>>Hey everyone, welcome to the Cube's coverage of AWS Reinvent 2022. Lisa Martin here with you with Subaru ier, one of our alumni who's now the CEO of Aerospike. Sabu. Great to have you on the program. Thank you for joining us. >>Great as always, to be on the cube. Luisa, good to meet you. >>So, you know, every company these days has got to be a data company, whether it's a retailer, a manufacturer, a grocer, a automotive company. But for a lot of companies, data is underutilized, yet a huge asset that is value added. Why do you think companies are struggling so much to make data a value added asset? >>Well, you know, we, we see this across the board when I talk to customers and prospects. There's a desire from the business and from it actually to leverage data to really fuel newer applications, newer services, newer business lines, if you will, for companies. I think the struggle is one, I think one the, you know, the plethora of data that is created, you know, surveys say that over the next three years data is gonna be, you know, by 2025, around 175 zetabytes, right? A hundred and zetabytes of data is gonna be created. And that's really a, a, a growth of north of 30% year over year. But the more important, and the interesting thing is the real time component of that data is actually growing at, you know, 35% cagr. And what enterprises desire is decisions that are made in real time or near real time. >>And a lot of the challenges that do exist today is that either the infrastructure that enterprises have in place was never built to actually manipulate data in real time. The second is really the ability to actually put something in place which can handle spikes yet be cost efficient if you'll, so you can build for really peak loads, but then it's very expensive to operate that particular service at normal loads. So how do you build something which actually works for you, for both you, both users, so to speak? And the last point that we see out there is even if you're able to, you know, bring all that data, you don't have the processing capability to run through that data. So as a result, most enterprises struggle with one, capturing the data, you know, making decisions from it in real time and really operating it at the cost point that they need to operate it at. >>You know, you bring up a great point with respect to real time data access. And I think one of the things that we've learned the last couple of years is that access to real time data, it's not a nice to have anymore. It's business critical for organizations in any industry. Talk about that as one of the challenges that organizations are facing. >>Yeah. When, when, when we started Aerospike, right when the company started, it started with the premise that data is gonna grow, number one, exponentially. Two, when applications open up to the internet, there's gonna be a flood of users and demands on those applications. And that was true primarily when we started the company in the ad tech vertical. So ad tech was the first vertical where there was a lot of data both on the supply side and the demand side from an inventory of ads that were available. And on the other hand, they had like microseconds or milliseconds in which they could make a decision on which ad to put in front of you and I so that we would click or engage with that particular ad. But over the last three to five years, what we've seen is as digitization has actually permeated every industry out there, the need to harness data in real time is pretty much present in every industry. >>Whether that's retail, whether that's financial services, telecommunications, e-commerce, gaming and entertainment. Every industry has a desire. One, the innovative companies, the small companies rather, are innovating at a pace and standing up new businesses to compete with the larger companies in each of these verticals. And the larger companies don't wanna be left behind. So they're standing up their own competing services or getting into new lines of business that really harness and are driven by real time data. So this compelling pressures, one, the customer exp you know, customer experience is paramount and we as customers expect answers in, you know, an instant in real time. And on the other hand, the way they make decisions is based on a large data set because you know, larger data sets actually propel better decisions. So there's competing pressures here, which essentially drive the need. One from a business perspective, two from a customer perspective to harness all of this data in real time. So that's what's driving an inces need to actually make decisions in real or near real time. >>You know, I think one of the things that's been in short supply over the last couple of years is patients we do expect as consumers, whether we're in our business lives, our personal lives that we're going to be getting, be given information and data that's relevant, it's personal to help us make those real time decisions. So having access to real time data is really business critical for organizations across any industries. Talk about some of the main capabilities that modern data applications and data platforms need to have. What are some of the key capabilities of a modern data platform that need to be delivered to meet demanding customer expectations? >>So, you know, going back to your initial question Lisa, around why is data really a high value but underutilized or underleveraged asset? One of the reasons we see is a lot of the data platforms that, you know, some of these applications were built on have been then around for a decade plus and they were never built for the needs of today, which is really driving a lot of data and driving insight in real time from a lot of data. So there are four major capabilities that we see that are essential ingredients of any modern data platform. One is really the ability to, you know, operate at unlimited scale. So what we mean by that is really the ability to scale from gigabytes to even petabytes without any degradation in performance or latency or throughput. The second is really, you know, predictable performance. So can you actually deliver predictable performance as your data size grows or your throughput grows or your concurrent user on that application of service grows? >>It's really easy to build an application that operates at low scale or low throughput or low concurrency, but performance usually starts degrading as you start scaling one of these attributes. The third thing is the ability to operate and always on globally resilient application. And that requires a, a really robust data platform that can be up on a five, nine basis globally, can support global distribution because a lot of these applications have global users. And the last point is, goes back to my first answer, which is, can you operate all of this at a cost point? Which is not prohibitive, but it makes sense from a TCO perspective. Cuz a lot of times what we see is people make choices of data platforms and as ironically their service or applications become more successful and more users join their journey, the revenue starts going up, the user base starts going up, but the cost basis starts crossing over the revenue and they're losing money on the service, ironically, as the service becomes more popular. So really unlimited scale, predictable performance always on, on a globally resilient basis and low tco. These are the four essential capabilities of any modern data platform. >>So then talk to me with those as the four main core functionalities of a modern data platform. How does aerospace deliver that? >>So we were built, as I said, from the from day one to operate at unlimited scale and deliver predictable performance. And then over the years as we work with customers, we build this incredible high availability capability which helps us deliver the always on, you know, operations. So we have customers who are, who have been on the platform 10 years with no downtime for example, right? So we are talking about an amazing continuum of high availability that we provide for customers who operate these, you know, globally resilient services. The key to our innovation here is what we call the hybrid memory architecture. So, you know, going a little bit technically deep here, essentially what we built out in our architecture is the ability on each node or each server to treat a bank of SSDs or solid state devices as essentially extended memory. So you're getting memory performance, but you're accessing these SSDs, you're not paying memory prices, but you're getting memory performance as a result of that. >>You can attach a lot more data to each node or each server in your distributed cluster. And when you kind of scale that across basically a distributed cluster you can do with aerospike, the same things at 60 to 80% lower server count and as a result 60 to 80% lower TCO compared to some of the other options that are available in the market. Then basically, as I said, that's the key kind of starting point to the innovation. We layer around capabilities like, you know, replication change, data notification, you know, synchronous and asynchronous replication. The ability to actually stretch a single cluster across multiple regions. So for example, if you're operating a global service, you can have a single aerospace cluster with one node in San Francisco, one northern New York, another one in London. And this would be basically seamlessly operating. So that, you know, this is strongly consistent. >>Very few no SQL data platforms are strongly consistent or if they are strongly consistent, they will actually suffer performance degradation. And what strongly consistent means is, you know, all your data is always available, it's guaranteed to be available, there is no data lost anytime. So in this configuration that I talked about, if the node in London goes down, your application still continues to operate, right? Your users see no kind of downtime and you know, when London comes up, it rejoins the cluster and everything is back to kind of the way it was before, you know, London left the cluster so to speak. So the op, the ability to do this globally resilient, highly available kind of model is really, really powerful. A lot of our customers actually use that kind of a scenario and we offer other deployment scenarios from a higher availability perspective. So everything starts with HMA or hybrid memory architecture and then we start building out a lot of these other capabilities around the platform. >>And then over the years, what our customers have guided us to do is as they're putting together a modern kind of data infrastructure, we don't live in a silo. So aerospace gets deployed with other technologies like streaming technologies or analytics technologies. So we built connectors into Kafka, pulsar, so that as you're ingesting data from a variety of data sources, you can ingest them at very high ingest speeds and store them persistently into Aerospike. Once the data is in Aerospike, you can actually run spark jobs across that data in a, in a multithreaded parallel fashion to get really insight from that data at really high, high throughput and high speed, >>High throughput, high speed, incredibly important, especially as today's landscape is increasingly distributed. Data centers, multiple public clouds, edge IOT devices, the workforce embracing more and more hybrid these days. How are you ex helping customers to extract more value from data while also lowering costs? Go into some customer examples cause I know you have some great ones. >>Yeah, you know, I think we have, we have built an amazing set of customers and customers actually use us for some really mission critical applications. So, you know, before I get into specific customer examples, let me talk to you about some of kind of the use cases which we see out there. We see a lot of aerospace being used in fraud detection. We see us being used in recommendations and since we use get used in customer data profiles or customer profiles, customer 360 stores, you know, multiplayer gaming and entertainment, these are kind of the repeated use case digital payments. We power most of the digital payment systems across the globe. Specific example from a, from a specific example perspective, the first one I would love to talk about is PayPal. So if you use PayPal today, then you know when you actually paying somebody your transaction is, you know, being sent through aero spike to really decide whether this is a fraudulent transaction or not. >>And when you do that, you know, you and I as a customer not gonna wait around for 10 seconds for PayPal to say yay or me, we expect, you know, the decision to be made in an instant. So we are powering that fraud detection engine at PayPal for every transaction that goes through PayPal before us, you know, PayPal was missing out on about 2% of their SLAs, which was essentially millions of dollars, which they were losing because, you know, they were letting transactions go through and taking the risk that it, it's not a fraudulent transaction with the aerospace. They can now actually get a much better sla and the data set on which they compute the fraud score has gone up by, you know, several factors. So by 30 x if you will. So not only has the data size that is powering the fraud engine actually grown up 30 x with Aerospike. Yeah. But they're actually making decisions in an instant for, you know, 99.95% of their transactions. So that's, >>And that's what we expect as consumers, right? We want to know that there's fraud detection on the swipe regardless of who we're interacting with. >>Yes. And so that's a, that's a really powerful use case and you know, it's, it's a great customer, great customer success story. The other one I would talk about is really Wayfair, right? From retail and you know, from e-commerce. So everybody knows Wayfair global leader in really, you know, online home furnishings and they use us to power their recommendations engine and you know, it's basically if you're purchasing this, people who bought this but also bought these five other things, so on and so forth, they have actually seen the card size at checkout go by up to 30% as a result of actually powering their recommendations in G by through Aerospike. And they, they were able to do this by reducing the server count by nine x. So on one ninth of the servers that were there before aerospace, they're now powering their recommendation engine and seeing card size checkout go up by 30%. Really, really powerful in terms of the business outcome and what we are able to, you know, drive at Wayfair >>Hugely powerful as a business outcome. And that's also what the consumer wants. The consumer is expecting these days to have a very personalized, relevant experience that's gonna show me if I bought this, show me something else that's related to that. We have this expectation that needs to be really fueled by technology. >>Exactly. And you know, another great example you asked about, you know, customer stories, Adobe, who doesn't know Adobe, you know, they, they're on a, they're on a mission to deliver the best customer experience that they can and they're talking about, you know, great customer 360 experience at scale and they're modernizing their entire edge compute infrastructure to support this. With Aerospike going to Aerospike, basically what they have seen is their throughput go up by 70%, their cost has been reduced by three x. So essentially doing it at one third of the cost while their annual data growth continues at, you know, about north of 30%. So not only is their data growing, they're able to actually reduce their cost to actually deliver this great customer experience by one third to one third and continue to deliver great customer 360 experience at scale. Really, really powerful example of how you deliver Customer 360 in a world which is dynamic and you know, on a dataset which is constantly growing at north, north of 30% in this case. >>Those are three great examples, PayPal, Wayfair, Adobe talking about, especially with Wayfair when you talk about increasing their cart checkout sizes, but also with Adobe increasing throughput by over 70%. I'm looking at my notes here. While data is growing at 32%, that's something that every organization has to contend with data growth is continuing to scale and scale and scale. >>Yep. I, I'll give you a fun one here. So, you know, you may not have heard about this company, it's called Dream 11 and it's a company based out of India, but it's a very, you know, it's a fun story because it's the world's largest fantasy sports platform and you know, India is a nation which is cricket crazy. So you know, when, when they have their premier league going on, you know, there's millions of users logged onto the dream alone platform building their fantasy lead teams and you know, playing on that particular platform, it has a hundred million users, a hundred million plus users on the platform, 5.5 million concurrent users and they have been growing at 30%. So they are considered a, an amazing success story in, in terms of what they have accomplished and the way they have architected their platform to operate at scale. And all of that is really powered by aerospace where think about that they are able to deliver all of this and support a hundred million users, 5.5 million concurrent users all with you know, 99 plus percent of their transactions completing in less than one millisecond. Just incredible success story. Not a brand that is you know, world renowned but at least you know from a what we see out there, it's an amazing success story of operating at scale. >>Amazing success story, huge business outcomes. Last question for you as we're almost out of time is talk a little bit about Aerospike aws, the partnership GRAVITON two better together. What are you guys doing together there? >>Great partnership. AWS has multiple layers in terms of partnerships. So you know, we engage with AWS at the executive level. They plan out, really roll out of new instances in partnership with us, making sure that, you know, those instance types work well for us. And then we just released support for Aerospike on the graviton platform and we just announced a benchmark of Aerospike running on graviton on aws. And what we see out there is with the benchmark, a 1.6 x improvement in price performance and you know, about 18% increase in throughput while maintaining a 27% reduction in cost, you know, on graviton. So this is an amazing story from a price performance perspective, performance per wat for greater energy efficiencies, which basically a lot of our customers are starting to kind of talk to us about leveraging this to further meet their sustainability target. So great story from Aero Aerospike and aws, not just from a partnership perspective on a technology and an executive level, but also in terms of what joint outcomes we are able to deliver for our customers. >>And it sounds like a great sustainability story. I wish we had more time so we would talk about this, but thank you so much for talking about the main capabilities of a modern data platform, what's needed, why, and how you guys are delivering that. We appreciate your insights and appreciate your time. >>Thank you very much. I mean, if, if folks are at reinvent next week or this week, come on and see us at our booth. We are in the data analytics pavilion. You can find us pretty easily. Would love to talk to you. >>Perfect. We'll send them there. So Ira, thank you so much for joining me on the program today. We appreciate your insights. >>Thank you Lisa. >>I'm Lisa Martin. You're watching The Cubes coverage of AWS Reinvent 2022. Thanks for watching.

>>from around the globe. It's the Cube covering Google Cloud. Next on Air 20. Hi, I'm Stew Minimum And and this is the Cube's coverage of Google Cloud next 20 on air, Of course. Last year we were all in person in San Francisco. This year it's an online experience. It's actually spanning many weeks and this week when we're releasing the Cube interviews, talking about application modernization, happy to welcome back program two of our Cube alumni. Chris Well, I've got Aparna Sinha, Uh, who is the director of product management, and joining her is Pali Bhat, who's the vice president of product and design, both with Google Cloud Poly. Welcome back. Thanks so much for joining us. >>Thank you. Good to be here. >>Well, so it goes without saying it. That 2020 has had quite a lot of changes. Really affect it. Start with you. You know, obviously there's been a lot of discussion is what is the impact of the global pandemic? The ripple in the economy on cloud. So I would love to hear a little bit. You know what you're hearing from your customers. What? That impact has been on on you and your business. >>Yes to thank thank you for asking as I look at our customers, what's been most inspiring for me to see is how organizations and the people in those organizations are coming together to help each other during this unprecedented event. And one of the things I wanted to highlight is, as we all adjust to this sort of new normal, there are two things that I keep seeing across every one of our customers. Better operation efficiency, with the focus on cost saving is something that's a business imperative and has drawn urgency. And the second bit is an increased focus on agility and business innovation. In the current atmosphere, where digital has truly become gone from being one of the channels being D channel, we're seeing our customers respond by being more innovative and reaching their customers in the way that they want to be rich. And that's been, for me personally, very inspiring to see. And we turned on Google Cloud to be a part of helping our customers in this journey in terms of our business itself. We're seeing tremendous momentum around our organization business because it plays directly into these two business imperatives around operational efficiency, cost saving and, of course, business innovation and agility. In Q two of 2020 we saw more than 100,000 companies use our application modernization platform across G ke and those cloud functions Cloud Run and our developers tools. So we've been, uh, just tagged with the response of how customers are using our tools in order to help them run their businesses, operate more efficiently and be more innovative on behalf of their customers. So we're seeing customers use everything from building mission critical applications who then securing, migrating and then operating our services. And we've also seen that customers get tremendous benefits. We've seen up to a 35% increase simply by using our own migration tools. And we've also seen it up to 75% improvement to all of the automation and re platform ing that they can do with our monetization platform. That's been incredible. What I do want to do. Those have a partner chime in on some of the complexity that these customers are seeing and how we're going about trying to address that >>Yes, eso to help our customers with the application modernization journey. Google Cloud really offers three highly differentiated capabilities. Us to the first one is really providing a consistent development and operations experience, and this is really important because you want the same experience, regardless of whether you're running natively in Google Cloud or you're running across clouds or you're running hybrid or you're running at the edge. And I think this is a truly unique differentiator off what we offer. Secondly, we really give customers and their developers industry leading guidance. And this is particularly important because there's a set of best practices on how you do development, how you run these applications, how you operate them in production for high reliability, a exceptional security staff, the stature and for the maximum developer efficiency on. And we provide the platform and the tooling to do that so that it can be customized to it's specific customers needs and their specific place on that modernization journey. And then the third thing on and I think this is incredibly important as well is that we would ride a data driven approach, a data driven optimization and benchmarking approach so that we can tell you where you are with regard to best practice and then help you move towards best practice, no matter where you're starting. >>Yeah, well, thank you, Aparna and Polly definitely resonates with what we're hearing. You know, customers need to be data driven. And then there's the imperative Now that digital movement Pali last year at the show, of course, Antos was, you know, really the talk of the conference years gone by. We know things move really fast, so if you could, you know, probably don't have time to get all of the news, but share with us the updates what differentiated this year along from a new standpoint, >>Yeah, So we've got tremendous set off improvements to the platform. And one of the things that I wanted to just share was that our customers as they actually migrate on to onto the cloud and begin the modernization journeys in their digital transformation programs. What we're seeing over and over is those customers that start with the platform as opposed to an individual application, are set up for success in the future. The platform, of course, is an tos where your application modernization journey begins. In terms of updates, we're gonna share a series off updates in block post, etcetera. I just want to highlight a few. We're sharing their availability off Antos for their middle swathe things that our customers have been asking about. And now our customers get to run on those on Prem and at the edge without the need for a hyper visor. What this does is helps organizations minimize unnecessary overhead and ultimately unlock all of the new cloud and edge use case. The second bit is we're not in the GF our speech to text on prem capability, but this is our first hybrid AI capability. So customers like Iron Mountain get to use hybrid AI, so they have full control of the infrastructure and have control off their data so they can implement data residency and compliance while still leveraging all of Google Cloud AI capabilities. Third services identity again. This extends existing identity solutions so that you can seamlessly work on and those workloads again. This is going to be generally available for on premise customers and better for Antos on AWS, and you're going to see more and more customers be able to leverage their existing identity investments while still getting the consistency that Anton's provides across environments. In the last one that I like to highlight is on those attached clusters, which lets customers bring any kubernetes conforming cluster on Toronto's and still take advantage of the advanced capabilities that until provides like declarative configurations and service automation. So one of the customers I just want to call out is Cold just built it. Entire hybrid cloud strategy on Anton's Day began with the platform first, and now we're seeing a record number of customers on Cold Start camaraderie. Take advantage of Mantel's tempting. With Macquarie Bank played, there's a number of use cases. I am particularly excited about major league baseball. I'm a big fan of baseball, and Major League Baseball is now using and those for 2020 season and all of the stadium across, trusting a large amount of data and gives them the capability to get those capabilities in stadiums very, really acceptable. All of those >>Okay, quick, quick. Follow up on that and those attached clusters because it was one of the questions I had last year. Google Cloud has partnerships with VM Ware for what they're doing. You know, Red Hat and Pivotal also is part of the VM Ware families, and they have their own kubernetes offering. So should I be thinking of this as a management capability that's similar to like what? What Andrew does Or maybe as your arca, Or is it just a kind of interoperability piece? How do we understand how these multiple kubernetes fit together? >>Yeah. So what we've done with Antos has really taken the approach that we need to help our customers are made and manage the infrastructure to specifically what Antos attach clusters gives our customers is they can have any kubernetes cluster as long as it's kubernetes conformance, they can benefit from all of the things that we provide in terms of automation. One of the challenges, of course, is you know, those two is configuring these very, very large instances in walls. A lot of handcrafting today we can provide declarative configuration. So you automate all of that. So think of this as configures code I think of this is infrastructure scored management scored. We're providing that service automation layer on top of any kubernetes conforming cluster with an tools. >>Great. Alright, uh, it's at modernization weeks, so Ah, partner, maybe bring us in aside. You were talking about your customers and what their what they're doing to modernize what's new that they should be aware of this year. >>Yeah, so So, First of all, you know, our mission is really to accelerate innovation in every organization through making their developers more productive as well as automating their operations. And this is something that is resonating even more in these times. Specifically, I think the biggest news that we have is really around, how we're going to help companies get started with the application modernization so that they can maximize the impact of their modernization efforts. And to do this, we're introducing what we're calling. The Google Cloud Application Modernization program or a Google camp for short on Google Camp has three pieces. It has an assessment, which is really data driven and fact based. It's a baseline assessment that helps organizations understand where they are in terms of their maturity with application modernization. Secondly, we give them a blueprint. This is something that is, is it encapsulates a specific set of best practices, proven best practices from development to security to operations, and it's something that they can put into practice and implement immediately. These practices, they cover the entire application lifecycle from writing the code to the See I CD to running it and operating it for maximum reliability and security. And then the third aspect, of course, is the application platform. And this is a modern platform, but also extremely extensible. And, as you know, it spans across clouds on this enables organizations to build, run and secure and, of course, manage both legacy as well as new applications. And the good news, of course, here is you know, this is a time tested platform. It's something that we use internally as well. For our Cloud ML services are being query omni service capability as well as for apogee, hot hybrid and many more at over time. So with the Google campus really covered all aspects of the application lifecycle. And we think it's extremely important for enterprises to have this capability. >>Yeah, so a party when you talk about the extent ability, I would expect that Google Cloud Run is one of the options there to help give us a bridge to get to server list. If that's where customers looking to my right on >>that, that's rights to the camp program provides is holistic, and it brings together many of our capabilities. So Cloud Code Cloud See I CD Cloud Run, which is our server less offering and also includes G ki e and and those best practices. Because customers for their applications, they're usually using multiple platforms. Now, in the case of Cloud Run, in particular, I want to highlight that there's been a lot of interest in the serverless capability during this last few months. In particular, I think, disproportionate amount of interest and server lists on container Native. In fact, according to the CNC F 2020 State of Cloud Native Development Report, you might have seen that, you know, they noted that 2.7 million cloud native developers are using kubernetes and four million are using serverless architectures or cloud functions, and that about 60% of back and developers are now using containers. So this just points to the the usage that was happening already and is now really disproportionately accelerated. In our case, you know, we've we've worked with several customers at the New York State Department and Media Market. Saturn are two that are really excellent stories with the New York State Department. They had a unemployment claims crisis. There was a lot. Ah, volume. That was difficult for their application to handle. And so we worked with them to re architect their application as a set of micro services on Google Cloud on our public sector team of teamed up with them to roll out a new unemployment website in record time. That website was able to handle the 1600% increase in Web traffic compared to a typical week. And this is very much do, too, the dev ops tooling that we provided and we worked with them on and then with Media market Saturn. This is really an excellent example in EMEA based example of a retailer that was able to achieve an eight X increase in speed as well as a 40% cost reduction. And these are really important metrics in these times in particular because for a retailer in the Cove in 19 crisis, to be able to bring new applications and new features to the hands of their customers is ultimately something that impacts their business is extremely valuable. >>Yeah, you think you bring up a really great point of partner when I traditionally think of application modernization. Maybe I've been in the space to long. But it is. Simplicity is not. The first thing that comes to mind is probably pointed out right now. There's an imperative people need to move fast, so I want to throw it out to both of you. How is Google's trying to make sure that, you know, in these uncertain times that customers can move fast and that with all these technology options that it could be just a little bit simpler? >>Yeah, I think I just, uh you know, start off by saying the first thing we've done is build all of our services from the ground up with automation, simplicity and agility in mind. So we've designed for development teams and operations teams be able to take these solutions and get productive with them right away. In addition, we understand that some of our largest customers actually need dedicated program where they can actually assess where they are and then map out a plan for incremental improvement so they can get on their journey to application modernization. But do it with the highest our way. And that was Google camp that apartment talked about ultimately at Google Cloud. Our mission, of course, is to accelerate innovation. Every organization toe hold developer velocity improvements, but also giving them the operation automation that we talked about with that application modernization platform. So we're very excited to be able to do this with every organization. >>Great. Well, Aparna, I'll let you have the final word Is the application modernization week here at Google Cloud. Next online, you can have the final take away for customers. >>Well, thank you, cio. You know, we are extremely passionate about developers on. We want to make sure that it is easy for anyone, anywhere to be able to get started with development as well as to have a path to, uh, accelerated path to production for their applications. So some of what we've done in terms of simplicity, which, as you said is extremely important in this environment, is to really make it easy to get started on. Some of the announcements are around build packs and the integration of cloud code are plug ins to the development environment directly into our serverless environment. And that's the type of thing that gets me excited. And I think I'm very passionate about that because it's something that applies to everyone. Uh, you know, regardless of where they are or what type of person they are, they can get started with development. And that can be a path to economic renewal and growth not just for companies, but for individuals. And that's a mission that we're extremely passionate about. Google Cloud >>Apartment Poly Thank you so much for sharing all the updates. Congratulations to the team. And definitely great to hear about how you're helping customers in these challenging times. >>Thank you for having us on. >>Thank you. So great to see you again. >>Alright. Stay tuned for more coverage from stew minimum and, as always, Thank you for watching the Cube. Yeah, yeah.

>> Announcer: Live from Las Vegas, it's theCUBE, covering AWS re:Invent 2019. Brought to you by Amazon Web Services and Intel along with its ecosystem partners. >> Welcome back to the eighth year of AWS re:Invent. It's 2019. There's over 60,000 in attendance. Seventh year of theCUBE. Wall-to-wall coverage, covering all the angles of this broad and massively-growing ecosystem. I am Stu Miniman. My co-host is Justin Warren, and one of our Cube alumni are back on the program. Ramin Sayar, who is the president and CEO of Sumo Logic. >> Stu: Booth always at the front of the expo hall. I think anybody that's come to this show has one of the Sumo-- >> Squishies. >> Stu: Squish dolls there. I remember a number of years you actually had live sumos-- >> Again this year. >> At the event, so you know, bring us, the sixth year you've been at the show, give us a little bit of the vibe and your experience so far. >> Yeah, I mean, naturally when you've been here so many times, it's interesting to be back, not only as a practitioner who's attended this many years ago, but now as a partner of AWS, and seeing not only our own community growth in terms of Sumo Logic, but also the community in general that we're here to see. You know, it's a good mix of practitioners and business folks from DevOps to security and much, much more, and as we were talking right before the show, the vendors here are so different now then it was three years go, let alone six years ago. So, it's nice to see. >> All right, a lot of news from Amazon. Anything specific jump out from you from their side, or I know Sumo Logic has had some announcements this week. >> Yeah, I mean, like, true to Amazon, there's always a lot of announcements, and, you know, what we see is customers need time to understand and digest that. There's a lot of confusion, but, you know, selfishly speaking from the Sumo side, you know, we continue to be a strong AWS partner. We announced another set of services along with AWS at this event. We've got some new competencies for container, because that's a big aspect of what customers are doing today with microservices, and obviously we announced some new capabilities around our security intelligence capabilities, specifically for CloudTrail, because that's becoming a really important aspect of a lot of customers maturation of cloud and also operating in the cloud in this new world. >> Justin: So walk us through what customers are using CloudTrail to do, and how the Sumo Logic connection to CloudTrail actually helps them with what they're trying to do. >> Well, first and foremost, it's important to understand what Sumo does and then the context of CloudTrail and other services. You know, we started roughly a decade ago with AWS, and we built and intelligence platform on top of AWS that allows us to deal with the vast amount of unstructured data in specific use cases. So one very common use case, very applicable to the users here, is around the DevOps teams. And so, the DevOps teams are having a much more complicated and difficult time today understanding, ascertaining, where trouble, where problems reside, and how to go troubleshoot those. It's not just about a siloed monitoring tool. That's just not enough. It doesn't the analytics or intelligence. It's about understanding all the data, from CloudTrail, from EC2, and non-AWS services, so you can appropriately understand these new modern apps that are dependent on these microservices and architectures, and what's really causing the performance issue, the availability issue, and, God forbid, a security or breach issue, and that's a unique thing that Sumo provides unlike others here. >> Justin: Yeah, now I believe you've actually extended the Sumo support beyond CloudTrail and into some of the Kubernetes services that Amazon offers like AKS, and you also, I believe it's ESC FireLens support? >> Ramin: Yeah, so, and that's just a continuation of a lot of stuff we've done with respect to our analytics platform, and, you know, we introduced some things earlier this year at re:Inforce with AWS as well so, around VPC Flow Logs and the like, and this is a continuation now for CloudTrail. And really what it helps our customers and end users do is better better and more proactively be able to detect potential issues, respond to those security issues, and more importantly, automate the resolution process, and that's what's really key for our users, because they're inundated with false positives all the time whether it's on the ops side let alone the security side. So Sumo Logic is very unique back to our value prop, but providing a horizontal platform across all these different use cases. One being ops, two being cybersecurity and threat, and three being line-of-business users who are trying to understand what their own users on their digital apps are doing with their services and how to better deliver value. >> Justin: Now, automation is so important when you've got this scope and scale of cloud and the pace of innovation that's happening with all the technology that's around us here at the show, so the automation side of things I think is a little bit underappreciated this year. We're talking about transformation and we're talking about AI and ML. I think, with the automation piece, is one thing that's a little bit underestimated from this year's show. What do you think about that? >> Yeah, I mean, our philosophy all along has been, you can't automate without AI and ML, and it's proven fact that, you know, by next year the machine data growth is going to be 16 zettabytes. By 2025, it's going to be 75 zettabytes of data. Okay, while that's really impressive in terms of volume of data, the challenge is, the tsunami of data that's being generated, how to go decipher what's an important aspect and what's not an important aspect, so you first have to understand from the streaming data services, how to be able to dynamically and schema on read, be able to analyze that data, and then be able to put in context to those use cases I talked about, and then to drive automation remediation, so it's a multifaceted problem that we've been solving for nearly a decade. In a given day, we're analyzing several hundred petabytes of data, right? And we're trying to distill it down to the most important aspects for you, for your particular role and your responsibility. >> Stu: Yeah, um, we've talked a lot about transformation at this show, and one of the big challenges for customers is, they're going through that application modernization journey. I wonder if you could bring us inside some of your customers, you know, where are they having success, where are some of the bottlenecks slowing them down from moving along on this transformation journey? >> Yeah, so, it's interesting because, whether you're a cloud-native company like Sumo Logic or you're aspiring to be a cloud-native company or a cloud-first project going through migration, you have similar problems. It's now become a machine-scale problem, not a human-scale problem, back to the data growth, right? And so, some of our customers, regardless of their maturation, are really trying to understand, you know, as they embark on these digital transformations, how do they solve, what we call, the intelligence gap? And that is, because there's so much silos across the enterprise organizations today, across development, operations, IT, security, lines of business, in its context, in its completeness, it's creating more complexity for our customers. So, what Sumo tries to help solve, do, is, solve that intelligence gap in this new intelligence economy by providing an intelligence platform we call "continuous intelligence". So what do customers do? So, some of our customers use Sumo to monitor and troubleshoot their cloud workloads. So whether it's, you know, the Netflix team themselves, right, because they're born and bred in the cloud or it's Hudl, who's trying to provide, you know, analytics and intelligence for players and coaches, right, to insurance companies that are going through the migration journey to the cloud, Hartford Insurance, New York Life, to sports and media companies, Major League Baseball, with the whole cyber SOC, and what they're trying to do there on the backs of Sumo, to even trucking companies like Packard, who's trying to do driverless, autonomous cars. It doesn't matter what industry you're in, everyone is trying to do through the digital transformation or be disrupted. Everyone's trying to gain that intelligence or not just be left behind but be lapped, and so what Sumo really helps them do is provide one single intelligence platform across dev, sec, and ops, bringing these teams together to be able to collaborate much more efficiently and effectively through the true multi-tenant SaaS platform that we've optimized for 10 years on AWS. >> Justin: So we heard from Andy yesterday that one of the important ways to drive that transformational change is to actually have the top-down support for that. So you mentioned that you're able to provide that one layer across multiple different teams who traditionally haven't worked that well together, so what are you seeing with customers around, when they put in Sumo Logic, where does that transformational change come from? Are we seeing the top-down driven change? Is that were customers come from, or is it a little bit more bottom-up, were you have developers and operations and security all trying to work together, and then that bubbles up to the rest of the organization? >> Ramin: Well, it's interesting, it's both for us because a lot of times, it depends on the size of the organization, where the responsibilities reside, so naturally, in a larger enterprise where there's a lot of forces of mass because of the different siloed organizations, you have to, often times, start with the CISO, and we make sure the CISO is a transformation agent, and if they are the transformation agent, then we partner with them to really help get a handle and control on their cybersecurity and threat, and then he or she typically sponsors us into other parts of the line of business, the DevOps teams, like, for example, we've seen with Hartford Insurance, right, or that we saw with F5 Networks and many more. But then, there's a flip side of that where we actually start in, let's use another example, uh, you know, with, for example, Hearst Media, right. They actually started because they were doing a lift-and-shift to the cloud and their DevOps team, in one line of business, started with Sumo, and expanded the usage and growth. They migrated 32 applications over to AWS, and then suddenly the security teams got wind of it and then we went top-down. Great example of starting, you know, bottom-up in the case of Hearst or top-down in the case of other examples. So, the trick here is, as we look at embarking upon these journeys with our customers, we try to figure out which technology partners are they using. It's not only in the cloud provider, but it's also which traditional on-premise tools versus potentially cloud-native services and SaaS applications they're adopting. Second is, which sort of organizational models are they adopting? So, a lot of people talk about DevOps. They don't practice DevOps, and then you can understand that very quickly by asking them, "What tools are you using?" "Are you using GitHub, Jenkins, Artifactory?" "Are you using all these other tools, "and how are you actually getting visibility "into your pipeline, and is that actually speeding "the delivery of services and digital applications, "yes or no?" It's a very binary answer, and if they can't answer that, you know they're aspiring to be. So therefore, it's a consultative sale for us in that mode. If they're already embarking upon that, however, then we use a different approach, where we're trying to understand how they're challenged, what they're challenged with, and show other customers, and then it's really more of a partnership. Does that makes sense? >> Justin: Yeah, makes perfect sense to me. >> So, one of the debates we had coming into this show is, a lot of discussion at multicloud around the industry. Of course, Amazon doesn't talk specifically about multicloud all that well. If you look historically, attempts to manage lots of different environments under a single pane of glass, we always say, "pane is spelled P-I-A-N", when you try to do that. There's been great success. If you look at VMware in the data center, VMware didn't cover the entire environment, but vCenter was the center of your, you know, admin's world, and you would edge cases to manage some of the other environments here. Feels that AWS is extending their footprint with thing like Outposts and the environments, but there are lots of things that won't be on Amazon, whether it be a second cloud provider, my legacy data center pieces, or anything else there. Sounds like you touch many of the pieces, so I'm curious if you, just, weigh in on what you hear from customers, how they get their arms around the heterogeneous mess that IT traditionally is, and what we need to do as an industry to make things better. >> You know, for a long time, many companies have been bi-modal, and now they're tri-modal, right, meaning that, you know, they have their traditional and their new aspects of IT. Now they're tri-modal in the sense of, now they have a third leg of that complexity in stool, which is public cloud, and so, it's a reality regardless of Amazon or GCP or Azure, that customers want flexibility and choice, and if fact, we see that with our own data. Every year, as you guys well know, we put out an intelligence report that actually shows year-over-year, the adoption of not only various technologies, but adoption of technologies used across one cloud provider versus multicloud providers, and earlier this year in September when we put the new release of the report out, we saw that year-over-year, there was more than 2x growth in the user of Kubernetes in production, and it was almost three times growth year-over-year in use of Kubernetes across multiple cloud providers. That tells you something. That tells you that they don't want lock-in. That tells you that they also want choice. That tells you that they're trying to abstract away from the IaaS layer, infrastructure-as-a-service layer, so they have portability, so to speak, across different types of providers for the different types of workload needs as well as the data sovereignty needs they have to constantly manage because of regulatory requirements, compliance requirements and the like. And so, this is actually it benefits someone like Sumo to provide that agnostic platform to customers so they can have the choice, but also most importantly, the value, and this is something that we announced also at this event where we introduced editions to our Cloud Flex licensing model that allows you to not only address multi-tiers of data, but also allows you to have choice of where you run those workloads and have choice for different types of data for different types of use cases at different cost models. So again, delivering on that need for customers to have flexibility and choice, as well as, you know, the promise of options to move workloads from provider to provider without having to worry about the headache of compliance and audit and security requirements, 'cause that's what Sumo uniquely does versus point tools. >> Well, Ramin, I think that's a perfect point to end on. Thank you so much for joining us again. >> Thanks for having me. >> Stu: And looking forward to catching up with Sumo in the future. >> Great to be here. >> All right, we're at the midway point of three days, wall-to-wall coverage here in Las Vegas. AWS re:Invent 2019. He's Justin Warren, I'm Stu Miniman, and you're watching theCUBE. (upbeat music)

>> Announcer: Live from Orlando Florida, it's theCUBE, covering Microsoft Ignite. Brought to you by Cohesity. >> Good morning everyone. You are watching theCUBE's live coverage of Microsoft Ignite 2019 here in Orlando, Florida. I'm your host Rebecca Knight, co-hosting alongside of Stu Miniman. We are joined by Donovan Brown. He is the Principal Cloud Advocate Manager of Methods and Practices Organizations at Microsoft. (laughing) A mouthful of a title. >> Yes. >> Rebecca: We are thrilled to welcome you on. >> Thank you so much. >> You are the man in the black shirt. >> I have been dubbed the man in the black shirt. >> So tell us what that's all about. You're absolutely famous. Whenever we were saying Donovan Brown's going to be here. "The man in the black shirt?" >> Yes. >> So what's that about? >> So it was interesting. The first time I ever got to keynote in an event was in New York in 2015 for Scott Guthrie, the guy who only wears a red shirt. And I remember, I was literally, and this is no exaggeration, wearing this exact black shirt, right, because I bring it with me and I can tell because the tag in the back is worn more than the other black shirts I have just like this one. And I bring this one out for big events because I was in a keynote yesterday and I knew I was going to be on your show today. And I wore it and it looked good on camera. I felt really good. I'm an ex-athlete. We're very superstitious. I'm like I have to wear that shirt in every keynote that I do from now on because if you look further back, you'll see me in blue shirts and all other colored shirts. But from that day forward, it's going to be hard pressed for you to find me on camera on stage without this black shirt on or a black shirt of some type. And there's a really cool story about the black shirt that was. This is what\ I knew it was a thing. So I pack about six or seven black shirts in every luggage. I'm flying overseas to Germany to go Kampf to do a keynote for, I think it was Azure Saturday. Flights were really messed up. they had to check my bag which makes me very uncomfortable because they lose stuff. I'm not too worried about it, it'll be okay. Check my bag, get to Europe. They've been advertising that the black shirt is coming for months and they lose my luggage. And I am now, heart's pounding out of my chest. (laughing) We go to the airport. I'm shopping in the airport because I don't even have luggage. I cannot find a black shirt and I am just thinking this is devastating. How am I going to go to a conference who's been promoting "the black shirt's coming" not wearing a black shirt? And my luggage does not show up. I show up at the event I'm thinking okay, maybe I'll get lucky and the actual conference shirt will be black and then we're all good. I walk in and all I see are white shirts. I'm like this could not be worse. And then now the speakers show up. They're wearing blue shirts, I'm like this cannot be happening. So I'm depressed, I'm walking to the back and everyone's starts saying, "Donovan's here, Donovan's here." And I'm looking to find my polo, my blue polo I'm going to put on. They're like no, no, no, no Donovan. They printed one black shirt just for me. I was like oh my goodness, this is so awesome. So I put the black shirt on, then I put a jacket on over it and I go out and I tell the story of how hard it was to get here, that they lost my luggage, I'm not myself without a black shirt. But this team had my back. And when I unzipped my shirt, the whole place just starts clapping 'cause I'm wearing >> Oh, I love it. >> a black shirt. >> Exactly. So now to be seen without a black shirt is weird. Jessica Dean works for me. We were in Singapore together and it was an off day. So I just wore a normal shirt. She had to take a double take, "Oh no, is that Donovan, my manager "'cause he's not wearing a black shirt?" I don't wear them all the time but if I'm on camera, on stage you're going to see me in a black shirt. >> Rebecca: All right, I like it. >> Well, Donovan, great story. Your team, Methods and Practices makes up a broad spectrum of activities and was relatively recently rebranded. >> Yeah. >> We've talked to some of your team members on theCUBE before, so tell our audience a little bit about the bridges Microsoft's building to help the people. >> Great. No, so that's been great. Originally, I built a team called The League. Right, there's a really small group of just DevOps focused diehards. And we still exist. A matter of fact, we're doing a meet and greet tonight at 4:30 where you can come and meet all five of the original League members. Eventually, I got tasked with a much bigger team. I tell the story. I was in Norway, I went to sleep, I had four direct reports. I literally woke up and I had 20 people reporting to me and I'm like what just happened? And the team's spanned out a lot more than just DevOps. So having it branded as the DevOps Guy doesn't really yield very well for people who aren't diehard DevOps people. And what we feared was, "Donovan there's people who are afraid of DevOps "who now report to you." You can't be that DevOps guy anymore. You have to broaden what you do so that you can actually focus on the IT pros in the world, the modern operations people, the lift and shift with Jeremy, with what Jeramiah's doing for me right, with the lift and shift of workloads . And you still have to own DevOps. So what I did is I pulled back, reduced my direct reports to four and now I have teams underneath me. Abel Wang now runs DevOps. He's going to be the new DevOps guy for me. Jeramiah runs our lift and shift. Rick Klaus or you know the Hat, he runs all my IT Pro and then Emily who's just an amazing speaker for us, runs all of my modern operations. So we span those four big areas right. Modern operations which is sort of like the ops side of DevOps, IT pros which are the low level infrastructure, diehard Windows server admins and then we have DevOps run by Abel which is still, the majority of The League is over there. And then we have obviously the IT pros, modern ops, DevOps and then the left and shift with Jeramiah. >> I'd like to speak a little bit as to why you've got these different groups? How do you share information across the teams but you know really meet customers where they are and help them along 'cause my background's infrastructure. >> Donovan: Sure. >> And that DevOps, was like that religion pounding at you, that absolutely, I mean, I've got a closet full of hoodies but I'm not a developer. Understand? >> Understood. (laughs) It's interesting because when you look at where our customers are today, getting into the cloud is not something you do overnight. It takes lots of steps. You might start with a lift and shift, right? You might start with just adding some Azure in a hybrid scenario to your on-prem scenario. So my IT pros are looking after that group of people that they're still on prem majority, they're trying to dip those toes into the cloud. They want to start using things like file shares or backups or something that they can have, disaster recovery offsite while they're still running the majority of what they're doing on-prem. So there's always an Azure pool to all four of the teams that I actually run. But I need them to take care of where our customers are today and it's not just force them to be where we want them tomorrow and they're not ready to go there. So it's kind of interesting that my team's kind of have every one of those stages of migration from I'm on-prem, do I need to lift and shift do I need to do modern operations, do I need to be doing full-blown DevOps pull all up? So, I think it's a nice group of people that kind of fit the spectrum of where our customers are going to be taking that journey from where they are to enter the cloud. So I love it. >> One of the things you said was getting to the cloud doesn't happen overnight. >> No, it does not. >> Well, you can say that again because there is still a lot of skepticism and reluctance and nervousness. How do you, we talked so much about this digital transformation and technology is not the hard part. It's the people that pose the biggest challenges to actually making it happen. >> Donovan: Right. >> So we're talking about meeting customers where they are in terms of the tools they need. But where do you meet them in terms of where they are just in their approach and their mindset, in terms of their cloud readiness? >> You listen. Believe it or not, you can't just go and tell people something. You need to listen to them, find out what hurts and then start with that one thing is what I tell people. Focus on what hurts the most first. Don't do a big bang change of any type. I think that's a recipe for disaster. There's too many variables that could go wrong. But when I sit down with a customer is like tell me where you are, tell me what hurts, like what are you afraid of? Is it a compliancies? Let me go get you in contact with someone who can tell you about all the comp. We have over 90 certifications on Azure. Let me. whatever your fear is, I bet you I can get you in touch with someone that's going to help you get past that fear. But I don't say just lift, shift, move it all like stop wasting, like no. Let's focus on that one thing. And what you're going to do is you're going to start to build confidence and trust with that customer. And they know that I'm not there just trying to rip and replace you and get out high levels of ACR. I'm trying to succeed with you, right, empower every person in every organization on the planet to achieve more. You do that by teaching them first, by helping them first. You can sell them last, right? You shouldn't have to sell them at all once they trust that what we we're trying to do together is partner with you. I look at every customer more as a partner than a customer, like how can I come with you and we do better things together than either one of us could have done apart. >> You're a cloud psychologist? Almost, right because I always put myself in their position. If I was a customer, what would I want that vendor to do for me? How would they make me feel comfortable and that's the way that I lead. Right, I don't want you going in there selling anything right. We're here to educate them and if we're doing our job on the product side, the answer is going to be obvious that you need to be coming with us to Azure. >> All right. So Donovan, you mentioned you used to be an athlete? >> Donovan: Yes. >> According to your bio, you're still a bit of an athlete. >> Donovan: A little bit, a little bit. >> So there's the professional air hockey thing which has a tie to something going on with the field. Give us a little bit of background. I've got an air hockey table in my basement. Any tips for those of us that aren't, you know? You were ranked 11th in the world. >> At one point, yeah, though I went to the World Championships. It was interesting because that World Championships I wasn't prepared. My wife plays as well. We were like we're just going to go, we're going to support the tournament. We had no expectations whatsoever. Next thing you know, I'm in the round playing for the top 10 in the world. And that's when it got too serious for me and I lost, because I started taking it too serious. I put too much pressure on myself. But professionally, air hockey's like professional foosball or pool. It's grown men taking this sport way too seriously. It's the way I'd describe it. It is not what you see at Chuck E. Cheese. And what was interesting is Damien Brady who works for me found that there is an AI operated air hockey table here on this floor. And my wife was like, oh my gosh, we have to find this machine. Someone tape Donovan playing it. Six seconds later, my first shot I scored it. And I just looked at the poor people who built it and I'm like yeah, I'm a professional air hockey player. This thing is so not ready for professional time but they took down all my information and said we'd love to consult with you. I said I'd love to consult with you too because this could be a lot of fun. Maybe also a great way for professionals to practice, right, because you don't always have someone who's willing to play hours and hours which it takes to get at the professional level. But to have an AI system that I could even teach up my attack, forcing me to play outside of my comfort zone, to try something other than a left wall under or right well over but have to do more cuts because it knows to search for that. I can see a lot of great applications for the professionalized player with this type of AI. It would actually get a lot better. Literally, someone behind me started laughing. "That didn't take long" because it in six seconds I had scored on it already. I'm like okay, I was hoping it was going to be harder than this. >> I'm thinking back to our Dave Cahill interview of AI for everyone, and this is AI for professional air hockey players. >> It is and in one of my demos, Kendra Havens showed AI inside of your IDE. And I remember I tell the story that I remember I started writing software back in the 90s. I remember driving to a software store. You remember we used to have to drive and you'd buy a box and the box would be really heavy because the manuals are in there, and not to mention a stack of floppy discs that you're going to spend hours putting in your computer. And I bought visual C++ 1.52 was my first compiler. I remember going home so excited. And it had like syntax highlighting and that was like this cool new thing and you had all these great breakpoints and line numbers. And now Kendra's on stage typing this repetitives task and then the editor stops her and says, "It looks like you need to do this a little bit more. "You want me to do this for you?" And I'm like what just happened? This is not syntax highlighting. This is literally watching what you do, identifying a repetitive task, seeing the pattern in your code and suggesting that I can finish writing this code for you. It's unbelievable. >> You bring up a great point. Back when I used to write, it was programming. >> Yes. >> And we said programming was you learn the structure, you learn the logic and you write all the lines of what's going to be there. Coding on the other hand usually is taking something that is there, pulling in the pieces, making the modification. >> Right. >> It sounds like we're talking about even the next generation where the intelligence is going to take over. >> It's built right inside of your IDE which is amazing. You were talking about artificial intelligence, not only for the air hockey. But I love the fact that in Azure, we have so many cognitive services and you just like pick these off the shelf. When I wanted to learn artificial intelligence when I was in the university, you had to go for another language called Lisp. That scared half of us away from artificial intelligence because you have to learn another language just to go do this cool thing that back then was very difficult to do and you could barely get it to play chess, let alone play air hockey. But today, cognitive services search, decision-making, chat bots, they're so easy. Anyone, even a non developer, can start adding the power of AI into their products thanks to the stuff that we're doing in Azure. And this is just lighting up all these new possibilities for us, air hockey, drones that are able to put out fires. I've just seen amazing stuff where they're able to use AI and they add it with as little as two lines of code. And all of a sudden, your app is so much more powerful than it was before. >> Donovan, one of the things that really struck me over the last couple years, looking at Microsoft, is it used to be, you'd think about the Microsoft stack. When I think about developers it's like, oh wait are you a .NET person? Well, you're going to be there. The keynote this morning, one of your team members was on stage with Scott Hanselman and was you know choose your language, choose your tools and you're going to have all of them out there. So talk to us a little bit about that transition inside Microsoft. >> Sure. One of the mantras that I've been saying for a while is "any language, any platform". No one believes me . So I had to start proving it. I'm like so I got on stage one year. It was interesting and this is a really rough year because I flew with three laptops. One had Mac OS on it, one of them had Linux on it and one of them had Windows. And what I did is I created a voting app and what I would do is I'd get on stage and say okay everyone that's in this session, go to this URL and start voting. They got to pick what computer I use, they got to pick what language I programmed in and they got to pick where in Azure-eyed I deployed it to. Was it to an app service was it to Docker? I'm like I'm going to prove to you I can do any language in any platform. So I honestly did not know what demo I was going to do. 20 minutes later, after showing them some slides, I would go back to the app and say what did you pick? And I would move that computer in front of me and right there on stage completely create a complete CI/CD pipeline for the language that that audience chose to whatever resources that they wanted on whatever platform that they wanted me. Was like, have I proven this to you enough or not? And I did that demo for an entire year. Any language that you want me to program in and any platform you want me to target, I'm going to do that right now and I don't even know what it's going to be. You're going to choose it for me. I can't remember the last time I did a .NET demo on stage. I did Python this week when I was on stage with Jason Zander. I saw a lot of Python and Go and other demos this year. We love .NET. Don't get us wrong but everyone knows we can .NET. What we're trying to prove right now is that we can do a lot of other things. It does not matter what language you program in. It does not matter where you want to deploy. Microsoft is here to help you. It's a company created by developers and we're still obsessed with developers, not just .NET developers, all developers even the citizen developer which is a developer which is a developer who doesn't have to see the code anymore but wants to be able to add that value to what they're doing in their organization. So if you're a developer, Microsoft is here to help full-stop. It's a powerful mission and a powerful message that you are really empowering everyone here. >> Donovan: Right. >> Excellent. >> And how many developers only program in one language now, right? I thought I remember I used to be a C++ programmer and I thought that was it, right. I knew the best language, I knew the fastest language. And then all of a sudden, I knew CSharp and I knew Java and I knew JavaScript and I brought a lot of PowerShell right now and I write it on and noticed like wow, no one knows one language. But I never leave Visual Studio code. I deploy all my workloads into Azure. I didn't have to change my infrastructure or my tools to switch languages. I just switched languages that fit whatever the problem was that I was trying to solve. So I live the mantra that we tell our customers. I don't just do .NET development. Although I love .NET and it's my go-to language if I'm starting from scratch but sometimes I'm going to go help in an open source project that's written in some other language and I want to be able to help them. With Visual Studio online, we made that extremely easy. I don't even have to set up my development machine anymore. I can only click a link in a GitHub repository and the environment I need will be provisioned for me. I'll use it, check in my commits and then throw it away when I'm done. It's the world of being a developer now and I always giggle 'cause I'm thinking I had to drive to a store and buy my first compiler and now I can have an entire environment in minutes that is ready to rock and roll. It's just I wish I would learn how to program now and not when I was on bulletin boards asking for help and waiting three days for someone to respond. I didn't have Stack Overflow or search engines and things like that. It's just an amazing time to be a developer. >> Yes, indeed. Indeed it is Donovan Brown, the man in the black shirt. Thank you so much for coming on theCUBE. >> My pleasure. Thank you for having me. >> It was really fun. Thank you. >> Take care. >> I'm Rebecca Knight for Stu Miniman. Stay tuned for more of theCUBE's live coverage of Microsoft Ignite. (upbeat music)

>> Announcer: Live from Miami Beach, Florida, it's theCUBE, covering UiPath Forward Americas, brought to you by UiPath. >> Welcome back to Miami, everybody. You're watching theCUBE. We're at UiPath Forward Americas. Dave Vellante with Stu Miniman. TheCUBE is the leader of, what are we the leader of? (laughs) >> Live tech coverage, Dave. >> The leader in live tech coverage. I've been blowing that line every week. Thanks for watching, everybody. We've got a great segment here. Jean Younger is here. She's the vice president, Six Sigma Leader, Security Benefit. She's to my near left, and Donna O'Donnell is here, director of key accounts at UiPath all the way from New York. Donna, thanks for coming on. >> Thank you. >> Dave: Great to see you guys. >> Thank you for having us. >> Thank you. >> All right, so we're well into day one. We're getting the Kool-Aid injection from customers and UiPath constituents, but Jean, let's start with you. Talk about your role, what's the company do, fill us in. >> Our company is an annuity company. We sell financial products for life insurance and annuities. We have about 30 billion under management, so it's a fairly large company out of Kansas. So, my role there is as a Six Sigma leader. We go in and we look at areas that need improvement or across the company, and one of the things I found, I'd been with the company almost five years now, and what I found is a lot of times, we're really good about putting manual processes in and never getting rid of 'em. We have day two issues of a tech. A tech goes live and you got a list of day two stuff that didn't get fixed, never gets fixed. It's just easier to do it, and cheaper, to leave it manual. So we have a lot of that in the company. With my job, seeing the various processes throughout the company, I was like what can we do to get rid of some of this stuff, get rid of that, get knowledge work back on the worker's plate instead of manipulating a spreadsheet or creating a report that they do every morning and it takes 'em the first 30 minutes or the first seven hours of their day is creating this one report every single day. We started looking at technology and came across UiPath. >> See, we call it GRS, getting rid of stuff. >> Jean: Yep. >> So, Donna, your job is to make these guys successful, right? >> Absolutely, so basically I just facilitate the smart people within the company. I listen to the business needs that Jean and other large clients have. We bring the resources, the products, and if we can't find it, we will absolutely find it and do everything we can to meet the needs. >> So, what's your automation story? When'd you get started? Paint a picture for us, the size, the scope. >> Okay, so last year about this time is when I started looking into it. I had just rolled out of another area that we had completely destroyed and built back up, and I was on to my next escapade in security benefit. >> Dave: Are you a silo buster, is that the new-- >> Yeah, I kind of go in and fix things. I'm kind of a fixer is basically what my job is. We'd rolled out and came back into Six Sigma to start looking and this came up. I'd seen the technology and I was like I wonder if it could work in our company. And so, we started doing kind of dog and pony show. We'd pull the different silos in, talk to 'em and say hey, here's what RPA can do for you. Is that something that you have some processes that might work? And we knew that there were processes in there, but we brainstormed with 'em for about 30 minutes. And out of that 30 minute, hour long conversation, we came out of there with about a hundred processes that people had already identified. And we kept going through there, I took that information, I built a business case, 'cause I knew to get the money, I needed to show them that there would be cost out potentially, and/or that I could take resources and move 'em into more critical areas that we didn't have the staffing. And so I had instances where, one of them that we're doing is out of our HR department. During the raise and salary time, they had two individuals that spent 60 hours a week for four months doing the same thing, same report over and over, and that's one of the processes we're actually going to implement here pretty soon. So, I came up with 'em and put the business plan together and asked for the money, and after kind of a long journey, I got the money. >> Long journey. (Jean and Donna laugh) >> It's never short enough is it? Jean, I mean, one of the things, Six Sigma is really good at measuring things. I mean, that's how you understand everything. You want to reduce variation. There was a line that really jumped out at me at the keynote is I want to go from pretty accurate to perfectly accurate, and when you were describing that there were a lot of things that were manually done. I mean, I lived in engineering for a lot of years and it was anything that somebody had to manually do, it was like oh wait, how can we change that? Because we didn't have RPA 10 years ago when I was looking at this, but how are you measuring these results? You talked about people doing repetitive tasks and the like. What other things are you finding to help get you along those reducing the variation inside the company? >> You know, it's interesting because I also teach the Six Sigma courses there, and one of my slides I've had for years teaching that class is most business processes are between 3.2 and 3.6, 3.8 sigma, which is like 95 to 98% accurate, and I said that's all the better we can usually do because of the expense that it would normally be to get us to a Six Sigma. You look at the places that have Six Sigma. It's life threatening, airplane engines. You hope they're at least Seven Sigma, those type of things, but business processes? 3.5, 3.2. But now, I get to change that because with RPA, I can make them Six Sigma very cheap, very cheaply, because I can pull 'em in, I got my bot, it comes over, pulls the information, and there's no double-keying. There's no miskeys. It's accuracy, 100% accuracy. >> So, what's the ripple effect in terms of the business impact? >> Cost savings, efficiency, customer experience. I mean, think about it. You're a customer, you get your policy, your name's wrong. How happy are you? You're not real happy. You send it back. So, no more of that. I mean, that's huge. So anything touching the customer going out of our business should be exactly how they put it on the application, especially if it was typed. Now, if it was handwritten, all hands are down on that, but if it was typed, it should be accurate. >> Donna, that's really powerful. I worked in a large corporation, we had a Six Sigma initiative and we know how much time and effort and people we were going to put in to have this little movements. >> Incremental change. >> An incremental change here to say. >> Donna: Pretty amazing. >> Blown away to tell you, Six Sigma and it's cheap. Well, what are you seeing? >> And I absolutely see. So, in addition to cost savings, it makes her more agile. But the big thing is, it makes it error free. The robots work 24 hours a day, seven days a week. Runs on itself, and Jean's going to get those efficiencies that she needed. >> How about let's talk more about the business case? I'm interested in the hard dollar piece of it. I've talked to a number of people at this show and others, and they tend not to just fire people. They got to redeploy 'em. Sometimes the CFO goes well, where's my hard dollars come from? So, where did your hard dollars come from? (laughs) >> From the CFO. (laughs) And right now, I have to prove that out yet. We're just in its infancy. We're just starting to bring up processes. In fact, yesterday and today we're dealing with several processes coming up, and so realistically, right now I've got about 300 processes. We haven't timed 'em all out yet, but I know right now that's between probably 12 and 15,000 hours of time savings that we will get on an annual basis. >> Okay, what one of the customers said today is, one example they used is they actually put it in next year's budget >> Correct. >> Which I loved. In other words, we're going to do more revenue for the same headcount, or less cost or whatever it is. That's a reasonable justification, maybe even better, right? Because it's got some forward motion to it. Is that kind of discussion and thinking come up? >> Headcount is under discussion right now. We're going through budgeting right now, and so yeah, that was part of the way that we justified the less headcount. Instead of hiring to fill another position, we would remove jobs from a certain person and be able to shift them into another role. And so that savings, non-hiring, as well as one of the processes we're doing is in our investment area. They couldn't afford to get another person. They couldn't get another headcount, so I gave them a headcount with a bot. I'm doing all their processes that they've only been able to do on a monthly basis, we're doing 'em every day. It's 52 processes they're going to do every day. It's an amazing, I gave 'em a head right there, bam. >> But we're also finding that the folks that were doing the mundane and repetitive tasks can focus on more creative work, more interesting work that they believe in and that they're motivated to do. We see that happening all the time. >> What does that mean for culture inside your company? Was there resistance at first? I have to think it's got to improve morale that it's like oh wait, I'm not getting in trouble for making mistakes now and I can go focus on things that fit better. >> You know, I think ultimately it will. Initially, there was a feeling gosh, the bots are going to take my job. But that was one thing we were pretty careful about initially going out and just saying what is it that you can't do? We all work 50, 60, some of them people are working 70 hours a week, and if I can take 10 or 20 hours away from them, they are lovin' us. There's individuals that are saying come here. I'll show you what I need. They also realize the ability of the bot to do it right all the time takes a little stress off of them, because they know they're going to get the right numbers, then, to analyze, 'cause that's a big thing. In the finance area, in the close, in the accounting area, what we're doing there is we're taking a lot of those simple process that somebody has to do and do them for them so then guess what? We can close earlier, get our books closed earlier in the month, as well as allow them longer time to analyze the results. So instead of the book's closed and then we go uh-oh, found a problem. Got to reopen the ledger and make an entry, we have less and less of that. Those are expectations that are set right now for our teams is that hey, let's get rid of the stuff that we can, and then let's see what's left. >> And Dave, I used to meet with clients and they used to say wow, this is a really interesting technology. Tell me about it. And now they're like holy crap, I'm behind the eight ball with my competition. How do we get this going quick? How do we get it going fast? In 2016, it was a $250 million industry, and by 2021, it's going to be a $3 billion industry we learned today. So it's pretty powerful. >> I think those numbers are low, by the way. >> I think they're low, too. What they said today, it's going to be a $3.4 billion industry. >> I think it's a 10x factor, probably by 2023 to 2025, I think this is going to be a $10 billion business. I've done a lot of forecasting in my life. That's just a gut feel swag, but it sort of feels like that. I think there's some pieces that are, there's some blind spots in terms of use cases and applications that we can't even see yet. Culturally, the light bulb moment, just listening to you, Jean, was the, first of all you're saying, you want your weekends back? Yeah! And then the second is it sounds like the employees are involved in sort of defining those processes, so they own it. >> And that's how we're scaling. I mean, we already realized we're a bottleneck. Our COE is a bottleneck and so we're like hey, right now, finance, it's not the end of the year. It's end of quarter, but those process are lighter than end of the year. So hey, can we get anything done? They're doing our documentation for us. They're actually taping themselves doing it, they're writing up the documentation. We come in and we look at it, and then we have a programmer doing it, but we're talking about how do we move that programming piece down to them as well, so we can get our scale up? Because I can't get through 300 processes in my small COE without a lot of help from the business. >> But Dave, most of our clients, the way that they scale very quickly is through partners. So, partners can do one of many things. They can be the developers, they can be the implementers, they could create the center of excellence, or they could pick which are the low-hanging processes. When we started off with Jean while she was going through the approval process, I brought out four partners, I gave 'em my own little mini RFP. They each had a four-hour time slot. They presented in front of Jean and we narrowed it down to two, and two of the partners are here at this event today. Most clients need to depend on partners. >> Well, that's key Donna, right? And I've said before, when you start seeing the big names that are around here, you know it's an exciting space. They don't just tiptoe and play around and games. They do some serious work for businesses. We got to turn the conversation to diversity, generally, but I also want to ask you specifically about women in tech. So, Stu and I were in a conference at Splunk earlier this week. The CEO of Carnival had a great line about diversity. He said, a big believer in diversity, of course. He's African American, and he said 40 years ago when I cracked in business, there weren't a lot of people I worked with that looked like me. I thought that was striking, Stu. I think there's always been women in tech, but not enough and a lot of stories about things that have happened to women in tech. It's changing slowly. A lot of women enter the field and then leave because they don't see a path to their future in things they like. What do you guys think about the topic, two women here on the panel today, which is our pleasure to have you. You can see, we need help. We have women working for us, (Jean and Donna laugh) but there's an imbalance there. >> You're right. >> What do you tell someone like us who's trying to find more women or more diversity and bring them into their-- >> Jean has many opinions in this space. Go ahead, Jean, I love your opinions in this space. >> I told the story at one of the UiPath events. I've been, as a lawyer, chemist, I've always been in pretty much a man's world. That's been my life in corporate America, and all along as I looked back, Donna was the first woman that sat across me to negotiate a contract. The entire time that I've been in the tech world, in the business world in corporate America, I had women working for me when I was at an insurance company negotiation very large contracts and stuff. They were on my side of the table. She was the first woman that I negotiated with on the other side of the table, and I think that's really sad, and I think we all have to look and say, how can we do better than that? How can we make us diverse? I look around here and you have all colors, all sizes, it's wonderful and it energizes you. And I am really a true believer in a really diverse workforce. I look at that and I think, 'cause they bring so many cool ideas, they think differently. Young, old, you put 'em all in a room and it's just amazing what they come up with, and I think if business leaders would hear that and think about that instead of hearing the same type of person, what's that same type of person that has your same background going to give you? He's not going to give you the transformation, or he or she. It's going to be kind of the same, what you're used to. You need that jolt, and I believe the more diverse people that we have around the table trying to solve the problem, it's amazing. I sat, last week, and I had a 22-year-old woman come into my office, Shirat, who's 30-ish and from India, and I had Amy who grew up in Topeka, hasn't left Topeka, myself. We were sitting around a table and another guy came and he probably 30. So you had a big, broad range. Somebody just out of college, me that's been out of college a long time, sitting around the table and we came up with, they thought they were dead in the water, and within 30 minutes of us just throwing different ideas out, we came up with a solution that we could continue going with. I mean, their faces were downtrodden and everything when they walked in, and when they left, we were excited, we were ready to go. Now, if we don't nurture that type of conversation, we're never going to get diversity. That's what diversity's about. If you think about it that way, wow. We went from having a problem that was a total dead stop and we weren't going to be able to proceed to 30 minutes later having a great solution and keeping running. And I truly believe it was because we had a diverse group of people around that table. >> Studies have been done of the clear value of diversity, the decisions that are made are better and drive business value. One of the challenges is finding the people, and it was pointed out to me one time, it's just because you're looking inside your own network. You got to go outside your own network, and it takes longer, it's more work. You just got to allocate the time, and it's good advice. It's hard work, but you got to do it to make change. >> And sometimes you got to take a chance. Sometimes, because it is outside of your network, you're not comfortable necessarily with the answers they give you or the way they approach a subject. I mean, you've got to feel comfortable, and CEOs and CFOs and the C-suite has to start thinking about that, because if you wanted to be transforming, that's how you transform. You don't transform thinking the same way every day. You're not going to transform. >> Let me ask you a question. You said you're a fixer, so I wrote down the adjectives that I would use to describe a fixer, and I want to know if this has been the way in which people have described you. You got to be smart, you got to be a quick study, you got to be a good listener, you got to be confident, self-assured, tough, decisive, collaborative. Are those the adjectives that have described you as a fixer over the years? >> Yes, I think those are you qualities, by the way. >> I don't doubt they're your qualities. Is that how people refer to you in business? >> Yeah, I think so. I mean, I've done the test where they say are you a collaborator or do you push? And I get the mix. I'm either a collaborator or I'm a person that's pushing her own belief, and I know exactly who said I was a person that was only pushing her own belief, and I know the ones that said I was a collaborator. But that is, you got to be collaborative. >> I believe you have those attributes, but the reason for my question is a lot of times when it's a woman fixer, those aren't the adjectives that they would use to describe you. It would be abrasive or combative. I mean, you hear adjectives like that. Same exactly attributes as a male fixer, just described differently. Has that changed in your view? >> I go about things probably a little bit differently than men do, and I've had to adapt. Like I said, I've been a chemist. What was I? 8% of the community of chemists is a woman, so I've had to adapt my style. And I do a lot of drive-bys, I do a lot of one-on-one discussions over the lunch, over hey, do you have a few minutes? I need to talk to you. So, I do a lot of that type of collaboration before I ever get into that big meeting where I'm pushing my one direction. I've got my buddies all lined up already, and so I don't think it feels like I'm abrasive or that I'm, because I've fought those battles privately already. So maybe I've adapted my style that I don't get those types of reactions, but that's what you got to do. You've got to learn how to work the system. >> At the same time, I think that, and this is a compliment, I think Jean on the outside, it's tough to earn her respect in the beginning, but if you do, there's nobody more fiercely loyal than Jean. So you got to earn your way in there, and that's got to be consistent, like a 15-step process to get there. (Jean laughs) >> Yeah. >> And you can't let go because if you let go-- >> Dave: They're hard to get, huh? >> She's going to make you think on number six day you're not good enough, and then you just got to keep on going. So I understand what you're stating, Dave. You have to keep on going, and if you get there there's nothing that Jean wouldn't do for you. As she's here, she's on the advisory board of UiPath. She is the most, once you prove yourself, that's it. It's going to be hard to change that, but it's not easy to get there. >> So this inherent bias, people are tribal in nature and they're biased. Does things like automation and RPA, AI, does it eliminate that bias or does it codify it? >> Wow, interesting question. I don't know, I don't know the answer to that. >> Dave: I don't think anybody knows. >> I don't know that either. >> I've never really thought about it. I mean, to me RPA is just another tool in my toolkit, you know? And if I can fix it with AI, great, or UiPath, if I can use that to fix it with RPA, great. If I need another toolkit, I'll go use that toolkit. But I do know that it's a way that individuals, you can get a lot of young people into your organization that have great ideas. I'm stocking up with interns and I'm using, like I said, woman we hired, she was my intern, graduated in May, and the next day she had a full-time job. And she's done a phenomenal job. And that's what RPA has done for our business, because it's an entree in that then they're in and they're doing this simpler technology, then people see how wonderful they are and they can go and move into bigger and better roles. And that's what I'm trying to encourage is get some really smart people in with this tool, and that's what UiPath has enabled, I think, people that maybe they're not the best coders, or maybe they're not the best BAs, but you put that together and they're knocking it out of the park. The young ones are knocking it out of the park on this technology. It's amazing. >> We did several blockchain and Crypto conferences this year, you want to talk about diversity, and I mean it's old money, it's new money, it's women, it's people in turbans, it's people with color. It's actually quite amazing, and one of the older investors, I asked him what's your secret? He said, "I surround myself with millennials." (laughs) >> Jean: Correct. >> That was good advice, but very diverse crowd in Crypto. You don't have to be Ivy League, Silicon Valley and white, Caucasian, to be successful, right? >> Dave, I was representing RPA at a Women in Tech conference last week in the FinTech environment, and I was talking, sitting next to Crypto and Bitcoin and at the end, the lines lined up for RPA. And I would say to the girls, why are you lined up for RPA? And they basically said you are the disruptor. RPA is the disruptor. There was a speaker here today that says RPA's the gateway drug to artificial intelligence, which is absolutely true. RPA is operational right now, it's working today, and there's elements of AI that are here today, but there's elements that are future technology. But RPA's completely ready to go, operational, mainstream in most enterprise companies. >> And I know we kind of went off topic there but it's relevant and it's important and it's a passion of ours, so really appreciate you guys coming on theCUBE. >> Thank you. >> Thank you, Dave. Thank you, Stu. >> All right, keep it right there everybody. Stu and I will be back with our next guest right after this short break. You're watching theCUBE live from UiPath Forward in Miami. Right back. (upbeat electronic music)

(upbeat electronic music) >> Live from Bahrain. It's theCUBE. Covering AWS summit Bahrain. Brought to you by Amazon Web Services. >> And welcome back to theCUBE's live coverage here in Bahrain for the exclusive coverage of the AWS's summit and their announcement and their execution of a new region which should be online here in early 2019. I'm John Furrier, your host with SiliconANGLE Media theCUBE, extracting the signal from the noise, meeting all the people. First time the Middle East and the region should be a big impact, having a digital footprint as size of Amazon Web Services, bringing energy and entrepreneurship and innovation and economic revitalization and enablement. We'd love the coverage, we meet a lot of great people. Our next guest is Simon Martin who's the ambassador of the British embassy here in Bahrain. Simon, welcome to theCUBE. >> Thanks. >> Thanks for joining us. >> My pleasure. >> So, OK, so I want, I want to just kind of get your perspective. I met the US ambassador yesterday the last night at dinner. He's kind of new to the area and the job. >> But he's already, >> You've got experience, >> But he's already well informed, I can tell you (laughs). >> He's well informed (laughs). Birth by fire, thrown in the deep end. You've been here for a few years. >> Yeah, three. >> Take a minute to talk about the environment here, because we're first time here. We're learning or observing. I'm certainly surprised. My daughter was asking me: What are the women like there? We had a women's breakfast yesterday. 70 plus people. The energy, the diversity, interesting culture. Feels like very open, what's your thoughts. >> Well, very much so, I mean, Bahrain has been at the sort of crossroads of international travel for hundreds and hundreds of years. The UK's relationship with Bahrain, the formal one, goes back just over 200. And that was all to do with trade. Manama means the place of sleep. And it was the place that people used to stop to rest on their way across the Arabian subcontinent and towards the Indian subcontinent, and so on. So, it's a place which is naturally welcoming of foreigners and outside ideas. And I think that's what Amazon have found here. So, there is an often lot of change going on in this part of the world. Bahrain is relatively small economy compared to its neighbors. It was the place that oil was first discovered in the Gulf, but, actually, once they discovered it, they realized that she had rather less than most of the neighbors and, therefore, it's an economy which has had to adapt to keep, keep growing. In contrast, >> Mainly, mainly the dependence on oil, other oil-rich areas. >> Yeah. >> Right, is that it? >> Yeah. So, that's been the main stay of the economy for some time, but there is not the, there is not yet the potential for the growth that's needed in order to help develop an economy with its, with the necessary modern infrastructure. A growing population, a need for, for quality employment for young people which is something that we've heard a lot of in the last few years. >> Talk about your history, how long have you been in the job you're in, what's the background, what are some of the things that you've done >> OK >> at the government in the UK. >> Yeah, well, Thank you, so I've been here for three years. Before that, I was working, actually, for His Royal Highness the Prince of Wales. And in that role, visited this part of the world on a couple of occasions, and so, and so the impact of that very important part of our relationship, royal family's relationship with the royal families in this part of the Gulf, and it just opened my eyes a bit to the, to the importance of having multifaceted relationships. And, again, this is what we're now, this is what we are now seeing here, that Amazon Web Services with the cloud region that they are building here have brought a new dimension, >> (laughs) The fly got... >> Not surprised, to the Bahraini economy. >> So, tell me about the multifaceted piece of news. What I'm fascinated by is the Dubai dynamic, right. You know, I see Dubai, a lot of events there, Blockchain events, AI events, a lot of tech events. Feels like New York to me, using the American metaphor. It's kind of like a Silicon Valery kind of vibe. But they all work, been working together for years. What's the historical relationships, how have they changed, and how does cloud computing make up for that? How does that play into it? >> Well, of course there've been, it's been a very collaborative and yet competitive relationship between the different, particularly the finance centers of the Gulf for many years. The economic success story of Dubai is very well known. Bahrain has continued to develop, but without the resources that underpin the UAE success, has done so on a more, more progressive way. But this is always be, going to be a much smaller economy and Bahrain has to, has to compete in niches in which it has the competitive advantage. And it's this, what we have now happening here, is creating a wonderful new niche opportunity for Bahrain. But, of course, I don't think am letting out any secrets to say that each of the countries in the Gulf would love to have been hosting the new cloud region. >> Yeah. >> So Bahrain had try incredibly hard to present an environment in which to host this kind of, this kind of investment which requires regulation. It requires openness and ease of doing business and it also requires an openness to developing the labor force to support not only the Amazon, but all of the train of companies that we're expecting to invest along behind it. >> Well, Simon, I really appreciate your experience and candor here on theCUBE. Certainly, for us it's a new area and you have certainly a perspective for, for the Royal Family in the UK, and now being here. But one of the interesting things I'd like to get your perspective on is, you know, you look at globalization and you look at regulations, you look at digital, things like GDPR, you see all traditional things, you mean, you can go back when I was a young kid growing up, I remember the pound and the French franc and all the different currencies going on, and then EU comes together. And now you have Asia and cryptocurrency. So you have a whole another cloud computing generation coming where that might reimagine the political landscape, might imagine the economic relationships. These are opportunities, but also threats. And so how people handle it is interesting. So, how do you, when you look at that kind of dynamic, you got a little bit uncertainty and opportunity at the same time, depending how you look at it, it's the glass half full or the glass half empty. >> Exactly. >> How should executives and government officials start thinking about this new model, this new marketplace. London is certainly the center of the action and connects now into Bahrain, could be a different dynamic, frictionless, digital. >> Mhmm. >> People living across borders. These are new dynamics. What's your thoughts on this new melting pot of digital impact? >> Well, of course, everybody wants a piece of it, everybody wants to be at the center of a new melting pot. And for Bahrain, they're looking to be the of it within, within this region, but of course, the Dubai Finance Center and, you know, Abu Dhabi and Kuwait, and so on are also, are also very keen, and no one, no one is expecting to be the dominant player. And certainly from Bahrain's perspective, it's very much about creating the environment in which companies will see, this is a good place to start. The Gulf region is a coherent region with an incipient single market, and so on, within the GCC, and so, naturally, investors from the outside are going to look at one place to start. And so what Bahrain has done, and I think it's, it's been very well founded, it's just taken place over the three years that I've been here, it's to dramatically increase the ease of doing business, and then find proportionate ways in which the government can support new companies to get them established. So, you mentioned GDPR and, you know, how's this going to affect a company in the Gulf. Well, I was at the launch of a very interesting new big data software project by one of the, in fact British owned new startups in the FinTech Bay here which is supported by the Economic Development Board. They're starting point is that the product that we are selling out of Bahrain is GDPR compliant, which gives you an idea of the way, >> Yeah. >> in which even from, from this relatively small island in the Gulf, >> Yeah. >> the global perspective has been taken. >> And certainly with, you know, digital currency, the Know Your Customer Anti Money Laundering is the big thing too, you got to get that right. >> Yeah, absolutely. >> So, they have an opportunity with FinTech. Final question for you, as you look out and see the human capital market and the future of work. >> Yeah. >> It's a big conversation we're always having and certainly I live in Silicon Valley where everyone's, no secret that there's a migration out of Silicon Valley due to the prices of living there, but yet concentration of entrepreneurship. People are going to have engineering teams all over the world. so you have a disperse workforce now crossing borders and not just the domicile issue, that's one, you know, taxes, where to domicile, outside say the US or other countries. So, you have a combination of diverse workforces. >> Mhmm. >> This is big, this is a big opportunity too, challenge and opportunity. >> It is, it is. And, of course, there are not just big changes, now, there's constant fluctuation in the way the workforce and the populations in this part of the world and within the gulf are changing. Look at Vision 2030 in Saudi Arabia, the big increase in the Saudi workforce, both through the policy of Saudization and through the creation of many more opportunities for women in the workforce. That's affecting Bahrain. But Bahrain has always been a place where people come to work and sometimes to work remotely, sometimes to live here and work across in Saudi Arabia. So, the Bahrainis feel that they are very, very attuned to these challenges. But I might just mention as well that this is not just about economics. And what impresses me about the reform program you see going on here is that, the idea is that we will create a broader and wider spread opportunity, particularly through the opportunities for young professionals working in AWS, but also in the environment all the way around it, for all communities in Bahrain, not just the wealthy, not just the sort of Ivy league equivalent graduates. >> Yeah. >> And so that's why the academy that they're setting up here can, >> And then network does emerge in social networking is going to bring people closer together. >> Yeah. >> OK, great to have you on. Final question is, as people look at this moment in time, maybe an inflection point, shot heard around the Gulf, if you will, of Amazon, certainly they did this with CIA in our country, the said success is coming in, and kind of changing how things do, reimagining value creation and value capture. What do you see as the impact of the, a diverse region have been in this area and the geography? Just your thoughts on what the impact's going to be. >> Well, of course, this is a virtual world and a cloud region is the virtual concept, so it's easy to say, well it shouldn't take an Amazon Web Services cloud region to transform the way in which governments work here. In practice, what AWS have seen wherever they have established cloud regions, it's a magnet for other businesses to develop around it, and it provides the reassurance that governments need to take that step forward. I don't know whether you heard Max Peterson and his presentation this morning saying he was amazed at the speed with which the entire Bahraini government system has embraced the move to the cloud which, indeed, my own government is doing as we speak. And this, I think, is going to be one of the really big, the really big impacts which will allow governments to get smaller and more efficient and more transparent >> And serve their citizens in a different way, in a better way. >> But one last thing, John. Because, you may not have heard about this is, we're hearing a lot about the shift towards renewable energy in this part of the world, and people say, why on earth would we need renewable energy which is, you know, so much of the world's petrol carbon resources are based here, but, of course, if you don't burn them, you can sell them. And that's very simple economics. The fact is that it has taken longer than other parts of the world for the transition to renewable energy, even though we have so much sunshine and at times quite a lot of wind. The government here just put out a tender for a 100 megawatt solar farm. And the driving force behind that is because AWS have said: we want to power our cloud region from renewable energy. And this is an example of industry and the big investors actually applying a positive force to speed up the direction of the government policy already. And it's something that has been well. >> It's happened fast, this private partnership public relationship, that's a success story. >> And I think there are lots of other ways we will see this happening, as I say, you can't have over 2000 people here all focusing on the cloud technology without bringing an awful lot of extra attention to and focus on what else is going on in Bahrain. >> Yeah. >> From my perspective, the Bahrain government is saying we welcome, we welcome this, this publicity, and we look forward to explaining ourselves. And I think we'll see a lot of further development in this area. >> Simon that's a great point. Sustainable energy and the trade-off between industry, private industry trying to make money, but contributing technology and a co-creation with the government. >> Yeah. >> I mean, data center, it's hot here, you need cooling, you got sun power, you see, you got to have that solution. >> Absolutely, yeah. >> You can't burn it, you can sell it, so good opportunity. >> Yeah, yeah. >> Simon Martin, ambassador, the British ambassador to the embassy here in Bahrain. Thank yo for sharing your insights and color commentary. >> Pleasure to meet you, John. >> Appreciate it. Okay, live coverage here. I'm John Furrier with theCUBE bringing you all the new observations. Our first time in the Middle East region well coherent structure, great economics, great society benefits, cloud computing, Amazon Web Services region opening up in 2019. Exclusive coverage. Stay with us fore more after this short break. (upbeat electronic music)

>> Live from Washington D.C, it's the Cube, covering inforum DC 2018 Brought to you by inforum. >> And welcome back to Washington D.C. I think you kind of guess where we are. [Mumbles] Over my shoulder there inforum 2018, along with Dave Vellante. I'm John Walls, we are live here at the Walter Washington convention center for this years show. We are joined by Martine Cadet, who is vice president of the educational arts program at Inform. Martine, good to see you. >> Thank you. >> And Jennifer Bucanan who is manger and head of the Inforum practice and CFO Solutions. Jennifer, good to see you. >> Thank You >> Let's talk about the program. Martine, ou can give us, first off, the inside scope of this. And then Jennifer you're on the other side of the fence. >> Right >> Because the very people, the individuals that Martine and her program are training, you're hiring. >> Absolutley. >> So I want to hear, first off lets talk about the program in general and then to you, Jennifer for about why. And what do you find really attractive about these people? >> Absolutley >> So the Education Alliance Program is 4 years old this year actually, so we're very excited about that. And what we really set out to do was build a talent pipeline that our company and our ecosystem of partners and customers can tap into. So there's been a lot of conversation about the fact that there's not enough talent to fill the positions that are in the industry. So we wanted to do something different where by we could actually grow that talent organically. So instead of going to the oricals and the SAP And having that revolving desk of okay we can leave this here and go to this competetor and work for them and be experienced. We need to find another way to be able to grow our talent so that as an organization, we can continue to be innovative and grow as we move forward. So EAP really kind of serves within that gap, but we do it a little differently than some of the other programs. We really focus on not just identifying talent, but really training them on the industry skill set. So what people `learn in school is amazing, but it is usually more theory. How do you take that theory and really apply it to what is really needed in the industry today, In a real job today? And that where the Education Alliance Program really kind of serves that niche. >> And generally speaking, the age group would be what? Of the trainees. >> The majority of the people that we train are more college age. So 18 all the way up to, I would say early 30's. So early in career talent is what we think about. >> Okay, are they in school? >> Some of, yeah. >> The majority of them right now are in school, but we also welcome people who are outside of school. We've kind of evolved our program where its not just partnerships with colleges and universities, but we train people who come from training organizations; like Yes We Code, and New York Urban League and things of that nature. And You're Up is another great partnership that we started having relationships with. So we do everything from the traditional integrate within a classroom setting, to more of a bootcamp model where talent gets trained over the course of a couple of months to meet this business needs that we have and are here now. >> Okay, and so Jennifer, then on the hiring side of this the advantage to you is what? >> Well for us, we are always looking for folks that coming out of school, whether it's a masters degree or a bachelors degree, that they have a little bit of a focus going into consulting and implementation services. There's a mix of skills that you look for. Some of it is that commitment to being a forthcoming service orientated person. Somebody with a little bit of perspective. And when we met the EAP students, that ambition just comes right out of those folks. And they have purpose in mind because they started to get a little bit of a taste of the real world of what they want to do. So they've got context and they've got direction and a lot of the folks that we've met with had some good accounting and finance knowledge, which we value. Plus they had the IT component, where traditionally if I just go to try to do some campus recruiting I might get one or the other, but it's hard to get both. >> So training that revolving door, that martine described which, there's still some of that going on, but you get a lot of viable knowledge. You know where the skeletons are buried. They can fast pass some of it. Trading that for excitement, diversity, maybe a different type of creativity. Certainly not as much well that's the way it's done in the enterprise. Right? Maybe discuss that a little bit. >> Yeah absolutley, the folks that, what we need coming in have that creative element and they're not just, ya know, crunching out and doing maybe the theory that you have mentioned. They've had a little bit of experience at a practical site of understanding how to apply technology and solve buisiness problems. Cause that experience that they go through in the EAP program is almost like a simulation and gives them a little taste of that. And when we talk about what we do and we introduce them to our business and try to look for a fit. They have a better understanding of what we are talking about and do. >> So Martine, in the 4 years since we've first met, what has changed? Has the scope, the goals expanded? What did you not forget that actually happened? >> Yeah Maybe you could share some of your experiences. >> Yeah, so in the four years we've gone deeper around world based training. So when we started, it was more around exposing students to different career opportunities, to what is EAP. I've been in the industry forever, but I was always more on the consumer side. So I didn't know what ERP was. [Laughing] Is this even professional? So helping students see the opportunities there, was kind of the initial focus and getting them to have kind of a toe in the water. We've gone much deeper this year, in particular going to role based training. So, what do you need? What skills encompanies you to be an amazing sales professional versus somebody who's going into the development space, or somebody who's going to manage kind of the cloud space, which is where our company is focused. So that's been one of the biggest evolutions in which we have done within EAP over the last couple years and were much more global than we were when we first started. So we are excited about that as well. And then in terms of things that surprised us. I think of of the areas that surprised us, it was a little bit harder to place students frankly, than we had initially had thought it would be. And so one of the ways that we've worked through that is we've worked with our talent science team, and they've been phenomenal at really helping identify fit. And so now we can have much richer conversations with the hiring managers around. Yes, I know that you would like an expereinced hire perhaps, but this is a reason why one of these more inexperienced hires is actually a great fit and will be your next superstar on you team. And on the flip side, we can have conversations with the talent around career opportunities that they might not have even thought about before. Cause now we've got kind of there fit for different roles. >> So were certainly seeing in many buisiness settings, that gut feel is being replaced by, you know, data and fact. When it comes to hiring people though, there's still that, well there's several things. There's gut feel, there's repor, there's biases, so are technologies like machines intelligence, and programs like this cutting through that? >> Yeah that's what we're trying to have happen. I mean it's hard, it's hard. Everyones trying to tackle these issues, but with technologies like talent signs, with having programs which address the feedback of well I don't know where to find a first talent. Well if you go to the same three schools that you always go to, that are by nature not diverse, then you're not going to find the diverse talent you are looking for. So if you can tap into a program where we go out of our way to make sure we're actually fishing in new ponds, and that we're bringing in amazing talent to the forefront that people can tap into. And we are really proud about that. >> Well, what's really key about that, and we were having this conversation earlier. Is that if you really want to bring diversity into your organization, you have to look beyond your inner circle. But it's a pain to do that, its time consuming. So what you've done, is you've fast passed that. Right? And give an opportunity for somebody to dive in. >> Yeah >> Yeah >> Sure, and some of those folks became part of our circle. Cause a year ago we met a wide group of the folks in the EAP program, and we were impressed by the broadness. Like I mentioned earlier, you've got some folks that are still sophomore, junior year that are just getting started. We've got relationships foudning with those folks. We have folks that are just getting ready to graduate, and we have folks that have been in the workplace, came back. So we've got a breath of experience, but folks from all over. And we were one of those folks who would go to the same school over and over. And you know, we would get good talent but, it's that breath, and that new perspective that comes in. And now that's our pipeline. We've got folks at different levels in their educational career that we stay in touch with. And a lot of the students reaching back to us is what helped us make connections for folks to bring on. >> So how do you find me? If I'm an interested student? >> Yeah >> Or how do I find you? >> Yeah >> If I am at a school. That's one question, and secondly once I'm in, is it like ROTC? Like I have a three year commitment after that? [Laughing] [Mumbling] >> You're invested in me. >> That's a great idea. [Laughing] >> A lot of resource time is being put into me, developing me, so what am I going to give you back? >> Yeah absolutely. >> Take on both of those if you would. >> Its more about finding the member institutions and then finding me the right talent within those organizations. Right? And so we do a lot of research and analysis on where we want to go. So we do want to make sure we are building pipelines that fit the busininesses needs first and foremost. So where do we have a majority of our offices? Where do our partners and customers have a majority of their offices? Where are the hiring needs and the types of roles? Then based on that we look for organizations that actually have core programs that align to that. And those are the ones that we want to have relationships with first and foremost. And then we seek out the talent. We actually have marketing communications people who are out there and promoting the courses and the partnerships that we have in the classroom to hopefully get the talent to actually apply to the class itself.. >> Alright so once I'm in. >> Once your in. >> You've got me. >> Yeah. >> I'm a junior, I'm studying, I'm doing my thing. >> Yeah. >> You're training me. Well I'm going to graduate in a year. So am I on the hook, or will you place me? >> We investin their training, and we also try to provide wrap around support services. So we've got people on the team who are beyond passionate and focused on making sure they've got the soft skills, who are also focused on making sure they are introduced to hiring managers within our ecosystem and within our organization who might be interested in talking to them. We set up kind of meet and greets as well, where we have events around that so placements important to us. We can't commit and guarantee a role per say, but we can open up opportunities perhaps the students didn't have before. And give them the training so that when they are compared against their peer, they can come out ahead. >> So having that Charles several times and interviewing him a number of times, this is, it certainly feels more than optics. What are the success metrics that you look at, and can you share some with us? >> Yeah, so we do look at how many people we actually trained and made it through the program. We look at how many people have been placed within Inforum as well as our ecosystem. We are looking to see how many students will actually pursue a path to certification, and go through the deeper training and learning. And then we look and see how many people are actually liking the program. Like what they're getting out of it. We'd love to see, I'd personally love to see in a couple years that people will have gone through EAP are now future customers, your future partners. You know, placement is one piece, but its also how are we influencing the industry as a whole? And for competitors, hiring EAP students, that actually goodness. Like we are trying to really change what is going on in the injustry perspective on how we grow and change talents. Because the way its working today doesn't work for everybody. So we've got to do something different. And the fact that Inforum has stepped up to actually grow it organically, I think is you know, a testimony to Charles. >> Great mindset, I mean you're not trying to just hang on and you're certainly embracing this. But if when an individual leaves, to whether even a competitor, there's some pride in that. Like hey we trained this individual and we're changing the industry. And you know, sometimes those things just have a way of coming back around. >> Yes, yeah, yes >> Absolutely >> So Jennifer, from the clients side if you will, how big could this program be for you? Like how helpful has it been, and how much more do you need it in order to meet these employee gaps? Cause as we've heard, the numbers aren't adding up right now. >> Right, right. So for us we've been having some conversations about how do we grow together on this? They've offered to say hey, CFO Solutions, would you like to be involved in some of the teaching opportunities? So we've been having dialouge about how that might be. And we've been talking about particular skill sets. You know, they start out with kind of a broad skill set and we work with a very specialized component of that. So we've been talking about the product mix that they involve in their program and bringing something more direct to what we're working with. So that's a big. >> Personalized learning. >> Yes. So its helping us kind of refine our pipeline because we know what's going to be coming out of it and we know that is is getting that slice of this US and the world if necessary right? It gives us a little assurity that we can get folks at different levels of their career. We can start talking to them now and we can start working with these guys on honing the skillset that they'll be coming in with. The soft skills piece that you had mentioned earlier was on of the real standout skills that we saw come out of this. All these folks, I can't overemphasize the driviness, the commitment they had, the communicating with me over a year period. And they're boldness, cause that's one of the main things that we need out of the folks that we bring and put in front of our clients. >> So this is all awesome, touchy feely stuff too, but at the end of the day, I've read that it has a buisiness impact. >> Absolutely, absolutley. >> So what's your experience been in terms of the bottom line? >> Well, so buiness impact wise, when we take a risk and bring somebody fresh out of school, and we bring him to a porject where you require very specialized skills, we need people that we can take a risk on, who will hit the ground running. So if I go and grab somebody from anywhere, I don't know what I'm going to get. I don't know if they're going to like their career. I don't know if they're going to understand what we are doing. And there's a lot of ramp up time, time before I can bill for that source, just to be practical. And when we bring in Eap students, I know they've had a taste of it, and they're ambitious and driven for it. And I can get them billable more quickly. And then I can be proud to have them out in front, because they can tell a story. A lot of this is a relationship business. I can have them come to a project kickoff and they can talk about what they've come from. And that they've had an involvement with Inforum, not just hey I'm fresh out of school. I don't know what I'm doing here. It's hey, I've been working with a product for a couple years. Even now and they hit the ground running just so much more quickly. >> So faster time to value. >> Yes, faster time to value. We've seen internally for the folks we've hired, that we've got one hundred percent voluntary retention rate. >> That's amazing. >> For early retention rate. For early career talent, four years into the program, where that's about 20% better than the rest of the talent that we have, right? So we're looking at retention, cause we know if you lose somebody, that's nine months of salary probably to replace them and to retrain somebody else. >> That's right, yes >> Absolutely >> Much easier to hang onto somebody than go find somebody new. >> Okay so you're getting the billing faster, higher quality, I heard. Which means better customer retention and less employee turnover. >> Less employee turnover. >> Which means lower cost. >> Absolutely. >> And on the recruiting side of things too, the development of trying to find talent, there's a lot of time invested and we're a firm that has a very lean operations team. A lot of us wear many hats. So one of my hats is my recruiting and development, and this just streamlines things for me and makes it so much easier. I don't have to spread myself thin trying to find folks. I know I've got kind of a pipeline and I've been sharing it with my other leadership in the other practices to kind of share that along the firm >> And to put it in context, I mean so for the trainings that are around rules and careers, were looking at getting the students to have 200 plus hours of training over bootcamp experience. Now, put that against somebody else who has zero right? You're getting to faster productivity, you're shaving off anywhere from 3 to 4 to 6 months of on boarding time by hiring somebody through this program. >> Yeah. >> And minimizing on boarding frustration which would help. >> Yeah, yeah. >> Sympathize with. >> Makes perfect sense. Great sounding program, we appreciate the insight today. Thanks for being with us. >> Martini- Yeah, thank you. >> And you're wearing many hats, you'll need a rain hat out there today. >> I will, I will. [Laughing] >> Congratulations. >> One more, yeah. Great program, thank you ladies. We're back with more on Live. The Cube is in Washington D.C at Inforum 2018.

>> Live from Toronto, Canada, it's theCUBE, covering Blockchain Futurist Conference 2018. Brought to you by theCUBE. >> Hello everyone and welcome back to theCUBE's exclusive coverage here in Toronto, Canada, in Ontario. We are here live breaking down what's going on in the Blockchain world. It's the Untraceables event here, Tracy and team doing a great job of Untraceable. They're putting on the Blockchain Futurist conference. This is about the future, bringing the industry together. All the luminaries are here; Bounds of Ethereum, Ackerson Ecosystem influencers, original gangsters- OGs-are here, of course theCube, we got 2018 coverage breaking it down, I'm John Furrier with Dave Vellante. Wrapping up day one Dave, I know you got to take off and head back on a flight home, let's break down and analyze what's going on in the industry. Yesterday we had the first annual ever, first inaugural Cloud and Blockchain summit, global Blockchain and Cloud summit, two worlds coming together. Here it's a little bit different this is all about cryptocurrency, it's all about blockchain. Big movements, speculators versus builders is my theme and everyone's recognizing the trend of price shifts billions lost in market gap that were gained last year but still some are up. But the focus is about entrepreneurship on a global scale, this is the focus here, right? It's a lot of VIPs, a lot of players coming together. I don't see people crying in their wine about the prices- although you can see it on Anthony Di Iorio's face, probably a setback or the Ethereum community on the price but still, the long game is what they're going after. Your thoughts and analysis? >> Well you definitely seeing a lot of talk about the boom and bust cycles. And we're hearing a lot from people -but by the way, there are a couple of guys who went big, maybe hedge fund guys or other fund guys that are taking a bath, maybe they got in big in January, December, not the best time to get in. So you are seeing some long faces there, but generally the sentiment is: hey, these boom and bust cycles they come and they go we've seen them before, now's the time to hunker down and innovate, execute, and figure out how to add substance and value. Now, first of all, I would say a couple things. One is those guys probably have... a store of fiat currency that they cashed out, number one, so they're feeling pretty good. Two is, the big difference to me John, is in 2018, crypto is much more in the mainstream news. You see it on CNBC, you see it in every medium po- every day you get a medium post, everybody's blogging about it, whereas obviously we've been blogging about bitcoin for five, six years but the mainstream media has picked up on it. >> Seven years. >> Seven years, there you go. So the mainstream media has picked up on it so it's much more front and center than it ever has been in the past. So I think that's a different dynamic. There seems to be still a lot of opportunistic sentiment, people are sanguine about the future and I think that's because we're seeing some real hardcore innovation going on in real use cases. Now, having said all that, the other scenario is there's just a lot of competition for quality projects, we're hearing too many coins out there, you're seeing all these ICOs tied to Ethereum in an oversupply right now, and you're clearly seeing that affect the price of Ethereum, which has dropped, on a percentage basis, much more than bitcoin. It's down considerably this year, whereas bitcoin actually is still up. Ethereum's trading about where it was last September, Bitcoin's up considerably since last September. So you know, a lot of cycles, a lot of instability still, but a lot of optimism. >> The bottom line for me is that the big question that's coming out of this event and this whole week here in Toronto is why do cryptocurrencies matter, the mass influence and adoption of Blockchain technology, where is that on the progress bar? This is the topic, and again, a lot of people that are "poo pooing" this revolution and I'm seeing on my Facebook feed all the time, "bitcoin's at zero," there's a lot of nonbelievers. Here's what I would say, here's my analysis. I think that the comparisons to the dot-com bubble with all the irrational exuberance that was part of that phase, this ICO phase, is crashing. No doubt about them. The ICOs in the United States are down, almost to nill. Certainly a lot of action going outside the United States, still unregulated, still wild, wild, east- or west depending how you call it. So yeah, that's happening and a lot of the bad stuff's being filtered out there's an emphasis on build which you mentioned. But here's the thing that no one might not see in the mainstream. During the dot-com bubble, there was all this companies that were started to it public and that was because the market wanted it. That's what happened with the cryptocurrency ICOs, the market wanted more products, then just manufactured it and then they realized, oh shit too many tokens. But if you look at the internet revolution, and I think this is the comparison with blockchain and crypto. You got blockchain technology, cryptocurrency, which is token economics are absolute gamechangers and the demand for that is very high and there are more people coming on every day in a mass adoption basis. The internet actually never stopped, if you looked at internet penetration rates, Mary Meeker would point out at Morgan Stanley, now she's at Kleiner Perkins, that the internet adoption rate of the internet during the bubble and then post-bubble continued to accelerate. That means more people got on the internet. So therefore the population of users became larger and larger every day. That really level-setted the reality that this was not a fad, not going away. I see blockchain and token economics having the same trajectory where there'll be more people adopting the technology then putting it into use than ever before. That's the tell sign. If that trend line continues to grow, the corrections will all take place, cycles will happen, but the entrepreneurs will follow the money, they're going to follow the user experience, they're going to follow the demand for opportunity. That to me is going to be the major tell sign. I think that's the general sentiment that I'm feeling here is screw the price of the tokens, yes there's too many tokens, clear out the dead wood, get back down to building companies, that's validated by the fact that there are more deals being done from a financing standpoint that are starting to look like traditional funding structures. Security tokens, equities, starting to see people talk and fundraising, lower rounds, not the big mega rounds. Money that's going to be around 7 to 30 million, 30 to 50, 50 to 100, 100 plus. This is going to be traditional structures, not the land grab utility token which gets you into the tailspin of basically managing coins distribution, managing all these things. There'll be a balance, but that's really kind of what's happening. >> So that's great analysis John, I would add to that that the fundamentals are still in place, blockchain attacks inefficiencies. Where there's a middle man and there's inefficiencies and there are waste, blockchain is being applied to attack those inefficiencies. I think the second thing is that new capital-raising vehicles have catalyzed massive investments and are catalyzing innovation and a whole new breed of developers. The third point is a global phenomemon. You don't have to be in Silicon Valley, or New York City, or Boston, or Austin, in the United states, or from an Ivy League school, it's happening around the world, you're seeing non-US countries and island countries invite developers in, giving them tax havens, and as a result, it's becoming much more of a global phenomenon than a lot of the internet startups were. There are a lot of adoption barriers. I mean you have the cyclicality and the volatility, you've got industries that are essentially entrenched: financial services, healthcare, lots of defense and aerospace industries, very much entrenched, it's going to take a long time for that collaboration to come together. And you also have a lot of scams. >> Yeah >> There's going to be a shake out, we predicted that I think in February in the Bahamas, we predicted the flight to quality, people are trying to figure out where that quality is right now. And to your point, you're also seeing more hybrid models, more traditional equity models combined with token models, and that's not a surprise. You're going to see more and more of that as a hedge. The token model still gives people the potential for liquidity, and as long as that fundamental remains in place, I think that dynamic will- is here to stay. >> And also, you and I have seen many cycles of innovation you talk about in the industry, many waves. The people that we talked to that have been through multiple waves like Brailey Rodder, (mumbles) and others, experience, they all know what's going on. The difference here that I think is interesting is that the smart contrast, the flight to quality, the companies that have buildable products, are going to get the attention. Now the difference now in this community that I think is interesting that makes the funding dynamic different is you have now community dynamics. You've got open source software, Cloud computing, and new technology with new capital formation dynamics. I think those three things are the perfect storm of innovation that's being overlooked. and the interplay between that is going to give us a look and feel of an industry that we've never seen before. So we can compare and contrast waves "oh, BC, Client-Server, blah blah blah," I don't think this is going to look like any of those waves, it's going to look different. And that's going to be really the shake out between the pundits who claim they know what's going on, or... predictions whatnot. Talking to the people, putting the ear to the ground in the communities, that's key. And for the companies, the ones that are going to win are the ones that can build community, tap into communities, and grow communities because they're now part of the ecosystem. It's not just selling products to them, they got to be a bidirectional, symbiotic relationship between communities at large, in this ecosystem. I think these are going to be new dynamics they're going to be- impact valuation, it's going to impact time to market, time to value, and ultimately give the entrepreneurs and the investors what they need, which is good outcomes in the process. >> You know it's interesting you were saying about the waves. And the waves in the past, and certainly looking back, were quite easy to identify, they tended to be architectural, you know centralized mainframe, and they went to client server, then you went to the sort of public internet, and then this cloud of remote services. The next wave is maybe not ... blatantly architectural, but it's this blending of digital services that's ubiquitous across all industries. And I think the key is, there's an automation layer on top of these digital services, which is powered by AI and machine intelligence, machine learning, and deep learning, and blockchain is part of that automation layer. And people are building new businesses on top of that and disrupting existing industries. I think there's no industry that's safe from disruption as I put it before, there are some entrenched, high-risk industries like financial services, healthcare, defense, aerospace, education, that are going to take longer but ultimately there's waste in all of those businesses and I will say I think a lot of the incumbents are going to hop on this trend and do very well picking up blockchain and defending against the disruptors. Not all will make it, but a lot of the big guys are going to put some serious resources into this and they're going to lead in to blockchain in a big way. >> Yeah and just to kind of wrap up, I think you're the fact that what we're seeing here is that engineering-led dynamics are happening, blockchain's going to lay down the plumbing, it's got to be stable, desensualized applications over the top with token economics is the business model of innovation. We got technology theater booming with innovation with engineering-led initiatives, that's got to accelerate, that's infrastructure, that's got to be more cloud-like, that's got to be much more stable, that's got to get laid down, got to put the roads down if you will, and then the business model innovation coming from the software this is the game changer so you're looking at all the smart money, smart money is saying okay, we see guys building product, let's see some unique IP, let's see some token economics that are nobel and different for what's happening, that to me is going to be the new investor algorithm if you will, for vetting. And it's been that way in a way, the smart money follows the smart engineers, what are you building? And then they vet that with other stands so again, big engineering-led focus. >> So what would you do now- okay, soyou were hearing this week, too many damn tokens, everything's tied to Ethereum, most ICOs, what would you do now if you're an entrepreneur, you have an idea, you have a potential to build a community, where would you focus, would you just try to float another token? Would you go overseas? What would you do in that situation? >> I would look at the regulatory frameworks as a way, as a guidepost to risk management, right. I think you're going to see some regulatory regimes try to manage the bridge between slow changing, old guard, to new fast, and loose. Crypto-'Cause look at it. It's fast and loose, but there's real people that are working on it. I would focus on the real people that have builders, I'd look at the mechanisms where they're domiciling, and what they do with the economics or the tokens. One thing I will tell you that is that, as an entrepreneur, this is like, a golden rule, your focus is everything: focus, focus, focus. If you're focused on managing distribution of coins, and the arbitrage of coin pricing, that takes away form the focus of engineering and building. I think that's going to be an easy binary test for an investor to say, "what are these guys working on?" Is the token working for the venture, or is the venture working for the token? That is a fundamental mindset, if that is... Not in the right position, it should be: the token works for the venture, not the venture working for the token. That to me, I would run for the hills, if I see someone working for the token, I'd say, "I don't want to fly at all at that deal." Because you could maybe pass up some money right in the short term, but you're going to miss the long game. That's the way I look at it. >> And again, I would add to that, I mean, yeah, okay, so there are a bunch of crypto-billionaires that got minted, and they got in early and good for them, but that doesn't mean there's not more opportunities. And when I think of a company like Dell Michael Dell wasn't the first in PC's, you know? Compact was the first, you know, Rod Canion, the back of the napkin, that urban legend. But what Michael Dell did is he improved on the system. He took inefficiencies out of the supply chain, and became the dominant player! So first move advantage, yes, okay, great, you missed being a billionaire potentially. But the wave tends to get bigger after the market matures. And as a result, I think my focus would be on building, to your theme, building that community, demonstrating value, and then, eventually, I think you're going to be able to use Block Chain, Crypto currencies, tokenization, crypto economics to power your business. But figure out a way to actually execute today and prove value; that's what I would do. >> Again, all great stuff, great analysis, Dave, Good to see you here, where again: this is theCUBE's coverage in Toronto for Block Chain Futurist Conference. Again, this is part of our 2018 initiating coverage of the Block Chain Industry with our video presence. Engaging the community is an upstream content project sharing the data with you, so you can make your decisions, and understand who to connect with. That's our model, we're going to do it. We've been covering BitCoin and Block Chain since 2011, on siliconangle.com, that's our journalism site. Go to theCUBE.net, that's where we have all the videos, and soon to be our CUBE token coming out, be part of our network. Join our community if you wannna get engaged, we're happy to have you. Thanks for watching Day 1 of the Futurist Conference here in Toronto, Ontario. Thanks for watching.

live from New York it's the cube covering AWS summit New York 2018 hot GUI Amazon Web Services and its ecosystem partners hello and welcome back here live cube coverage in New York City for AWS Amazon Web Services summit 2018 I'm John Fourier with Jeff Rick here at the cube our next guest is dr. Matt wood general manager of artificial intelligence with Amazon Web Services keep alumnae been so busy for the past year and been on the cubanía thanks for coming back appreciate you spending the time so promotions keep on going on you got now general manager of the AI group AI operations ai automation machine learning offices a lot of big category of new things developing and a you guys have really taken AI and machine learning to a whole new level it's one of the key value propositions that you guys now have for not just a large enterprise but down to startups and developers so you know congratulations and what's the update oh well the update is this morning in the keynote I was lucky enough to introduce some new capabilities across our platform when it comes to machine learning our mission is that we want to be able to take machine learning and make it available to all developers we joke internally that we just want to we want to make machine learning boring we wanted to make it vanilla it's just it's another tool in the tool chest of any developer and any any data data scientist and we've done that this idea of taking technology that is traditionally only within reached a very very small number of well-funded organizations and making it as broadly distributed as possible we've done that pretty successfully with compute storage and databases and analytics and data warehousing and we want to do the exact same thing for the machine learning and to do that we have to kind of build an entirely new stack and we think of that stack in in three different tiers the bottom tier really for academics and researchers and data scientists we provide a wide range of frameworks open source programming libraries the developers and data scientists use to build neural networks and intelligence they're things like tend to flow and Apache mx9 and by torch and they're really they're very technical you can build you know arbitrarily sophisticated says most she open source to write mostly open source that's right we contribute a lot of our work back to MX net but we also contribute to buy torch and to tend to flow and there's big healthy open source projects growing up around you know all these popular frameworks plus more like chaos and gluon and horror boredom so that's a very very it's a key area for for researchers and academics the next level up we have machine learning platforms this is for developers and data scientists who have data they see in the clout although they want to move to the cloud quickly but they want to be able to use for modeling they want to be able to use it to build custom machine learning models and so here we try and remove as much of the undifferentiated heavy lifting associated with doing that as possible and this is really where sage maker fits in Cersei's maker allows developers to quickly fill train optimize and host their machine learning models and then at the top tier we have a set of AI services which are for application developers that don't want to get into the weeds they just want to get up and running really really quickly and so today we announced four new services really across those their middle tier in that top tier so for Sage maker we're very pleased to introduce a new streaming data protocol which allows you to take data straight from s3 and pump it straight into your algorithm and straight onto the computer infrastructure and what that means is you no longer have to copy data from s3 onto your computer infrastructure in order to be able to start training you just take away that step and just stream it right on there and it's an approach that we use inside sage maker for a lot of our built-in algorithms and it significantly increases the the speed of the algorithm and significantly of course decreases the cost of running the training because you pay by the second so any second you can save off it's a coffin for the customer and they also it helps the machine learn more that's right yeah you can put more data through it absolutely so you're no longer constrained by the amount of disk space you're not even constrained by the amount of memory on the instance you can just pump terabyte after terabyte after terabyte and we actually had another thing like talked about in the keynote this morning a new customer of ours snap who are routinely training on over 100 terabytes of image data using sage maker so you know the ability to be able to pump in lots of data is one of the keys to building successful machine learning applications so we brought that capability to everybody that's using tensorflow now you can just have your tensor flow model bring it to Sage maker do a little bit of wiring click a button and you were just start streaming your data to your tents upload what's the impact of the developer time speed I think it is it is the ability to be able to pump more data it is the decrease in time it takes to start the training but most importantly it decreases the training time all up so you'll see between a 10 and 25 percent decrease in training time some ways you can train more models or you can train more models per in the same unit time or you can just decrease the cost so it's a completely different way of thinking about how to train over large amounts of data we were doing it internally and now we're making it available for everybody through tej matrix that's the first thing the second thing that we're adding is the ability to be able to batch process and stage make them so stage maker used to be great at real-time predictions but there's a lot of use cases where you don't want to just make a one-off prediction you want to predict hundreds or thousands or even millions of things all at once so let's say you've got all of your sales information at the end of the month you want to use that to make a forecast for the next month you don't need to do that in real-time you need to do it once and then place the order and so we added batch transforms to Sage maker so you can pull in all of that data large amounts of data batch process it within a fully automated environment and then spin down the infrastructure and you're done it's a very very simple API anyone that uses a lambda function it's can take advantage of this again just dramatically decreasing the overhead and making it so much easier for everybody to take advantage of machine load and then at the top layer we had new capabilities for our AI services so we announced 12 new language pairs for our translation service and we announced new transcription so capability which allows us to take multi-channel audio such as might be recorded here but more commonly on contact centers just like you have a left channel on the right channel for stereo context centers often record the agent and the customer on the same track and today you can now pass that through our transcribed service long-form speech will split it up into the channels or automatically transcribe it will analyze all the timestamps and create just a single script and from there you can see what was being talked about you can check the topics automatically using comprehend or you can check the compliance did the agents say the words that they have to say for compliance reasons at some point during the conversation that's a material new capability for what's the top surface is being used obviously comprehend transcribe and barri of others you guys have put a lot of stuff out there all kinds of stuff what's the top sellers top use usage as a proxy for uptake you know I think I think we see a ton of we see a ton of adoption across all of these areas but where a lot of the momentum is growing right now is sage maker so if you look at a formula one they just chose Formula One racing they just chose AWS and sage maker as their machine learning platform the National Football League Major League Baseball today announcer they're you know re offering their relationship and their strategic partnership with AWS cream machine learning so all of these groups are using the data which just streams out of these these races all these games yeah and that can be the video or it can be the telemetry of the cars or the telemetry of the players and they're pumping that through Sage maker to drive more engaging experiences for their viewers so guys ok streaming this data is key this is a stage maker quickly this can do video yeah just get it all in all of it well you know we'd love data I would love to follow up on that so the question is is that when will sage maker overtake Aurora as the fastest growing product in history of Amazon because I predicted that reinvent that sage maker would go on err is it looking good right now I mean I sorta still on paper you guys are seeing is growing but see no eager give us an indicator well I mean I don't women breakout revenue per service but even the same excitement I'll say this the same excitement that I see Perseids maker now and the same opportunity and the same momentum it really really reminds me of AWS ten years ago it's the same sort of transformative democratizing approach to which really engages builders and I see the same level of the excitement as levels are super super high as well no super high in general reader pipe out there but I see the same level of enthusiasm and movement and the middle are building with it basically absolutely so what's this toy you have here I know we don't have a lot of time but this isn't you've got a little problem this is the world's first deep learning in April were on wireless video camera we thought it D blends we announced it and launched it at reinvent 2017 and actually hold that but they can hold it up to the camera it's a cute little device we modeled it after wall-e the Pixar movie and it is a HD video camera on the front here and in the base here we have a incredibly powerful custom piece of machine learning hardware so this can process over a billion machine learning operations per second you can take the video in real time you send it to the GPU on board and we'll just start processing the stream in real time so that's kind of interesting but the real value of this and why we designed it was we wanted to try and find a way for developers to get literally hands-on with machine learning so the way that build is a lifelong learners right they they love to learn they have an insatiable appetite for new information and new technologies and the way that they learn that is they experiment they start working and they kind of spin this flywheel where you try something out it works you fiddle with it it stops working you learn a little bit more and you want to go around around around that's been tried and tested for developers for four decades the challenge with machine learning is doing that is still very very difficult you need a label data you need to understand the algorithms it's just it's hard to do but with deep lens you can get up and running in ten minutes so it's connected back to the cloud it's good at about two stage makeup you can deploy a pre-built model down onto the device in ten minutes to do object detection we do some wacky visual effects with neural style transfer we do hot dog and no hot dog detection of course but the real value comes in that you can take any of those models tear them apart so sage maker start fiddling around with them and then immediately deploy them back down onto the camera and every developer on their desk has things that they can detect there are pens and cups and people whatever it is so they can very very quickly spin this flywheel where they're experimenting changing succeeding failing and just going round around a row that's for developers your target audience yes right okay and what are some of the things that have come out of it have you seen any cool yes evolutionary it has been incredibly gratifying and really humbling to see developers that have no machine learning experience take this out of the box and build some really wonderful projects one in really good example is exercise detection so you know when you're doing a workout they build a model which detects the exerciser there and then detects the reps of the weights that you're lifting now we saw skeletal mapping so you could map a person in 3d space using a simple camera we saw security features where you could put this on your door and then it would send you a text message if it didn't recognize who was in front of the door we saw one which was amazing which would read books aloud to kids so you would hold up the book and they would detect the text extract the text send the text to paly and then speak aloud for the kids so there's games as educational tools as little security gizmos one group even trained a dog detection model which detected individual species plug this into an enormous power pack and took it to the local dog park so they could test it out so it's all of this from from a cold start with know machine learning experience you having fun yes absolutely one of the great things about machine learning is you don't just get to work in one area you get to work in you get to work in Formula One and sports and you get to work in healthcare and you get to work in retail and and develop a tool in CTO is gonna love this chief toy officers chief toy officers I love it so I got to ask you so what's new in your world GM of AI audition intelligence what does that mean just quickly explain it for our our audience is that all the software I mean what specifically are you overseeing what's your purview within the realm of AWS yeah that's that's a totally fair question so my purview is I run the products for deep learning machine learning and artificial intelligence really across the AWS machine learning team so I get I have a lot of fingers in a lot of pies I get involved in the new products we're gonna go build out I get involved in helping grow usage of existing products I get it to do a lot of invention it spent a ton of time with customers but overall work with the rest of the team on setting the technical and pronto strategy for machine learning at AWS when what's your top priorities this year adoption uptake new product introductions and you guys don't stop it well we do sync we don't need to keep on introducing more and more things any high ground that you want to take what's what's the vision I didn't the vision is to is genuinely to continue to make it as easy as possible for developers to use Ruggiero my icon overstate the importance or the challenge so we're not at the point where you can just pull down some Python code and figure it out we're not even we don't have a JVM for machine learning where there's no there's no developer tools or debuggers there's very few visualizers so it's still very hard if you kind of think of it in computing terms we're still working in assembly language and you're seen learning so there's this wealth of opportunity ahead of us and the responsibility that I feel very strongly is to be able to continually in crew on the staff to continually bring new capabilities to mortar but well cloud has been disrupting IT operations AI ops with a calling in Silicon Valley and the venture circuit Auto ml as a term has been kicked around Auto automatic machine learning you got to train the machines with something data seems to be it strikes me about this compared to storage or compared to compute or compared to some of the core Amazon foundational products those are just better ways to do something they already existed this is not a better way to do something that are exists this is a way to get the democratization at the start of the process of the application of machine learning and artificial intelligence to a plethora of applications in these cases that is fundamentally yeah different in it just a step up in terms of totally agree the power to the hands of the people it's something which is very far as an area which is very fast moving and very fast growing but what's funny is it totally builds on top of the cloud and you really can't do machine learning in any meaningful production way unless you have a way that is cheap and easy to collect large amounts of data in a way which allows you to pull down high-performance computation at any scale that you need it and so through the cloud we've actually laid the foundations for machine learning going forwards and other things too coming oh yes that's a search as you guys announced the cloud highlights the power yet that it brings to these new capabilities solutely yeah and we get to build on them at AWS and at Amazon just like our customers do so osage make the runs on ec2 we wouldn't we won't be able to do sage maker without ec2 and you know in the fullness of time we see that you know the usage of machine learning could be as big if not bigger than the whole of the rest of AWS combined that's our aspiration dr. Matt would I wish we had more time to Chad loved shopping with you I'd love to do a whole nother segment on what you're doing with customers I know you guys are great customer focus as Andy always mentions when on the cube you guys listen to customers want to hear that maybe a reinvent will circle back sounds good congratulations on your success great to see you he showed it thanks off dr. Matt would here in the cube was dreaming all this data out to the Amazon Cloud is whether they be hosts all of our stuff of course it's the cube bringing you live action here in New York City for cube coverage of AWS summit 2018 in Manhattan we'll be back with more after this short break

>> Narrator: Live from Nassau in the Bahamas. It's The Cube! Covering PolyCon 18. Brought to you by PolyMath. >> Welcome back everyone, we're live here in the Bahamas with The Cube's exclusive coverage of PolyCon 18, I'm John Furrier with my co-host Dave Vellante, both co-founders of SiliconANGLE. We start our coverage of the crypto-currency ICO, blockchain, decentralized world internet that it is becoming. It's the beginning of our tour, 2018. Our next guest is Hartej Sawhney who's the advisor at Pink Sky Capital, but also the co-founder of Hosho.io. Welcome to The Cube. >> Thank you so much. >> Hey thanks for coming on. Thanks for coming on. >> Thanks guys. >> We had a great chat last night, and you do some real good work. You're one of the smartest guys in the business. Got a great reputation. A lot of good stuff going on. So, take a minute to talk about who you are, what you're working on, what you're doing, and the projects you're involved in. >> So first of all, thank you so much for having me, it's really exciting to see the progress of high-quality content being created in the space. So my name is Hartej Sawhney. We have a team based in Las Vegas. I've been based in Las Vegas for about five years. But I was born and raised in central New Jersey, in Princeton. And my co-founder is Yo Sup Quan. We started this company about seven months ago and my co-founder's background was he's the co-founder of Coin Sighter in Exchange out of New York, which exited to Kraken. After that he started Launch Key which exited to Iovation. And prior to this company, my previous company was Zuldi, Z-U-L-D-I .com where we had a mobile point of sale system specifically for high volume food and beverage companies and businesses. So we were focused on Fintech and mobile point of sale and payment processing. So both of us have a unique background in both Fintech and cyber-security and my co-founder Yo, he's a managing partner of a crypto hedge fund named Pink Sky Capital. And he was doing diligence for Pink Sky, and he realized that the quality of the smart contracts he was seeing for deals that he wanted to participate as an investor in, and I'm an advisor in that hedge fund, we both realized that essentially the quality of these smart contracts is extremely low. And that there was nobody in this space that we saw laser focused on just blockchain security. And all the solutions that would be entailed in there. And so we began focusing on just auditing smart contracts, doing a line-by-line code review of each smart contract that's written, conducting a GAS analysis, and conducting a static analysis, making sure that the smart contract does what the white paper says, and then putting a seal of approval on that smart contract to mitigate risk. So that the code has not been changed once we've done an analysis of it, that there's no security vulnerabilities in this code, and that we can mitigate the risks for exchanges and for investors that someone has done a thorough code analysis of this. That there's no chance that this is going to be hacked, that money won't be stolen, money won't be lost, and that there's no chance of a security vulnerability on this. And we put our company's name and reputation on this. >> And what was the problem that is the alternative to that? Was there just poorly written code? Was it updated code? Was it gas was too expensive? They were doing off-chain transactions. I mean what are some of the dynamics that lead you guys down this path? I mean this makes sense. You're kind of underwriting the code, or you're ensuring it or I don't know what you call it, but essentially verifying it. What was the problem? And what were some of the use cases of problems? >> I would say that the underlying problem today in this whole industry, of the blockchain space, is that the most commonly found blockchain is Ethereum. The language behind Ethereum is called Solidity. Solidity is a brand new software language that very few people in the world are sufficient programmers in Solidity. On top of that, Solidity is updated, as a language on a weekly basis. So there are a very limited number of engineers in the world who are full-stack engineers, that have studied and understand Solidity, that have a security background, and have a QA mindset. Everything that I just said does exist on this Earth today and if it does, there's a chance that that person has made too much money to want to get out of bed. Because Ethereum's price has gone up. So the quality of smart contracts that we're seeing being written by even development shops, the developers building them are actually not full-stack engineers, they're web developers who have learned the language Solidity and so thus we believe that the quality of the code has been significantly low. We're finding lots of critical vulnerabilities. In fact, 100% of the time that Hosho has audited code for a smart contract, we have found at least a couple of vulnerabilities. Even as a second or the third auditor after other companies conduct an audit, we always find a vulnerability. >> And is it correct that Solidity is much more easy to work with than say, Bitcoin scripting language, so you can do a lot more with it, so you're getting a lot more, I don't want to say rogue code, but maybe that's what it is. Is that right? Is that the nature of the theory? >> Compared to Bitcoin script, yes. But compared to JavaScript, no. Because Fortune 500 companies have rooms full of Java engineers, Java developers. And now the newer blockchains are being written, are being written on in block JavaScript, right? So you have IBM's Hyperledger program, you have EOS, you have ICX, Cardano, Stellar, Waves, Neo, there's so many new projects that are coming, that all of them are flexing about the same thing. Including Rootstock, RSK. RSK is a project where they're allowing smart contracts to be tied to the Bitcoin blockchain for the first time ever. Right, so Fortune 500 companies may take advantage of the fact that they have Java developers to take advantage of already, that already work for them, who could easily write to a new blockchain, and possibly these new blockchains are more enterprise grade and able to take more institutional capital. But only time will tell. And us as the auditor, we want to see more code from these newer blockchains, and we want to see more developers actually put in commits. Because it's what matters the most, is where are the developers putting in commits and right now maximum developers are on the Ethereum blockchain. >> Is that, the numbers I mean. Just take a step there. So the theory of blockchain. Percentage of developers vis-a-vis other platforms percentages-- >> By far the most is on developed on Ethereum. >> And in terms of code, obviously the efficiencies that are not yet realized, 'cause there's not enough cycles of coding going on, it's evolution, right? >> Yes. >> Seems to be the problem, wouldn't you say? So a combination of full-stack developer requirements, >> Yes. >> To people who aren't proficient in all levels of the stack. >> Yes. >> Just are inefficient in the coding. It's not a ding on the developers, it's just they're writing code and they miss something, right? Or maybe they're not sufficient in the language-- >> It's a new language. The functions are being updated on a weekly basis, so sometimes you copied and pasted a part of another contract, that came from a very sophisticated project, so they'll say to us, well we copied and pasted this portion from EOS, so it should be great. But what that's leading to is either A, they're using a function that's now outdated, or B, by copying and pasting someone else's code from their smart contract, this smart contract is no longer doing what you intended it to do. >> So now Hartej, how much of your capability is human versus machine? >> Yeah I was going to ask that. >> ML, AI type stuff? >> So we're increasingly becoming automated, but because of the over, there's so much demand in the space. And we've had so much demand to consistently conduct audits, it's tough to pull my engineers away from conducting an audit to work on the tooling to automate the audit, right? And so we are building a lot of proprietary tooling to speed up the process, to automate conducting a GAS analysis, where we make sure you're not clogging up the blockchain by using too much GAS. Static analysis, we're trying to automate that as fast as possible. But what's a bit more difficult to automate, at least right now, is when we have a qualified full-stack engineer read the white paper or the source of truth and make sure the smart contract actually does it, that is, it's a bit longer tail where you're leveraging machine learning and AI to make that fully automated. (talking over each other) >> But maybe is that, I'm sorry John. Is that the long term model or do you think you can actually, I mean there's people that say augmented intelligence is going to be a combination of humans and machines, what do you think? >> I think it's going to be a combination for a long time. Every single day that we audit code, our process gets faster and faster and faster because once we find a vulnerability, finding that same vulnerability next time will be faster and easier and faster and easier. And so as time goes on, we see it as, since the bundle of our work today is ICOs, token generation events, there are ERC 20 tokens on the Ethereum blockchain. And we don't know how long this party will last. Like maybe in a couple years or a couple months, we have a big twist in the ICO space that the numbers will drastically go down. The long tail of Hosho's business for us, is to keep track of people writing smart contracts, period. But we think they are going to become more functional smart contracts where the entire business is on a smart contract and they've cut out sophisticated middle men. Right and it may be less ICOs, and in those cases I mean, if you're a publicly traded company, and you're going from R&D phase where you wrote a smart contract and now actually going to deploy it, I think the publicly traded company's going to do three to five audits. They're going to do multiple audits and take security as a very major concern. And in the space today, security is not being discussed nearly as much as it should. We have the best hedge funds cutting checks into companies, before the smart contract is even written, let alone audited. And so we're trying to partner with all the biggest hedge funds and tell the hedge funds to mandate that if you cut a check into a company that is going to do a token generation event, that they need to guarantee that they're going to at least value security, both in-house for the company and for the smart contract that's going to be written. >> How much do you charge for this? I mean just ballpark. Is it a range of purchase price, sales price? What's the average engagement go for, is it on a scope of work? Statement of work? Or is it license? I mean how does it work? >> So first it depends is it a penetration test of the website or the exchange? Penetration testing of exchanges are far more complex than just a website. Or if it's a smart contract audit, is it an ICO or is it a functional smart contract? In either case for the smart contract audit, we have to build a long set of custom tooling to attack each and every smart contract. So it's definitely very case-by-case. But a ballpark that we could maybe give is somewhere around the lines of 10 to 15 thousand dollars per 100 lines of functional code. And we ask for about three weeks of lead time for both a smart contract audit and a penetration test. And surprisingly in this space, some of the highest caliber companies and high caliber projects with the best teams, are coming to us far too late to get a security audit and a penetration test. So after months of fundraising and a private pre-sale and another pre-sale, and going and throwing parties and events and conferences to increase the excitement for participating in their token sale, what we think is the most important part, the security audit for a smart contract is left to the last week before your ICO. And a ridiculous number of companies are coming to us within seven days of the token sale, >> John: Scrambling. >> Scrambling, and we're saying but we've seen you at seven conferences, I think that we need to delay your ICO by two or three weeks. We can assure you that all of your investors will say thank you for valuing security, because this is irreversible. Once this goes live and the smart contract is deployed. >> Horse is out of the barn. >> It's irreversible. >> Right right. >> And once we seal the code, no one should touch it. >> It's always the case with security, it's bolted on at the last minute. >> It's like back road recovery too, oh we'll just back it up. It's an architectural decision we should have made that months ago. So question for you, the smart contract, because again I'm just getting my wires crossed, 'cause there's levels of smart contracts. So if we, hypothetical ICO or we're doing smart contracts for our audience that's going to come out soon. But see that's more transactional. There's security token sales, >> Yes. >> That are essentially, can be ERC 20 tokens, and that's not huge numbers. It could be big, but not massive. Not a lot transaction costs. That's a contract, right? That's a smart contract? >> People are writing smart contracts to conduct a token generational event, most commonly for an ERC 20 token, that's correct. >> Okay so that's the big, I call that the big enchilada. That's the big-- >> Right now that is the most important, the most common. >> Okay so as you go in the future, I can envision a day where in our community, people going to be doing smart contracts peer-to-peer. >> Sure. >> How does that work? Is that a boiler plate? Is is audited, then it's going to be audited every time? Do the smart contracts get smaller? I mean what's your vision on that? Because we are envisioning a day where people in our audience will say hey Hartej, let's do a white paper together, let's write it together, have a handshake, do a smart contract click, click. Lock it in. And charge a dollar a download, get a million downloads, we split it. >> I envision a day where you can have a more drag and drop smart contract and not need a technical developer to be a full-stack engineer to have to write your smart contract. Yes I totally envision that day. >> John: But that's not today. >> We are very far from that today. >> Dave, kill that project. >> We're so far, we're very far from that. We're light years far from that. >> Okay well look. If we can't eliminate the full-stack engineers, I'm okay with that. Can we eliminate the lawyers? At least minimize them. >> We can minimize them possibly, but we have five stacks of lawyers for our company, I don't see them going anywhere. We need lawyers all the time. >> I see that in the press sometimes, yeah it's going to get disrupted. I don't see it happening. Okay we were having a great conversation off-camera about what makes a good ICO. You see, you have a huge observation space. And you were very opinionated. A lot of companies are out there just floating a token because they're trying to raise money. And they could do the same thing with Ethereum or Bitcoin. >> That's correct. >> Your thoughts? >> My thoughts are that it's very important for companies who are sophisticated, I think, to start by giving away a little bit of equity in the business. And that if you want to be in the blockchain space, and you really firmly believe you have a model to have a token within a decentralized application, I would still start by finding quality investors in the space, in the world. They might be still in Silicon Valley. Silicon Valley didn't just disappear overnight now that the blockchain is out. I am all for the fact that Silicon Valley no longer has as much of a grip on tech because of their blockchain world. And they're not seeing as much deal flow, and there's not as much reliance on venture capitalists, that's exciting to me. But let's not forget the value, that top-tier VCs like Andreessen Horowitz and Vinod Khosla. and Fintech VCs like Commerce Ventures and Nyca Partners in New York, Propel VC, these are good Fintech VC arms that continue to time and time again add immense value to companies. >> And they have networks. They add value. >> They have strong-valued networks, but they're just not going to disappear. And those VCs, if they've invested into a company, took a board seat, fostered their growth, taught them what it means to actually be a real business that's growing at 7-15% week over week, maybe two years down the line, after they've given away a board seat to someone like Nyca Partners, I would be interested in understanding what your token economics look like. Now that you have a revenue generating business, how you've placed a token model into this already running business that makes 25 to 50 grand a month and you have a team of 10, self-sustaining themselves off of revenue. Much more intriguing of a conversation. What's happening today in the space is, hey my buddy Jim and Steve and I came up with an idea for this business. There's going to be a token, and we're starting a private pre-sale tomorrow. I'm going to give you 300% bonus and will you be my advisor? And they're going to start raising capital because of an idea. You know what we used to say in the Silicon Valley startup world, you can raise on just a PowerPoint. I think in the blockchain world, you could raise on just an idea? And then maybe a white paper? And the white paper is one page? And so you've raised a bunch of capital, you have a white paper. >> Now you got to build it. >> Now you got to build, you got to write a smart contract, you got to build it, you got to do it, and then everyone loses excitement and it goes back to our previous conversation the development talent. So, another thing not being discussed in the space is company employee retention, right? So if you have a growing number of ICOs, that have very large budgets because investors have found a way to sink millions of dollars into a company early, you've got $5 million in the hands of a company to start, well this company can afford to pay someone a very ridiculous salary to come join them to write the smart contract now. So they could offer an engineer 500 Eth a month to come join them for three months. So you have good engineers just bouncing from one ICO to the next and as soon as the ICO goes live, they quit. This is a problem to companies who are-- >> It's migration, out migration. >> How do you retain, even capital? >> Companies like Hosho, ShapeShift, companies that are selling picks and shovels of the industry, that want to be household names in the space, we have to really think about how we're going to retain our employees in the space. >> So the recruitment and bringing on the new generation, we were also talking off camera about Bill Tye and the younger generation and kind of riffing on the notion that, because there is a new set of mission-driven developers and builders, on the business side as well. Your thoughts and reaction to what you see and what you see that's good and what you see that we need more of? >> So the most powerful thing in the blockchain space that I think is so exciting is that you have a lot of people between the age of 25 and 35 that don't come from money, that didn't go to Stanford, didn't go to Y Combinator, they're probably not white, from-- >> John: Ivy League schools. >> Ivy League schools. I'm not trying to make it about race, but if you're a white male and went to Stanford and went to Y Combinator, chances of you raising VC money on sand hill are a lot higher, right? And you have a guy looking like me who didn't go to Stanford, doesn't come from money, running up and down sand hill, I have personally faced that battle and it wasn't easy. And we were based in Vegas and so being based in Vegas, I'd also have to deal with so why do you live in Vegas? When are you going to move to Silicon Valley? And if we invest in you, you're going to open an office in sand hill right? And now in the blockchain world, what's exciting is you have so many heavy-hitters running as founders, some of the most successful companies in the space, who don't come from money and a big prestigious background, but they're honest, they're hard-working, they're putting in 12 to 15 hours of work every single day, seven days a week. And to space, six weeks is like six years. And we all have a level of trust that goes back to times when we were all running struggling startups. And so our bond is, to me, even more significant than what must have been between Keith Rabois and Peter Thiel in the PayPal Mafia. We have our own mafias being formed of much stronger bonds of younger people who will be able to share much more significant deal flow so if the PayPal Mafia was able to join forces to punch out companies like eBay and Square, wait 'til companies in this space, we have young, heavy-hitters right now who are non-reliant on some of the more traditional older folks. Wait 'til you see what happens in the next couple years. >> Hartej, great conversation. And I want to get one more question in. We've seen Keiretsu Forum, mafias, teams more than ever as community becomes an integral part of vetting and by the way trust, you have unwritten rules. I mean baseball, Dave and I used to do sports analogies. >> Self-governance. >> Reggie Jackson talked about unwritten rules and it works. If you beam the batter, the other guy, your best star, your side's going to get beamed. That's an unwritten rule. These are what keeps things going, balanced through the course of a season. What are the unwritten rules in the Ethos right now? >> Honesty, transparency, and that's the key. We need self-governance. This is a very unregulated market. There's rules being broken by people who are ignorant to the rules. The most common rule I've seen being broken is by people who are not broker dealers, running around fundraising capital, they don't even know what an institutional advisor license is. They don't know what a Series 7 and a Series 63 is. I asked a guy just last night, he said I'm pooling capital, I'm syndicating, let me know if you want in on the deal. And I said when did you take your Series 7? He goes what's that? Get away from me. You're an American, you need to look up what US securities laws are and make sure that you're playing by the rules and if someone who doesn't know the rules has entered our inner circle of investors, of advisors, of people sharing deal flow, we have a good network of people that are closing the loop for companies, whether it's lawyers, investors, exchanges, security auditors, people who write smart contracts, dev shops, people who write white papers, PR marketing, people who do the road show, there's a full circle-- >> So people are actually doing work to put into the community, to know your neighbor if you will, know the deals that are going down, to identify potential trip wires that are being established by either bad actors or-- >> KYC, AML, this is a new space that's also attracting people that have a criminal background. Right? And that's just a harsh reality of the space. That in the United States if you have a felony on your record, maybe getting a job has become really difficult and you figured let's do an ICO, no one's going to check my record. That is a reality of the space. Another reality is the money that was invested into this entire ICO clean. Right, that's a massive issue for the US government right now. It's been less than 15 hours since the SEC has issued actually subpoenas to people on this exact topic, today. >> This is a great topic, we'd like to do more on. >> Dozens of them. >> We'd like to continue to keep in touch with you on The Cube. Obviously you're welcome anytime, loved your insight. Certainly we'd love to have you be an advisor on our mission, you're welcome anytime. >> For sure, let's talk about it. Come out to Las Vegas. Hosho's always happy to host you. >> John And Dave: We're there all the time. >> The Cube lives at the sands. >> It's our second home. >> Come by Hosho's office and let us know. Vegas is our home. We are hosting a conference in Vegas after DEFCON. So DEFCON is the biggest security conference in the world. You have the best black hats and white hats show up as security experts in Vegas and right on the tail end of it, Hosho's going to host a very exclusive invite-only conference. >> What's it called? Just Hosho Conference? >> Just Blockchain. It'll be called the just, it'll be by the Just Blockchain Group and Hosho's the main backer behind it. >> Well we appreciate your integrity and your sharing here on The Cube, and again you're paying it forward in the community, that's great. Ethos we love that. That's our mission here, paying it forward content. Here in the Bahamas. Live coverage here at PolyCon 18. We're talking about securitized token, a decentralized future for awesome things happening. I'm Jeff Furrier, Dave Vellante. We'll be back with more after this short break. (upbeat music)

>> There's a movement that's sweeping across businesses everywhere here in this country and around the world. And it's all about data. Today businesses are being inundated with data. To the tune of over two and a half million gigabytes that'll be generated in the next 60 seconds alone. What do you do with all that data? To extract insights you typically turn to a data scientist. But not necessarily anymore. At least not exclusively. Today the ability to extract value from data is becoming a shared mission. A team effort that spans the organization extending far more widely than ever before. Today, data science is being democratized. >> Data Sciences for All: It's a Whole New Game. >> Welcome everyone, I'm Katie Linendoll. I'm a technology expert writer and I love reporting on all things tech. My fascination with tech started very young. I began coding when I was 12. Received my networking certs by 18 and a degree in IT and new media from Rochester Institute of Technology. So as you can tell, technology has always been a sure passion of mine. Having grown up in the digital age, I love having a career that keeps me at the forefront of science and technology innovations. I spend equal time in the field being hands on as I do on my laptop conducting in depth research. Whether I'm diving underwater with NASA astronauts, witnessing the new ways which mobile technology can help rebuild the Philippine's economy in the wake of super typhoons, or sharing a first look at the newest iPhones on The Today Show, yesterday, I'm always on the hunt for the latest and greatest tech stories. And that's what brought me here. I'll be your host for the next hour and as we explore the new phenomenon that is taking businesses around the world by storm. And data science continues to become democratized and extends beyond the domain of the data scientist. And why there's also a mandate for all of us to become data literate. Now that data science for all drives our AI culture. And we're going to be able to take to the streets and go behind the scenes as we uncover the factors that are fueling this phenomenon and giving rise to a movement that is reshaping how businesses leverage data. And putting organizations on the road to AI. So coming up, I'll be doing interviews with data scientists. We'll see real world demos and take a look at how IBM is changing the game with an open data science platform. We'll also be joined by legendary statistician Nate Silver, founder and editor-in-chief of FiveThirtyEight. Who will shed light on how a data driven mindset is changing everything from business to our culture. We also have a few people who are joining us in our studio, so thank you guys for joining us. Come on, I can do better than that, right? Live studio audience, the fun stuff. And for all of you during the program, I want to remind you to join that conversation on social media using the hashtag DSforAll, it's data science for all. Share your thoughts on what data science and AI means to you and your business. And, let's dive into a whole new game of data science. Now I'd like to welcome my co-host General Manager IBM Analytics, Rob Thomas. >> Hello, Katie. >> Come on guys. >> Yeah, seriously. >> No one's allowed to be quiet during this show, okay? >> Right. >> Or, I'll start calling people out. So Rob, thank you so much. I think you know this conversation, we're calling it a data explosion happening right now. And it's nothing new. And when you and I chatted about it. You've been talking about this for years. You have to ask, is this old news at this point? >> Yeah, I mean, well first of all, the data explosion is not coming, it's here. And everybody's in the middle of it right now. What is different is the economics have changed. And the scale and complexity of the data that organizations are having to deal with has changed. And to this day, 80% of the data in the world still sits behind corporate firewalls. So, that's becoming a problem. It's becoming unmanageable. IT struggles to manage it. The business can't get everything they need. Consumers can't consume it when they want. So we have a challenge here. >> It's challenging in the world of unmanageable. Crazy complexity. If I'm sitting here as an IT manager of my business, I'm probably thinking to myself, this is incredibly frustrating. How in the world am I going to get control of all this data? And probably not just me thinking it. Many individuals here as well. >> Yeah, indeed. Everybody's thinking about how am I going to put data to work in my organization in a way I haven't done before. Look, you've got to have the right expertise, the right tools. The other thing that's happening in the market right now is clients are dealing with multi cloud environments. So data behind the firewall in private cloud, multiple public clouds. And they have to find a way. How am I going to pull meaning out of this data? And that brings us to data science and AI. That's how you get there. >> I understand the data science part but I think we're all starting to hear more about AI. And it's incredible that this buzz word is happening. How do businesses adopt to this AI growth and boom and trend that's happening in this world right now? >> Well, let me define it this way. Data science is a discipline. And machine learning is one technique. And then AI puts both machine learning into practice and applies it to the business. So this is really about how getting your business where it needs to go. And to get to an AI future, you have to lay a data foundation today. I love the phrase, "there's no AI without IA." That means you're not going to get to AI unless you have the right information architecture to start with. >> Can you elaborate though in terms of how businesses can really adopt AI and get started. >> Look, I think there's four things you have to do if you're serious about AI. One is you need a strategy for data acquisition. Two is you need a modern data architecture. Three is you need pervasive automation. And four is you got to expand job roles in the organization. >> Data acquisition. First pillar in this you just discussed. Can we start there and explain why it's so critical in this process? >> Yeah, so let's think about how data acquisition has evolved through the years. 15 years ago, data acquisition was about how do I get data in and out of my ERP system? And that was pretty much solved. Then the mobile revolution happens. And suddenly you've got structured and non-structured data. More than you've ever dealt with. And now you get to where we are today. You're talking terabytes, petabytes of data. >> [Katie] Yottabytes, I heard that word the other day. >> I heard that too. >> Didn't even know what it meant. >> You know how many zeros that is? >> I thought we were in Star Wars. >> Yeah, I think it's a lot of zeroes. >> Yodabytes, it's new. >> So, it's becoming more and more complex in terms of how you acquire data. So that's the new data landscape that every client is dealing with. And if you don't have a strategy for how you acquire that and manage it, you're not going to get to that AI future. >> So a natural segue, if you are one of these businesses, how do you build for the data landscape? >> Yeah, so the question I always hear from customers is we need to evolve our data architecture to be ready for AI. And the way I think about that is it's really about moving from static data repositories to more of a fluid data layer. >> And we continue with the architecture. New data architecture is an interesting buzz word to hear. But it's also one of the four pillars. So if you could dive in there. >> Yeah, I mean it's a new twist on what I would call some core data science concepts. For example, you have to leverage tools with a modern, centralized data warehouse. But your data warehouse can't be stagnant to just what's right there. So you need a way to federate data across different environments. You need to be able to bring your analytics to the data because it's most efficient that way. And ultimately, it's about building an optimized data platform that is designed for data science and AI. Which means it has to be a lot more flexible than what clients have had in the past. >> All right. So we've laid out what you need for driving automation. But where does the machine learning kick in? >> Machine learning is what gives you the ability to automate tasks. And I think about machine learning. It's about predicting and automating. And this will really change the roles of data professionals and IT professionals. For example, a data scientist cannot possibly know every algorithm or every model that they could use. So we can automate the process of algorithm selection. Another example is things like automated data matching. Or metadata creation. Some of these things may not be exciting but they're hugely practical. And so when you think about the real use cases that are driving return on investment today, it's things like that. It's automating the mundane tasks. >> Let's go ahead and come back to something that you mentioned earlier because it's fascinating to be talking about this AI journey, but also significant is the new job roles. And what are those other participants in the analytics pipeline? >> Yeah I think we're just at the start of this idea of new job roles. We have data scientists. We have data engineers. Now you see machine learning engineers. Application developers. What's really happening is that data scientists are no longer allowed to work in their own silo. And so the new job roles is about how does everybody have data first in their mind? And then they're using tools to automate data science, to automate building machine learning into applications. So roles are going to change dramatically in organizations. >> I think that's confusing though because we have several organizations who saying is that highly specialized roles, just for data science? Or is it applicable to everybody across the board? >> Yeah, and that's the big question, right? Cause everybody's thinking how will this apply? Do I want this to be just a small set of people in the organization that will do this? But, our view is data science has to for everybody. It's about bring data science to everybody as a shared mission across the organization. Everybody in the company has to be data literate. And participate in this journey. >> So overall, group effort, has to be a common goal, and we all need to be data literate across the board. >> Absolutely. >> Done deal. But at the end of the day, it's kind of not an easy task. >> It's not. It's not easy but it's maybe not as big of a shift as you would think. Because you have to put data in the hands of people that can do something with it. So, it's very basic. Give access to data. Data's often locked up in a lot of organizations today. Give people the right tools. Embrace the idea of choice or diversity in terms of those tools. That gets you started on this path. >> It's interesting to hear you say essentially you need to train everyone though across the board when it comes to data literacy. And I think people that are coming into the work force don't necessarily have a background or a degree in data science. So how do you manage? >> Yeah, so in many cases that's true. I will tell you some universities are doing amazing work here. One example, University of California Berkeley. They offer a course for all majors. So no matter what you're majoring in, you have a course on foundations of data science. How do you bring data science to every role? So it's starting to happen. We at IBM provide data science courses through CognitiveClass.ai. It's for everybody. It's free. And look, if you want to get your hands on code and just dive right in, you go to datascience.ibm.com. The key point is this though. It's more about attitude than it is aptitude. I think anybody can figure this out. But it's about the attitude to say we're putting data first and we're going to figure out how to make this real in our organization. >> I also have to give a shout out to my alma mater because I have heard that there is an offering in MS in data analytics. And they are always on the forefront of new technologies and new majors and on trend. And I've heard that the placement behind those jobs, people graduating with the MS is high. >> I'm sure it's very high. >> So go Tigers. All right, tangential. Let me get back to something else you touched on earlier because you mentioned that a number of customers ask you how in the world do I get started with AI? It's an overwhelming question. Where do you even begin? What do you tell them? >> Yeah, well things are moving really fast. But the good thing is most organizations I see, they're already on the path, even if they don't know it. They might have a BI practice in place. They've got data warehouses. They've got data lakes. Let me give you an example. AMC Networks. They produce a lot of the shows that I'm sure you watch Katie. >> [Katie] Yes, Breaking Bad, Walking Dead, any fans? >> [Rob] Yeah, we've got a few. >> [Katie] Well you taught me something I didn't even know. Because it's amazing how we have all these different industries, but yet media in itself is impacted too. And this is a good example. >> Absolutely. So, AMC Networks, think about it. They've got ads to place. They want to track viewer behavior. What do people like? What do they dislike? So they have to optimize every aspect of their business from marketing campaigns to promotions to scheduling to ads. And their goal was transform data into business insights and really take the burden off of their IT team that was heavily burdened by obviously a huge increase in data. So their VP of BI took the approach of using machine learning to process large volumes of data. They used a platform that was designed for AI and data processing. It's the IBM analytics system where it's a data warehouse, data science tools are built in. It has in memory data processing. And just like that, they were ready for AI. And they're already seeing that impact in their business. >> Do you think a movement of that nature kind of presses other media conglomerates and organizations to say we need to be doing this too? >> I think it's inevitable that everybody, you're either going to be playing, you're either going to be leading, or you'll be playing catch up. And so, as we talk to clients we think about how do you start down this path now, even if you have to iterate over time? Because otherwise you're going to wake up and you're going to be behind. >> One thing worth noting is we've talked about analytics to the data. It's analytics first to the data, not the other way around. >> Right. So, look. We as a practice, we say you want to bring data to where the data sits. Because it's a lot more efficient that way. It gets you better outcomes in terms of how you train models and it's more efficient. And we think that leads to better outcomes. Other organization will say, "Hey move the data around." And everything becomes a big data movement exercise. But once an organization has started down this path, they're starting to get predictions, they want to do it where it's really easy. And that means analytics applied right where the data sits. >> And worth talking about the role of the data scientist in all of this. It's been called the hot job of the decade. And a Harvard Business Review even dubbed it the sexiest job of the 21st century. >> Yes. >> I want to see this on the cover of Vogue. Like I want to see the first data scientist. Female preferred, on the cover of Vogue. That would be amazing. >> Perhaps you can. >> People agree. So what changes for them? Is this challenging in terms of we talk data science for all. Where do all the data science, is it data science for everyone? And how does it change everything? >> Well, I think of it this way. AI gives software super powers. It really does. It changes the nature of software. And at the center of that is data scientists. So, a data scientist has a set of powers that they've never had before in any organization. And that's why it's a hot profession. Now, on one hand, this has been around for a while. We've had actuaries. We've had statisticians that have really transformed industries. But there are a few things that are new now. We have new tools. New languages. Broader recognition of this need. And while it's important to recognize this critical skill set, you can't just limit it to a few people. This is about scaling it across the organization. And truly making it accessible to all. >> So then do we need more data scientists? Or is this something you train like you said, across the board? >> Well, I think you want to do a little bit of both. We want more. But, we can also train more and make the ones we have more productive. The way I think about it is there's kind of two markets here. And we call it clickers and coders. >> [Katie] I like that. That's good. >> So, let's talk about what that means. So clickers are basically somebody that wants to use tools. Create models visually. It's drag and drop. Something that's very intuitive. Those are the clickers. Nothing wrong with that. It's been valuable for years. There's a new crop of data scientists. They want to code. They want to build with the latest open source tools. They want to write in Python or R. These are the coders. And both approaches are viable. Both approaches are critical. Organizations have to have a way to meet the needs of both of those types. And there's not a lot of things available today that do that. >> Well let's keep going on that. Because I hear you talking about the data scientists role and how it's critical to success, but with the new tools, data science and analytics skills can extend beyond the domain of just the data scientist. >> That's right. So look, we're unifying coders and clickers into a single platform, which we call IBM Data Science Experience. And as the demand for data science expertise grows, so does the need for these kind of tools. To bring them into the same environment. And my view is if you have the right platform, it enables the organization to collaborate. And suddenly you've changed the nature of data science from an individual sport to a team sport. >> So as somebody that, my background is in IT, the question is really is this an additional piece of what IT needs to do in 2017 and beyond? Or is it just another line item to the budget? >> So I'm afraid that some people might view it that way. As just another line item. But, I would challenge that and say data science is going to reinvent IT. It's going to change the nature of IT. And every organization needs to think about what are the skills that are critical? How do we engage a broader team to do this? Because once they get there, this is the chance to reinvent how they're performing IT. >> [Katie] Challenging or not? >> Look it's all a big challenge. Think about everything IT organizations have been through. Some of them were late to things like mobile, but then they caught up. Some were late to cloud, but then they caught up. I would just urge people, don't be late to data science. Use this as your chance to reinvent IT. Start with this notion of clickers and coders. This is a seminal moment. Much like mobile and cloud was. So don't be late. >> And I think it's critical because it could be so costly to wait. And Rob and I were even chatting earlier how data analytics is just moving into all different kinds of industries. And I can tell you even personally being effected by how important the analysis is in working in pediatric cancer for the last seven years. I personally implement virtual reality headsets to pediatric cancer hospitals across the country. And it's great. And it's working phenomenally. And the kids are amazed. And the staff is amazed. But the phase two of this project is putting in little metrics in the hardware that gather the breathing, the heart rate to show that we have data. Proof that we can hand over to the hospitals to continue making this program a success. So just in-- >> That's a great example. >> An interesting example. >> Saving lives? >> Yes. >> That's also applying a lot of what we talked about. >> Exciting stuff in the world of data science. >> Yes. Look, I just add this is an existential moment for every organization. Because what you do in this area is probably going to define how competitive you are going forward. And think about if you don't do something. What if one of your competitors goes and creates an application that's more engaging with clients? So my recommendation is start small. Experiment. Learn. Iterate on projects. Define the business outcomes. Then scale up. It's very doable. But you've got to take the first step. >> First step always critical. And now we're going to get to the fun hands on part of our story. Because in just a moment we're going to take a closer look at what data science can deliver. And where organizations are trying to get to. All right. Thank you Rob and now we've been joined by Siva Anne who is going to help us navigate this demo. First, welcome Siva. Give him a big round of applause. Yeah. All right, Rob break down what we're going to be looking at. You take over this demo. >> All right. So this is going to be pretty interesting. So Siva is going to take us through. So he's going to play the role of a financial adviser. Who wants to help better serve clients through recommendations. And I'm going to really illustrate three things. One is how do you federate data from multiple data sources? Inside the firewall, outside the firewall. How do you apply machine learning to predict and to automate? And then how do you move analytics closer to your data? So, what you're seeing here is a custom application for an investment firm. So, Siva, our financial adviser, welcome. So you can see at the top, we've got market data. We pulled that from an external source. And then we've got Siva's calendar in the middle. He's got clients on the right side. So page down, what else do you see down there Siva? >> [Siva] I can see the recent market news. And in here I can see that JP Morgan is calling for a US dollar rebound in the second half of the year. And, I have upcoming meeting with Leo Rakes. I can get-- >> [Rob] So let's go in there. Why don't you click on Leo Rakes. So, you're sitting at your desk, you're deciding how you're going to spend the day. You know you have a meeting with Leo. So you click on it. You immediately see, all right, so what do we know about him? We've got data governance implemented. So we know his age, we know his degree. We can see he's not that aggressive of a trader. Only six trades in the last few years. But then where it gets interesting is you go to the bottom. You start to see predicted industry affinity. Where did that come from? How do we have that? >> [Siva] So these green lines and red arrows here indicate the trending affinity of Leo Rakes for particular industry stocks. What we've done here is we've built machine learning models using customer's demographic data, his stock portfolios, and browsing behavior to build a model which can predict his affinity for a particular industry. >> [Rob] Interesting. So, I like to think of this, we call it celebrity experiences. So how do you treat every customer like they're a celebrity? So to some extent, we're reading his mind. Because without asking him, we know that he's going to have an affinity for auto stocks. So we go down. Now we look at his portfolio. You can see okay, he's got some different holdings. He's got Amazon, Google, Apple, and then he's got RACE, which is the ticker for Ferrari. You can see that's done incredibly well. And so, as a financial adviser, you look at this and you say, all right, we know he loves auto stocks. Ferrari's done very well. Let's create a hedge. Like what kind of security would interest him as a hedge against his position for Ferrari? Could we go figure that out? >> [Siva] Yes. Given I know that he's gotten an affinity for auto stocks, and I also see that Ferrari has got some terminus gains, I want to lock in these gains by hedging. And I want to do that by picking a auto stock which has got negative correlation with Ferrari. >> [Rob] So this is where we get to the idea of in database analytics. Cause you start clicking that and immediately we're getting instant answers of what's happening. So what did we find here? We're going to compare Ferrari and Honda. >> [Siva] I'm going to compare Ferrari with Honda. And what I see here instantly is that Honda has got a negative correlation with Ferrari, which makes it a perfect mix for his stock portfolio. Given he has an affinity for auto stocks and it correlates negatively with Ferrari. >> [Rob] These are very powerful tools at the hand of a financial adviser. You think about it. As a financial adviser, you wouldn't think about federating data, machine learning, pretty powerful. >> [Siva] Yes. So what we have seen here is that using the common SQL engine, we've been able to federate queries across multiple data sources. Db2 Warehouse in the cloud, IBM's Integrated Analytic System, and Hortonworks powered Hadoop platform for the new speeds. We've been able to use machine learning to derive innovative insights about his stock affinities. And drive the machine learning into the appliance. Closer to where the data resides to deliver high performance analytics. >> [Rob] At scale? >> [Siva] We're able to run millions of these correlations across stocks, currency, other factors. And even score hundreds of customers for their affinities on a daily basis. >> That's great. Siva, thank you for playing the role of financial adviser. So I just want to recap briefly. Cause this really powerful technology that's really simple. So we federated, we aggregated multiple data sources from all over the web and internal systems. And public cloud systems. Machine learning models were built that predicted Leo's affinity for a certain industry. In this case, automotive. And then you see when you deploy analytics next to your data, even a financial adviser, just with the click of a button is getting instant answers so they can go be more productive in their next meeting. This whole idea of celebrity experiences for your customer, that's available for everybody, if you take advantage of these types of capabilities. Katie, I'll hand it back to you. >> Good stuff. Thank you Rob. Thank you Siva. Powerful demonstration on what we've been talking about all afternoon. And thank you again to Siva for helping us navigate. Should be give him one more round of applause? We're going to be back in just a moment to look at how we operationalize all of this data. But in first, here's a message from me. If you're a part of a line of business, your main fear is disruption. You know data is the new goal that can create huge amounts of value. So does your competition. And they may be beating you to it. You're convinced there are new business models and revenue sources hidden in all the data. You just need to figure out how to leverage it. But with the scarcity of data scientists, you really can't rely solely on them. You may need more people throughout the organization that have the ability to extract value from data. And as a data science leader or data scientist, you have a lot of the same concerns. You spend way too much time looking for, prepping, and interpreting data and waiting for models to train. You know you need to operationalize the work you do to provide business value faster. What you want is an easier way to do data prep. And rapidly build models that can be easily deployed, monitored and automatically updated. So whether you're a data scientist, data science leader, or in a line of business, what's the solution? What'll it take to transform the way you work? That's what we're going to explore next. All right, now it's time to delve deeper into the nuts and bolts. The nitty gritty of operationalizing data science and creating a data driven culture. How do you actually do that? Well that's what these experts are here to share with us. I'm joined by Nir Kaldero, who's head of data science at Galvanize, which is an education and training organization. Tricia Wang, who is co-founder of Sudden Compass, a consultancy that helps companies understand people with data. And last, but certainly not least, Michael Li, founder and CEO of Data Incubator, which is a data science train company. All right guys. Shall we get right to it? >> All right. >> So data explosion happening right now. And we are seeing it across the board. I just shared an example of how it's impacting my philanthropic work in pediatric cancer. But you guys each have so many unique roles in your business life. How are you seeing it just blow up in your fields? Nir, your thing? >> Yeah, for example like in Galvanize we train many Fortune 500 companies. And just by looking at the demand of companies that wants us to help them go through this digital transformation is mind-blowing. Data point by itself. >> Okay. Well what we're seeing what's going on is that data science like as a theme, is that it's actually for everyone now. But what's happening is that it's actually meeting non technical people. But what we're seeing is that when non technical people are implementing these tools or coming at these tools without a base line of data literacy, they're often times using it in ways that distance themselves from the customer. Because they're implementing data science tools without a clear purpose, without a clear problem. And so what we do at Sudden Compass is that we work with companies to help them embrace and understand the complexity of their customers. Because often times they are misusing data science to try and flatten their understanding of the customer. As if you can just do more traditional marketing. Where you're putting people into boxes. And I think the whole ROI of data is that you can now understand people's relationships at a much more complex level at a greater scale before. But we have to do this with basic data literacy. And this has to involve technical and non technical people. >> Well you can have all the data in the world, and I think it speaks to, if you're not doing the proper movement with it, forget it. It means nothing at the same time. >> No absolutely. I mean, I think that when you look at the huge explosion in data, that comes with it a huge explosion in data experts. Right, we call them data scientists, data analysts. And sometimes they're people who are very, very talented, like the people here. But sometimes you have people who are maybe re-branding themselves, right? Trying to move up their title one notch to try to attract that higher salary. And I think that that's one of the things that customers are coming to us for, right? They're saying, hey look, there are a lot of people that call themselves data scientists, but we can't really distinguish. So, we have sort of run a fellowship where you help companies hire from a really talented group of folks, who are also truly data scientists and who know all those kind of really important data science tools. And we also help companies internally. Fortune 500 companies who are looking to grow that data science practice that they have. And we help clients like McKinsey, BCG, Bain, train up their customers, also their clients, also their workers to be more data talented. And to build up that data science capabilities. >> And Nir, this is something you work with a lot. A lot of Fortune 500 companies. And when we were speaking earlier, you were saying many of these companies can be in a panic. >> Yeah. >> Explain that. >> Yeah, so you know, not all Fortune 500 companies are fully data driven. And we know that the winners in this fourth industrial revolution, which I like to call the machine intelligence revolution, will be companies who navigate and transform their organization to unlock the power of data science and machine learning. And the companies that are not like that. Or not utilize data science and predictive power well, will pretty much get shredded. So they are in a panic. >> Tricia, companies have to deal with data behind the firewall and in the new multi cloud world. How do organizations start to become driven right to the core? >> I think the most urgent question to become data driven that companies should be asking is how do I bring the complex reality that our customers are experiencing on the ground in to a corporate office? Into the data models. So that question is critical because that's how you actually prevent any big data disasters. And that's how you leverage big data. Because when your data models are really far from your human models, that's when you're going to do things that are really far off from how, it's going to not feel right. That's when Tesco had their terrible big data disaster that they're still recovering from. And so that's why I think it's really important to understand that when you implement big data, you have to further embrace thick data. The qualitative, the emotional stuff, that is difficult to quantify. But then comes the difficult art and science that I think is the next level of data science. Which is that getting non technical and technical people together to ask how do we find those unknown nuggets of insights that are difficult to quantify? Then, how do we do the next step of figuring out how do you mathematically scale those insights into a data model? So that actually is reflective of human understanding? And then we can start making decisions at scale. But you have to have that first. >> That's absolutely right. And I think that when we think about what it means to be a data scientist, right? I always think about it in these sort of three pillars. You have the math side. You have to have that kind of stats, hardcore machine learning background. You have the programming side. You don't work with small amounts of data. You work with large amounts of data. You've got to be able to type the code to make those computers run. But then the last part is that human element. You have to understand the domain expertise. You have to understand what it is that I'm actually analyzing. What's the business proposition? And how are the clients, how are the users actually interacting with the system? That human element that you were talking about. And I think having somebody who understands all of those and not just in isolation, but is able to marry that understanding across those different topics, that's what makes a data scientist. >> But I find that we don't have people with those skill sets. And right now the way I see teams being set up inside companies is that they're creating these isolated data unicorns. These data scientists that have graduated from your programs, which are great. But, they don't involve the people who are the domain experts. They don't involve the designers, the consumer insight people, the people, the salespeople. The people who spend time with the customers day in and day out. Somehow they're left out of the room. They're consulted, but they're not a stakeholder. >> Can I actually >> Yeah, yeah please. >> Can I actually give a quick example? So for example, we at Galvanize train the executives and the managers. And then the technical people, the data scientists and the analysts. But in order to actually see all of the RY behind the data, you also have to have a creative fluid conversation between non technical and technical people. And this is a major trend now. And there's a major gap. And we need to increase awareness and kind of like create a new, kind of like environment where technical people also talks seamlessly with non technical ones. >> [Tricia] We call-- >> That's one of the things that we see a lot. Is one of the trends in-- >> A major trend. >> data science training is it's not just for the data science technical experts. It's not just for one type of person. So a lot of the training we do is sort of data engineers. People who are more on the software engineering side learning more about the stats of math. And then people who are sort of traditionally on the stat side learning more about the engineering. And then managers and people who are data analysts learning about both. >> Michael, I think you said something that was of interest too because I think we can look at IBM Watson as an example. And working in healthcare. The human component. Because often times we talk about machine learning and AI, and data and you get worried that you still need that human component. Especially in the world of healthcare. And I think that's a very strong point when it comes to the data analysis side. Is there any particular example you can speak to of that? >> So I think that there was this really excellent paper a while ago talking about all the neuro net stuff and trained on textual data. So looking at sort of different corpuses. And they found that these models were highly, highly sexist. They would read these corpuses and it's not because neuro nets themselves are sexist. It's because they're reading the things that we write. And it turns out that we write kind of sexist things. And they would sort of find all these patterns in there that were sort of latent, that had a lot of sort of things that maybe we would cringe at if we sort of saw. And I think that's one of the really important aspects of the human element, right? It's being able to come in and sort of say like, okay, I know what the biases of the system are, I know what the biases of the tools are. I need to figure out how to use that to make the tools, make the world a better place. And like another area where this comes up all the time is lending, right? So the federal government has said, and we have a lot of clients in the financial services space, so they're constantly under these kind of rules that they can't make discriminatory lending practices based on a whole set of protected categories. Race, sex, gender, things like that. But, it's very easy when you train a model on credit scores to pick that up. And then to have a model that's inadvertently sexist or racist. And that's where you need the human element to come back in and say okay, look, you're using the classic example would be zip code, you're using zip code as a variable. But when you look at it, zip codes actually highly correlated with race. And you can't do that. So you may inadvertently by sort of following the math and being a little naive about the problem, inadvertently introduce something really horrible into a model and that's where you need a human element to sort of step in and say, okay hold on. Slow things down. This isn't the right way to go. >> And the people who have -- >> I feel like, I can feel her ready to respond. >> Yes, I'm ready. >> She's like let me have at it. >> And the people here it is. And the people who are really great at providing that human intelligence are social scientists. We are trained to look for bias and to understand bias in data. Whether it's quantitative or qualitative. And I really think that we're going to have less of these kind of problems if we had more integrated teams. If it was a mandate from leadership to say no data science team should be without a social scientist, ethnographer, or qualitative researcher of some kind, to be able to help see these biases. >> The talent piece is actually the most crucial-- >> Yeah. >> one here. If you look about how to enable machine intelligence in organization there are the pillars that I have in my head which is the culture, the talent and the technology infrastructure. And I believe and I saw in working very closely with the Fortune 100 and 200 companies that the talent piece is actually the most important crucial hard to get. >> [Tricia] I totally agree. >> It's absolutely true. Yeah, no I mean I think that's sort of like how we came up with our business model. Companies were basically saying hey, I can't hire data scientists. And so we have a fellowship where we get 2,000 applicants each quarter. We take the top 2% and then we sort of train them up. And we work with hiring companies who then want to hire from that population. And so we're sort of helping them solve that problem. And the other half of it is really around training. Cause with a lot of industries, especially if you're sort of in a more regulated industry, there's a lot of nuances to what you're doing. And the fastest way to develop that data science or AI talent may not necessarily be to hire folks who are coming out of a PhD program. It may be to take folks internally who have a lot of that domain knowledge that you have and get them trained up on those data science techniques. So we've had large insurance companies come to us and say hey look, we hire three or four folks from you a quarter. That doesn't move the needle for us. What we really need is take the thousand actuaries and statisticians that we have and get all of them trained up to become a data scientist and become data literate in this new open source world. >> [Katie] Go ahead. >> All right, ladies first. >> Go ahead. >> Are you sure? >> No please, fight first. >> Go ahead. >> Go ahead Nir. >> So this is actually a trend that we have been seeing in the past year or so that companies kind of like start to look how to upscale and look for talent within the organization. So they can actually move them to become more literate and navigate 'em from analyst to data scientist. And from data scientist to machine learner. So this is actually a trend that is happening already for a year or so. >> Yeah, but I also find that after they've gone through that training in getting people skilled up in data science, the next problem that I get is executives coming to say we've invested in all of this. We're still not moving the needle. We've already invested in the right tools. We've gotten the right skills. We have enough scale of people who have these skills. Why are we not moving the needle? And what I explain to them is look, you're still making decisions in the same way. And you're still not involving enough of the non technical people. Especially from marketing, which is now, the CMO's are much more responsible for driving growth in their companies now. But often times it's so hard to change the old way of marketing, which is still like very segmentation. You know, demographic variable based, and we're trying to move people to say no, you have to understand the complexity of customers and not put them in boxes. >> And I think underlying a lot of this discussion is this question of culture, right? >> Yes. >> Absolutely. >> How do you build a data driven culture? And I think that that culture question, one of the ways that comes up quite often in especially in large, Fortune 500 enterprises, is that they are very, they're not very comfortable with sort of example, open source architecture. Open source tools. And there is some sort of residual bias that that's somehow dangerous. So security vulnerability. And I think that that's part of the cultural challenge that they often have in terms of how do I build a more data driven organization? Well a lot of the talent really wants to use these kind of tools. And I mean, just to give you an example, we are partnering with one of the major cloud providers to sort of help make open source tools more user friendly on their platform. So trying to help them attract the best technologists to use their platform because they want and they understand the value of having that kind of open source technology work seamlessly on their platforms. So I think that just sort of goes to show you how important open source is in this movement. And how much large companies and Fortune 500 companies and a lot of the ones we work with have to embrace that. >> Yeah, and I'm seeing it in our work. Even when we're working with Fortune 500 companies, is that they've already gone through the first phase of data science work. Where I explain it was all about the tools and getting the right tools and architecture in place. And then companies started moving into getting the right skill set in place. Getting the right talent. And what you're talking about with culture is really where I think we're talking about the third phase of data science, which is looking at communication of these technical frameworks so that we can get non technical people really comfortable in the same room with data scientists. That is going to be the phase, that's really where I see the pain point. And that's why at Sudden Compass, we're really dedicated to working with each other to figure out how do we solve this problem now? >> And I think that communication between the technical stakeholders and management and leadership. That's a very critical piece of this. You can't have a successful data science organization without that. >> Absolutely. >> And I think that actually some of the most popular trainings we've had recently are from managers and executives who are looking to say, how do I become more data savvy? How do I figure out what is this data science thing and how do I communicate with my data scientists? >> You guys made this way too easy. I was just going to get some popcorn and watch it play out. >> Nir, last 30 seconds. I want to leave you with an opportunity to, anything you want to add to this conversation? >> I think one thing to conclude is to say that companies that are not data driven is about time to hit refresh and figure how they transition the organization to become data driven. To become agile and nimble so they can actually see what opportunities from this important industrial revolution. Otherwise, unfortunately they will have hard time to survive. >> [Katie] All agreed? >> [Tricia] Absolutely, you're right. >> Michael, Trish, Nir, thank you so much. Fascinating discussion. And thank you guys again for joining us. We will be right back with another great demo. Right after this. >> Thank you Katie. >> Once again, thank you for an excellent discussion. Weren't they great guys? And thank you for everyone who's tuning in on the live webcast. As you can hear, we have an amazing studio audience here. And we're going to keep things moving. I'm now joined by Daniel Hernandez and Siva Anne. And we're going to turn our attention to how you can deliver on what they're talking about using data science experience to do data science faster. >> Thank you Katie. Siva and I are going to spend the next 10 minutes showing you how you can deliver on what they were saying using the IBM Data Science Experience to do data science faster. We'll demonstrate through new features we introduced this week how teams can work together more effectively across the entire analytics life cycle. How you can take advantage of any and all data no matter where it is and what it is. How you could use your favorite tools from open source. And finally how you could build models anywhere and employ them close to where your data is. Remember the financial adviser app Rob showed you? To build an app like that, we needed a team of data scientists, developers, data engineers, and IT staff to collaborate. We do this in the Data Science Experience through a concept we call projects. When I create a new project, I can now use the new Github integration feature. We're doing for data science what we've been doing for developers for years. Distributed teams can work together on analytics projects. And take advantage of Github's version management and change management features. This is a huge deal. Let's explore the project we created for the financial adviser app. As you can see, our data engineer Joane, our developer Rob, and others are collaborating this project. Joane got things started by bringing together the trusted data sources we need to build the app. Taking a closer look at the data, we see that our customer and profile data is stored on our recently announced IBM Integrated Analytics System, which runs safely behind our firewall. We also needed macro economic data, which she was able to find in the Federal Reserve. And she stored it in our Db2 Warehouse on Cloud. And finally, she selected stock news data from NASDAQ.com and landed that in a Hadoop cluster, which happens to be powered by Hortonworks. We added a new feature to the Data Science Experience so that when it's installed with Hortonworks, it automatically uses a need of security and governance controls within the cluster so your data is always secure and safe. Now we want to show you the news data we stored in the Hortonworks cluster. This is the mean administrative console. It's powered by an open source project called Ambari. And here's the news data. It's in parquet files stored in HDFS, which happens to be a distributive file system. To get the data from NASDAQ into our cluster, we used IBM's BigIntegrate and BigQuality to create automatic data pipelines that acquire, cleanse, and ingest that news data. Once the data's available, we use IBM's Big SQL to query that data using SQL statements that are much like the ones we would use for any relation of data, including the data that we have in the Integrated Analytics System and Db2 Warehouse on Cloud. This and the federation capabilities that Big SQL offers dramatically simplifies data acquisition. Now we want to show you how we support a brand new tool that we're excited about. Since we launched last summer, the Data Science Experience has supported Jupyter and R for data analysis and visualization. In this week's update, we deeply integrated another great open source project called Apache Zeppelin. It's known for having great visualization support, advanced collaboration features, and is growing in popularity amongst the data science community. This is an example of Apache Zeppelin and the notebook we created through it to explore some of our data. Notice how wonderful and easy the data visualizations are. Now we want to walk you through the Jupyter notebook we created to explore our customer preference for stocks. We use notebooks to understand and explore data. To identify the features that have some predictive power. Ultimately, we're trying to assess what ultimately is driving customer stock preference. Here we did the analysis to identify the attributes of customers that are likely to purchase auto stocks. We used this understanding to build our machine learning model. For building machine learning models, we've always had tools integrated into the Data Science Experience. But sometimes you need to use tools you already invested in. Like our very own SPSS as well as SAS. Through new import feature, you can easily import those models created with those tools. This helps you avoid vendor lock-in, and simplify the development, training, deployment, and management of all your models. To build the models we used in app, we could have coded, but we prefer a visual experience. We used our customer profile data in the Integrated Analytic System. Used the Auto Data Preparation to cleanse our data. Choose the binary classification algorithms. Let the Data Science Experience evaluate between logistic regression and gradient boosted tree. It's doing the heavy work for us. As you can see here, the Data Science Experience generated performance metrics that show us that the gradient boosted tree is the best performing algorithm for the data we gave it. Once we save this model, it's automatically deployed and available for developers to use. Any application developer can take this endpoint and consume it like they would any other API inside of the apps they built. We've made training and creating machine learning models super simple. But what about the operations? A lot of companies are struggling to ensure their model performance remains high over time. In our financial adviser app, we know that customer data changes constantly, so we need to always monitor model performance and ensure that our models are retrained as is necessary. This is a dashboard that shows the performance of our models and lets our teams monitor and retrain those models so that they're always performing to our standards. So far we've been showing you the Data Science Experience available behind the firewall that we're using to build and train models. Through a new publish feature, you can build models and deploy them anywhere. In another environment, private, public, or anywhere else with just a few clicks. So here we're publishing our model to the Watson machine learning service. It happens to be in the IBM cloud. And also deeply integrated with our Data Science Experience. After publishing and switching to the Watson machine learning service, you can see that our stock affinity and model that we just published is there and ready for use. So this is incredibly important. I just want to say it again. The Data Science Experience allows you to train models behind your own firewall, take advantage of your proprietary and sensitive data, and then deploy those models wherever you want with ease. So summarize what we just showed you. First, IBM's Data Science Experience supports all teams. You saw how our data engineer populated our project with trusted data sets. Our data scientists developed, trained, and tested a machine learning model. Our developers used APIs to integrate machine learning into their apps. And how IT can use our Integrated Model Management dashboard to monitor and manage model performance. Second, we support all data. On premises, in the cloud, structured, unstructured, inside of your firewall, and outside of it. We help you bring analytics and governance to where your data is. Third, we support all tools. The data science tools that you depend on are readily available and deeply integrated. This includes capabilities from great partners like Hortonworks. And powerful tools like our very own IBM SPSS. And fourth, and finally, we support all deployments. You can build your models anywhere, and deploy them right next to where your data is. Whether that's in the public cloud, private cloud, or even on the world's most reliable transaction platform, IBM z. So see for yourself. Go to the Data Science Experience website, take us for a spin. And if you happen to be ready right now, our recently created Data Science Elite Team can help you get started and run experiments alongside you with no charge. Thank you very much. >> Thank you very much Daniel. It seems like a great time to get started. And thanks to Siva for taking us through it. Rob and I will be back in just a moment to add some perspective right after this. All right, once again joined by Rob Thomas. And Rob obviously we got a lot of information here. >> Yes, we've covered a lot of ground. >> This is intense. You got to break it down for me cause I think we zoom out and see the big picture. What better data science can deliver to a business? Why is this so important? I mean we've heard it through and through. >> Yeah, well, I heard it a couple times. But it starts with businesses have to embrace a data driven culture. And it is a change. And we need to make data accessible with the right tools in a collaborative culture because we've got diverse skill sets in every organization. But data driven companies succeed when data science tools are in the hands of everyone. And I think that's a new thought. I think most companies think just get your data scientist some tools, you'll be fine. This is about tools in the hands of everyone. I think the panel did a great job of describing about how we get to data science for all. Building a data culture, making it a part of your everyday operations, and the highlights of what Daniel just showed us, that's some pretty cool features for how organizations can get to this, which is you can see IBM's Data Science Experience, how that supports all teams. You saw data analysts, data scientists, application developer, IT staff, all working together. Second, you saw how we support all tools. And your choice of tools. So the most popular data science libraries integrated into one platform. And we saw some new capabilities that help companies avoid lock-in, where you can import existing models created from specialist tools like SPSS or others. And then deploy them and manage them inside of Data Science Experience. That's pretty interesting. And lastly, you see we continue to build on this best of open tools. Partnering with companies like H2O, Hortonworks, and others. Third, you can see how you use all data no matter where it lives. That's a key challenge every organization's going to face. Private, public, federating all data sources. We announced new integration with the Hortonworks data platform where we deploy machine learning models where your data resides. That's been a key theme. Analytics where the data is. And lastly, supporting all types of deployments. Deploy them in your Hadoop cluster. Deploy them in your Integrated Analytic System. Or deploy them in z, just to name a few. A lot of different options here. But look, don't believe anything I say. Go try it for yourself. Data Science Experience, anybody can use it. Go to datascience.ibm.com and look, if you want to start right now, we just created a team that we call Data Science Elite. These are the best data scientists in the world that will come sit down with you and co-create solutions, models, and prove out a proof of concept. >> Good stuff. Thank you Rob. So you might be asking what does an organization look like that embraces data science for all? And how could it transform your role? I'm going to head back to the office and check it out. Let's start with the perspective of the line of business. What's changed? Well, now you're starting to explore new business models. You've uncovered opportunities for new revenue sources and all that hidden data. And being disrupted is no longer keeping you up at night. As a data science leader, you're beginning to collaborate with a line of business to better understand and translate the objectives into the models that are being built. Your data scientists are also starting to collaborate with the less technical team members and analysts who are working closest to the business problem. And as a data scientist, you stop feeling like you're falling behind. Open source tools are keeping you current. You're also starting to operationalize the work that you do. And you get to do more of what you love. Explore data, build models, put your models into production, and create business impact. All in all, it's not a bad scenario. Thanks. All right. We are back and coming up next, oh this is a special time right now. Cause we got a great guest speaker. New York Magazine called him the spreadsheet psychic and number crunching prodigy who went from correctly forecasting baseball games to correctly forecasting presidential elections. He even invented a proprietary algorithm called PECOTA for predicting future performance by baseball players and teams. And his New York Times bestselling book, The Signal and the Noise was named by Amazon.com as the number one best non-fiction book of 2012. He's currently the Editor in Chief of the award winning website, FiveThirtyEight and appears on ESPN as an on air commentator. Big round of applause. My pleasure to welcome Nate Silver. >> Thank you. We met backstage. >> Yes. >> It feels weird to re-shake your hand, but you know, for the audience. >> I had to give the intense firm grip. >> Definitely. >> The ninja grip. So you and I have crossed paths kind of digitally in the past, which it really interesting, is I started my career at ESPN. And I started as a production assistant, then later back on air for sports technology. And I go to you to talk about sports because-- >> Yeah. >> Wow, has ESPN upped their game in terms of understanding the importance of data and analytics. And what it brings. Not just to MLB, but across the board. >> No, it's really infused into the way they present the broadcast. You'll have win probability on the bottom line. And they'll incorporate FiveThirtyEight metrics into how they cover college football for example. So, ESPN ... Sports is maybe the perfect, if you're a data scientist, like the perfect kind of test case. And the reason being that sports consists of problems that have rules. And have structure. And when problems have rules and structure, then it's a lot easier to work with. So it's a great way to kind of improve your skills as a data scientist. Of course, there are also important real world problems that are more open ended, and those present different types of challenges. But it's such a natural fit. The teams. Think about the teams playing the World Series tonight. The Dodgers and the Astros are both like very data driven, especially Houston. Golden State Warriors, the NBA Champions, extremely data driven. New England Patriots, relative to an NFL team, it's shifted a little bit, the NFL bar is lower. But the Patriots are certainly very analytical in how they make decisions. So, you can't talk about sports without talking about analytics. >> And I was going to save the baseball question for later. Cause we are moments away from game seven. >> Yeah. >> Is everyone else watching game seven? It's been an incredible series. Probably one of the best of all time. >> Yeah, I mean-- >> You have a prediction here? >> You can mention that too. So I don't have a prediction. FiveThirtyEight has the Dodgers with a 60% chance of winning. >> [Katie] LA Fans. >> So you have two teams that are about equal. But the Dodgers pitching staff is in better shape at the moment. The end of a seven game series. And they're at home. >> But the statistics behind the two teams is pretty incredible. >> Yeah. It's like the first World Series in I think 56 years or something where you have two 100 win teams facing one another. There have been a lot of parity in baseball for a lot of years. Not that many offensive overall juggernauts. But this year, and last year with the Cubs and the Indians too really. But this year, you have really spectacular teams in the World Series. It kind of is a showcase of modern baseball. Lots of home runs. Lots of strikeouts. >> [Katie] Lots of extra innings. >> Lots of extra innings. Good defense. Lots of pitching changes. So if you love the modern baseball game, it's been about the best example that you've had. If you like a little bit more contact, and fewer strikeouts, maybe not so much. But it's been a spectacular and very exciting World Series. It's amazing to talk. MLB is huge with analysis. I mean, hands down. But across the board, if you can provide a few examples. Because there's so many teams in front offices putting such an, just a heavy intensity on the analysis side. And where the teams are going. And if you could provide any specific examples of teams that have really blown your mind. Especially over the last year or two. Because every year it gets more exciting if you will. I mean, so a big thing in baseball is defensive shifts. So if you watch tonight, you'll probably see a couple of plays where if you're used to watching baseball, a guy makes really solid contact. And there's a fielder there that you don't think should be there. But that's really very data driven where you analyze where's this guy hit the ball. That part's not so hard. But also there's game theory involved. Because you have to adjust for the fact that he knows where you're positioning the defenders. He's trying therefore to make adjustments to his own swing and so that's been a major innovation in how baseball is played. You know, how bullpens are used too. Where teams have realized that actually having a guy, across all sports pretty much, realizing the importance of rest. And of fatigue. And that you can be the best pitcher in the world, but guess what? After four or five innings, you're probably not as good as a guy who has a fresh arm necessarily. So I mean, it really is like, these are not subtle things anymore. It's not just oh, on base percentage is valuable. It really effects kind of every strategic decision in baseball. The NBA, if you watch an NBA game tonight, see how many three point shots are taken. That's in part because of data. And teams realizing hey, three points is worth more than two, once you're more than about five feet from the basket, the shooting percentage gets really flat. And so it's revolutionary, right? Like teams that will shoot almost half their shots from the three point range nowadays. Larry Bird, who wound up being one of the greatest three point shooters of all time, took only eight three pointers his first year in the NBA. It's quite noticeable if you watch baseball or basketball in particular. >> Not to focus too much on sports. One final question. In terms of Major League Soccer, and now in NFL, we're having the analysis and having wearables where it can now showcase if they wanted to on screen, heart rate and breathing and how much exertion. How much data is too much data? And when does it ruin the sport? >> So, I don't think, I mean, again, it goes sport by sport a little bit. I think in basketball you actually have a more exciting game. I think the game is more open now. You have more three pointers. You have guys getting higher assist totals. But you know, I don't know. I'm not one of those people who thinks look, if you love baseball or basketball, and you go in to work for the Astros, the Yankees or the Knicks, they probably need some help, right? You really have to be passionate about that sport. Because it's all based on what questions am I asking? As I'm a fan or I guess an employee of the team. Or a player watching the game. And there isn't really any substitute I don't think for the insight and intuition that a curious human has to kind of ask the right questions. So we can talk at great length about what tools do you then apply when you have those questions, but that still comes from people. I don't think machine learning could help with what questions do I want to ask of the data. It might help you get the answers. >> If you have a mid-fielder in a soccer game though, not exerting, only 80%, and you're seeing that on a screen as a fan, and you're saying could that person get fired at the end of the day? One day, with the data? >> So we found that actually some in soccer in particular, some of the better players are actually more still. So Leo Messi, maybe the best player in the world, doesn't move as much as other soccer players do. And the reason being that A) he kind of knows how to position himself in the first place. B) he realizes that you make a run, and you're out of position. That's quite fatiguing. And particularly soccer, like basketball, is a sport where it's incredibly fatiguing. And so, sometimes the guys who conserve their energy, that kind of old school mentality, you have to hustle at every moment. That is not helpful to the team if you're hustling on an irrelevant play. And therefore, on a critical play, can't get back on defense, for example. >> Sports, but also data is moving exponentially as we're just speaking about today. Tech, healthcare, every different industry. Is there any particular that's a favorite of yours to cover? And I imagine they're all different as well. >> I mean, I do like sports. We cover a lot of politics too. Which is different. I mean in politics I think people aren't intuitively as data driven as they might be in sports for example. It's impressive to follow the breakthroughs in artificial intelligence. It started out just as kind of playing games and playing chess and poker and Go and things like that. But you really have seen a lot of breakthroughs in the last couple of years. But yeah, it's kind of infused into everything really. >> You're known for your work in politics though. Especially presidential campaigns. >> Yeah. >> This year, in particular. Was it insanely challenging? What was the most notable thing that came out of any of your predictions? >> I mean, in some ways, looking at the polling was the easiest lens to look at it. So I think there's kind of a myth that last year's result was a big shock and it wasn't really. If you did the modeling in the right way, then you realized that number one, polls have a margin of error. And so when a candidate has a three point lead, that's not particularly safe. Number two, the outcome between different states is correlated. Meaning that it's not that much of a surprise that Clinton lost Wisconsin and Michigan and Pennsylvania and Ohio. You know I'm from Michigan. Have friends from all those states. Kind of the same types of people in those states. Those outcomes are all correlated. So what people thought was a big upset for the polls I think was an example of how data science done carefully and correctly where you understand probabilities, understand correlations. Our model gave Trump a 30% chance of winning. Others models gave him a 1% chance. And so that was interesting in that it showed that number one, that modeling strategies and skill do matter quite a lot. When you have someone saying 30% versus 1%. I mean, that's a very very big spread. And number two, that these aren't like solved problems necessarily. Although again, the problem with elections is that you only have one election every four years. So I can be very confident that I have a better model. Even one year of data doesn't really prove very much. Even five or 10 years doesn't really prove very much. And so, being aware of the limitations to some extent intrinsically in elections when you only get one kind of new training example every four years, there's not really any way around that. There are ways to be more robust to sparce data environments. But if you're identifying different types of business problems to solve, figuring out what's a solvable problem where I can add value with data science is a really key part of what you're doing. >> You're such a leader in this space. In data and analysis. It would be interesting to kind of peek back the curtain, understand how you operate but also how large is your team? How you're putting together information. How quickly you're putting it out. Cause I think in this right now world where everybody wants things instantly-- >> Yeah. >> There's also, you want to be first too in the world of journalism. But you don't want to be inaccurate because that's your credibility. >> We talked about this before, right? I think on average, speed is a little bit overrated in journalism. >> [Katie] I think it's a big problem in journalism. >> Yeah. >> Especially in the tech world. You have to be first. You have to be first. And it's just pumping out, pumping out. And there's got to be more time spent on stories if I can speak subjectively. >> Yeah, for sure. But at the same time, we are reacting to the news. And so we have people that come in, we hire most of our people actually from journalism. >> [Katie] How many people do you have on your team? >> About 35. But, if you get someone who comes in from an academic track for example, they might be surprised at how fast journalism is. That even though we might be slower than the average website, the fact that there's a tragic event in New York, are there things we have to say about that? A candidate drops out of the presidential race, are things we have to say about that. In periods ranging from minutes to days as opposed to kind of weeks to months to years in the academic world. The corporate world moves faster. What is a little different about journalism is that you are expected to have more precision where people notice when you make a mistake. In corporations, you have maybe less transparency. If you make 10 investments and seven of them turn out well, then you'll get a lot of profit from that, right? In journalism, it's a little different. If you make kind of seven predictions or say seven things, and seven of them are very accurate and three of them aren't, you'll still get criticized a lot for the three. Just because that's kind of the way that journalism is. And so the kind of combination of needing, not having that much tolerance for mistakes, but also needing to be fast. That is tricky. And I criticize other journalists sometimes including for not being data driven enough, but the best excuse any journalist has, this is happening really fast and it's my job to kind of figure out in real time what's going on and provide useful information to the readers. And that's really difficult. Especially in a world where literally, I'll probably get off the stage and check my phone and who knows what President Trump will have tweeted or what things will have happened. But it really is a kind of 24/7. >> Well because it's 24/7 with FiveThirtyEight, one of the most well known sites for data, are you feeling micromanagey on your people? Because you do have to hit this balance. You can't have something come out four or five days later. >> Yeah, I'm not -- >> Are you overseeing everything? >> I'm not by nature a micromanager. And so you try to hire well. You try and let people make mistakes. And the flip side of this is that if a news organization that never had any mistakes, never had any corrections, that's raw, right? You have to have some tolerance for error because you are trying to decide things in real time. And figure things out. I think transparency's a big part of that. Say here's what we think, and here's why we think it. If we have a model to say it's not just the final number, here's a lot of detail about how that's calculated. In some case we release the code and the raw data. Sometimes we don't because there's a proprietary advantage. But quite often we're saying we want you to trust us and it's so important that you trust us, here's the model. Go play around with it yourself. Here's the data. And that's also I think an important value. >> That speaks to open source. And your perspective on that in general. >> Yeah, I mean, look, I'm a big fan of open source. I worry that I think sometimes the trends are a little bit away from open source. But by the way, one thing that happens when you share your data or you share your thinking at least in lieu of the data, and you can definitely do both is that readers will catch embarrassing mistakes that you made. By the way, even having open sourceness within your team, I mean we have editors and copy editors who often save you from really embarrassing mistakes. And by the way, it's not necessarily people who have a training in data science. I would guess that of our 35 people, maybe only five to 10 have a kind of formal background in what you would call data science. >> [Katie] I think that speaks to the theme here. >> Yeah. >> [Katie] That everybody's kind of got to be data literate. >> But yeah, it is like you have a good intuition. You have a good BS detector basically. And you have a good intuition for hey, this looks a little bit out of line to me. And sometimes that can be based on domain knowledge, right? We have one of our copy editors, she's a big college football fan. And we had an algorithm we released that tries to predict what the human being selection committee will do, and she was like, why is LSU rated so high? Cause I know that LSU sucks this year. And we looked at it, and she was right. There was a bug where it had forgotten to account for their last game where they lost to Troy or something and so -- >> That also speaks to the human element as well. >> It does. In general as a rule, if you're designing a kind of regression based model, it's different in machine learning where you have more, when you kind of build in the tolerance for error. But if you're trying to do something more precise, then so much of it is just debugging. It's saying that looks wrong to me. And I'm going to investigate that. And sometimes it's not wrong. Sometimes your model actually has an insight that you didn't have yourself. But fairly often, it is. And I think kind of what you learn is like, hey if there's something that bothers me, I want to go investigate that now and debug that now. Because the last thing you want is where all of a sudden, the answer you're putting out there in the world hinges on a mistake that you made. Cause you never know if you have so to speak, 1,000 lines of code and they all perform something differently. You never know when you get in a weird edge case where this one decision you made winds up being the difference between your having a good forecast and a bad one. In a defensible position and a indefensible one. So we definitely are quite diligent and careful. But it's also kind of knowing like, hey, where is an approximation good enough and where do I need more precision? Cause you could also drive yourself crazy in the other direction where you know, it doesn't matter if the answer is 91.2 versus 90. And so you can kind of go 91.2, three, four and it's like kind of A) false precision and B) not a good use of your time. So that's where I do still spend a lot of time is thinking about which problems are "solvable" or approachable with data and which ones aren't. And when they're not by the way, you're still allowed to report on them. We are a news organization so we do traditional reporting as well. And then kind of figuring out when do you need precision versus when is being pointed in the right direction good enough? >> I would love to get inside your brain and see how you operate on just like an everyday walking to Walgreens movement. It's like oh, if I cross the street in .2-- >> It's not, I mean-- >> Is it like maddening in there? >> No, not really. I mean, I'm like-- >> This is an honest question. >> If I'm looking for airfares, I'm a little more careful. But no, part of it's like you don't want to waste time on unimportant decisions, right? I will sometimes, if I can't decide what to eat at a restaurant, I'll flip a coin. If the chicken and the pasta both sound really good-- >> That's not high tech Nate. We want better. >> But that's the point, right? It's like both the chicken and the pasta are going to be really darn good, right? So I'm not going to waste my time trying to figure it out. I'm just going to have an arbitrary way to decide. >> Serious and business, how organizations in the last three to five years have just evolved with this data boom. How are you seeing it as from a consultant point of view? Do you think it's an exciting time? Do you think it's a you must act now time? >> I mean, we do know that you definitely see a lot of talent among the younger generation now. That so FiveThirtyEight has been at ESPN for four years now. And man, the quality of the interns we get has improved so much in four years. The quality of the kind of young hires that we make straight out of college has improved so much in four years. So you definitely do see a younger generation for which this is just part of their bloodstream and part of their DNA. And also, particular fields that we're interested in. So we're interested in people who have both a data and a journalism background. We're interested in people who have a visualization and a coding background. A lot of what we do is very much interactive graphics and so forth. And so we do see those skill sets coming into play a lot more. And so the kind of shortage of talent that had I think frankly been a problem for a long time, I'm optimistic based on the young people in our office, it's a little anecdotal but you can tell that there are so many more programs that are kind of teaching students the right set of skills that maybe weren't taught as much a few years ago. >> But when you're seeing these big organizations, ESPN as perfect example, moving more towards data and analytics than ever before. >> Yeah. >> You would say that's obviously true. >> Oh for sure. >> If you're not moving that direction, you're going to fall behind quickly. >> Yeah and the thing is, if you read my book or I guess people have a copy of the book. In some ways it's saying hey, there are lot of ways to screw up when you're using data. And we've built bad models. We've had models that were bad and got good results. Good models that got bad results and everything else. But the point is that the reason to be out in front of the problem is so you give yourself more runway to make errors and mistakes. And to learn kind of what works and what doesn't and which people to put on the problem. I sometimes do worry that a company says oh we need data. And everyone kind of agrees on that now. We need data science. Then they have some big test case. And they have a failure. And they maybe have a failure because they didn't know really how to use it well enough. But learning from that and iterating on that. And so by the time that you're on the third generation of kind of a problem that you're trying to solve, and you're watching everyone else make the mistake that you made five years ago, I mean, that's really powerful. But that doesn't mean that getting invested in it now, getting invested both in technology and the human capital side is important. >> Final question for you as we run out of time. 2018 beyond, what is your biggest project in terms of data gathering that you're working on? >> There's a midterm election coming up. That's a big thing for us. We're also doing a lot of work with NBA data. So for four years now, the NBA has been collecting player tracking data. So they have 3D cameras in every arena. So they can actually kind of quantify for example how fast a fast break is, for example. Or literally where a player is and where the ball is. For every NBA game now for the past four or five years. And there hasn't really been an overall metric of player value that's taken advantage of that. The teams do it. But in the NBA, the teams are a little bit ahead of journalists and analysts. So we're trying to have a really truly next generation stat. It's a lot of data. Sometimes I now more oversee things than I once did myself. And so you're parsing through many, many, many lines of code. But yeah, so we hope to have that out at some point in the next few months. >> Anything you've personally been passionate about that you've wanted to work on and kind of solve? >> I mean, the NBA thing, I am a pretty big basketball fan. >> You can do better than that. Come on, I want something real personal that you're like I got to crunch the numbers. >> You know, we tried to figure out where the best burrito in America was a few years ago. >> I'm going to end it there. >> Okay. >> Nate, thank you so much for joining us. It's been an absolute pleasure. Thank you. >> Cool, thank you. >> I thought we were going to chat World Series, you know. Burritos, important. I want to thank everybody here in our audience. Let's give him a big round of applause. >> [Nate] Thank you everyone. >> Perfect way to end the day. And for a replay of today's program, just head on over to ibm.com/dsforall. I'm Katie Linendoll. And this has been Data Science for All: It's a Whole New Game. Test one, two. One, two, three. Hi guys, I just want to quickly let you know as you're exiting. A few heads up. Downstairs right now there's going to be a meet and greet with Nate. And we're going to be doing that with clients and customers who are interested. So I would recommend before the game starts, and you lose Nate, head on downstairs. And also the gallery is open until eight p.m. with demos and activations. And tomorrow, make sure to come back too. Because we have exciting stuff. I'll be joining you as your host. And we're kicking off at nine a.m. So bye everybody, thank you so much. >> [Announcer] Ladies and gentlemen, thank you for attending this evening's webcast. If you are not attending all cloud and cognitive summit tomorrow, we ask that you recycle your name badge at the registration desk. Thank you. Also, please note there are two exits on the back of the room on either side of the room. Have a good evening. Ladies and gentlemen, the meet and greet will be on stage. Thank you.

>> Narrator: Live from the Javits Center in New York City. It's the Cube, covering the Inforum 2017. Brought to you by Infor. >> We are wrapping up the Cube's day two coverage of conference here in New York City at Inforum. My name is Rebecca Knight, along with my cohost Dave Vellante. We're joined by Dan Barnhardt. He is the Infor Vice President of Communications. Thanks so much for joining us. >> Yes, thank you for having me. Thank you for being here two days in a row. >> It's been a lot of fun. We've had a great time. So yeah, congratulations, it's been a hugely successful conference, a lot of buzz. Recap it for us, what's been most exciting for you? >> Sure, this was our second year having a forum in New York, which is our home town. I think it was a more exciting conference than last year. We unveiled some incredible development updates, led by Coleman, our AI offering, which is an incredible announcement for us, as well as Networked CloudSuites, which takes the functionality from our GT Nexus commerce network, and bakes it into our CloudSuites, the mission critical industry CloudSuites, that we offer on the Amazon Web Services cloud. Those were really exciting developments, as well as some other announcements we made with regard to product. And then, in addition to product, we had a lot of customer momentum that we shared. Last year, we had customers like Whole Foods and Travis Perkins up here. We continued the momentum with big enterprise customers making big bets on Infor, led by Koch Industries who invested more than two billion dollars this year at Infor, and are now modernizing their human resources and their financial operations with Infor CloudSuites. Moving to the cloud HR for 130,000 employees at Koch Industries which is an incredible achievement for the product, and for cloud HR. And, that's very exciting, as well as other companies like FootLocker, which were recognized with the Innovation Award for our Progress Makers Award. They're using talent science, data science to power their employees, not to power their employees, but to drive their employees towards greater productivity and greater happiness, because they've got the right people in the right fit for FootLocker, that's very exciting. And, of course, Bank of America, our Customer of the Year, which uses our HR solutions for their workforce, which obviously is exceptionally large. >> Yes, there was a great ceremony this morning, with a lot of recognition. So, let's talk a little bit more about Coleman, this was the big product announcement, really the first product in AI for Infor. Tell us a little bit about the building blocks. >> For certain. We have a couple of AI offerings now, like predictive hotel pricing, predictive demand and assortment planning in retail, but we have been building towards Coleman and what we consider the age of networked intelligence for multiple years. Since we architected Infor CloudSuite to run mission critical ERP in the cloud, we developed the capability of having data, mission critical data that really runs a business, your manufacturing, finance, distribution core functions, in the cloud on AWS, which gives us hyper-scale compute power to crunch incredible data. So, that really became possible once we moved CloudSuite in 2014. And then in 2015, we acquired GT Nexus, which is a commerce network that unites, that brings in the 80 percent of enterprise data that lies outside the four walls, among suppliers, and logistics providers, and banks. That unified that into the CloudSuite and brought that data in, and we're able to crunch that using the compute power of AWS. And then last year at Inforum, we announced the acquisition of Predictix, which is a predictive solutions for retail. And when building those, Predictix was making such groundbreaking development in the area of machine learning that they spun off a separate group called Logicblox, just to focus on machine learning. And Inforum vested heavily, we didn't talk a lot about Logicblox, but that was going to deliver a lot of the capabilities along with Amazon's developments with Lex and Alexa to enable Coleman to come to reality. So we were able then to acquire Birst. Birst is a BI program that takes, and harmonizes, the data that comes across CloudSuite and GT Nexus in a digestible form that with the machine learning power from Logicblox can power Coleman. So now we have AI that's pervasive underneath the application, making decisions, recommending advice so that people can maximize their potential at work, not have to do more menial tasks like search and gather, which McKenzie has shown can take 20 percent of your work week just looking for the information and gathering the information to make decisions. Now, you can say Coleman get me this information, and Coleman is able to return that information to you instantly, and let you make decisions, which is very, very exciting breakthrough. >> So there's a lot there. When you and I talked prior to the show, I was kind of looking for okay, what's going to be new and different, and one of the things you said was we're really going to have a focus on innovation. So, in previous Inforums it's really been about, to me anyway, we do a lot of really hard work. We're hearing a lot about acquisitions, certainly AI and Coleman, how those acquisitions come together with your, you know, what Duncan Angove calls the layer cake, you know the wedding cake stack, the strategy stack, I call it. So do you feel like you've achieved those objectives of messaging that innovation, and what's the reaction then from the customer base? >> Without a doubt. I wouldn't characterize anything that we said last year as not innovative, we announced H&L Digital, our digital transformation arm which is doing some incredible custom projects, like for the Brooklyn Nets, essentially money balling the NBA. Look forward to seeing that in next season a little bit, and then more in the season to come. Some big projects with Travis Perkins and with some other customers, care dot com, that were mentioned. But this year we're unveiling Coleman, which takes a lot of pieces, as Duncan said sort of the wedding cake, and puts them together. This has been a development for years. And now we're able to unveil it, and we've chosen to name it Coleman in honor of Katherine Coleman Johnson, one of the ladies whose life was told in the movie Hidden Figures, and she was a pioneer African-American woman in Stem, which is an important cause for us. You know, Infor years ago when we were in New Orleans unveiled the Infor Education Alliance program so that we can invest in increasing Stem education among young people, all young people with a particular focus on minorities and women to increase the ranks of underrepresented communities in the technology industry. So this, Coleman, not only pays honor to Katherine Johnson the person, but also to her mission to increase the number of people that are choosing careers in Stem, which as we have shown is the future of work for human beings. >> So talk a little bit more about Infor's commitment to increasing number to increasing, not only Stem education, but as you said increasing the number of women and minorities who go into Stem careers. >> Certainly. We, you know Pam Murphy who is our chief operating officer, this has been an incredibly important cause to her as well as Charles Phillips our CEO. We launched the Women's Infor Network, WIN, several years ago and that's had some incredible results in helping to increase the number of women at Infor. Many years ago, I think it was Google that first released their diversity report, and it drew a lot of attention to how many women and how many minorities are in technology. And they got a lot of heat, because it was about 30, 35 percent of their workforce was female, and then as other companies started rolling out their diversity report, it was a consistent number between 30 to 35 percent, and what we identified from that was not that women are not getting the jobs, it's that there aren't as many women pursuing careers in this type of field. >> Rebecca: Pipeline. >> Yes. So in order to do that, we need to provide an environment that nurtures some of the specific needs that women have, and that we're promoting education. So we formed the WIN program to do that first task, and this year on International Women's Day in early March, we were able to show some of the results that came from that, particularly in senior positions, SVP, VP, and director level positions at Infor. Some have risen 60 percent the number of women in those roles since we launched the Women's Infor Network just a couple of years ago. And then we launched the Education Alliance Program. We partnered with institutions, like CUNY the City University of New York, the New York Urban League, and universities now across the globe, we've got them in India, in Thailand and China, in South Korea to help increase the number of people who are pursuing careers in Stem. We've also sponsored PBS series and Girls Who Code, we have a hack-athon going on here at Inforum with a bunch of young people who are building, sort of, add-on apps and widgets that go to company Infor. We're investing a lot in the growth of Stem education, and the next generation. >> And by the way, those numbers that you mentioned for Google and others at around 30, 34 percent, that's much better than the industry average. They're doing quote, unquote well and still far below the 50 percent which is what you would think, you know, based on population it would be. So mainly the average is around, or the actual number's around 17 percent in the technology business, and then the other thing I would add is Amazon, I believe, was pretty forthcoming about its compensation, you know. >> Salesforce really started it, Marc Benioff. >> And they got a lot of heat for it, but it's transparency is really the starting point, right? >> It was clear really early for companies like Salesforce, and Amazon, and Google, and Infor that this was not something that we needed to create talking points about, we were going to need to effect real change. And that was going to take investment and time, and thankfully with leadership like Charles Phillips, our CEO, and Marc Benioff were making investments to help make sure that the next generation of every human, but particularly women and minorities that are underrepresented right now in technology, have those skills that will be needed in the years to come. >> Right, you have to start with a benchmark and then know where you're moving from. >> Absolutely, just like if you're starting a project to transform your business, where do you want to go and what are the steps that are going to help you get there? >> Speaking of transforming your business, this is another big trend, is digital transformation. So now that we are at nearing the end of day two of this conference, what are you hearing from customers about this jaunting, sometimes painful process that they must endure, but really they must endure it in order to stay alive and to thrive? >> Without a doubt. A disruption is happening in every industry that we're seeing, and customers across all of the industries that Infor serves, like manufacturing, healthcare, retail, distribution, they are thinking about how do we survive in the new economy, when everything is digital, when every company needs to be a technology company. And we are working with our customers to help first modernize their systems. You can't be held back by old technology, you need to move to the cloud to get the flexibility and the agility that can adapt to changing business conditions and disruptions. No longer do you have years to adapt to things, they're happening overnight, you must have flexible solutions to do that. So, we have a lot of customers. We just had a panel with Travis Perkins, and with Pilot Flying J, who was on the Cube earlier, talking about how their, and Cook Industries our primary investor now, talking about how they're re-architecting their IT infrastructure to give them that agility so they can start thinking about what sort of projects could open up new streams of revenue. How could we, you know, do something else that we never thought of, but now we have the capability to do digitally that could be the future of our business? And it's really exciting to have all the CIOs, and SVPs of technology, VPs of technology, that are here at Inforum talking about what they're doing, and how they're imagining their business. It's really incredible to get a peek at what they're doing. >> You know, we were talking to Debbie earlier. One of the interesting things that I, my takeaway is on the digital transformation, is you know, we always say digital is data and then what we talked about was the ability to traverse industry value change, not just vertically but horizontally. Amazon buying Whole Foods is a perfect example, Amazon's a content company, Apple's getting into financial services. I wonder if you could comment on your thoughts on because you're so deep into micro-verticals, and what Debbie said was well I gave a consumer package good example to a process manufacturing company. And they were like what are you talking about, and she said look, let me connect the dots and the light bulbs went off. And they said wow, we could take that CPG example and apply it, so I wonder when we talk about digital transformation, if you see or can foresee your advantage in micro-verticals as translating across those verticals. >> Without a doubt. We talk about it as adjacent innovation. And Charles points back to an example, way back from the creation of the niche in glass, and how that led to additional businesses and industries like eyeglasses and fire preparedness, and we look at it that way for certain. We dive very deep into key industries, but when we look at them holistically across and we say oh, this is happening within the retail industry, we can identify key functionality that might change the industry of disruption, not disruption, distribution. Might disrupt the distribution industry, and we can apply the lessons learned by having that industry specialization into other industries and help them realize a potential that they weren't aware of before, because we uncovered it in one place. That's happening an awful lot with what we do with retail and assortment planning and healthcare. We run 70 percent of the large hospitals in the US, and we're learning a lot from retail and how we might help hospitals move more quickly. When you are managing life and death situations, if you are planning assortment or inventory for those key supplies within a hospital, and you can make even small adjustments that can have huge impact on patient care, so that's one of the benefits of our industry-first strategy, and the adjacent innovation that we cultivate there. >> I know we're not even finished with Inforum 2017, but we must look ahead to 2018. Talk a little bit about what your goals for next year's conference are. >> For sure. You're correct, we're not finished yet with Inforum. I know everyone here is really excited about Bruno Mars who's entertaining tonight, but we are looking forward to next year's conference as well, we're already talking about some of the innovative things that we'll announce, and the customer journeys that are beginning now, which we'd like to unveil there. We are going to be moving the conference from New York, we're going to move to Washington DC in late-September, September 24th to 27th in Washington DC, which we're very excited about to let our customers, they come back every year to learn more. We had seven thousand people attending this year, we want to give them a little bit of a variety, while still making sure that they can reach, you know, with one stop from Europe and from Asia, cause customers are traveling from all over the world, but we're very excited to see the growth that would be shared. This year, for instance, if you look at the sponsors, we had our primary SI partner Avaap was platinum partner last year. In addition to Avaap this year, we were joined by Accenture, and Deloitte, Capgemini, Grant Thorton, all of whom have built Infor practices over the last 12 months because there's so much momentum over our solutions that that is a revenue opportunity for them that they want to take advantage of. >> And the momentum is just going to keep on going next year in September. So I'll see you in September. >> Yeah, thank you very much. I appreciate you guys being here with us for the third year, second year in a row in New York. >> Indeed, thank you. I'm Rebecca Knight for Dave Vellante, we will have more from Inforum 2017 in a bit.