>> Announcer: From London, England, it's theCUBE! Covering Coupa Insp!re'19 EMEA. Brought to you by Coupa. >> Hey, welcome to theCUBE. Lisa Martin on the ground in London at Coupa Insp!re'19. Can you hear all the buzz around me? You probably can hear it, it's electric. The keynote just ended, and I'm very pleased to welcome, fresh from the keynote stage, we have Rachel Botsman, author and trust expert from Oxford University. Rachel, welcome to theCUBE! >> Thank you for having me. >> Your talk this morning about the intersection of trust and technology, to say it's interesting is an understatement. You had some great examples where you showed some technology brands, that we all know, and have different relationships with: Uber, Facebook, and Amazon. And the way that you measured the audience is great, you know, clap the brand that you trust the most. And it was so interesting, because we expect these technology brands to, they should be preserving our information, but we've also seen recent history, some big examples, of that trust being broken. >> Rachel: Yeah, yeah. >> Talk to us about your perspectives. >> So what I thought was interesting, well kind of unexpected for me, was no one clapped for Facebook, not one person in the room. And this is really interesting to me, because the point that I was making is that trust is really, really contextual, right? So if I had said to people, do you trust on Facebook that you can find your friends from college, they probably would've clapped. But do I trust them with my data, no. And this distinction is so important, because if you lose trust in one area as a company or a brand, and it can take time, you lose that ability to interact with people. So our relationship and our trust relationship with brands is incredibly complicated. But I think, particular tech brands, what they're realizing is that, how badly things go wrong when they're in a trust crisis. >> Talk to me about trust as a currency. You gave some great examples this morning. Money is the currency for transactions, where trust is the currency of interactions. >> Yeah, well I was trying to frame things, not because they sound nice, but how do you create a lens where people can really understand, like what is the value of this thing, and what is the role that it plays? And I'm never going to say money's not important; money is very important. But people can understand money; people value money. And I think that's because it has a physical, you can touch it, and it has an agreed value, right? Trust I actually don't believe can be measured. Trust is, what is it? It's something there, there's a connection between people. So you know when you have trust because you can interact with people. You know when you have trust because you can place their faith in them, you can share things about yourself and also share things back. So it's kind of this idea that, think of it as a currency, think of it as something that you should really value that is incredibly fragile in any situation in any organization. >> How does a company like Coupa, or an Amazon or a Facebook, how do they leverage trust and turn it into a valuable asset? >> Yeah, I don't like the idea that you sort of unlock trust. I think companies that really get it right are companies that think day in and day out around behaviors and culture. If you get behaviors and culture right, like the way people behave, whether they have empathy, whether they have integrity, whether you feel like you can depend on them, trust naturally flows from that. But the other thing that often you find with brands is they think of trust as like this reservoir, right? So it's different from awareness and loyalty; it's not like this thing that, you can have this really full up battery which means then you can launch some crazy products and everyone will trust it. We've seen this with like, Mattel, the toy brand. They launched a smart system for children called Aristotle, and within six months they had to pull it because people didn't trust what it was recording and watching in people's bedrooms. We were talking about Facebook and the cryptocurrency Libra, their new smart assistants; I wouldn't trust that. Amazon have introduced smart locks; I don't know if you've seen these? >> Lisa: Yes. >> Where if you're not home, it's inconvenient for a very annoying package slip. So you put in an Amazon lock and the delivery person will walk into your home. I trust Amazon to deliver my parcels; I don't trust them to give access to my home. So what we do with the trust and how we tap into that, it really depends on the risk that we're asking people to take. >> That's a great point that you bring about Amazon, because you look at how they are infiltrating our lives in so many different ways. There's a lot of benefits to it, in terms of convenience. I trust Amazon, because I know when I order something it's going to arrive when they say it will. But when you said about trust being contextual and said do you trust that Amazon pays their taxes, I went wow, I hadn't thought of it in that way. Would I want to trust them to come into my home to drop off a package, no. >> Rachel: Yeah. >> But the, I don't know if I want to say infiltration, into our lives, it's happening whether we like it or not. >> Well I think Amazon is really interesting. First of all because so often as consumers, and I'm guilty, we let convenience trump trust. So we talk about trust, but, you know what, like, if I don't really trust that Uber driver but I really want to get somewhere, I'll get in the car, right? I don't really trust the ethics of Amazon as a company or like what they're doing in the world, but I like the convenience. I predict that Amazon is actually going to go through a major trust crisis. >> Lisa: Really? >> Yeah. The reason why is because their trust is largely, I talked about capability and character. Amazon's trust is really built around capability. The capability of their fulfillment centers, like how efficient they are. Character wobbles, right? Like, does Bezos have integrity? Do we really feel like they care about the bookshops they're eating up? Or they want us to spend money on the right things? And when you have a brand and the trust is purely built around capability and the character piece is missing, it's quite a precarious place to be. >> Lisa: I saw a tweet that you tweeted recently. >> Uh oh! (laughs) >> Lisa: On the difference between capability and character. >> Yes, yeah. >> Lisa: And it was fascinating because you mentioned some big examples, Boeing. >> Yes. >> The two big air disasters in the last year. Facebook, obviously, the security breach. WeWork, this overly aggressive business model. And you said these companies are placing the blame, I'm not sure if that's the right word-- >> No no, the blame, yeah. >> On product or service capabilities, and you say it really is character. Can you talk to our audience about the difference, and why character is so important. >> Yeah, it's so interesting. So you know, sometimes you post things. I actually post more on LinkedIn, and suddenly like, you hit a nerve, right? Because I don't know, it's something you're summarizing that many people are feeling. And so the point of that was like, if you look at Boeing, Theranos was another example, WeWork, hundreds of banks, when something goes wrong they say it was a flaw in the product, it was a flaw in the system, it's a capability problem. And I don't think that's the case. Because the root cause of capability problems come from character and culture. And so, capability is really about the competence and reliability of someone or a product or service. Character is how someone behaves. Character gets to their intentions and motives. Character gets to, did they know about it and not tell us. Even VW is another example. >> Lisa: Yes. >> So it's not the product that is the issue. And I think we as consumers and citizens and customers, where many companies get it wrong in a trust crisis is they talk about the product fix. We won't forgive them, or we won't start giving them our trust again until we really believe something's changed about their character. I'm not sure anything has changed with Facebook's culture and character, which is why they're struggling with every move that they take, even though their intentions might be good. That's not how people in the world are viewing them. >> Do you think, taking Boeing as an example, I fly a lot, I'm sure you do as well. >> Rachel: Yeah. >> When those accidents happened, I'm sure everybody, including myself, was checking, what plane is this? >> Rachel: Yeah. >> Because when you know, especially once data starts being revealed, that demonstrated pilots, test pilots, were clearly saying something isn't right here, why do you think a company like Boeing isn't coming out and addressing that head on from an integrity perspective? Do you think that could go a long way in helping their brand reputation? >> I never, I mean I do get it, I'm married to a lawyer so I understand, legal gets involved, governance gets involved, so it's like, let's not disclose that. They're so worried about the implications. But it's this belief they can keep things hidden. It's a continual pattern, right? And that they try to show empathy, but really it comes across as some weird kind of sympathy. They don't really show humility. And so, when the CEO sits there, I have to believe he feels the pain of the human consequence of what happened. But more importantly, I have to believe it will never happen again. And again, it's not necessarily, do I trust the products Boeing creates, it's do I trust the people? Do I trust the decisions that they're making? And so it's really interesting to watch companies, Samsung, right? You can recover from a product crisis, with the phones, and they kind of go away. But it's much harder to recover from what, Boeing is a perfect example, has become a cultural crisis. >> Right, right. Talk to us about the evolution of trust. You talked about these three waves. Tell our audience about that, and what the third wave is and why we're in it, benefits? And also things to be aware of. >> Yes! (laughs) I didn't really talk about this today, because it's all about inspiration. So just to give you a sense, the way I think about trust is three chapters of human history. So the first one is called local trust; all running around villages and communities. I knew you, I knew your sister, I knew whoever was in that village. And it was largely based on reputation. So, I borrowed money from someone I knew, I went to the baker. Now this type of trust, it was actually phenomenally effective, but we couldn't scale it. So when we wanted to trade globally, the Industrial Revolution, moving to cities, we invented what I call institutional trust. And that's everything from financial systems to insurance products, all these mechanisms that allow trust to flow on a different level. Now what's happening today, it's not those two things are going away and they're not important; they are. It's that what technology inherently does, particularly networks, marketplaces, and platforms, is it takes this trust that used to be very hierarchical and linear, we used to look up to the CEO, we used to look up to the expert, and it distributes it around networks and platforms. So you can see that at Coupa, right? And this is amazing because it can unlock value, it can create marketplaces. It can change the way we share, connect, collaborate. But I think what's happened is that, sort of the idealism around this and the empowerment is slightly tinged, in a healthy way, realizing a lot can go wrong. So distributed trust doesn't necessarily mean distributed responsibility. My biggest insight from observing many of these communities is that, we like the idea of empowerment, we like the idea of collaboration, and we like the idea of control, but when things go wrong, they need a center. Does that make sense? >> Lisa: Absolutely, yes. >> So, a lot of the mess that we're seeing in the world today is actually caused by distributed trust. So when I like, read a piece of information that isn't from a trusted source and I make a decision to vote for someone, just an example. And so we're trying to figure out, what is the role of the institution in this distributed world? And that's why I think things have got incredibly messy. >> It certainly has the potential for that, right? Looking at, one of the things that I also saw that you were talking about, I think it was one of your TED Talks, is reputation capital. And you said you believe that will be more powerful than credit history in the 21st century. How can people, like you and I, get, I want to say control, over our reputation, when we're doing so many transactions digitally-- >> Rachel: I know. >> And like I think you were saying in one of your talks, moving from one country to another and your credit history doesn't follow you. How can somebody really control their trust capital and creative positive power from it? >> They can't. >> They can't? Oh no! >> I don't want to disappoint you, but there's always something in a TED speech that you wish you could take out, like 10 years later, and be like, not that you got it wrong, but that there's a naivety, right? So it is working in some senses. So what is really hard is like, if I have a reputation on Airbnb, I have a reputation on Amazon, on either side of the marketplace, I feel like I own that, right? That's my value, and I should be able to aggregate that and use that to get a loan, or get a better insurance, because it's a predictor of how I behave in the future. So I don't believe credit scores are a good predictor of behavior. That is very hard to do, because the marketplaces, they believe they own the data, and they have no incentive to share the reputation. So believe me, like so many companies after, actually it was wonderful after that TED Talk, many tried to figure out how to aggregate reputation. Where I have seen it play out as an idea, and this is really very rewarding, is many entrepreneurs have taken the idea and gone to emerging markets, or situations where people have no credit history. So Tala is a really good example, which is a lending company. Insurance companies are starting to look at this. There's a company called Traity. Where they can't get a loan, they can't get a product, they can't even open a bank account because they have no traditional credit history. Everyone has a reputation somewhere, so they can tap into these networks and use that to have access to things that were previously inaccessible. So that's the application I'm more excited about versus having a trust score. >> A trust score that we would be able to then use for our own advantages, whether it's getting a job, getting a loan. >> Yeah, and then unfortunately what also happened was China, and God forbid that I in any way inspired this decision, decided they would have a national trust score. So they would take what you're buying online and what you were saying online, all these thousands of interactions, and that the government would create a trust score that would really impact your life: the schools that your children could go to, and there's a blacklist, and you know, if you jaywalk your face is projected and your score goes down. Like, this is like an episode of Black Mirror. >> It's terrifying. >> Yeah. >> There's a fine line there. Rachel, I wish we had more time, because we could keep going on and on and on. But I want to thank you-- >> A pleasure. >> For coming right from the keynote stage to our set; it was a pleasure to meet you. >> On that dark note. >> Yes! (laughing) For Rachel Botsman, I'm Lisa Martin. You're watching theCUBE from Coupa Insp!re London '19. Thanks for watching. (digital music)

(upbeat music) >> Welcome back to London everybody, this is Dave Vellante with The Cube, the leader in tech coverage, and we're here at AWS. We wanted to cover deeper the public sector activity. We've been covering this segment for quite some time, with the public sector summit in DC, went to Bahrain last year, and we wanted to extend that to London. We're doing a special coverage here with a number of public sector folks. Anjenesh Babu is here, he's a network manager at Oxford GLAM. Thanks very much for coming on The Cube, it's good to see you. >> Thank you.], thanks. >> GLAM, I love it. Gardens, libraries and museums, you even get the A in there, which everybody always leaves out. So tell us about Oxford GLAM. >> So we are part of the heritage collection side of the University. And I'm here representing the gardens and museums. In the divisions we've got world renown collections, which has been held for 400 years or more. It comprises of four different museums and the Oxford University Botanic Gardens and Arboretum. So in total, we're looking at five different divisions, spread across probably sixteen different sites, physical sites. And the main focus of the division is to bring out collections to the world, through digital outreach, engagement and being fun, bringing fun into the whole system. Sustainment is big, because we are basically custodians of our collections and it has to be here almost forever, in a sense. And we can only display about 1% of our collections at any one point and we've got about 8.5 million objects. So as you can imagine, the majority of that is in storage. So one way to bring this out to the wider world is to digitize them, curate them and present them, either online or in another form. So that is what we do. >> In your role as the network manager is to makes sure everything connects and works and stays up? Or maybe describe that a little more. >> So, I'm a systems architect and network manager for gardens and museums, so in my role, my primary focus is to bridge the gap between technical and the non-technical functions, within the department. And I also look after network and infrastructure sites, so there's two parts to the role, one is a BAU business as usual function where we keep the networks all going and keep the lights on, basically. The second part is bringing together designs, it's not just solving technical problems, so if I'm looking at a technical problem I step out and almost zoom out to see, what else are we looking at which could be connected, and solve the problem. For example, we could be looking at a web design solution in one part of the project, but it's not relevant just to that project. If you step out and say, we could do this in another part of the program, and we may be operating in silence and we want to breakdown those, that's part of my role as well. >> Okay, so you're technical but you also speak the language of the organization and business. We put it in quotes because you're not a business per say. Okay, so you're digitizing all these artifacts and then making them available 24/7, is that the idea? What are some of the challenges there? >> So the first challenge is only 3% of objects are actually digitized. So we have 1% on display, 3% is actually digitized, it's a huge effort, it's not just scanning or taking photographs, you've got cataloging, accessions and a whole raft of databases that goes behind. And museums historically have got their own separate database collection which is individually held different collection systems, but as public, you don't care, we don't care, we just need to look at the object. You don't want to see, that belongs to the Ashmolean Museum or the picture does. You just want to see, and see what the characteristics are. For that we are bringing together a layer, which integrates different museums, it sort of reflects what we're doing in out SIT. The museums are culturally diverse institutions and we want to keep them that way, because each has got its history, a kind of personality to it. Under the hood, the foundational architecture, systems remain the same, so we can make them modular, expandable and address the same problems. So that's how we are supporting this and making it more sustainable at the same time. >> So you have huge volume, quality is an issue because people want to see beautiful images. You got all this meta data that you're collecting, you have a classification challenge. So how are you architecting this system and what role does the Cloud play in there? >> So, in the first instance we are looking at a lot of collections were on premises in the past. We are moving as a SaaS solution at the first step. A lot of it requires cleansing of data, almost, this is the state of the images we aren't migrating, we sort of stop here let's cleanse it, create new data streams and then bring it to the Cloud. That's one option we are looking at and that is the most important one. But during all this process in the last three years with the GLAM digital program there's been huge amount of changes. To have a static sort of golden image has been really crucial. And to do that if we are going down rate of on premise and trying to build out, scale out infrastructures, it would have a huge cost. The first thing that I looked at was, explore the Cloud options and I was interested in solutions like Snowball and the Storage Gateway. Straightforward, loads up the data and it's on the Cloud, and then I can fill out the infrastructure as much as I want, because we can all rest easy, the main, day one data is in the Cloud, and it's safe, and we can start working on the rest of it. So it's almost like a transition mechanism where we start working on the data before it goes to the Cloud anyway. And I'm also looking at a Cloud clearing house, because there's a lot of data exchanges that are going to come up in the future, vendor to vendor, vendor to us and us to the public. So it sort of presents itself a kind of junction, who is going to fill the junction? I think the obvious answer is here. >> So Snowball or Gateway, basically you either Snowball or Gateway the assets into the Cloud and you decide which one to use based on the size and the cost associated with doing that, is that right? >> Yes, and convenience. I was saying this the other day at another presentation, it's addictive because it's so simple and straight forward to use, and you just go back and say it's taken me three days to transfer 30 terabytes into a Snowball appliance and on the fourth day, it appears in in my packets, so what are we missing? Nothing. Let's do it again next week. So you got the Snowball for 10 days, bring it in transfer, so it's much more straightforward than transferring it over the network, and you got to keep and eye on things. Not that it's not hard, so for example, the first workloads we transferred over to the file gateway, but there's a particular server which had problems getting things across the network, because of out dated OS on it. So we got the Snowball in and in a matter of three days the data was on the Cloud, so to effect every two weeks up on the Snowball, bring it in two weeks, in three days it goes up back on the Cloud. So there's huge, it doesn't cost us any more to keep it there, so the matter of deletions are no longer there. So just keep it on the Cloud shifting using lifecycle policies, and it's straight forward and simple. That's pretty much it. >> Well you understand physics and the fastest way to get from here to there is a truck sometimes, right? >> Well, literally it is one of the most efficient ways I've seen, and continues to be so. >> Yeah, simple in concept and it works. How much are you able to automate the end-to-end, the process that you're describing? >> At this point we have a few proof of concept of different things that we can automate, but largely because a lot of data is held across bespoke systems, so we've got 30 terabytes spread across sixteen hard disks, that's another use case in offices. We've got 22 terabytes, which I've just described, it's on a single server. We have 20 terabytes on another Windows server, so it's quite disparate, it's quite difficult to find common ground to automate it. As we move forward automation is going to come in, because we are looking at common interface like API Gateways and how they define that, and for that we are doing a lot of work with, we have been inspired a lot by the GDS API designs, and we are just calling this off and it works. That is a road we are looking at, but at the moment we don't have much in the way of automation. >> Can you talk a bit more about sustainability, you've mentioned that a couple of times, double click on that, what's the relevance, how are you achieving sustainability? Maybe you could give some examples. >> So in the past sustainability means that you buy a system and you over provision it, so you're looking for 20 terabytes over three years, lets go 50 terabytes. And something that's supposed to be here for three years gets kept going for five, and when it breaks the money comes in. So that was the kind of very brief way of sustaining things. That clearly wasn't enough, so in a way we are looking for sustainability from a new function say, we don't need to look at long-term service contracts we need to look at robust contracts, and having in place mechanisms to make sure that whatever data goes in, comes out as well. So that was the main driver and plus with the Cloud we are looking at the least model. We've got an annual expenditure set aside and that keeps it, sustainability is a lot about internal financial planning and based on skill sets. With the Cloud skill sets are really straightforward to find and we have engaged with quite a few vendors who are partnering with us, and they work with us to deliver work packages, so in a way even though we are getting there with the skills, in terms of training our team we don't need to worry about complex deployments, because we can outsource that in sprints. >> So you have shipped it from a CAPX to an OPX model, is that right? >> Yes >> So what was that like, I mean, was that life changing, was it exhilarating? >> It was exhilarating, it was phenomenally life changing, because it set up a new direction within the university, because we were the first division to go with the public Cloud and set up a contract. Again thanks to the G-Cloud 9 framework, and a brilliant account management team from AWS. So we shifted from the CAPX model to the OPX model with an understanding that all this would be considered as a leased service. In the past you would buy an asset, it depreciates, it's no longer the case, this is a leased model. The data belongs to us and it's straight forward. >> Amazon continues to innovate and you take advantage of those innovations, prices come down. How about performance in the cloud, what are you seeing there relative to your past experiences? >> I wouldn't say it's any different, perhaps slightly better, because the new SDS got the benefit of super fast bandwidth to the internet, so we've got 20 gigs as a whole and we use about 2 gigs at the moment, we had 10 gig. We had to downgrade it because, we didn't use that much. So from a bandwidth perspective that was the main thing. And a performance perspective what goes in the Cloud you frankly find no different, perhaps if anything they are probably better. >> Talk about security for a moment, how early on in the Cloud people were concerned about security, it seems to have attenuated, but security in the Cloud is different, is it not, and so talk about your security journey and what's your impression and share with our audience what you've learned. >> So we've had similar challenges with security, from security I would say there's two pots, one's the contractual security and one is the technical security. The contractual security, if we had spun up our own separate legal agreement with AWS or any other Cloud vendor, it would have taken us ages, but again we went to the digital marketplace, used the G-Cloud 9 framework and it was no brainer. Within a week we had things turned around, and we were actually the first institution to go live with and account with AWS. That is the taken care of. SDS is a third party security assessment template, which we require all our vendors to sign. As soon as we went through that it far exceeds what the SDS requires, and it's just a tick box exercise. And things like data encryption at rest, in transit it actually makes it more secure than what we are running on premise. So in a way technically it's far more secure than what we could ever have achieved that's on premise, and it's all taken care of, straight forward. >> So you've a small fraction of your artifacts today that are digitized. What's the vision, where do you want to take this? >> We're looking at, I'm speaking on behalf of gardens, this is not me, per say, I'm speaking on behalf of my team, basically we are looking at a huge amount of digitization. The collection should be democratized, that's the whole aspect, bringing it out to the people and perhaps making them curators in some form. We may not be the experts for a massive collection from say North America or the Middle East, there are people who are better than us. So we give them the freedom to make sure they can curate it in a secure, scalable manner and that's where the Cloud comes in. And we backend it using authentication that works with us, logs that works with us and roll-back mechanisms that works with us. So that's were we are looking at in the next few years. >> How would you do this without the Cloud? >> Oh. If you're doing it without the Cloud-- >> Could you do it? >> Yes, but we would be wholly and solely dependent on the University network, the University infrastructure and a single point. So when you're looking at the bandwidth it's shared by students using it network out of the university and our collection visitors coming into the university. And the whole thing, the DS infrastructure, everything's inside the university. It's not bad in its present state but we need to look at a global audience, how do you scale it out, how do you balance it? And that's what we're looking at and it would've been almost impossible to meet the goals that we have, and the aspirations, and not to mention the cost. >> Okay so you're going to be at the summit, the Excel Center tomorrow right? What are you looking forward to there for us from a customer standpoint? >> I'm looking at service management, because a lot of our work, we've got a fantastic service desk and a fantastic team. So a lot of that is looking at service management, how to deliver effectively. As you rightly say Amazon is huge on innovation and things keep changing constantly so we need to keep track of how we deliver services, how do we make ourselves more nimble and more agile to deliver the services and add value. If you look at the OS stack, that's my favorite example, so you look at the OS stack you've got seven layers going up from physical then all the way to the application. You can almost read an organization in a similar way, so you got a physical level where you've got cabling and all the way to the people and presentation layer. So right now what we are doing is we are making sure we are focusing on the top level, focusing on the strategies, creating strategies, delivering that, rather than looking out for things that break. Looking out for things that operationally perhaps add value in another place. So that's where we would like to go. >> Anjenesh, thanks so much for coming on The Cube. >> Thank you >> It was a pleasure to have you. All right and thank you for watching, keep right there we'll be back with our next guest right after this short break. You're watching The Cube, from London at Amazon HQ, I call it HQ, we're here. Right back. (upbeat music)

>>Hey, everyone. Welcome back to accelerating analytics, maturity. I'm your host. Lisa Martin, Alan Jacobson joins me next. The chief data and analytics officer at Altrix Ellen. It's great to have you on the program. >>Thanks Lisa. >>So Ellen, as we know, everyone knows that being data driven is very important. It's a household term these days, but 93% of organizations are not utilizing the analytics skills of their employees, which is creating a widening analytics gap. What's your advice, your recommendations for organizations who are just starting out with analytics >>And you're spot on many organizations really aren't leveraging the, the full capability of their knowledge workers. And really the first step is probably assessing where you are on the journey, whether that's you personally, or your organization as a whole, we just launched an assessment tool on our website that we built with the international Institute of analytics, that in a very short period of time, in about 15 minutes, you can go on and answer some questions and understand where you sit versus your peer set versus competitors and kind of where you are on the journey. >>So when people talk about data analytics, they often think, ah, this is for data science experts like people like you. So why should people in the lines of business like the finance folks, the marketing folks, why should they learn analytics? >>So domain experts are really in the best position. They, they know where the gold is buried in their companies. They know where the inefficiencies are, and it is so much easier and faster to teach a domain expert a bit about how to automate a process or how to use analytics than it is to take a data scientist and try to teach them to have the knowledge of a 20 year accounting professional or a, or a logistics expert of your company. It much harder to do that. And really, if you think about it, the world has changed dramatically in a very short period of time. If, if you were a marketing professional 30 years ago, you likely didn't need to know anything about the internet, but today, do you know what you would call that marketing professional? If they didn't know anything about the internet, probably unemployed or retired. And so knowledge workers are having to learn more and more skills to really keep up with their professions. And analytics is really no exception. Pretty much in every profession, people are needing to learn analytics, to stay current and, and be capable for their companies. And companies need people who can do that. >>Absolutely. It seems like it's table stakes. These days, let's look at different industries. Now, are there differences in how you see analytics in automation being employed in different industries? I know Altrix is being used across a lot of different types of organizations from government to retail. I also see you're now with some of the leading sports teams, any differences in industries. >>Yeah. There's an incredible actually commonality between domains industry to industry. So if you look at what an HR professional is doing, maybe attrition analysis, it's probably quite similar, whether they're in oil and gas or in a high tech software company. And so really the similarities are, are much larger than you might think. And even on the, on, on the, on the sports front, we see many of the analytics that sports teams perform are very similar. So McLaren is one of the great partners that we work with and they use TRICS across many areas of their business from finance to production, extreme sports, logistics, wind tunnel engineering, the marketing team analyzes social media data, all using Altrics. And if I take as an example, the finance team, the finance team is trying to optimize the budget to make sure that they can hit the very stringent targets that F1 sports has. And I don't see a ton of difference between the optimization that they're doing to hit their budget numbers and what I see fortune 500 finance departments doing to optimize their budget. And so really the, the commonality is very high. Even across industries. >>I bet every F fortune 500 or even every company would love to be compared to the same department within McLaren F1, just to know that wow, what they're doing is so in incre incredibly important as is what we are doing. Absolutely. So talk about lessons learned, what lessons can business leaders take from those organizations like McLaren, who are the most analytically mature >>Probably first and foremost, is that the ROI with analytics and automation is incredibly high. Companies are having a ton of success. It's becoming an existential threat to some degree, if, if your company isn't going on this journey and your competition is it, it can be a, a huge problem. IDC just did a recent study about how companies are unlocking the ROI using analytics. And the data was really clear organizations that have a higher percentage of their workforce using analytics are enjoying a much higher return from their analytic investment. And so it's not about hiring two double PhD statisticians from Oxford. It really is how widely you can bring your workforce on this journey. Can they all get 10% more capable? And that's having incredible results at businesses all over the world. An another key finding that they had is that the majority of them said that when they had many folks using analytics, they were going on the journey faster than companies they didn't. And so picking technologies, that'll help everyone do this and, and do this fast and do it easily. Having an approachable piece of software that everyone can use is really a key, >>So faster able to move faster, higher ROI. I also imagine analytics across the organization is a big competitive advantage for organizations in any industry. >>Absolutely the IDC or not. The IDC, the international Institute of analytics showed huge correlation between companies that were more analytically mature versus ones that were not. They showed correlation to growth of the company. They showed correlation to revenue and they showed correlation to shareholder values. So across really all of the, the, the key measures of business, the more analytically mature companies simply outperformed their competition. >>And that's key these days is to be able to outperform your competition. You know, one of the things that we hear so often, Alan, is people talking about democratizing data and analytics. You talked about the line of business workers, but I gotta ask you, is it really that easy for the line of business workers who aren't trained in data science, to be able to jump in, look at data, uncover and extract business insights to make decisions. >>So in, in many ways, it really is that easy. I have a 14 and 16 year old kid. Both of them have learned Altrics they're, Altrics certified. And, and it was quite easy. It took 'em about 20 hours and they were, they, they were off to the races, but there can be some hard parts. The hard parts have more to do with change management. I mean, if you're an accountant, that's been doing the best accounting work in your company for the last 20 years. And all you happen to know is a spreadsheet for those 20 years. Are you ready to learn some new skills? And, and I would suggest you probably need to, if you want, keep up with your profession. The, the big four accounting firms have trained over a hundred thousand people in Altrix just one firm has trained over a hundred thousand. >>You, you can't be an accountant or an auditor at some of these places with, without knowing Altrix. And so the hard part, really in the end, isn't the technology and learning analytics and data science. The harder part is this change management change is hard. I should probably eat better and exercise more, but it's, it's hard to always do that. And so companies are finding that that's the hard part. They need to help people go on the journey, help people with the change management to, to help them become the digitally enabled accountant of the future. The, the logistics professional that is E enabled that that's the challenge. >>That's a huge challenge. Cultural, cultural shift is a challenge. As you said, change management. How, how do you advise customers? If you might be talking with someone who might be early in their analytics journey, but really need to get up to speed and mature to be competitive, how do you guide them or give them recommendations on being able to facilitate that change management? >>Yeah, that's a great question. So, so people entering into the workforce today, many of them are starting to have these skills Altrics is used in over 800 universities around the globe to teach finance and to teach marketing and to teach logistics. And so some of this is happening naturally as new workers are entering the workforce, but for all of those who are already in the workforce have already started their careers, learning in place becomes really important. And so we work with companies to put on programmatic approaches to help their workers do this. And so it's, again, not simply putting a box of tools in the corner and saying free, take one. We put on hackathons and analytic days, and it can, it can be great fun. We, we have a great time with, with many of the customers that we work with helping them, you know, do this, helping them go on the journey and the ROI, as I said, you know, is fantastic. And not only does it sometimes affect the bottom line, it can really make societal changes. We've seen companies have breakthroughs that really make great impact to society as a whole. >>Isn't that so fantastic to see the, the difference that that can make. It sounds like you're, you guys are doing a great job of democratizing access to alter X to everybody. We talked about the line of business folks and the incredible importance of enabling them and the, the ROI, the speed, the competitive advantage. Can you share some specific examples that you think of Alter's customers that really show data breakthroughs by the lines of business using the technology? >>Yeah, absolutely. So, so many to choose from I'll I'll, I'll give you two examples. Quickly. One is armor express. They manufacture life saving equipment, defensive equipments, like armor plated vests, and they were needing to optimize their supply chain, like many companies through the pandemic. We, we see how important the supply chain is. And so adjusting supply to, to match demand is, is really vital. And so they've used all tricks to model some of their supply and demand signals and built a predictive model to optimize the supply chain. And it certainly helped out from a, a dollar standpoint, they cut over a half a million dollars of inventory in the first year, but more importantly, by matching that demand and supply signal, you're able to better meet customer customer demand. And so when people have orders and are, are looking to pick up a vest, they don't wanna wait. >>And, and it becomes really important to, to get that right. Another great example is British telecom. They're, they're a company that services the public sector. They have very strict reporting regulations that they have to meet and they had, and, and this is crazy to think about over 140 legacy spreadsheet models that they had to run to comply with these regulatory processes and, and report, and obviously running 140 legacy models that had to be done in a certain order and linked incredibly challenging. It took them over four weeks, each time that they had to go through that process. And so to, to save time and have more efficiency in doing that, they trained 50 employees over just a two week period to start using Altrix and, and, and learn Altrix. And they implemented an all new reporting process that saw a 75% reduction in the number of man hours. >>It took to run in a 60% runtime performance. And so, again, a huge improvement. I can imagine it probably had better quality as well, because now that it was automated, you don't have people copying and past data into a spreadsheet. And that was just one project that this group of, of folks were able to accomplish that had huge ROI, but now those people are moving on and automating other processes and performing analytics in, in other areas, you can imagine the impact by the end of the year that they will have on their business, you know, potentially millions upon millions of dollars. This is what we see again. And again, company after company government agency, after government agency is how analytics are really transforming the way work is being done. >>That was the word that came to mind when you were describing the all three customer examples, the transformation, this is transformative. The ability to leverage alters to, to truly democratize data and analytics, give access to the lines of business is transformative for every organization. And, and also the business outcomes. You mentioned, those are substantial metrics based business outcomes. So the ROI and leveraging a technology like alri seems to be right there, sitting in front of you. >>That's right. And, and to be honest, it's not only important for these businesses. It's important for, for the knowledge workers themselves. I mean, we, we hear it from people that they discover Alrich, they automate a process. They finally get to get home for dinner with their families, which is fantastic, but, but it leads to new career paths. And so, you know, knowledge workers that have these added skills have so much larger opportunity. And I think it's great when the needs of businesses to become more analytics and analytic and automate processes actually matches the needs of the employees. And, you know, they too wanna learn these skills and become more advanced in their capabilities, >>Huge value there for the business, for the employees themselves to expand their skillset, to, to really open up so many opportunities for not only the business to meet the demands of the demanding customer, but the employees to be able to really have that breadth and depth in their field of service. Great opportunities there. Alan, is there anywhere that you wanna point the audience to go, to learn more about how they can get started? >>Yeah. So one of the things that we're really excited about is how fast and easy it is to learn these tools. So any of the listeners who wanna experience Altrix, they can go to the website, there's a free download on the website. You can take our analytic maturity assessment, as we talked about at the beginning and, and see where you are on the journey and just reach out. You know, we'd love to work with you and your organization to see how we can help you accelerate your journey on, on analytics and automation, >>Alan, it was a pleasure talking to you about democratizing data and analytics, the power in it for organizations across every industry. We appreciate your insights and your time. >>Thank you so much >>In a moment, Paula Hanson, who is the president and chief revenue officer of ultras and Jackie Vander lay graying. Who's the global head of tax technology at eBay will join me. You're watching the cube, the leader in high tech enterprise coverage.

>> It's no surprise that 73% of organizations indicate analytics spend will outpace other software investments in the next 12 to 18 months. After all as we know, data is changing the world and the world is changing with it. But is everyone's spending resulting in the same ROI? This is Lisa Martin. Welcome to "theCUBE"'s presentation of democratizing analytics across the enterprise, made possible by Alteryx. An Alteryx commissioned IDC info brief entitled, "Four Ways to Unlock Transformative Business Outcomes from Analytics Investments" found that 93% of organizations are not utilizing the analytics skills of their employees, which is creating a widening analytics gap. On this special "CUBE" presentation, Jason Klein, product marketing director of Alteryx, will join me to share key findings from the new Alteryx commissioned IDC brief and uncover how enterprises can derive more value from their data. In our second segment, we'll hear from Alan Jacobson, chief data and analytics officer at Alteryx. He's going to discuss how organizations across all industries can accelerate their analytic maturity to drive transformational business outcomes. And then in our final segment, Paula Hansen, who is the president and chief revenue officer of Alteryx, and Jacqui Van der Leij Greyling, who is the global head of tax technology at eBay, they'll join me. They're going to share how Alteryx is helping the global eCommerce company innovate with analytics. Let's get the show started. (upbeat music) Jason Klein joins me next, product marketing director at Alteryx. Jason, welcome to the program. >> Hello, nice to be here. >> Excited to talk with you. What can you tell me about the new Alteryx IDC research, which spoke with about 1500 leaders, what nuggets were in there? >> Well, as the business landscape changes over the next 12 to 18 months, we're going to see that analytics is going to be a key component to navigating this change. 73% of the orgs indicated that analytics spend will outpace other software investments. But just putting more money towards technology, it isn't going to solve everything. And this is why everyone's spending is resulting in different ROIs. And one of the reasons for this gap is because 93% of organizations, they're still not fully using the analytics skills of their employees, and this widening analytics gap, it's threatening operational progress by wasting workers' time, harming business productivity and introducing costly errors. So in this research, we developed a framework of enterprise analytics proficiency that helps organizations reap greater benefits from their investments. And we based this framework on the behaviors of organizations that saw big improvements across financial, customer, and employee metrics, and we're able to focus on the behaviors driving higher ROI. >> So the info brief also revealed that nearly all organizations are planning to increase their analytics spend. And it looks like from the info brief that nearly three quarters plan on spending more on analytics than any other software. And can you unpack, what's driving this demand, this need for analytics across organizations? >> Sure, well first there's more data than ever before, the data's changing the world, and the world is changing data. Enterprises across the world, they're accelerating digital transformation to capitalize on new opportunities, to grow revenue, to increase margins and to improve customer experiences. And analytics along with automation and AI is what's making digital transformation possible. They're providing the fuel to new digitally enabled lines of business. >> One of the things that the study also showed was that not all analytics spending is resulting in the same ROI. What are some of the discrepancies that the info brief uncovered with respect to the changes in ROI that organizations are achieving? >> Our research with IDC revealed significant roadblocks across people, processes, and technologies. They're preventing companies from reaping greater benefits from their investments. So for example, on the people side, only one out of five organizations reported a commensurate investment in upskilling for analytics and data literacy as compared to the technology itself. And next, while data is everywhere, most organizations, 63% from our survey, are still not using the full breadth of data types available. Yet data's never been this prolific, it's going to continue to grow, and orgs should be using it to their advantage. And lastly organizations, they need to provide the right analytics tools to help everyone unlock the power of data. >> So they- >> They instead rely on outdated spreadsheet technology. In our survey, nine out of 10 respondents said less than half of their knowledge workers are active users of analytics software beyond spreadsheets. But true analytic transformation can't happen for an organization in a few select pockets or silos. We believe everyone regardless of skill level should be able to participate in the data and analytics process and be driving value. >> Should we retake that, since I started talking over Jason accidentally? >> Yep, absolutely we can do so. We'll just go, yep, we'll go back to Lisa's question. Let's just, let's do the, retake the question and the answer, that'll be able to. >> It'll be not all analytics spending results in the same ROI, what are some of the discrepancies? >> Yes, Lisa, so we'll go from your ISO, just so we get that clean question and answer. >> Okay. >> Thank you for that. On your ISO, we're still speeding, Lisa, so give it a beat in your head and then on you. >> Yet not all analytics spending is resulting in the same ROI. So what are some of the discrepancies that the info brief uncovered with respect to ROI? >> Well, our research with IDC revealed significant roadblocks across people, processes, and technologies, all preventing companies from reaping greater benefits from their investments. So on the people side, for example, only one out of five organizations reported a commensurate investment in upskilling for analytics and data literacy as compared to the technology itself. And next, while data is everywhere, most organizations, 63% in our survey, are still not using the full breadth of data types available. Data has never been this prolific. It's going to continue to grow and orgs should be using it to their advantage. And lastly, organizations, they need to provide the right analytic tools to help everyone unlock the power of data, yet instead they're relying on outdated spreadsheet technology. Nine of 10 survey respondents said that less than half of their knowledge workers are active users of analytics software. True analytics transformation can't happen for an organization in a few select pockets or silos. We believe everyone regardless of skill level should be able to participate in the data and analytics process and drive value. >> So if I look at this holistically, then what would you say organizations need to do to make sure that they're really deriving value from their investments in analytics? >> Yeah, sure. So overall, the enterprises that derive more value from their data and analytics and achieve more ROI, they invested more aggressively in the four dimensions of enterprise analytics proficiency. So they've invested in the comprehensiveness of analytics across all data sources and data types, meaning they're applying analytics to everything. They've invested in the flexibility of analytics across deployment scenarios and departments, meaning they're putting analytics everywhere. They've invested in the ubiquity of analytics and insights for every skill level, meaning they're making analytics for everyone. And they've invested in the usability of analytics software, meaning they're prioritizing easy technology to accelerate analytics democratization. >> So very strategic investments. Did the survey uncover any specific areas where most companies are falling short, like any black holes that organizations need to be aware of at the outset? >> It did, it did. So organizations, they need to build a data-centric culture. And this begins with people. But what the survey told us is that the people aspect of analytics is the most heavily skewed towards low proficiency. In order to maximize ROI, organizations need to make sure everyone in the organization has access to the data and analytics technology they need. And then the organizations also have to align their investments with upskilling in data literacy to enjoy that higher ROI. Companies who did so experience higher ROI than companies who underinvested in analytics literacy. So among the high ROI achievers, 78% have a good or great alignment between analytics investment and workforce upskilling compared to only 64% among those without positive ROI. And as more orgs adopt cloud data warehouses or cloud data lakes, in order to manage the massively increasing workloads- Can I start that one over. >> Sure. >> Can I redo this one? >> Yeah. >> Of course, stand by. >> Tongue tied. >> Yep, no worries. >> One second. >> If we could do the same, Lisa, just have a clean break, we'll go your question. >> Yep, yeah. >> On you Lisa. Just give that a count and whenever you're ready. Here, I'm going to give us a little break. On you Lisa. >> So are there any specific areas that the survey uncovered where most companies are falling short? Like any black holes organizations need to be aware of from the outset? >> It did. You need to build a data-centric culture and this begins with people, but we found that the people aspect of analytics is most heavily skewed towards low proficiency. In order to maximize ROI organizations need to make sure everyone has access to the data and analytics technology they need. Organizations that align their analytics investments with upskilling enjoy higher ROI than orgs that are less aligned. For example, among the high ROI achievers in our survey, 78% had good or great alignment between analytics investments and workforce upskilling, compared to only 64% among those without positive ROI. And as more enterprises adopt cloud data warehouses or cloud data lakes to manage increasingly massive data sets, analytics needs to exist everywhere, especially for those cloud environments. And what we found is organizations that use more data types and more data sources generate higher ROI from their analytics investments. Among those with improved customer metrics, 90% were good or great at utilizing all data sources, compared to only 67% among the ROI laggards. >> So interesting that you mentioned people, I'm glad that you mentioned people. Data scientists, everybody talks about data scientists. They're in high demand, we know that, but there aren't enough to meet the needs of all enterprises. So given that discrepancy, how can organizations fill the gap and really maximize the investments that they're making in analytics? >> Right, so analytics democratization, it's no longer optional, but it doesn't have to be complex. So we at Alteryx, we're democratizing analytics by empowering every organization to upskill every worker into a data worker. And the data from this survey shows this is the optimal approach. Organizations with a higher percentage of knowledge workers who are actively using analytics software enjoy higher returns from their analytics investment than orgs still stuck on spreadsheets. Among those with improved financial metrics, AKA the high ROI achievers, nearly 70% say that at least a quarter of their knowledge workers are using analytics software other than spreadsheets compared to only 56% in the low ROI group. Also among the high ROI performers, 63% said data and analytic workers collaborate well or extremely well compared to only 51% in the low ROI group. The data from the survey shows that supporting more business domains with analytics and providing cross-functional analytics correlates with higher ROI. So to maximize ROI, orgs should be transitioning workers from spreadsheets to analytics software. They should be letting them collaborate effectively and letting them do so cross-functionally. >> Yeah, that cross-functional collaboration is essential for anyone in any organization and in any discipline. Another key thing that jumped out from the survey was around shadow IT. The business side is using more data science tools than the IT side. And it's expected to spend more on analytics than other IT. What risks does this present to the overall organization, if IT and the lines of business guys and gals aren't really aligned? >> Well, there needs to be better collaboration and alignment between IT and the line of business. The data from the survey, however, shows that business managers, they're expected to spend more on analytics and use more analytics tools than IT is aware of. And this isn't because the lines of business have recognized the value of analytics and plan to invest accordingly, but a lack of alignment between IT and business. This will negatively impact governance, which ultimately impedes democratization and hence ROI. >> So Jason, where can organizations that are maybe at the outset of their analytics journey, or maybe they're in environments where there's multiple analytics tools across shadow IT, where can they go to Alteryx to learn more about how they can really simplify, streamline, and dial up the value on their investment? >> Well, they can learn more on our website. I also encourage them to explore the Alteryx community, which has lots of best practices, not just in terms of how you do the analytics, but how you stand up in Alteryx environment, but also to take a look at your analytics stack and prioritize technologies that can snap to and enhance your organization's governance posture. It doesn't have to change it, but it should be able to align to and enhance it. >> And of course, as you mentioned, it's about people, process, and technologies. Jason, thank you so much for joining me today, unpacking the IDC info brief and the great nuggets in there. Lots that organizations can learn and really become empowered to maximize their analytics investments. We appreciate your time. >> Thank you, it's been a pleasure. >> In a moment, Alan Jacobson, who's the chief data and analytics officer at Alteryx is going to join me. He's going to be here to talk about how organizations across all industries can accelerate their analytic maturity to drive transformational business outcomes. You're watching "theCUBE", the leader in tech enterprise coverage. >> Somehow many have come to believe that data analytics is for the few, for the scientists, the PhDs, the MBAs. Well, it is for them, but that's not all. You don't have to have an advanced degree to do amazing things with data. You don't even have to be a numbers person. You can be just about anything. A titan of industry or a future titan of industry. You could be working to change the world, your neighborhood, or the course of your business. You can be saving lives or just looking to save a little time. The power of data analytics shouldn't be limited to certain job titles or industries or organizations because when more people are doing more things with data, more incredible things happen. Analytics makes us smarter and faster and better at what we do. It's practically a superpower. That's why we believe analytics is for everyone, and everything, and should be everywhere. That's why we believe in analytics for all. (upbeat music) >> Hey, everyone. Welcome back to "Accelerating Analytics Maturity". I'm your host, Lisa Martin. Alan Jacobson joins me next. The chief of data and analytics officer at Alteryx. Alan, it's great to have you on the program. >> Thanks, Lisa. >> So Alan, as we know, everyone knows that being data driven is very important. It's a household term these days, but 93% of organizations are not utilizing the analytics skills of their employees, which is creating a widening analytics gap. What's your advice, your recommendations for organizations who are just starting out with analytics? >> You're spot on, many organizations really aren't leveraging the full capability of their knowledge workers. And really the first step is probably assessing where you are on the journey, whether that's you personally, or your organization as a whole. We just launched an assessment tool on our website that we built with the International Institute of Analytics, that in a very short period of time, in about 15 minutes, you can go on and answer some questions and understand where you sit versus your peer set versus competitors and kind of where you are on the journey. >> So when people talk about data analytics, they often think, ah, this is for data science experts like people like you. So why should people in the lines of business like the finance folks, the marketing folks, why should they learn analytics? >> So domain experts are really in the best position. They know where the gold is buried in their companies. They know where the inefficiencies are. And it is so much easier and faster to teach a domain expert a bit about how to automate a process or how to use analytics than it is to take a data scientist and try to teach them to have the knowledge of a 20 year accounting professional or a logistics expert of your company. Much harder to do that. And really, if you think about it, the world has changed dramatically in a very short period of time. If you were a marketing professional 30 years ago, you likely didn't need to know anything about the internet, but today, do you know what you would call that marketing professional if they didn't know anything about the internet, probably unemployed or retired. And so knowledge workers are having to learn more and more skills to really keep up with their professions. And analytics is really no exception. Pretty much in every profession, people are needing to learn analytics to stay current and be capable for their companies. And companies need people who can do that. >> Absolutely, it seems like it's table stakes these days. Let's look at different industries now. Are there differences in how you see analytics in automation being employed in different industries? I know Alteryx is being used across a lot of different types of organizations from government to retail. I also see you're now with some of the leading sports teams. Any differences in industries? >> Yeah, there's an incredible actually commonality between the domains industry to industry. So if you look at what an HR professional is doing, maybe attrition analysis, it's probably quite similar, whether they're in oil and gas or in a high tech software company. And so really the similarities are much larger than you might think. And even on the sports front, we see many of the analytics that sports teams perform are very similar. So McLaren is one of the great partners that we work with and they use Alteryx across many areas of their business from finance to production, extreme sports, logistics, wind tunnel engineering, the marketing team analyzes social media data, all using Alteryx, and if I take as an example, the finance team, the finance team is trying to optimize the budget to make sure that they can hit the very stringent targets that F1 Sports has, and I don't see a ton of difference between the optimization that they're doing to hit their budget numbers and what I see Fortune 500 finance departments doing to optimize their budget, and so really the commonality is very high, even across industries. >> I bet every Fortune 500 or even every company would love to be compared to the same department within McLaren F1. Just to know that wow, what they're doing is so incredibly important as is what we're doing. >> So talk- >> Absolutely. >> About lessons learned, what lessons can business leaders take from those organizations like McLaren, who are the most analytically mature? >> Probably first and foremost, is that the ROI with analytics and automation is incredibly high. Companies are having a ton of success. It's becoming an existential threat to some degree, if your company isn't going on this journey and your competition is, it can be a huge problem. IDC just did a recent study about how companies are unlocking the ROI using analytics. And the data was really clear, organizations that have a higher percentage of their workforce using analytics are enjoying a much higher return from their analytic investment, and so it's not about hiring two double PhD statisticians from Oxford. It really is how widely you can bring your workforce on this journey, can they all get 10% more capable? And that's having incredible results at businesses all over the world. An another key finding that they had is that the majority of them said that when they had many folks using analytics, they were going on the journey faster than companies that didn't. And so picking technologies that'll help everyone do this and do this fast and do it easily. Having an approachable piece of software that everyone can use is really a key. >> So faster, able to move faster, higher ROI. I also imagine analytics across the organization is a big competitive advantage for organizations in any industry. >> Absolutely the IDC, or not the IDC, the International Institute of Analytics showed huge correlation between companies that were more analytically mature versus ones that were not. They showed correlation to growth of the company, they showed correlation to revenue and they showed correlation to shareholder values. So across really all of the key measures of business, the more analytically mature companies simply outperformed their competition. >> And that's key these days, is to be able to outperform your competition. You know, one of the things that we hear so often, Alan, is people talking about democratizing data and analytics. You talked about the line of business workers, but I got to ask you, is it really that easy for the line of business workers who aren't trained in data science to be able to jump in, look at data, uncover and extract business insights to make decisions? >> So in many ways, it really is that easy. I have a 14 and 16 year old kid. Both of them have learned Alteryx, they're Alteryx certified and it was quite easy. It took 'em about 20 hours and they were off to the races, but there can be some hard parts. The hard parts have more to do with change management. I mean, if you're an accountant that's been doing the best accounting work in your company for the last 20 years, and all you happen to know is a spreadsheet for those 20 years, are you ready to learn some new skills? And I would suggest you probably need to, if you want to keep up with your profession. The big four accounting firms have trained over a hundred thousand people in Alteryx. Just one firm has trained over a hundred thousand. You can't be an accountant or an auditor at some of these places without knowing Alteryx. And so the hard part, really in the end, isn't the technology and learning analytics and data science, the harder part is this change management, change is hard. I should probably eat better and exercise more, but it's hard to always do that. And so companies are finding that that's the hard part. They need to help people go on the journey, help people with the change management to help them become the digitally enabled accountant of the future, the logistics professional that is E enabled, that's the challenge. >> That's a huge challenge. Cultural shift is a challenge, as you said, change management. How do you advise customers if you might be talking with someone who might be early in their analytics journey, but really need to get up to speed and mature to be competitive, how do you guide them or give them recommendations on being able to facilitate that change management? >> Yeah, that's a great question. So people entering into the workforce today, many of them are starting to have these skills. Alteryx is used in over 800 universities around the globe to teach finance and to teach marketing and to teach logistics. And so some of this is happening naturally as new workers are entering the workforce, but for all of those who are already in the workforce, have already started their careers, learning in place becomes really important. And so we work with companies to put on programmatic approaches to help their workers do this. And so it's, again, not simply putting a box of tools in the corner and saying free, take one. We put on hackathons and analytic days, and it can be great fun. We have a great time with many of the customers that we work with, helping them do this, helping them go on the journey, and the ROI, as I said, is fantastic. And not only does it sometimes affect the bottom line, it can really make societal changes. We've seen companies have breakthroughs that have really made great impact to society as a whole. >> Isn't that so fantastic, to see the difference that that can make. It sounds like you guys are doing a great job of democratizing access to Alteryx to everybody. We talked about the line of business folks and the incredible importance of enabling them and the ROI, the speed, the competitive advantage. Can you share some specific examples that you think of Alteryx customers that really show data breakthroughs by the lines of business using the technology? >> Yeah, absolutely, so many to choose from. I'll give you two examples quickly. One is Armor Express. They manufacture life saving equipment, defensive equipments, like armor plated vests, and they were needing to optimize their supply chain, like many companies through the pandemic. We see how important the supply chain is. And so adjusting supply to match demand is really vital. And so they've used Alteryx to model some of their supply and demand signals and built a predictive model to optimize the supply chain. And it certainly helped out from a dollar standpoint. They cut over a half a million dollars of inventory in the first year, but more importantly, by matching that demand and supply signal, you're able to better meet customer demand. And so when people have orders and are looking to pick up a vest, they don't want to wait. And it becomes really important to get that right. Another great example is British Telecom. They're a company that services the public sector. They have very strict reporting regulations that they have to meet and they had, and this is crazy to think about, over 140 legacy spreadsheet models that they had to run to comply with these regulatory processes and report, and obviously running 140 legacy models that had to be done in a certain order and length, incredibly challenging. It took them over four weeks each time that they had to go through that process. And so to save time and have more efficiency in doing that, they trained 50 employees over just a two week period to start using Alteryx and learn Alteryx. And they implemented an all new reporting process that saw a 75% reduction in the number of man hours it took to run in a 60% run time performance. And so, again, a huge improvement. I can imagine it probably had better quality as well, because now that it was automated, you don't have people copying and pasting data into a spreadsheet. And that was just one project that this group of folks were able to accomplish that had huge ROI, but now those people are moving on and automating other processes and performing analytics in other areas. So you can imagine the impact by the end of the year that they will have on their business, potentially millions upon millions of dollars. And this is what we see again and again, company after company, government agency after government agency, is how analytics are really transforming the way work is being done. >> That was the word that came to mind when you were describing the all three customer examples, transformation, this is transformative. The ability to leverage Alteryx, to truly democratize data and analytics, give access to the lines of business is transformative for every organization. And also the business outcome you mentioned, those are substantial metrics based business outcomes. So the ROI in leveraging a technology like Alteryx seems to be right there, sitting in front of you. >> That's right, and to be honest, it's not only important for these businesses. It's important for the knowledge workers themselves. I mean, we hear it from people that they discover Alteryx, they automate a process, they finally get to get home for dinner with their families, which is fantastic, but it leads to new career paths. And so knowledge workers that have these added skills have so much larger opportunity. And I think it's great when the needs of businesses to become more analytic and automate processes actually matches the needs of the employees, and they too want to learn these skills and become more advanced in their capabilities. >> Huge value there for the business, for the employees themselves to expand their skillset, to really open up so many opportunities for not only the business to meet the demands of the demanding customer, but the employees to be able to really have that breadth and depth in their field of service. Great opportunities there, Alan. Is there anywhere that you want to point the audience to go to learn more about how they can get started? >> Yeah, so one of the things that we're really excited about is how fast and easy it is to learn these tools. So any of the listeners who want to experience Alteryx, they can go to the website, there's a free download on the website. You can take our analytic maturity assessment, as we talked about at the beginning, and see where you are on the journey and just reach out. We'd love to work with you and your organization to see how we can help you accelerate your journey on analytics and automation. >> Alan, it was a pleasure talking to you about democratizing data and analytics, the power in it for organizations across every industry. We appreciate your insights and your time. >> Thank you so much. >> In a moment, Paula Hansen, who is the president and chief revenue officer of Alteryx, and Jacqui Van der Leij Greyling, who's the global head of tax technology at eBay, will join me. You're watching "theCUBE", the leader in high tech enterprise coverage. >> 1200 hours of wind tunnel testing, 30 million race simulations, 2.4 second pit stops. >> Make that 2.3. >> Sector times out the wazoo. >> Way too much of this. >> Velocities, pressures, temperatures, 80,000 components generating 11.8 billion data points and one analytics platform to make sense of it all. When McLaren needs to turn complex data into winning insights, they turn to Alteryx. Alteryx, analytics automation. (upbeat music) >> Hey, everyone, welcome back to the program. Lisa Martin here, I've got two guests joining me. Please welcome back to "theCUBE" Paula Hansen, the chief revenue officer and president at Alteryx, and Jacqui Van der Leij Greyling joins us as well, the global head of tax technology at eBay. They're going to share with you how Alteryx is helping eBay innovate with analytics. Ladies, welcome, it's great to have you both on the program. >> Thank you, Lisa, it's great to be here. >> Yeah, Paula, we're going to start with you. In this program, we've heard from Jason Klein, we've heard from Alan Jacobson. They talked about the need to democratize analytics across any organization to really drive innovation. With analytics, as they talked about, at the forefront of software investments, how's Alteryx helping its customers to develop roadmaps for success with analytics? >> Well, thank you, Lisa. It absolutely is about our customers' success. And we partner really closely with our customers to develop a holistic approach to their analytics success. And it starts of course with our innovative technology and platform, but ultimately we help our customers to create a culture of data literacy and analytics from the top of the organization, starting with the C-suite. And we partner with our customers to build their roadmaps for scaling that culture of analytics, through things like enablement programs, skills assessments, hackathons, setting up centers of excellence to help their organization scale and drive governance of this analytics capability across the enterprise. So at the end of the day, it's really about helping our customers to move up their analytics maturity curve with proven technologies and best practices, so they can make better business decisions and compete in their respective industries. >> Excellent, sounds like a very strategic program, we're going to unpack that. Jacqui, let's bring you into the conversation. Speaking of analytics maturity, one of the things that we talked about in this event is the IDC report that showed that 93% of organizations are not utilizing the analytics skills of their employees, but then there's eBay. How Jacqui did eBay become one of the 7% of organizations who's really maturing and how are you using analytics across the organization at eBay? >> So I think the main thing for us is when we started out was is that, our, especially in finance, they became spreadsheet professionals instead of the things that we really want our employees to add value to. And we realized we had to address that. And we also knew we couldn't wait for all our data to be centralized until we actually start using the data or start automating and being more effective. So ultimately we really started very, very actively embedding analytics in our people and our data and our processes. >> Starting with people is really critical. Jacqui, continuing with you, what were some of the roadblocks to analytics adoption that you faced and how did you overcome them? >> So I think eBay is a very data driven company. We have a lot of data. I think we are 27 years around this year, so we have the data, but it is everywhere. And how do you use that data? How do you use it efficiently? How do you get to the data? And I believe that that is definitely one of our biggest roadblocks when we started out and just finding those data sources and finding ways to connect to them to move forward. The other thing is that people were experiencing a lot of frustration. I mentioned before about the spreadsheet professionals. And there was no, we were not independent. You couldn't move forward, you would've put it on somebody else's roadmap to get the data and to get the information if you want it. So really finding something that everybody could access analytics or access data. And finally we have to realize is that this is uncharted territory. This is not exactly something that everybody is used to working with every day. So how do you find something that is easy, and that is not so daunting on somebody who's brand new to the field? And I would call those out as your major roadblocks, because you always have, not always, but most of the times you have support from the top, and in our case we have, but at the end of the day, it's our people that need to actually really embrace it, and making that accessible for them, I would say is definitely not per se, a roadblock, but basically a block you want to be able to move. >> It's really all about putting people first. Question for both of you, and Paula we'll start with you, and then Jacqui we'll go to you. I think the message in this program that the audience is watching with us is very clear. Analytics is for everyone, should be for everyone. Let's talk now about how both of your organizations are empowering people, those in the organization that may not have technical expertise to be able to leverage data, so that they can actually be data driven. Paula. >> Yes, well, we leverage our platform across all of our business functions here at Alteryx. And just like Jacqui explained, at eBay finance is probably one of the best examples of how we leverage our own platform to improve our business performance. So just like Jacqui mentioned, we have this huge amount of data flowing through our enterprise and the opportunity to leverage that into insights and analytics is really endless. So our CFO Kevin Rubin has been a key sponsor for using our own technology. We use Alteryx for forecasting all of our key performance metrics, for business planning, across our audit function, to help with compliance and regulatory requirements, tax, and even to close our books at the end of each quarter. So it's really going to remain across our business. And at the end of the day, it comes to how do you train users? How do you engage users to lean into this analytic opportunity to discover use cases? And so one of the other things that we've seen many companies do is to gamify that process, to build a game that brings users into the experience for training and to work with each other, to problem solve and along the way, maybe earn badges depending on the capabilities and trainings that they take. And just have a little healthy competition as an employee base around who can become more sophisticated in their analytic capability. So I think there's a lot of different ways to do it. And as Jacqui mentioned, it's really about ensuring that people feel comfortable, that they feel supported, that they have access to the training that they need, and ultimately that they are given both the skills and the confidence to be able to be a part of this great opportunity of analytics. >> That confidence is key. Jacqui, talk about some of the ways that you're empowering folks without that technical expertise to really be data driven. >> Yeah, I think it means to what Paula has said in terms of getting people excited about it, but it's also understanding that this is a journey and everybody is at a different place in their journey. You have folks that's already really advanced who has done this every day. And then you have really some folks that this is brand new or maybe somewhere in between. And it's about how you get everybody in their different phases to get to the initial destination. I say initial, because I believe a journey is never really complete. What we have done is that we decided to invest, and built a proof of concept, and we got our CFO to sponsor a hackathon. We opened it up to everybody in finance in the middle of the pandemic. So everybody was on Zoom and we told people, listen, we're going to teach you this tool, it's super easy, and let's just see what you can do. We ended up having 70 entries. We had only three weeks. So and these are people that do not have a background. They are not engineers, they're not data scientists. And we ended up with a 25,000 hour savings at the end of that hackathon from the 70 entries with people that have never, ever done anything like this before. And there you have the result. And then it just went from there. People had a proof of concept. They knew that it worked and they overcame the initial barrier of change. And that's where we are seeing things really, really picking up now. >> That's fantastic. And the business outcome that you mentioned there, the business impact is massive, helping folks get that confidence to be able to overcome sometimes the cultural barriers is key here. I think another thing that this program has really highlighted is there is a clear demand for data literacy in the job market, regardless of organization. Can each of you share more about how you're empowering the next generation of data workers? Paula, we'll start with you. >> Absolutely, and Jacqui says it so well, which is that it really is a journey that organizations are on and we as people in society are on in terms of upskilling our capabilities. So one of the things that we're doing here at Alteryx to help address this skillset gap on a global level is through a program that we call SparkED, which is essentially a no-cost analytics education program that we take to universities and colleges globally to help build the next generation of data workers. When we talk to our customers like eBay and many others, they say that it's difficult to find the skills that they want when they're hiring people into the job market. And so this program's really developed just to do just that, to close that gap and to work hand in hand with students and educators to improve data literacy for the next generation. So we're just getting started with SparkED. We started last May, but we currently have over 850 educational institutions globally engaged across 47 countries, and we're going to continue to invest here because there's so much opportunity for people, for society and for enterprises, when we close the gap and empower more people with the necessary analytics skills to solve all the problems that data can help solve. >> So SparkED has made a really big impact in such a short time period. It's going to be fun to watch the progress of that. Jacqui, let's go over to you now. Talk about some of the things that eBay is doing to empower the next generation of data workers. >> So we basically wanted to make sure that we kept that momentum from the hackathon, that we don't lose that excitement. So we just launched the program called eBay Masterminds. And what it basically is, is it's an inclusive innovation in each other, where we firmly believe that innovation is for upskilling for all analytics roles. So it doesn't matter your background, doesn't matter which function you are in, come and participate in in this where we really focus on innovation, introducing new technologies and upskilling our people. We are, apart from that, we also said, well, we shouldn't just keep it to inside eBay. We have to share this innovation with the community. So we are actually working on developing an analytics high school program, which we hope to pilot by the end of this year, where we will actually have high schoolers come in and teach them data essentials, the soft skills around analytics, but also how to use Alteryx. And we're working with, actually, we're working with SparkED and they're helping us develop that program. And we really hope that at, say, by the end of the year, we have a pilot and then also next year, we want to roll it out in multiple locations in multiple countries and really, really focus on that whole concept of analytics for all. >> Analytics for all, sounds like Alteryx and eBay have a great synergistic relationship there that is jointly aimed at especially going down the stuff and getting people when they're younger interested, and understanding how they can be empowered with data across any industry. Paula, let's go back to you, you were recently on "theCUBE"'s Supercloud event just a couple of weeks ago. And you talked about the challenges the companies are facing as they're navigating what is by default a multi-cloud world. How does the Alteryx Analytics Cloud platform enable CIOs to democratize analytics across their organization? >> Yes, business leaders and CIOs across all industries are realizing that there just aren't enough data scientists in the world to be able to make sense of the massive amounts of data that are flowing through organizations. Last I checked, there was 2 million data scientists in the world, so that's woefully underrepresented in terms of the opportunity for people to be a part of the analytics solution. So what we're seeing now with CIOs, with business leaders is that they're integrating data analysis and the skillset of data analysis into virtually every job function, and that is what we think of when we think of analytics for all. And so our mission with Alteryx Analytics Cloud is to empower all of those people in every job function, regardless of their skillset, as Jacqui pointed out from people that are just getting started all the way to the most sophisticated of technical users. Every worker across that spectrum can have a meaningful role in the opportunity to unlock the potential of the data for their company and their organizations. So that's our goal with Alteryx Analytics Cloud, and it operates in a multi cloud world and really helps across all sizes of data sets to blend, cleanse, shape, analyze, and report out so that we can break down data silos across the enterprise and help drive real business outcomes as a result of unlocking the potential of data. >> As well as really lessening that skill gap. As you were saying, there's only 2 million data scientists. You don't need to be a data scientist, that's the beauty of what Alteryx is enabling and eBay is a great example of that. Jacqui, let's go ahead and wrap things with you. You talked a great deal about the analytics maturity that you have fostered at eBay. It obviously has the right culture to adapt to that. Can you talk a little bit and take us out here in terms of where Alteryx fits in as that analytics maturity journey continues and what are some of the things that you are most excited about as analytics truly gets democratized across eBay? >> When we're starting up and getting excited about things when it comes to analytics, I can go on all day, but I'll keep it short and sweet for you. I do think we are on the top of the pool of data scientists. And I really feel that that is your next step, for us anyways, is that how do we get folks to not see data scientists as this big thing, like a rocket scientist, it's something completely different. And it's something that is in everybody in a certain extent. So again, partnering with Alteryx who just released the AI ML solution, allowing folks to not have a data scientist program, but actually build models and be able to solve problems that way. So we have engaged with Alteryx and we purchased the licenses, quite a few. And right now through our Masterminds program, we're actually running a four month program for all skill levels, teaching them AI ML and machine learning and how they can build their own models. We are really excited about that. We have over 50 participants without a background from all over the organization. We have members from our customer services. We have even some of our engineers are actually participating in the program. We just kicked it off. And I really believe that that is our next step. I want to give you a quick example of the beauty of this is where we actually just allow people to go out and think about ideas and come up with things. And one of the people in our team who doesn't have a data scientist background at all, was able to develop a solution where there is a checkout feedback functionality on the eBay side where sellers or buyers can verbatim add information. And she built a model to be able to determine what relates to tax specific, what is the type of problem, and even predict how that problem can be solved before we as a human even step in, and now instead of us or somebody going to verbatim and try to figure out what's going on there, we can focus on fixing the error versus actually just reading through things and not adding any value, and it's a beautiful tool and I was very impressed when I saw the demo and definitely developing that sort of thing. >> That sounds fantastic. And I think just the one word that keeps coming to mind, and we've said this a number of times in the program today is empowerment. What you're actually really doing to truly empower people across the organization with varying degrees of skill level, going down to the high school level, really exciting. We'll have to stay tuned to see what some of the great things are that come from this continued partnership. Ladies, I want to thank you so much for joining me on the program today and talking about how Alteryx and eBay are really partnering together to democratize analytics and to facilitate its maturity. It's been great talking to you. >> Thank you, Lisa. >> Thank you so much. (cheerful electronic music) >> As you heard over the course of our program, organizations where more people are using analytics who have deeper capabilities in each of the four Es, that's everyone, everything, everywhere, and easy analytics, those organizations achieve more ROI from their respective investments in analytics and automation than those who don't. We also heard a great story from eBay, great example of an enterprise that is truly democratizing analytics across its organization. It's enabling and empowering line of business users to use analytics, not only focused on key aspects of their job, but develop new skills rather than doing the same repetitive tasks. We want to thank you so much for watching the program today. Remember you can find all of the content on thecube.net. You can find all of the news from today on siliconangle.com and of course alteryx.com. We also want to thank Alteryx for making this program possible and for sponsoring "theCUBE". For all of my guests, I'm Lisa Martin. We want to thank you for watching and bye for now. (upbeat music)

>>Well, welcome back to the cubes coverage of AWS reinvent 2021. We're here joined by Ian buck, general manager and vice president of accelerated computing at Nvidia I'm. John Ford, your host of the QB. And thanks for coming on. So in video, obviously, great brand congratulates on all your continued success. Everyone who has does anything in graphics knows the GPU's are hot and you guys get great brand great success in the company, but AI and machine learning was seeing the trend significantly being powered by the GPU's and other systems. So it's a key part of everything. So what's the trends that you're seeing, uh, in ML and AI, that's accelerating computing to the cloud. Yeah, >>I mean, AI is kind of drape bragging breakthroughs innovations across so many segments, so many different use cases. We see it showing up with things like credit card, fraud prevention and product and content recommendations. Really it's the new engine behind search engines is AI. Uh, people are applying AI to things like, um, meeting transcriptions, uh, virtual calls like this using AI to actually capture what was said. Um, and that gets applied in person to person interactions. We also see it in intelligence systems assistance for a contact center, automation or chat bots, uh, medical imaging, um, and intelligence stores and warehouses and everywhere. It's really, it's really amazing what AI has been demonstrated, what it can do. And, uh, it's new use cases are showing up all the time. >>Yeah. I'd love to get your thoughts on, on how the world's evolved just in the past few years, along with cloud, and certainly the pandemics proven it. You had this whole kind of full stack mindset initially, and now you're seeing more of a horizontal scale, but yet enabling this vertical specialization in applications. I mean, you mentioned some of those apps, the new enablers, this kind of the horizontal play with enablement for specialization, with data, this is a huge shift that's going on. It's been happening. What's your reaction to that? >>Yeah, it's the innovations on two fronts. There's a horizontal front, which is basically the different kinds of neural networks or AIS as well as machine learning techniques that are, um, just being invented by researchers for, uh, and the community at large, including Amazon. Um, you know, it started with these convolutional neural networks, which are great for image processing, but as it expanded more recently into, uh, recurrent neural networks, transformer models, which are great for language and language and understanding, and then the new hot topic graph neural networks, where the actual graph now is trained as a, as a neural network, you have this underpinning of great AI technologies that are being adventure around the world in videos role is try to productize that and provide a platform for people to do that innovation and then take the next step and innovate vertically. Um, take it, take it and apply it to two particular field, um, like medical, like healthcare and medical imaging applying AI, so that radiologists can have an AI assistant with them and highlight different parts of the scan. >>Then maybe troublesome worrying, or requires more investigation, um, using it for robotics, building virtual worlds, where robots can be trained in a virtual environment, their AI being constantly trained, reinforced, and learn how to do certain activities and techniques. So that the first time it's ever downloaded into a real robot, it works right out of the box, um, to do, to activate that we co we are creating different vertical solutions, vertical stacks for products that talk the languages of those businesses, of those users, uh, in medical imaging, it's processing medical data, which is obviously a very complicated large format data, often three-dimensional boxes in robotics. It's building combining both our graphics and simulation technologies, along with the, you know, the AI training capabilities and different capabilities in order to run in real time. Those are, >>Yeah. I mean, it's just so cutting edge. It's so relevant. I mean, I think one of the things you mentioned about the neural networks, specifically, the graph neural networks, I mean, we saw, I mean, just to go back to the late two thousands, you know, how unstructured data or object store created, a lot of people realize that the value out of that now you've got graph graph value, you got graph network effect, you've got all kinds of new patterns. You guys have this notion of graph neural networks. Um, that's, that's, that's out there. What is, what is a graph neural network and what does it actually mean for deep learning and an AI perspective? >>Yeah, we have a graph is exactly what it sounds like. You have points that are connected to each other, that established relationships and the example of amazon.com. You might have buyers, distributors, sellers, um, and all of them are buying or recommending or selling different products. And they're represented in a graph if I buy something from you and from you, I'm connected to those end points and likewise more deeply across a supply chain or warehouse or other buyers and sellers across the network. What's new right now is that those connections now can be treated and trained like a neural network, understanding the relationship. How strong is that connection between that buyer and seller or that distributor and supplier, and then build up a network that figure out and understand patterns across them. For example, what products I may like. Cause I have this connection in my graph, what other products may meet those requirements, or also identifying things like fraud when, when patterns and buying patterns don't match, what a graph neural networks should say would be the typical kind of graph connectivity, the different kind of weights and connections between the two captured by the frequency half I buy things or how I rate them or give them stars as she used cases, uh, this application graph neural networks, which is basically capturing the connections of all things with all people, especially in the world of e-commerce, it's very exciting to a new application, but applying AI to optimizing business, to reducing fraud and letting us, you know, get access to the products that we want, the products that they have, our recommendations be things that, that excited us and want us to buy things >>Great setup for the real conversation that's going on here at re-invent, which is new kinds of workloads are changing. The game. People are refactoring their business with not just replatform, but actually using this to identify value and see cloud scale allows you to have the compute power to, you know, look at a note on an arc and actually code that. It's all, it's all science, all computer science, all at scale. So with that, that brings up the whole AWS relationship. Can you tell us how you're working with AWS before? >>Yeah. 80 of us has been a great partner and one of the first cloud providers to ever provide GPS the cloud, uh, we most more recently we've announced two new instances, uh, the instance, which is based on the RA 10 G GPU, which has it was supports the Nvidia RTX technology or rendering technology, uh, for real-time Ray tracing and graphics and game streaming is their highest performance graphics, enhanced replicate without allows for those high performance graphics applications to be directly hosted in the cloud. And of course runs everything else as well, including our AI has access to our AI technology runs all of our AI stacks. We also announced with AWS, the G 5g instance, this is exciting because it's the first, uh, graviton or ARM-based processor connected to a GPU and successful in the cloud. Um, this makes, uh, the focus here is Android gaming and machine learning and France. And we're excited to see the advancements that Amazon is making and AWS is making with arm and the cloud. And we're glad to be part of that journey. >>Well, congratulations. I remember I was just watching my interview with James Hamilton from AWS 2013 and 2014. He was getting, he was teasing this out, that they're going to build their own, get in there and build their own connections, take that latency down and do other things. This is kind of the harvest of all that. As you start looking at these new new interfaces and the new servers, new technology that you guys are doing, you're enabling applications. What does, what do you see this enabling as this, as this new capability comes out, new speed, more, more performance, but also now it's enabling more capabilities so that new workloads can be realized. What would you say to folks who want to ask that question? >>Well, so first off I think arm is here to stay and you can see the growth and explosion of my arm, uh, led of course, by grab a tiny to be. I spend many others, uh, and by bringing all of NVIDIA's rendering graphics, machine learning and AI technologies to arm, we can help bring that innovation. That arm allows that open innovation because there's an open architecture to the entire ecosystem. Uh, we can help bring it forward, uh, to the state of the art in AI machine learning, the graphics. Um, we all have our software that we released is both supportive, both on x86 and an army equally, um, and including all of our AI stacks. So most notably for inference the deployment of AI models. We have our, the Nvidia Triton inference server. Uh, this is the, our inference serving software where after he was trained to model, he wanted to play it at scale on any CPU or GPU instance, um, for that matter. So we support both CPS and GPS with Triton. Um, it's natively integrated with SageMaker and provides the benefit of all those performance optimizations all the time. Uh, things like, uh, features like dynamic batching. It supports all the different AI frameworks from PI torch to TensorFlow, even a generalized Python code. Um, we're activating how activating the arm ecosystem as well as bringing all those AI new AI use cases and all those different performance levels, uh, with our partnership with AWS and all the different clouds. >>And you got to making it really easy for people to use, use the technology that brings up the next kind of question I want to ask you. I mean, a lot of people are really going in jumping in the big time into this. They're adopting AI. Either they're moving in from prototype to production. There's always some gaps, whether it's knowledge, skills, gaps, or whatever, but people are accelerating into the AI and leaning into it hard. What advancements have is Nvidia made to make it more accessible, um, for people to move faster through the, through the system, through the process? >>Yeah, it's one of the biggest challenges. The other promise of AI, all the publications that are coming all the way research now, how can you make it more accessible or easier to use by more people rather than just being an AI researcher, which is, uh, uh, obviously a very challenging and interesting field, but not one that's directly in the business. Nvidia is trying to write a full stack approach to AI. So as we make, uh, discover or see these AI technologies come available, we produce SDKs to help activate them or connect them with developers around the world. Uh, we have over 150 different STKs at this point, certain industries from gaming to design, to life sciences, to earth scientist. We even have stuff to help simulate quantum computing. Um, and of course all the, all the work we're doing with AI, 5g and robotics. So, uh, we actually just introduced about 65 new updates just this past month on all those SDKs. Uh, some of the newer stuff that's really exciting is the large language models. Uh, people are building some amazing AI. That's capable of understanding the Corpus of like human understanding, these language models that are trained on literally the continent of the internet to provide general purpose or open domain chatbots. So the customer is going to have a new kind of experience with a computer or the cloud. Uh, we're offering large language, uh, those large language models, as well as AI frameworks to help companies take advantage of this new kind of technology. >>You know, each and every time I do an interview with Nvidia or talk about Nvidia my kids and their friends, they first thing they said, you get me a good graphics card. Hey, I want the best thing in their rig. Obviously the gaming market's hot and known for that, but I mean, but there's a huge software team behind Nvidia. This is a well-known your CEO is always talking about on his keynotes, you're in the software business. And then you had, do have hardware. You were integrating with graviton and other things. So, but it's a software practices, software. This is all about software. Could you share kind of more about how Nvidia culture and their cloud culture and specifically around the scale? I mean, you, you hit every, every use case. So what's the software culture there at Nvidia, >>And it is actually a bigger, we have more software people than hardware people, people don't often realize this. Uh, and in fact that it's because of we create, uh, the, the, it just starts with the chip, obviously building great Silicon is necessary to provide that level of innovation, but as it expanded dramatically from then, from there, uh, not just the Silicon and the GPU, but the server designs themselves, we actually do entire server designs ourselves to help build out this infrastructure. We consume it and use it ourselves and build our own supercomputers to use AI, to improve our products. And then all that software that we build on top, we make it available. As I mentioned before, uh, as containers on our, uh, NGC container store container registry, which is accessible for me to bus, um, to connect to those vertical markets, instead of just opening up the hardware and none of the ecosystem in develop on it, they can with a low-level and programmatic stacks that we provide with Kuda. We believe that those vertical stacks are the ways we can help accelerate and advance AI. And that's why we make as well, >>Ram a little software is so much easier. I want to get that plug for, I think it's worth noting that you guys are, are heavy hardcore, especially on the AI side. And it's worth calling out, uh, getting back to the customers who are bridging that gap and getting out there, what are the metrics they should consider as they're deploying AI? What are success metrics? What does success look like? Can you share any insight into what they should be thinking about and looking at how they're doing? >>Yeah. Um, for training, it's all about time to solution. Um, it's not the hardware that that's the cost, it's the opportunity that AI can provide your business and many, and the productivity of those data scientists, which are developing, which are not easy to come by. So, uh, what we hear from customers is they need a fast time to solution to allow people to prototype very quickly, to train a model to convergence, to get into production quickly, and of course, move on to the next or continue to refine it often. So in training is time to solution for inference. It's about our, your ability to deploy at scale. Often people need to have real time requirements. They want to run in a certain amount of latency, a certain amount of time. And typically most companies don't have a single AI model. They have a collection of them. They want, they want to run for a single service or across multiple services. That's where you can aggregate some of your infrastructure leveraging the trading infant server. I mentioned before can actually run multiple models on a single GPU saving costs, optimizing for efficiency yet still meeting the requirements for latency and the real time experience so that your customers have a good, a good interaction with the AI. >>Awesome. Great. Let's get into, uh, the customer examples. You guys have obviously great customers. Can you share some of the use cases, examples with customers, notable customers? >>Yeah. I want one great part about working in videos as a technology company. You see, you get to engage with such amazing customers across many verticals. Uh, some of the ones that are pretty exciting right now, Netflix is using the G4 instances to CLA um, to do a video effects and animation content. And, you know, from anywhere in the world, in the cloud, uh, as a cloud creation content platform, uh, we work in the energy field that Siemens energy is actually using AI combined with, um, uh, simulation to do predictive maintenance on their energy plants, um, and, and, uh, doing preventing or optimizing onsite inspection activities and eliminating downtime, which is saving a lot of money for the engine industry. Uh, we have worked with Oxford university, uh, which is Oxford university actually has over two, over 20 million artifacts and specimens and collections across its gardens and museums and libraries. They're actually using convenient GPS and Amazon to do enhance image recognition, to classify all these things, which would take literally years with, um, uh, going through manually each of these artifacts using AI, we can click and quickly catalog all of them and connect them with their users. Um, great stories across graphics, about cross industries across research that, uh, it's just so exciting to see what people are doing with our technology together with, >>And thank you so much for coming on the cube. I really appreciate Greg, a lot of great content there. We probably going to go another hour, all the great stuff going on in the video, any closing remarks you want to share as we wrap this last minute up >>Now, the, um, really what Nvidia is about as accelerating cloud computing, whether it be AI, machine learning, graphics, or headphones, community simulation, and AWS was one of the first with this in the beginning, and they continue to bring out great instances to help connect, uh, the cloud and accelerated computing with all the different opportunities integrations with with SageMaker really Ks and ECS. Uh, the new instances with G five and G 5g, very excited to see all the work that we're doing together. >>Ian buck, general manager, and vice president of accelerated computing. I mean, how can you not love that title? We want more, more power, more faster, come on. More computing. No, one's going to complain with more computing know, thanks for coming on. Thank you. Appreciate it. I'm John Farrell hosted the cube. You're watching Amazon coverage reinvent 2021. Thanks for watching.

(bright music) >> Well, welcome back to theCUBE's coverage of AWS re:Invent 2021. We're here joined by Ian Buck, general manager and vice president of Accelerated Computing at NVIDIA. I'm John Furrrier, host of theCUBE. Ian, thanks for coming on. >> Oh, thanks for having me. >> So NVIDIA, obviously, great brand. Congratulations on all your continued success. Everyone who does anything in graphics knows that GPU's are hot, and you guys have a great brand, great success in the company. But AI and machine learning, we're seeing the trend significantly being powered by the GPU's and other systems. So it's a key part of everything. So what's the trends that you're seeing in ML and AI that's accelerating computing to the cloud? >> Yeah. I mean, AI is kind of driving breakthroughs and innovations across so many segments, so many different use cases. We see it showing up with things like credit card fraud prevention, and product and content recommendations. Really, it's the new engine behind search engines, is AI. People are applying AI to things like meeting transcriptions, virtual calls like this, using AI to actually capture what was said. And that gets applied in person-to-person interactions. We also see it in intelligence assistance for contact center automation, or chat bots, medical imaging, and intelligence stores, and warehouses, and everywhere. It's really amazing what AI has been demonstrating, what it can do, and its new use cases are showing up all the time. >> You know, Ian, I'd love to get your thoughts on how the world's evolved, just in the past few years alone, with cloud. And certainly, the pandemic's proven it. You had this whole kind of fullstack mindset, initially, and now you're seeing more of a horizontal scale, but yet, enabling this vertical specialization in applications. I mean, you mentioned some of those apps. The new enablers, this kind of, the horizontal play with enablement for, you know, specialization with data, this is a huge shift that's going on. It's been happening. What's your reaction to that? >> Yeah. The innovation's on two fronts. There's a horizontal front, which is basically the different kinds of neural networks or AIs, as well as machine learning techniques, that are just being invented by researchers and the community at large, including Amazon. You know, it started with these convolutional neural networks, which are great for image processing, but has expanded more recently into recurrent neural networks, transformer models, which are great for language and language and understanding, and then the new hot topic, graph neural networks, where the actual graph now is trained as a neural network. You have this underpinning of great AI technologies that are being invented around the world. NVIDIA's role is to try to productize that and provide a platform for people to do that innovation. And then, take the next step and innovate vertically. Take it and apply it to a particular field, like medical, like healthcare and medical imaging, applying AI so that radiologists can have an AI assistant with them and highlight different parts of the scan that may be troublesome or worrying, or require some more investigation. Using it for robotics, building virtual worlds where robots can be trained in a virtual environment, their AI being constantly trained and reinforced, and learn how to do certain activities and techniques. So that the first time it's ever downloaded into a real robot, it works right out of the box. To activate that, we are creating different vertical solutions, vertical stacks, vertical products, that talk the languages of those businesses, of those users. In medical imaging, it's processing medical data, which is obviously a very complicated, large format data, often three-dimensional voxels. In robotics, it's building, combining both our graphics and simulation technologies, along with the AI training capabilities and difference capabilities, in order to run in real time. Those are just two simple- >> Yeah, no. I mean, it's just so cutting-edge, it's so relevant. I mean, I think one of the things you mentioned about the neural networks, specifically, the graph neural networks, I mean, we saw, I mean, just go back to the late 2000s, how unstructured data, or object storage created, a lot of people realized a lot of value out of that. Now you got graph value, you got network effect, you got all kinds of new patterns. You guys have this notion of graph neural networks that's out there. What is a graph neural network, and what does it actually mean from a deep learning and an AI perspective? >> Yeah. I mean, a graph is exactly what it sounds like. You have points that are connected to each other, that establish relationships. In the example of Amazon.com, you might have buyers, distributors, sellers, and all of them are buying, or recommending, or selling different products. And they're represented in a graph. If I buy something from you and from you, I'm connected to those endpoints, and likewise, more deeply across a supply chain, or warehouse, or other buyers and sellers across the network. What's new right now is, that those connections now can be treated and trained like a neural network, understanding the relationship, how strong is that connection between that buyer and seller, or the distributor and supplier, and then build up a network to figure out and understand patterns across them. For example, what products I may like, 'cause I have this connection in my graph, what other products may meet those requirements? Or, also, identifying things like fraud, When patterns and buying patterns don't match what a graph neural networks should say would be the typical kind of graph connectivity, the different kind of weights and connections between the two, captured by the frequency of how often I buy things, or how I rate them or give them stars, or other such use cases. This application, graph neural networks, which is basically capturing the connections of all things with all people, especially in the world of e-commerce, is very exciting to a new application of applying AI to optimizing business, to reducing fraud, and letting us, you know, get access to the products that we want. They have our recommendations be things that excite us and want us to buy things, and buy more. >> That's a great setup for the real conversation that's going on here at re:Invent, which is new kinds of workloads are changing the game, people are refactoring their business with, not just re-platforming, but actually using this to identify value. And also, your cloud scale allows you to have the compute power to, you know, look at a note in an arc and actually code that. It's all science, it's all computer science, all at scale. So with that, that brings up the whole AWS relationship. Can you tell us how you're working with AWS, specifically? >> Yeah, AWS have been a great partner, and one of the first cloud providers to ever provide GPUs to the cloud. More recently, we've announced two new instances, the G5 instance, which is based on our A10G GPU, which supports the NVIDIA RTX technology, our rendering technology, for real-time ray tracing in graphics and game streaming. This is our highest performance graphics enhanced application, allows for those high-performance graphics applications to be directly hosted in the cloud. And, of course, runs everything else as well. It has access to our AI technology and runs all of our AI stacks. We also announced, with AWS, the G5 G instance. This is exciting because it's the first Graviton or Arm-based processor connected to a GPU and successful in the cloud. The focus here is Android gaming and machine learning inference. And we're excited to see the advancements that Amazon is making and AWS is making, with Arm in the cloud. And we're glad to be part of that journey. >> Well, congratulations. I remember, I was just watching my interview with James Hamilton from AWS 2013 and 2014. He was teasing this out, that they're going to build their own, get in there, and build their own connections to take that latency down and do other things. This is kind of the harvest of all that. As you start looking at these new interfaces, and the new servers, new technology that you guys are doing, you're enabling applications. What do you see this enabling? As this new capability comes out, new speed, more performance, but also, now it's enabling more capabilities so that new workloads can be realized. What would you say to folks who want to ask that question? >> Well, so first off, I think Arm is here to stay. We can see the growth and explosion of Arm, led of course, by Graviton and AWS, but many others. And by bringing all of NVIDIA's rendering graphics, machine learning and AI technologies to Arm, we can help bring that innovation that Arm allows, that open innovation, because there's an open architecture, to the entire ecosystem. We can help bring it forward to the state of the art in AI machine learning and graphics. All of our software that we release is both supportive, both on x86 and on Arm equally, and including all of our AI stacks. So most notably, for inference, the deployment of AI models, we have the NVIDIA Triton inference server. This is our inference serving software, where after you've trained a model, you want to deploy it at scale on any CPU, or GPU instance, for that matter. So we support both CPUs and GPUs with Triton. It's natively integrated with SageMaker and provides the benefit of all those performance optimizations. Features like dynamic batching, it supports all the different AI frameworks, from PyTorch to TensorFlow, even a generalized Python code. We're activating, and help activating, the Arm ecosystem, as well as bringing all those new AI use cases, and all those different performance levels with our partnership with AWS and all the different cloud instances. >> And you guys are making it really easy for people to use use the technology. That brings up the next, kind of, question I wanted to ask you. I mean, a lot of people are really going in, jumping in big-time into this. They're adopting AI, either they're moving it from prototype to production. There's always some gaps, whether it's, you know, knowledge, skills gaps, or whatever. But people are accelerating into the AI and leaning into it hard. What advancements has NVIDIA made to make it more accessible for people to move faster through the system, through the process? >> Yeah. It's one of the biggest challenges. You know, the promise of AI, all the publications that are coming out, all the great research, you know, how can you make it more accessible or easier to use by more people? Rather than just being an AI researcher, which is obviously a very challenging and interesting field, but not one that's directly connected to the business. NVIDIA is trying to provide a fullstack approach to AI. So as we discover or see these AI technologies become available, we produce SDKs to help activate them or connect them with developers around the world. We have over 150 different SDKs at this point, serving industries from gaming, to design, to life sciences, to earth sciences. We even have stuff to help simulate quantum computing. And of course, all the work we're doing with AI, 5G, and robotics. So we actually just introduced about 65 new updates, just this past month, on all those SDKs. Some of the newer stuff that's really exciting is the large language models. People are building some amazing AI that's capable of understanding the corpus of, like, human understanding. These language models that are trained on literally the content of the internet to provide general purpose or open-domain chatbots, so the customer is going to have a new kind of experience with the computer or the cloud. We're offering those large language models, as well as AI frameworks, to help companies take advantage of this new kind of technology. >> You know, Ian, every time I do an interview with NVIDIA or talk about NVIDIA, my kids and friends, first thing they say is, "Can you get me a good graphics card?" They all want the best thing in their rig. Obviously the gaming market's hot and known for that. But there's a huge software team behind NVIDIA. This is well-known. Your CEO is always talking about it on his keynotes. You're in the software business. And you do have hardware, you are integrating with Graviton and other things. But it's a software practice. This is software. This is all about software. >> Right. >> Can you share, kind of, more about how NVIDIA culture and their cloud culture, and specifically around the scale, I mean, you hit every use case. So what's the software culture there at NVIDIA? >> Yeah, NVIDIA's actually a bigger, we have more software people than hardware people. But people don't often realize this. And in fact, that it's because of, it just starts with the chip, and obviously, building great silicon is necessary to provide that level of innovation. But it's expanded dramatically from there. Not just the silicon and the GPU, but the server designs themselves. We actually do entire server designs ourselves, to help build out this infrastructure. We consume it and use it ourselves, and build our own supercomputers to use AI to improve our products. And then, all that software that we build on top, we make it available, as I mentioned before, as containers on our NGC container store, container registry, which is accessible from AWS, to connect to those vertical markets. Instead of just opening up the hardware and letting the ecosystem develop on it, they can, with the low-level and programmatic stacks that we provide with CUDA. We believe that those vertical stacks are the ways we can help accelerate and advance AI. And that's why we make them so available. >> And programmable software is so much easier. I want to get that plug in for, I think it's worth noting that you guys are heavy hardcore, especially on the AI side, and it's worth calling out. Getting back to the customers who are bridging that gap and getting out there, what are the metrics they should consider as they're deploying AI? What are success metrics? What does success look like? Can you share any insight into what they should be thinking about, and looking at how they're doing? >> Yeah. For training, it's all about time-to-solution. It's not the hardware that's the cost, it's the opportunity that AI can provide to your business, and the productivity of those data scientists which are developing them, which are not easy to come by. So what we hear from customers is they need a fast time-to-solution to allow people to prototype very quickly, to train a model to convergence, to get into production quickly, and of course, move on to the next or continue to refine it. >> John Furrier: Often. >> So in training, it's time-to-solution. For inference, it's about your ability to deploy at scale. Often people need to have real-time requirements. They want to run in a certain amount of latency, in a certain amount of time. And typically, most companies don't have a single AI model. They have a collection of them they want to run for a single service or across multiple services. That's where you can aggregate some of your infrastructure. Leveraging the Triton inference server, I mentioned before, can actually run multiple models on a single GPU saving costs, optimizing for efficiency, yet still meeting the requirements for latency and the real-time experience, so that our customers have a good interaction with the AI. >> Awesome. Great. Let's get into the customer examples. You guys have, obviously, great customers. Can you share some of the use cases examples with customers, notable customers? >> Yeah. One great part about working at NVIDIA is, as technology company, you get to engage with such amazing customers across many verticals. Some of the ones that are pretty exciting right now, Netflix is using the G4 instances to do a video effects and animation content from anywhere in the world, in the cloud, as a cloud creation content platform. We work in the energy field. Siemens energy is actually using AI combined with simulation to do predictive maintenance on their energy plants, preventing, or optimizing, onsite inspection activities and eliminating downtime, which is saving a lot of money for the energy industry. We have worked with Oxford University. Oxford University actually has over 20 million artifacts and specimens and collections, across its gardens and museums and libraries. They're actually using NVIDIA GPU's and Amazon to do enhanced image recognition to classify all these things, which would take literally years going through manually, each of these artifacts. Using AI, we can quickly catalog all of them and connect them with their users. Great stories across graphics, across industries, across research, that it's just so exciting to see what people are doing with our technology, together with Amazon. >> Ian, thank you so much for coming on theCUBE. I really appreciate it. A lot of great content there. We probably could go another hour. All the great stuff going on at NVIDIA. Any closing remarks you want to share, as we wrap this last minute up? >> You know, really what NVIDIA's about, is accelerating cloud computing. Whether it be AI, machine learning, graphics, or high-performance computing and simulation. And AWS was one of the first with this, in the beginning, and they continue to bring out great instances to help connect the cloud and accelerated computing with all the different opportunities. The integrations with EC2, with SageMaker, with EKS, and ECS. The new instances with G5 and G5 G. Very excited to see all the work that we're doing together. >> Ian Buck, general manager and vice president of Accelerated Computing. I mean, how can you not love that title? We want more power, more faster, come on. More computing. No one's going to complain with more computing. Ian, thanks for coming on. >> Thank you. >> Appreciate it. I'm John Furrier, host of theCUBE. You're watching Amazon coverage re:Invent 2021. Thanks for watching. (bright music)

(upbeat electronic music) >> Okay thanks, Adam, and the studio. We're here on the floor in Cloud City, right in the middle of all the action, the keynotes are going on in the background. It's a packed house. I'm John Furrier. Dave Vellante's on assignment, digging in, getting those stories. He'll have the analysis, he'll be back on theCUBE, but I want to welcome Chloe Richardson, who has been holding down the main stage here in Cloud City with amazing content that she's been hosting. Chloe, great to see you. Thanks for coming on theCUBE, and kicking it off day two with me. >> No, not at all. Thank you for having me! It's very exciting! I love what you guys have got over here, very fun! >> We're inside theCUBE. This is where all the action is, and also, Cloud City is really changing the game. If you look at what's going on here in Cloud City, it's pretty spectacular. >> No, I mean, the atmosphere is absolutely palpable. Isn't it? You can just feel it. People walk in and see what the future looks like for the telecoms industry. Very exciting. >> And you've been doing a great job on the main stage, we're really loving your content. Let's get into some of the content here. After the keynotes are going on, we're going to have DR maybe fly by the set later, we're going to check that out. But let's check out this videotape. This is TelcoDR. You got to check out this reel, and we'll be right back, and we'll talk about it. (smooth electronic music) >> TelcoDR burst onto the global telecom scene this year, making headlines for taking over the huge Erickson space at MWC 21, and for building Cloud City in just a hundred days. But why did the company go to such trouble? And what is their unique offering to the telecoms industry? And what drives their dynamic CEO, Danielle Royston, or DR, as everyone calls her? Cloud City Live caught up with DR, away from the hustle and bustle of the city to find out. (upbeat instrumental music) >> Hi, I'm Danielle Royston, coming to you from beautiful Barcelona! I'm here for MWC 21. About a hundred days ago, I decided to take over the iconic Erickson booth to turn it into Cloud City. Cloud City has over 30 vendors, and 70 demos, to introduce telco to what I think is the future for our industry. We're going to have three awesome experiences. We're going to talk about the new subscriber experience. We're going to talk about what's in store for the new network, and the future of work. And I'm really excited to create a community, and invite awesome telco executives to see this new feature. It's been a really tough 18 months, and we didn't know what MWC 21 was going to be like in terms of attendance. And so from the get-go, we planned this amazing experience that we call Cloud City Live. At Cloud City Live, we have two main components. We have the speaker series, where we have over 50 speakers from Amazon, Google, Microsoft, as well as CSPs, and awesome vendors, talking about the public cloud in telco. The second part of Cloud City Live is theCUBE. Think of this as like an ESPN desk of awesome tech interviews focused on telco and the public cloud, hosted by John furrier and Dave Vallente. Dave and John are going to talk to a variety of guests focused on telco in the public cloud. It's a great way for our virtual participants to feel like they're at the show, experiencing what's going on here. So excited to have them as part of the Cloud City booth. There's a ton of innovation going on in telco, and 20 years ago, Elon Musk set on his mission to Mars. I, like Elon Musk, am on a quest to take telco to the public cloud. Every year at MWC, there's always a flurry of announcements, and this year is no different. At this year's MWC, Totogi, a startup that I invested $1,000,000 in, will be launching. Totogi is introducing two products to the market this week at MWC. The first is at planetary scale charger. More than a charger, it's an engagement, coupling your network data with charging information to drive subscriber engagement, and doubling your ARPU. The second product that Totogi is introducing is a planetary scale BSS system, built on top of the TM Forum Open APIs. Both of these products will be available for viewing in the virtual booth, as well as on the show floor. The public cloud is an unstoppable mega trend that's coming to telco! I'm super excited to bring to you the vendors, the products, the demonstrations, and the speakers, both to people here in Barcelona, and virtually around the world! (upbeat instrumental music) Well, that was a fascinating insight into the origins of TelcoDR, why public cloud is going to truly disrupt the telecoms industry, and why DR herself is so passionate about it. If you'd like to find out more, come and see us at Cloud City. (groovy electronic music) >> Okay, thanks. Just rolling that reel. Chloe, I mean, look at that reel, I mean, DR, Danielle Royston, she's a star. And I've seen a lot of power players in the industry. She's got guts and determination, and she's got a vision, and she's not just, you know, making noise about telco and cloud, there's actually a lot of real good vision there! I mean, it's just so impressive. >> No, it really is. And for me, it's almost like the next moonshot. It's the moonshot of the telco world! She's innovative, she's exciting. And if we've learned anything over the last 18 months, it's that we need that in this industry, to grow for the future of the industry. So, so exciting. I think she's a real inspiration! >> And I love the fact that she's so takes the tiger by the tail. Because the telco industry is being disrupted, she's just driving the bus here. And I remember, I did a story on Teresa Carlson, who was with Amazon Web Services, she was running the public sector, and she was doing the same exact thing in that public sector world in DC, and around the world. She opened up regions in Bahrain, which as a woman, that was an amazing accomplishment. And she wasn't just a woman, she was just a power player! And she was an exceptional leader. I see DR doing the same thing, and people aren't going to like that, I'll tell you right now. People are going to be like, "Whoa, what's going on here?" >> Now of course, it's always that way we pioneers though, isn't it? At the time, people thinking what is going on here, we don't like change, why are being shaken up? But actually, afterwards, in retrospect, they think, "Oh, okay. I see why that happened, and we needed it." So, really exciting stuff. >> Making things happen, that's what we're doing here on theCUBE. Obviously, the main stage's doing a great job. Let's go check out this highlight reel. If you're watching and you missed some of the action, this is obviously the physical event back since 2019 in February, but there's also a hybrid event, a lot of virtual action going on. So, you got theCUBE Virtual, you got a lot of content on virtual sites. But in person here, we're going to go show you a highlight reel from what we did yesterday, and what was happening around the show. Enjoy this quick highlight reel from yesterday. (groovy electronic music) (cheerful instrumental music) (groovy electronic music) Okay. We're back here in theCUBE. We're on the main floor out here with Chloe, who is emceeing, hosting, and driving the content on the Cloud City main stage. Chloe, it's been great here. I mean so far, day one, I was watching your presentations and fireside chats you've been hosting. Awesome content. I mean, people are like jazzed up. >> Yeah, no, for sure. We had Scott Brighton on yesterday, who was our opening keynote on the live stage, and his session was all about the future of work, which is so relevant and so pertinent to now. And he talked about the way it's changing. And in 10 years, it's going to be a trillion dollar industry to be in the cloud at work. So, really interesting! I mean, yeah, the atmosphere here is great. Everyone's excited. It's new content everyday. And that's the thing, it's not stale content! It's stuff that people want to hear. People are here for the new hot trends, the new hot topics. It's very exciting. >> Yeah, the next big thing. And also it's a fiscal event, so since 2019, this Mobile World Congress has been a massive event, and hasn't happened since February, 2019. That's a lot of time that's elapsed in the industry because of COVID, and people are glad to be here. But a lot of stuff's changed! >> Yeah. It's a different world, right? I mean, two years in the telco industry is like a hundred years elsewhere. Everything has changed! Digital transformation migration, obviously cloud, which is what we're talking about over here at Cloud City Live. I'm wondering though, John, I'd like to pick your brains on something. >> John: Sure. >> It has changed in the last two years. We know that! But what about the future of Mobile World Congress? How do you see it changing in the next few years. >> Oh, man. That's a great question. I mean, my observation, I've been coming to the show for a very long time, over a decade and a half, and it's been a nerdy show about networks, and telecom, which is basically radios, and wireless, and then mobile. But it's very global, a lot of networks. But now it's evolving! And many people are saying, and we were talking on theCUBE yesterday, Dave Vellante was commenting, that this show is turning into a consumer like show. So CES is the big consumer electronics show in the US, in Las Vegas every year. This show has got a vibe, because of all the technology from the cloud players, and from the chips, getting smaller, faster, cheaper, more capability, lower power. So people look at the chips, the hardware. It's less about the speeds and feeds, it's more about the consumer experience. We got cars. I was talking to a guy yesterday, he said, "Vehicle e-commerce is coming." I went, "What the hell his vehicle e-commerce?" And you could be on your app driving down the freeway and go, "Hey, I want some food." Instead of having it delivered to you, you order it, you pick it up. So that's kind of what can be happening now in real time, you can do all kinds of other things. So, a lot of new things are happening. >> Yeah, I think so. Do you see that as another disruption for the industry? That is, the fact that it's moving to be more consumer focused? Is there anything we should be worried about in that space? >> Well, I think the incumbents are going to lose their positions. So I think in any new shift, new brands come in out of nowhere. And it's the people that you don't think about. It's the the company that you don't see. (audience in background applauding) And we got DR on the main stage right here, look at this! We saw her walk out with the confidence of a pro. >> Chloe: Yeah, for sure. >> She just walked out there, and she's not afraid. >> Well, as she said in her video, she is ready to wake them up! And you can see as soon as she walks out, that is what she intends to do today. >> I love her mojo. She's got a lot of energy. And back to the show, I mean, she's just an example of what I was saying. Like in every market shift, a new brand emerges. >> Chloe: Yep. >> I mean, even when Apple was tainted, they were about to shut down, they were going to run out of cash, when Steve Jobs brought back Apple, he consolidated and rebooted the company, the iPad was a seminal, iPod, a seminal moment. Then the iPhone, and just, the rest is history. That kind of disruption is coming. You're going to see that now. >> Oh, it's exciting though, isn't it? To be future ready, rather than future proof! But actually I wanted to ask you something as well, because we are seeing all these cloud players getting hot under the collar about telco. Why are they so excited? What's the buzz about wire, as you're on AWS and Google Cloud, why do they want to have a slice of the pie? >> Well, I think they're hot and heavy on the fact that telco is a ripe opportunity. And it used to be this boring, slow moving glacier. It's almost like global warming now, the icebergs are melting, and it's going to just change. And because of the edge, 5G is not a consumer wireless thing, it's not like a better phone. It's a commercial app opportunity, because it's high bandwidth. We've all been to concerts, or football games, or sporting events where a stadium is packed. Everyone gets bars on their wifi, but can't get out. Can't upload their picture to Instagram. Why? Because it's choking them on the network. That's where 5G solves a problem. It brings a lot of bandwidth, and that's going to bring the edge to life, and that's money. So when you got money, and greed, and power, changing hands, if it's on the table, and the wheel's spinning, it could be double zero, or it could be lucky seven. You don't know! >> Oh, for sure. And that's certainly enough to get all the big players hot and bothered about getting involved! And I suppose it circles back to the fact that DR is really leading the charge, and they're probably thinking, "Okay, what's going on here? This is different. We want something new." You did notice it, OpenRAN is something that we've been talking about over the last day or so. We've had quite a few of us speakers over here at Cloud City Live mention OpenRAN. What is it all about, Don? Because why all the buzz if 5G is such a hot topic? Why are we get excited about it? >> That's a great thing. The 5G certainly will drive the main trend, for sure. OpenRAN is essentially an answer to the fact that 5G is popular, and they need more infrastructure. So open source, the Linux Foundation, has been the driver for most of the open source software. So, they're trying to make open software, and open architectures, to create more entrepreneurial activity around hardware, and around infrastructure, because we need more infrastructure, we need more antennas, we need more transceivers, we need more devices. That could be open. So in order to do that, you got to open up the technology, and you want to minimize the licensing, and minimize a lot of these, you know, proprietary aspects. >> What did we look at? So on Wednesday, we've got a great keynote from Phillip Langlois, who is CEO and founder of P1 Security. And he's coming to talk to us about cybersecurity within the cloud, and within telco. So you just mentioned that OpenRAN is all about having open source, about having that space where we can share more efficiently and easily more easily. What does that mean for security though? Is it at risk? >> I think it's going to increase the value of security, and minimize the threats. Because open source, even though it's open, the more people that are working on it, the more secure it could be. So yes, it could be more open in a sense that could be explored by hackers, but open can also protect. And I think we've seen open source, and cloud in particular, be more secure. Because everyone said, cloud is not secure, open source is insecure. And as it turns out, when the collective hive minds of developers work on things, it gets secure. >> And it is interesting, isn't it? Because we have seen that there has been an uptick in cyber security threats, but actually I was speaking to some leaders across various industries, and particularly in tech, and they were saying, actually, there's not been an uptick in attempted threats, there's been an uptick because with this open-source environment, we are able to track them, and measure them, and defend more efficiently. So actually, they're being batted away. But the number is probably the same as it always was, we just didn't know about them before we had this open source environment. >> There's more money in threats, and there's more surface area. So as the tide rises, so to the threats. So on a net basis, it's more, because there's more volume, but it's pretty much the same. And look it, there's money involved, they are organized. There's a business model on attacking and getting the cash out of your bank, or ransomware is at an all time high. >> Yes! >> So this is like a big problem, and it's beyond the government. It's around individual freedom. So, security is huge. And I think open source and cloud are going to be, I think, the answer to that. >> Yeah, for sure. And it's, again, about collaboration, isn't it? Which we talk about all the time, but without collaboration, the industries are going to have to work together to promote this environment. So yeah, it should be good to talk with Phillip on Wednesday. >> I'd just say on security, don't download that PDF, if you don't know who it came from. The phishing is always good. Well, we got some great stuff coming up. We're going to have a great day. We got a video here of Mobile World Live. We're going to show this next segment, and we're going to toss it to a video. And this is really about to give the experience, Chloe, for people who aren't here. To get a feel for what's going on in Barcelona, and all the action. And if you look at the video, enjoy it. >> Hi, I'm Daniel Royston, CEO and founder of TelcoDR. But you can call me DR! Ready for some more straight talk about telco? It's go time! Let's do it. Holy shit! It sure is a great time to be a tech company! I mean, if you're Amazon, Microsoft, Google, Grab, Twilio, Door Dash, or Uber, life's pretty great! Just look at these stock prices over the past five years, with their shareholder value going up and to the right. Totally amazing! But where's telco? Dare I add our stocks to this awesome chart? Let's compare these fabulous tech stocks to AT&T, Vodafone, Telefonica, TIM, America Movil, and Zain Group. Huh. Not so great, right? Yep. I'm talking directly to you, senior telco execs. I'm here to wake you up! Why is it that Wall Street doesn't see you as tech? Why aren't CSPs seen as driving all the tech change? Why is it always Apple, Amazon and Google who get the big buzz? But more importantly, why isn't it you? Before I came to this industry, I always thought of carriers as tech companies. I gave more of my money to AT&T than to Apple, because I really cared about the quality of the network. But I also wondered why on earth the carriers allowed all the other tech companies to take center stage. After spending the last few years in telco, I now understand why. It's because you are network people, you are not customer people! I get it. You have the security blanket. You're a network oligopoly. It's crazy expensive to build a network, and it's expensive to buy spectrum. It takes operational chops to run a killer network, and it takes great skill to convince Wall Street to finance all of it. You telco execs are amazing at all those things. But because you focus on the network, it means you don't focus on the customer. And so far, you haven't had to. Every Telco's KPI is to be less shitty than their next competitor. You don't have to be the best. Just don't be last. Everyone else's NPS is in the thirties too. Their mobile app ratings are just as terrible as yours. Everyone's sucks at customer sat. And it's widely acknowledged and accepted. Let's talk about the cost of that. The cost is not measured on market share against other MNOs. The cost is measured in lost ARPU that the tech guys are getting. Everyone knows about the loss of texting to WeChat, WhatsApp, and the other OTT apps. But it is not just texting. The total adjustable market, or TAM, of the mobile app disruptors is huge! Instead of remaining network focused, you should be leveraging your network into a premier position. And because you're network people, I bet you think I'm talking about coercive network leverage. That is not what I'm talking about! I'm talking about love, customer love. There is one thing the highly valued tech companies all have in common. They all crush it on customer love! They look at every interaction with the customer and say, how do we make the customer love this? Like Netflix has easy monthly cancellation, Amazon does no questions asked returns, Uber gives users a real time view into driver rating and availability. Compare those ideas to the standard telco customer interaction. The highly valued tech companies don't have the network oligopoly to fall back on like you do. To survive, they must make customers love them. So, they focus on it in a big way! And it pays off. Their NPS is close to 70, and they have app ratings of 4.5 or higher. A far cry from your thirties NPS, and app ratings of 3.5. If you want to have those huge tech multiples for yourself, you have to start thinking about these guys as your new competition, not the other telcos in your market. The crazy thing is, if you give up using your network as a crutch, and put all of your focus on the customer, the network becomes an asset worth more than all the super apps. Let's step back and talk about the value of super apps, and becoming customer centered! Retooling around the customer is a huge change, so let's make sure it's worth it. We aren't talking about 25% improvement. I'm going to show you that if you become customer centric, you can double your ARPU, double your valuation multiples, and drive big shareholder value, just like the tech companies on that chart! Now let's talk about the customer focused super apps. There are hundreds of companies in a variety of categories vying for your subscribers' disposable income. Movies, food delivery, financial services. Who are they? And why does Wall Street give them such high valuations and like them so much? Well first, look at what they are telling Wall Street about their TAM. They broadcast ridiculously huge TAMs that are greater than the telco TAMs. You know, who should have a ridiculously huge TAM? You! Hello? What I'm saying is that if you got what's yours, you double in size. And if you take the TAMs they throw around, you'd be five times as big. When I think about the opportunity to double ARPU, without having to double the cap ex to build out the network, I say to myself, hell yeah! We should totally go do it, and do whatever it takes to go get it. For example, let's talk about Grab. Grab is a Southeast Asian super app company with an expected $40 billion valuation. Grab's customer focus started in Rideshare, but then leveraged its customer love into wallet deliveries, hospitality, and investing. Their ARPU is now larger than a Telco's ARPU in countries where they compete, and they have a higher valuation than those telcos too. Imagine if you could combine a great user experience with the valuable services that helped grow your ARPU. That would be huge! So, how do you build a super app? I bet right about now, you're wishing you had a super app. Everyone wants a super app! A lot of money has been unsuccessfully spent by telcos trying to build their own. I bet you're saying to yourself, "DR, your pie in the sky sounds great, but it has no chance of success." Well, I'm betting things are about to change. There is a public cloud startup called to Totogi that is going to help carriers build world-class super apps. To have a successful super app, there is one key metric you need to know. It is the KPI that determines if your super app will be a success or a flop. It's not about the daily active users. It's not the average order value. It's not even gross merchandise value. It's all about the frequency of use per day by the user. That's the metric that matters. How many have you used that metric in your telco apps? Do you have a team driving up user app interactions every day? Most telco apps are used for top-up, or to check a bill. This is a huge missed opportunity. Super app companies excel at building great experiences and driving a huge amount of interactions. They have to, their business depends on it. They have to be customer focused. They have to keep bringing the user back to the app, every day, multiple times a day. And you know what? They do a great job. Customers love their super apps. They have great user experiences. Like Apple credit cards, no information required application process. They have high net promoter scores because of customer friendly policies. Like how Door Dash retroactively credits fees when you move to a better plan. And they have great app store ratings, because they do simple things, like remember your last order, or allow you to use the app, rather than forced you to call customer service. Customers of successful super apps love it when new services are added. And because of the customer love, every time something is added to the app, customers adopt it immediately. New services drive frequent daily user interactions. So our problem in telco is we have an app that is only open once per month, not multiple times per day. And without frequent opens, there is no super app. Hm, what do we have in telco that we could use to help with this problem? I wonder. While you don't currently have a mobile app that subscribers use multiple times a day, you have something that's 10 times better! You have a network. Subscribers already interact with your network. 10 times more frequently than any user with any of the super apps. But telcos don't leverage those interactions into the insanely valuable engagements they could be. Worse, even if you wanted to, your crappy, over customized, on-premise solutions, make it impossible. Thankfully, there's this new tech that's come around, you may have heard of it, the public cloud. When you bring the enabling technology of the public cloud, you can turn your network interactions into valuable super app interactions. And there's a special new startup that's going to help you do it, Totogi! Totogi will leverage all those network interactions, and turn them into valuable customer interactions. Let me repeat that. Totogi will leverage all those network interactions, and turn them into valuable customer interactions. Totogi allows the carrier to leverage its network, and all the network interactions, into customer engagement. This is something that super apps don't have, but will wish they did. But this magic technology is not enough. Telcos also need to move from being network focused to being customer focused. Totogi enables telcos to chase exciting revenue growth without that annoying, massive cap ex investment. Totogi is going to help you transform your sucky mobile apps, with the crappy customer ratings, into something your subscribers want to open multiple times a day, and become a platform for growth. I'm so excited about Totogi, I'm investing $100,000,000 into it. You heard me right. $100,000,000. Is this what it feels like to be SoftBank? I'm investing into Totogi because it's going to enable telcos to leverage their network interactions into super app usage! Which will lead to an improved subscriber experience, and will give you a massive jump in your ARPU. And once you do that, all those telco valuations will go from down here, (buzzes lips) to up here. And so I've been talking to some folks, you know, checking in, feeling them out, getting their thoughts. And I've been asking them, what do you think about telcos building super apps? And the response has been, "Click. Eh." Everyone says, no way. Telcos can't do it. Zero chance. Total goose egg. (egg cracking) One suggested I build a bonfire with a hundred million dollars, because then at least I wouldn't waste years of my life. Well, I think those people are dead wrong! I do believe that telcos can build super apps and make them super successful. The public cloud is changing all parts of telco, and Totogi and super apps are fundamentally changing the customer relationships. In one month at MWC, people will see what Totogi has to offer, and they will understand why I'm making this bold call. Because Totogi takes the value of the network, and the power of the public cloud, to help telcos move from being network centric, to being customer centric. Boom! If you want to make this transformation and reap all the financial benefits, you will have to compete for customers with a whole new set of players. You will no longer compete with the network focused guys, like the other telcos. Instead, you will be competing against the customer focused companies. These players don't have a network to fall back on like your old competitors, they know they have to make customers love them. Their customer loyalty is so off the charts, their customers are called fans. So if you want that big money, you will have to compete on their turf, and make the customers want to choose you. You need Apple level loyalty. That bar is uber high. We'll have to give up the security blanket of the network, and change. Instead of NPS at the thirties, it needs to be in the seventies. Instead of mobile app ratings in the threes, they need to get five stars. I'm betting big that Totogi will make that possible! I'm going to help you every step of the way, starting with my keynote next month at MWC. Join me, and I'll share the secrets to converting your super valuable network interactions to make your super app a massive success. We're going to have an amazing time, and I can't wait to see you there! >> Okay. We're back here in theCUBE here at Mobile World Congress in Cloud City. I'm John Furrier. Chloe Richardson's filling in for Dave Vellante who's out on assignment. He's out getting all the data out there and getting stories. Chloe, what a great keynote by Danielle Royston. We just heard her involving major action, major pump you up, punch in the face, "Wake the heck up cloud people, cloud is here!" She didn't pull any punches. >> No, I mean the thing is, John, there's trillions of dollars on the table, and everyone seems to be fighting for it. >> And you heard her up there, if you're not on the public cloud, you're not going to get access to that money. It's a free for all. And I think the cloud people are like, they might think they're going to walk right in, and the telco industry is going to just give it up. >> No, of course. >> And it's not going to be, it's going to be a fight! Who will win? >> Who will win, but also who will build the next big thing? (John laughing) >> Someone needs to die in the media conversations. It's always a fight. Something's dead. Something's dead but keeps the living. All that kidding aside, this is really about partnering. Think what's happened is Telco's already acknowledged that they need to change. And the 5G edge conversation, the chip acceleration. Look at Apple. They've got their own processors, Nvidia, Amazon makes their own chips, Intel's pumping stuff out, you've got Qualcomm. You've got all these new things. So, the chips are getting faster, and the software's more open source. And I'm telling you, the cloud is just going to drive that bus right down Cloud Street, and it's going to be in Cloud City everywhere. >> And it's going to be peepin' on the board as it drives down. (John laughing) John, I'm not a stalker, but I have read some of the things that you've written, and one of the things you mentioned that was really interesting was the difference between building and operating. Break it down for me, what does that mean? >> That means basically in mature markets, and growing markets, things behave differently, and certainly economics, and the people, and the makeup, and the mindset. So the telco has been kind of this mature market, it's been changing and growing, but not like radically. Cost optimization, make profit. You know, to install a lot of cable, you got to get the rents out of that infrastructure. And that's kind of gone on for too long. Cloud is a growth market. And it's about building, not just operating. And you've got operators, carriers are operating networks. So you're going to see the convergence of operators and builders coming together. Builders being software developers, new technology, and executives that think about building. And you want people on your team that are going to be, I won't say war time, you know, lieutenants or generals, but people who can handle the pace of change. Because the change and the nature is different. And some people want slow and steady, keep the boat from rocking. But in a growth market, it's turbulent, and the ride might not be quiet, first-class ticket to paradise. It's bumpy, but it's thrilling. >> No, of course. Is it similar to the old sales adage of hunter versus farmer? Are there parallels there? >> Yeah. I mean, there's a mindset. If you have a team of people that aren't knocking down new opportunities and building the next big thing, fixing your house, get your house in order, you know, refactor, reset, reboot, replatform with the cloud, and then refactor your business! If you don't have the people thinking like that, you're probably either going to be taken over, or go out of business. And that's what the telcos with all these assets, they're going to get bought, rolled into a SPAC, Special Purpose Acquisition Company, which is super hot in the United States. A lot of roll-ups going on with private equity. So a lot of these telcos, if they don't refactor, or replatform then refactor, they're going to be toast, and they're going to get rolled up, and eaten up by somebody else. >> Yeah, sure. It's interesting though, isn't it? Because when we think of telco in tech, we often think of, obviously we've got the triad, people, process, technology, and we think, process and technology really to the forefront here. But like you said there, people are also so important because if you don't have this right balance, you're not going to be able to drive that change. We had, obviously, Scott Brighton on the stage yesterday, and after his session, somebody came up to me and just said, "I'm interested to hear what that means for education." So how can we establish this new generation of tech and telco leaders from the grassroots with educational associations, establishments. How can we encourage that? I wonder, is this something that you talk about? >> Yeah. I mean, education's huge, and this highlights the change that telco's now part of. Telco used to be a boring industry that ran the networks, or moving packets around, and mobile was there. But once the iPhone came out in 2007, the life has changed, society has changed, education's changed, how people interact has changed. So, you start to see people now aware of the value. And if you look at during COVID, the internet didn't crash, the telcos actually saved our asses, and everyone survived because the network didn't break. Yeah, we had some bad Zoom meetings here and there, and some teleconferences that didn't go well, but for the most part we survived, and they really saved everybody. So, they should get kudos for that. But now they're dependent upon healthcare, education. People care about that stuff, so now you're going to start to see an elevated focus on what telecom is doing. That's why the edge has got trillions of dollars up for grabs. But education, there's negative unemployment in cybersecurity and in cloud. So for the people who say, "Oh, there's no jobs." Or, "I can't work." That's a bunch of BS, because you can just get online, get on YouTube, and just get a degree. You can get a degree. You can get an Amazon job. It pays a hundred thousand dollars a year! American. You can make a hundred thousand pounds, and be unemployed six months, and then be employed. So negative unemployment means, there's more jobs than people to fill them all, in fact. >> Yeah, it's interesting you mentioned that, because I was talking to a cyber security leader who was saying in something, I think there were now 3 million vacancies in cybersecurity. And there's such a skill shortage. There is nobody around to fill it! So it's an interesting problem to have, isn't it? Because it's reversed to what we've been used to for the last few decades! And obviously, telco is in the same space. But what can we do about it do you think, to actually -- >> I think it's going to take leadership, and I'm a big proponent of kids not going to university if they don't have to. Why spend the dough, money, if you don't have to? You can get online. I mean, the data's there. But to me, it's the relationships, the mentorship. You're starting to see a women in tech, and underrepresented minorities in the tech field, where mentorship is more important than curriculum. Community is more important than just going through a linear courseware. Nobody wants to sit online and go through linear courseware. Now, if they have to get a certificate, or degree, and accreditation, no problem. But the communities are out there, so that's a big change over, I'm a big fan of that. And I think people should, you know, get some specialized skills. You can get that online, so why even go to school? So, people are figuring that out. >> For sure. And also, even transferring. I mean, so many skills are transferable nowadays, aren't they, so we could easily be talking to people from other industries, and bringing them into telco, and saying, "Look, bring what you know from your retail background, or your healthcare background, and help us at telco to, again, drive forwards." Just like DR was saying, it's all about the next big thing. >> Well, Danielle is always also driving a lot of change. And if you think about the jobs, and the pedigree of going to a university, oh, Harvard, all the big Ivy Leagues, Oxford in your area. So it's like, if you go to the school like that, and you get a pedigree, you instantly get a job. Now the jobs that are available weren't around five years ago, so there's no like pedigree or track record. There's no like, everyone's equal. >> Yeah. >> So you could, the democratization of the internet now, from a job standpoint, is people are leveling up faster. So it's not about the Ivy League, or the big degree, or silver spoon in your mouth, you've got the entitlement. So you start to see people emerging and making things happen. Entrepreneurship in America, immigrant entrepreneurship. People are billionaires that have no high school diplomas! >> It's interesting you mention that, John, because we can't have more than five years experience in this space, we know that. But in telco, there is a problem. And maybe it's, again, it's a flipped problem where telco recruiters, or talent acquisition leaders, are now asking for kind of 10, 20 years experience when they're sending out job descriptions. So does that mean that we are at fault for not being able to fill all these vacancies? >> I think that's just, I mean I think there's a transition of the new skill set happening, one. But two, I think, you know, to be like a chip engineer, (laughs) you can't learn that online. But if you want to run a cloud infrastructure, you can. But I think embedded systems is an area that I was talking to an engineer, there's a huge shortage of engineers who code on the microprocessors, on the chips. So, embedded systems is a big career. So there's definitely paths you can specialize. Space is another area you've seen a lot of activity on. You see Jeff Bezos and Elon Musk is going to be here on a virtual keynote, trying to go to Mars. And you know, Danielle Royston always says, "What's going to happen first, Mars colony, or telco adopting public cloud?" And some people think Mars will happen first, but. >> What do you think, John? >> I think Telco's going to get cloud. I mean first of all, public cloud is now hybrid cloud, and the edge, this whole internet edge, 5G, is so symbolic and so important, because it's an architectural beachhead. And that's where the trillion dollar baby is. So, the inside baseball, and the inside money, and all the investors are focusing on the edge, because whoever can command the edge, wins all the dollars. So everyone kind of knows, it's a public secret, and it's fun to watch everyone jockey for the positions. >> Yeah no, it really is. But it's also quite funny, isn't it? Because the edge is almost where we were decades ago, but we're putting the control back in the hands of consumers. So, it's an interesting flip. And I wonder if, with the edge, we can really enhance this acceleration of product development, this efficiency, this frictionless system in which we live in. And also, I've heard you say hybrid a few times, John. >> John: Yeah. >> Is hybrid going to be the future of the world no matter what industry you're in? >> Hybrid is everything now. So, we're the hybrid CUBE, we've got hybrid cloud. >> Exactly. >> You got hybrid telco, because now you've got the confluence of online and offline coming together. That is critical dynamic! And you're seeing it. Like virtual reality, for instance, now you're seeing things, I know you guys are doing some great work at your company around creating experiences that are virtual. You got, companies like Roblox went public recently. Metaverse. It's a good time to be in that business, because experiential human relations are coming. So, I think that's going to be powered by 5G. You know, gamers. So, all good stuff. Chloe, great to be with you here on theCUBE, and we're looking forward to seeing your main stage. >> Great. >> And then we're going to send it back to the studio, Adam, and the team. We're waiting for DR to arrive here in Cloud City. And this is theCUBE, from Cloud City, back to you, Adam, and the studio.

(upbeat music) >> Okay, thanks Adam in the studio. We're here on the floor in Cloud City, right in the middle of all the action. The keynotes are going on in the background, it's a packed house. I'm John Furrier. Dave Vellante is on assignment, digging in, getting those stories. He'll have the analysis, he'll be back on theCUBE but I want to welcome Chloe Richardson, who has been holding down the main stage here in Cloud City, with amazing content that she's been hosting. Chloe, great to see you. Thanks for coming on theCUBE and kicking it up day two with me. >> No, not at all. Thank you for having me. It's very exciting. I love what you guys have got over here, very fun. >> We're inside theCUBE. This is where all the action is. And also the Cloud City is really changing the game. If you look at what's going on here in Cloud City, it's pretty spectacular. >> Know, I mean the atmosphere is absolutely palpable, isn't it? You can just feel as people walk in and see what the future looks like to the Telecoms industry, it's very exciting. >> And you've been doing a great job on the main stage. We've been really loving your content. Let's get into some of the content here. Actually the keynote is going on, we're going to have DR, maybe fly by the set later, we're going to check that up. But let's check out this videotape of, this is TelcoDR. You got to check out this reel and we'll be right back, we'll talk about it. (upbeat music) >> TelcoDR burst onto the global telecom scene this year, making headlines for taking over the huge Erickson's space at MWC21. And for building Cloud City in just a hundred days. But why did the company go to such trouble? And what is the unique offering to the telecoms industry? And what drives their dynamic CEO, Danielle Royston or DR as everyone calls her? Cloud City Live caught up with DR, away from the hustle and bustle of the city to find out. (upbeat music) >> Hi, I'm Danielle Royston, coming to you from beautiful Barcelona. I'm here for MWC21. About a hundred days ago, I decided to take over the iconic Erickson booth to turn it into Cloud City. Cloud City has over 30 vendors and 70 demos to introduce telco to what I think is the future for our industry. We're going to have three awesome experiences. We're going to talk about the new subscriber experience, we're going to talk about what's in store for the new network and the future of work. I'm really excited to create a community and invite awesome telco executives to see this new future. It's been a really tough 18 months, and we didn't know what MWC21 was going to be like in terms of attendance. And so from the get go we plan this amazing experience that we call, Cloud City Live. At Cloud City Live, we have two main components. We have the speaker series where we have over 50 speakers from Amazon, Google, Microsoft, as well as CSPs and awesome vendors talking about the public cloud in telco. The second part of Cloud City Live, is theCUBE. Think of this as like an ESPN desk of awesome tech interviews focused on telco and the public cloud hosted by John Furrier and Dave Vellante. Dave and John are going to talk to a variety of guests, focused on telco and the public cloud. It's a great way for our virtual participants to feel like they're at the show, experiencing what's going on here. So excited to have them as part of the Cloud City booth. There's a ton of innovation going on in telco. And 20 years ago, Elon Musk set on his mission to Mars. I, like Elon Musk, I'm on a quest to take telco to the public cloud. Every year at MWC, there's always a flurry of announcements and this year is no different. At this year's MWC, Totogi, a startup that I invested a hundred million dollars in, will be launching. Totogi is introducing two products to the market, this week at MWC. The first is a planetary scale charger. More than a charger, it's an engagement coupling dual network data with charging information to drive subscriber engagement and doubling your ARPU. The second product that Totogi is introducing, is a planetary scale BSS system built on top of the TM forum, open APIs. Both of these products will be available for viewing in the virtual booth, as well as on the show for. The public cloud is an unstoppable mega trend that's coming to telco. I'm super excited to bring to you, the vendors, the products, the demonstrations, and the speakers, both to people here in Barcelona and virtually around the world. (upbeat music) >> Well, that was a fascinating insight into the origins of TelcoDR, why public cloud is going to truly disrupt the telecoms industry and why DR herself is so passionate about it. If you'd like to find out more, come and see us at Cloud City. (upbeat music) >> Okay, thanks. Just roll on that reel. Chloe, I mean, look at that reel. I mean, DR, Danielle Royston, she's a star and I've seen a lot of power players in the industry. She's got guts and determination, and she's got a vision and she's not just, you know, making noise about telco and cloud, there's actually a lot of real good vision there. I mean, it's just so impressive. >> No, really isn't. And for me, it's almost like the next moonshot. It's the moonshot of the telco world. She's innovative, she's exciting and if we've learned anything over the last 18 months is that we need to in this industry to grow and for the future of the industry. So, it's so exciting. I think she's a real inspiration. >> And I love the fact that she's so, takes a tiger by the tail, because the telco industry is being disrupted. She's just driving the bus here and I remember I did a story on Teresa Carlson, who was with Amazon web services, she was running the public sector and she was doing the same exact thing in that public sector world in DC and around the world. She opened up regions in Bahrain, which as a woman, that was an amazing accomplishment. And she wasn't just a woman, she was just a power player. And she was exceptional leader. I see DR doing the same thing and people aren't going to like that, I'll tell you right now. People are going to be like, whoa, what's going on here? >> And of course, it's always the way we pioneers though, isn't it? At the time people thinking what's going, we don't like change, why are we being shaken up. But actually afterwards, in retrospect, they think, oh, okay, I see why that happened and we needed it. So really exciting stuff. >> Making things happen, that's what we're doing here in theCUBE. Obviously the main stage's doing a great job. Let's go check out this highlight reel. If you're watching and you miss some of the action, this is, I'll see the physical event back since 2019 in February, but there's also a Hybrid event. A lot of virtual action going on. So you got theCUBE virtual, you got a lot of content on virtual sites, but in person here, we're going to go show you a highlight reel from what we did yesterday, what was happening around the show? Enjoy this quick highlight reel from yesterday. (upbeat music) (upbeat music) (upbeat music) Okay. We're back here in theCUBE. We're the main floor out here with Chloe Richardson, who is emceeing, hosting and driving the content on the Cloud City main stage. Chloe, it's been great here. I mean, so far day one, I was watching your presentations and inspire site chats you've been hosting. Awesome content. I mean, people are like jazzed up. >> Yeah, I know for sure. We had Scott Brighton on yesterday, who was our opening keynote on the live stage. And his session was all about the future of work, which is so relevant and so pertinent to now. And he talked about the way it's changing and in 10 years it's going to be a trillion dollar industry to be in the cloud at work. So really interesting. I mean, yeah, the atmosphere here is great, everyone's excited, there's new content everyday. And that's the thing, it's not stale content. It's stuff that people want to hear. People are here for the new hot trends, the new hot topics. Really exciting. >> Yeah, the next big thing. And also it's a fiscal event. So since 2019, this Mobile World Congress has been a massive event and hasn't happened since February, 2019. That's a lot of time that's elapsed in the industry cause of COVID and people are glad to be here, but a lot of stuff's changed. >> Yeah, it's a different world, right? I mean, two years in the telco industry is like a hundred years elsewhere. Everything has changed, digital transformation migration, obviously cloud, which is what we're talking about over here at Cloud City Live. I'm wondering though John, I'd like to pick your brains on something. >> Sure. >> It has changed in the last two years, we know that, but what about the future of Mobile World Congress? How do you see it changing in the next few years? >> Oh man, that's a great question. I mean, my observation, I've been coming to the show for a very long time, over a decade and a half, and it's been a nerdy show about networks and telecom, which is basically radios and wireless and then mobile. It's very global, a lot of networks, but now it's evolving and many people are saying, and we were talking on theCUBE yesterday, Dave Vellante was commenting that this show is turning into a consumer like show. So CES is the big consumer electronics show in the US, in Las Vegas every year. This show has got a vibe because what's all the technology from the cloud players and from the chips, are getting smaller, faster, cheaper, more capability, lower power. So if you look at the chips, the hardware, it's less about the speeds and feeds. It's more about the consumer experience. You got cars. I was talking to a guy yesterday, he said, "Vehicle e-commerce is coming." I'm like, "What the hell his vehicle e-commerce?" And you could be on your app, driving down the freeway and go, "Hey, I want some food." Instead of having it delivered to you, if you order it you pick it up. So that's kind of can be happening now in real time, you can do all kinds of other things. so a lot of new things are happening. >> Yeah, I think so. Do you see that as another disruption for the industry that is the fact that it's moving to be more consumer focused? Is that anything we should be worried about in that space? >> Well I think the incumbents are going to lose their position. So I think in any new shift, new brands come in out of nowhere. >> For sure. >> And it's the people that you don't think about. It's the company that's not, that you don't see. And we got DR on the main stage right here, look at this. You saw her walk out with the confidence of a pro. She just walked out there and she's not afraid. >> No. Well, as she said in her video, she is ready to wake them up and you can see as soon as she worked out. That is what she intends to do. >> I love her mojo, she's got a lot of energy. And back to the show, I mean, she's just an example of what I was saying. Like in every market shift, a new brand emerges. >> Yep. >> I mean, even when apple was tainted, they were about to shut down, they were going to run out of cash. When Steve Jobs brought back apple, he consolidated and rebooted the company. The iPad was a similar moment, then the iPhone and just the rest is history. That kind of disruption's coming. You're going to see that here. >> Yeah. Oh, it's exciting though isn't it? To be future ready rather than future proof but actually I wanted to ask you something as well, because we are seeing all these cloud players getting hot under the collar about telco. Why are they so excited? What's the buzz about why, as you're in MWS and Google Cloud? Why do they want to have a slice of the pie? >> Well, I think they're hot, hot and heavy on the fact that telco is a ripe opportunity and it used to be this boring, slow moving glacier. >> Okay. >> It's almost like global warming now. The icebergs are melting and it's going to just change and because of the edge, 5G is not a consumer wireless thing. It's not like a better phone, it's a commercial app opportunity cause it's high bandwidth. We've all been to concerts or football games or sporting events where a stadium is packed. Everyone gets bars on their wifi, but can't get out, can't upload their pictures on Instagram. Why? Because it's choking them in the network. That's where 5G solves the problem. It brings a lot of bandwidth and that's going to bring the edge to life and that's money. So when you got money and greed and power changing hands, it's every, it's on the table and the wheel's spinning, and it could be double zero, or it could be lucky seven. You don't know. >> Yeah, for sure. And that's certainly enough to get all the big players hot and bothered about getting involved. And I suppose it circles back to the fact that, DR is really leading the charge and they're probably thinking, okay, what's going on here? This is different, we want something new. You didn't know it's an open run or something that we've been talking about over the last day or so. We've had quite a few of us speakers over here constantly. I've mentioned open run. What is it all about John? Because why all the bars, if 5G is such a hot topic? Why are we getting excited about it? >> That's a great thing. 5G certainly is Google Drive the main trend for sure. OpenRent is essentially an answer to the fact that 5G is popular and they need more infrastructure. So open source, the Linux Foundation has been the driver for most of the open source software. So they're trying to bring software and open architectures to create more entrepreneurial activity around hardware and around infrastructure because we need more infrastructure. We need more antennas, we need more transceivers, we need more devices that could be open. So in order to do that, you got to open up the technology and you want to minimize the licensing and minimize a lot of these, you know, proprietary aspects. >> What if we look at, so on Wednesday, we've got a great keynote from Philippe Langlois, who is CEO and founder of P1 Security. And he's coming to talk to us about cybersecurity within the cloud and within telco. So you just mentioned that. Open mind, it's all about having open source, about having that space where we can share more efficiently and easy, more easily. What does that mean for security though? Is it a risk? >> I think that's going to increase the value of security and minimize the threats. Because open source, even though it's open, the more people that are working on it, the more secure it could be. So yes, it could be more open in sense that could be explored by hackers, but it can be open to also protect. And I think we've seen open source and cloud in particular be more secure because everyone said, "Cloud is not secure, open source is not secure." And as it turns out when the collective hive minds of developers work on things, it gets secure. >> And it is interesting, isn't it? Because we have seen that there has been an uptick in cyber security and threats. But actually I was speaking to some leaders in across various industries and particularly in tech. And they were saying, "Actually there's not been an uptick in attempted threats, there's been an uptick because with this open source environment. We are able to track them and measure them and defend more efficiently. So actually they're being battered away, but the number is probably the same as it always was. We just didn't know about them before we had this open source environment. >> There's more money in threats and there's more surface area. So as the tide rises, so do the threats. So on a net basis it's more because there's more volume, but it's pretty much the same. And look at it, there's money involved, they're organized, there's a business model on attacking and getting the cash out of your bank or ransomwares at an all time high. So this is like a big problem and it's beyond the government, it's our individual freedom. So security its huge and I think open source and cloud are going to be, I think the answer to that. >> Yeah, for sure. And it's again about collaboration, isn't it? Which we talk about all the time but without collaboration that the industries aren't going to have to work together to promote this environment. So yeah, it should be good to talk with Phillip on Wednesday. >> I just say in security, don't download that PDF if you don't know who came from. The fishing is always good. Well, we got some great stuff coming up. We're going to have a great day. We got a video here on Mobile World Live, we're going to show this next segment and we're going to toss it to a video. And this is really about to give the experience Chloe, for people who aren't here, right? >> Yeah. >> To get a feel for what's going on in Barcelona and all the actions. And if you look at the video, enjoy it. >> Hi, I'm Danielle Royston, CEO and founder of TelcoDr, but you can call me DR. Ready for some more straight talk about telco? It's go time, let's do it. Holy shit. It sure is a great time to be a tech company. I mean, if you're Amazon, Microsoft, Google, Grab, Twilio, DoorDash or Uber, life's pretty great. Just look at these stock prices over the past five years with their shareholder value going up into the right. Totally amazing. But where's telco? There I add our stocks to this awesome chart. Let's compare these fabulous tech stocks to AT&T, Vodafone, Telefonica, Tim, America Movil and Zain group. Huh, not so great, right? Yep. I'm talking directly to you senior telco execs. I'm here to wake you up. Why is it that Wall Street doesn't see you as tech? Why aren't CSPs seen as driving all the tech change? Why is it always Apple, Amazon and Google who get the big buzz? But more importantly, why isn't it you? Before I came to this industry, I always thought of carriers as tech companies. I gave more of my money to AT&T and to Apple because I really cared about the quality of the network. But I also wondered why on earth, the carriers allowed all the other tech companies to take center stage. After spending the last few years in telco, I now understand why. It's because you are network people, you are not customer people. I get it, you have the security blanket, you're a network oligopoly. It's crazy expensive to build a network and it's expensive to buy spectrum. It takes operational chops to run a killer network and it takes great skill to convince Wall Street, to finance all of it. You telco execs are amazing at all those things, but because you focus on the network, it means you don't focus on the customer. And so far you haven't had to. Every telco's KPI is to be less shitty than their next competitor. You don't have to be the best, just don't be last. Everyone else's NPS, is in the thirties too. Their mobile app ratings are just as terrible as yours. Everyone's sucks at customer sat and it's widely acknowledged and accepted. Let's talk about the cost of that. The cost is not measured on market share against other MNOs. The cost is measured in lost ARPU that the tech guys are getting. Everyone knows about the loss of texting, to WeChat, WhatsApp and the other OTT apps, but it is not just texting. The total adjustable market or term of the mobile app disruptors is huge. Instead of remaining network focused, you should be leveraging your network into a premier position. And because you're a network people, I bet you think I'm talking about coercive network leverage. That is not what I'm talking about. I'm talking about love, customer love. There is one thing the highly valued tech companies all have in common. They all crush it on customer love. They look at every interaction with the customer and say, "How do we make the customer love this?" Like Netflix has easy monthly cancellation, Amazon does no questions asked returns, Uber gives users a real time view into driver rating and availability. Compare those ideas to the standard telco customer interaction. The highly valued tech companies, don't have the network oligopoly to fall back on like you do. To survive they must make customers love them. So they focus on it in a big way and it pays off. Their NPS is close to 70 and they have app ratings of 4.5 or higher. A far cry from your thirties NPS and app ratings of 3.5. If you want to have those huge tech multiples for yourself, you have to start thinking about these guys as your new competition, not the other telcos in your market. The crazy thing is, if you give up using your network as a crutch and put all of your focus on the customer, the network becomes an asset worth more than all the super apps. Let's step back and talk about the value of super apps and becoming customer centric. Retooling around the customer is a huge change. So let's make sure it's worth it. We aren't talking about 25% improvement. I'm going to show you that if you become customer centric, you can double your ARPU, double your valuation multiples and drive big shareholder value just like the tech companies on that chart. Now let's talk about the customer focused super apps. There are hundreds of companies and a variety of categories vying for your subscriber's disposable income. Movies, food delivery, financial services, who are they? And why does Wall Street give them such high evaluations and like them so much? Well first, look at what they are telling Wall Street about their TAM. They broadcast ridiculously huge TAMs that are greater than the telco TAMs. You know, who should have a ridiculously huge TAM? You. Hello. What I'm saying is that if you got what's yours, you double in size. And if you take the TAAMs they throw around, you'll be five times as big. When I think about the opportunity to double ARPU, without having to double the CapEx, to build out the network, I say to myself, "Hell yeah, we should totally go do it and do whatever it takes to go get." For example, let's talk about Grab. Grab is a southeast Asian super app company with an expected $40 billion valuation. Grab's customer focused started in rideshare, but then leverage its customer love into wallet deliveries, hospitality, and investing. Their ARPU is now larger than a telco's ARPU in countries where they compete, and they have a higher valuation than those telcos too. Imagine if you could combine a great user experience with a valuable services that helped grow your ARPU, that would be huge. So how do you build a super app? I bet right about now, you're wishing you had a super app. Everyone wants a super app. A lot of money has been unsuccessfully spent by telcos trying to build their own. I bet you're saying to yourself, "DR, your pie in the sky sounds great but it has no chance of success." Well, I'm betting things are about to change. There is a public cloud startup called Totogi that is going to help carriers build world class super apps. To have a successful super app, there is one key metric you need to know. It is the KPI that determines if your super app will be a success or a flop. It's not about the daily active users, it's not the average order value, it's not even gross merchandise value. It's all about the frequency of use per day by the user, that's the metric that matters. How many of you use that metric in your telco apps? Do you have a team driving up user app interactions every day? Most telco apps are used for top up or to check a bill. This is a huge missed opportunity. Super app companies excel at building great experiences and driving a huge amount of interactions. They have to, their business depends on it. They have to be customer focused. They have to keep bringing the user back to the app, every day, multiple times a day. And you know what? They do a great job. Customers love their super apps. They have great user experiences like Apple credit cards, no information required, application process. They have high net promoter scores because of customer friendly policies, like how DoorDash retroactively credits fees when you move to a better plan. And they have great app store ratings because they do simple things like remember your last order, or allow you to use the app rather than force you to call customer service. Customers of successful super apps love it when new services are added. And because of the customer love, every time something is added to the app, customers adopt it immediately. New services drive frequent daily user interactions. So our problem in telco is we have an app that is only open once per month, not multiple times per day. And without frequent opens, there is no super app. What do we do we have in telco that we could use to help with this problem? I wonder, why you don't currently have a mobile app that subscribers use multiple times a day. You have something that's 10 times better. You have a network. Subscribers already interact with your network 10 times more frequently than any user with any of the super apps. But telcos don't leverage those interactions into the insanely valuable engagements they could be. Worse, even if you wanted to your crappy over customized on premise solutions, make it impossible. Thankfully, there's this new tech that's come around, you may have heard of it. The public cloud. When you bring the enabling technology of the public cloud, you can turn your network interactions into valuable super app interactions. And there's a special new startup that's going to help you do it, Totogi. Totogi will leverage all those network interactions and turn them into valuable customer interactions. Let me repeat that. Totogi will leverage all those network interactions and turn them into valuable customer interactions. Totogi allows the carrier to leverage its network and all the network interactions into customer engagement. This is something the super apps don't have but will wish they did. But this magic technology is not enough. Telcos also need to move from being network focus to being customer focused. Totogi enables telcos to chase exciting revenue growth without that annoying massive CapEx investment. Totogi is going to help you transform your sucky mobile apps with the crappy customer ratings, into something your subscribers want to open multiple times a day and become a platform for growth. I'm so excited about Totogi, I'm investing $100 million into it. You heard me right, $100 million. Is this what it feels like to be soft bank? I'm investing in Totogi because it's going to enable telcos to leverage the network interactions into super app usage. Which will lead to an improved subscriber experience and will give you a massive jump in your ARPU. And once you do that, all those Telco valuations will go from down here to up here. And so I've been talking to some folks, you know, checking in, feeling them out, getting their thoughts, and I've been asking them, what do you think about telcos building super apps? And the response has been, click, everyone says, "No way, telcos can't do it." Zero chance, total goose egg. One suggested I build a bonfire with 100 million dollars, because then at least I wouldn't waste years of my life. Well I think those people are dead wrong. I do believe that telcos can build super apps and make them super successful. The public cloud is changing all parts of telco and Totogi and super apps are fundamentally changing, the customer relationships. In one month at MWC, people will see what Totogi has to offer, and they will understand why I'm making this bold call. Because the Totogi takes the value of the network and the power of the public cloud to help telcos move from being network centric, to being customer centric. Boom! If you want to make this transformation and reap all the financial benefits, you will have to compete for customers with a whole new set of players. You will no longer compete with the network focus guys like the other telcos, instead you will be competing against the customer focused companies. These players don't have a network to fall back on like your old competitors. They know they have to make customers love them. Their customer loyalty is so off the charts, their customers are called fans. So if you want that big money, you will have to compete on their turf and make the customers want to choose you, you need Apple level loyalty. That bar is uber high. We will have to give up the security blanket of the network and change. Instead of NPS of the thirties, it needs to be in the 70s. Instead of mobile app ratings in the threes, they need to get five stars. I'm betting big that Totogi will make that possible. I'm going to help you every step of the way, starting with my keynote next month at MWC. Join me and I'll share the secrets to converting your super valuable network interactions to make your super app a massive success. We're going to have an amazing time and I can't wait to see you there. >> Okay. We're back here in theCUBE here at Mobile World Congress in Cloud City. I'm John Furrier, Chloe Richardson filling it for Dave Vellante who's out on assignment. He's out getting all the data out there and getting stories. Chloe, what a great keynote by Danielle Royston. We just heard her and while with major action, major pump me up, punch in the face, wake the heck up cloud people, cloud is here. She didn't pull any punches. >> No, I mean the thing is John, there's trillions of dollars on the table and everyone seems to be fighting for it. >> And you heard her up there, if you're not on the public cloud, you're not going to get access to that money. It's a free for all. And I think the cloud people are like, they might think they're going to walk right in and the telco industry is going to just give it up. >> No, of course. >> There's not going to be, it's going to be a fight, who will win. >> Who will win but also who will build the next big thing? >> Someone needs to die in the media conversation, it's always a fight, something's dead, something's dead but keeps the living. All that kidding aside, this is really about partnering. I think what's happened is, telco's already acknowledged that they need to change in the 5G edge conversation, the chip acceleration. Look at Apple, they've got their own processors, Nvidia, Amazon makes their own chips, Intel's pumping stuff out, you've got Qualcomm, you've got all these new things. So the chips are getting faster and the software's more open source and I'm telling you, cloud is just going to drive that bus right down clouds street and it's going to be in Cloud City everywhere. >> And it's going to be peeping on the board as it drives down. John, I'm not a stalker, but I have read some of the things that you've written. And one of the things you mentioned that was really interesting was the difference between building and operating. Break it down for me. What does that mean? >> That means basically in mature markets and growing markets things behave differently and certainly economics and the people and the makeup and the mindset. >> Okay. >> So the telco has been kind of this mature market. It's been changing and growing but not like radically. Cost optimization, make profit, you know, install a lot of cable. You got to get the rents out of that infrastructure and that's kind of gone on for too long. Cloud is a growth market, and it's about building, not just operating and you've got operators, carriers are operating networks. So you're going to see the convergence of operators and builders coming together, builders being software developers, new technology and executives that think about building. And you want people on your team that are going to be, I won't say war time, you know, lieutenants or generals, but people who can handle the pace of change. >> Okay. >> Because the change and the nature is different. And some people want slow and steady, keep the boat from rocking, but in a growth market, it's turbulent and ride might not be quiet, first class ticket to paradise, but it's bumpy, but it's thrilling. >> No, of course. Is it similar to the old sales adage of hunter versus farmer and the parallels? >> Yeah. I mean, the mindset. If you have a team of people that aren't knocking down new opportunities and building the next big thing, fixing your house, get your house in order, you know, refactor, reset, reboot, re platform with the cloud and then refactor your business. If you don't have the people thinking like that, you're probably either going to be taken over or go out of business. And that's what the telco with all these assets, they're going to get bought roll into a SPAC, special purpose acquisition company was a super hot in the United States. A lot of roll ups going on with Private equity. So a lot of these telcos, if they don't refactor or re platform, then refactor, they're going to be toast and they're going to get rolled up and eaten up by somebody else. >> Yeah, sure. It's interesting though, isn't it? Because when we think of telco in tech, we often think of, obviously we've got the triad. People process technology, and we think process and technology really take the forefront here but like you said there, people are also so important because if you don't have this right balance, you're not going to be able to drive that change. We had, obviously Scott Brighton on the stage yesterday and after his session, somebody came up to me and just said, "I'm interested to hear what that means for education." So how can we establish this new generation of tech and telco leaders from the grassroots with educational associations establishments? How can we encourage that? I wonder, is this something that you talk about often? >> Yeah. I mean, education is huge and this highlights the change that telcos now part of. Telco used to be a boring industry that ran the networks, or moving packets around and mobile was there, but once the iPhone came out in 2007, the life has changed, society has changed, education's changed, how people interact has changed. So you start to see people now aware of the value and if you look at the, during the COVID, the internet didn't crash, the telcos actually saved our asses and everyone was, survive because the network didn't break. Yeah, we had some bad zoom meetings here and there and some teleconferences that didn't go well but for the most part we survived and they really saved everybody, my goodness. So they should get kudos for that. But now they're dependent upon healthcare, education, people care about that stuff. So now you're going to start to see an elevated focus on what telecom is doing. That's why The Edge has checked trillions of dollars up for grabs. But education, there's negative unemployment in cybersecurity and in cloud. So for the people who say, oh, there's no jobs or I can't work, that's a bunch of BS because you can just get online, get on YouTube and just get a degree. You can get a degree, you can get an Amazon job, it pays a hundred thousand dollars a year, American. You can make a hundred thousand pounds and be unemployed six months and then be employed. So negative unemployment means there's more jobs than people to fill them qualify. >> Yeah, it's interesting you mentioned that because I was talking to a cyber security leader who was saying in some of the things there were now 3 million vacancies in cybersecurity and there's such a skill shortage, there is nobody around to fill it. So it's an interesting problem to have isn't it? Cause it's reversed to what we've been used to for the last few decades and obviously telco is in the same space. What can we do about it? Do you think it will actually bring people in? >> I think it's going to take leadership and I'm a big proponent of kids not going to university, they don't have to. Why spend the dough, money if you don't have to? You can get online. I mean, the data's there, but to me it's the relationships, the mentorship. You starting to see women in tech and underrepresented minorities in the tech field, where mentorship is more important than curriculum. Community is more important than just going through a linear course where nobody wants to sit online and go through linear courseware. Now, if they have to get a certificate or degree and accreditation no problem, but communities are out there. So that's a big change over, I'm a big fan of that and I think people should, you know, get some specialized skills, you can get that online. So why even go to school? So people are figuring that out. >> For sure. And also even transferring, I mean, so many skills are transferable nowadays, aren't there? So we could easily be talking to people from other industries and bringing them into telco and saying, look, bring what you know from your retail background or your healthcare background and help us at telco to again, drive forward, just like DR is saying it's all about the next big thing. >> Danielle, I was also driving a lot of change and if you think about the jobs and a pedigree of going to a university, oh, Harvard, all the big Ivy leagues, Oxford in your area. So it's like, if you go to a school like that and you get a pedigree, you instantly get a job. Now, the jobs that are available, weren't around five years ago. So there's no like pedigree or track record, there's no like, everyone's equal. >> Yeah. >> So you could, the democratization of the internet now is, from a job standpoint is, people are leveling up faster. So it's not about the Ivy league or the big degree or silver spoon in your mouth, you've got the entitlement. So you start to see people emergent and make things happen, entrepreneurship in America, immigrant entrepreneurship. People are billionaires that have no high school diplomas. >> It's interesting you mentioned that John, because we can have more than five years experience in this space, we know that but in telco there is a problem and maybe it's, again it's a flipped problem where, telco recruiters or talent acquisition leaders, are now asking for kind of 10, 20 years experience when they're sending out job descriptions. So does that mean that we are at fault for not being able to fill all these vacancies? >> Well, I mean, I think that's just, I mean, I think there's a transition of the new skill set happening one, but two, I think, you know, you've got to be like a chip engineer, you can't learn that online, but if you want to run a cloud infrastructure, you can. But I think embedded systems is an area that I was talking to an engineer, there's a huge shortage of engineers who code on the microprocessors, on the chips. So embedded systems is a big career. So there's definitely parts, you can specialize, space is another area you've seen a lot of activity on, obviously Jeff Bezos and Elon Musk is going to be here on virtual keynote, trying to go to Mars. And, you know, Danielle Royston always says, who's going to happen first, Mars, colony, or telco adopting public cloud? Some people think Mars will happen first but. >> What do you think John? >> I think telco's going to get cloud. I mean, first of all, public cloud is now hybrid cloud and the edge, this whole internet edge, 5G, is so symbolic and so important because it's an architectural beachhead. >> Yeah. >> And that's where the trillion dollar baby is. >> Of course. >> So the inside baseball and the inside money and all the investors are focusing on the edge because whoever can command the edge, wins all the dollars. So everyone kind of knows it's a public secret and it's fun to watch, everyone jockey for the positions. >> Yeah, know, it really is. But it's also quite funny, isn't it? Because the edge is almost where we were decades ago, but we're putting the control back in the hands of consumers. So it's an interesting flip and I wonder if with the edge, we can really enhance this acceleration of product development its efficiency, this frictionless system in which we live in. And also, I've heard you say hybrid a few times John. >> Yeah. >> Is hybrid going to be the future of the world no matter what industry you're in? >> Hybrid is everything now. So it's, we're the hybrid cube, we've got hybrid cloud. >> Exactly. >> You got hybrid telco, because now you've got the confluence of online and offline coming together. >> Yeah. >> That is critical dynamic, and you seeing it. Like virtual reality for instance, now you seeing things, I know you guys are doing some great work at your company around creating experiences that are virtual. >> Exactly. >> You got, like Roblox went public recently. >> Yeah. >> Metaverse is a good time to be in that business because experiential human relations are coming. So I think that's going to be powered by 5G, you know, gamers. So all good stuff, Chloe, great to be with you here in theCUBE. >> Thank you. >> And we're looking forward to seeing your main stage. >> Great. >> And then we're going to send it back to the studio, Adam and the team, we're waiting for DR to arrive here in Cloud City and this is theCUBE, from Cloud City back to you, Adam in the studio.

>>Kirk focuses on the approach to modern data quality and how it can enable the continuous delivery of trusted data. Take it away. Kirk >>Trusted data has been a focus of mine for the last several years. Most particularly in the area of machine learning. Uh, I spent much of my career on wall street, writing models and trying to create a healthy data program, sort of the run the bank and protect the franchise and how to do that at scale for larger organizations. Uh, I'm excited to have the opportunity today sitting with me as Victor to have a fireside chat. He is an award-winning and best-selling author of delete big data and most currently framers. He's also a professor of governance at Oxford. So Victor, my question for you today is in an era of data that is always on and always flowing. How does CDOs get comfortable? You know, the, I can sleep at night factor when data is coming in from more angles, it's being stored in different formats and varieties and probably just in larger quantities than ever before. In my opinion, just laws of large numbers with that much data. Is there really just that much more risk of having bad data or inaccuracy in your business? >>Well, thank you Kirk, for having me on. Yes, you're absolutely right. That the real problem, if I were to simplify it down to one statement is that incorrect data and it can lead to wrong decisions that can be incredibly costly and incredibly costly for trust for the brand, for the franchise incredibly costly, because they can lead to decisions that are fundamentally flawed, uh, and therefore lead the business in the wrong direction. And so the, the, the real question is, you know, how can you avoid, uh, incorrect data to produce incorrect insights? And that depends on how you view trust and how you view, uh, data and correctness in the first place. >>Yeah, that's interesting, you know, in my background, we were constantly writing models, you know, we're trying to make the models smarter all the time, and we always wanted to get that accuracy level from 89% to 90%, you know, whatever we could be, but there's this popular theme where over time the models can diminish an accuracy. And the only button we really had at our disposal was to retrain the model, uh, oftentime I'm focused on, should we be stress testing the data, it almost like a patient health exam. Uh, and how do we do that? Where we could get more comfortable thinking about the quality of the data before we're running our models and our analytics. >>Yeah, absolutely. When we look at the machine learning landscape, even the big data landscape, what we see is that a lot of focus is now put on getting the models, right, getting it worked out, getting the kinks worked out, but getting sort of the ethics, right. The value, right. That is in the model. Um, uh, and what is really not looked at what is not focused enough that, um, is the data. Now, if you're looking at it from a compliance viewpoint, maybe it's okay if you just look at the model, maybe not. But if you understand that actually using the right data with the right model gives you a competitive advantage that your competitors don't have, then it is far more than compliance. And if it is far more compliance, then actually the aperture for strategy opens up and you should not just look at models. You should actually look at the data and the quality and correctness of the data as a huge way by which you can push forward your competitive advantage. >>Well, I haven't even trickier one for you. I think, you know, there's so much coming in and there's so much that we know we can measure and there's so much we could replay and do what if analysis on and kind of back tests, but, you know, do you see organizations doing things to look around the corner? And maybe an interesting analogy would be something like with Tesla is doing whether it's sensors or LIDAR, and they're trying to bounce off every object they know, and they can make a lot of measurements, but the advancements in computer vision are saying, I might be able to predict what's around the corner. I might be able to be out ahead of the data error. I'm about to see tomorrow. Um, you know, do you see any organizations trying to take that futuristic step to sort of know the unknown and be more predictive versus reactive? >>Absolutely. Tesla is doing a bit Lincoln, uh, but so are others in that space and not autonomous driving space, um, uh, Waymo, the, uh, the, the, uh, Google company that is, uh, doing autonomous driving for a long period of time where they have been doing is collecting training data, uh, through their cars and then running a machine learning on the training data. Now they hit a wall a couple of years ago because the training data wasn't diverse enough. It didn't have that sort of Moore's law of insight anymore, even though it was more and more training data. Um, and so the, the Delta, the additional learning was just limited. So what they then decided to do was to build a virtual reality called car crafting, which were actually cars would drive around and create, uh, uh, predictive training data. Now, what is really interesting about that is that that is isn't a model. It is a model that creates predictive data. And this predictive is the actual value that is added to the equation here. And with this extra predictive data, they were able to improve their autonomous driving quite significantly. Uh, five years ago, their disengagement was, uh, raped was every, uh, 2000 miles on average. And, uh, last year, uh, five years later, it was every 30,000 miles on average, that's a 15 K improvement. And that wasn't driven by a mysterious model. It was driven by predictive data. >>Right, right. You know, that's interesting. I, I'm also a fan of trying to use data points that don't exist in the data sets. So it sounds like they were using more data data that was derived from other sources. And maybe the most simple format that I usually get started with was, you know, what, if I was looking at data from Glassdoor and I wanted to know if it was valid, if it was accurate, but of course there's going to be numbers in the age, field and salary and years of experience in different things. But what if the years of experience and age and academic level of someone no longer correlates to the salary yet that correlation component is not a piece of data that even lives in the column, the row, the cell. So I do think that there's a huge area for improvement and just advancement in the role data that we see in collect, but also the data science metrics, something like lift and correlation between the data points that really helped me certify and feel comfortable that this data makes sense. Otherwise it could just be numbers in the field >>Indeed. And, and this challenge of, of finding the data and focusing on the right subset of the data and manipulating it, uh, in the right, in a qualitatively right way is really something that has been with us for quite a number of years. There's a fabulous, uh, case, um, a few years back, uh, when, um, in Japan, when there was the suspicion that in Sumo wrestling, there was match fixing going on massive max fiction. Um, and, and so investigators came in and they took the data from the championship bouts and analyzed them and, uh, didn't find anything. And, uh, what was, what was really interesting is then later researchers came in and read the rules and regulations of Sumo wrestling and understood that it's not just the championship bouts that matter, but it's also sometimes the relegation matches that matter. And so then they started looking at those secondary matches that nobody looked at before and that subset of data, and they discovered there's massive match fixing going on. It's just, nobody looked at it because nobody just, as you said, that connection, uh, between th those various data sources or the sort of causal connectivity there. And so it's, it's, it's really crucial to understand, uh, that, uh, driving insight out of data, isn't a black box thing where you feed the data in and get it out. It really requires deep thinking about how to wire it up from the very beginning. >>No, that's an interesting story. I kind of wonder if the model in that case is almost the, the wrestlers themselves or the output, but definitely the, the data that goes into it. Um, yeah. So, I mean, do you see a path where organizations will achieve a hundred percent confidence? Because we all know there's a, I can't sleep at night factor, but there's also a case of what do I do today. It's, I'm probably not living in a perfect world. I might be sailing a boat across an ocean that already has a hole in it. So, you know, we can't turn everything off. We have to sort of patch the boat and sail it at the same time. Um, what do you think the, a good approaches for a large organization to improve their posture? >>You know, if you focus on perfection, you never, you never achieved that perfection a hundred percent perfection or so is never achievable. And if you want some radical change, then that that's admirable. But a lot of times it's very risky. It's a very risky proposition. So rather than doing that, there is a lot of low hanging fruit than that incremental, pragmatic step-by-step approach. If I can use an analogy from history, uh, we, we, we talk a lot about, um, the data revolution and before that, the industrial revolution, and when we think about the industrial revolution, we think about the steam engine, but the reality is that the steam engine, wasn't just one radical invention. In fact, there were a myriad of small incremental invade innovations over the course of a century that today we call the industrial revolution. And I think it's the various same thing when the data revolution where we don't have this one silver bullet that radically puts us into data Nirvana, but it is this incremental, pragmatic step-by-step change. It will get us closer. Um, pragmatic, can you speak in closer to where we want to be, even though there was always more work for us left? >>Yeah, that's interesting. Um, you know, that one hits home for me because we ultimately at Collibra take an incremental approach. We don't think there's a stop the world event. There's, you know, a way to learn from the past trends of our data to become incrementally smarter each day. And this kind of stops us from being in a binary project mode, right. Where we have to wait right. Something for six months and then reassess it and hope, you know, we kind of wonder if you're at 70% accuracy today is being at 71% better tomorrow, right? At least there's a measurable amount of improvement there. Uh, and it's a sort of a philosophical difference. And it reminds me of my banking days. When you say, uh, you know, past performance is no guarantee of future results. And, um, it's a nice disclaimer, you can put in everything, but I actually find it to be more true in data. >>We have all of these large data assets, whether it's terabytes or petabytes, or even if it's just gigabytes sitting there on all the datasets to learn from. And what I find in data is that the past historical values actually do tell us a lot about the future and we can learn from that to become incrementally smarter tomorrow. And there's really a lot of value sitting there in the historical data. And it tells me at least a lot about how to forecast the future. You know, one that's been sitting on the top of my mind recently, especially with COVID and the housing market a long time back, I competed with automation, valuation modeling, which basically means how well can you predict the price of a house? And, you know, that's always a fun one to do. And there's some big name brands out there that do that pretty well. >>Back then when I built those models, I would look at things like the size of the yard, the undulation of the land, uh, you know, whether a pool would award you more or less money for your house. And a lot of those factors were different than they are now. So those models ultimately have already changed. And now that we've seen post COVID people look for different things in housing and the prices have gone up. So we've seen a decline and then a dramatic increase. And then we've also seen things like land and pools become more valuable than they were in the housing model before, you know, what are you seeing here with models and data and how that's going to come together? And it's just, is it always going to change where you're going to have to constantly recalibrate both, you know, our understanding of the data and the models themselves? >>Well, indeed the, the problem of course is almost eternal. Um, oftentimes we have developed beautiful models that work really well. And then we're so wedded to this model or this particular kind of model. And we can fathom to give them up. I mean, if I think of my students, sometimes, you know, they, they, they, they have a model, they collect the data, then they run the analysis and, uh, it basically, uh, tells them that their model was wrong. They go out and they collect more data and more data and more data just to make sure that it isn't there, that, that, that their model is right. But the data tells them what the truth is that the model isn't right anymore that has context and goals and circumstances change the model needs to adapt. And we have seen it over and over again, not just in the housing market, but post COVID and in the COVID crisis, you know, a lot of the epidemiologists looked at life expectancy of people, but when you, when you look at people, uh, in the intensive care unit, uh, with long COVID, uh, suffering, uh, and in ICU and so on, you also need to realize, and many have that rather than life expectancy. >>You also need to look at life quality as a mother, uh, kind of dimension. And that means your model needs to change because you can't just have a model that optimizes on life expectancy anymore. And so what we need to do is to understand that the data and the changes in the data that they NAMIC of the data really is a thorn in our thigh of revisiting the model and thinking very critically about what we can do in order to adjust the model to the present situation. >>But with that, Victor, uh, I've really enjoyed our chat today. And, uh, do you have any final thoughts, comments, questions for me? >>Uh, you know, Kirk, I enjoyed it tremendously as well. Uh, I do think that, uh, that what is important, uh, to understand with data is that as there is no, uh, uh, no silver bullet, uh, and there is only incremental steps forward, this is not actually something to despair, but to give and be the source of great hope, because it means that not just tomorrow, but even the day after tomorrow and the day after the day after tomorrow, we still can make headway can make improvement and get better. >>Absolutely. I like the hopeful message I live every day to, uh, to make data a better place. And it is exciting as we see the advancements in what's possible on what's kind of on the forefront. Um, well with that, I really appreciate the chat and I would encourage anyone. Who's interested in this topic to attend a session later today on modern data quality, where I go through maybe five key flaws of the past and some of the pitfalls, and explain a little bit more about how we're using unsupervised learning to solve for future problems. Thanks Victor. Thank you, Kurt. >>Thanks, Kirk. And Victor, how incredible was that?

>>welcome back everyone. We're back to intermission. I'm hama in case you forgot and hear them with Brett and Peter. So what a great morning afternoon. We've had like we're now in the home stretch and you know, I really want to give a shout out to all of you who are sticking with us, especially if you're in different time zone than pacific. So I then jumped into the community rooms. The spanish won, the Brazilian won the french one. Everybody is just going strong. So again, so much so gratitude for that. Thank you for being so involved and really participating the chat rooms in the community. The chat windows in the community rooms are just going nuts. So it's, it's really good to see that. And as usual, Peter and brat had some great, very interactive panels and that was very exciting to watch. But you know, since they were on the panels, I decided to go and see some other things and I actually attended the last mile of container ization. That was, that was actually a very good session. We had a lot of good interactivity there. Yeah. And then while also talked about the container security in the cloud native world. So that was, I think that was your panel peter. That was, that was very exciting. And um, I want to share with everybody the numbers that we've been seeing for dr khan live. So as, as of, I'm sorry, said we need a drumroll. We do need a drum roll. Can you do a drum roll for me? No, no, no. >>Just a >>symbol. Okay, good. Go. Uh, we're at over 22,000 attendees um, today. So that's amazing. That's great. I love the sound effect. That's a great sound effect. The community rooms continue to be really engaged. We're still seeing hundreds of people in those rooms. So again shout out to everyone who is participating. And I felt again like a kid in a candy store didn't know which sessions to attend. They were all very interesting and you know, we're getting some good feedback on twitter. I want to read out some more tweets that we got and one in particular, I don't know whether to feel happy for this person or sad for this person, but it's uh well the initials are P. W. And he said that he was up at two am to watch the keynotes. So again, I'll let you decide whether you're it's a good thing or not, but we're happy to have you PW is awesome. Um as well. There was someone who said that these features are so needed. The things that dr announced this morning in the keynotes and that doctor has reacted to our pains and I think they mean has addressed their pain. So that was really gratifying to read. Yeah, really wonderful. That's some other countries that I didn't shout out before this just tells you what the breadth and scope of our community is. Indonesia, la paz Bolivia, Greece, Munich, Ukraine, oxford UK Australia Philippines. And there's just more and I'm going to do a special shadow to Montreal because that's where I'm from. So yes, applause for that. It was really great. And so I just want to thank all of you. Um, I want to encourage you when we talked about the power of community. Remember we're doing a fundraiser. So to combat Covid for Covid relief or actually all that money is going to go to UNICEF. Docker is contributing 10,000 and we're doing a go fund me. And the link is there on the screen. So please donate. You know, just $1. 1 person each of you donates $1. We would have raised over $22,000. So please please find it within you to contribute because again, our communities that are, that are the most effective are India and brazil, which are are very active doctor affinity. So please give back. I really appreciate that >>highlighted by the brazil. Yeah. >>You're going to brazil room and get them all to donate. Exactly. Um, also want to encourage, you know, if you're interested in participating in our, in our road map. Our public road map is on GIT hub. So it's get home dot com slash docker slash roadmap. And that's something that you can participate in and vote up features that you want to see. We love to get the community involved and participating in our, in our road map. So make sure to check that out. And I also want to note on that >>Hello can real quick. I'm sorry. Yeah, I talk about our road map all the time, but honestly folks out there are PMS are in their our ceo is in there that we do watch that. That is our roadmap is extremely, extremely important to us. So any features complaints, right, joining the conversation. That's a great way to get uh to interact with Docker in our products. Right. We we really highly valued the road map. Okay, back to your mama, sorry. >>Oh absolutely. And if you want to see us be even more responsive to what you need to participate in that road map discussion. That's really great. Um a couple of things coming up, just want to put the spotlight on. We have at 3 15 what's new with with desktop from our own ue cow. So I highly recommend that you attend that session and of course there's the Woman in tech live panel. So this is very exciting, moderated by yours truly and it has putting a spotlight on our women captains and our women developers. So that's very exciting. But I also hear that we're doing there's a session with jay frog coming up so peter, why don't you talk about that a little bit? >>Yeah, we have a session coming up from our partners from jay frog around devops patterns and anti patterns for continuous software updates. And another one that I'm extremely excited about is uh RM one talk from our very own Tony's from Docker. So if you have an M one and you're interested in multi arc architecture builds, check that out. It's gonna be a great, great talk. Um and then we have melissa McKay also from jay frog, talking about Docker and the container ecosystem and last but definitely not least. We'll check them all out there. Going to be great. But Brett is going to be doing I think the best panel that I'm gonna go watch and he made up a new word, it's called say this. I'm all about the trending new words today about this is gonna be awesome. Yeah. Yeah >>I'm going to have the battle bottle of the panels. >>Yeah. Yeah well mine's before years so we're not competing. So yeah we have we have two excellent panels in a row to finish off the day and just seven list is basically how to run, how can we run containers without managing servers? So it doesn't mean you don't actually have infrastructure just let's not manage service. Um Yeah and we we uh need to wrap it up and >>Uh before we do that I just want to um tell everyone that we actually have a promotion going on. So we um for those that sign up for a pro or team subscription, we're offering a 20% off so there's the U. R. L.. You can check out what the promotion is and this is for a new and returning users so you can use the promo code dr khan 21 all the information is on the website are really encourage you to check that out promotion for 20% off, join us for our panels. And we're doing a wrap up at five p.m. Where we'll have our own Ceo and that wrap up portion. Look forward to seeing there. All right, >>thank you too. All right everyone we'll see you on the next go around coming up next me and some other people awesome and Yeah. Mhm. Mhm. Yeah. >>Yeah. Yeah. Mhm. Is >>a really varied community. There's a lot of people with completely different backgrounds, completely different experience levels and completely different goals about how they want to use Docker. And I think that's really interesting. It's always easy to talk about the technology that I've used for so many years. I really love Doctor and I can find so many ways that it's useful and it's great to use in your day to day work clothes. I've >>used doctor for anything from um tracking airplanes with my son, which was a kind of cool project to more professional projects where we actually Built one of the first database as his services using docker even before it was 10 and I was released and we took it further and we start composing monitoring tools. We really start taking it to the next level. And we got to the point where I was trying to make everything in a container, I love to use >>doctor to make disposable project so I can download the project and it's been that up using Docker compose or something like that in a way that every developer that works in the project doesn't even need to know the underlying technology doesn't just need to run Docker compose up and the whole project is going to be up and running even if >>you're not using doctor and production, there are a lot of other ways that you can use doctor to make your life so much easier. As a developer, you can run your projects on your machine locally. Um as a tester you can actually launch test containers and be able to run um dependencies that your project requires. You can run real life versions so that um you're as close to production as possible. >>I was able to migrate most of the workloads from our on from uh to the cloud. Running complete IEDs inside a docker or running it or using it basically to replace their build scripts or using it to run not web applications but maybe compile c plus plus code or compile um projects that really just require some sort of consistency across their team, >>whether it be a web app or a database, I can control these all the same. That was really the power I saw within Doctors standardization and the portability >>doctor isn't the one that created containers uh and uh but it's the one that made it uh democratically possible, so everyone use it. And this effort has made the technology environment so much better for everyone that uses it, both for developers and for end users. So this >>past year has been quite interesting and I think we're all in the same boat here, so no one has, no one is an exception to this, but what we all learn from it is, you know, the community is very important and to lean on other people for help for assistance. >>Yeah, it's been really challenging of course, but I think the biggest and most obvious thing that I've learned both on a personal and a business perspective is just to be ready to adapt to change and don't be afraid of it either. I think it's worth noting that you should never really take it for granted that the paradigms of, you know, the world or technology or something like that aren't going to shift drastically and very, very quickly. >>I'm looking forward to what is coming down the pipe with doctor. What more are they going to throw our way in order to make our lives easier? >>It's very interesting to see the company grow and adapt the way it has. I mean it as well as the community, it's been very interesting to see, you know, how, you know, the return to develop our focus is now the main focus and I find that's very interesting because, you know, developers are the ones that really boost the doctor to where it is today. And if we keep on encouraging these developer innovation, we'll just see more tools being developed on top of Doctor in the future, and that's what I'm really excited to see with Doctor and the technology in the future.

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR, this is Braking Analysis with Dave Vellante. >> Intel's big announcement this week underscores the threat that the United States faces from China. The US needs to lead in semiconductor design and manufacturing. And that lead is slipping because Intel has been fumbling the ball over the past several years, a mere two months into the job, new CEO Pat Gelsinger wasted no time in setting a new course for perhaps, the most strategically important American technology company. We believe that Gelsinger has only shown us part of his plan. This is the beginning of a long and highly complex journey. Despite Gelsinger's clear vision, his deep understanding of technology and execution ethos, in order to regain its number one position, Intel we believe we'll need to have help from partners, competitors and very importantly, the US government. Hello everyone and welcome to this week's Wikibon CUBE insights powered by ETR. In this breaking analysis we'll peel the onion Intel's announcement of this week and explain why we're perhaps not as sanguine as was Wall Street on Intel's prospects. And we'll lay out what we think needs to take place for Intel to once again, become top gun and for us to gain more confidence. By the way this is the first time we're broadcasting live with Braking Analysis. We're broadcasting on the CUBE handles on Twitch, Periscope and YouTube and going forward we'll do this regularly as a live program and we'll bring in the community perspective into the conversation through chat. Now you may recall that in January, we kind of dismissed analysis that said Intel didn't have to make any major strategic changes to its business when they brought on Pat Gelsinger. Rather we said the exact opposite. Our view at time was that the root of Intel's problems could be traced to the fact that it wasn't no longer the volume leader. Because mobile volumes dwarf those of x86. As such we said that Intel couldn't go up the learning curve for next gen technologies as fast as its competitors and it needed to shed its dogma of being highly vertically integrated. We said Intel needed to more heavily leverage outsourced foundries. But more specifically, we suggested that in order for Intel to regain its volume lead, it needed to, we said at the time, spin out its manufacturing, create a joint venture sure with a volume leader, leveraging Intel's US manufacturing presence. This, we still believe with some slight refreshes to our thinking based on what Gelsinger has announced. And we'll talk about that today. Now specifically there were three main pieces and a lot of details to Intel's announcement. Gelsinger made it clear that Intel is not giving up its IDM or integrated device manufacturing ethos. He called this IDM 2.0, which comprises Intel's internal manufacturing, leveraging external Foundries and creating a new business unit called Intel Foundry Services. It's okay. Gelsinger said, "We are not giving up on integrated manufacturing." However, we think this is somewhat nuanced. Clearly Intel can't, won't and shouldn't give up on IDM. However, we believe Intel is entering a new era where it's giving designers more choice. This was not explicitly stated. However we feel like Intel's internal manufacturing arm will have increased pressure to serve its designers in a more competitive manner. We've already seen this with Intel finally embracing EUV or extreme ultraviolet lithography. Gelsinger basically said that Intel didn't lean into EUV early on and that it created more complexity in its 10 nanometer process, which dominoed into seven nanometer and as you know the rest of the story and Intel's delays. But since mid last year, it's embraced the technology. Now as a point of reference, Samsung started applying EUV for its seven nanometer technology in 2018. And it began shipping in early 2020. So as you can see, it takes years to get this technology into volume production. The point is that Intel realizes it needs to be more competitive. And we suspect, it will give more freedom to designers to leverage outsource manufacturing. But Gelsinger clearly signaled that IDM is not going away. But the really big news is that Intel is setting up a new division with a separate PNL that's going to report directly to Pat. Essentially it's hanging out a shingle and saying, we're open for business to make your chips. Intel is building two new Fabs in Arizona and investing $20 billion as part of this initiative. Now well Intel has tried this before earlier last decade. Gelsinger says that this time we're serious and we're going to do it right. We'll come back to that. This organizational move while not a spin out or a joint venture, it's part of the recipe that we saw as necessary for Intel to be more competitive. Let's talk about why Intel is doing this. Look at lots has changed in the world of semiconductors. When you think about it back when Pat was at Intel in the '90s, Intel was the volume leader. It crushed the competition with x86. And the competition at the time was coming from risk chips. And when Apple changed the game with iPod and iPhone and iPad, the volume equation flipped to mobile. And that led to big changes in the industry. Specifically, the world started to separate design from manufacturing. We now see firms going from design to tape out in 12 months versus taking three years. A good example is Tesla and his deal with ARM and Samsung. And what's happened is Intel has gone from number one in Foundry in terms of clock speed, wafer density, volume, lowest cost, highest margin to falling behind. TSMC, Samsung and alternative processor competitors like NVIDIA. Volume is still the maker of kings in this business. That hasn't changed and it confers advantage in terms of cost, speed and efficiency. But ARM wafer volumes, we estimate are 10x those of x86. That's a big change since Pat left Intel more than a decade ago. There's also a major chip shortage today. But you know this time, it feels a little different than the typical semiconductor boom and bust cycles. Semiconductor consumption is entering a new era and new use cases emerging from automobiles to factories, to every imaginable device piece of equipment, infrastructure, silicon is everywhere. But the biggest threat of all is China. China wants to be self-sufficient in semiconductors by 2025. It's putting approximately $60 billion into new chip Fabs, and there's more to come. China wants to be the new economic leader of the world and semiconductors are critical to that goal. Now there are those poopoo the China threat. This recent article from Scott Foster lays out some really good information. But the one thing that caught our attention is a statement that China's semiconductor industry is nowhere near being a major competitor in the global market. Let alone an existential threat to the international order and the American way of life. I think Scotty is stuck in the engine room and can't see the forest of the trees, wake up. Sure. You can say China is way behind. Let's take an example. NAND. Today China is at about 64 3D layers whereas Micron they're at 172. By 2022 China's going to be at 128. Micron, it's going to be well over 200. So what's the big deal? We say talk to us in 2025 because we think China will be at parody. That's just one example. Now the type of thinking that says don't worry about China and semi's reminds me of the epic lecture series that Clay Christiansen gave as a visiting professor at Oxford University on the history of, and the economics of the steel industry. Now if you haven't watched this series, you should. Basically Christiansen took the audience through the dynamics of steel production. And he asked the question, "Who told the steel manufacturers that gross margin was the number one measure of profitability? Was it God?" he joked. His point was, when new entrance came into the market in the '70s, they were bottom feeders going after the low margin, low quality, easiest to make rebar sector. And the incumbents nearly pulled back and their mix shifted to higher margin products and their gross margins went up and life was good. Until they lost the next layer. And then the next, and then the next, until it was game over. Now, one of the things that got lost in Pat's big announcement on the 23rd of March was that Intel guided the street below consensus on revenue and earnings. But the stock went up the next day. Now when asked about gross margin in the Q&A segment of the announcement, yes, gross margin is a if not the key metric in semi's in terms of measuring profitability. When asked Intel CFO George Davis explained that with the uptick in PCs last year there was a product shift to the lower margin PC sector and that put pressure on gross margins. It was a product mix thing. And revenue because PC chips are less expensive than server chips was affected as were margins. Now we shared this chart in our last Intel update showing, spending momentum over time for Dell's laptop business from ETR. And you can see in the inset, the unit growth and the market data from IDC, yes, Dell's laptop business is growing, everybody's laptop business is growing. Thank you COVID. But you see the numbers from IDC, Gartner, et cetera. Now, as we pointed out last time, PC volumes had peaked in 2011 and that's when the long arm of rights law began to eat into Intel's dominance. Today ARM wafer production as we said is far greater than Intel's and well, you know the story. Here's the irony, the very bucket that conferred volume adventures to Intel PCs, yes, it had a slight uptick last year, which was great news for Dell. But according to Intel it pulled down its margins. The point is Intel is loving the high end of the market because it's higher margin and more profitable. I wonder what Clay Christensen would say to that. Now there's more to this story. Intel's CFO blame the supply constraints on Intel's revenue and profit pressures yet AMD's revenue and profits are booming. So RTSMCs. Only Intel can't seem to thrive when there's this massive chip shortage. Now let's get back to Pat's announcement. Intel is for sure, going forward investing $20 billion in two new US-based fabrication facilities. This chart shows Intel's investments in US R&D, US CapEx and the job growth that's created as a result, as well as R&D and CapEx investments in Ireland and Israel. Now we added the bar on the right hand side from a Wall Street journal article that compares TSMC CapEx in the dark green to that of Intel and the light green. You can see TSMC surpass the CapEx investment of Intel in 2015, and then Intel took the lead back again. And in 2017 was, hey it on in 2018. But last year TSMC took the lead, again. And appears to be widening that lead quite substantially. Leading us to our conclusion that this will not be enough. These moves by Intel will not be enough. They need to do more. And a big part of this announcement was partnerships and packaging. Okay. So here's where it gets interesting. Intel, as you may know was late to the party with SOC system on a chip. And it's going to use its packaging prowess to try and leap frog the competition. SOC bundles things like GPU, NPU, DSUs, accelerators caches on a single chip. So better use the real estate if you will. Now Intel wants to build system on package which will dis-aggregate memory from compute. Now remember today, memory is very poorly utilized. What Intel is going to do is to create a package with literally thousands of nodes comprising small processors, big processors, alternative processors, ARM processors, custom Silicon all sharing a pool of memory. This is a huge innovation and we'll come back to this in a moment. Now as part of the announcement, Intel trotted out some big name customers, prospects and even competitors that it wants to turn into prospects and customers. Amazon, Google, Satya Nadella gave a quick talk from Microsoft to Cisco. All those guys are designing their own chips as does Ericsson and look even Qualcomm is on the list, a competitor. Intel wants to earn the right to make chips for these firms. Now many on the list like Microsoft and Google they'd be happy to do so because they want more competition. And Qualcomm, well look if Intel can do a good job and be a strong second sourced, why not? Well, one reason is they compete aggressively with Intel but we don't like Intel so much but it's very possible. But the two most important partners on this slide are one IBM and two, the US government. Now many people were going to gloss over IBM in this announcement, but we think it's one of the most important pieces of the puzzle. Yes. IBM and semiconductors. IBM actually has some of the best semiconductor technology in the world. It's got great architecture and is two to three years ahead of Intel with POWER10. Yes, POWER. IBM is the world's leader in terms of dis-aggregating compute from memory with the ability to scale to thousands of nodes, sound familiar? IBM leads in power density, efficiency and it can put more stuff closer together. And it's looking now at a 20x increase in AI inference performance. We think Pat has been thinking about this for a while and he said, how can I leave leap frog system on chip. And we think he thought and said, I'll use our outstanding process manufacturing and I'll tap IBM as a partner for R&D and architectural chips to build the next generation of systems that are more flexible and performant than anything that's out there. Now look, this is super high end stuff. And guess who needs really high end massive supercomputing capabilities? Well, the US military. Pat said straight up, "We've talked to the government and we're honored to be competing for the government/military chips boundary." I mean, look Intel in my view was going to have to fall down into face not win this business. And by making the commitment to Foundry Services we think they will get a huge contract from the government, as large, perhaps as $10 billion or more to build a secure government Foundry and serve the military for decades to come. Now Pat was specifically asked in the Q&A section is this Foundry strategy that you're embarking on viable without the help of the US government? Kind of implying that it was a handout or a bailout. And Pat of course said all the right things. He said, "This is the right thing for Intel. Independent of the government, we haven't received any commitment or subsidies or anything like that from the US government." Okay, cool. But they have had conversations and I have no doubt, and Pat confirmed this, that those conversations were very, very positive that Intel should head in this direction. Well, we know what's happening here. The US government wants Intel to win. It needs Intel to win and its participation greatly increases the probability of success. But unfortunately, we still don't think it's enough for Intel to regain its number one position. Let's look at that in a little bit more detail. The headwinds for Intel are many. Look it can't just flick a switch and catch up on manufacturing leadership. It's going to take four years. And lots can change in that time. It tells market momentum as well as we pointed out earlier is headed in the wrong direction from a financial perspective. Moreover, where is the volume going to come from? It's going to take years for Intel to catch up for ARMS if it never can. And it's going to have to fight to win that business from its current competitors. Now I have no doubt. It will fight hard under Pat's excellent leadership. But the Foundry business is different. Consider this, Intel's annual CapEx expenditures, if you divide that by their yearly revenue it comes out to about 20% of revenue. TSMC spends 50% of its revenue each year on CapEx. This is a different animal, very service oriented. So look, we're not pounding the table saying Intel's worst days are over. We don't think they are. Now, there are some positives, I'm showing those in the right-hand side. Pat Gelsinger was born for this job. He proved that the other day, even though we already knew it. I have never seen him more excited and more clearheaded. And we agreed that the chip demand dynamic is going to have legs in this decade and beyond with Digital, Edge, AI and new use cases that are going to power that demand. And Intel is too strategic to fail. And the US government has huge incentives to make sure that it succeeds. But it's still not enough in our opinion because like the steel manufacturers Intel's real advantage today is increasingly in the high end high margin business. And without volume, China is going to win this battle. So we continue to believe that a new joint venture is going to emerge. Here's our prediction. We see a triumvirate emerging in a new joint venture that is led by Intel. It brings x86, that volume associated with that. It brings cash, manufacturing prowess, R&D. It brings global resources, so much more than we show in this chart. IBM as we laid out brings architecture, it's R&D, it's longstanding relationships. It's deal flow, it can funnel its business to the joint venture as can of course, parts of Intel. We see IBM getting a nice licensed deal from Intel and or the JV. And it has to get paid for its contribution and we think it'll also get a sweet deal and the manufacturing fees from this Intel Foundry. But it's still not enough to beat China. Intel needs volume. And that's where Samsung comes in. It has the volume with ARM, has the experience and a complete offering across products. We also think that South Korea is a more geographically appealing spot in the globe than Taiwan with its proximity to China. Not to mention that TSMC, it doesn't need Intel. It's already number one. Intel can get a better deal from number two, Samsung. And together these three we think, in this unique structure could give it a chance to become number one by the end of the decade or early in the 2030s. We think what's happening is our take, is that Intel is going to fight hard to win that government business, put itself in a stronger negotiating position and then cut a deal with some supplier. We think Samsung makes more sense than anybody else. Now finally, we want to leave you with some comments and some thoughts from the community. First, I want to thank David Foyer. His decade plus of work and knowledge of this industry along with this collaboration made this work possible. His fingerprints are all over this research in case you didn't notice. And next I want to share comments from two of my colleagues. The first is Serbjeet Johal. He sent this to me last night. He said, "We are not in our grandfather's compute era anymore. Compute is getting spread into every aspect of our economy and lives. The use of processors is getting more and more specialized and will intensify with the rise in edge computing, AI inference and new workloads." Yes, I totally agree with Sarbjeet. And that's the dynamic which Pat is betting and betting big. But the bottom line is summed up by my friend and former IDC mentor, Dave Moschella. He says, "This is all about China. History suggests that there are very few second acts, you know other than Microsoft and Apple. History also will say that the antitrust pressures that enabled AMD to thrive are the ones, the very ones that starved Intel's cash. Microsoft made the shift it's PC software cash cows proved impervious to competition. The irony is the same government that attacked Intel's monopoly now wants to be Intel's protector because of China. Perhaps it's a cautionary tale to those who want to break up big tech." Wow. What more can I add to that? Okay. That's it for now. Remember I publish each week on wikibon.com and siliconangle.com. These episodes are all available as podcasts. All you got to do is search for Braking Analysis podcasts and you can always connect with me on Twitter @dvellante or email me at david.vellante, siliconangle.com As always I appreciate the comments on LinkedIn and in clubhouse please follow me so that you're notified when we start a room and start riffing on these topics. And don't forget to check out etr.plus for all the survey data. This is Dave Vellante for theCUBE insights powered by ETR, be well, and we'll see you next time. (upbeat music)

>> Hello everyone. On today's panel, the theme is Drug Discovery and how Artificial Intelligence can make a difference. On the panel today, we are honored to have Dr. Ryan Yates, principal scientist at The National Center for Natural Products Research, with a focus on botanicals specifically the pharmacokinetics, which is essentially how the drug changes over time in our body and pharmacodynamics which is essentially how drugs affects our body. And of particular interest to him is the use of AI in preclinical screening models to identify chemical combinations that can target chronic inflammatory processes such as fatty liver disease, cognitive impairment and aging. Welcome, Ryan. Thank you for coming. >> Good morning. Thank you for having me. >> The other distinguished panelist is Dr. Rangan Sukumar, our very own, is a distinguished technologist at the CTO office for High Performance Computing and Artificial Intelligence with a PHD in AI and 70 publications that can be applied in drug discovery, autonomous vehicles and social network analysis. Hey Rangan, welcome. Thank you for coming, by sparing the time. We have also our distinguished Chris Davidson. He is leader of our HPC and AI Application and Performance Engineering team. His job is to tune and benchmark applications, particularly in the applications of weather, energy, financial services and life sciences. Yes so particular interest is life sciences he spent 10 years in biotech and medical diagnostics. Hi Chris, welcome. Thank you for coming. >> Nice to see you. >> Well let's start with your Chris, yes, you're regularly interfaced with pharmaceutical companies and worked also on the COVID-19 White House Consortium. You know tell us, let's kick this off and tell us a little bit about your engagement in the drug discovery process. >> Right and that's a good question I think really setting the framework for what we're talking about here is to understand what is the drug discovery process. And that can be kind of broken down into I would say four different areas, there's the research and development space, the preclinical studies space, clinical trial and regulatory review. And if you're lucky, hopefully approval. Traditionally this is a slow arduous process it costs a lot of money and there's a high amount of error. Right, however this process by its very nature is highly iterate and has just huge amounts of data, right it's very data intensive, right and it's these characteristics that make this process a great target for kind of new approaches in different ways of doing things. Right, so for the sake of discussion, right, go ahead. >> Oh yes, so you mentioned data intensive brings to mind Artificial Intelligence, you know, so Artificial Intelligence making the difference here in this process, is that so? >> Right, and some of those novel approaches are actually based on Artificial Intelligence whether it's deep learning and machine learning, et cetera, you know, prime example would say, let's just say for the sake of discussion, let's say there's a brand new virus, causes flu-like symptoms, shall not be named if we focus kind of on the R and D phase, right our goal is really to identify target for the treatment and then screen compounds against it see which, you know, which ones we take forward right to this end, technologies like cryo-electron, cryogenic electron microscopy, just a form of microscopy can provide us a near atomic biomolecular map of the samples that we're studying, right whether that's a virus, a microbe, the cell that it's attaching to and so on, right AI, for instance, has been used in the particle picking aspect of this process. When you take all these images, you know, there are only certain particles that we want to take and study, right whether they have good resolution or not whether it's in the field of the frame and image recognition is a huge part of this, it's massive amounts of data in AI can be very easily, you know, used to approach that. Right, so with docking, you can take the biomolecular maps that you achieved from cryo-electron microscopy and you can take those and input that into the docking application and then run multiple iterations to figure out which will give you the best fit. AI again, right, this is iterative process it's extremely data intensive, it's an easy way to just apply AI and get that best fit doing something in a very, you know, analog manner that would just take humans very long time to do or traditional computing a very long time to do. >> Oh, Ryan, Ryan, you work at the NCNPR, you know, very exciting, you know after all, you know, at some point in history just about all drugs were from natural products yeah, so it's great to have you here today. Please tell us a little bit about your work with the pharmaceutical companies, especially when it is often that drug cocktails or what they call Polypharmacology, is the answer to complete drug therapy. Please tell us a bit more with your work there. >> Yeah thank you again for having me here this morning Dr. Goh, it's a pleasure to be here and as you said, I'm from the National Center for Natural Products Research you'll hear me refer to it as the NCNPR here in Oxford, Mississippi on the Ole Miss Campus, beautiful setting here in the South and so, what, as you said historically, what the drug discovery process has been, and it's really not a drug discovery process is really a therapy process, traditional medicine is we've looked at natural products from medicinal plants okay, in these extracts and so where I'd like to begin is really sort of talking about the assets that we have here at the NCNPR one of those prime assets, unique assets is our medicinal plant repository which comprises approximately 15,000 different medicinal plants. And what that allows us to do, right is to screen mine, that repository for activities so whether you have a disease of interest or whether you have a target of interest then you can use this medicinal plant repository to look for actives, in this case active plants. It's really important in today's environment of drug discovery to really understand what are the actives in these different medicinal plants which leads me to the second unique asset here at the NCNPR and that is our what I'll call a plant deconstruction laboratory so without going into great detail, but what that allows us to do is through a how to put workstation, right, is to facilitate rapid isolation and identification of phytochemicals in these different medicinal plants right, and so things that have historically taken us weeks and sometimes months, think acetylsalicylic acid from salicylic acid as a pain reliever in the willow bark or Taxol, right as an anti-cancer drug, right now we can do that with this system on the matter of days or weeks so now we're talking about activity from a plant and extract down to phytochemical characterization on a timescale, which starts to make sense in modern drug discovery, alright and so now if you look at these phytochemicals, right, and you ask yourself, well sort of who is interested in that and why, right what are traditional pharmaceutical companies, right which I've been working with for 20, over 25 years now, right, typically uses these natural products where historically has used these natural products as starting points for new drugs. Right, so in other words, take this phytochemical and make chemicals synthetic modifications in order to achieve a potential drug. But in the context of natural products, unlike the pharmaceutical realm, there is often times a big knowledge gap between a disease and a plant in other words I have a plant that has activity, but how to connect those dots has been really laborious time consuming so it took us probably 50 years to go from salicylic acid and willow bark to synthesize acetylsalicylic acid or aspirin it just doesn't work in today's environment. So casting about trying to figure out how we expedite that process that's when about four years ago, I read a really fascinating article in the Los Angeles Times about my colleague and business partner, Dr. Rangan Sukumar, describing all the interesting things that he was doing in the area of Artificial Intelligence. And one of my favorite parts of this story is basically, unannounced, I arrived at his doorstep in Oak Ridge, he was working Oak Ridge National Labs at the time, and I introduced myself to him didn't know what was coming, didn't know who I was, right and I said, hey, you don't know me you don't know why I'm here, I said, but let me tell you what I want to do with your system, right and so that kicked off a very fruitful collaboration and friendship over the last four years using Artificial Intelligence and it's culminated most recently in our COVID-19 project collaborative research between the NCNPR and HP in this case. >> From what I can understand also as Chris has mentioned highly iterative, especially with these combination mixture of chemicals right, in plants that could affect a disease. We need to put in effort to figure out what are the active components in that, that affects it yeah, the combination and given the layman's way of understanding it you know and therefore iterative and highly data intensive. And I can see why Rangan can play a huge significant role here, Rangan, thank you for joining us So it's just a nice segue to bring you in here, you know, given your work with Ryan over so many years now, tell I think I'm also quite interested in knowing a little about how it developed the first time you met and the process and the things you all work together on that culminated into the progress at the advanced level today. Please tell us a little bit about that history and also the current work. Rangan. >> So, Ryan, like he mentioned, walked into my office about four years ago and he was like hey, I'm working on this Omega-3 fatty acid, what can your system tell me about this Omega-3 fatty acid and I didn't even know how to spell Omega-3 fatty acids that's the disconnect between the technologist and the pharmacologist, they have terms of their own right since then we've come a long way I think I understand his terminologies now and he understands that I throw words like knowledge graphs and page rank and then all kinds of weird stuff that he's probably never heard in his life before right, so it's been on my mind off to different domains and terminologies in trying to accept each other's expertise in trying to work together on a collaborative project. I think the core of what Ryan's work and collaboration has led me to understanding is what happens with the drug discovery process, right so when we think about the discovery itself, we're looking at companies that are trying to accelerate the process to market, right an average drug is taking 12 years to get to market the process that Chris just mentioned, Right and so companies are trying to adopt what's called the in silico simulation techniques and in silico modeling techniques into what was predominantly an in vitro, in silico, in vivo environment, right. And so the in silico techniques could include things like molecular docking, could include Artificial Intelligence, could include other data-driven discovery methods and so forth, and the essential component of all the things that you know the discovery workflows have is the ability to augment human experts to do the best by assisting them with what computers do really really well. So, in terms of what we've done as examples is Ryan walks in and he's asking me a bunch of questions and few that come to mind immediately, the first few are, hey, you are an Artificial Intelligence expert can you sift through a database of molecules the 15,000 compounds that he described to prioritize a few for next lab experiments? So that's question number one. And he's come back into my office and asked me about hey, there's 30 million publications in PubMag and I don't have the time to read everything can you create an Artificial Intelligence system that once I've picked these few molecules will tell me everything about the molecule or everything about the virus, the unknown virus that shows up, right. Just trying to understand what are some ways in which he can augment his expertise, right. And then the third question, I think he described better than I'm going to was how can technology connect these dots. And typically it's not that the answer to a drug discovery problem sits in one database, right he probably has to think about uniproduct protein he has to think about phytochemical, chemical or informatics properties, data and so forth. Then he talked about the phytochemical interaction that's probably in another database. So when he is trying to answer other question and specifically in the context of an unknown virus that showed up in late last year, the question was, hey, do we know what happened in this particular virus compared to all the previous viruses? Do we know of any substructure that was studied or a different disease that's part of this unknown virus and can I use that information to go mine these databases to find out if these interactions can actually be used as a repurpose saying, hook, say this drug does not interact with this subsequence of a known virus that also seems to be part of this new virus, right? So to be able to connect that dot I think the abstraction that we are learning from working with pharma companies is that this drug discovery process is complex, it's iterative, and it's a sequence of needle in the haystack search problems, right and so one day, Ryan would be like, hey, I need to match genome, I need to match protein sequences between two different viruses. Another day it would be like, you know, I need to sift through a database of potential compounds, identified side effects and whatnot other day it could be, hey, I need to design a new molecule that never existed in the world before I'll figure out how to synthesize it later on, but I need a completely new molecule because of patentability reasons, right so it goes through the entire spectrum. And I think where HP has differentiated multiple times even the recent weeks is that the technology infusion into drug discovery, leads to several aha! Moments. And, aha moments typically happened in the other few seconds, and not the hours, days, months that Ryan has to laboriously work through. And what we've learned is pharma researchers love their aha moments and it leads to a sound valid, well founded hypothesis. Isn't that true Ryan? >> Absolutely. Absolutely. >> Yeah, at some point I would like to have a look at your, peak the list of your aha moments, yeah perhaps there's something quite interesting in there for other industries too, but we'll do it at another time. Chris, you know, with your regular work with pharmaceutical companies especially the big pharmas, right, do you see botanicals, coming, being talked about more and more there? >> Yeah, we do, right. Looking at kind of biosimilars and drugs that are already really in existence is kind of an important point and Dr. Yates and Rangan, with your work with databases this is something important to bring up and much of the drug discovery in today's world, isn't from going out and finding a brand new molecule per se. It's really looking at all the different databases, right all the different compounds that already exist and sifting through those, right of course data is mind, and it is gold essentially, right so a lot of companies don't want to share their data. A lot of those botanicals data sets are actually open to the public to use in many cases and people are wanting to have more collaborative efforts around those databases so that's really interesting to kind of see that being picked up more and more. >> Mm, well and Ryan that's where NCNPR hosts much of those datasets, yeah right and it's interesting to me, right you know, you were describing the traditional way of drug discovery where you have a target and a compound, right that can affect that target, very very specific. But from a botanical point of view, you really say for example, I have an extract from a plant that has combination of chemicals and somehow you know, it affects this disease but then you have to reverse engineer what those chemicals are and what the active ones are. Is that very much the issue, the work that has to be put in for botanicals in this area? >> Yes Doctor Goh, you hit it exactly. >> Now I can understand why a highly iterative intensive and data intensive, and perhaps that's why Rangan, you're highly valuable here, right. So tell us about the challenge, right the many to many intersection to try and find what the targets are, right given these botanicals that seem to affect the disease here what methods do you use, right in AI, to help with this? >> Fantastic question, I'm going to go a little bit deeper and speak like Ryan in terminology, but here we go. So with going back to about starting of our conversation right, so let's say we have a database of molecules on one side, and then we've got the database of potential targets in a particular, could be a virus, could be bacteria, could be whatever, a disease target that you've identified, right >> Oh this process so, for example, on a virus, you can have a number of targets on the virus itself some have the spike protein, some have the other proteins on the surface so there are about three different targets and others on a virus itself, yeah so a lot of people focus on the spike protein, right but there are other targets too on that virus, correct? >> That is exactly right. So for example, so the work that we did with Ryan we realized that, you know, COVID-19 protein sequence has an overlap, a significant overlap with previous SARS-CoV-1 virus, not only that, but it overlap with MERS, that's overlapped with some bad coronavirus that was studied before and so forth, right so knowing that and it's actually broken down into multiple and Ryan I'm going to steal your words, non-structural proteins, envelope proteins, S proteins, there's a whole substructure that you can associate an amino acid sequence with, right so on the one hand, you have different targets and again, since we did the work it's 160 different targets even on the COVID-19 mark, right and so you find a match, that we say around 36, 37 million molecules that are potentially synthesizable and try to figure it out which one of those or which few of those is actually going to be mapping to which one of these targets and actually have a mechanism of action that Ryan's looking for, that'll inhibit the symptoms on a human body, right so that's the challenge there. And so I think the techniques that we can unrule go back to how much do we know about the target and how much do we know about the molecule, alright. And if you start off a problem with I don't know anything about the molecule and I don't know anything about the target, you go with the traditional approaches of docking and molecular dynamics simulations and whatnot, right. But then, you've done so much docking before on the same database for different targets, you'll learn some new things about the ligands, the molecules that Ryan's talking about that can predict potential targets. So can you use that information of previous protein interactions or previous binding to known existing targets with some of the structures and so forth to build a model that will capture that essence of what we have learnt from the docking before? And so that's the second level of how do we infuse Artificial Intelligence. The third level, is to say okay, I can do this for a database of molecules, but then what if the protein-protein interactions are all over the literature study for millions of other viruses? How do I connect the dots across different mechanisms of actions too? Right and so this is where the knowledge graph component that Ryan was talking about comes in. So we've put together a database of about 150 billion medical facts from literature that Ryan is able to connect the dots and say okay, I'm starting with this molecule, what interactions do I know about the molecule? Is there a pretty intruding interaction that affects the mechanism of pathway for the symptoms that a disease is causing? And then he can go and figure out which protein and protein in the virus could potentially be working with this drug so that inhibiting certain activities would stop that progression of the disease from happening, right so like I said, your method of options, the options you've got is going to be, how much do you know about the target? How much do you know the drug database that you have and how much information can you leverage from previous research as you go down this pipeline, right so in that sense, I think we mix and match different methods and we've actually found that, you know mixing and matching different methods produces better synergies for people like Ryan. So. >> Well, the synergies I think is really important concept, Rangan, in additivities, synergistic, however you want to catch that. Right. But it goes back to your initial question Dr. Goh, which is this idea of polypharmacology and historically what we've done with traditional medicines there's more than one active, more than one network that's impacted, okay. You remember how I sort of put you on both ends of the spectrum which is the traditional sort of approach where we really don't know much about target ligand interaction to the completely interpretal side of it, right where now we are all, we're focused on is, in a single molecule interacting with a target. And so where I'm going with this is interesting enough, pharma has sort of migrate, started to migrate back toward the middle and what I mean by that, right, is we had these in a concept of polypharmacology, we had this idea, a regulatory pathway of so-called, fixed drug combinations. Okay, so now you start to see over the last 20 years pharmaceutical companies taking known, approved drugs and putting them in different combinations to impact different diseases. Okay. And so I think there's a really unique opportunity here for Artificial Intelligence or as Rangan has taught me, Augmented Intelligence, right to give you insight into how to combine those approved drugs to come up with unique indications. So is that patentability right, getting back to right how is it that it becomes commercially viable for entities like pharmaceutical companies but I think at the end of the day what's most interesting to me is sort of that, almost movement back toward that complex mixture of fixed drug combination as opposed to single drug entity, single target approach. I think that opens up some really neat avenues for us. As far as the expansion, the applicability of Artificial Intelligence is I'd like to talk to, briefly about one other aspect, right so what Rang and I have talked about is how do we take this concept of an active phytochemical and work backwards. In other words, let's say you identify a phytochemical from an in silico screening process, right, which was done for COVID-19 one of the first publications out of a group, Dr. Jeremy Smith's group at Oak Ridge National Lab, right, identified a natural product as one of the interesting actives, right and so it raises the question to our botanical guy, says, okay, where in nature do we find that phytochemical? What plants do I go after to try and source botanical drugs to achieve that particular end point right? And so, what Rangan's system allows us to do is to say, okay, let's take this phytochemical in this case, a phytochemical flavanone called eriodictyol and say, where else in nature is this found, right that's a trivial question for an Artificial Intelligence system. But for a guy like me left to my own devices without AI, I spend weeks combing the literature. >> Wow. So, this is brilliant I've learned something here today, right, If you find a chemical that actually, you know, affects and addresses a disease, right you can actually try and go the reverse way to figure out what botanicals can give you those chemicals as opposed to trying to synthesize them. >> Well, there's that and there's the other, I'm going to steal Rangan's thunder here, right he always teach me, Ryan, don't forget everything we talk about has properties, plants have properties, chemicals have properties, et cetera it's really understanding those properties and using those properties to make those connections, those edges, those sort of interfaces, right. And so, yes, we can take something like an eriodictyol right, that example I gave before and say, okay, now, based upon the properties of eriodictyol, tell me other phytochemicals, other flavonoid in this case, such as that phytochemical class of eriodictyols part right, now tell me how, what other phytochemicals match that profile, have the same properties. It might be more economically viable, right in other words, this particular phytochemical is found in a unique Himalayan plant that I've never been able to source, but can we find something similar or same thing growing in, you know a bush found all throughout the Southeast for example, like. >> Wow. So, Chris, on the pharmaceutical companies, right are they looking at this approach of getting, building drugs yeah, developing drugs? >> Yeah, absolutely Dr. Goh, really what Dr. Yates is talking about, right it doesn't help us if we find a plant and that plant lives on one mountain only on the North side in the Himalayas, we're never going to be able to create enough of a drug to manufacture and to provide to the masses, right assuming that the disease is widespread or affects a large enough portion of the population, right so understanding, you know, not only where is that botanical or that compound but understanding the chemical nature of the chemical interaction and the physics of it as well where which aspect affects the binding site, which aspect of the compound actually does the work, if you will and then being able to make that at scale, right. If you go to these pharmaceutical companies today, many of them look like breweries to be honest with you, it's large scale, it's large back everybody's clean room and it's, they're making the microbes do the work for them or they have these, you know, unique processes, right. So. >> So they're not brewing beer okay, but drugs instead. (Christopher laughs) >> Not quite, although there are pharmaceutical companies out there that have had a foray into the brewery business and vice versa, so. >> We should, we should visit one of those, yeah (chuckles) Right, so what's next, right? So you've described to us the process and how you develop your relationship with Dr. Yates Ryan over the years right, five years, was it? And culminating in today's, the many to many fast screening methods, yeah what would you think would be the next exciting things you would do other than letting me peek at your aha moments, right what would you say are the next exciting steps you're hoping to take? >> Thinking long term, again this is where Ryan and I are working on this long-term project about, we don't know enough about botanicals as much as we know about the synthetic molecules, right and so this is a story that's inspired from Simon Sinek's "Infinite Game" book, trying to figure it out if human population has to survive for a long time which we've done so far with natural products we are going to need natural products, right. So what can we do to help organizations like NCNPR to stage genomes of natural products to stage and understand the evolution as we go to understand the evolution to map the drugs and so forth. So the vision is huge, right so it's not something that we want to do on a one off project and go away but in the process, just like you are learning today, Dr. Goh I'm going to be learning quite a bit, having fun with life. So, Ryan what do you think? >> Ryan, we're learning from you. >> So my paternal grandfather lived to be 104 years of age. I've got a few years to get there, but back to "The Infinite Game" concept that Rang had mentioned he and I discussed that quite frequently, I'd like to throw out a vision for you that's well beyond that sort of time horizon that we have as humans, right and that's this right, is our current strategy and it's understandable is really treatment centric. In other words, we have a disease we develop a treatment for that disease. But we all recognize, whether you're a healthcare practitioner, whether you're a scientist, whether you're a business person, right or whatever occupation you realize that prevention, right the old ounce, prevention worth a pound of cure, right is how can we use something like Artificial Intelligence to develop preventive sorts of strategies that we are able to predict with time, right that's why we don't have preventive treatment approach right, we can't do a traditional clinical trial and say, did we prevent type two diabetes in an 18 year old? Well, we can't do that on a timescale that is reasonable, okay. And then the other part of that is why focus on botanicals? Is because, for the most part and there are exceptions I want to be very clear, I don't want to paint the picture that botanicals are all safe, you should just take botanicals dietary supplements and you'll be safe, right there are exceptions, but for the most part botanicals, natural products are in fact safe and have undergone testing, human testing for thousands of years, right. So how do we connect those dots? A preventive strategy with existing extent botanicals to really develop a healthcare system that becomes preventive centric as opposed to treatment centric. If I could wave a magic wand, that's the vision that I would figure out how we could achieve, right and I do think with guys like Rangan and Chris and folks like yourself, Eng Lim, that that's possible. Maybe it's in my lifetime I got 50 years to go to get to my grandfather's age, but you never know, right? >> You bring really, up two really good points there Ryan, it's really a systems approach, right understanding that things aren't just linear, right? And as you go through it, there's no impact to anything else, right taking that systems approach to understand every aspect of how things are being impacted. And then number two was really kind of the downstream, really we've been discussing the drug discovery process a lot and kind of the kind of preclinical in vitro studies and in vivo models, but once you get to the clinical trial there are many drugs that just fail, just fail miserably and the botanicals, right known to be safe, right, in many instances you can have a much higher success rate and that would be really interesting to see, you know, more of at least growing in the market. >> Well, these are very visionary statements from each of you, especially Dr. Yates, right, prevention better than cure, right, being proactive better than being reactive. Reactive is important, but we also need to focus on being proactive. Yes. Well, thank you very much, right this has been a brilliant panel with brilliant panelists, Dr. Ryan Yates, Dr. Rangan Sukumar and Chris Davidson. Thank you very much for joining us on this panel and highly illuminating conversation. Yeah. All for the future of drug discovery, that includes botanicals. Thank you very much. >> Thank you. >> Thank you.

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

>>Hello. I'm Professor Toru Suzuki Cherif cardiovascular medicine on associate dean of the College of Life Sciences at the University of Leicester in the United Kingdom, where I'm also director of the Lester Life Sciences accelerator. I'm also honorary consultant cardiologist within our university hospitals. It's part of the national health system NHS Trust. Today, I'd like to talk to you about our Lester Clinical Data Science Initiative. Now brief background on Lester. It's university in hospitals. Lester is in the center of England. The national health system is divided depending on the countries. The United Kingdom, which is comprised of, uh, England, Scotland to the north, whales to the west and Northern Ireland is another part in a different island. But national health system of England is what will be predominantly be discussed. Today has a history of about 70 years now, owing to the fact that we're basically in the center of England. Although this is only about one hour north of London, we have a catchment of about 100 miles, which takes us from the eastern coast of England, bordering with Birmingham to the west north just south of Liverpool, Manchester and just south to the tip of London. We have one of the busiest national health system trust in the United Kingdom, with a catchment about 100 miles and one million patients a year. Our main hospital, the General Hospital, which is actually called the Royal Infirmary, which can has an accident and emergency, which means Emergency Department is that has one of the busiest emergency departments in the nation. I work at Glen Field Hospital, which is one of the main cardiovascular hospitals of the United Kingdom and Europe. Academically, the Medical School of the University of Leicester is ranked 20th in the world on Lee, behind Cambridge, Oxford Imperial College and University College London. For the UK, this is very research. Waited, uh, ranking is Therefore we are very research focused universities as well for the cardiovascular research groups, with it mainly within Glenn Field Hospital, we are ranked as the 29th Independent research institution in the world which places us. A Suffield waited within our group. As you can see those their top ranked this is regardless of cardiology, include institutes like the Broad Institute and Whitehead Institute. Mitt Welcome Trust Sanger, Howard Hughes Medical Institute, Kemble, Cold Spring Harbor and as a hospital we rank within ah in this field in a relatively competitive manner as well. Therefore, we're very research focused. Hospital is well now to give you the unique selling points of Leicester. We're we're the largest and busiest national health system trust in the United Kingdom, but we also have a very large and stable as well as ethnically diverse population. The population ranges often into three generations, which allows us to do a lot of cohort based studies which allows us for the primary and secondary care cohorts, lot of which are well characterized and focused on genomics. In the past. We also have a biomedical research center focusing on chronic diseases, which is funded by the National Institutes of Health Research, which funds clinical research the hospitals of United Kingdom on we also have a very rich regional life science cluster, including med techs and small and medium sized enterprises. Now for this, the bottom line is that I am the director of the letter site left Sciences accelerator, >>which is tasked with industrial engagement in the local national sectors but not excluding the international sectors as well. Broadly, we have academics and clinicians with interest in health care, which includes science and engineering as well as non clinical researchers. And prior to the cove it outbreak, the government announced the £450 million investment into our university hospitals, which I hope will be going forward now to give you a brief background on where the scientific strategy the United Kingdom lies. Three industrial strategy was brought out a za part of the process which involved exiting the European Union, and part of that was the life science sector deal. And among this, as you will see, there were four grand challenges that were put in place a I and data economy, future of mobility, clean growth and aging society and as a medical research institute. A lot of the focus that we have been transitioning with within my group are projects are focused on using data and analytics using artificial intelligence, but also understanding how chronic diseases evolved as part of the aging society, and therefore we will be able to address these grand challenges for the country. Additionally, the national health system also has its long term plans, which we align to. One of those is digitally enabled care and that this hope you're going mainstream over the next 10 years. And to do this, what is envision will be The clinicians will be able to access and interact with patient records and care plants wherever they are with ready access to decision support and artificial intelligence, and that this will enable predictive techniques, which include linking with clinical genomic as well as other data supports, such as image ing a new medical breakthroughs. There has been what's called the Topol Review that discusses the future of health care in the United Kingdom and preparing the health care workforce for the delivery of the digital future, which clearly discusses in the end that we would be using automated image interpretation. Is using artificial intelligence predictive analytics using artificial intelligence as mentioned in the long term plans. That is part of that. We will also be engaging natural language processing speech recognition. I'm reading the genome amusing. Genomic announced this as well. We are in what is called the Midland's. As I mentioned previously, the Midland's comprised the East Midlands, where we are as Lester, other places such as Nottingham. We're here. The West Midland involves Birmingham, and here is ah collective. We are the Midlands. Here we comprise what is called the Midlands engine on the Midland's engine focuses on transport, accelerating innovation, trading with the world as well as the ultra connected region. And therefore our work will also involve connectivity moving forward. And it's part of that. It's part of our health care plans. We hope to also enable total digital connectivity moving forward and that will allow us to embrace digital data as well as collectivity. These three key words will ah Linkous our health care systems for the future. Now, to give you a vision for the future of medicine vision that there will be a very complex data set that we will need to work on, which will involve genomics Phanom ICS image ing which will called, uh oh mix analysis. But this is just meaning that is, uh complex data sets that we need to work on. This will integrate with our clinical data Platforms are bioinformatics, and we'll also get real time information of physiology through interfaces and wearables. Important for this is that we have computing, uh, processes that will now allow this kind of complex data analysis in real time using artificial intelligence and machine learning based applications to allow visualization Analytics, which could be out, put it through various user interfaces to the clinician and others. One of the characteristics of the United Kingdom is that the NHS is that we embrace data and captured data from when most citizens have been born from the cradle toe when they die to the grave. And it's important that we were able to link this data up to understand the journey of that patient. Over time. When they come to hospital, which is secondary care data, we will get disease data when they go to their primary care general practitioner, we will be able to get early check up data is Paula's follow monitoring monitoring, but also social care data. If this could be linked, allow us to understand how aging and deterioration as well as frailty, uh, encompasses thes patients. And to do this, we have many, many numerous data sets available, including clinical letters, blood tests, more advanced tests, which is genetics and imaging, which we can possibly, um, integrate into a patient journey which will allow us to understand the digital journey of that patient. I have called this the digital twin patient cohort to do a digital simulation of patient health journeys using data integration and analytics. This is a technique that has often been used in industrial manufacturing to understand the maintenance and service points for hardware and instruments. But we would be using this to stratify predict diseases. This'll would also be monitored and refined, using wearables and other types of complex data analysis to allow for, in the end, preemptive intervention to allow paradigm shifting. How we undertake medicine at this time, which is more reactive rather than proactive as infrastructure we are presently working on putting together what's it called the Data Safe haven or trusted research environment? One which with in the clinical environment, the university hospitals and curated and data manner, which allows us to enable data mining off the databases or, I should say, the trusted research environment within the clinical environment. Hopefully, we will then be able to anonymous that to allow ah used by academics and possibly also, uh, partnering industry to do further data mining and tool development, which we could then further field test again using our real world data base of patients that will be continually, uh, updating in our system. In the cardiovascular group, we have what's called the bricks cohort, which means biomedical research. Informatics Center for Cardiovascular Science, which was done, started long time even before I joined, uh, in 2010 which has today almost captured about 10,000 patients arm or who come through to Glenn Field Hospital for various treatments or and even those who have not on. We asked for their consent to their blood for genetics, but also for blood tests, uh, genomics testing, but also image ing as well as other consent. Hable medical information s so far there about 10,000 patients and we've been trying to extract and curate their data accordingly. Again, a za reminder of what the strengths of Leicester are. We have one of the largest and busiest trust with the very large, uh, patient cohort Ah, focused dr at the university, which allows for chronic diseases such as heart disease. I just mentioned our efforts on heart disease, uh which are about 10,000 patients ongoing right now. But we would wish thio include further chronic diseases such as diabetes, respiratory diseases, renal disease and further to understand the multi modality between these diseases so that we can understand how they >>interact as well. Finally, I like to talk about the lesser life science accelerator as well. This is a new project that was funded by >>the U started this January for three years. I'm the director for this and all the groups within the College of Life Sciences that are involved with healthcare but also clinical work are involved. And through this we hope to support innovative industrial partnerships and collaborations in the region, a swells nationally and further on into internationally as well. I realized that today is a talked to um, or business and commercial oriented audience. And we would welcome interest from your companies and partners to come to Leicester toe work with us on, uh, clinical health care data and to drive our agenda forward for this so that we can enable innovative research but also product development in partnership with you moving forward. Thank you for your time.

(clicking) >> Hey, welcome back everybody, Jeff Frick here with theCUBE, we're in downtown San Francisco, the Hotel Nikko, it's International Women's Day, March 8th, stuff happening all around the world If you haven't seen it, jump on social. I think there's more hashtags than I even know what to do with. Thankfully we have 240 characters now. (Lisa laughs) But we're excited to be here at the Accenture event, it's Getting To Equal. Accenture's made a commitment to get to 50% gender equality by 2025, and this is a terrific event, 400 people, a lot of panels, a lot of real-world conversations. So we're excited to be here and our next guests are joining us, it's Lisa Bridgett, she's the COO of The Modist. Welcome. >> Thank you so much. >> And Amy Fuller, chief marketing and communications officer from Accenture. Thank you for having us. >> Thank you. >> So for folks that aren't familiar with The Modist, give us a little overview. >> Hi everyone, we are a year old today. >> A year today? >> A year old on International Women's Day. >> Happy birthday. >> Thank you so much. And we are a luxury ecommerce platform between Dubai and London, that has an assortment and a curation of luxury fashion, 150 brands, but all with the sensibility around modesty, so we think about hemlines, we think about opacity, we think about loose fits, all with luxury fashion on top of it, but making sure that we cater for our customers' needs in mind. >> How could this not have existed before, 366 days ago. >> This is the age old question, and our founder Ghizlan Guenez has been asked that time and time again. We have numerous places where you can go and find anything that will reveal, but there wasn't a one-stop place that really had curation and styling thought through from a modest perspective. And the customer base spans women who think about modesty from a religious perspective, businesswomen, curvier women, older women, high fashionistas that love a layered look, really, it's a niche, but it's massive. It's a massive global niche. >> Again, we're here, Macy's is right across the street, we're right downtown San Francisco, Nordstrom's, this is the big retail hub of San Francisco, one of the bigger retail hubs in the United States. And we know, we were talking before we turned on the cameras, I have teenage girls, and you go to the store, and you're like, "Oh my gosh, "is there nothing else that you can buy, "besides what's on there?" Why is this so underserved, or was underserved? >> I mean, I think that the fashion industry is going through a massive overhaul now, as one thinks about whether you're designing for aspiration, or whether you're designing and selling for really the reality of what the consumer segment is out there. And that goes for a Western woman, and when you think about the global fashion industry, are we thinking about fashion that resonates in India, or the Middle East, or in Asia, or are you sticking to a more conformed, idealized persona of what the customer is. And so this is very much on top of minds of all retail at the moment, and you will have seen shifts into larger sizes, very well-known fashion designers thinking about how do I design and cater for women that don't subscribe to an idealized format, it's quite a reflective thing that the fashion industry's going through at the moment. >> It's interesting Amy, a lot of conversations about communications, and objectives, not necessarily about what's comfortable and what I want to wear. As you look at this world and how it evolves, what's your take? Because, designing for an aspiration, that's a really interesting way, versus just designing practical clothes, we haven't seen the practical side. >> Well I think that what Lisa and her company are doing has potential to be quite transformational, and, I'll just plug a piece of research that we're publishing in honor of International Women's Day, which looked at, how do we get to equality in the workplace. Massive research, analytics, surveyed 22,000 working adults, men and women, in 34 countries, and what we were trying to get at, and did get at, are things about the culture. So what are the cultural factors that actually make a difference? So this is a very long way of getting to the point, but one of the questions we asked was, have you ever been asked to change clothing, hair, tattoos, et cetera, things about personal appearance to fit and conform in the workplace. A lot of people had been asked, sadly. And this was across 34 countries. But what we further found was, if you had not been asked to conform to the workplace, in other words, if you are allowed to dress as you wish to dress, that that was a factor that drove equality in the workplace. So, the idea that a woman with fabulous taste, who wishes to dress modestly, and Lisa described, there are a lot of people out there with that point of view, have a place to go to get absolutely stunning stuff, and dress as they wish to dress, and therefore, be the persona they want to be in the workplace is really powerful. And there were a lot of other factors, but that was the one that I found really, really, really interesting, and we found out before we had even invited Lisa to talk to us today, so it was a coming together of things that do matter. >> It's interesting because dress in the workplace, in the context of the workplace, is an interesting topic, if you go to Wall Street, everybody's got to buy the super nice suits, and then we got this kind of Casual Friday thing a few years ago, and people were very confused, how casual do I get on Casual Friday, and then, you've kind of got the whole joke about the baristas, with tats, and ripped up t-shirts-- >> There you go. >> And the getting that blended into traditional corporate cultures, little bit of a shocker. >> Well, there are a lot of questions that come into play, and I was having a long chat with one of my male colleagues, last night about how things have changed, and how much trickier it is to navigate, and he described that early on, cut to a couple of decades ago, men had to wear white shirts and ties at Accenture, and there was a young man who came to work in a blue Oxford, tie, suit, perfectly appropriate-- >> But blue, not white. >> On a Monday, yes, taken to task, and drawn aside, and said, "Blue shirts are for Fridays." >> Wow. >> So, from there, we go, and one of the things we really love about Accenture is that, you can wear what you want to wear, and it really has such a profound impact on how you feel in the workplace. And, if I can pull in a little AI stuff as well, when we look at AI, and the impact it will have on the workforce, what really, really matters is the things that humans are uniquely able to do. And what AI is uniquely able to deliver, that's the big win for all of us, for business, and when you think about the uniquely human characteristics, creativity, comfort that leads to creativity, and being able to freely think, is one of the most valuable qualities we have as humans. And, oddly, or not oddly, what you wear allows you to feel comfortable, or not. So coming back to what the Modist really provides women with great taste-- >> Great taste. >> To something that they feel comfortable with, and they can be more productive, and more successful. >> Yeah, I'll halo just a couple of those points. The first one is about choice. So, we were saying earlier on, we're in a luxurious environment where we are able to say, "You can choose," because it has not been that way and still continues not to be that way for many people. And that's why we really are for a mission and a purpose, because here we provide you with this element of choice, and you don't need to be ashamed of it, and you'd need to be proud of it. The second part was that modesty didn't need to equate to frumpiness, why can't I dress elegantly and magnificently beautifully, and there is something about dress, and fashion, that really provides a sense of identity, that's an age old desire for society, and for women, a lot, and this is a place where you can be modest, but luxuriously, and beautifully dressed up. And be proud of that, and not necessarily conformed into a box of frumpiness, or less stylish wear. >> The other big interchange, I think, which drove a lot of the traditional norms around clothing, was when you interface with a customer. It was how do you represent the customer, I'm sure that was a lot of what the story that you said, or in the investment bankers, where, we want you to have a certain look, because you're representing the company, it's that company's look that you are personifying when you go out and talk to your customer. Well today, a lot of customer interactions, let's take banking for one, is done on a mobile app. People don't go to the bank, I don't expect the guy to come out from the back with the beautiful pinstripe suit, who knows me anymore. I wonder, do you think that's had a part of the impact on this? Or just more of our acceptance in general of people that don't necessarily look like me? Whether that be in skin color, dress, the way they speak, et cetera. >> Yeah, those are great-- >> You go, and I've got a-- >> Well, I think it's both, and I love both of those points, more virtual interaction clearly takes clothing out of the equation, as well as a lot of other things, and that can be liberating, though I think we have a thirst for the in real life, and the person-to-person which isn't going away. But, I grew up in the advertising business, and, at ad agencies, they were pretty loose. But you always dressed for your client, so that certainly was a dynamic. But of course, now, dressing for your client doesn't imply a suit. And it makes it slightly more work, in fact, 'cause you have to do some anthropological study of what is the client environment like, and that how would you be most comfortable, and appropriate in that environment, so, certainly both of those factors come into play. >> And I feel the hyperdigitalization of the way we interact actually allows for more authenticity. Because you don't have to dress up in the suit that's the conform, you know. Your digital interaction and the work effect is happening, and so people behind that wanting to know who are you really? And authenticity is a way in which you get your own identifical message through, and dressing is one of the elements that comprises that. >> Alright, so before we wrap, Lisa, I want to get your take, so, you've been in business for a year-- >> Yes. >> Again, happy birthday. >> Thank you. >> If we get together, a year from now, you've, say, got over the hurdle, you're up, you're running, you're shipping, what are some of your objectives for the next year? >> Well, we have an amazing strategic roadmap ahead, we have got a very secret launch around product that will be coming out shortly, and that's something that we've been deep in. We are really developing the personalization and the AI component of our shopping experience, so we're really targeting what works best for this consumer, how and where, and that goes all the way from her marketing, through to her experience inside, and through to the retention side. And, just increasly, continually growing globally. We ship to 120 countries, our first market is UAE, our second is America, third is UK, fourth Saudi Arabia, fifth Canada, sixth Hong Kong. So we're global at the get-go, and it's just continuing to grow our customer base in this magnificently beautiful parts of the world that love modest fashion. >> Well, congratulations-- >> Thank you so much. >> And what a great story, we'll continue to watch it. >> Thank you so much. >> So Lisa, thank you, Amy, thanks for spending some time with us. >> Thanks so much! >> Alright, I'm Jeff Frick, you're watching theCUBE, We're at the Accenture International Women's Day event, in San Francisco, California. Thanks for watching. (mellow electronic music)