(gentle music) >> Hey, everyone. Welcome back to New York City. Lisa Martin and John Furrier here with theCUBE, covering AWS Summit NYC. This is a series of summits this year. There's about 15 of them globally. We are excited to be here with a couple of guests. We have an alumni back with us. Couple of guests from Caylent, Stephen Garden joins us, the Executive Chairman, and Valerie Henderson, Chief Revenue Officer. Guys, welcome to the program. >> Thank you. >> Thank you. Thank you for having us. >> Great to have you, welcome back. >> Appreciate it, from 2016. >> 2016, it's been a minute. >> Yep. >> But that was before Caylent. Talk to us about Caylent, what do you guys do? What do you deliver? How are you affiliated with AWS? >> Sure, so we were founded in 2015, initially as a container management product. So our roots are very deeply centered around Cloud native. We've since evolved and become a Cloud native consultancy. We're all in with AWS. We were actually just awarded AWS Premier Partner a couple of weeks ago, so we're pretty pumped about that, but we're about 250 people now, across North and South America. And our goal is really to work with customers that are looking to innovate and evolve and use AWS as a catalyst to build new products for their business. >> As a catalyst, I like that. Valerie, talk about the customer. Obviously so much tumbled in the last couple of years. Still going through it. >> Yeah, of course. >> How have customer conversations evolved and changed in the last couple of years, from your perspective? >> Yeah, I think from my perspective it is such a unique time and it's a time that is constantly changing. And I think change breeds opportunity, and I feel like customers see that, and they're leaning in. They want the opportunity to create new revenue streams, do more, more efficiently, and I think that's the key. And the questions are really asking, how can we take our data, and turn it into something that we can monetize? How can we be smarter with what we have? And I think it's an incredible time to be in the space that we're in. Every conversation I have is really forward thinking, and about the business. And I've been in this space for a while, and that was not always that case. And I think now people are shifting that IT shop to IP shop, and that's so key, from my perspective. >> Interesting, interesting shift there. Every company has to be a data company these days, to be competitive, the last couple of years it was, how did we survive? Pivot, pivot, pivot. But to be a data company, means you have to be able to extract the value and insights from that data and act on it, to your point, develop new products, new revenue streams, new opportunities. How do you enable companies, and maybe this is a question that you can both answer, to truly become data companies? >> The whole model from a service's perspective is not a do-for model, it is a do-with model. And any time we go into a customer, it's like, where are they on the curve? From monolith application, to microservices, where do they sit today? And I think when you dig in, you assess, you deeply understand where they are, you can get them to where they want to be, and build a plan. And the way our model works is, we're doing it with them, and what that means is we're enabling them, documentation, we're supporting them, that if we're not there, they're going to be able to carry it forward and continue to do more. So, that's so so important. I'd love Stephen's take on it. >> Yeah, I think the other trend that we're seeing in data more recently is that customers need to share their information with other partners, collaborate. And AWS is just the perfect platform to be able to do that, enable that sharing. And you're seeing even businesses like Snowflake build a data Cloud on top of AWS. So, I think that's a new angle that we're seeing which is really bringing together way more innovation- >> What about that data clean-room trend that's going on, Snowflake's doing a lot of that. But some of them have a little lock in spec there, versus being open, security, privacy, governance, what's the balance between open sharing and the requirements you need to be secure and compliant? >> Yeah, I think very simplistically, the information that you are using to deliver your product and service to customers generally safer, more public and available, the information that's confidential to your business behind the scenes, obviously, you use the right protocols to lock it out. But it is a very hot topic in today's world, especially with Web3 and people seeking to get their information back, so... >> So you mentioned you guys around since 2015, if you go back in time, it seems like yesterday, but Cloud time, it's like two generations ago. Why is data now more relevant? Is it because the technology's gotten better and easier, or more maturization of the client's understanding, or being full with data, having a data problem and hence an opportunity? Or is it open source has evolved? Or all three, what's your reaction to that? Why is it exploding now when it's been around for a while? >> It keeps exponentially growing, right? The more and more data. There was a stat four or five years ago about, hey, we're taking more photographs in a single year now than all of mankind, leading up to that date, but I think just the sheer quantities and the way people are managing it now, and being able to actually capture information points of everything across their entire business, just presents a much bigger opportunity to be able to take and form decisions of the back of that. >> So do you see the customers having more data full problems, that they're having more data? So that's... And in that one >> 100%. >> Of the consequences of not leveraging it? >> Yeah, it's what to do. Yeah, absolutely, and if you think about when you wake up in the morning if you ask Alexa what the weather is, and like, you're creating data, in every engagement with the world. So I think it's this explosion of it, but then it exists, and what do you do, and having a strategy. I still think one of the biggest gaps is people, and talent, and expertise to do the work, frankly. Which is, the hypothesis of Caylent existing. >> Yeah, I think a data concept and application, because what's the weather to Alexa, is an application of what's the weather, it's a request, but it's actually the data's built into the app. >> It's built in. >> So data as code is a new trend. >> Yes, yeah, yeah, and I think it's funny to answer the question. There's more data points surrounding how to leverage your data, and I'm like, it's crazy, I think you're really seeing that working- >> We have an old data warehouse, we can't get the weather data, although it's there somewhere. But that's the problem. Getting the data, in the applications, this is not... Wasn't around 10 years ago. No one was talking like that. Now it's more standard. That sounds like DevOps to me, a DevOps problem. >> Yeah, moving from the monolithic to the microservice is wild, and just the way that people are building applications today. The users, their customers are demanding more from the service, and AWS is able to deliver that. >> What are some of your customers doing with you guys, can you give some examples and scope the scale of your relationship with the customers, vis-a-vis AWS and the Cloud, how they're using you guys and the Cloud. >> Yeah, yeah, for sure, a customer of ours, Allergen, which is an incredible organization, really had a large effort to modernize. And they actually have a data lab within their company called Allergen Data Labs, and they leveraged us to truly just modernize this containerization effort. How they can do more with less, and that serverless experience. So, I think from my perspective what we're seeing is also a need to be thoughtful about DevOps retooling and tooling because talent wants to work with the best toolset, the hottest stuff on the street, and again, to keep talent is key, in any organization's success. >> Valerie, how does Caylent help with that from a talent perspective? Obviously there's talent shortage, we're also still in the great resignation. >> Oh my gosh. >> How do you help organizations bridge the gap so that they can glean insights from data and be competitive and win? >> Yeah, we actually just published a case study with Novus which was bought by SEI, which is a huge financial firm. Where they said, "Listen, it's human nature to say I have a gap, and I need to fill it, I'm going to hire somebody." That's human nature to say, okay, this is what we're going to do. But the reality is, I think companies are starting to see the advantage of using a partner and say, okay, I could hire one person or I could bring in a partner who's going to have a team of five, works incrementally for a period of time, does with, helps coach my team up, document all of that, and I think that they're seeing value from that. And ultimately, it's not that we don't want them to eventually hire. When they do hire, we want that person to come in and have the best experience. >> And sometimes the people aren't even available, right? >> Correct, yeah. >> So you have a combination of managed services, a plethora of managed services that are also involved with the customers. So, it's that integration, scale, and partnering and sharing. You mentioned sharing data earlier, how do you guys view that integration piece, 'cause if you have a modern architecture, you got to have that decomposed, decoupled but integrated approach. >> Yeah, we really believe that the whole world of project services and managed services is coming together as one. So we have a single delivery model which we're really passionate about. And we look at it as an embedded team within our customers, embedded DevOps to support them, basically on anything that could be from a modernizing a new application through to addressing a more traditional Cloud architecture framework that's in place. But yeah, the trick to it is, as Val said earlier is the do with approach, not just do for, right? I think customers need to learn about the Cloud. They need to understand the technology that they're using. They want to have that understanding. And we found a way of fitting in our services to help them accelerate that part. >> So Valerie, I got to ask you the question. So, in sports you talk about the modern era of baseball or whatever, we're in the modern era of Cloud, going next generation. We call it Super Cloud, a concept that Dave and I put out at re:Invent. If someone asks you, what does the modern era look like? As you look at your customer base and the data you guys have, how would you describe this modern era? What is it made up of? Is it outcomes versus solutions? Is it technology that's decentralized? How do you talk about it? What is the modern era, if you were- >> Not to oversimplify it, but I'm going to, the idea that somebody could come into work and all they have to think about is business outcomes and the data points that they need to achieve said business outcomes. I'm the biggest fan of measure what matters, I think it is an incredibly powerful methodology. And I think anybody who thinks about running business, they know that it's a scale. The amount of companies that are in that place is very small right now. So I think modern era is really that running an IT company to an IP company. >> So Stephen, if you unpack that, what's under the covers to make that happen? Automation, machines, what's your assessment of that outcome, which by the way was well said. Beautiful, beautiful comment. What makes that happen? >> I think it is around automation. It is around do once and then apply many times. That is key. Obviously it's a fundamental principle of the Cloud, is that consistency in that repeatability. So when you can simplify services down to a point, click, deploy, I think you're in a much better position to be able to move quickly and then not have to worry about anything under the hood and just focus, like Val said, on the business outcomes. >> That's more creative. They're focusing on the problems, to not do the rock fetches and the heavy lifting that's not differentiated. >> I find that what gives people energy generates opportunity. And I think when people hit those roadblocks of, these things don't work together. There's all these interdependencies. It's really challenging. So I love what's happening. I think there's never been a better time to be in this business. >> Not a dull moment, That's for darn sure. >> Not a dull moment. >> Valerie, talk about outcomes. You mentioned a couple of customers that you're working with, some case studies. It is all about outcomes these days. That's the conversations that we have with the entire ecosystem is all about business outcomes. What are some of those key transformative business outcomes that Caylent is helping customers to achieve? >> Yeah, to me one thing that is key is, anytime I'm meeting with a customer, I want to understand who their customers are. I'm like, who is your customer? And how can we create a better experience for that customer. Whether it's their end users or their external customers. And I think that is a huge element. What we're seeing is that sassification of, how do I make it easier for my customers to procure and engage with my platform? And a lot of what we're doing right now is helping clients with that. And it's not a flip of a switch, it's not a click of a button, it's complicated. But that is what we are here to help, help simplify, help create that understanding of what's possible. >> How do you guys talk to your customers, take a minute to give a plug for the company. What are you looking for? What's the stats? How many employees you guys hiring, and what's the pitch to customers? >> Yeah, so I think every organization is on their journey to the Cloud now. It's gotten to that point where if you're not working with a public Cloud provider, you're part of a very, very small group. We like to say that we'll meet customers where they are, and help evolve them as a business, help evolve their teams. And that's what we mean when we say do with, so it's a pretty broad spectrum. We're big in healthcare. We're big in FinTech. We've worked with a lot of startup customers. We have about 250 customers today, 250 employees. And we're scaling rapidly. We've grown that from about 50 employees a year ago. >> Oh, wow. >> Yes, when I started, we were just around 60 people and we're at 260 today. >> And why are people working with you? What are you guys, solving a problem? Are you enabling them? What's the pitch? >> Without a doubt, I love that. Being in sales my whole career, somebody asking me for a pitch is my favorite. >> Okay, let's go. >> Yeah, yeah, the true value prop of what we do is all of the above. We enable, we help customers do more faster, but again, we do not want customers to walk away from an engagement with us saying, oh no, we don't know what to do. We want them to feel empowered. I still think the biggest gap from everything being in that IP business outcome is people. And for us, we're so passionate about that, and building a company that really truly believes that. And that's part of who we are as a company and our value system. >> And the digital transformation, ultimately what they're going through, you get them there faster. They get the outcomes and they're operational. >> Absolutely, and also to be clear, when a customer has a great experience working with you, they want to tell other people about the experience. And for us, like the referrals that we get, the partnership with Amazon is so key. >> What are some reactions after you go through an engagement? We've been riffing on this concept of Super Cloud where you're starting to see people build on top of, not the AWSs, but their partners that work with them. And so the customers are getting their own Cloud experience at scale. What are some of the comments you hear from your successful customers? What are some anecdotal feedback? >> Yeah, yeah. >> I'm so glad we did this because now I'm selling more, I'm doing this, what are some of the things that they're thinking? >> Yeah, yeah, I think ultimately the consistent theme that we get is, "I'm so glad that I didn't let fear hold me back from engaging a partner," because a lack of control scares a lot of customers. It does. And I think customers that are willing to say, "Okay, I'm going to have a little faith, trust in the process." They thank us. They do, and we've seen that across the board. I think that crossing that chasm is not to be underestimated without a doubt. >> Great story, congratulations. >> Oh, thank you. >> Well, there's nothing more powerful and potent than the voice of the customer. >> Without a doubt. And really you have to listen. >> Yes, yes, definitely. Stephen, Valerie, thank you so much for joining Dave and me on the program today, talking about Caylent, what you guys are doing for customers with AWS, empowering, enabling, collaboration. I love it, thank you. >> Yeah, thank you both. >> All right, our pleasure. For John Furrier, I'm Lisa Martin. You're watching theCUBE live in New York City, we are at AWSO in NYC, John and I will be right back with our next guest. (gentle music)

>>From around the globe. It's the cube with digital coverage of AWS reinvent 2020 special coverage sponsored by AWS global partner network. >>Welcome everyone to the cube live covering AWS reinvent 2020. I'm your host Rebecca Knight. Today. We are joined by Joshua Virgin. He is the general manager at AWS outposts. Thanks so much for coming on the cube. Joshua, >>Thank you for having me. It's great to be here. >>It's great to have you, so tell our viewers a little bit about AWS, AWS outposts. >>Oh sure. It's one of my favorite subjects, obviously. So outposts is a service from AWS that allows you to use the same tools, technology, API APIs, programming interfaces that you do in the cloud, but install this and run it on your own premises or in a co-location facility. So it really extends the reach of AWS to far more locations than you could otherwise use it. >>So what are some of the advancements, uh, this year >>It's, it's been an amazingly busy year, even under unprecedented kind of circumstances where we've tried to turn the crank really hard and deliver value for our customers. We increased the number of countries you could order outposts in up to 51 countries. You can now connect outpost to all 22 AWS regions and our gov cloud regions, everything outside of China. And we delivered 15 new services or incremental features, including a S3 on outposts, which was the top thing that customers asked for, but also our application load balancer, Alasta cash, our relational database service RDS. Uh, you know, there's probably more than I'm missing here, but, um, you know, and we're definitely not slowing down in that regard. 2021 will probably be an even bigger year. >>So tell us a little bit about the, the response from customers since the launch of AWS outpost last year. What are, what are you hearing? >>Yeah, I mean, we're, we're hearing a lot, uh, I think we've been pleasantly surprised by the, the breadth and the depth of the customer use cases. One of the biggest things we heard from people was, you know, the, the outposts are great, but it's a, it's a full rack of compute or many racks of compute in some cases and storage, you know, their locations that people wanted to put it in that were smaller where their space constrained, maybe a restaurant or a factory floor or a small medical facility, uh, you know, a telco like a cell site. And so what we did based on that is something that we actually just announced in Andy's keynote. Uh, just a few days ago here, which is the new small form factor outposts that are one you and two use size, uh, servers. It's about the size of one or two pizza boxes stacked on top of each other. >>So that's even going to make outposts available to even more use cases, uh, you know, early on, uh, we kind of said to ourselves that it's important to kind of give people that consistent experience wherever they might need the compute and the storage and the other services. And so I've been, I've been really pleasantly surprised, as I mentioned earlier, by how many people have talked to us. We have customers like Phillips healthcare. They are, uh, they're bringing their medical imaging solution to outposts and it allows them to kind of modernize the way they deliver services to hospitals and medical research centers around the world. Something that really wouldn't be possible without having AWS everywhere. >>And that is much, much needed today. Um, tell us a little bit about more about this year in particular, you said it yourself at the beginning of our conversation, this is an unprecedented year for so many different reasons. How has the COVID-19 pandemic effected AWS outposts and how your team interacts with customers and, and gets your job done? >>Yeah, we, I think we have some unique challenges in that regard, obviously, as I mentioned earlier, AWS outposts are installed in a co-location facility or on a customer's own premises in a data center, you know, other things like that. So obviously we have to get our technicians out there to roll them in and hook them up to your network and, you know, to get them powered up. So that means that we are complying with, uh, COVID restrictions. And as I mentioned, 51 different countries. So there was even an install earlier this year at a mining location, uh, you know, far outside the U S where we had to get technicians working with, uh, local technicians from the customer following COVID guidelines, wearing protective gear, and actually installing the outpost, uh, you know, using kind of satellite conductivity and phones to phone home, and talk to us during the installation of course, cause it's not hooked up yet. So those are just kind of examples of the, the links to which we'll go to make sure that, of course we're safe, the customers are safe, but that they can kind of continue to modernize their application portfolio and get benefits from the outposts. >>And what are you hearing from clients and customers in terms of how they're thinking about their technology needs now and in the coming year? >>Yeah, that's a, that's a great question. I mean, it, it really varies by market segment. So you have customers like Ericsson and Telefonica. They're going to be using outpost to, uh, kind of run their 5g packet core technology, Abe it's, it's gotta be run at the edge right there, telcos, they need to minimize latency single digit milliseconds, or you might have a customer like Lockheed Martin. And what they've told us is they have projects that are subject to government contracts and regulations. And not only do they have of course compliance regimes, like fed ramp that they need to be aware of, but there's data residency requirements. So whether they're deploying in the United States or, you know, with our allies all around the world, the compute and the storage, they need to run in specific locations. So now outposts are going to be a key, uh, advancement and kind of a key differentiator for them in how they deliver services to their customers and still meet those data residency or compliance requirement. >>Joshua, tell our viewers more about AWS outpost ready? >>Oh, that's a, it's another thing I'm really glad you mentioned. So the outposts ready program, these are solutions from our APN and our Amazon AWS partner network that are, uh, validated and following our best practices on AWS outposts. They're certified to work and, you know, they're generally available to customers. And so it's a program where, you know, ISV and SAS providers can ensure that the technology that they provide that this third-party technology is going to work in the outpost environment. And there's, there's something about outposts that I think makes this, uh, a differentiator and uniquely valuable when I mentioned kind of that consistent hybrid experience. When you think about how outposts are deployed, you know, in a customer's data center, maybe alongside other technology they're already using. And so customers say, look, these AWS services are great, but I already use a variety of third party technology maybe from Veritas or trend micro Palo Alto networks Convolt size sense, PagerDuty, pure storage NetApp, or the, you know, the list is actually pretty extensive of what people are already using. >>And so they've said, you know, I do plan on using AWS services, but I also don't want to give up, uh, you know, what my team is already familiar with. So can you make sure that's going to work for me, whether I'm using it in the region or on the AWS outposts. And so the, the, the interest in kind of demand for this, both from customers and the enthusiasm from the partners has been off the charts. We started the program in just September, which is not that long ago, and we had 32 partners. And, uh, as of today we have an additional, uh, additional 25 partners, right? It's 57 partners, total 64 certified solutions. So that's a lot of momentum and just kind of a, a short amount of time. And I'm really happy that we can deliver that to the customer. >>So it does, it's already showing tremendous momentum. How do you think about it in terms of the primary benefits that it gives to customers and how it helps customers and partners, >>You know, in order to qualify, the solution has to be tested and validated upon, uh, against a bunch of, uh, criteria that we have very specific technical criteria, security requirements, operational, and, you know, they're, they're supported for customers with clear deployment guidelines. So, you know, the customers can kind of think of this as a guarantee that we're not just saying maybe this could work, but, but this will work. If you're already using it, it's going to continue to work in a way that's familiar to you. And again, that's important that consistent hybrid experience, whether you're using a solution from a third party or from AWS, whether you're using it in the region or on a local zone or in a wavelength zone, and some of our other kind of innovative infrastructure deployments or using it on an outpost, no matter where you're using it, it has to work the same way. >>And so this is something that customers have said, I want to be able to get up and running quickly. We had a customer riot games, uh, th they're the maker of league of legends, but also when they were launching their new game, Valerie hunt, uh, in, in June of 2020, they deployed outposts in four different locations to kind of ensure a level playing field in terms of latency and what they told us, uh, you know, very much like the service ready program is they were able to get up and running in just a matter of days once the outpost was deployed. And it's because we gave them those same API APIs, that same tooling. So I think that's really important for people and, you know, I hope we can continue to deliver on that promise. >>So to close this out here, I want you to look into your crystal ball and think ahead, 12 and 24 months, when you know, fingers crossed, things are back to somewhat more normal. Uh, what's in store for AWS outposts. >>Yeah. I mean, we're going to deliver on what we announced here at reinvent, which has the new small form factor outposts. And I think what we're going to continue to do is listen to customers. We develop outposts from the very beginning because customer said, could, could you deploy outposts in our, in our data center or, sorry, can you deploy AWS? And our data center didn't have a name back then. And so that's really the hallmark of AWS. You know, somewhere around 90% of our roadmaps are based on what customers tell us they want. And the other 10% is when we kind of look around the corner and hopefully delight people with something they didn't even know they needed. And I really hope for my team and that that's what 2021 and 2022 brings is more countries, more services, more value, more compliance, certifications, you know, all the things that people tell us they want. We're going to keep turning the crank as hard as we can. And delivering that as quickly as possible >>With the trademark Amazon customer delight. Yes, absolutely. Excellent. Well, Joshua, Virgin, thank you so much for coming on the cube. It was a pleasure having you. >>It was a pleasure talking to you. Thank you very much. >>I'm Rebecca Knight for more of the cubes coverage of AWS reinvent 2020 stay tuned.

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

>>Yeah, >>yeah. Hello, everyone. And welcome to the cultivating a data slowing culture. Jack, my name is Paula Johnson. I'm thought Spots head of community, and I am so excited to be your host heared at beyond. One of my favorite things about beyond is connecting with everyone and just feeling that buzz and energy from you all. So please don't be shy and engage in the chat. I'll be there shortly. We all know that when it comes to being fluent in a language, it's all about how do you take data in the sense and turn it into action? We've seen that in the hands of employees. Once they have access to this information, they are more engaged in their role. They're more productive, and most importantly, they're making better decisions. I think all of us want a little bit more of that, don't we? In today's track, you'll hear from expert partners and our customers and best practices that you could start applying to build that data. Fluent culture in your organization that we're seeing is powering the digital transformation across all industries will also discuss the role that the analysts of the future plays when it comes to this cultural shift and how important it is for diversity in data that helps us prevent bias at scale. To start us off our first session of the day is cultivating a data fluent culture, the essence and essentials. Our first speaker, CEO and founder of the Data Lodge, Valerie Logan. Valerie, Thank you for joining us today of passings over to you Now. >>Excellent. Thank you so much while it's so great to be here with the thought spot family. And there is nothing I would love to talk more about than data literacy and data fluency. And I >>just want to take a >>second and acknowledge I love how thought spot refers to this as data fluency and because I really see data literacy and fluency at, you know, either end of the same spectrum. And to mark that to commemorate that I have decorated the Scrabble board for today's occasion with fluency and literacy intersecting right at the center of the board. So with that, let's go ahead and get started and talking about how do you cultivate a data fluent culture? So in today's session, I am thrilled to be able to talk through Ah, few dynamics around what's >>going >>on in the market around this area. Who are the pioneers and what are they doing to drive data fluent culture? And what can you do about it? What are the best practices that you can apply to start this? This momentum and it's really a movement. So how do you want to play a part in this movement? So the market in the myths, um you know, it's 2020. We have had what I would call an unexpected awakening for the topic of data literacy and fluency. So let's just take a little trip down memory lane. So the last few years, data literacy and data fluency have been emerging as part of the chief data officer Agenda Analytics leaders have been looking at data culture, um, and the up skilling of the workforce as a key cornerstone to how do you create Ah, modern data and analytic strategy. But often this has been viewed as kind of just training or visualization or, um, a lot of focus on the upscaling side of data literacy. So there's >>been >>some great developments over the past few years with I was leading research at Gartner on this topic. There's other work around assessments and training Resource is. But if I'm if I'm really honest, they a lot of this has been somewhat viewed as academic and maybe a bit abstract. Enter the year 2020 where data literacy just got riel and it really can no longer be ignored. And the co vid pandemic has made this personal for all of us, not only in our work roles but in our personal lives, with our friends and families trying to make critical life decisions. So what I'd ask you to do is just to appreciate that this topic is no longer just a work thing. It is personal, and I think that's one of the ways you start to really crack. The culture code is how do you make this relevant to everyone in their personal lives? And unfortunately, cove it did that, and it has brought it to the forefront. But the challenge is how do you balance how do analytics leaders balance the need to up skill the workforce in the culture, with all of these competing needs around modernizing the platform and, um, driving trusted data and data governance? So that's what we'll be exploring is how to do this in parallel. So the very first thing that we need to do is start with the definition and I'd like to share with you how I framed data literacy for any industry across the globe. Which is first of all to appreciate that data literacy as a foundation capability has really been elevated now as >>an >>equivalent to people process and technology. And, you know, if you've been around a while, you know that classic trinity of people process and technology, It's the way that we have thought about how do you change an organization but with the digitization of our work, our lives, our society, you know anything from how do we consume information? How do we serve customers? Um, you know, we're walking sensors with our smartphones are worlds are digital now, and so data has been elevated as an equivalent Vector two people process and technology. And this is really why the role of the chief data officer in the analytics leader has been elevated to a C suite role. And it's also why data literacy and fluency is a workforce competency, not just for the specialist eso You know, I'm an old math major quant. So I've always kind of appreciated the role of data, but now it's prevalent to all right in work in life. So this >>is a >>mindset shift. And in addition to the mindset shift, let's look at what really makes up the elements of what does it mean to be data literate. So I like to call it the ability to read, write and communicate with data in context in both work in life and that it has two pieces. It has a vocabulary, so the vocabulary includes three basic sets of terms. So it includes data terms, obviously, so data sources, data attributes, data quality. There are analysis methods and concepts and terms. You know, it could be anything from, ah, bar Chart Thio, an advanced machine learning algorithm to the value drivers, right? The business acumen. What problems are resolving. So if you really break it down, it's those three sets of terms that make up the vocabulary. But it's not just the terms. It's also what we do with those terms and the skills and the skills. I like to refer to those as the acronym T T E a How do you think? How do you engage with others and how do you act or apply with data constructively? So hopefully that gives you a good basis for how we think about data literacy. And of course, the stronger you get in data literacy drives you towards higher degrees of data fluency. So I like to say we need to make this personal. And when we think about the different roles that we have in life and the different backgrounds that we bring, we think about the diversity and the inclusion of all people and all backgrounds. Diversity, to me is in addition to diversity of our gender identification, diversity of our racial backgrounds and histories. Diversity is also what is what is our work experience in our life experience. So one of the things I really like to do is to use this quote when talking about data literacy, which is we don't see things as they are. We see them as we are. So what we do is we create permission to say, you know what? It's okay that maybe you have some fear about this topic, or you may have some vulnerability around using, um you know, interactive dashboards. Um, you know, it's all about how we each come to this topic and how we support each other. So what I'd like to dio is just describe how we do that and the way that I like to teach that is this idea that we we foster data literacy by acknowledging that really, you learn this language, you learn this through embracing it, like learning a second language. So just take a second and think about you know what languages you speak right? And maybe maybe it's one. Maybe it's too often there's, you know, multiple. But you can embrace data literacy and fluency like it's a language, and somehow that creates permission for people to just say, you know, it's OK that I don't necessarily speak this language, but but I can try. So the way that we like to break this down and I call this SL information as a second language built off of the SL construct of English as a second language and it starts with that basic vocabulary, right? Every language has a vocabulary, and what I mentioned earlier in the definition is this idea that there are three basic sets of terms, value information and analysis. And everybody, when they're learning things like Stow have like a little pneumonic, right? So this is called the V A model, and you can take this and you can apply it to any use case. And you can welcome others into the conversation and say, You know, I really understand the V and the I, but I'm not a Kwan. I don't understand the A. So even just having this basic little triangle called the Via Model starts to create a frame for a shared conversation. But it's not just the vocabulary. It's also about the die elects. So if you are in a hospital, you talk about patient outcomes. If you are in insurance, you talk about underwriting and claims related outcomes. So the beauty of this language is there is a core construct for a vocabulary. But then it gets contextualized, and the beauty of that is, even if you're a classic business person that don't you don't think you're a data and analytics person. You bring something to the party. You bring something to this language, which is you understand the value drivers, so hopefully that's a good basis for you. But it's not just the language. It's also the constructs. How do you think? How do you interact and how do you add value? So here's a little double click of the T E. A acronym to show you it's Are you aware of context? So when you're watching the news, which could be interesting these days, are you actually stepping back and taking pause and saying E wonder what the source of that ISS? I wonder what the assumptions are or when you're in interacting with others. What is your degree of the ability? Thio? Tele Data story, Right? Do you have comfort and confidence interacting with others and then on the applying? This is at the end of the day, this is all about helping people make decisions. So when you're making a decision, are you being conscientious of the ethics right, the ethics or the potential bias in what you're looking at and what you're potentially doing? So I hope this provides you a nice frame. Just if you take nothing else away, take away the V A model as a way to think about a use case and application of data that there's different dialects. So when you're interacting with somebody, think of what dialect are they speaking? And then these three basic skill sets that were helping the workforce to up skill on. But the last thing is, um, you know, there's there's different levels of proficiency, and this is the point of literacy versus fluency. Depending on your role. Not everyone needs to speak data at the same level. So what we're trying to do is get everyone, at least to a shared level of conversational data, right? A basic level of foundation literacy. But based on your role, you will develop different degrees of fluency. The last point of treating this as a language is the idea that we don't just learn language through training. We learn language through interaction and experience. So I would encourage you. Just think about all what are all the different ways you can learn language and apply those to your relationship with data. Hopefully, that makes sense. Um, >>there's a >>few myths out there around this topic of data literacy, and I just want to do a little myth busting real quickly just so you can be on the lookout for these. So first of all data literacy is not about just about training. Training and assessments are certainly a cornerstone, however, when you think about developing a language, yeah, you can use a Rosetta Stone or one of those techniques, but that only gets you. So far. It's conversations you have. It's immersion. Eso keep in mind. It's not just about training. There are many ways to develop language. Secondly, data literacy is not just about internal structure, data and statistics. There are so many different types of data sets, audio, video, text, um, and so many different methods for synthesizing that content. So keep in mind, this isn't just about kind of classic data and methods. The third is visualization and storytelling are such a beautiful way to bring data literacy toe life. But it's not on Lee about visualization and storytelling, right? So there are different techniques. There are different methods on. We'll talk in a minute about health. Top Spot is embedding a lot of the data literacy capabilities into the environment. So it's not just about visualization and storytelling, and it's certainly not about making everybody a junior data scientist. The key is to identify, you know, if you are a call center representative. If you are a Knop orations manager, if you are the CEO, what is the appropriate profile of literacy and fluency for you? The last point and hopefully you get this by now is thistle is not just a work skill. And I think this is one of the best, um, services that we can provide to our employees is when you train an employee and help them up. Skill their data fluency. You're actually up Skilling, the household and their friends and their family because you're teaching them and then they can continue to teach. So at the >>end of >>the day, when we talk about what are the needs and drivers like, where's the return and what are the main objectives of, you know, having a C suite embrace state illiteracy as, ah program? There are primarily four key themes that come up that I hear all the time that I work with clients on Number one is This is how you help accelerate the shift to a data informed, insight driven culture. Or I actually like how thought spot refers to signals, right? So it's not even just insights. It's How do you distill all this noise right and and respond to the signals. But to do that collectively and culturally. Secondly, this is about unlocking what I call radical collaboration so well, while these terms often, sometimes they're viewed as, oh, we need to up skill the full population. This is as much about unlocking how data scientists, data engineers and business analysts collaborate. Right there is there is work to be done there, an opportunity there. The third is yes, we need to do this in the context of up Skilling for digital dexterity. So what I mean by that is data literacy and fluency is in the context of whole Siris of other up Skilling objectives. So becoming more agile understanding, process, automation, understanding, um, the broader ability, you know, ai and in Internet of things sensors, right? So this is part of a portfolio of up skilling. But at the end of the day, it comes down to comfort and confidence. If people are not comfortable with decision making in their role at their level in their those moments that matter, you won't get the kind of engagement. So this is also about fostering comfort and confidence. The last thing is, you know, you have so much data and analytics talent in your organization, and what we want to do is we want to maximize that talent. We really want to reduce dependency on reports and hey, can you can you put that together for me and really enable not just self service but democratizing that access and creating that freedom of access, but also freed up capacity. So if you're looking to build the case for a program, these air the primary four drivers that you can identify clear r A y and I call r o, I, I refer to are oh, I two ways return on investment and also risk of ignoring eso. You gotta be careful. You ignore these. They're going to come back to haunt you later. Eso Hopefully this helps you build the case. So let's take a look at what is a data literacy program. So it's one thing to say, Yeah, that sounds good, but how do you collectively and systemically start to enable this culture change? So, in pioneering data literacy programs, I like to call a data literacy program a commitment. Okay, this is an intentional commitment to up skill, the workforce in the culture, and there's really three pieces to that. The first is it has to be scoped to say we are about enabling the full potential of all associates. And sometimes some of my clients are extending that beyond the virtual walls of their organization to say S I'm working with a U. S. Federal agency. They're talking about data literacy for citizens, right, extending it outside the wall. So it's really about all your constituents on day and associates. Secondly, it is about fostering shared language and the modern data literacy abilities. The third is putting a real focus on what are the moments that matter. So with any kind of heavy change program, there's always a risk that it can. It can get very vague. So here's some examples of the moments that you're really trying to identify in the moments that matter. We do that through three things. I'll just paint those real quick. One is engagement. How do you engage with the leaders? How do you develop community and how do you drive communications? Secondly, we do that through development. We do that through language development, explicitly self paced learning and then of course, broader professional development and training. The third area enablement. This one is often overlooked in any kind of data literacy program. And this is where Thought spot is driving innovation left and right. This is about augmentation of the experience. So if we expect data literacy and data fluency to be developed Onley through training and not augmenting the experience in the environment, we will miss a huge opportunity. So thought spot one. The announcement yesterday with search assist. This is a beautiful example of how we are augmenting guided data literacy, right to support unending user in asking data rich questions and to not expect them to have to know all the forms and features is no different than how a GPS does not tell you. Latitude, longitude, a GPS tells you, Turn left, turn right. So the ability to augment that the way that thought spot does is so powerful. And one of my clients calls it data literacy by design. So how are we in designing that into the environment? And at the end of the day, the last and fourth lever of how you drive a program is you've gotta have someone orchestrating this change. So there is a is an art and a science to data literacy program development. So a couple of examples of pioneers So one pioneer nationwide building society, um, incredible work on how they are leveraging thought spot In particular, Thio have conversations with data. They are creating frictionless voyages with data, and they're using the spot I Q tool to recommend personalized insight. Right? This is an example of that enablement that I was just explaining. Second example, Red hat red hat. They like to describe this as going farther faster than with a small group of experts. They also refer to it as supporting data conversations again with that idea of language. So what's the difference between pioneers and procrastinators? Because what I'm seeing in the market right now is we've got these frontline pioneers who are driving these programs. But then there's kind of a d i Y do it yourself mentality going on. So I just wanted to share what I'm observing as this contrast. So procrastinators are kind of thinking I have no idea where they even start with us, whereas pioneers air saying, you know what, this is absolutely central. Let's figure it out procrastinators are saying. You know what? This probably isn't the right time for this program. Other things are more important and pioneers air like you know what? We don't have an option fast forward a year from now. Do we really think this is gonna organically change? This is pervasive to everything we dio procrastinators. They're saying I don't even know who to put in charge for this. And pioneers there saying this needs a lead. This needs someone focusing on it and a network of influencers. And then finally, procrastinators, They're generally going, you know, we're just gonna wing this and we'll just we'll stand up in academy. We'll put some courses together and pioneers air saying, You know what? We need to work smart. We need a launch, We need a leverage and we need to scale. So I hope that this has inspired you that, you know, there really are many ways to go forward, as FDR said, and only one way of standing still. So not taking an action is a choice. And there were, you know, it does have impact. So a couple of just quick things to wrap up one is how do you get started with the data literacy program, so I recommend seven steps. Who's your sponsor and who is the lead craft? Your case for change. Make it explicit. Developed that narrative craft a blueprint that's scalable but that has an initial plan where data literacy is part of not separate. Run some pilot workshops. These can be so fun and you can tackle the fear and vulnerability concern with really going after, Like how? How do we speak data across different diverse parts of the team. Thes are so fun. And what I find is when I teach people how to run a workshop like this, they absolutely want to repeat it and they get demand for more and more workshops launch pragmatically, right? We don't have any time or energy for big, expansive programs. Identify some quick winds, ignite the grassroots movement, low cost. There are many ways to do that. Engage the influencers right, ignite this bottom up movement and find ways to welcome all to the party. And then finally, you gotta think about scale right over time. This is a partnership with learning and development partnership with HR. This becomes the fabric of how do you onboard people. How do you sustain people? How do you develop? So the last thing I wanted to just caution you on is there's a few kind of big mistakes in this area. One is you have to be clear on what you're solving for, right? What does this really mean? What does it look like? What are the needs and drivers? Where is this being done? Well, today, to be very clear on what you're solving for secondly, language matters, right? If if that has not been clear, language is the common thread and it is the basis for literacy and fluency. Third, going it alone. If you try to tackle this and try to wing it. Google searching data literacy You will spend your time and energy, which is as precious of a currency as your money on efforts that, um, take more time. And there is a lot to be leveraged through through various partnerships and leverage of your vendor providers like thought spot. Last thing. A quick story. Um, over 100 years ago, Ford Motor Company think about think about who the worker population was in the plants. They were immigrants coming from all different countries having different native languages. What was happening in the environment in the plants is they were experiencing significant safety issues and efficiency issues. The root issue was lack of a shared language. I truly believe that we're at the same moment where we're lacking a shared language around data. So what Ford did was they created the Ford English school and they started to nurture that shared language. And I believe that that's exactly what we're doing now, right? So I couldn't I couldn't leave this picture, though, and not acknowledge. Not a lot of diversity in that room. So I know we would have more diversity now if we brought everyone together. But I just hope that this story resonates with you as the power of language as a foundation for growing literacy and fluency >>for joining us. We're actually gonna be jumping into the next section, so grab a quick water break, but don't wander too far. You definitely do not want to miss the second session of today. We're going to be exploring how to scale the impact and how to become a change agent in your organization and become that analysts of the future. So season

>>from >>around the globe. It's the Cube with digital coverage of AWS reinvent 2020. Special coverage sponsored by AWS Global Partner Network >>Right. Welcome, everyone to the Cube. Live covering aws reinvent 2020. I'm your host, Rebecca Knight. Today we're joined by Joshua Virgin. He is the general manager at AWS Outpost. Thanks so much for coming on the Cube. Joshua, >>thank you for having me. It's great to be here. >>Well, it's great to have you So tell our viewers a little bit about aws out AWS Outpost. >>Sure, it's the one of my favorite subjects, obviously. So outpost is a service from AWS that allows you to use the same tools technology ap ice. You know, programming interfaces that you do in the cloud, but install this and run it on your own premises or in a co location facility. So it really extends the reach of A W S two far more locations than you could otherwise use it. >>So what are some of the advancements this year? >>It's been an amazingly you know, busy year, even under unprecedented kind of circumstances, where we've tried to turn the crank really hard and deliver value for our customers. We increase the number of countries you could order outposts in up to 51 countries. You can now connect outpost all 22 AWS regions and or govcloud regions everything outside of China. On we delivered 15 new services or incremental features, including S three on outpost, which was the top thing that customers asked for. But also our application load balancer, elastic cash are relational database service RDS. You know, there's probably more that I'm missing here, but, you know, and we're definitely not slowing down in that regard. 2021 will probably be an even bigger year. >>So tell us a little bit about the response from customers since the launch of a W s outpost last year. What are you hearing? >>Yeah, I mean, we're hearing a lot. I think we've been pleasantly surprised by the breadth and the depth of the customer use cases. One >>of the >>biggest things we heard from people was, you know, the the outposts are great, but it's a it's a full rack of compute or many racks of compute in some cases in storage, you know, their locations that people wanted to put it in that were smaller where their space constrained. Maybe a restaurant or a factory floor or ah, you know, small medical facility. You know, a telco like a cell site. And and so what we did, based on that is something that we actually just announced and Andy's keynote just a few days ago here, which is the new small form factor outposts that are one you and to you size servers. It's about the size of one or two pizza boxes stacked on top of each other. So that's even going to make outposts available toe even Mawr use cases. Uh, you know, early on we kind of said to ourselves that it's important to kind of give people that consistent experience wherever they might need the compute and storage and the other services. And so I've been I've been really pleasantly surprised, as I mentioned earlier by how many people have talked to us. We have customers like Philips Healthcare. They are. They're bringing their medical imaging solution toe outposts, and it allows them to kind of modernize the way they deliver services, the hospitals and medical research centers around the world, something that really wouldn't be possible without having A W s everywhere, >>and that is much, much needed today. Um, tell us a little bit about Maura. About this year in particular. You said it yourself at the beginning of our conversation. This is an unprecedented year for so many different reasons. How has the cove in 19 pandemic affected AWS outpost and how your team interacts with customers and get your job done? >>Yeah, we I >>think we have >>some unique, you know, challenges in that regard. Obviously, as I mentioned earlier, a W s outposts are installed in a co location, facility or on a customer's own premises in a data center. You know, other things like that. So obviously we have to get our technicians out there toe, roll them in and hook them up to your network and, you know, to get them powered up. So that means that we are complying with, uh, covert restrictions. And as I mentioned 51 different countries. So there was even an install earlier this year at a mining location, you know, far outside the U. S. Where we had to get technicians working with, uh, local technicians from the customer following Kobe guidelines wearing protective gear and actually installing the outpost. You know, using kind of satellite connectivity and phones, toe phone home and talk to us during the installation, of course, because it's not hooked up yet. So those were just kind of examples of the lengths to which will go to make sure that, of course, we're safe. The customers were safe, but that they can kind of continue to modernize their application portfolio and get benefits from the outpost. >>And what are you hearing from clients and customers in terms of how they're thinking about their technology needs now and in the coming year? >>Yeah, that's a That's a great question. I mean, it really varies by market segment. So you have customers like Cisco and Ericsson and Telefonica. They're gonna be using Outpost Thio kind of run their five g packet core technology. It it's got to be run at the edge right there. Telcos. They need to minimize Leighton, see single digit milliseconds, or you might have a customer like Lockheed Martin, And what they've told us is they have projects that are subject to government contracts and regulations. And not only do they have, of course, compliance regimes like Fed ramp that they need to be aware of. But there's data residency requirements. So whether they're deploying in the United States or, you know, with our allies all around the world, the compute in the storage that they need to run in specific locations. So now outposts are going to be a key advancement and kind of a key differentiator for them in how they deliver services to their customers and still meet those data residency or compliance requirements. >>Joshua, tell our viewers more about AWS Outpost ready? >>Oh, that zits. Another thing. I'm really glad you mentioned. So the Outpost Ready program. These are solutions from our a Pienaar Amazon AWS partner Network that are validated in following our best practices on AWS outposts. They're certified toe work and you know they're generally available to customers. And so it's a program where, you know, I SVs and saz providers can ensure that the technology that they provide this third party technology is going to work in the outpost environment. And and there's there's something about outpost that I think makes this, uh, differentiator and uniquely valuable. When I mentioned kind of that consistent hybrid experience. When you think about how outposts are deployed, you know, in a customer's data center, Mike. Maybe alongside other technology they're already using. And so customers say, Look, these AWS services are great, but I already use a variety of, you know, third party technology, maybe from Veritas or Trend Micro Palo Alto Networks. Con vault sigh since pager duty Pure storage Netapp. You know, the list is actually pretty extensive of what people are already using. And so they said, you know, I do plan on using AWS services, but I also don't want to give up. You know what what my team is already familiar with, So can you make sure that's gonna work for me, whether I'm using it in the region or on the AWS outposts? And so the interest and kind of demand for this both from customers and the enthusiasm from the partners has been off the charts. We started the program in just September, which is not that long ago, and we had 32 partners, and as of today we have an additional, uh, additional 25 partners, right? It's 57 partners, total 64 certified solutions so that that's a lot of momentum in just kind of, ah, short amount of time. And I'm really happy that we can deliver that to the customers >>so it doesn't. It's already showing tremendous momentum. How do you think about it in terms of the primary benefits that it gives to customers and how it helps customers and partners? >>Yeah, I think, you know, in order to qualify, the solution has to be tested and validated upon against a bunch of criteria that we have very specific technical criteria, security requirements operational and you know, they're they're supported for customers with clear deployment guidelines. So you know, the customers can kind of think of this as a guarantee that we're not just saying maybe this could work, but but this will work. If you're already using it, it's going to continue to work in a way that's familiar to you and and again, that's important. That consistent hybrid experience, whether you're using a solution from a third party or from AWS, whether you're using it in the region or on a local zone or in a wavelength zone, some of our other, you know, kind of innovative infrastructure deployments or using it on outpost, no matter where you're using it, it has to work the same way. And so this is something that customers have said. I want to be able to get up and running quickly. We had a customer riot games. They're the maker of league of Legends. But also when they were launching their new game, Valerie Int, in June of 2020 they deployed outpost in four different locations to kind of ensure a level playing field in terms of latency. What they told us, you know, very much like this service ready program is they were able to get up and running in just a matter of days once the outpost was deployed. And it's because we gave them those same a p I s that same tooling. So I think that's really important for people. And, you know, I hope we can continue to deliver on that promise. >>So the closest out here, I want you to look into your crystal ball and think ahead 12 and 24 months when you know, fingers crossed things are back to somewhat more normal. What? What's in store for AWS Outpost? >>Yeah, I mean, we're going to deliver on what we announced here at reinvent, which is the new small form factor outposts on. I think what we're going to continue to do is listen to customers. We developed outpost from the very beginning because customers said Could could you deploy outposts in our in our data center or Sorry, can you deploy eight of us? And our data center didn't have a name back then. And so that's really the hallmark of AWS, you know, somewhere around 90% of our road maps or based on what customers tell us they want, then the other 10% is when we kind of look around the corner and hopefully delight people with something they didn't even know they needed. And I really hope for my team. And that that's what 2021 2022 brings is, you know, more countries, more services, more value, more compliance certifications. You know, all the things that people tell us they want. We're going to keep turning the crank as hard as we can and delivering that as quickly as possible >>with the trademark Amazon customer delight. >>Yes, absolutely >>excellent. Well, Joshua Virgin. Thank you so much for coming on the Cube. It was a pleasure having you. >>That was a pleasure talking to you. Thank you very much. >>I'm Rebecca night for more of the cubes. Coverage of AWS reinvent 2020. Stay tuned. >>Yeah.