>>Mhm >>Welcome to this preview of couch based connect online 2021. My name is Dave Volonte with the cube and we're here with couch based ceo matt cain matt. Good to see you again. Welcome >>Thanks Dave. Great to be here. >>We are super excited at the Cube to partner with Couch Base this year to share the news, the analysis from connect online 21. What can attendees expect from the event this year? What's the theme? What can people really take away? >>They were fired up, you know, there is no different. Our theme this year is modernized now and it's something that we're hearing from our customers across the world is they think about leveraging technology to get closer to their customers and at the top of every one of their strategic agendas is figuring out how to build the best applications to service our needs as our personal lives and in our business lives and we're really focused on talking about the technology that we uniquely architected to enable this that stands aspects of relational database technology and new capabilities, leveraging those people technology, putting that into an integrated platform and really supporting customers. And we love talking about what we built Dave but we love even more when our customers share what they've been doing with our platform, customers like Pepsi and Amadeus and American greetings, you're going to hear them and their development meant teams talk about how they have leverage couch base to solve some of the most fundamental application challenges and how that's really opening up new businesses for them in their end markets. >>Let's talk a little bit more mad about that, the modernized now. I mean the trends that we're seeing in in the market place, they were in motion before the pandemic. But digital digitization and modernization has really become a high priority. Talk about why in your view now is the time to modernize and what's the mandate for enterprises? >>Well look, I think digital transformation has been a focus point for some time and I think that's going to continue as we go forward. But I think as enterprise think about the challenges they have in front of them to successfully transform digitally, they may be thinking about the problem a little bit differently in light of current circumstances. Uh and what we're seeing is enterprises have the desire to innovate, but they may not always have the resources or the capabilities to do it at the place they want to. And so how do they approach this challenge first and foremost, they need a platform that can help bridge the legacy world and the new one that they can safely evolve applications and modernize, you know, workloads that were dependent on relational databases while putting them into new platforms. At the same time, they need people to do this work. So if I'm an democrats, almost an insatiable demand to build new applications, but I don't necessarily have all the people and teams and capability and skill sets needed to support that. So as a technology company, we've got to think through how do we help provide the tools that will open up more people's ability to contribute to that digital transformation leveraging things like sequel is the fact of language in the database technology allowing enterprises to repurpose workforces free up investment dollars, free up people to really focus on the next generation of properties that are going to change their businesses. And so I think the current economic conditions haven't changed the fact that digital transformation is the top of the priority list. If anything, that reinforced the urge with which enterprises need to go after this, but also the way that they need to do it. But I can't just continue to throw niche technologies of problems. I've really got to think about what kind of platforms tonight and then in the future and a couch base as the modern database for enterprise applications. This is what we're going to spend time talking about and helping customers understand the value that we can unlock for them as they invest in the couch based platform. >>Super relevant now since we last talked matt, he made some big moves, not the least of which is you're now a public company. We've been following that. But what's changed what's new product wise and maybe one of the fundamentals of the market that that your your customers and your culture or driving. >>Yeah, well let's talk about first, what's not changed? We continue to be long term oriented couch basically believe we're in truly what we call a generational market transition and the challenges in front of enterprises unparalleled. So too are the opportunities for enterprises that get this right? They will innovate and thriving in their respective markets. And so as a company, we pride ourselves in being maniacally focused at solving unmet, underserved needs in the world of databases. And really thinking through what technological challenges do we need to innovate on store, customers can take that technology and successful. That's not, that hasn't changed that, that won't change. We continue to be insanely customer focused and really studying those problems and making sure that we're adding value in everything we do from a product perspective services, how we show up to help our customers and that's really important. Um certainly as you said, it's a big milestone for the company step in the public market. We're very proud of what we've accomplished over our first decade or so of existence. But we truly believe that we've been built for this moment and that market transition that ever have referred to um that that movement into the public markets allows us to talk more broadly about all that we've built and how customers are taking advantage of our technology case in point is probably the biggest release in company history couch based server seven oh, so while we were busy taking a step into the public market, we also continue to innovate as I said and are very pleased to be a market with couch base 70 which fundamentally bridges for enterprise customers to move from relational to modern databases and do that in a single integrated platform. And we're going to talk about that connect in more detail and how application developers can re platform applications in a much more seamless way and then start to innovate in a way, you know, that they never have. So a ton of work underway. We've got some really exciting announcements which I think we'll talk about here in the second at least plant the seeds on those. But we're going to be really focused on the innovation that we've delivered up to this point because it's so fundamentally valuable to the enterprise customers we serve and couldn't be more excited to share the benefits of that. That's actually what we're going to help customers do as we go forward. >>Well, we see a lot of companies and as as we evaluated, you've hit critical mass in terms of how we think about it successful I. P. O. Your surpassed $100 million in revenue 500 plus customers talk about the opportunity for couch based to continue to grow. What's in store. What's the focus? >>Well, as I mentioned, we're going to we're going to continue to innovate and so you know, ahead of the conference. We're going to talk about some really important upcoming innovations and I'm not gonna steal too much of the thunder from the show, people are really pumped and putting that material together? We we focus a lot on ensuring that we have the best database in the market, particularly for enterprise applications. Uh and really thinking through the architecture that will support applications today and going forward and we've been really successful with that date. As you mentioned, we have not only a lot of enterprise customers and we're really proud of those customers would support what were even more proud of though. Dave is the mission critical nature the enterprise nature of those applications. These companies are truly running their businesses on applications powered by pouches as we go forward. We have almost unlimited potential for new opportunity in acquiring new customers. Um and we're really focused on that and evangelizing what we've done successfully with our existing base to new customers and their respective markets and we continue to acquire those and you know will successfully expand because of the power of our platform. We've done a lot to invest in our partner ecosystem. So you know we have many I. S. V. S. That are taking our solutions to market. We have G. S. I. S. That are building practices around couch base because our database provides capabilities that others don't and they can run their businesses and help their end customers transform with the power of couch face. But Dave what we like to talk about a couch bases, we have opportunities to really help customers once we get in. We think about many factors of growth. So when we support a customer with an application, what often happens is that application growth because the enterprises successful and they put more users in or they deployed a new new geography at the same time, they realize, wow, if I can support highly interactive, highly scalable distributed applications in this particular area, I have hundreds, if not thousands of those in my enterprise, so I can use the platform for that. Then one of the things that we focus on is giving more and more capabilities to developers to enhance the performance and the personalization of their applications I mentioned, we support the sequel query language, we've got operational analytics, we've embedded full text search, we have things like eventing all of these are elegantly architected features that allow developers to build great applications and the more that were successful in helping developers do that, you know, the more, the more the company is going to grow. Um and then on top of all that we couldn't be more excited about about cloud and couch based cloud from the very outset has been a cloud, native platform, are enterprises are running this and multi and hybrid cloud deployments, but what we really have an opportunity to do is help them and run more of the service of, of that cloud solution and we're gonna be talking a lot more uh you know, come come the show about some specifics around that offering and could be more excited about augmenting or portfolio with some new capabilities there >>lot to learn at this event. Tons of meat on the bone. Okay. Matt, we're gonna leave it right there. Couch based, connect online. It's a two day event, october 20th to the 21st. More than 80 sessions geared for architects, developers, business users, open source advocates. Now the easiest way to register, all you gotta do is go to couch base dot com. You'll see the link there. There's a hackathon with prizes. So start developing and win. And while you're there, check out the free downloads with a number of different deployment options. Couch based, connect 2021 modernized. Now we'll see you there. >>Mhm mm.

>>from around the globe. It's the Cube with digital coverage of Dell Technologies. World Digital Experience Brought to You by Dell Technologies. >>Welcome to the Cubes Coverage of Dell Technologies World 2020. The Digital Experience. I'm Lisa Martin, and I'm pleased to welcome back a Cube alumni and a new Cube member to the program today. China. My Mondal is back with US Director of Solutions Marketing for Dell Technologies China. But it's great to see you at Dell Technologies world, even though we're very specially death. >>Happy to be back. Thank you, Lisa. >>And Thomas Henson is joining us for the first time. Global business development manager for a I and analytics. Thomas, Welcome to the Cube. >>I am excited to be here. It's my first virtual cube. >>Yeah, well, you better make it a good one. All right. I said we're talking about a I so so much has changed John to me. The last time I saw you were probably were sitting a lot closer together. So much has changed in the last 67 months, but a lot has changed with the adoption of Ai Thomas. Kick us off. What are some of the big things feeling ai adoption right now? >>Yeah, I >>would have to >>say the two biggest things right now or as we look at accelerated compute and by accelerated compute we're not just talking about the continuation of Moore's law, but how In Data Analytics, we're actually doing more processing now with GP use, which give us faster insights. And so now we have the ability to get quicker insights in jobs that may have taken, you know, taking weeks to months a song as we were measuring. And then the second portion is when we start to talk about the innovation going on in the software and framework world, right? So no longer do you have toe know C plus plus or a lower level language. You can actually do it in Python and even pull it off of Get Hub. And it's all part of that open source community. So we're seeing Mawr more folks in the field of data science and deep learning that can actually implement some code. And then we've got faster compute to be able to process that. >>Tell me, what are your thoughts? >>Think I want to add? Is the explosive growth off data on that's actually are fulfilling the AI adoption. Think off. Like all the devices we have, the i o t. On age devices are doing data are pumping data into the pipeline. Our high resolution satellite imagery, all social media generating data. No. All of this data are actually helping the adoption off a I because now we have very granular data tow our friend the AI model Make the AI models are much better. Besides, so the combination off both in, uh, data the power off Like GPU, power surfers are coupled with the inefficient in the eye after and tools helping off. Well, the AI growth that we're seeing today >>trying to make one of the things that we've known for a while now is that it's for a I to be valuable. It's about extracting value from that. Did it? You talked about the massive explosion and data, but yet we know for a long time we've been talking about AI for decades. Initiatives can fail. What can Dell Technologies do now to help companies have successfully I project? >>Yeah, eso As you were saying, Lisa, what we're seeing is the companies are trying to add up AI Technologies toe Dr Value and extract value from their data set. Now the way it needs to be framed is there is a business challenge that customers air trying to solve. The business challenge gets transformed into a data science problem. That data scientist is going toe work with the high technology, trained them on it. That data science problem gets to the data science solution on. Then it needs to be mapped to production deployment as a business solution. What happens? Ah, lot off. The time is the companies do not plan for output transition from all scale proof of concept that it a scientists are playing with, like a smaller set of data two, when it goes toe the large production deployment dealing with terabytes toe terabyte self data. Now that's where we come in. At their technologies, we have into end solutions for the, uh for the ai for pollution in the customers journeys starting from proof of concept to production. And it is all a seamless consular and very scalable. >>So if some of the challenges there are just starting with iterations. Thomas question for you as business development manager, those folks that John um I talked about the data scientists, the business. How are you helping them come together from the beginning so that when the POC is initiated, it actually can go on the right trajectory to be successful? >>No, that's a great point. And just to kind of build off of what Shonda my was talking about, You know, we call it that last mile, right? Like, Hey, I've got a great POC. How do I get into production? Well, if you have executive sponsorship and it's like, Hey, everybody was on board, but it's gonna take six months to a year. It's like, Whoa, you're gonna lose some momentum. So where we help our customers is, you know, by partnering with them to show them how to build, you know, from an i t. And infrastructure perspective what that ai architectural looks like, right? So we have multiple solutions around that, and at the end of the day, it's about just like Sean. Um, I was saying, You know, we may start off with a project that maybe it's only half a terabyte. Maybe it's 10 terabytes, but once you go into production, if it turns out to be three petabytes four petabytes. Nobody really, you know, has the infrastructure built unless they built on those solid practices. And that's where our solutions come in. So we can go from small scale laboratory all the way large scale production without having to move any of that data. Right? So, you know, at the heart of that is power scale and giving you that ability to scale your data and no more data migration so that you can handle one PC or multiple PCs as those models continue to improve as you start to move into production >>and I'm sticking with you 1st. 2nd 0, sorry. Trying to go ahead. >>So I was going to add that, uh, just like posthumous said right. So if you were a data scientist, you are working with this data science workstations, but getting the data from, uh, L M c our scales thes scale out platform and, uh, as it is growing from, you see two large kills production data can stay in place with the power scale platform. You can add notes, and it can grow to petabytes. And you can add in not just the workstations, but also our They'll power it, solve our switches building out our enter A I ready solutions are already solution for your production. Giving are very seamless experience from the data scientist with the i t. >>So China may will stick with you then. I'm curious to know in the last 6 to 7 months since 2020 has gone in a very different direction thing we all would have predicted our last Dell Technologies world together. What are you seeing? China. My in terms of acceleration or maybe different industries. What our customers needs, how they changed. I guess I should say in the in 2020. >>So in 2020 we're seeing the adoption off a I even more rapidly. Uh, if you think about customers ranging from like say, uh, media and entertainment industry toe, uh, the customer services off any organization to, uh the healthcare and life sciences with lots off genome analysts is going on in all of these places where we're dealing with large are datasets. We're seeing ah, lot off adoption foster processing off A. I R. Technologies, uh, giving with, say, the all the research that the's Biosciences organizations are happening. Uh, Thomas, I know like you are working with, like, a customer. So, uh, can you give us a little bit more example in there? >>Yes, one of the areas. You know, we're talking about 2021 of the things that we're seeing Mawr and Mawr is just the expansion of Just look at the need for customer support, right arm or folks working remotely their arm or folks that are learning remote. I know my child is going through virtual schools, So think about your I t organization and how Maney calls you're having now to expand. And so this is a great area where we're starting to see innovation within a I and model building to be ableto have you know, let's call it, you know, the next generation of chatbots rights. You can actually build these models off the data toe, augment those soup sports systems >>because you >>have two choices, right? You can either. You know, you you can either expand out your call center right for for we're not sure how long or you can use AI and analytics to help augment to help maybe answer some of those first baseline questions. The great thing about customers who are choosing power scale and Dell Technologies. Their partner is they already have. The resource is to be able to hold on to that data That's gonna help them train those models to help. >>So, Thomas, whenever we're talking about data, the explosions it brings to mind compliance. Protection, security. We've seen ransom where really skyrocket in 2020. Just you know, the other week there was the VA was hit. Um, I think there was also a social media Facebook instagram ticktock, 235 million users because there was an unsecured cloud database. So that vector is expanding. How can you help customers? Customers accelerate their AI projects? Well, ensuring compliance and protection and security of that data. >>Really? That's the sweet spot for power scale. We're talking with customers, right? You know, built on one FS with all the security features in mind. And I, too, came from the analytics world. So I remember in the early days of Hadoop, where, you know, as a software developer, we didn't need security, right? We you know, we were doing researching stuff, but then when we took it to the customer and and we're pushing to production, But what about all the security features. We needed >>the same thing >>for artificial intelligence, right? We want toe. We want to make sure that we're putting those security features and compliance is in. And that's where you know, from from an AI architecture perspective, by starting with one FS is at the heart of that solution. You can know that you're protecting for you know, all the enterprise features that you need, whether it be from compliance, thio, data strategy, toe backup and recovery as well. >>So when we're talking about big data volumes Chanda, mind we have to talk about the hyper scale er's talk to us about, you know, they each offer azure A W s Google cloud hundreds of AI services. So how does DEL help customers use the public cloud the data that's created outside of it and use all of those use that the right AI services to extract that value? >>Yeah. Now, as you mentioned, all of these hyper scholars are they differentiate with our office is like a i m l r Deep Learning Technologies, right? And as our customer, you want toe leverage based off all the, uh, all the cloud has to offer and not stuck with one particular cloud provider. However, we're talking about terabytes off data, right? So if you are happy with what doing service A from cloud provider say Google what you want to move to take advantage off another surface off from Asia? It comes with a very high English p a migration risk on time it will take to move the data itself. Now that's not good, right? As the customer, we should be able to live for it. Best off breed our cloud services for AI and for that matter, for anything across the board. Now, how we help customers is you can have all of your data say, in a managed, uh, managed cloud service provider running on power scale. But then you can connect from this managed cloud service provider directly toe any off the hyper scholars. You can connect toe aws, azure, Google Cloud and even, like even, uh, the in place analytics that power scale offers you can run. Uh, those, uh I mean, run those clouds AI services directly on that data simultaneously from these three, and I'll add like one more thing, right? Thes keep learning. Technologies need GPU power solvers, right? and cloud even within like one cloud is not homogeneous environment. Like sometimes you'll find a US East has or gp part solvers. But like you are in the West and the same for other providers. No, with our still our technologies cloud power scale for multi cloud our scale is sitting outside off those hyper scholars connected directly to our any off this on. Then you can burst into different clouds, take advantage off our spot. Instances on are like leverage. All the GP is not from one particular service provider part. All of those be our hyper scholars. So those are some examples off the work we're doing in the multi cloud world for a I >>So that's day. You're talking about data there. So powers failed for multi cloud for data that's created outside the public club. But Thomas, what about for data that's created inside the cloud? How does Del help with that? >>Yes. So, this year, we actually released a solution, uh, in conjunction with G C. P. So within Google Cloud, you can have power scale for one fs, right? And so that's that native native feature. So, you know, goes through all the compliance and all the features within being a part of that G c p natively eso counts towards your credits and your GP Google building as well. But it's still all the features that you have. And so we've been running some, actually, some benchmarks. So we've got a couple of white papers out there, that kind of detail. You know what we can do from an artificial intelligence perspective back to Sean Demise Example. We were just talking about, you know, being able to use more and more GPU. So we we've done that to run some of our AI benchmarks against that and then also, you know, jumped into the Hadoop space. But because you know, that's 11 area from a power scale, prospective customers were really interested. Um, and they have been for years. And then, really, the the awesome portion about this is for customers that are looking for a hybrid solution. Or maybe it's their first kickoff to it. So back Lisa to those compliance features that we were talking about those air still inherent within that native Google G C P one fs version, but then also for customers that have it on prim. You can use those same features to burst your data into, um, your isil on cluster using all the same native tools that you've been using for years within your enterprise. >>God, it's so starting out for power. Skill for Google Cloud Trying to get back to you Kind of wrapping things up here. What are some of the things that we're going to see next from Dell from an AI Solutions perspective? >>Yes. So we are working on many different interesting projects ranging from, uh, the latest, uh, in video Salford's that they have announced d d x a 100. And in fact, two weeks ago at GTC, uh, Syria announced take too far parts with, uh, it takes a 100 solvers. We're part off that ecosystem. And we are working with, uh, the leading, uh uh, solutions toe benchmark, our ai, uh, environments, uh, for all the storage, uh, ensuring, like we are providing, like, all the throughput and scalability that we have to offer >>Thomas finishing with you from the customer perspective. As we talked about so many changes this year alone as we approach calendar year 2021 what are some of the things that Dell is doing with its customers with its partners, the hyper scale er's and video, for example, Do you think customers are really going to be able to truly accelerate successful AI projects? >>Yeah. So the first thing I'd like to talk about is what we're doing with the D. G. S A 100. So this month that GTC you saw our solution for a reference architecture for the G s, a 100 plus power scale. So you talk about speed and how we can move customers insights. I mean, some of the numbers that we're seeing off of that are really a really amazing right. And so this is gives the customers the ability to still, you know, take all the features and use use I salon and one f s, um, like they have in the past, but now combined with the speed of the A 100 still be ableto speed up. How fast they're using those building out those deep learning models and then secondly, with that that gives them the ability to scale to. So there's some features inherent within this reference architecture that allow for you to make more use, right? So bring mawr data scientists and more modelers GP use because that's one thing you don't see Data scientist turning away right there always like, Hey, you know, I mean, this this project here needs needs a GPU. And so, you know, from a power scale one fs perspective, we want to be able to make sure that we're supporting that. So that as that data continues to grow, which, you know we're seeing is one of the large factors. Whenever we're talking about artificial intelligence is the scale for the data. We wanna them to be able to continue to build out that data consolidation area for all these multiple different workloads. That air coming in. >>Excellent, Thomas. Thanks for sharing that. Hopefully next time we get to see you guys in person and we can talk about a customer who has done something very successful with you guys. Kind of me. Always great to talk to you. Thank you for joining us. >>Thank you. Thank you >>for China. May Mandel and Thomas Henson. I'm Lisa Martin. You're watching the cubes Coverage of Dell Technologies, World 2020

>>Hey, everyone, Thanks for taking them to join the story. Hope you and your loved ones are safe during these tough times. Let me start by introducing myself. My name is Michelle. When I walk for GlaxoSmithKline, GSK as an engineering manager in my current role, A little protocol platform A P s, which is part of the already data platform here in G S, K R and D Tech. I live in Dallas, Texas. I have a Masters degree in computer science on a bachelor's in electronics and communication engineering. I started my career as a software developer on over these years again a lot of experience in leading and building, not scale and predicts products and solutions. I also have a complete accountability for container platforms here at GSK or any tick. I've been working very closely with Dr Enterprise, which is no Miranda's for more than three years to enable container platforms that yes, came on mainly in our own Itek. So that's me. Let >>me give you a quick overview on agenda for today's talk. I'll start with what we do here at GSK on what is RND data platform. Then I'll give you an overview on What are the business drivers that >>motivated US toe? Take this container Germany on some insight into learnings on accomplishments over these years. Working with Dr Enterprise on the container platforms Lately, you must have seen a lot of articles off there which talk about how ts case liberating technologies like artificial intelligence, mission learning, UN data and analytics for the Douglas Corey process. I'm very excited to see the progress we have made in technology, but what makes us truly unique is our commitment to the patient. >>We're G escape, help millions of people, do more, feel better and live longer. Wear a global company that is focused on three were tickles pharmaceuticals vaccines on consumer healthcare. Our main intent is to lower the >>burden on the impact of diseases on the patients. Here at GSK, we allow science to drive the technology. This helps us toe build innovative products. That's helps our scientists to make better and faster additions throughout the drug discovery by plane. >>With that, let me give you some >>context on what currently data platform is how it is enabled. A T escape started in mid 2016. What used to be called us are any information platform whose main focus was to centralize curate on rationalized all the data produced within the others are in the business systems in orderto drive, a strategic business value, standardization of clinical trials, Genome Wide Association Study Analysis, also known as Jesus Storage and Crossing Off Rheal. World Evidence data some of the examples off how the only platform was used to deliver the business value four years later. No, a new set off business rivals of changing our landscape. The irony Information Platform is evolving to be a hybrid, multi cloud solution and is known as already did a platform refering to 20 >>19 GSK's annual report. These are the four teams that there are any platform will be mainly focused on. We're expanding our data capabilities to support the use. Escape by a former company on evolving into a hybrid medical platform is one of the many steps that we're taking to be future ready. Our key focus will still be making >>greater recommendations better and faster by using that wants us. We're making the areas like artificial intelligence and machine learning. No doc brings us toe. What is Germany is important. Why are we taking this German with that? Let me take you to the next topic off. Like the process of discovery, Francisco is not an easy process. Talking about the recent events occurred over the last few months on the way. How all our lives are impacted. It is a lot of talk on information going about. Why did drug discovery process is so tough working for a global health care company? I get asked this question very frequently. From many people I interact with. Question is like, Why is that? This car is so tough on why it takes so much time. Drug discovery is a complex process that involves multiple different stages on at each and every stage. There is huge amounts of data that the scientists have took process to make some decisions. Studies have shown that only 3% off small molecules entering the human studies actually become medicines. If you're new to drug discovery, you may ask, like what is the targets? Targets so low? We humans are very complex species, >>not going into the details of the process. We're G escape >>have made a lot of investments into technology that enabled us to make data river conditions. Throw the drug Discovery pipeline >>as we implement. As we started implementing these tools and technologies to enable already did a platform, we started to get a better appreciation off how these tools in track on integrate >>with each other. Our goal wants to make this platform a jail, the platform that can work at scale so that we can provide a great user experience and contribute back to the bread discovery pipeline so that the scientists can make faster editions. We want our ardently users to consume the data, and the service is available on the platform seamlessly in a self service fashion. And we also have to accomplish this by establishing trust. And then we have to end also enable the academic partnerships, acquisitions, collaborations that DSK has, which actually brings a lot of data on value to our scientists. So when we talk about so many collaborations and a lot of these systems, what this brings in is wide range off systems and platforms that are fundamentally built on different infrastructure. This is where Doctor comes into fiction on our containers significance. >>We have realized the power of containers on how we can simplify this complex ecosystem by using containers and provide a faster access off data to war scientists who didn't go >>back and contribute back to the drug discovery by play. >>With that, let me take talk to you about >>the containers journey and she escaped. So we started our container journey in late 2017. We started working with Dr Enterprise to enable the container platform. This is on our on prem infrastructure Back then, or first year or so we walked through multiple Pelosis did a lot of testing to make sure our platform is stable before we onboard either the data or the user applications. I was part of this complete journey on Dr Stream has worked with us very closely towards you. The first milestone off establishing a stable container platform. A tsk. Now, getting into 2019 we started deploying our applications in production environment. I cannot go into the details of what this Absar, but they do include both data pipelines as well as Web services. You know, initial days we have worked a lot on swamp, but in 2019 is when we started looking into communities in the same year, we enable kubernetes orchestration on the doctor and replace platform here at GSK and also made it as a de facto orchestra coming into 2020. All our micro service applications are undead. A pipelines are migrated to the container platforms on all of these are orchestrated by Cuban additional on these air applications that are running in production. As of today, we have made the container forced approach as an architectural standard across already taking GSK. We also started deploying our AML training models onto containers on All this work is happening on our Doctor Enterprise platform. Also as part off are currently platforms hybrid multicolored journey. We started enabling container and kubernetes based platforms on public clubs. Now going into 2021 on future. Enabling our RND users to easily access data and applications in a platform agnostic way is very crucial for our success because previously we had only onto him. Now we have public clothes that are getting involved on One of >>the many steps we're taking through this journey is to >>watch allies the data on ship data and containers or kubernetes volumes on demand to our our end users of scientists. And this allows us to deliver data to our scientists wherever they want in a very security on. We're leveraging doctor to do it. So that's >>our future. Learning on with that, let's take a deep dive into fuel for >>our accomplishments over these years. I want to start with a general demand and innovative one very interesting use case that we developed on Dr. This is a rapid prototyping capability that enabled our scientists seamlessly to Monday cluster communication. This was one off the biggest challenges which way his face for a long time and with the help of containers, were able to solve this on provide this as a capability to our scientists. We actually have shockers this capability in one of the doctor conferences before next. As I've said before, by migrating all over web services into containers, we not only achieved horizontal scalability for those specific services, but also saved more than 50% in support costs for the applications which we have migrated by making Docker image as an immutable artifact In our bill process, we are now able to deploy our APS or models in any container or Cuban, its base platform, either in on Prem or in a public club. We also made significant improvements towards the process. A not a mission By leveraging docker containers, containers have played a significant role in keeping US platform agnostic and thus enabling our hybrid multi cloud Germany valuable for out already did scientists. As I mentioned before, data virtualization is another viewpoint we have in terms off our next steps off where we want to take kubernetes on where we wanna leverage open it. Us. What you see here are just a few off many accomplishments which we have our, um, achieved by using containers for the past three years or so. So with that before I close all the time and acknowledge all our internal partners who has contributed a lot to this journey mainly are in the business are on the deck on the broader take. Organizations that escape also want to time document present Miranda's for being such a great partner throughout this journey and also giving us an opportunity to share this success story today. Lastly, thanks for everyone to listening to the stop and please feel free to reach out. If you have any questions or suggestions, let's be fit safe. Thank you

>> from Cambridge, Massachusetts. It's three Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back. We're here to wrap up the M I T. Chief data officer officer, information quality. It's hashtag m i t CDO conference. You're watching the Cube. I'm David Dante, and Paul Gill is my co host. This is two days of coverage. We're wrapping up eyes. Our analysis of what's going on here, Paul, Let me let me kick it off. When we first started here, we talked about that are open. It was way saw the chief data officer role emerged from the back office, the information quality role. When in 2013 the CEO's that we talked to when we asked them what was their scope. We heard things like, Oh, it's very wide. Involves analytics, data science. Some CEOs even said Oh, yes, security is actually part of our purview because all the cyber data so very, very wide scope. Even in some cases, some of the digital initiatives were sort of being claimed. The studios were staking their claim. The reality was the CDO also emerged out of highly regulated industries financialservices healthcare government. And it really was this kind of wonky back office role. And so that's what my compliance, that's what it's become again. We're seeing that CEOs largely you're not involved in a lot of the emerging. Aye, aye initiatives. That's what we heard, sort of anecdotally talking to various folks At the same time. I feel as though the CDO role has been more fossilized than it was before. We used to ask, Is this role going to be around anymore? We had C I. Ose tell us that the CEO Rose was going to disappear, so you had both ends of the spectrum. But I feel as though that whatever it's called CDO Data's our chief analytics off officer, head of data, you know, analytics and governance. That role is here to stay, at least for for a fair amount of time and increasingly, issues of privacy and governance. And at least the periphery of security are gonna be supported by that CD a role. So that's kind of takeaway Number one. Let me get your thoughts. >> I think there's a maturity process going on here. What we saw really in 2016 through 2018 was, ah, sort of a celebration of the arrival of the CDO. And we're here, you know, we've got we've got power now we've got an agenda. And that was I mean, that was a natural outcome of all this growth and 90% of organizations putting sea Dios in place. I think what you're seeing now is a realization that Oh, my God, this is a mess. You know what I heard? This year was a lot less of this sort of crowing about the ascendance of sea Dios and Maura about We've got a big integration problem of big data cleansing problem, and we've got to get our hands down to the nitty gritty. And when you talk about, as you said, we had in here so much this year about strategic initiatives, about about artificial intelligence, about getting involved in digital business or customer experience transformation. What we heard this year was about cleaning up data, finding the data that you've got organizing it, applying meditator, too. It is getting in shape to do something with it. There's nothing wrong with that. I just think it's part of the natural maturation process. Organizations now have to go through Tiu to the dirty process of cleaning up this data before they can get to the next stage, which was a couple of three years out for most of >> the second. Big theme, of course. We heard this from the former head of analytics. That G s K on the opening keynote is the traditional methods have failed the the Enterprise Data Warehouse, and we've actually studied this a lot. You know, my analogy is often you snake swallowing a basketball, having to build cubes. E D W practitioners would always used to call it chasing the chips until we come up with a new chip. Oh, we need that because we gotta run faster because it's taking us hours and hours, weeks days to run these analytics. So that really was not an agile. It was a rear view mirror looking thing. And Sarbanes Oxley saved the E. D. W. Business because reporting became part of compliance thing perspective. The master data management piece we've heard. Do you consistently? We heard Mike Stone Breaker, who's obviously a technology visionary, was right on. It doesn't scale through this notion of duping. Everything just doesn't work and manually creating rules. It's just it's just not the right approach. This we also heard the top down data data enterprise data model doesn't works too complicated, can operationalize it. So what they do, they kick the can to governance. The Duke was kind of a sidecar, their big data that failed to live up to its promises. And so it's It's a big question as to whether or not a I will bring that level of automation we heard from KPMG. Certainly, Mike Stone breaker again said way heard this, uh, a cz well, from Andy Palmer. They're using technology toe automate and scale that big number one data science problem, which is? They spend all their time wrangling data. We'll see if that if that actually lives up >> to his probable is something we did here today from several of our guests. Was about the promise of machine learning to automate this day to clean up process and as ah Mark Ramsay kick off the conference saying that all of these efforts to standardize data have failed in the past. This does look, He then showed how how G s K had used some of the tools that were represented here using machine learning to actually clean up the data at G S. K. So there is. And I heard today a lot of optimism from the people we talked to about the capability of Chris, for example, talking about the capability of machine learning to bring some order to solve this scale scale problem Because really organizing data creating enterprise data models is a scale problem, and the only way you can solve that it's with with automation, Mike Stone breaker is right on top of that. So there was optimism at this event. There was kind of an ooh, kind of, ah, a dismay at seeing all the data problems they have to clean up, but also promised that tools are on the way that could do that. >> Yeah, The reason I'm an optimist about this role is because data such a hard problem. And while there is a feeling of wow, this is really a challenge. There's a lot of smart people here who are up for the challenge and have the d n a for it. So the role, that whole 360 thing. We talked about the traditional methods, you know, kind of failing, and in the third piece that touched on, which is really bringing machine intelligence to the table. We haven't heard that as much at this event. It's now front and center. It's just another example of a I injecting itself into virtually every aspect every corner of the industry. And again, I often jokes. Same wine, new bottle. Our industry has a habit of doing that, but it's cyclical, but it is. But we seem to be making consistent progress. >> And the machine learning, I thought was interesting. Several very guest spoke to machine learning being applied to the plumbing projects right now to cleaning up data. Those are really self contained projects. You can manage those you can. You can determine out test outcomes. You can vet the quality of the of the algorithms. It's not like you're putting machine learning out there in front of the customer where it could potentially do some real damage. There. They're vetting their burning in machine, learning in a environment that they control. >> Right, So So, Amy, Two solid days here. I think that this this conference has really grown when we first started here is about 130 people, I think. And now it was 500 registrants. This'd year. I think 600 is the sort of the goal for next year. Moving venues. The Cube has been covering this all but one year since 2013. Hope to continue to do that. Paul was great working with you. Um, always great work. I hope we can, uh we could do more together. We heard the verdict is bringing back its conference. You put that together. So we had column. Mahoney, um, had the vertical rock stars on which was fun. Com Mahoney, Mike Stone breaker uh, Andy Palmer and Chris Lynch all kind of weighed in, which was great to get their perspectives kind of the days of MPP and how that's evolved improving on traditional relational database. And and now you're Stone breaker. Applying all these m i. Same thing with that scale with Chris Lynch. So it's fun to tow. Watch those guys all Boston based East Coast folks some news. We just saw the news hit President Trump holding up jet icon contractors is we've talked about. We've been following that story very closely and I've got some concerns over that. It's I think it's largely because he doesn't like Bezos in The Washington Post Post. Exactly. You know, here's this you know, America first. The Pentagon says they need this to be competitive with China >> and a I. >> There's maybe some you know, where there's smoke. There's fire there, so >> it's more important to stick in >> the eye. That's what it seems like. So we're watching that story very closely. I think it's I think it's a bad move for the executive branch to be involved in those type of decisions. But you know what I know? Well, anyway, Paul awesome working with you guys. Thanks. And to appreciate you flying out, Sal. Good job, Alex Mike. Great. Already wrapping up. So thank you for watching. Go to silicon angle dot com for all the news. Youtube dot com slash silicon angles where we house our playlist. But the cube dot net is the main site where we have all the events. It will show you what's coming up next. We've got a bunch of stuff going on straight through the summer. And then, of course, VM World is the big kickoff for the fall season. Goto wicked bond dot com for all the research. We're out. Thanks for watching Dave. A lot day for Paul Gillon will see you next time.

>> from Cambridge, Massachusetts. It's the Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back to Cambridge, Massachusetts. Everybody, You're watching the Cube, the leader in live tech coverage, and we're covering the M I t CDO conference M I t. CDO. My name is David Monty in here with my co host, Paul Galen. Mike Stone breakers here. The legend is founder CTO of Of Tamer, as well as many other companies. Inventor Michael. Thanks for coming back in the Cube. Good to see again. Nice to be here. So this is kind of ah, repeat pattern for all of us. We kind of gather here in August that the CDO conference You're always the highlight of the show. You gave a talk this week on the top 10. Big data mistakes. You and I are one of the few. You were the few people who still use the term big data. I happen to like it. Sad that it's out of vogue already, but people associated with the doo doop it's kind of waning, but regardless, so welcome. How'd the talk go? What were you talking about. >> So I talked to a lot of people who were doing analytics. We're doing operation Offer operational day of data at scale, and they always make most of them make a collection of bad mistakes. And so the talk waas a litany of the blunders that I've seen people make, and so the audience could relate to the blunders about most. Most of the enterprise is represented. Make a bunch of the blunders. So I think no. One blunder is not planning on moving most everything to the cloud. >> So that's interesting, because a lot of people would would would love to debate that, but and I would imagine you probably could have done this 10 years ago in a lot of the blunders would be the same, but that's one that wouldn't have been there. But so I tend to agree. I was one of the two hands that went up this morning, and vocalist talk when he asked, Is the cloud cheaper for us? It is anyway. But so what? Why should everybody move everything? The cloud aren't there laws of physics, laws of economics, laws of the land that suggest maybe you >> shouldn't? Well, I guess 22 things and then a comment. First thing is James Hamilton, who's no techies. Techie works for Amazon. We know James. So he claims that he could stand up a server for 25% of your cost. I have no reason to disbelieve him. That number has been pretty constant for a few years, so his cost is 1/4 of your cost. Sooner or later, prices are gonna reflect costs as there's a race to the bottom of cloud servers. So >> So can I just stop you there for a second? Because you're some other date on that. All you have to do is look at a W S is operating margin and you'll see how profitable they are. They have software like economics. Now we're deploying servers. So sorry to interrupt, but so carry. So >> anyway, sooner or later, they're gonna have their gonna be wildly cheaper than you are. The second, then yet is from Dave DeWitt, whose database wizard. And here's the current technology that that Microsoft Azure is using. As of 18 months ago, it's shipping containers and parking lots, chilled water in power in Internet, Ian otherwise sealed roof and walls optional. So if you're doing raised flooring in Cambridge versus I'm doing shipping containers in the Columbia River Valley, who's gonna be a lot cheaper? And so you know the economies of scale? I mean, that, uh, big, big cloud guys are building data centers as fast as they can, using the cheapest technology around. You put up the data center every 10 years on dhe. You do it on raised flooring in Cambridge. So sooner or later, the cloud guys are gonna be a lot cheaper. And the only thing that isn't gonna the only thing that will change that equation is For example, my lab is up the street with Frank Gehry building, and we have we have an I t i t department who runs servers in Cambridge. Uh, and they claim they're cheaper than the cloud. And they don't pay rent for square footage and they don't pay for electricity. So yeah, if if think externalities, If there are no externalities, the cloud is assuredly going to be cheaper. And then the other thing is that most everybody tonight that I talk thio including me, has very skewed resource demands. So in the cloud finding three servers, except for the last day of the month on the last day of the month. I need 20 servers. I just do it. If I'm doing on Prem, I've got a provision for peak load. And so again, I'm just way more expensive. So I think sooner or later these combinations of effects was going to send everybody to the cloud for most everything, >> and my point about the operating margins is difference in price and cost. I think James Hamilton's right on it. If he If you look at the actual cost of deploying, it's even lower than the price with the market allows them to their growing at 40 plus percent a year and a 35 $40,000,000,000 run rate company sooner, Sooner or >> later, it's gonna be a race to the lot of you >> and the only guys are gonna win. You have guys have the best cost structure. A >> couple other highlights from your talk. >> Sure, I think 2nd 2nd thing like Thio Thio, no stress is that machine learning is going to be a game is going to be a game changer for essentially everybody. And not only is it going to be autonomous vehicles. It's gonna be automatic. Check out. It's going to be drone delivery of most everything. Uh, and so you can, either. And it's gonna affect essentially everybody gonna concert of, say, categorically. Any job that is easy to understand is going to get automated. And I think that's it's gonna be majorly impactful to most everybody. So if you're in Enterprise, you have two choices. You can be a disrupt or or you could be a disruptive. And so you can either be a taxi company or you can be you over, and it's gonna be a I machine learning that's going going to be determined which side of that equation you're on. So I was a big blunder that I see people not taking ml incredibly seriously. >> Do you see that? In fact, everyone I talked who seems to be bought in that this is we've got to get on the bandwagon. Yeah, >> I'm just pointing out the obvious. Yeah, yeah, I think, But one that's not quite so obvious you're is a lot of a lot of people I talked to say, uh, I'm on top of data science. I've hired a group of of 10 data scientists, and they're doing great. And when I talked, one vignette that's kind of fun is I talked to a data scientist from iRobot, which is the guys that have the vacuum cleaner that runs around your living room. So, uh, she said, I spend 90% of my time locating the data. I want to analyze getting my hands on it and cleaning it, leaving the 10% to do data science job for which I was hired. Of the 10% I spend 90% fixing the data cleaning errors in my data so that my models work. So she spends 99% of her time on what you call data preparation 1% of her time doing the job for which he was hired. So data science is not about data science. It's about data integration, data cleaning, data, discovery. >> But your new latest venture, >> so tamer does that sort of stuff. And so that's But that's the rial data science problem. And a lot of people don't realize that yet, And, uh, you know they will. I >> want to ask you because you've been involved in this by my count and starting up at least a dozen companies. Um, 99 Okay, It's a lot. >> It's not overstated. You estimated high fall. How do you How >> do you >> decide what challenge to move on? Because they're really not. You're not solving the same problems. You're You're moving on to new problems. How do you decide? What's the next thing that interests you? Enough to actually start a company. Okay, >> that's really easy. You know, I'm on the faculty of M i t. My job is to think of news new ship and investigate it, and I come up. No, I'm paid to come up with new ideas, some of which have commercial value, some of which don't and the ones that have commercial value, like, commercialized on. So it's whatever I'm doing at the time on. And that's why all the things I've commercialized, you're different >> s so going back to tamer data integration platform is a lot of companies out there claim to do it day to get integration right now. What did you see? What? That was the deficit in the market that you could address. >> Okay, great question. So there's the traditional data. Integration is extract transforming load systems and so called Master Data management systems brought to you by IBM in from Attica. Talent that class of folks. So a dirty little secret is that that technology does not scale Okay, in the following sense that it's all well, e t l doesn't scale for a different reason with an m d l e t l doesn't scale because e t. L is based on the premise that somebody really smart comes up with a global data model For all the data sources you want put together. You then send a human out to interview each business unit to figure out exactly what data they've got and then how to transform it into the global data model. How to load it into your data warehouse. That's very human intensive. And it doesn't scale because it's so human intensive. So I've never talked to a data warehouse operator who who says I integrate the average I talk to says they they integrate less than 10 data sources. Some people 20. If you twist my arm hard, I'll give you 50. So a Here. Here's a real world problem, which is Toyota Motor Europe. I want you right now. They have a distributor in Spain, another distributor in France. They have a country by country distributor, sometimes canton by Canton. Distribute distribution. So if you buy a Toyota and Spain and move to France, Toyota develops amnesia. The French French guys know nothing about you. So they've got 250 separate customer databases with 40,000,000 total records in 50 languages. And they're in the process of integrating that. It was single customer database so that they can Duke custom. They could do the customer service we expect when you cross cross and you boundary. I've never seen an e t l system capable of dealing with that kind of scale. E t l dozen scale to this level of problem. >> So how do you solve that problem? >> I'll tell you that they're a tamer customer. I'll tell you all about it. Let me first tell you why MGM doesn't scare. >> Okay. Great. >> So e t l says I now have all your data in one place in the same format, but now you've got following problems. You've got a d duplicated because if if I if I bought it, I bought a Toyota in Spain, I bought another Toyota in France. I'm both databases. So if you want to avoid double counting customers, you got a dupe. Uh, you know, got Duke 30,000,000 records. And so MGM says Okay, you write some rules. It's a rule based technology. So you write a rule. That's so, for example, my favorite example of a rule. I don't know if you guys like to downhill downhill skiing, All right? I love downhill skiing. So ski areas, Aaron, all kinds of public databases assemble those all together. Now you gotta figure out which ones are the same the same ski area, and they're called different names in different addresses and so forth. However, a vertical drop from bottom to the top is the same. Chances are they're the same ski area. So that's a rule that says how to how to put how to put data together in clusters. And so I now have a cluster for mount sanity, and I have a problem which is, uh, one address says something rather another address as something else. Which one is right or both? Right, so now you want. Now you have a gold. Let's call the golden Record problem to basically decide which, which, which data elements among a variety that maybe all associated with the same entity are in fact correct. So again, MDM, that's a rule's a rule based system. So it's a rule based technology and rule systems don't scale the best example I can give you for why Rules systems don't scale. His tamer has another customer. General Electric probably heard of them, and G wanted to do spend analytics, and so they had 20,000,000 spend transactions. Frank the year before last and spend transaction is I paid $12 to take a cab from here here to the airport, and I charged it to cost center X Y Z 20,000,000 of those so G has a pre built classification system for spend, so they have parts and underneath parts or computers underneath computers and memory and so forth. So pre existing preexisting class classifications for spend they want to simply classified 20,000,000 spent transactions into this pre existing hierarchy. So the traditional technology is, well, let's write some rules. So G wrote 500 rules, which is about the most any single human I can get there, their arms around so that classified 2,000,000 of the 20,000,000 transactions. You've now got 18 to go and another 500 rules is not going to give you 2,000,000 more. It's gonna give you love diminishing returns, right? So you have to write a huge number of rules and no one can possibly understand. So the technology simply doesn't scale, right? So in the case of G, uh, they had tamer health. Um, solve this. Solved this classification problem. Tamer used their 2,000,000 rule based, uh, tag records as training data. They used an ML model, then work off the training data classifies remaining 18,000,000. So the answer is machine learning. If you don't use machine learning, you're absolutely toast. So the answer to MDM the answer to MGM doesn't scale. You've got to use them. L The answer to each yell doesn't scale. You gotta You're putting together disparate records can. The answer is ml So you've got to replace humans by machine learning. And so that's that seems, at least in this conference, that seems to be resonating, which is people are understanding that at scale tradition, traditional data integration, technology's just don't work >> well and you got you got a great shot out on yesterday from the former G S K Mark Grams, a leader Mark Ramsay. Exactly. Guys. And how they solve their problem. He basically laid it out. BTW didn't work and GM didn't work, All right. I mean, kick it, kick the can top down data modelling, didn't work, kicked the candid governance That's not going to solve the problem. And But Tamer did, along with some other tooling. Obviously, of course, >> the Well, the other thing is No. One technology. There's no silver bullet here. It's going to be a bunch of technologies working together, right? Mark Ramsay is a great example. He used his stream sets and a bunch of other a bunch of other startup technology operating together and that traditional guys >> Okay, we're good >> question. I want to show we have time. >> So with traditional vendors by and large or 10 years behind the times, And if you want cutting edge stuff, you've got to go to start ups. >> I want to jump. It's a different topic, but I know that you in the past were critic of know of the no sequel movement, and no sequel isn't going away. It seems to be a uh uh, it seems to be actually gaining steam right now. What what are the flaws in no sequel? It has your opinion changed >> all? No. So so no sequel originally meant no sequel. Don't use it then. Then the marketing message changed to not only sequel, So sequel is fine, but no sequel does others. >> Now it's all sequel, right? >> And my point of view is now. No sequel means not yet sequel because high level language, high level data languages, air good. Mongo is inventing one Cassandra's inventing one. Those unless you squint, look like sequel. And so I think the answer is no sequel. Guys are drifting towards sequel. Meanwhile, Jason is That's a great idea. If you've got your regular data sequel, guys were saying, Sure, let's have Jason is the data type, and I think the only place where this a fair amount of argument is schema later versus schema first, and I pretty much think schema later is a bad idea because schema later really means you're creating a data swamp exactly on. So if you >> have to fix it and then you get a feel of >> salary, so you're storing employees and salaries. So, Paul salaries recorded as dollars per month. Uh, Dave, salary is in euros per week with a lunch allowance minds. So if you if you don't, If you don't deal with irregularities up front on data that you care about, you're gonna create a mess. >> No scheme on right. Was convenient of larger store, a lot of data cheaply. But then what? Hard to get value out of it created. >> So So I think the I'm not opposed to scheme later. As long as you realize that you were kicking the can down the road and you're just you're just going to give your successor a big mess. >> Yeah, right. Michael, we gotta jump. But thank you so much. Sure appreciate it. All right. Keep it right there, everybody. We'll be back with our next guest right into the short break. You watching the cue from M i t cdo Ike, you right back

>> from Mountain View, California It's the Cube covering the fifteenth annual Grow Awards. Brought to you by A C. G S V. >> Hi, Lisa Martin on the ground with the Cube at the Computer History Museum for the fifteenth annual TGS Grow Awards. Can you hear the collaboration of the innovation going on behind me? Very excited to welcome to the Cube, one of tonight's award winners from a Jaso Technologies, Norby, Jericho B and the President and CEO of Modesto. Congratulations on the Emerging Growth Award that adjuster has been honored with tonight. >> Thank you very much. We're very honored to be here. So you've been at >> the helm of a desert for a long time. I'd like our audience to hear a little bit from you about whom destiny is what you do. What makes you different. >> Perfect. So we are at a technology company on our products are used primarily in Internet of things, applications across many, many segments. Most off our businesses within the industrial segment on our customers use our products to actually build a Iot solutions for their end markets. Our products include semiconductor chips that are used at the edge of Coyote EJ gateway devices that connects the local networks to the more broad networks on. Basically, we enable our customers to take data from the physical world and send it up into the clouds >> to you guys. Our have had a great great trajectory, obviously being recognized by the emerging growth winner from a C. G S B. Tell me a little bit about it was looking at some information from you guys and on twenty eighteen, You guys did a great job of executing on your strategic initiatives to really make twenty eighteen a transformative year couple of acquisitions to us about the last year, in particular in the group that you have seen the momento and you're bringing into twenty nineteen. >> Correct? Correct. So we started. We enter twenty eighteen as a provider up application specific memory devices for I ot however, we realize that for our customers to take true benefit off the technologies we provide, we need to be a more holistic supplier of solutions. So as a result, we went through a whole process off looking at other technologies that can complement what we have in a very similar way, with strategic focus in the markets that we were focused, and as a result, we made two acquisitions in past summer that ended up its expanding our market opportunity, broadening our reach within existing customer and significantly expanding our offering portfolio to foreign markets. >> Negroes have a really strong position with tear one customers in the industrial sector. You mentioned that expecting Don't be a little bit more than about your leadership here in what makes these large industrial cheer. One players say Augusto is for us, >> right? So before I asked her that let me talk a little bit about the difference between industrial I ot and Consumer >> Riley's Yes, >> So if you think about consumer, I ot, it's what grabs headlines. It's the fitness trackers, the latest home smart thermostats, and the smartwatch is on so forth. The's are new markets. Volumes are girl very fast, but if next year and new shiny object is created, it's easy for the consumers to replace. They basically buy the new one. Repent replaced the old. One interesting thing about industrial I ot is that industrial I ot has this fragmented legacy systems that today run in their businesses. So if you look at the building we're in Today there is a fired and safety system that runs there's H Vac system that runs the business. There's a security systems, and this could have been installed here decades ago. There are billions of connected things in that industrial network today, but the data is unable to go up into the cloud. Where come cloud providers? Aye, aye. Providers can actually take the data on provide benefits to the business owners. We understand the language of industrial I ot very well because off our roots in that space. And we also understand this universe very well because of our roots being in Silicon Valley. So for industrial customers to benefit from this transformation, it's very important to be able to understand the OT world operational technology world of old days on the IT world that we're very familiar with. So with addition off these acquisitions that we've done this summer very well, positions with the building blocks that way can put together on offer differentiated solutions to our customers? >> Well, no, but it's been a pleasure having you on the queue. But the fifteenth annual acey GSP grow words. Congratulations to adjust of your whole team for the emerging growth award. And we look forward to seeing what happens this year in the space with you. Thank >> you. Thank you very much. Thank you. >> Lisa. Martin, you're watching the Cube. Thanks for watching.

>> Live from Las Vegas. It's the Cube covering Adobe Summit twenty nineteen brought to you by X Ensure Interactive. >> Hey, welcome back, everyone. Cubes live coverage here in Las Vegas for W twenty nineteen. I'm Jeffery Jeffery, my co host. Next guy's a demon Danza whose the global president of a censure interactive operations Welcome to the Cube. Thanks for joining us. >> Thank you. Thanks for having me. >> So learning a lot about the interactive piece I sent you. Interactive. What is the Centre Interactive operations? What does that do? What's the function? >> Extension Interactive Operations is the manage service arm of Accenture Interactive. And together we design, build and activate scale the best experiences on the planet for our clients. What we were finding is you know, a lot of clients were very happy with our design lead creation of experiences, but they really wanted more help to activate, operate and scale those experiences across the world. I think scale ability is now becoming the crux of many of our CMO conversations. And so it was very important for us to build out Accenture Interactive operations to scale those experiences for our clients, >> given example of what it entails. And I'm just trying to follow it through. So, like, operations, meaning like Okay, that interactive team sets up everything they hated off to you and you guys wired together is in the cloud. Isn't analytics think us through how the operations workflow is? >> Yeah, well, except your interactive very much design and build the solution for clients. And absolutely, we then come into play to make sure that way Developer, man and machine operating model. So basically, we spoke marketing engines for clients that a data powered and also we design hub and spoke models for clients across the world to give them their speed, scale and agility that they now need in their coms. So very much, you know, we architect the right or model that is needed. The client that's the marketing operating model as well as the content operating model, so that we can effectively taken experience and scale it across multiple touchpoints seamlessly with huge brand consistency across every single consumer touch. >> So they stand out very quickly then, so that their goal is okay. Get it into market quickly, stand it up, get it going >> absolutely. And rapid. Standard is really so important because there's a speed in in sort of compression of go to market, and now clients can't have weeks and months of lag time between a creation off a product and the deployment of the broader on DH. So that's why we critically have come to the party with a very man and machine and data driven model. Teo, Give them that speed. So it really is from idea mediation, proof of concept, out to market. We truncated that whole supply chain and marketing supply chain quite significantly >> so that you talked about scale and global reach. But at the same time, your warrants personalization, right? So the challenge of personalization at scale is very different than just scale for scale sake. So how are you helping clients address that part of the equation? >> Well, first and foremost with any approach to hyper personalization, the way that we actually consume and segment the data is critical. So at its centre, interactive operations will play a key role in dealing with first, second and third party data off a client to be able to devise the right cohort groups that they need to effectively target in a world of hyper personalization that's directly related to their growth ambitions. Then we will make sure that the data actually feed the content creation and customization, so that the right message conversation experiences going out to the right client at the right time in the right way. And I think that, you know, we've really hone that for a lot of clients. BMW, Disney, Malia Hotels, G S, k, Et cetera, et cetera. And it's proving unbelievably successful. >> You guys are a huge partner of Adobe and partner of the Year, pretty much every year. Great presence globally. You've got to be excited when you look at the charts on yesterday's Kino when they lay out the platform because they're setting up exactly the kinds of mechanisms around data pipeline ing, semantic translations and then really time personal. I mean, they're building everything into a platform to make a data driven, and that's the hard part of all this. I mean, what you just laid out is a hugely complex story, and to do that quickly is huge task. >> Oh, absolutely. You >> gotta like what adobes doing now with their Platform >> I am loving water job he's doing, and they're making it easier. They are almost accelerating. Where now referring to is the platform ization of marketing on DH. You know, marketing technology is now circle thirty percent off the marketing budget. That's a lot, that's I mean, that's obviously the highest it's ever been. And it's only going to go one way. So now, to be able to actually set up designed the right marketing technology, leverage it fully Onda. Also, once you've got foundational tech like Adobe Toe, also build additional vertical bespoke technology onto that really starts to get clients too competitive. And so >> that's some of the challenges markers have because we've seen the evolution of the Internet infrastructure since their Web one dot oto whatever version you call it now. But in my mind, I just see this montage of this Martek stack that logo slides that comes out every year and it's Oh my God, it's like, huge. So So So the question is, has Martek failed to live up to his expectations? All these point solutions? Or is it just natural evolution that these things are kind of consolidating into kind of pillars of of technologies with more business conversations over the top? Because that's the question that way. Here, a lot from practitioners. Just look, I don't need another platform. I don't need another tool. I got tons of tool, got tons of platform options. I just want this stuff to work. Absolutely. How do you see this? Key challenges from marketers >> I think I think it's incredibly challenging, just challenging into your reference to the Loom Escape. I mean, the Loomascape has over six thousand Martech and adtech companies in it, and we're going to see an acceleration of that consolidation in that landscape. You're absolutely right. The point solutions are going quickly accelerate to an end to end solution. So everyone is a bit of musical chairs going on at the moment. With regards to the M in a landscape on DH, it's getting more acute, actually buy them by the week. So market is a very, very challenged still, to be able to procure the right technology to be able to also make sure that they're getting maximum utilization from that technology. Some of that technology is very, very expensive, so they have bought the licenses. But actually they don't necessarily have the skills, the talent, the capabilities to drive the technology effectively. It's almost like having a Ferrari but not having a driving licence. So we're helping clients to be able to properly drive the technology and to be able to also ascertain if they have the right technology in the first place, because the landscape is moving so quickly >> or the more the wrong technology and repurpose it and re skill. I mean, it's a huge operational challenge. Absolutely. Your operation comes in and this is This comes up a lot in our conversations. I love the new capability. I just wish I knew howto implement it >> and >> then operationalize it and staff around it so that everything's in my marketing mix and in agility way, not a waterfall kind of >> completely. And that's what we do in terms of our human and machine model. We look at the ad tech Martek stacks that we're building for clients. Make sure that they're truly proprietary, bespoke doing the job that they're intended to do in terms of marketing for growth and then literally we help clients maximize everything that they can get out of that technology and making sure that really data and analytics is driving the content creation, driving the content customization cause you're now in a world of algorithmic optimization when it comes to atomic content, lots and lots of little pieces of content that I needed to fire at loading loads of different cohort groups. We could take that all on and actually make it pretty painless for a client to do that across multiple countries. >> Thank you. What about from the other side of the equation? The receiver of all this micro targeted atomic for major consumer? This's so much stuff. I was like It's like it's like driving through a snowstorm with your headlights at night. You know, it's just like, how do you get through the tent? How did you get people's attention? How are you helping people get attention in this increasingly cluttered, busy and just, you know, over sensitized, you know, kind of inbound world in which we live as consumers? And it's one thing for me to see. I think of the poor B to B marketers. Oh my goodness, what a crazy challenge they have now. >> Yeah, I mean, I think it's a great question, and I think that now it's it's less about attention. Necessarily. It's more about relevance because if you manage to achieve hyper relevance in your communication, you know, customer first communication, then by default, you are going to get the right attention and you're going to get the right result from that experience, conversation, communication, etcetera, etcetera. So really, I think being able to really excel at hyper personalization is really what we're focused on now. And data is the answer to that. And data hand in hand with artificial intelligence and machine learning really gives us an unbelievable combination on puts hyper personalization on steroids. >> I'm gonna ask you on that point, cause content becomes a key part of the marketing mix at all at levels er known and all well paid all that good stuff. But content has is about data to because being relevant is also contextually aligned with targeted distribution of that of that that those audiences. So the question is we're seeing with our video's content drives a lot of community engagement. How are customs? Think about the role of community because as the users become part of their brand engine, this is now part of a new closed loop that's developing. How do you guys see that connecting? Because if you get the content right and you get the targeting through your operations, you then will they be able to put certain content in certain channels with the right data. That means the programming has to be relevant, which is another task. But if they get that right, the community engagement goes off the charts. How do you see the community part of developing? What is the brand marketers do after that? >> I think the community aspect is critically important, and it's hand in hand with the importance of first party data and everything that I mean, we really are gravitating towards a world of first party marketing activation. The first party data that clients hold is unbelievably potent, and there in lies your your the secrets of success to creating a highly engaged community. And, yes, we are taking a leadership role now in producing long and short form content. When it comes to making sure that it's laser focus to that particular Koval group, it has to be hyper relevant on DH. Absolutely, to your point, some of the community members want to create that content themselves. So we also play a part in whether it's the finer points of influence of marketing, making sure that we're helping thes stakeholders create the right content and then helping them distributed effectively and efficiently >> and then scoring users and reputation Relevant Reputation >> comes yes, because they become I mean, key influences in B to C and beater B to B are so important is when it as it pertains to the viral ity of the communication. So they're almost like channels, you know, the influences are almost like channels in off themselves, and they can actually, you know, put the communication on steroids if they are effective at there >> for the news. I think I get what you're saying. That the new formula is a collection of niches is the new reach number. It's a rather nice blast to the reaches. It's a collection of niches that are programmable, inexpressible >> absolutely. It's almost like the collection of cohort groups together gives you that mass communication. >> I'm curious kind of the take on softer some of the softer types of communications that content around, you know, mission. And we heard you know, sati and the keynote earlier today. Talking about mission and a lot of people are are really not so much concerned, but they care. They care about what the mission of the company is in some of these kind of social and, you know, not necessarily direct attributes of the product or direct benefits of using the product, but more of a private company, not necessarily product that they sell. How do you see that evolving in kind of the marketers tool kit and kind of the rising importance of that type of of engagement with community? >> Yeah, when we told took Teo client CM o's and CDO. Specifically, we talk about purpose as well as the product differentiation. I think in today's world, you have to have both on by purpose. You don't necessarily have to have a lofty purpose because not everyone can look at a lot of CPG clients. They can't have a lofty purpose, but they can be purposeful. They could be hyper relevant in your life, and that's what we try and attain and achieve. So I think it's very, very important reading a lot of work at the moment, with clients almost stepping back and saying, Well, what business are you actually in? What is your raison Detrol? What is your purpose in life and how do we amplify that then through all forms of communication? Because then once you've got that sussed. You really do have the the critical ingredients off designing, creating the best experiences on the planet and activating them. >> Transparency becomes a big part of the user trust equation as well as a user experience and relevance. Because of your transparent, they want to see the day that this becomes a whole new dynamic. >> Transparency is critical because anyone can find out anything in two minutes, you know, on the interweb. So you know, you have to. Transparency is not trust. Transparency is not >> enough. All right, so I gotta ask you about the conflict between innovation and regulation and market. A name is because we've seen innovation always run hard and fast, and then regulation tries to catch up and kind of fit in first party date. It's super important as this new shift digital was happening where it's kind of moving from the old, you know, email blast to the old communications static channels to more dynamic, You starting to see the rise of distribution platforms like Facebook, LinkedIn, Twitter, among other zillion other third party AP. I driven platform. They're all having third party data. So how do you How does the customer your customer brands balance The need for first party information that they have and or are now putting their content out in these channels is a huge thing because not everyone has opened data. So how do you guys review that trend? And how early is it? What needs to be done? Is it okay? >> Yeah. I mean, first and foremost, the clients that do have very rich first party data, particularly financial clients, telco clients, etcetera. We really helped them amplify that first party data to help them activate with clients that don't necessarily have rich first parties ater like a lot of CPG clients, we help them build that first party data. And that's also sometimes where the purpose comes in on the community building comes in because when you get those two things, you know when you hone those two things, you can actually start to build a community, and then you can start to build Richard first party data so that we can help clients activate off of that third party data. We're getting a little bit more forensic with regards to whether or not that third party data is truly additive. And sometimes it's the smaller third party verticals specific to travel, etcetera, farmer, et cetera, et cetera. Where the third party data is actually most potent. So it's important. Teo. Almost look att depth more than bread when it comes to you >> and blending the data together. >> Exactly. But it has to be additive because there are some third party data sources which aren't truly additive toe activation. Therefore, we can discard them. >> Nikki. Great content. You're amazing. Insights are broad and great. Really relevant. Thank you for sharing data here on the Q. Appreciate it. >> Thanks for having me. It's been fun. >> Live coverage here, too, Joe be summat. Twenty nineteen. I'm Jeffery, Jeff Frick, Dave Tune from or day to coverage after this short break.

(upbeat music) >> Narrator: Live from Los Angeles it's The Cube covering Open Source Summit North America 2017. Brought to you by the Lennox Foundation and Red Hat. >> Welcome back, everyone. Live in Los Angeles for The Cube's exclusive coverage of the Open Source Summit North America. I'm John Furrier, your host, with my co-host, Stu Miniman. Our next guest is Elaine Yeung, @egsy on Twitter, check her out. Student at Holberton School? >> At Holberton School. >> Holberton School. >> And that's in San Francisco? >> I'm like reffing the school right here. (laughs) >> Looking good. You look great, so. Open Source is a new generation. It's going to go from 64 million libraries to 400 million by 2026. New developers are coming in. It's a whole new vibe. >> Elaine: Right. >> What's your take on this, looking at this industry right now? Looking at all this old, the old guard, the new guard's coming in, a lot of cool things happening. Apple's new ARKit was announced today. You saw VR and ARs booming, multimedia. >> Elaine: Got that newer home button. Right, like I-- >> It's just killer stuff happening. >> Stu: (laughs) >> I mean, one of the reason why I wanted to go into tech, and this is why I, like, when I told them that I applied to Holberton School, was that I really think at whatever next social revolution we have, technology is going to be somehow interval to it. It's probably not even, like, an existing technology right now. And, as someone who's just, like, social justice-minded, I wanted to be able to contribute in that way, so. >> John: Yeah. >> And develop a skillset that way. >> Well, we saw the keynote, Christine Corbett Moran, was talking really hardcore about code driving culture. This is happening. >> Elaine: Right. So this is not, like, you know, maybe going to happen, we're starting to see it. We're starting to see the culture being shaped by code. And notions of ruling classes and elites potentially becoming democratized 100% because now software, the guys and gals doing it are acting on it and they have a mindset-- >> Elaine: Right. >> That come from a community. So this is interesting dynamic. As you look at that, do you think that's closer to reality? Where in your mind's eye do you see it? 'Cause you're in the front lines. You're young, a student, you're immersed in that, in all the action. I wish I was in your position and all these great AI libraries. You got TensorFlow from Google, you have all this goodness-- >> Elaine: Right. >> Kind of coming in, I mean-- >> So you're, so let me make sure I am hearing your question right. So, you're asking, like, how do I feel about the democratization of, like, educ-- >> John: Yeah, yeah. Do you feel it? Are you there? Is it happening faster? >> Well, I mean, things are happening faster. I mean, I didn't have any idea of, like, how to use a terminal before January. I didn't know, like, I didn't know my way around Lennox or GitHub, or how to push a commit, (laughs) until I started at Holberton School, so. In that sense, I'm actually experiencing this democratization of-- >> John: Yeah. >> Of education. The whole, like, reason I'm able to go to this school is because they actually invest in the students first, and we don't have to pay tuition when we enroll. It's only after we are hired or actually, until we have a job, and then we do an income-share agreement. So, like, it's really-- >> John: That's cool. >> It's really cool to have, like, a school where they're basically saying, like, "We trust in the education that we're going to give you "so strongly that you're not going to pay up front. >> John: Yeah. >> "Because we know you're going to get a solid job and "you'll pay us at that point-- >> John: Takes a lot of pressure off, too. >> Yeah. >> John: 'Cause then you don't have to worry about that overhang. >> Exactly! I wrote about that in my essay as well. Yeah, just, like because who wants to, like, worry about student debt, like, while you're studying? So, now I can fully focus on learning C, learning Python (laughs) (mumbles) and stuff. >> Alright, what's the coolest thing that you've done, that's cool, that you've gotten, like, motivated on 'cause you're getting your hands dirty, you get the addiction. >> Stu: (laughs) >> Take us through the day in the life of like, "Wow, this is a killer." >> Elaine: I don't know. Normally, (laughs) I'm just kind of a cool person, so I feel like everything I-- no, no. (laughs) >> John: That's a good, that's the best answer we heard. >> (laughs) Okay, so we had a battle, a rap battle, at my school of programming languages. And so, I wrote a rap about Bash scripts and (laughs) that is somewhere on the internet. And, I'm pretty sure that's, like, one of the coolest things. And actually, coming out here, one of my school leaders, Sylvain, he told me, he was like, "You should actually put that, "like, pretty, like, front and center on your "like, LinkedIn." Or whatever, my profile. And what was cool, was when I meet Linus yesterday, someone who had seen my rap was there and it's almost like it was, like, set up because he was like, "Oh, are you the one "that was rapping Bash?" And, I was like, "Well, why yes, that was me." (laughs) >> John: (laughs) >> And then Linus said it was like, what did he say? He was like, "Oh, that's like Weird Al level." Like, just the fact that I would make up a rap about Bash Scripts. (laughs) >> John: That's so cool. So, is that on your Twitter handle? Can we find that on your Twitter handle? >> Yes, you can. I will-- >> Okay, E-G-S-Y. >> Yes. >> So, Elaine, you won an award to be able to come to this show. What's your take been on the show so far? What was exciting about you? And, what's your experience been so far? >> To come to the Summit. >> Stu: Yeah. >> Well, so, when I was in education as a dean, we did a lot of backwards planning. And so, I think for me, like, that's just sort of (claps hands). I was looking into the future, and I knew that in October I would need to, like, start looking for an internship. And so, one of my hopes coming out here was that I would be able to expand my network. And so, like that has been already, like that has happened like more than I even expected in terms of being able to meet new people, come out here and just, like, learn new things, but also just like hear from all these, everyone's experience in the industry. Everyone's been just super awesome (laughs) and super positive here. >> Yeah. We usually find, especially at the Open Source shows, almost everyone's hiring. You know, there's huge demand for software developers. Maybe tell us a little bit about Holberton school, you know, and how they're helping, you know, ramp people up and be ready for kind of this world? >> Yeah. So, it's a two-year higher education alternative, and it is nine months of programming. So, we do, and that's split up into three months low-level, so we actually we did C, where we, you know, programmed our own shell, we programmed printf. Then after that we followed with high-levels. So we studied Python, and now we're in our CIS Admin track. So we're finishing out the last three months. And, like, throughout it there's been a little bit, like, intermix. Like, we did binary trees a couple weeks ago, and so that was back in C. And so, I love it when they're, like, throwing, like, C at us when we've been doing Python for a couple weeks, and I'm like, "Dammit, I have to put semicolons (laughs) >> John: (laughs) >> "And start compiling. "Why do we have to compile this?" Oh, anyway, so, offtrack. Okay, so after those nine months, and then it's a six month internship, and after that it's nine months of specialization. And so there's different spec-- you can specialize in high-level, low-level, they'll work with you in whatever you, whatever the student, their interests are in. And you can do that either full-time student or do it part-time. Which most of the students that are in the first batch that started in January 2016, they're, most of them are, like, still working, are still working, and then they're doing their nine month specialization as, like, part-time students. >> Final question for you, Elaine. Share your personal thoughts on, as you're immersed in the coding and learning, you see the community, you meet some great people here, network expanding, what are you excited about going forward? As you look out there, as you finish it up and getting involved, what's exciting to you in the world ahead of you? What do you think you're going to jump into? What's popping out and revealing itself to you? >> I think coming to the conference and hearing Jim speak about just how diversity is important and also hearing from multiple speakers and sessions about the importance of collaboration and contributions, I just feel like Lennox and Open Source, this whole movement is just a really, it's a step in the right direction, I believe. And it's just, I think the recognition that by being diverse that we are going to be stronger for it, that is super exciting to me. >> John: Yeah. >> Yeah, and I just hope to be able to-- >> John: Yeah (mumbles) >> I mean, I know I'm going to be able to add to that soon. (laughs) >> Well, you certainly are. Thanks for coming on The Cube. Congratulations on your success. Thanks for coming, appreciate it. >> Elaine: Thank you, thank you. >> And this is The Cube coverage, live in LA, for Open Source Summit North America. I'm John Furrier, Stu Miniman. More live coverage after this short break. (upbeat music)