usually expect to see at Red Hat summit so what else do you want to help share where the community is final closing thoughts well I think that you know we're not done yet we have been so fortunate to be able to highlight you know the contributions that women make to open source and that is a honor that we get to take that role but we need to continue to go down this path we are not we're not done we have not made the improvement in terms of the the representative in our communities that will actually foster all of the improvements and all the solutions that need to happen in the world though we're going to keep down this pathway and really encourage everyone to think through how you can have a more inclusive team how you can make someone feel included if you're participating in a community or in an organization so that we really continue to bring in more diversity and have more innovation well excellent thank you so much Alisa for sharing it thank you too - both of you Award winners and really look forward to reading more online definitely checking out some of the initiatives that you've shared valuable pieces that hopefully everybody can leverage all right lots more coverage from Red Hat summit 2020 I'm Stu minimun and as always thank you for watching the cube [Music]

>>Live from Las Vegas. It's the cube covering UI path forward Americas 2019 brought to you by UI path. Hello. We've already welcome to Las Vegas. This is day two of the year. >>Path forward conference UI path forward three. So what UI Pat does is they named their events one two three last year we were at Miami in the year before was one. Their North American event, which was in New York city. Here is three at the Bellagio hotel in in Las Vegas. 3000 people here for this rocket ship company growing revenues, they've got over $300 million in annual recurring revenue. That's up from 25 million in 2017 so you're talking about greater than 12 X increase in annual recurring revenues over 3000 employees. Now, Daniel Dienes, the CEO just named the industries the tech industry's latest billionaire. He's now dressing like a billionaire last year. He's in a tee shirt this year. He looks more like a more like a CEO. So we're going to be interviewing him later on today, but let's get right into it. The keynotes today comprised God Kirkwood who gave some predictions and that's her. >>I'm going to go, I'm going to talk about his predictions. I'm going to make some comments on those predictions and give you some thoughts of my own. Maybe throw in a few predictions of from Dave Vellante and then Craig LeClaire from Forrester gave a keynote. He was on the QBs today. Very knowledgeable analysts, probably one of the industry's top analysts, and I'll make some comments on some of the things he said. So let me get right into it. You got Kirkwood when you do these predictions, you know I put 'em out there. Of course it is smart. He's going to do these things and make them somewhat self-serving for RPA and UI path. So I'll make some comments on that as first one. One those was, there'll be a global economic downturn. I can't remember if he actually pinned a date, but I think he said it's in paint pending. >>Let's let's say 2020 he said that's good for RPA. Why would that be good for RPA? Because if there's an economic downturn, people are gonna want to get more. For less, and they're going to want to automate. They're gonna want to spend money and get fast ROI. And RPA potentially is a way to do that. It's not necessarily good news for low wage workers. They're doing mundane tasks. But nonetheless, he made the statement that it's good for our RPA. I would say this, I think a lot of this is going to depend on 2020 and the election in the United States as to what happens. I think it's very unclear right now. You saw the democratic debates last night. It's very clear that there's a, there's a swing to the left. Elizabeth Warren is, is kind of appears to be the front runner. So I would, I would make this prediction. >>I actually think Trump was gonna win the election. You know, don't hate me for saying that all you Trump haters, but I think whatever happens, maybe, maybe doesn't win the election. Maybe he wins the election and then, and then the subsequent election goes to the Democrats. But I think there's going to be a major swing back to the left. And I think that what that's gonna do, it's gonna open up the checkbooks and put more pressure on debt and I don't think there's a real issue right now of too fast economic growth of inflation. It's obviously something that economists watch, but if interest rates start rising back to the Clinton era levels, that means big trouble for the economy. But I don't see that necessarily happening in 2020 I think 2020 we'll see some moderation. I definitely think we're seeing less tech spending expected for Q four and I think that'll spill into 2020 based on the ETR and enterprise technology research data that we see. >>But I think it's actually a healthy pullback. I kind of agree with guy on that front. I actually think it is good for RPA. I think RPA is one of those sectors that you see in the ETR surveys that is gaining share relative to other tech spending and I think that will continue in any downturn. So I expect softness. However you define downturn, I don't think it's going to be falling off the cliff or a disaster, but I definitely think spending will be more tepid. Second thing he said is RPA will become the YouTube for automations. Think of YouTube as a container. I am not going to spend a lot of time on this one. A YouTube and RPA. I think no one's a consumer, but his, his analogy was around a container for automations, just like YouTube was a container for for video. I think they have aspirations to scale like YouTube, but if you look at RPA is a right now a back office, B2B business function and I think it'll stay that way for a couple of years. >>I'll make some statements on that. Automations will move from snowflake to snowball. What does he mean by that? Well today automations are all unique. Every company, and he made this statement feels like it's automations are a snowflake there. Everyone is different and what he's predicting is that over time these automations will become, there'd be more commonality in those automations. I think that's true. I do think while there are definite business processes that are unique to companies that there are a lot of similarities. Things like the UI path marketplace will allow people to share automations and I think there will be much more commonality. I think it's critical for scale. Number four, he said students entering the workforce will force employers to use automation. He didn't give a timeframe on this, but I'll tell you one thing. At a 2020 I've got three kids in college with two kids in college, one that's recently, recently graduated, who does something. >>Most kids in college have no clue what robotic process automation is, let alone what the acronym RPA stands for. So this is going to take some time. asked a hundred college kids what RPA is and I bet you maybe one or two have heard of it, even know what it is. So that's not happening today. I think that'll take probably another two cycles of graduate's before that really hits. We heard from the college of William and Mary yesterday where Tom Clancy and the college have partnered to really push in RPA into the curriculum and I think that's great. I'm going to talk, Tom Clancy's, a expert in the area of training and education that's going to take some time to bake out. So I would put that again. Guy didn't give a timeframe, but I would, I would say that's, that's five to eight years away. Number five, we'll continue to be surprised by the intelligence of machines and the stupidity of humans. >>Well, what he meant by that was there are some things that humans do that are repetitive, that are mistakes. They make the same mistakes over and over and over again, and machines won't necessarily do that. I do think this, that the gap or the number of things, if you make a list between the number of things that humans can do versus what robots can do with a physical or software robots, that gap is closing. There's no question about it. It's, you know, short few years ago, robots couldn't even climb stairs and now they can and you're, you're seeing things like chatbots improving. There's still, you know, a lot of them are still crap frankly, but, but you're going to see a lot of money go into chatbots. And so I do think that that gap will, will close. And I think it's, it's gonna, it's gonna come down to education and creativity in terms of the impact on job loss. >>And I'll make some comments about that in a moment. The six prediction, there are seven overall, so bear with me here. Automation will be discussed in the United nations con and the context will be jobs, wages and global economics. That's already happened. It's already happening. People are concerned about the impact on productivity and, and so, you know, that's a lock. The last one was consolidation amongst RPA vendors and automation led services will accelerate. I totally agree with this. He mentioned work fusion and amp works as two companies that are gonna. We're going to where we're going to see consolidation. We've already seen it. SAP got bought Contexto so you see in the big whales come into this market in four talks a lot about RPA. Anytime there's a fast growing software segment like RPA and as a leader like UI path, would you other companies all you know on their tail automation anywhere and blue prism automation anywhere in UI path have a ton of dough. >>You're going to see the big software companies say, wait a minute, I need a piece of that pie. Because software companies generally feel like every dime that's spent on software should go to them. That's the mentality of an SAP or an Oracle or even IBM and so either, unquestionably, you're going to see some consolidation. You mentioned service providers as well. Companies like symphony. I've been making a lot of comparisons this week between what I see in the UI path ecosystem and what I saw way back in the early part of this decade in the service now ecosystem. You had a company with Fritz like cloud sharper, which nobody ever heard of. They were a service management ITSMs expert and Accenture eventually snapped them up and came in. You saw DXC or CSC at the time do the same thing. And so I think you'll see the same thing here in this ecosystem. >>This ecosystem here is happening. It's buzzing, but it's got to grow and, and you're already seeing Deloitte and cognizant and E Y and PWC. The big guys could have jump in here. I often say that SIS love to eat at the trough and they know where the money is and the money appears to be in RPA because really there's so many screwed up processes inside companies. RPA is actually can give them a quick ROI. Now let me turn to some of my thoughts on this. Let me talk about the job impact of automation the vendors would have. You believe that it's all good, that people love this and and when they bring in software robots, it makes their lives better because they're doing less money, less money, less of the mundane tasks, and they're able to focus on new, more strategic things to our customer that we've talked to here in the cube. >>And also privately. This is true, people do love your software. Robots. When we were Jean younger yesterday from security benefit. If you Civ most excited she's ever been, you know, having said that, Craig Le Claire's research shows that over the next 10 years we will see a 16% job loss of jobs will disappear, rolls will disappear, and by the way, foresters at the low end of the spectrum of that forecast. Most forecast say 30 40% of jobs are going to get disrupted. I tend to believe that Craig's number is probably a better one at the lower end of that spectrum, but that's still a huge number. You are going to see unquestionably job impact from automation. Absolutely. No question in my mind. I think you're already seeing it now. Look it. Humans have always been replaced by machines, but for the first time in history we're seeing Keith cognitive functions replacing humans and as going to have a big disruptive impact on the workforce. >>And the other piece of this I would predict we are going to see a productivity boost. I think a significant productivity boost. Let me share you some data with the Bureau of labor statistics, which you know, you may look at that, you know in question some of their methodologies, but over the longterm, I think it's a viable metric from 2007 to 2018 productivity grew at 1.3% that's an anemic rate from from 1947 to 2018 productivity grew at 2.1% so Oh seven to 18 half the longterm productivity gain, 2000 to 2007 2.7% and then from, and then what we saw in Q one of 19 3.4% uptick in productivity. Is that sustainable? I think it is. I think we're now entering a, a new phase of productivity growth and I think it's gonna be driven by things like RPA and other automation. So that is going to have an impact back to the earlier statements on job loss. >>Okay. The other thing is I want to talk about the forecast, the market. Last year at UI path two in Miami, I said that I thought that forecast was low. They had like $4 billion by 2020 and I sort of called out Craig LaClaire on that, you know, and so I said this could be 10 billion by 2020 now he clarified that today up on stage. I was including services in, in my prediction, correct. Declares follows this market much more closely than I do. So I'll defer to him on, on on that. But he put in the services number and he showed the services to license ratio of around, you know, three X or so. But he actually had this very serial number about 10 billion by 2020 so I felt, felt good about that. That kind of bat my back of napkin prediction. I used to do this stuff at IDC for a living. >>So you know, actually got a little knack for that on an analog basis. Then he showed sort of his, his forecast for the market, you know, growing at a very linear rate. Now I'll say this, I think hot markets like RPA, they generally don't grow at a, at a, at a linear steady rate. If you look at some of the emerging forecasts that I, you know, for instance, IDC had in my years there, we would always have these linear like smooth growth forecasts. You know, some of those big markets, you know, think, you know, early days of the PC, the, the, the, the internet flash storage, you know, things of that nature. They tend to, these disruptive technologies tend to grow in an curve or an S curve. So what you see is sort of this momentum building where the market is being seeded. Know Gardner has RPA now in the trough of disillusionment. >>So you're seeing some of this, okay, the little engine that could, and then what you see is this steep part of the S curve growing and then after it explodes and hits escape velocity, it's sort of stretches out into maturity. And I think that's what you're going to see with RPA. But some things have to happen before that happens. And one is specifically the RPA has to move from the back office to the front office. It has to move from only really dealing with pretty simple, mundane tasks to more complicated automations. It's got to be able to deal with unstructured data. It's gotta be able to handle on attended or rather attended bots where you're injecting humans into the equation and you're actually using machine learning and artificial intelligence to to learn and then identify other areas of automation and actually have systems of agency that can act. >>In other words, a bot will call another bot that actually can complete a transaction and so you're going to see a lot of money spent here. This is a big chasm. I think that RPA has to cross. We're going to talk to Daniel DNAs about this. He's a big ticker. He's a go big or go home guy, and so I think those things I would predict those things actually are going to happen because you're going to see so much effort and money and emphasis put into AI and for competitive advantage that I actually think that RPA can lead that and then again come back to the consolidation. I think you will see some consolidation. I think you're seeing UI path. Try to take the lead automation anywhere is kind of pressing the lead if you will. Both companies have raised a couple of billion dollars if you combine them and I think the way this market shakes out is any and you're going to have some of the big whales come in like SAP. >>I think the way this happened is you're going to see one or two specialists emerge. I think UI path is on its way there automation anywhere as well and and the number one player is going to make a lot of money. The number two players going to do two. OK the number three player is going to struggle and everybody else is kinda be either break even or they're going to bundle it in like SAP as part of their overall portfolio and compete on that basis. So I would predict that UI path will maintain its lead. I think its got the culture to do that. I think automation anywhere also could company is going to keep pressing that lead and those should are two companies you know that you need to watch me. Interesting to see. Blue prism, I think they are somewhat under capitalized. They went to the public markets. >>The spending data actually shows all three of these companies as well as some of the legacy companies like Pega systems actually gaining could have more share relative to other initiatives. So I think even some of these legacy companies are going to continue to chug along and actually do pretty well in the business. But, but the real darling, you know, I think it's going to be UI path. All the bankers are hovering around earlier on this week trying to get their business. They know there's an IPO coming at some point. Again, we'll ask Daniel Dienes about that today. You have it. That's my intro. Some of my predictions. Some a guy Kirkwood's predictions. Wall-to-wall coverage on the cube today, day two at UI path forward three from Las Vegas. We'll be right back right after this short break.

>> Announcer: Live, from Copenhagen, Denmark, it's theCUBE! Covering Nutanix.NEXT 2019. Brought to you by Nutanix. >> Welcome back everyone to theCUBE's live coverage of Nutanix.NEXT. We are at the Bella Center in Copenhagen, Denmark. I'm your host, Rebecca Knight, alongside of Stu Miniman, of course. We are joined by a good friend of theCUBE, Ray Wang, principal analyst and CEO of Constellation Research. Thank you so much for returning to theCUBE. >> Hey, how you doing? Good morning! >> Good morning, good morning! >> Good morning! (laughing) >> Good morning! >> I don't know. I get all my accents wrong out here. >> (laughing) So, you got a shout out on the main stage this morning, from Monica Kumar, congratulations on that. She talked about you and your research on the infinite role of computing. You also do a lot with the future of work. I know that that is really right in your wheelhouse right now. What are you hearing, what are you seeing, what kinds of conversations are you having that are interesting you? >> Yeah, so, this infinite computing option, it's one of the that we're talking about, the fact that you can scale out forever, right? And the problem that's holding us back has been technical debt, right? So all that legacy that everyone's got to figure out. It's like, my connections, my server, my disk-rack recovery, my disaster recovery, my backup, everything. It's a pain in the butt. And I'm still trying to get onto the cloud. So on that end, we're like, okay, all this stuff is holding us back, how do we get there? Now, the future of work is a little bit different. We're seeing a very very different set of work. People have talked about where we are the gig economy, but that's just one aspect of it. Everything is being decomposed into microservices. Large processes are becoming smaller and smaller microservices, they're being reusable, well our work and tasks are following the same way. We're getting smaller and smaller tasks, some are more repetitive, some are going to be automated, and it's really about where we actually find the difference between augmentation of humanity, and full automation, and that's where the next battle's going to be. >> Yeah, Ray, some of the discussions we've been having this week, is how do we really simplify the environment? The balance I hear from customers, on the one hand, they're always like, I don't have enough money, I don't have enough personnel, on the other hand, oh my gosh, that full automation sounds like you're going to put me out of a job. We know we're not putting everybody out of work in the next couple of years. There are challenges; we worry about the hollowing out of the center of the economy, but here, what Nutanix is trying to do, of course, is, I don't want to have to thrive in that complexity anymore, I want to be able to drive innovation, keep up with that, take advantage of that unlimited resources out there, so, where do you see, you've been here at the show, what are you hearing from the customers here? Anything different in Europe versus back in North America that you'd share about that journey onto the changing roles? >> Oh it's a great point. It's about simplifying everything where you can, it's about areas of automation where they make sense. Here in Europe it's slightly different because a lot of the focus in Europe has been about cost and efficiency, followed by of course regulatory. Those have been the two drivers. And they've been battling that in order to be, even they will look at some level of innovation. Where in the US, people are head on doing innovation, regulatory and operational efficiency at the same time. So that creates a very very different environment. But what we have noticed are some patterns, especially when we look at automation and AI; there are four areas out of seven where we see a lot more automation that's happening. The first one is massively repetitive tasks, those are things, yeah, got to get that out of the way, we don't do this very very well. The second one is really thinking about massive nodes of interaction. When you're connected to multiple places, multiple organizations, multiple instances, that's something where we start to get overwhelmed, and then of course, there's lots of volume. If you've got lots of volume or requests that are coming through, you can't possibly handle that, and that's a place where we see a lot of machine scale. And the last piece is really when you have to scale, humans don't scale very well. However, it's actually not a hollowing out of the middle; it's actually a hollowing out of the ends in a very, very real end, because really really simple tasks go away, super complex tasks go away, and the middle actually remains, and the middle is things that are complex that cannot be recreated by math, they're also areas that require a lot of creativity, humans make the rules, we break the rules, and then the last part is really fine motor skills and presence, the machines still aren't as good. So we still have some hope. So the middle stays, it's the hollowing out of the ends, the high end jobs and the low end jobs are the ones where we're going to see a lot of risk. >> So what does that mean? So we have, leaving the middle there, and as you said, the high end jobs and the low end jobs go away, but what does that mean in terms of the skills? In terms of what employers are looking for, in terms of what they need in their prospective applicants and hirees. >> That's a great point. Soft skills are important; it's the qualitative skills that become even more important, it's also being able to manage and orchestrate the hard skills; because you don't necessarily have to know how to do the calculation, you have to just know which algorithm to apply. >> Okay, and then also, these soft skills of managing people, I'm assuming too? Because computers are not so good at that either. >> Yes. Soft skills are managing people, but also manage the human and machine equation that's going to happen. Because we have to train the machines, the machines aren't going to know that level of intuition, and there's a large amount of training that's going to happen over time. >> All right. So, Ray, one of the things Nutanix is doing is, as they've been transforming to not only subscription, software's always been at their core, but they're starting to do not just infrastructure software, but application software. I know you live in that world quite a lot, so when you hear Nutanix talking about building databases, delivering these services, it's something that I look at, Amazon does some of that, but for the most part they're infrastructure and build on top of us. How do you think, how is Nutanix doing, what are some of the challenges for them, going up against some of the bellwethers out there in tech, and all the open source projects that are out there. >> So the challenge is always going to be, there is a one dominant player in every market. And what they're providing is an alternative to allow the orchestration of not having that, not only that dominant player, but a choice. So in every single market, they're focused on giving users choice, and giving the ability to aggregate, and bring everything into one single plane. That is tough to do, right? And the fact that they see that as their big hairy audacious goal, that's impressive. If you said they were going to do this three years ago, I wouldn't have believed them. >> Well yeah, I think back to, remember almost 10 years ago, VMware tried to get into applications, they bought Zimbra, they bought a few others. Cisco did like 26 adjacencies, they were going to take over video and do all these things, and we've seen lots of failures over the years. They refocused on their core, was a big thing that I heard, that the users seem to be excited about. Are there areas that you're find especially interesting as to where Nutanix is poking? >> So, I would say that Nutanix three years ago was a little bit sleepy. They got comfortable, they did the stuff that they did really well, and it feels like, maybe about 12 months ago, Dheeraj had a different vision. Like something snapped, something hit, he said this isn't working, we're going to change things, and we've seen a whole bunch of new talent come into play. We've also seen a huge expansion of what they're trying to do, and a cleanup of all those side projects that were all going on before. So I think they've actually honed in on, okay, if we can simplify this piece, this is a money-winning business for some time, and they're talking about 80% margins last quarter, I mean that's huge, and that's just trying to save customers money, and make their lives simpler. >> Do you think that they have the messaging right? Because, I mean, they're going to this Thoreauvian/Emersonian idea of simplify, simplify, simplify, and it does resonate, of course! What customer doesn't want a simpler computing experience? But do you think that they are reaching the right people, and they have obviously very passionate customers, but are they getting into new businesses. >> I think they're getting to the businesses that their customers are asking them to, those adjacencies are huge, I think and when you think about cleaning up technical debt, all that legacy debt that you actually have to fix, I mean, this is where you begin. It's so hard to make that cloud journey to begin with, it's even harder to carry all that legacy with you. And we're going to see a lot more of this going forward. >> All right. So, Ray, talk a little bit about, I loved an event you did last year, the people's centered digital future. Help explain to our audience what this is about, and where you're taking it again this year. >> So that event was a one-time event. We were celebrating the 70th anniversary of the United Nations founding, we were celebrating almost 50 years of the internet, and 50% of the world being connected to the internet. And part of the reason that was an important event was, we really felt that there was a need to get back to the roots of where the internet had begun, and more importantly, talk about where we are today in the world of privacy. One of the biggest challenges we have in the a digital world is that your personal data, your genomics, all this information about you is being brokered for free. And what we have to do is take that back. And by taking that back, what I mean is, we've got to make all these rights, property right. If we can make that a property right, we can leverage the existing rules and legislation that's there, and we can actually start paying people for that data through consent, and giving people that ability, on consent to data, could create lots of things, from universal basic income, to a brand new set of data economy that equalizes the playing field, while keeping the large tech giants. >> There's some of those big journeys that we went on, you talk about the internet, this year's 50th anniversary of the first walking on the moon, and you look at how entire countries rallied together, so much technology was-- >> Yeah, look at India. >> Spun off of what they've done there, it's like we need some rallying cries in today's day and age to solve some of these big day and age. Is that AI? Where are some of the big areas that you see tech needing to drive forward in the next decade? >> I think the big area's going to be around decentralization, giving individuals more empowerment. We've got large, big tech companies, that are, I'd say, imbalanced. We start companies right away, building monopolies on day one, and we don't open up those markets. And the question is, how do we create a level playing field for the individual to be to compete, to bring a new idea, and to innovate, if that's continuously stifled by big technology companies without an opportunity, we're in trouble. And so that starts by making data a property right, to the personal data. It starts by also creating marketplaces for that data, and those marketplaces have to have regulations, similar to capital market flows. The way treat exchanges, we treat marketplaces, we need to do the same thing with the way we do with data, and then the third piece, there has to be some level of a tax, that goes to all these data economies, so that they can fund the infrastructure and the watch dogs that are there. Now this is coming from a free market, I'm a free market capitalist, okay? I can't stand regulation, but I also realize that it's so important that we have a fair market. >> But do you, we know so much about how Americans are so much more cavalier about their privacy than even Europeans, what will it take to galvanize Americans to care about those little crumbs that they're leaving on the internet, that is the data that you say should be a property right, that we should be paid for? >> I think it's going to start with companies actually take, and do the right thing, where they actually give them that opportunity to monetize that information. >> Will they do that? >> I think the new set of startups are starting to do that, because they're looking at the risk that's being posed, at Facebook and Google and Amazon, on the anti-trust, DOJ, FCC, they're all coming in at the same time, the FTC, they're all wondering, do we break these companies up or not? The short answer is, I don't think they're going to, because we're competing with China, and when you're looking at that scale of data, where Amazon's transactions are only 1/10 of Ali Baba's? That's huge. So the consolidation has to happen, but we need to create a layer that actually democratizes and creates a fair trading play. >> And those startups, you think, can compete with established players? >> I think once we set the roles, and the ground rules, I think people are going to be able to do that, but once you free that data, what are we competing on now? You have to pay for my consent, you have to earn my business, you can't trade it for free, or just say, "Hey look, you are the product." That changes everything. >> Rebecca: Yeah, that's a good point. >> Ray, I know you spend a lot of time talking to, and giving advice to some of the leaders in technology, you're welcome to get into some specifics about Nutanix, or some of the cloud players, but what are some of the key themes, what are people getting right, and what are they still doing wrong? >> Okay, so theme number one, this is going to be a multicloud hybrid world for a long time. Anybody that's bucking the multicloud trend, they've missed the point, right? Because we want portability in data, there's only two or three players in every single market, if I can't move my data, my workloads, and my IO in and out, then you've actually created vendor lock-in from hell. And I think customers are going to protest against that. The second one, and you guys are probably following this trend a lot, is really about AI ethics and design principles for AI. So what is ethical AI? We've got five things that are important: The first one is make sure it's transparent. See the algorithms, see what they write. Second one, make sure it's explainable. Hey, bias is not a bad thing, so if I'm discriminating against redheads, with, left-handed, and that happened to like, I don't know, Oracle, fine. But, if that was unintended, and you're discriminating against that, then we have to get rid of that, right? And so we have to figure out how to reduce that kind of bias, if it's unwanted bias. If you discover that you're discriminating, and not being inclusive, you've got to make sure that you address that. So then the next part is, it's got to be reversible. And once you have that reversibility, we also make sure that we can train these systems over time. And then the last piece is, Musk could be right! Musk could be right, the machines might take over, but if you insert a human at the beginning of the process, and at the end of the process, you won't get taken over. >> I want to hear about what the future of work looks like for Ray Wang. You are on the road constantly, you are (laughs) you are moving your data from one place, you are everywhere, all the time. So what do you have on next, what's exciting you about your professional life? >> I think the challenge's that we are living in a world where there's too much information, too much content. And you guys say this all the time, right? Separating the signal from the noise. And people are willing to pay for that signal. But that is a very very tough job, right? It's about the analysis, the insights, and when you have that, people don't want to read through your reports. They don't want to watch through the videos. They just want to call you up and say, "Hey, what's going on?" And get the short version of it. And that's what's making it very interesting, because you would expect this would be in a chat bot, it'd be in a robo advisor, doesn't work that way. People still want the human connection, especially given all that data out there, they want the analysis and insights that you guys provide, that's very very important, but even more important right now, it's really about getting back to those relationships. I think people are very careful about the relationships they're keeping, they're also curating those relationships, and coming back to spending more time. And so we're seeing a lot more of in-person meetings, in-person events, very very small, curated conversations, and I think that's coming back. I mean that's why we do our conference every year, as well, we try to keep 200 to 300 people intimately together. >> Those human connections, not going away. (laughs) >> Nope, not going away, in an automated, AI, digital world! This is our post-digital future. >> That's excellent. Well Ray, thanks you so much for coming on theCUBE, it's always so much fun to talk to you. >> Hey, thanks a lot. >> High energy guy (laughs). >> Low energy. >> I'm Rebecca Knight for Stu Miniman, we will have more from the Bella Center at Nutanix.NEXT coming up in just a little bit. (upbeat music)

>> From Seattle Washington, it's theCUBE, covering AWS Imagine, nonprofit. Brought to you by Amazon Web Services. >> Hey welcome back everybody, Jeff Frick here with theCUBE. We're actually on the waterfront in Seattle at the AWS Imagine nonprofit event. We were here a couple weeks ago for the AWS Imagine education event. This is really about nonprofits and solving big, big problems. So Dave Levy and team have you know dedicated to some of these big problems. And one of the big problems in the world is human trafficking, and problems that people are encountering and all kinds of nasty situations all over the world. And we're really excited to have someone who's tackling that problem, and really trying to bring a voice to those people that wouldn't otherwise have a voice. And she's Hend Alhinnawai, she's the CEO of Humanitarian Tracker. Hend, good to see you. >> Thank you Jeff, good to be here. >> Absolutely. So before we jump into it, impressions on this event? >> Wonderful event bringing together technologists, people in nonprofits, really creating synergies for people to collaborate and talk to each other and network and learn how they can advance their organizations. >> Such important work. >> Yes. >> So give us kind of the background on what you're up to, what Humanitarian Tracker's all about. >> So Humanitarian Tracker's a nonprofit forum. It was created to connect and empower citizens using innovation and technology, but specifically for humanitarian events. We were among the first to combine crowdsourced reports with data mining and artificial intelligence and apply them to humanitarian disasters, conflicts, human rights violations, disease outbreak. All the way to tracking the UN's Sustainable Development Goals. Really giving a holistic view of what's happening. >> It's interesting, you know, it's probably like the middle eastern spring, I can't remember the exact term that people use, where it was kind of the first use of regular people using their mobile phones to kind of grab a ground swell of action. You're not looking at the politics specifically, you're looking more at humanitarian disasters. But pretty amazing kind of what a connected phone represents to anyone anywhere in the world now to communicate what's happening to them. To share that story. We really didn't have anything like that before. To get that personal event on the ground. >> No it's really a new way of consuming, creating and consuming information. So the cell phone has really given people on the ground a chance to tell their own story. But it's not enough. If you have an event that happens to you. Something happens to you. And you record it, it stops there. But the unique thing with Humanitarian Tracker is it gives people that forum to show the world and tell them what's happening to and around them. >> Right, but it's not just about the individual. And what you guys are doing is using cutting edge technology, obviously you're here as part of the AWS event. In terms of machine-learning and big data to grab a large number of these reported events and distill it into more of an overarching view of what is actually happening on the ground. How did you do that, where did you get that vision, how are you executing that? >> Well, we're all about empowering the citizen. And in our line of work we deal with a lot of data, a lot of information, most of it is unstructured, most of it is crowdsourced. So we use machine-learning to help us extract important details. Information on time. Event location, what is happening. And at the same time we really cared that this reporter, stays anonymous for their own safety. We, privacy and security is utmost importance to us. So that's always our focus. So in that space, we de-identify them. We take out any information that could be identifiable, that could lead to their arrest, or could lead to someone identifying that it was them that reported. >> And how do you get this information to the people that are suffering this activity ground? How do they know about you, how do they know that you are anonymizing their information so there's not going to be repercussions if they report. You know, how do, kind of I guess your go-to-market, to steal a business terms, in making sure that people know this tool's available for help? >> It depends on the situation. For example in the conflict situation, we rolled it out, and we kept it low key for awhile. Because we didn't want government attacks, we didn't want people to be arrested, or to be tried. So we rolled it out. And it was word of mouth that spreads. And people started submitting supports. Actually the first project we did with conflict, we weren't sure if we were going to get one report, zero reports. The first week we got nothing. And then slowly as people learned about it they started submitting their reports. And we see our job as really elevating the otherwise marginalized voice. So you submit a report to us, we then take it. We verify it. We make it public. And that, we welcome, we encourage, we want people to consume it. Whether you're a student, whether you're a journalist, whether you're a government, whether you work in a nonprofit, the UN. It's been used to address human rights violations, it's been used to identify humanitarian hotspots. The data's phenomenal, and what you get from it. It's not just collecting data. We're not just about collecting the data. We want to make sure it's meaningful, and we want to derive insights. So we want to know what is the data actually telling us? >> Right, right. So just to be clear for people that don't know, so you're making that data available, you're cleansing the data, you're running some AI on it to try to get a bigger picture, and anyone with a login, any kind of journalist can now access that data in support of whatever issue or topic or story they're chasing? >> That's it Jeff. >> That's phenomenal. And just kind of size and scope. You've been at this I think you said since 2011. You know kind of how many active, activities, crisis, I don't know, what the definition is of a bucket of these problems. Are you tracking historically at a given point in time? Give us some kind of basic sizing type of dimensions. >> It really ranges, because it could, when we were tracking conflict for example, we were really focused on one area, and the surrounding countries. Because you had refugee population, you had displacement, you had all sorts of issues. But it could be anywhere from five projects, it just depends. And we want to make sure that each project we're taking on we're giving it our full attention, full scope. And I like to run the organization like a two-team pizza team. And so I don't take on more than I could handle. >> Right, right. So then how did it morph from the conflict to the Global Sustainability Goal? So we've worked with Western Digital, they're doing a lot of work, ASP's doing a lot of work on kind of these global sustainability goals. How did you get involved in that, and how did the two kind of dovetail together? >> So the elasticity of the cloud has helped our operation scale tremendously. And in 2016 we were selected as a top 10 global innovation, that could be applied to the Sustainable Development Goals, and-- >> So they found you, the UN find you, or did you get nominated? How did that happen? >> We were nominated, and from over 1,000 solutions we were chosen. >> Congratulations. >> Thank you. And we were showcased at the Solutions Summit which is hosted at the United Nations. And just based on that experience of meeting people that were doing really cool things in their respective communities, we launched the Global Action Mosaic. Because we wanted to create one place where people that are doing projects in their communities could submit it, and have it showcased. And the goals are not only to crowdsource the SGD's, but to also be a part of the effort to track what's happening. Who's doing what where, make it easy for people to search say, Jeff you decided to get involved in a project with education. You can go onto our Global Action Mosaic, search projects on education in your community or in other parts of the world and then get involved in it. So it's really creating a centralized place where people can get information on the global goals. >> Awesome. So that's pretty much the Global Action Mosaic. It's pretty much focused on the UN global goals versus your core efforts around the Humanitarian Tracker. >> Yes. >> That's great. So we're here at AWS. Have you always been on AWS? Is this something new? How does being on kind of the AWS infrastructure help you do your mission better? >> We are, we've been partners in running AWS since we actually started. >> Since the beginning. >> Yes we have Yusheheedi as one of our partners, development partners, AWS. And because one of the core, one of the most important things to us is privacy and security, we want to make sure that whatever data is being handled and received is stored securely. >> Right, right. >> And that information transmitted, handled is also being done so in a secure way. Like I mentioned, the elasticity of the cloud has helped us scale our mission tremendously. It's affordable, we've been able to us it, we've learned their machine-learning stock to de-identify some of the data that comes in. So we're firm believers that AWS is essential to how we run our operation. >> Because do the individual conflicts kind of grow and shrink over time? Do you see it's really a collection of kind of firing up hotspots and then turning down versus one long, sustained, relatively flat, from kind of a utilization and capacity point of view? >> Yeah, no it definitely, it flares up and you'll have like a year, months, weeks sometimes where it's just focused on one area. But one of the things we focus on, it's not just. So what is the data actually telling us? So say you're focusing on point A. But just down the street in location B there is a dire humanitarian emergency that needs to be addressed. The crowdsourced reports, combined with the data mining and the AI, helps us identify those hotspots. So everybody could be focused here, but there could be an emergency down the street that needs to be addressed as well. It just depends. >> And do you have your own data scientists or do you, do other people take your data and run it through their own processes to try to find some of these insights? >> We have both. >> You have both. >> Yeah. >> So what's been the biggest surprise when you anonymize and aggregate the data around some of these hotspots? Is there a particular pattern that you see over and over? Is there some insight, that now that you've seen so much of it, from kind of the (muffled speaking) that you can share and reflect on? >> I think it' very unique to each project to do. But there is one thing that I strongly support, that I don't see enough of, and that's the sharing of data within the organizations. And so, for example just getting to that culture where sharing your data between organizations is encouraged and actually done. Could help create a, create a pool of knowledge. So, for example we worked with 13 different organizations that were all tackling humanitarian events. The same one, in Syria. And the 13 did not share data and did not talk to each other. And so we found that for example, they were all focused on one area. When just a few miles down, there was a need that wasn't being addressed. But because they don't share information, they had no idea. >> Right. >> It was only when we were able to take a look at it, kind of from the, from an overarching view, looking all their data, we were able to say you know, it would be helpful, it would actually, you could save on resources, and less time, and less effort, and you guys are tackling a small funding pool to begin with. If you shared information and tackled different things, instead of focusing on one area, because you don't know what the other guys doing. >> And were they using crowdsource data, is there source data, or were they just trying to collect their own from the field? >> They were collecting their own. >> So I assume that the depth, and the richness, and the broadness of data is nothing like you're collecting. >> Well you get a different kind of, you get different kind of information when the individuals actually telling you what's happening versus you asking a very direct question like, "Are you healthy? Yes or No?". Whereas you give them the chance, they might tell you that they haven't eaten, and their diabetic and you know, give you other pieces of information. Where they're living, are they refugees? Are they healthy? Are they not healthy? Do they go to school? Do their kids go to school? How many kids they have? Are they a female-run household? All this information could help guide development in the proper way. >> Right, right. All right. So give you the final word, how should people get involved if they want to help? >> You can go to humanitariantracker.org if you want to volunteer with us. And if you're doing a project that is related to the UN's Sustainable Development Goals, I would like you to go to globalactionmosaic.org, and map it there, and be part of our community. >> So Hend, thank you for taking a few minutes to share your story, and for all the good work that you're doing out there. >> Thank you Jeff it was a pleasure. >> All right, she's Hend, I'm Jeff, you're watching theCUBE, we're at AWS Imagine nonprofit. Thanks for watching we'll see you next time. (techno music)

>> Live from Washington DC, it's theCUBE, covering Inforum DC 2018, brought to you by Infor. >> Well, good morning. Welcome back here on theCUBE. We are live in Washington DC, at Inforum 2018. You can tell, Infor's just over the shoulder here. We're on top of the show floor, looking down, and a lot of buzz, a lot of activity out there. Good to be a part of that excitement here in DC. I'm John Walls, along with Dave Vellante, and we're joined by, he said, "Just call me Soma." Soma Somasundaram, who's the CTO at Infor. Soma, good job on the keynote stage this morning. Thanks for joining, appreciate that. >> Yeah, and yesterday. >> Yup, yup, thanks. >> So, talk about a couple of new products, one launched, one in beta. Why don't you go ahead and tell our audience a little bit about that, about what you're bringing to the marketplace now? >> Yeah, so, you know, we have, you know, as I mentioned in today's keynote, we're all about product innovation, and we're engineers. Charles is an engineer, I'm an engineer, and we're constantly driving new innovation. So, some of the innovation, there's fundamentally, we want to build what I would call a shared services platform that all of our cloud suites can utilize. There's no need for each of the applications to go reinvent the wheel to build a middleware, or a data lake, or an API layer, so we built a shared services platform, which is what we called Infor OS. As part of Infor OS, we continued to release new things. You heard today, we released something called Infor Go. As the name might suggest, the idea is that you as an employee in one of the customer organizations, you want to have, easily go to the app store, download something called Infor Go, it automatically is configured for your role. It gives you, if let's say you're a salesperson, it gives you access to CRM data, to curate your pipeline, it gives you access to employee data, because you're an employee of the organization, gives you ability to file expense reports, because you're a traveler. You get the idea. So, in a role, you don't want to be dealing with 20 different apps. It's just one thing. You just go in, one sign on, you get access to everything you need. That's one announcement we made. That's on the technology side. And on the functional side, you know, we launched a new CRM this morning, and the idea there again is that, we're in the CRM business not to build a horizontal CRM. Our idea is, you build, anything you do must be industry-specific, right? When you are selling and servicing an excavator, and you are a dealer of moving equipment, you want to know what kind of configuration installed, what kind of accessories I can sell to this farmer, what kind of terrain they're operating on. That is industry-specific. So to us, that is important. That's what we're doing with CRM. We built it on obviously our own platform, technology platform, multi-tenant, running in the cloud, but the main differentiation is industry, right? So that's something we announced. We've been on building a next generation HCM suite, which we talked about a lot yesterday. The final piece of that is payroll, which is important. So that payroll, which just went beta this morning. It's all built on the exact same platform, with Infor OS, multi-tenant, and it's highly extensible, so that completes our HCM suite on a unified platform. Those were the announcements we made today. >> So I wanted to talk a little bit about the platform. So last year, after Inforum 17, I wrote a blog post, and I put up the strategy and technology stack, and I kind of missed the OS underneath. So we'll come back and maybe course-correct that. But one of the problems with enterprise software, especially suites, is there are a lot of cul-de-sacs. You go down a road, and then you hit a dead end, and then you have to come all the way back, and if you want some other function, you have to go down and come all the way back, and it's a very frustrating user experience. So, I'm inferring that what you guys have done is try to address that and other problems with a platform approach. So a platform, in my view, beats products. So maybe talk about platform and what that means to you guys, and then I would love to get into the sort of conceptual and actual stack. >> Yeah, so, it is what should be common sense, in my opinion, that if you buy a HCM suite from a provider of software, you buy ERP from that same provider, you buy travel and expense application from the same provider. You would think that they all have the same user experience, and are integrated out of the box, they all seamlessly work together, with single sign-on. That would be a normal expectation as a customer, I would think, but unfortunately, the market's not going that way, right? Everybody's got their own, even within one company, you have multiple products, they don't work together well. Our idea is that if you buy an industry cloud suite, you must feel like it came from Infor, it all should have one single user experience, it all should work together as an integrated suite, it should all be sharing data for analytics, and so on and so forth. So that is the whole idea behind building this Infor OS. So, Infor OS has got several services underneath, starting with, you know, user experience, which is developing a hook and loop. So we have all of the controls, whether it's a dropdown box, or a grid control, or date picker, they all behave exactly the same way. Whether you're in CRM, or HCM, or inside a purchasing application, they all work the same, right? So, starting with that, then you go-- >> So if I can interrupt, so the Infor OS has the core services that you need, that the software needs to access for any function that you're building, correct? >> Exactly, yeah, yeah. >> Okay, please. >> So it's user experience, then you have integration. We have one integration layer called ION, and ION supports both an API layer, if you want to build a mobile app, you need APIs into the software, so built a lot of APIs into our applications. Those are exposed through a single gateway. There's one way to get into Infor applications through this API layer. We built that as part of Infor OS. We also built Coleman, which we announced last year. Coleman depends on two things. One, a lot of access to data, so I can crunch and do machine learning, and a lot of access to APIs. So what if you could create an acquisition, tucked into a device, versus having to open up a form, right? To do that, you need APIs. If you can order Domino's pizza from home, using Alexa, why can't you do that at work? So we built this framework for those kind of things. So it's got APIs, it's got Coleman, it's got data lake. So all of this data is in one place, so you can build analytics. We have Birst, which sits on top of the data lake, and I can go on. So that's really what we're doing with Infor OS. It's really, that's very, very important. It's not like your Intel Inside kind of thing. Without Infor OS, Infor apps don't work. >> So, if I can, if you bear with me, just to conceptualize the stack, the OS is at the bottom layer, and then you've got your micro-vertical functions as sort of the next layer, and then the cloud, which is really AWS, is the cloud infrastructure, then you've got the GT Nexus, essentially, the network commerce platform, so all those data and supply chain connection points that you have access to, Birst, the analytics, which was in acquisition last year, and then the Coleman AI completes the stack. My question is, as it relates to, for instance, Birst, that was an acquisition. So, you have to bring that in and do some engineering work to make it fit into the stack, is that right, or is it just kind of bolted on? >> No, you know, so, everything has to be done with the conscious way of design, right, so it just doesn't happen by itself. So, Birst is a fantastic world-class analytics platform, right? They as a company built a world-class platform that allows for department analytics, so if you're working in sales or working in marketing, you can go bring your own data, you can do analytics. It's great at that. At the same time, it's great at enterprise analytics, where you have all of this data in one place, you harmonize the data and do that. As a platform, it's a fantastic platform, but we're about delivering content on top of that platform, so we need to bring the network data, like you said, we need to bring the industry data, we need to bring the employee data from HCM. Bringing it all together and exposing that using Birst as the visualization layer is how we are exposing it. So to that extent, Birst was connected into the data lake, and it sits on top of the data lake, leverages that data. We built a semantic layer, which reflects the model of data that we have in the data lake, so yeah, it does, and we have the single sign-on, so it actually surfaces within Ming.le, within the homepage of a purchasing manager or whoever, and that's work, that's what we did. >> So you essentially re-platformed it. So of course, part of the due diligence is how challenging it's going to be to do that, how fast you can get that to market, but this is complicated. It requires a significant engineering resource on Infor's part. We talked about this a little bit at the analyst meeting last year, the industry analyst session. Couple things, one is the integration and exploitation of AWS cloud, and all the services there, the data pipelines, and the services there, but also modern software development. You know, microservices, and containers, and all of that good stuff. Can you talk about those sort of two dimension and any other points that you'd like to emphasize in terms of the things that Infor developers are doing to create this modern platform? >> Yeah, so, first of all, you know, we are all about applications, right, so we're not building databases, we're not building our own data centers, we're not building our own operating systems. We're a business software application company. Our belief is that if you try to verticalize and try to innovate on every single layer of what you do, it stifles innovation. Why not embrace industry's innovation, right? Can we out-spend AWS, in terms of building a cloud infrastructure? I don't think so. >> No way. >> No one can. And so, it's important to focus on what you do best, and leverage innovation that's coming in outside the four walls of Infor, to embrace that to deliver what the customer requires. So, what we really did is we took the AWS services, and we encapsulated them into our application, so when the application does disaster recovery, it's actually AWS services, right? When we call Elasticsearch, we're using AWS services there. We use DynamoDB for graphing the data in the data lake. Much like Facebook works on Open Graph, of trying to find people who are connected to each other, data inside the data lake is connected, right? Sales order is connected to a sales person. It's connected to a customer. Customer is connected to returns, and so on and so forth, so, we've done those kind of things. So, we've built a layer above the web services of AWS to actually create hooks into the application that leverages that, and we built our application itself in a sort of a microservices architecture. Granular APIs is a better way to describe how we did it, so that those granular APIs can be used in a digital project to create your own mobile app. It's the same APIs that are used in Coleman, for our digital assistant, or chat bots. All of those things require clear thought in terms of design, how you expose the functionality, and how you expose data, and that's what we did. >> Yeah, so, as a developer, in an engineering organization, having access to those primitives, those granular APIs, gives you what, greater flexibility, if the market turns, you can turn more quickly. I mean, it's more complicated, right, but it gives you finer grain control. Is that fair? >> Absolutely the case, yeah, and by the way, we know that the world is heterogeneous, right? I would love for a customer organization to just use Infor for everything, nothing else, right? But that's probably not realistic. So we built this to be able to work in a heterogeneous environment. So creating APIs and having this loosely-coupled architecture allows for that to happen. Ultimately, the customer has a choice. We obviously have to work to earn their business, but if they have other things outside of Infor that they're running in their ecosystem, you need to be able to embrace that. So this architecture actually allows for that. >> So it's the architecture, but if you're saying, if I'm a customer, and I want to run in the Google cloud, or Azure, technically, at least in theory, you can support that, but do you actually do that today, or is that sort of roadmap stuff? >> Technically, you could do that, right, but we obviously leveraged a lot of AWS services in our stack. What I meant to say in heterogeneity is that if you run a non-Infor application, right, so like, Salesforce for CRM, right? I would love for the customer to use Infor CRM, 'cause we think we are very competitive, but if they are running Salesforce, and they don't want to replace that, we need to be able to work in that environment, where it's running in a different cloud, it's running in a different architecture. So, we built Infor OS and the layer to be able to deal with that kind of hybrid deployments. >> Technically, what's the enabler there? Is it just sort of an API-based framework, or... >> It is API-based framework. It's also got federated security built into it. It's got the middleware understands, ION understands that data could come from a non-Infor system. As long as you're talking, you know, you go to United Nations, if everybody there has a headset, to really translate what anyone is saying, versus if everyone speaks English, well, world would be wonderful. >> But they spoke English yesterday. (John laughs) >> I got one more, I got one more geeky question. Anytime I get the head of engineering, you know, the CTO-- >> You love this. >> We love to get into it. The audience eats the stuff up. >> Yes. >> And we love the business talk too. But, I've heard a lot about multi-tenant architecture. My friends at servers now make a big deal about multi-instance, saying, oh, and I don't know if it's, if it can't fix a feature kind of thing, or if there's really, you know, additional value there, but the claim is it's more secure. Multi-tenant, I think conceptually, is certainly more cost-effective. What's your take on sort of multi-tenant? Why is it important? Maybe discuss the security levels that you guys engineer in, your comments. >> Yeah, yeah, if you have something that you can call it a feature, you can, like you said, but our belief is that multi-tenant architecture allows for faster innovation, easier update to the customer, to keep them current, and you know, you think about having thousands of individual instances that you have to update, on a weekly basis, because we will get to a weekly update. We are currently doing monthly update, and we get to a weekly update. That requires a natural act to create automation to be able to update all of them. I mean, there's, you know, you could argue which is really more pure, but multi-tenant architecture for us is one single application server farm that is able to work for different tenants, understanding their configuration, their business process, and operate the way they want it to be operated, but it is running in one single farm, that we can update as frequently as we need, without obviously causing disruption, so that is, I think is a good design scenario. Having said that, we actually isolate the data of a tenant, right, because you could have a scenario where all tenants' data is in one database. We don't do that. We actually insulate tenants so that data is not permeable. You can't go across tenants. So, we think that this is an elegant way to architect and keep it agile, and we can bring innovation faster to the customer. >> So when you go from monthly to weekly, to daily, to hourly, to minutely. Every customer comes with you, whereas in the multi-instance world, you actually have to plan for it. You've got to plan the migration. You're maybe N minus one, or maybe even N minus two, if that's supported, and it's more disruptive. >> That's correct. >> Okay, and then, you've got to engineer, you know, the security, and other factors. Thank you for that explanation. >> So, I always like to get back to, at the end of the day, you know, what are folks doing with what you're providing them, right? So, in kind of like your new services world, your new product world, what are some of the more, I guess, unique ways in which your customers are putting these great tools that you have to work for them, that you would like to use as kind of the poster child of success, and say, you know, we're providing this new value and these new enhancements, and give you the chance to take it to others, and use them as examples? >> Yeah, so, fundamentally, I'll be remiss if I don't start with the industry, right? So, it may not be very sexy, but ultimately, if I'm in a food and beverage industry, I really need to have a piece of software that understands that, right? Like for example, if you're an ice cream plant, you pay by part of a carton, you don't pay for the gallons of milk you get, right, so, does the software understand that? Right, if it don't, then you have to work around it, right? So, it may not sound sexy, but that's important to us, right? So, customers deploy without customization is very, very important for us. That's why we call it last man functionality. But if you flip to the technology side of things, I think that we're just scratching the surface in terms of what users want to do with Coleman. Coleman digital assistant, for example, like I earlier said about placing an acquisition target into a device. I think our idea is that every single employee of our customer organization should be using technology. Typical ERP, as it was deployed 20 years ago, only power users used it, right? Other people wrote on a piece of paper and sent it around. >> Same thing with decision support. There was like, three guys, two guys in the company who knew it. You had to go ask them to build a cube for you. >> Exactly. That doesn't scale, exactly. And we're living in a very diverse, global sort of set up. It doesn't work if you have three people who understand how to do BI, you know, two people who can create work flows, and I always like to use this example of this website called ifttt.com. I don't know if you've tried this or not. It literally stands for if this, then that. If I can go and describe something, and if this happens, then do that. Why can't we do that in enterprise software, right? Why is it that you have to go to knock on the door of IT to do it? So our idea is to bring that level of innovation, so we can innovate, our partners can innovate, customers can innovate, we don't step on each other. >> I got to ask you about a topic that we've heard a lot about this week, is robotic process automation, and you guys have essentially intimated, or at least, I've inferred that you've got quite a bit of capabilities in that regard. We're talking about software robots here, essentially, to replace sort of humans doing mundane tasks, or maybe augment humans. What is the capability that you have with RPA? Is it something that you're shipping today, and I have some follow up questions, if I may. >> Yeah, so, we built ION when we started building this years ago. We built it with the notion of build it on a data-rich architecture, right? What I mean by that is when something happens, an event happens in an application, a sales order is taken, or it's updated, give me a full copy of that document, that anyone can understand, right? That is a foundation of what you need to be able to externalize things like RPA. So we have access to the document as things happen. That's point number one. Point number two is that we built the Coleman AI platform, which we talked about earlier today. That actually leverages that workflow, as points in the workflow, to be able to go and do AI-based services that are hooks that are there in the workflow. So, where human beings need to intervene, I give an easy example. How often, like, there are people reporting to you, I do, and we get expense reports that people submit. First of all, I don't even look at them, Michelle looks at them, and do you think she opens and actually looks at how much somebody spent for dinner? No, you just push the button and approve. Why are we doing that, right? Why can't a robot figure out is there something that looks not quite right, then flag it, versus having to do this mundane work? So why can't Coleman do that? That's the way we've done it, and it's because we have a workflow engine, we have the API architecture, we have an AI platform, it's easy to wire these things together and having data externalized allows us to do that. >> So, in looking at the RPA market, there's several companies out there, and a lot of software companies, many of which are very, very complicated. You can't get your hands on the software. There is some, or maybe one in particular, it's easy, you download it, and it's low code or even no code, so I would imagine, I'm envisioning some kind of studio for a user like myself, who can, you know, is not technical, who can use it, and then maybe some kind of orchestrator, to be able to actually effect what I want done to get done. Is that something that you're shipping today, or how do I do it, as a user, and is it low code or no code? >> As an end user, if you are trying to figure out, yeah, I'll go to them to deploy, then obviously, you need a data scientist, okay? So, that part of it, we have a platform that is available for the data scientist, to be able to go look at the data, curate the data set, allow them to deploy different algorithms to figure out which one work, is the right for certain, then deploy that, and when you say deploy, it automatically creates an API, and allows for use anywhere. From an end user standpoint, like I said, this ifttt.com, you should be able to go in and say, set up your own alerts, that if I see, if you see, you know, X, Y, Z happen, let me know, or if I see X, Y, Z happen, you know, do this. So that part of capability exists in the platform, right? So, you can't completely replace data science and everything with the real end user doing it, but if you package the services in such a way that an end user can actually pick and choose and deploy, that can be done today. >> Your expense report, or approval example, and there are many, many others, so, are great, thank you for great. >> Soma, thank you, for the time too. We appreciate that. Thanks for dropping in, and again, great job on the keynote stage, and wish you success down the road here. >> Thanks a lot, appreciate it. >> I don't think you need it, though, I think you've got your, your act together really well. >> And your hands full. >> Yes, you do. A lot going on. All right, back with more here. We're live in Washington DC. You're watching theCUBE.