(upbeat music) >> Welcome to theCUBE coverage of women in tech. International Women's Day 2022. I'm your host, Lisa Martin. Karla Wong joins me next. Country Sales Leader for the Commercial Sector in Peru at AWS. Karla, welcome to theCUBE. >> Thank you so much Lisa and thank you for having me. It's a pleasure to be with you today. >> I'm looking forward to chatting with you. You've been in the tech industry for more than 20 years, you've been a leader in tech and sales and customer service, partners, organizations. Talk to me a little bit about your background. >> I am a system engineer. I have some studies from enterprise direction with a university in Savannah, Columbia and I have a digital transformation certified with MIT in Boston. >> Fantastic, were you always interested in technology or STEM or was it something that you pivoted into somewhere during your career? >> Yes, you know what? Since I was little, I was just fascinated with the technology and all the time I was just trying to figure out how to do things and how to build that things and I remember once I was just, of course many time long ago, I was with this BHS, right? An equipment and I tried to do and tried to understand how this works and just figure out I was with many parts of that equipment and then I didn't realize how to join that parts but it was really funny because all the time I was trying to understand what is behind that kind of equipment, how this works and all the time I was asking and my dad said, I was just feeling so curiosity about that and asking many questions and I have uncles that they are engineers. So I was just all the time asking about that and they said, you know what? You are good in math, maybe you can just decide for an engineering career. They were encouraged me for doing that. So I guess that was my first clue that I'm interested in technology. >> Well, you sounds like you have a natural curiosity that you had great role models in your parents and probably others along your educational route and your career route that kind of encouraged that curiosity and being curious is one of the things that's important to being at AWS. Am I right? >> Yes, it's really important because we promote, you know, our, one of the main leadership principles that you read is learn to be curious and they promote that one, right? They're encouraging you to innovate, to learn more, to try to understand more about our solutions, our customers, how to make the things better and you have the space to propose new things, to do the things better. So they encourage you and they empower you to do that and you feel like your curiosity that you have very natural here's improved and they just promote that you continue to do that. >> That curiosity is so important. I mean, when we think about women in technology and we think about bringing in more thought diversity and DEI, it's important to be curious, to be able to bring different thoughts in so that the organization can be more well rounded, it can learn, you also not only do you lead the sales organization, but you are someone that's very active in volunteering. Tell me a little bit about that and how do you balance leading a sales organization and volunteering at the same time? >> You know, when you talk about this is more like work life balance, right? And when we talk about that you can feel like you need it, right? You need to work on that. It's more like an attitude of it's extremely important to think about mental health for everyone because that of course have impact in your physical health and when you talk about this, it not only matters in terms of attitude, it's action and disciplines as well and you have to keep in mind that. The first thing I believe and all the time I do it give the right value for this balance because it's something that a lot of people want more than anything and I have more than some professional decision thinking about this precisely and I have to thinking of me as a person, my family, how to help the community and you cannot imagine the impact when you decide to go for a volunteering activities how can benefit you and not in only the personal way, in your professional way. Even though you didn't start a volunteering, trying to figure out how this help you in your professional life, you receive a lot of benefits from the volunteering activities and it's amazing how that one's impacting your professional life also. When you are a volunteer, you'll receive new and meaningful experiences. Volunteering can be an excellent getaway to find unique and valuable experiences that you are very difficult to find in a day to day basis, right? And you develop your real life skills, openness to criticism, responsibility, humility, commitment, service, attitude, many things that you can proactively include in your job with your team and you can join with them in teamwork and try to figure out how to engage with them in your activities. This is another way to motivate your team, to build your team, right? Talking with this very valuable experiences and also I find out that that improves your health and mood. >> Sounds very-- >> We talk having-- >> Sorry. >> I'm sorry, no don't worry. >> That's very complimentary, that the volunteer work with leading the sales organization that there's so much value that you're bringing into your sales leadership role from the volunteering that you do. I'm just curious, can you describe some of the volunteer organizations that you work with? I think it's pretty impressive. >> Yes, I started my volunteering 14 years ago I guess but I was in the volunteering activities from the school and my dad was a really strong influence for that because I joined, I remember joining with him and go to do some volunteering activities that he led and I start 14 years I went with Operation and Smile group and then in the last two or three years I start with Project of Love. We are focused on kids with cancer and try to help them to build the last wishes they have because they pass away and at the end of this, this two years ago, I start with local activity that we do for patients with rare diseases and we just try to join two great passion that I have. One is the dance that we have here. The name of our national dance is Marinera Norteña and we are just doing this with a group that they are passion at the same time with this volunteering activities and the dance and we just trying to be the ambassador for and the voice for these patients, try to share with the community, the hard health journey that they have trying to obtain a fair treatment, a fair diagnostic, because they are rare disease and here is very difficult that they investigate about that. So that's why we are just doing this using dance as a way to broadcast our voice and just share happiness and hope and health. >> Happiness and hope. Those are two great things. So as the female leader in the tech industry, what are some of the main challenges that you have found regarding cultural aspects, regarding geographical aspects and LATAM? Talk to me about some of those challenges. >> Let me share with you my personal journey. My challenges started with the moment I decided to start engineering. A career that is traditional considered for men only, although this changes over the time, you will realize that the stereotype remains in many people minds right? It happens not only in Peru I can see it in Latin America. Someone once asked me if I wouldn't like to study something easier for a woman, right? And I just, when I received that question, that helping me to reaffirm that it was taking the right decision and I have the fortune to work with companies that believe in female leadership and the importance of our contribution and empower me to do things differently. Although I must confess that this was not always like this. I experienced the situation when I have to show that I'm so much and more capable and prepared than a man to take a major challenge. So despite the fact in the recent years you have had the great advances in integration of women in the field of science and technology, the gap in equality in equality in this sector still continues and many times the attitude towards women is discriminatory considering that we don't have enough knowledge and we don't have enough strength to overcome challenge without the ability to give the extra mile that is often required, or simply because of a gender issue. And generally speaking, opportunities that they're not equal. Neither in salaries. Several studies have revealed that in the same position since at position level within company, men's salary or benefits are higher than the woman. In addition, sometimes the position for a woman is not necessarily for merit it's just to feel fulfill a gender quota and when it's fulfilled, there's no more opportunities. So it's still a long way to go. We are working in that, we are trying to inspire more women to be part of this world. This is an amazing world and this world needs our leadership, judgment, ambition, as a woman. So that's why we try to inspire and try to be a role model for some young ladies that they are thinking about this career in technology. >> Right, you bring up a great point though about one of the things in terms of hiring for quotas. And as we think about this International Women's Day, this year's theme is Breaking the Bias. Where do you think we are with that? >> I think we have a lot long, long way to go to. Today we don't see that we have more women in some leadership roles in technology. We see more young ladies studying engineering but you know what, when you talk about stereotypes we need to understand, or the bias, the bias is not only what the society it's giving you, it's also your own bias because we need to understand that technology careers is not only for men it's also for a woman. And we need to understand and change the perspective that we see the challenges that we have in our life because sometimes that could be a really stopper in your professional life. And for me, we don't, we really need to understand that it's important. We cannot stop believing in ourself and we can achieve whatever we want. So we never stop pursuing our goals and achieve what you really need to achieve and as I said all the time, get inspired by women with great achievements who have changed this world technology. We have many examples of that for many years. We have Eva Maria Kiesler, the core inventor of Wi-Fi, Radia Joy Perlman, known as the the mother of the internet and Ada Lovelace who became the first female computer programmer. So we have many examples in this story to understand that the limit is on you. So the bias we need to break the first one is the bias that you have of yourself. >> That's a good point. That's a really good point there. I'm curious, what would your recommendation be? You obviously had, you had that natural curiosity that we talked about. You also seems like you had great parents who were very encouraging of all of the different things that you were interested in. What do you recommend for women maybe starting out in the STEM area or in tech in particular? How do they get that courage to just try? >> You know what, the main thing I guess as I mentioned before, is to put aside the stereotypes, right? And get out of your head, the standing out career like science, technology and engineering is only for a man. All the time I have this list for me, that is lesson learned. And my lesson learned is please don't think that you cannot do it. Try it. If you go and the things do not work well, try it again and try it again. So don't feel stopped because you face your first challenge and the challenge it's very difficult, because we have the courage to do that and you know what? It is very and interesting to understand that women has resilience, we have the courage to do anything, we are multi tasking all the time they say women can do many things at the same time and we have this particular way to communicate. We are very inclusive. We make empathy. We're just leading with a cohesion concept of a team. So we need to explore more about our strengths and try to encourage from them. And one of the main things for me is don't feel afraid and transform, you know, when you feel like that, transfer that as your power, you're encouraged to continue. So we need to transform our fears in our, I always said our gasoline to continue and then your motive to be successful. So transform your fears. >> I love that. >> That's my main focus. >> Transform your fear. That's great advice there is. And I will say no, don't be afraid to raise your hand and ask a question 'cause I guarantee you, many people in the room whether it's a physical room these days or it's a virtual video conferencing room, probably have the same question. Be the one to raise your hand and ask. But I love how you're saying transform that fear 'cause it's there. Don't be afraid to fail but also we need to have those female role models, mentors and sponsors that we can see that can have help us kind of in that transformation process, that mentorship is really critical to help guide that along. >> Yes, yes, yes, that's correct and I will, I am, I was really fortunate because I have real role models in my life not only, as I mentioned my dad and also one of the things that I recognize in this company that I work for that empower leadership from women and I identify some role models I want to follow and I ask her in each particular company to be my coach and to be my mentor, because of course you are starting in the technology side and you need more from others that they can share with you her wisdom, right? And try to give you advice, how to work on that. And I always said, and I will always repeat because I sometimes I have the opportunity to mentor young ladies that they are very curious about the technology side and I share with them my experience, my lesson learned so they can build their own story to do this and I share all the time don't compete in a male environment in a gray suit. You have your own personality, you have your own strengths, you're a woman and you have your strength as a woman. Show that, be, you know, the black point in the middle of the white environment because you're different, your leadership is different. You have to understand that, value that and explore more about that so you can inspire others and you can inspire yourself and it's fair to say, please identify your achievements and value them because you deserve that, you fight for them and you have to be celebrate for that. >> Right. >> So that's the main, you know, the main idea when I share with these ladies but it's right, it's fair to be recognized for that. It's your effort, it's your way to do the things differently and it's very appreciated. >> Very appreciated and very inspiring. Thank you so much Karla for sharing your story, how you are balancing work life volunteerism, how it's complimentary. I found this conversation very inspiring so thank you so much for joining me today. >> Thank you. No, thank you so much Lisa. It was really a pleasure for me to be with you today. >> Excellent, likewise. For Karla Wong, I'm Lisa Martin. You're watching theCUBE's coverage of women in tech, International Women's Day 2022. 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>>from our studios in the heart of Silicon Valley. Palo ALTO, California It is a cute conversation. >>Hi, and welcome to the Cube Studios for another cube conversation where we go in depth with thought leaders driving innovation across the tech industry. I'm your host, Peter Boris. Everybody's talking about the cloud and with the cloud might be able to do for their business. The challenge is there are a limited number of people in the world who really understands what it means to build for the cloud utilizing the cloud. It's a lot of approximations out there, but not a lot of folks are deeply involved in actually doing it right. We've got one here with us today. Wound Junk is thesis CEO and co founder of Clue Meo Womb. Welcome to the Cube. >>Happy to be here. >>So let's start with this issue of what it means to build for the cloud. Now Lou Meows made the decision to have everything fit into that as a service model. What does that practically need? >>So from the engineering point of view, building our sauce application is fundamentally different. So the way that I'll go and say is that at Cuneo. We actually don't build software and ship software. What we actually do, it builds service and service is what you're actually shipped our customers. Uh, let me give you an example. In the case of Kun, you they say backups fail like so far sometimes fails. We get that failures too. The difference in between Clooney oh, and traditional solutions is that if something were to fail, we are they one detecting that failure before our customers do Not only that, when something fails, we actually know exactly why it failed. Therefore, we can actually troubleshoot it, and we can actually fix it and operate the service without the customer intervention. So it's not about the books also or about the troubleshooting aspect, but it's also about new features. If you were to introduce a new features, we can actually do this without having customers upgraded call. We will actually do it ourselves. So essentially it frees the customers from actually doing all these actions because we will do them on behalf of them >>at scale. And I think that's the second thing I want to talk about quickly. Is that the ability to use the cloud to do many of the things that you're talking about at scale creates incredible ranges of options that customers have at their disposal. So, for example, a W s customers of historically using like snapshots to provide ah modicum of data protection to their AWS workloads. But there are other new options that could be applied if the systems are built to supply them. Give us a sense of how clue Meal is looking at this question of, you know, snapshots were something else. >>Yes, so, basically, traditionally, even on their own prints, out of the things you have something called the snapshots and you had your backups right, and they're they're fundamentally different. But if you actually shift your gears and you look at what A. W S offers today, they actually offers the ability for you to take snapshots. But actually that's not a backup, right? And they're they're fundamentally different. So let's talk about it a little bit more what it means to be snapshots and a backup. Right? So they say, there's a bad actor and your account gets compromised like your AWS account gets compromised. So then the bad actor has access not only to the EBS volumes, but also to the snap shows. What that means is that that person can actually go in and delete the E. V s volume as well as the TVs. No options. Now, If you had a backup, let's say you are should take a backup of that TVs William to whom? You, that bad actor would have access to the CVS volumes. However, it won't be able to delete the backup that we actually have, including you. So in the whole thing. The idea off Romeo is that you should be able to protect all of your assets, that being either an on Prem or neither of us by setting up a single policies. And these are true backups and not just snapshots. >>And that leads to the last question I have, which is ultimately the ability to introduce thes capabilities. At scale creates a lot of new opportunities that customers can utilize to do a better job of building applications, but also, I presume, managing how they use AWS because snapshots and other types of service can expand dramatically, which can increase your cost. How is doing it better with things like native backup service is improve customers ability to administer the AWS spend and accounts. >>So great question. So, essentially, if you look at the enterprises today, obviously they have multiple on premise data centers and also a different car providers that they use like AWS and azure and also a few sauce applications. Right? So then the idea is for Camilo is to create this single platform. What? All of the stains can actually be backed up in a uniform way where you can actually manage all of them. And then the other thing is all doing it in the cloud. So if you think about it, if you don't solve the problem, fundamental in the car, their stings that you end up paying later on. So let's take an example. Right. Uh, moving bites moving bites in between one server to the other, traditionally basically moving bites from one rack to the other. It was always free. You never had to pay anything for that. >>Certainly in the data center, >>right? But if you actually go to the public cloud, you cannot say the same thing, right? Basically, moving by across AWS recent regions is not free anymore. Moving data from AWS to the on premises. That's not for either. So these are all the things that any, you know, cop provider service provider, because has to consider and actually solved so that the customers can on Lee back it up into Clem you. But then they actually can leverage different cloud providers, you know, in a seamless way, without having to worry all of this costs associated with it so criminal we should be able to back it up. But we should be able to also offer mobility in between either aws back up the M word or the M. C. >>So if I can kind of summarize what you just said that you want to be able to provide to an account to an enterprise the ability to not have to worry about the back and infrastructure from a technical and process standpoint, but not also have to worry so much about the back and infrastructure from a cost of financial standpoint that by providing a service and then administering how that service is optimally handled, the customer doesn't have to think about some of those financial considerations of moving data around in the same way that they used to have. I got that right? >>I absolutely yes. Basically multiple accounts, multiple regions, multiple couple providers. It is extremely hard to manage. What come you does? It will actually provide you a single pane of glass where you can actually manage them all. But then, if you actually think about just and manageability this actually you can actually do that by just building a management layer on top of it. But more importantly, you really need to have a single data repository for you. For us to be able to provide a true mobility in between them. One is about managing. But the other thing is about if you're done, if you're done with the real divide way, it provides you the belly to move them and leverages the cloud power so that you don't have to worry about the cloud expenses but whom you internally is the one that actually optimizing all of this for our customers. >>Wound young cto and co founder of Cleo. Thanks very much for being on the Q. Thank you. And thank you for joining us for another cube conversation. I'm Peter Bursts. See you next time

>>from our studios in the heart of Silicon Valley. Palo ALTO, California It is a cute conversation. >>Welcome to another wicked bond digital community event, this one sponsored by Clue Me. Oh, I'm your host, Peter Burroughs. Any business that aspires to be a digital business needs to think about its data differently. It needs to think about how data could be applied to customer experience, value propositions, operations and improve profitability and strategic options for the businesses that moves forward. But that means openly, either. We're thinking about how we embed data more deeply into our operations. That means we must also think about how we're going to protect that data. So the business is not suffer because someone got a hold of our data or corrupted our data or that system just failed and we needed to restore that data very quickly. Now what we want to be able to do is we're going to do that in a way that's natural and looks a lot like a cloud because we want that cloud experience in our data protection as well. So that's we're gonna talk about with Clue Meo Today, a lot of folks think in terms of moving all the data into the cloud. We think increasingly we have to recognize the cloud is not a strategy for centralizing data but rather distributing data and being able to protect that data where it is utilizing a simple, common cloudlike experience has become an increasingly central competitive need for a lot of digital enterprises. The first conversation we had was with poo John Kamar, who John is a CEO and co founder of Cuneo. Let's hear a Peugeot on had to say about data value. Data service is and clue Meo. John, Welcome to the show. >>Thank you. Very nice to be here. >>So give us the update. Include me. Oh, >>so come you. Ah, a two year old company, right? We dress recently launched out of stealth. So so far, you know, we we came out with the innovative offering which is a sass solution to go and protect on premises in November and vmc environments. That's what we launched out of style two months ago. We want our best of show. When we came out off Stilton in November 2019. But ultimately we started with a vision about protecting data respective off buried, recites So it was all about, you know, you know, on premises on Cloud and other SAS service is so one single service that protects data introspective about recites So far, we executed on on premises VM wear and Vmc. Today What we're announcing for the first time is our protection to go and protect applications natively built on aws. So these are application that ineptitude natively built on aws that clue me in as a service will protect respective off. You know them running, you know, in one region or cross region cross accounts and a single service little our customers to protect native AWS applications. The other big announcement we're making is a new round of financing, and that is testament to the interest in the space and the innovative nature off the platform that we have built. So when we came out of still, we announced we had raised two rounds of financing $51 million in series and series B round of financing. Today, what we're announcing is a serious see around the financing off $135 million the largest. I would say Siri see financing for a sass and the price company, especially a company that's a little over two years >>old. Look, graduations that's gonna buy a lot of new technology and a lot of customer engagement. But what customers is a set up from where customers are really looking for is they're looking for tooling and methods and capabilities that allow them to treat their data differently. Talk a bit about the central importance of data and how it's driving decisions. ACLU mia >>Yes, so fundamentally. You know, when we built out the data platform, it was about going after the data protection as the first use case in the platform. Longer term, the journey really is to go from a data protection company to a data management company, and this is possible for the first time because you have the public cloud on your side. If you're truly built a platform for the cloud on the public cloud, you have this distinct and want a JJ off. Now, taking the data that you're protecting and really leveraging it for other service is that you can enable the enterprise for, and this is exactly what and the prices are asking for, especially as they you know, you make a transition from on premises. So the public cloud where they're powering on more and more applications in the public cloud and they really, you know, sometimes have no idea in terms off where the data is sitting and how they can take advantage off all these data sources that ultimately clueless protecting >>Well, no idea where the data sitting take advantage of these data. Sources presumably facilitate new classes of integration because that's how you generate value out of data. That suggests that we're not just looking at protection as crucially important as it is we're looking at new classes of service is they're gonna make it possible to alter the way you think about data management. If I got that right and what are those in service is? >>Yes, it's It's a journey, As I said, very starting with Finnegan Data protection. It's also about doing there the protection across multiple clouds, right? So ultimately we had a platform. Even though we're announcing, you know, aws, you know, applications support. Today. We've already done the ember and BMC as we go along. You'll see us kind of doing this across multiple clouds, an application that's built on the cloud running across multiple clouds, AWS, Azure and DCP. Whatever it might be, you see, it's kind of doing there, the protection across in applications and multiple clouds. And then it's about going and saying, Can we take advantage of the data that we're protecting and really power on adjusting to use cases, they could be security use cases because we know exactly what's changing when it's changing. There could be infrastructure. Analytics use cases because people are running tens of thousands off instances and containers and envy EMS in the public cloud. And if a problem happens, nobody really knows what caused it. And we have all the data and we can kind off index it in the back end and lies in the back end without the customer needing to lift a finger and really show them what happened in their environment that didn't know about right. So there's a lot of interesting use cases that get powered on because you have the ability to index all the data year. You have the ability to essentially look at all the changes that are happening and really give that visibility. Tow the end customer and all of this one click and automating it without the customer needing to do much. >>I will tell you this that we've talked to a number of customers of Romeo and the fundamental choice. The clue. Meo choice was simplicity. How are you going to sustain that? Even as you have these new classes of service is >>that is the key right? And that is about the foundation we have built at the end of the day, right? So if you look at all of our customers that have on border today, it's really the experience where in less than 15 minutes they can essentially start enjoying the power of the platform and the back end that we have built. And the focus on design that we have is ultimately why we're able to do this with simplicity. So so when when we when we think about you know all the things we do in the back, and there's obviously a lot of complexity in the back end because it is a complex platform. But every time we ask ourselves the question that okay from a customer perspective, how do we make sure that it is one click and easy for them? So that focus and that attention to detail that we have behind the scenes to make sure that the customer ultimately should just consumed the service and should not need to do anything more than what they absolutely need to do so that they can essentially focus on what eggs value to the business >>takes a lot of technology, a lot of dedication to make complex things really simple. Absolutely. John Kumar, CEO and co founder of Coolio. Thanks very much for being on the Cube. Thank you. Great conversation with you, John. Data value leading to data service is now. Let's think a little bit more about how enterprises ultimately need to start thinking about how to manifest that in a cloud rich world, Chad Kenney is the vice president and chief acknowledges a Cuneo and Chad and I had an opportunity to sit down to talk about some of the interesting approach. Is that air possible because of cloud and very importantly, to talk about a new announcement that clue me is making as they expand their support of different cloud types? What's your Chad had to say? The notion of data service is has been around for a long time, but it's being upended, recast, reformed as a consequence of what cloud can do. But that also means that Cloud is creating new ways of thinking about data service. Is new opportunities to introduce and drive this powerful approach of thinking about digital businesses centralized assets and to have that conversation about what that means. We've got Chad Candy, who's a VP and chief technologist of Kumiko with us today. Chad, welcome to the Cube. >>Thanks so much for having me. >>Okay, so what? Start with that notion of data service is and the role because gonna play clue. Meo has looked at this problem or looked this challenge from the ground up. What does that mean? >>So if you look at the cloud is a whole customers have gone through a significant journey. We've seen you know that the first shadow I t kind of play out where people decided to go to the cloud I t was too slow. It moved into kind of a cloud first movement where people realize the power of cloud service is that then got them to understand a little bit of interesting things that played out one moving applications as they exist. We're not very efficient, and so they needed to re architect certain applications. Second, SAS was a core way of getting to the cloud in a very simplistic fashion without having to do much of whatsoever. And so, for applications that were not core competencies, they realized they should go sass. And for anything that was a core competency, they needed to really re architect to be able to take advantage of those very powerful cloud service is. And so when you look at it, if people were to develop applications today, cloud is the default. They'd go tours. And so for us, we had the luxury of building from the cloud up on these very powerful cloud service is to enable a much more simple model for our customers to consume. But even more so to be able to actually leverage the agility and elasticity of the cloud. Think about this for a quick second. We can take facilities, break them up, expand them across many different compute resource is within the cloud versus having to take kind of what you did on prim in a single server or multitudes of servers and try to plant that in the cloud from a customer's experience perspective. It's vastly different. You get a world where you don't think about how you manage the infrastructure, how you manage the service, you just consume it. And the value that customers get out of that is not only getting their data there, which is the on ramp around our data protection mechanisms, but also being able to leverage cloud. Native service is on top of that data in the longer term, as we have this one comment global index and platform. What we're super excited today to announce is that we're adding in eight of US native capabilities to be ableto protect that data in the public cloud. And this is kind of the default place where most people go to from a cloud perspective to really get their applications are up and running and take advantage of a lot of those cloud. Native service is >>well, if you're gonna be Claude native and promised to customers is going to support There were clothes. You've got to be obviously on eight of us, So congratulations on that. But let's go back to this notion of you use the word powerful 80 of the U. S. Is a mature platform, G C P is coming along very rapidly. Azure is also very, very good. There are others as well, but sometimes enterprises discover that they have to make some tradeoffs. To get the simplicity, they have to get less function, to get the reliability they have to get rid of simplicity. How does clue Meo think through those trade offs to deliver that simple? That powerful, that reliable platform for something is important. Data protection and data service is in general, >>so we wanted to create an experience that was single click, discover everything and be able to help people consume that service quickly. And if you look at the problem that people are dealing with a customer's talk to us about this all time is the power of the cloud resulted in hundreds, if not thousands of accounts within eight of us. And now you get into a world where you're having to try to figure out how did I manage all of these for one? Discover all of it and consistently make sure that my data, which, as you've mentioned, is incredibly important to businesses today as protected. And so having that one common view is incredibly important to start with, and the simplicity of that is immensely powerful. When you look at what we do as a business, to make sure that that continues to occur is first, we leverage cloud. Native Service is on the back, which are complex, and getting those things to run and orchestrate are things that we build on the back end on the front end. We take the customers view and looking at what is the most simple way of getting this experience to occur for both discovery as well as you know, backup recovery and even being able to search in a global fashion and so really taking their seats to figure out what would be the easiest way to both consume the service and then also be able to get value from it by running that service >>A W s has been around well, a ws in many respects founded the cloud industry. It's it's certainly sales force on the South side. But a W. S is the first company to make the promise that it was gonna provide this very flexible, very powerful, very agile infrastructures of service. And they've done absolutely marvelous job about it, and they've also advanced the stadium to the technology dramatically and in many respects, are in the driver's seat. What tradeoffs? What limits does your new platform faces? It goes to eight of us. Or is it the same Coolio experience, adding, Now all of the capabilities of eight of us? >>It's a great question. I think a lot of solutions out there today are different parts and pieces kind of club together. What we built is a platform that these new service is just get instantly added. Next time you log in to that service, you'll see that that available Thio and you could just go ahead and log in to your accounts and build to discover directly. And I think that the the power of sass is really that not only have we made it immensely secure, which is something that people think about quite a bit with having, you know, not only did in flight, but data at rest, encryption on and leveraging really the cloud capabilities of security. But we've made it incredibly simple for them to be able to consume that easily, literally not lift a finger to get anything done. It's available for you when you log into that system. And so having more and more data sources in one single pane of glass and being able to see all the accounts, especially in AWS, where you have quite a few of those accounts, and to be able to apply policies in a consistent fashion to ensure that your you know, compliant within the environment for whatever business requirements that you have around data protection is immensely powerful to our >>customers. Judd Jenny, chief technologist Clue me Oh, thanks very much for being on the Cube. Thank you. Great conversation. Chad especially interested in hearing about how Camilo is being extended to include eight of US service, is within its overall data protection approach and obviously into data service is let's take a little bit more into that clue. MEOWS actually generated and prepared a short video we could take a look at that goes a little bit more deeply into how this is all gonna work. >>Enterprises air moving rapidly to the cloud. Embracing sass for simplified delivery of key service is in this cloud centric world. I T teams could focus on more strategic work, accelerating digital transformation initiatives when it comes to backup. I t is stuck designing, patching and capacity planning for on Prem Systems. Snapshots alone for data protection in the public cloud is risky, and there are hundreds of unprotected SAS applications in the typical enterprise. Move to cloud should make backup simpler, but it can quickly become exponentially worse. It's time to rethink the backup experience. What if there were no hardware, software or virtual appliances to size, configure, manage or even by it all? And by adding enterprise backup, public cloud workloads are no longer exposed to accidental data Deletion and Ransomware and Clooney. Oh, we deliver secure data backup and recovery without any of that complexity or risk. We provide all of the critical functions of enterprise backup de Doop and scheduling user and key management and cataloging because were built in the public cloud, weaken rapidly, deliver new innovations and take advantage of inherent data security controls. Our mission is to protect your data wherever it's stored. The clue. Meo authentic SAS backup experience scales on demand to manage and protect your data more easily and efficiently. And without things like cloud bills or egress charges, Clooney oh gives you predictable costs. Monitor and global back of compliance is far simpler, and the built in always on security of clue. Meo means that your data is safe. Take advantage of the cloud for backup with no constraints. Clue. Meo Authentic sass for the Enterprise. >>Great video as we think about moving forward in the future and what customers are trying to do. We have to think more in terms of the native service is that cloud can provide and how to fully exploit them to increase the aggregate flexibility both within our enterprises, but also based on what our supplies have to offer. We had a great conversation with Runes Young, who is thesis CTO and co founder of Cuneo, about just that. Let's hear it wound had to say everybody's talking about the cloud and what the cloud might be able to do for their business. The challenge is there are a limited number of people in the world who really understands what it means to build for the cloud utilizing the cloud. It's a lot of approximations out there, but not a lot of folks are deeply involved in actually doing it right. We've got one here with us today, wound junk is thesis CEO and co founder of Clue Meo Womb. Welcome to the Cube. >>Happy to be here. >>So let's start with this issue of what it means to build for the cloud. Now Lou MEOWS made the decision to have everything fit into that as a service model. What is that practically need? >>So from the engineering point of view, building our sauce application is fundamentally different. So the way that I'll go and say is that at Cuneo we actually don't build software and ship software. What we actually do, it builds service and service is what you're actually shipped Our customers. Let me give you an example. In the case of Kun, you they say backups fail like so far sometimes fails. We get that failures too. The difference in between Clooney oh, and traditional solutions is that if something were to fail, we are they one detecting that failure before our customers do Not only that, when something fails, we actually know exactly why it failed. Therefore, we can actually troubleshoot it, and we can actually fix it and operate the service without the customer intervention. So it's not about the books also or about the troubleshooting aspect, but it's also about new features. If you were to introduce a new features, we can actually do this without having customers upgraded call. We will actually do it ourselves. So essentially it frees the customers from actually doing all these actions because we will do them on behalf of them >>at scale. And I think that's the second thing I want to talk about quickly. Is that the ability to use the cloud to do many of the things that you're talking about? At scale creates incredible ranges of options that customers have at their disposal. So, for example, a W s customers of historically used things like snapshots to provide ah modicum of data protection to their AWS workloads. But there are other new options that could be applied if the systems are built to supply them. Give us a sense of how clue Meal is looking at this question of, you know, snapshots were something else. >>Yes, So, basically, traditionally, even on the imprints, out of the things, you have something called the snapshots and you had your backups right, and they're they're fundamentally different. But if you actually shift your gears and you look at what A. W s offers today. They actually offers stability for you to take snapshots. But actually, that's not a backup, right, And they're fundamentally different. So let's talk about it a little bit more what it means to be snapshots and a backup, right? So they say, there's a bad actor and your account gets compromised like your AWS account gets compromised. So then the bad actor has access not only to the EBS volumes, but also to the snap shows. What that means is that that person can actually go in and delete the E. V s volume as well as the TVs nuptials. Now, if you had a backup, let's say you are should take a backup of that TVs William to whom you that bad actor would have access to the CVS volumes. However, it won't be able to delete the backup that we actually have, including you. So in the whole thing. The idea off Romeo is that you should be able to protect all of your assets, that being either an on Prem or neither of us by setting up a single policies. And these are true backups and not just snapshots >>and that leads to the last question I have, which is ultimately the ability to introduce thes capabilities. At scale creates a lot of new opportunities of customers can utilize to do a better job of building applications, but also, I presume, managing how they use AWS because snapshots and other types of service can expand dramatically, which can increase your cost. How is doing it better with things like Native Backup Service is improve customers ability to administer the AWS spend and accounts. >>So, great question. So, essentially, if you look at the enterprises today, obviously they have multiple on premise data centers and also a different car providers that they use like AWS and Azure and also a few SAS applications, Right? So then the idea is for Camilo is to create this single platform what all of the stains can actually be backed up in a uniform way where you can actually manage all of them. And then the other thing is all doing it in the cloud. So if you think about it, if you don't solve the problem, fundamental in the cow, their stings that you end up paying later on. So let's take an example. Right. Uh, moving bites. Moving bites in between one server to the other. Traditionally basically moving bites from one rack to the other. It was always free. You never had to pay anything for that. >>Certainly in the data center. >>Right? But if you actually go to the public cloud, you cannot say the same thing, right? Basically, moving by across AWS recent regions is not free anymore. Moving data from AWS to the on premises. That's not for either. So these are all the things that you know cop provider service providers are gods has to consider and actually solved so that the customers can on Lee back it up into come you. But then they actually can leverage different cloud providers, you know, in a seamless way, without having to worry all of this costs associated with it so criminal we should be able to back it up. But we should be able to also offer mobility in between either aws back up the M word or the M C. >>So if I can kind of summarize what you just said that you want to be able to provide to an account to an enterprise, the ability to not have to worry about the back and infrastructure from a technical and process standpoint, but not also have to worry so much about the back and infrastructure from a cost of financial standpoint that by providing a service and then administering how that service is optimally handled, the customer doesn't have to think about some of those financial considerations of moving get around in the same way that they used to. Have I got that right, >>I absolutely, yes, basically multiple accounts, multiple regions, multiple couple providers. It is extremely hard to manage. What come your does. It will actually provide you a single pane of glass where you can actually manage them all. But then, if you actually think about just and manageability this, actually you can actually do that by just building a management layer on top of it. But more importantly, you really need to have a single data repository for you. For us to be able to provide a true mobility in between them. One is about managing, but the other thing is about if you're done, if you're done in the real divide way, it provides you the ability to move them and leverages the cloud power so that you don't have to worry about the cloud expenses but whom you internally is the one that actually optimizing all of this for our customers. >>Wound young cto and co founder of Coolio. Thanks very much for being on the Q. Thank you. Thanks very much. Room I want to thank clue me Oh, for providing this important content about the increasingly important evolution of data protection Cloud. Now, here's your opportunity to weigh in on this crucially important arena. What do you think about this evolving relationship? How do you foresee it operating in your enterprise? What comments do you have? What questions do you have of the thought leaders from Clue Me? Oh, and elsewhere. That's what we gonna do now we're gonna go into the crowd chat. We're gonna hear from each other about this really important topic and what you foresee in your enterprise as your digital business transforms, it's crochet

our studios in the heart of Silicon Valley Palo Alto California this is a cute conversation welcome to another wiki bond digital community event this one's sponsored by Columbia I'm your host Peter Burris any business that aspires to be a digital business needs to think about its data differently it needs to think about how data can be applied to customer experience value propositions operations that improve profitability and strategic options for the business as it moves forward but that means openly either we're thinking about how we embed data more deeply into our operations that means we must also think about how we're going to protect that data so the business does not suffer because someone got a hold of our data or corrupted our data or that a system just failed and we needed to restore that data very quickly now what we want to be able to do is we want to do that in a way that's natural and looks a lot like a cloud because we want that cloud experience in our data protection as well so that's we're going to talk about with clue Meo today a lot of folks think in terms of moving all the data into the cloud we think increasingly we have to recognize a cloud is not a strategy for centralizing data but rather distributing data and being able to protect that data where it is utilizing a simple common cloud like experience it's becoming an increasingly central competitive need for a lot of digital enterprises the first conversation we had was with a puja and Kumar who John is a CEO and co-founder of comeö let's hear a puja I had to say about data value data services and clue me oh who john welcome to the show Thank You Bertram nice to be here so give us the update in Colombia so Tomio is a two year old company right we just recently launched out of stealth so so far you know we we came out with the innovative offering which is a SAS solution to go and protect on premises you know VMware and BMC environments that's what we launched out of style two months ago we our best of show when we came out of stealth in in VMware 2019 well ultimately we started with a vision about you know protecting data irrespective of where it resides so it was all about you know you know on-premises on cloud and other SAS services so one single service that protects data irrespective of where it resides so far we executed on on-premises VMware and VM see today what we are announcing for the first time is our protection to go and protect applications natively built on AWS so these are applications that an aptitude natively built on AWS that clue me or as a service will protect irrespective of you know them running you know in one region or cross region cross accounts and a single service that will allow our customers to protect native AWS applications the other big announcement we are making is a new round of financing and that is testament to the interest in the space and the innovative nature of the platform that we have built so when we came out of stealth we announced we had raised two rounds of financing 51 million dollars in series a and Series B rounds of financing today what we are announcing is a Series C round of financing of 135 million dollars the largest I would say Series C financing for a SAS enterprise company especially a company that's a little over two years old Oh congratulations that's gonna buy a lot of new technology and a lot of customer engagement but what customers as I said up from where customers are really looking for is they're looking for tooling and methods and capabilities that allow them to treat their data differently talk a little bit about the central importance of data and how it's driving decisions of Cluny oh yes so fundamentally you know when we built out the the data platform it was about going after the data protection as the first use case in the platform longer term the journey really is to go from a data protection company to a data management company and this is possible for the first time because you have the public cloud on your side if you truly built a platform for the cloud on the public cloud you have this distinct advantage of now taking the data that you're protecting and really leveraging it for others that you can enable the enterprise for and this is exactly what enterprises are asking for especially as they you know you know make a transition from on-premises to the public cloud where they are powering on more and more applications in the public cloud and they really you know sometimes have no idea in terms of where the data is sitting and how they can take advantage of all these data sources that ultimately Klum is protecting well no idea where the data is sitting take advantage of these data sources presumably facilitate new classes of integration because that's how you generate value out of data that suggests that we're not just looking at protection as crucially important as it is we're looking at new classes of services they're going to make it possible to alter the way you think about data management if I got that right and what are those new services yes it's it's a journey as I said right so starting with you know again data protection it's also about doing data protection across multiple clouds right so ultimately we are a platform even though we are announcing you know AWS you know application support today we've already done VMware and VM C as we go along you'll see us kind of doing this across multiple clouds so an application that's built on the cloud running across multiple clouds AWS asher and GC p or whatever it might be you see as kind of doing data protection across in applications in multiple clouds and then it's about going and saying you know can we take advantage of the data that we are protecting and really power on adjacent use cases you know they could be security use cases because we know exactly what's changing when it's changing there could be infrastructure analytics use cases because people are running tens of thousands of instances and containers and n VMs in the public cloud and if a problem happens nobody really knows what caused it and we have all the data and we can kind of you know index it in the backend analyze in the backend without the customer needing to lift a finger and really show them what happened in their environment that they didn't know about right so there's a lot of interesting use cases that get powered on because you have the ability to index all the data here you have the ability to essentially look at all the changes that are happening and really give that visibility to the end customer and all of this one-click and automating it without the customer needing to do much I will tell you this that we've talked to a number of customers of Cuneo and the fundamental choice the clue mio choice was simplicity how are you going to sustain that even as you add these new classes of services yes that is the key right and that is about the foundation we have built at the end of the day right so if you look at all of our customers that have you know on-boarded today it's really the experience where in less than you know 15 minutes they can essentially start enjoying the power of the platform and the backend that we have built and the focus on design that we have is ultimately why we are able to do this with simplicity so so when we when we think about you know all the things we do in the backend there's obviously a lot of complexity in the backend because it is a complex platform but every time we ask ourselves the question that okay from a customer perspective how do we make sure that it is one click and easy for them so that focus and that attention to detail that we have behind the scenes to make sure that the customer ultimately should just consume the service and should not need to do anything more than what they absolutely need to do so that they can essentially focus on what adds value to their business takes a lot of Technology a lot of dedication to make complex things really simple absolutely whoo John Kumar CEO and co-founder of Clue leo thanks very much for being on the cube Thank You bigger great conversation with poo John data value leading to data services now let's think a little bit more about how enterprises ultimately need to start thinking about how to manifest that in a cloud rich world Chad Kenny is the vice president and chief technologist at Kumi oh and Chad and I had an opportunity to sit down and talk about some of the interesting approaches that are possible because of cloud and very importantly to talk about a new announcement that clew mios making as they expand their support of different cloud types let's see what Chad had to say the notion of data services has been around for a long time but it's being upended recast reformed as a consequence of what cloud can do but that also means that cloud is creating new ways of thinking about data services new opportunities to entry and drive this powerful approach of thinking about digital businesses centralized assets and to have that conversation about what that means we've got Chad Kenny who's a VP and chief technologist of comeö with us today Chad welcome to the cube thanks so much for having me okay so let's start with that notion of data services and the role the clouds gonna play loomio has looked at this problem with this challenge from the ground up what does that mean so if you look at the the cloud as a whole customers have gone through a significant journey we've seen you know that the first shadow IT kind of play out where people decided to go to the cloud IT was too slow it moved into kind of a cloud first movement where people realize the power of cloud services that then got them to understand a little bit of interesting things that played out one moving applications as they exist were not very efficient and so they needed to react exort anapa second SAS was a core way of getting to the cloud in a very simplistic fashion without having to do much of whatsoever and so for applications that were not core competencies they realized they should go SAS and for anything that was a core competency they needed to really reaaargh attack to be able to take advantage of those you know very powerful cloud services and so when you look at it if people were to develop applications today cloud is the default that you'd go towards and so for us we had the luxury of building from the cloud up on these very powerful cloud services to enable a much more simple model for our customers to consume but even more so to be able to actually leverage the agility and elasticity of the cloud think about this for a quick second we can take facilities break them up expand them across many different computer resources within the cloud versus having to take kind of what you did on Prem in a single server or multitudes of servers and try to plant that in the cloud from a customer's experience perspective it's vastly different you get a world where you don't think about how you manage the infrastructure how you manage the service you just consume it and the value that customers get out of that is not only getting their data there which is the on-ramp around our data protection mechanisms but also being able to leverage cloud native services on top of that data in the longer term as we have this one common global index and path and what we're super excited today to announce is that we're adding in AWS native capabilities to be able to date and protect that data in the public cloud and this is kind of the default place where most people go to from a cloud perspective to really get their applications up and running and take advantage of a lot of those cloud native services well if you're gonna be cloud native and promised to customers as you can support their workloads you got to be obviously on AWS so congratulations on that but let's go back to this notion of user word powerful mm-hmm 80 of us is a mature platform GCPs coming along very rapidly asher is you know also very very good and there are others as well but sometimes enterprises discover that they have to make some trade-offs to get the simplicity they have to get less function to get the reliability they have to get rid of simplicity how does ku mio think through those trade-offs to deliver that simple that powerful that reliable platform for something as important as data protection and data services in general so we wanted to create an experience that was single click discover everything and be able to help people consume that service quickly and if you look at the problem that people are dealing with a customer's talked to us about this all the time is the power of the cloud resulted in hundreds if not thousands of accounts within AWS and now you get into a world where you're having to try to figure out how do I manage all of these for one discover all of it and consistently make sure that my data which as you've mentioned is incredibly important to businesses today as protect it and so having that one common view is incredibly important to start with and the simplicity of that is immensely powerful when you look at what we do as a business to make sure that that continues to occur is first we leverage cloud native services on the back which are complex and and and you know getting those things to run and orchestrate are things that we build on the back end on the front end we take the customer's view and looking at what is the most simple way of getting this experience to occur for both discovery as well as you know backup for recovery and even being able to search in a global fashion and so really taking their seats to figure out what would be the easiest way to both consume the service and then also be able to get value from it by running that service AWS has been around well AWS in many respects founded the cloud industry it's it's you know certainly Salesforce and the south side but AWS is the first company to make the promise that it was going to provide this very flexible very powerful very agile infrastructure as a service and they've done an absolutely marvelous job about it and they've also advanced the state of the art of the technology dramatically and in many respects are in the driver's seat what trade offs what limits does your new platform face as it goes to AWS or is it the same coolio experience adding now all of the capabilities of AWS it's a great question because I think a lot of solutions out there today are different parts and pieces kind of klom together well we built is a platform that these new services just get instantly added next time you log into that service you'll see that that available to you and you can just go ahead and log in to your accounts and be able to discover directly and I think that the Vout the power of SAS is really that not only have we made it immensely secure which is something that people think about quite a bit with having you know not only data in flight but data at rest encryption and and leveraging really the cloud capabilities of security but we've made it incredibly simple for them to be able to consume that easily literally not lift a finger to get anything done it's available for you when you log into that system and so having more and more data sources in one single pane of glass and being able to see all the accounts especially in AWS where you have quite a few of those accounts and to be able to apply policies in a consistent fashion to ensure that you're you know compliant within the environment for whatever business requirements that you have around data protection is immensely powerful to our customers Chad Kenney chief technologist plumie Oh thanks very much for being on the tube thank you great conversation Chad especially interested in hearing about how klum EO is being extended to include AWS services within its overall data protection approach and obviously into Data Services but let's take a little bit more into that Columbia was actually generated and prepared a short video that we could take a look at that goes a little bit more deeply into how this is all going to work [Music] enterprises are moving rapidly to the cloud embracing sass for simplified delivery of key services in this cloud centric world IT teams can focus on more strategic work accelerating digital transformation initiatives when it comes to backup IT is stuck designing patching and capacity planning for on-premise systems snapshots alone for data protection in the public cloud is risky and there are hundreds of unprotected SAS applications in the typical enterprise the move to cloud should make backup simpler but it can quickly become exponentially worse it's time to rethink the backup experience what if there were no hardware software or virtual appliances to size configure manage or even buy it all and by adding Enterprise backup public cloud workloads are no longer exposed to accidental data deletion and ransomware at Clube o we deliver secure data backup and recovery without any of that complexity or risk we provide all of the critical functions of enterprise backup d dupe and scheduling user and key management and cataloging because we're built in the public cloud we can rapidly deliver new innovations and take advantage of inherent data security controls our mission is to protect your data wherever it's stored the clew mio authentic SAS backup experienced scales on-demand to manage and protect your data more easily and efficiently and without things like cloud bills or egress charges pluto gives you predictable costs monitoring global backup compliance is far simpler and the built-in always-on security of Clue mio means that your data is safe take advantage of the cloud for backup with no constraints clew mio authentic SAS for the enterprise great video as we think about moving forward in the future and what customers are trying to do we have to think more in terms of the native services that cloud can provide and how to fully exploit them to increase the aggregate flexible both within our enterprises but also based on what our supplies have to offer we had a great conversation with wounds Young who is the CTO and co-founder of Clue mio about just that let's hear it wound had to say everybody's talking about the cloud and what the cloud might be able to do for their business the challenge is there are a limited number of people in the world who really understands what it means to build for the cloud utilizing the cloud it's a lot of approximations out there but not a lot of folks are deeply involved in actually doing it right we've got one here with us today woo Jung is the CTO and co-founder of Cluny o moon welcome to the cube how they tittie here so let's start with this issue of what it means to build for the cloud now loomio has made the decision to have everything fit into that as a service model what is that practically mean so from the engineering point of view building our SAS application is fundamentally different so the way that I'll go and say is that at Combe you know we actually don't build software and ship software what we actually do it will service and service is what we actually ship to our customers let me give you an example in the case of chromium they say backups fail like software sometimes fails and we get that failures too the difference in between criminal and traditional solutions is that if something were to fail we are the one detecting that failure before our customers - not only that when something fails we actually know exactly why you fail therefore we can actually troubleshoot it and we can actually fix it and upgrade the service without the customer intervention so it's not about the bugs also or about the troubleshooting aspect but it's also about new features if you were to introduce our new features we can actually do this without having customers upgraded code we will actually do it ourselves so essentially it frees the customers from actually doing all these actions because we will do them on behalf of them at scale and I think that's the second thing I want to talk about quickly is that the ability to use the cloud to do many of the things that you're talking about at scale creates incredible ranges of options that customers have at their disposal so for example AWS customers have historically used things like snapshots to provide a modicum of data protection to their AWS workloads but there are other new options that could be applied if the system's are built to supply them give us a sense of how kkumeul is looking at this question of you know snapshots versus something else yeah so basically traditionally even on the on print side of the things you have something called a snapshot and you had your backups right and they're they're fundamentally different but if you actually shift your gears and you look at what they WS offers today they actually offers the ability for you to take snapshots but actually that's not a backup right and they're fundamentally different so let's talk about it a little bit more what it means to be snapshots and a backup right so let's say there's a bad actor and your account gets compromised like your AWS account gets compromised so then the bad actor has access not only to the EBS volumes but also to the EBS snapshots what that means is that that person can actually go ahead and delete the EBS volume as well as the EBS snapshots now if you had a backup let's say you actually take a backup of that EBS volume to Kumu that bad actor will have access to the EBS volumes however you won't be able to delete the backup that we actually have in Kumu so in the whole thing the idea of Kumi on is that you should be able to protect all of your assets that being either an on-prem or an AWS by setting up a single policies and these are true backups and not just snapshots and that leads to the last question I have which is ultimately the ability to introduce these capabilities at scale creates a lot of new opportunities that customers can utilize to do a better job of building applications but also I presume managing how they use AWS because snapshots and other types of service can expand dramatically which can increase your cost how is doing it better with things like native backup services improve a customer's ability to administer their AWS spend and accounts great question so essentially if you look at the enterprise's today obviously they have multiple you know on-premise data centers and also a different cloud providers that the you like AWS and Azure and also a few SAS applications right so then the idea is for kkumeul is to create this single platform where all of these things can actually be backed up in a uniform way where you can actually manage all of them and then the other thing is all doing it in the cloud so if you think about it if you don't solve the poem fundamentally in the cloud there's things that you end up paying later on so let's take an example right moving bytes moving bytes in between one server to the other traditionally basically moving bytes from one rack to the other it was always free you never had to pay anything for that certainly in the data center alright but if you actually go to the public cloud you cannot say the same thing right basically moving by it across aw s recent regions is not free anymore moving data from AWS to the on premises that's not free either so these are all the things that any you know car provider service provider like ours has to consider and actually solve so that the customers can only back it up into Kumu but then they actually can leverage different cloud providers you know in a seamless way without having to worry all of this costs associated with it so kkumeul we should be able to back it up but we should be able to also offer mobility in between either AWS backup VMware or VNC so if I can kind of summarize what you just said that you want to be able to provide to an account to an enterprise the ability to not have to worry about the backend infrastructure from a technical and process standpoint but not also have to worry so much about the backend infrastructure from a cost and financial standpoint that by providing a service and then administering how that service is optimally handled the customer doesn't have to think about some of those financial considerations of moving data around in the same way that they used to oh I got that right I absolutely yes basically multiple accounts multiple regions multiple providers it is extremely hard to manage what Cuneo does it will actually provide you a single pane of glass where you can actually manage them all but then if you actually think about just and manageability this actually you can actually do that by just building a management layer on top of it but more importantly you and we need to have a single data you know repository for you for us to be able to provide a true mobility in between them one is about managing but the other thing is about if you're done if you're done it the real the right way it provides you the ability to move them and it leverages the cloud power so that you don't have to worry about the cloud expenses but kkumeul internally is the one are actually optimizing all of this try our customers wound jeong CTO and co-founder of Kaleo thanks very much for being on the queue thank you thanks very much moon I want to thank chromeo for providing this important content about the increasingly important evolution of data protection and cloud now here's your opportunity to weigh in on this crucially important arena what do you think about this evolving relationship how do you foresee it operating in your enterprise what comments do you have what questions do you have of the thought leaders from Cluny oh and elsewhere that's what we're gonna do now we're gonna go into the crowd chat and we're gonna hear from each other about this really important topic and what you foresee in your enterprise as your digital business transforms let's crouch at

and II just think about how data could be customer experience value propositions operations that improve profitability and strategic options for the business as it moves forward but that means openly either we're thinking about how we embed data more deeply into our operations that means we must also think about how we're going to protect that data so the business does not suffer because someone got a hold of our data or corrupted our data or that a system just failed and we needed to restore that data very quickly now what we want to be able to do is we want to do that in a way that's natural and looks a lot like a cloud because we want that cloud experience in our data protection as well so that's we're going to talk about with Klum you know today a lot of folks think in terms of moving all the data into the cloud we think increasingly we have to recognize a cloud is not a strategy for centralizing data but rather distributing data and being able to protect that data where it is utilizing a simple common cloud like experience it's becoming an increasingly central competitive need for a lot of digital enterprises the first conversation we had was with pooja Kumar who John is a CEO and co-founder of Kaleo let's hear a pooja I had to say about data value data services and Kumi Oh poo John welcome to the show thank you Peter nice to be here so give us the update in clue so comeö is a two year old company right we just recently launched out of stealth so so far you know we we came out with innovative offering which is a SAS solution to go and protect on premises you know VMware and BMC environments that's what we launched out of style two months ago we won our best of show when we came out of stealth in in VMware 2019 but ultimately we started with a vision about you know protecting data irrespective of where it resides so it was all about you know you know on-premises on cloud and other SAS services so one single service that protects data irrespective of where it resides so far we executed on on-premises VMware and BMC today what we are announcing for the first time is our protection to go and protect applications natively built on AWS so these are applications that are natively built on AWS that loomio as a service will protect irrespective of you know them running you know in one region or cross region cross accounts and a single service that will allow our customers to protect native AWS applications the other big announcement we are making is a new round of financing and that is testament to the interest in the space and the innovative nature of the platform that we have built so when we came out of stealth we announced we had raised two rounds of financing 51 million dollars in series a and Series B rounds of financing today what we are announcing is a Series C round of financing of 135 million dollars the largest I would say Series C financing for a SAS enterprise company especially a company that's a little over two years old Oh congratulations that's gonna buy a lot of new technology and a lot of customer engagement but what customers as I said up from what customers are really looking for is they're looking for tooling and methods and capabilities that allow them to treat their data differently talk a bit about the central importance of data and how it's driving decisions of Cluny oh yes so fundamentally you know when we built out the the data platform it was about going after the data protection as the first use case on the platform longer term the journey really is to go from a data protection company to a data management company and this is possible for the first time because you have the public cloud on your side if you truly built a platform for the cloud on the public cloud you have this distinct advantage of now taking the data that you're protecting and really leveraging it for other services that you can enable the enterprise for and this is exactly what enterprises are asking for especially as they you know you know make a transition from on-premises to the public cloud where they're powering on more and more applications in the public cloud and they really you know sometimes have no idea in terms of where the data is sitting and how they can take advantage of all these data sources that ultimately protecting well no idea where the data is sitting take advantage of these data sources presumably facilitate new classes of integration because that's how you generate value out of data that suggests that we're not just looking at protection as crucial and important as it is we're looking at new classes of services they're going to make it possible to alter the way you think about data management if I got that right and what are those new services yes it's it's a journey as I said right so starting with you know again data protection it's also about doing data protection across multiple clouds right so ultimately we are a platform even though we are announcing you know AWS you know application support today we've already done VMware and BMC as we go along you'll see us kind of doing this across multiple clouds so an application that's built on the cloud running across multiple clouds AWS ashore and GCP or whatever it might be you see as kind of doing data protection across in applications in multiple clouds and then it's about going and saying you know can we take advantage of the data that we are protecting and really power on adjacent use cases you know there could be security use cases because we know exactly what's changing when it's changing there could be infrastructure analytics use cases because people are running tens of thousands of instances and containers and VMs in the public cloud and if a problem happens nobody really knows what caused it and we have all the data and we can kind of you know index it in the backend analyze in the backend without the customer needing to lift a finger and really show them what happened in their environment that they didn't know about right so there's a lot of interesting use cases that get powered on because you have the ability to index all the data here you have the ability to essentially look at all the changes that are happening and really give that visibility to the end customer and all of this one-click and automating it without the customer needing to do much I will tell you this that we've talked to a number of customers of Cuneo and the fundamental choice the clue Meo choice was simplicity how are you going to sustain that even as you add these new classes of services that is the key right and that is about the foundation we have built at the end of the day right so if you look at all of our customers that have you know on boarded today it's really the experience we're in less than you know 15 minutes they can we start enjoying the power of the platform and the backend that we have built and the focus on design that we have is ultimately why we are able to do this with simplicity so so when we when we think about you know all the things we do in the back end there's obviously a lot of complexity in the back end because it is a complex platform but every time we ask ourselves the question that okay from a customer perspective how do we make sure that it is one click and easy for them so that focus and that attention to detail that we have behind the scenes to make sure that the customer ultimately should just consume the service and should not need to do anything more than what they absolutely need to do so that they can essentially focus on what adds value to their business takes a lot of technology a lot of dedication to make complex things really simple absolutely whoo John Kumar CEO and co-founder of coolio thanks very much for being on the cube Thank You bigger great conversation with poo John data value leading to data services now let's think a little bit more about how enterprises ultimately need to start thinking about how to manifest that in a cloud rich world Chad Kenney is the vice president and chief acknowledges of Cuneo and Chad and I had an opportunity to sit down and talk about some of the interesting approaches that are possible because of cloud and very importantly to talk about a new announcement that clue miios making as they expand their support of different cloud types let's see what Chad had to say the notion of data services has been around for a long time but it's being upended recast reformed as a consequence of what cloud can do but that also means that cloud is creating new ways of thinking about data services new opportunities to introduce and drive this powerful approach of thinking about digital businesses centralized assets and to have that conversation about what that means we've got Chad Kenny who's a VP and chief technologists of comeö with us today Chad welcome to the cube thanks so much for having me okay so let's start with that notion of data services and the role the clouds going to play Kumi always looked at this problem this challenge from the ground up what does that mean so if you look at the the cloud as a whole customers have gone through a significant journey we've seen you know that the first shadow IT kind of play out where people decided to go to the cloud IT was too slow it moved into kind of a cloud first movement where people realize the power of cloud services that then got them to understand a little bit of interesting things that played out one moving applications as they exist were not very efficient and so they needed to react attack certain applications second SAS was a core way of getting to the cloud in a very simplistic fashion without having to do much of whatsoever and so for applications that were not core competencies they realized they should go SACEM for anything that was a core competency they needed to really reaaargh attack to be able to take advantage of those you know very powerful cloud services and so when you look at it if people were to develop applications today cloud is the default that you'd go towards and so for us we had the luxury of building from the cloud up on these very powerful cloud services to enable a much more simple model for our customers to consume but even more so to be able to actually leverage the agility and elasticity of the cloud think about this for a quick second we can take facilities break them up expand them across many different compute resources within the cloud versus having to take kind of what you did on prim in a single server or multitudes of servers and try to plant that in the cloud from a customer's experience perspective it's vastly different you get a world where you don't think about how you manage the infrastructure how you manage the service you just consume it and the value that customers get out of that is not only getting their data there which is the on-ramp around our data protection mechanisms but also being able to leverage cloud native services on top of that data in the longer term as we have this one common global index and platform what we're super excited today to announce is that we're adding in AWS native capabilities to be able to date and protect that data in the public cloud and this is kind of the default place where most people go to from a cloud perspective to really get their applications up and running and take advantage a lot of those cloud native services well if you're gonna be cloud native and promise to customers as you're going to support their workloads you got to be obviously on AWS so congratulations on that but let's go back to this notion of user word powerful mm-hmm AWS is a mature platform GCPs coming along very rapidly asher is you know also very very good and there are others as well but sometimes enterprises discover that they have to make some trade-offs to get the simplicity they have to get less function to get the reliability they have to get rid of simplicity how does qu mio think through those trade-offs to deliver that simple that powerful that reliable platform for something as important as data protection and data services in general so we wanted to create an experience that was single click discover everything and be able to help people consume that service quickly and if you look at the problem that people are dealing with a customer's talked to us about this all the time is the power of the cloud resulted in hundreds if not thousands of accounts within AWS and now you get into a world where you're having to try to figure out how do I manage all of these for one discover all of it and consistently make sure that my data which as you've mentioned is incredibly important to businesses today as protect it and so having that one common view is incredibly important to start with and the simplicity of that is immensely powerful when you look at what we do as a business to make sure that that continues to occur is first we leverage cloud native services on the back which are complex and and and you know getting those things to run and orchestrate are things that we build on the back end on the front end we take the customer's view and looking at what is the most simple way of getting this experience to occur for both discovery as well as you know backup for recovery and even being able to search in a global fashion and so really taking their seats to figure out what would be the easiest way to both consume the service and then also be able to get value from it by running that service AWS has been around well AWS in many respects founded the cloud industry it's it's you know certainly Salesforce and the south side but AWS is that first company to make the promise that it was going to provide this very flexible very powerful very a a July infrastructure as a service and they've done an absolutely marvelous job about it and they've also advanced the state of your technology dramatically and in many respects are in the driver's seat what trade offs what limits does your new platform face as it goes to AWS or is it the same Coolio experience adding now all of the capabilities of AWS it's a great question because I think a lot of solutions out there today are different parts and pieces kind of clump together well we built is a platform that these new services just get instantly added next time you log into that service you'll see that that available to you and you can just go ahead and log in to your accounts and be able to discover directly and I think that the vow the power of SAS is really that not only have we made it immensely secure which is something that people think about quite a bit with having you know not only data in flight but data at rest encryption and and leveraging really the cloud capabilities of security but we've made it incredibly simple for them to be able to consume that easily literally not lift a finger to get anything done it's available for you when you log into that system and so having more and more data sources in one single pane of glass and being able to see all the accounts especially in AWS where you have quite a few of those accounts and to be able to apply policies in a consistent fashion to ensure that you're you know compliant within the environment for whatever business requirements that you have around data protection is immensely powerful to our customers Chad Denny Chief Technologist plumie oh thanks very much for being on the tube thank you great conversation Chad especially interested in hearing about how klum EO is being extended to include AWS services within its overall data protection approach and obviously into Data Services but let's take a little bit more into that Columbia was actually generated and prepared a short video that we could take a look at that goes a little bit more deeply into how this is all going to work enterprises are moving rapidly to the cloud embracing sass for simplified delivery of key services in this cloud centric world IT teams can focus on more strategic work accelerating digital transformation initiatives for when it comes to backup IT is stuck designing patching and capacity planning for on-premise systems snapshots alone for data protection in the public cloud is risky and there are hundreds of unprotected SAS applications in the typical enterprise the move to cloud should make backup simpler but it can quickly become exponentially worse it's time to rethink the backup experience what if there were no hardware software or virtual appliances to size configure manage or even buy it all and by adding Enterprise backup public cloud workloads are no longer exposed to accidental data deletion and ransomware and Clube o we deliver secure data backup and recovery without any of that complexity or risk we provide all of the critical functions of enterprise backup d dupe and scheduling user and key management and cataloging because we're built in the public cloud we can rapidly deliver new innovations and take advantage of inherent data security controls our mission is to protect your data wherever it's stored the clew mio authentic SAS backup experience scales on demand to manage and protect your data more easily and efficiently and without things like cloud bills or egress charges luenell gives you predictable costs monitoring global backup compliance is far simpler and the built-in always-on security of Clue mio means that your data is safe take advantage of the cloud for backup with no constraints clew mio authentic SAS for the enterprise great video as we think about moving forward in the future and what customers are trying to do we have to think more in terms of the native services that cloud can provide and how to fully exploit them to increase the aggregate flexible both within our enterprises but also based on what our supplies have to offer we had a great conversation with wounds Young who is the CTO and co-founder of Clue mio about just that let's hear it wound had to say everybody's talking about the cloud and what the cloud might be able to do for their business the challenges there are a limited number of people in the world who really understands what it means to build for the cloud utilizing the cloud it's a lot of approximations out there but not a lot of folks are deeply involved in actually doing it right we've got one here with us today woo Jung is the CTO and co-founder of Cluny Oh woo and welcome to the cube how they theny here so let's start with this issue of what it means to build for the cloud now loomio has made the decision to have everything fit into that as a service model what is that practically mean so from the engineering point of view building our SAS application is fundamentally different so the way that I'll go and say is that at Combe you know we actually don't build software and ship software what we actually do it will service and service is what we actually ship to our customers let me give you an example in the case of Kumu they say backups fail like software sometimes fails and we get that failures >> the difference in between chromeo and traditional solutions is that if something were to fail we are the one detecting that failure before our customers - not only that when something fails we actually know exactly why you fail therefore we can actually troubleshoot it and we can actually fix it and operate the service without the customer intervention so it's not about the bugs also or about the troubleshooting aspect but it's also about new features if you were to introduce our new features we can actually do this without having customers upgraded code we will actually do it ourselves so essentially it frees the customers from actually doing all these actions because we will do them on behalf of them at scale and I think that's the second thing I want to talk about quickly is that the ability to use the cloud to do many of the things that you're talking about at scale creates incredible ranges of options that customers have at their disposal so for example AWS customers have historically used things like snapshots to provide it a modicum of data protection to their AWS workloads but there are other new options that could be applied if the systems are built to supply them give us a sense of how kkumeul is looking at this question of no snapshots versus something else yeah so basically traditionally even on the on print side of the things you have something called the snapshots and you had your backups right and there they're fundamentally different but if you actually shift your gears and you look at what they Wis offers today they actually offers the ability for you to take snapshots but actually that's not a backup right and they're fundamentally different so let's talk about it a little bit more what it means to be snapshots and a backup right so let's say there's a bad actor and your account gets compromised like your AWS account gets compromised so then the bad actor has access not only to the EBS volumes but also to the EBS snapshots what that means is that that person can actually go ahead and delete the EBS volume as well as the EBS snapshots now if you had a backup let's say you actually take a backup of that EBS volume to Kumu that bad actor will have access to the EBS volumes however they won't be able to delete the backup that we actually have in Kumu so in the whole thing the idea of Kumi on is that you should be able to protect all of your assets that being either a non-prime or AWS by setting up a single policies and these are true backups and not just snapshots and that leads to the last question I have which is ultimately the ability to introduce these capabilities at scale creates a lot of new opportunities that customers can utilize to do a better job of building applications but also I presume managing how they use AWS because snapshots and other types of servers can expand dramatically which can increase your cost how is doing it better with things like native backup services improve a customer's ability to administer their AWS spend and accounts so great question so essentially if you look at the enterprise's today obviously they have multiple you know on-premise data centers and also a different card provide that they use like AWS and Azure and also a few SAS applications right so then the idea is for cumin is to create this single platform where all of these things can actually be backed up in a uniform way where you can actually manage all of them and then the other thing is all doing it in the cloud so if you think about it if you don't solve the poem fundamentally in the cloud there's things that you end up paying later on so let's take an example right moving bytes moving bytes in between one server to the other traditionally basically moving bytes from one rack to the other it was always free you never had to pay anything for that certainly in the data center all right but if you actually go to the public cloud you cannot say the same thing right basically moving by it across aw s recent regions is not free anymore moving data from AWS to the on premises that's not fair either so these are all the things that any you know car provider service provider like ours has to consider and actually solve so that the customers can only back it up into Kumu but then they actually can leverage different cloud providers you know in a seamless way without having to worry all of this costs associated with it so kkumeul we should be able to back it up but we should be able to also offer mobility in between either AWS back at VMware or VNC so if I can kind of summarize what you just said that you want to be able to provide to an account to an enterprise the ability to not have to worry about the back-end infrastructure from a technical and process standpoint but not also have to worry so much about the back-end infrastructure from a cost and financial standpoint that by providing a service and then administering how that service is optimally handled the customer doesn't have to think about some of those financial considerations of moving data around in the same way that they used to I got that right I absolutely yes basically multiple accounts multiple regions multiple providers it is extremely hard to manage what Cuneo does it will actually provide you a single pane of glass where you can actually manage them all but then if you actually think about just and manageability it's actually you can actually do that by just building a management layer on top of it but more importantly you and we need to have a single data you know repository for you for us to be able to provide a true mobility between them one is about managing but the other thing is about if you're done if you're done it the real the right way it provides you the ability to move them and it leverages the cloud power so that you don't have to worry about the cloud expenses but kkumeul internally is the one are actually optimizing all of this for our customers wound jeong CTO and co-founder of columbia thanks very much for being on the cube thank you thanks very much moon I want to thank chromeo for providing this important content about the increasingly important evolution of data protection and cloud now here's your opportunity to weigh in on this crucially important arena what do you think about this evolving relationship how do you foresee it operating in your enterprise what comments do you have what questions do you have of the thought leaders from clew mio and elsewhere that's what we're going to do now we're gonna go into the crowd chat and we're gonna hear from each other about this really important topic and what you foresee in your enterprise as your digital business transforms let's crouch at you [Music] [Music] [Music]

whether you're just getting started in terms of transforming your data center modernizing looking at hyperconvergence infrastructure or whether you're building out your cloud strategy thinking about private public distributed clouds Nutanix and the partners that you met here are committed to helping you on that journey I am very excited and proud to share that together we have made quite an impact on the four organizations that were here at dot next is part of our dot heart initiative girls intact the women's Sports Foundation move the foundation for hospital art and workforce opportunity services we have thanks to your generosity donated more than 7,500 dollars now we have a couple more hours to go to get to ten thousand and I know that we can do it so thank you so much for your generosity and be sure to take advantage of the last couple of hours I'm a solutions Expo to grab some tokens and get us to ten thousand dollars it is also my pleasure in the spirit of giving to award this tricked-out Nutanix colored Trek bicycle to one lucky winner from our social contest trek has been a customer of newt annexes since 2014 and like Nutanix they share a number of business values around giving back into the community creating positive change building things that last memories relationships products and so I'd like to take a moment to please welcome back out to stage Nutanix CMO Ben Gibson to help me do the honors [Music] [Applause] welcome back hey Julie that's a gorgeous bike yeah lights it's very like carbon fiber frame look at this thing Wow sleek can you tell me a little bit about it you know Julie this bike I mentioned before I'm a cyclist this is not only in beautiful Nutanix brand color it's got top-of-the-line component REE Shimano El Tigre this thing all comes together I'd like to think of this as hyperconvergence for the cyclist just as beautiful the same impute value it does have a Nutanix monkey on the front all right so we ready to unveil our winner let's do it all right so the winner of the trek social contest is Rob chlorine are you here yeah now Rob yeah come on up you got to come you know check this out we're not gonna ask you to clip in well actually maybe you should clip in I'm not sure we can ship it home for you so maybe you just ride it all the way home where you from New York from New York it's just a short ride back up there congratulations Rob we'll get this ship to you awesome thank you I think we have someone help you out done okay been in the spirit of giving I think I'm just gonna continue let the good times roll yeah in the beginning yesterday you also put out a challenge to our next audience trying to find the Easter eggs that were in the brand video yeah so we got some good responses people were hunting watching the video if you look carefully you could see some of these Easter eggs that were popping up on this video you look carefully you see some Nutanix logo you see prism up on the screen for someone who's showing freedom to get a lot of work done the one my favorite actually is number three there that's artistic the Nutanix acts in the sky so Julie we had a lot of people that tweet it in to try to identify and we have a winner don't worry yeah it was a little bit harder than I think people thought but there was one lucky winner who was one of the first people to find the Nutanix Easter eggs and that is Chad door are you here dad or are you here so you'll recall Chad gets to join us at dot next 2019 all expenses paid airfare hotel and pass very good we also intrude on X fashion like to reward the person who's been the most active on our mobile app so this is someone who's been giving us feedback being part of the community very prolific with the dot next app so congratulations to Faizal Joe waves if you are here you also will be joining us at Sonic's 2019 on us and then how many people are interested in the Nook anyone understand if we were given away all right congratulations to Mike Gellar nice job Mike so we have given away two prizes all expenses paid to dot next 2019 however we haven't mentioned where that's going to be Julie where is dot next 2019 well I think maybe in the spirit of the the culinary theme that we've been on we had Anthony Bourdain on stage we could make us a challenge we will throw out three culinary hints to you Nutanix employers you may not play and I think I have oh I do I have one more science cookbook for whoever gets it right all right so everything you get it right shout it out we've got people in the audience you're gonna be listening and then why don't you do the honors of the first clue all right first hint we're going to a place that's going undergoing a culinary revolution there's a famous chef named Jimmy Martinez just opened up a new restaurant renowned for handcrafted tacos I'm not sure with mr. Modine would say about that but handcrafted they sound delicious to me no I'm here too a few coming out let me give you a second I don't I'm not hearing it this city is also renowned the city is also renowned for celebrating the medieval era so you can enjoy a four-course meal while you're watching nice just for their honor I haven't yet say it again Anaheim yes so you will be joining us dot next 2019 in the sunny Southern California area of Anaheim so please mark your calendars May 7 through 9 and then of course if you really enjoyed the learning and the fun that we had this week and maybe some of your teammates weren't able to join you we will be out on dot next on tour coming to a city near you and if you enjoy the conference experience and would like to participate six months from now across the pond please feel free to join us at Dominic's conference in London from November 27th through 29 and I think maybe one of the the high notes to end on then might be to circle back on freedom yeah you know like I said when I opened our show this week this is a community we talked about enterprise cloud we explored a lot around what invisibility means and what we're achieving together on that front and we talked about why why are we together on it's about realizing new freedoms freedoms to build the data so do you want to build all the way through to freedom to play and I was at the party last night and I saw a good deal play last night too you know we were thrilled to have you join us here this week as a continuation of this growing community I want to thank you for your time and your energy and your engagement to make this what it is and I thought also I wanted to mention a couple things first of all thank you to all the wonderful Nutanix engineers and employees that worked so hard to bring this all together and there's two people in particular I want to acknowledge as part of our Newt annex family and broader community here that really put their heart and soul into making this experience what it is the first is Aaron Alonso and her wonderful team who create this entire event thank you very much Aaron amazing there's another gentleman named Rohit Goyal and he's not the only one but Rohit more than anyone else and his colleagues spend immeasurable amounts of time creating the wonderful content that showed up in all of our breakout sessions Rohit thank you so much and as a reminder those breakout sessions actually continue our last said right after this session here we'll go to our last set of breakout sessions and then we're going to depart for home so again Julie and I both thank you so much for being a part of this week hope to see you in London and if not London I heard a yes and if not London certainly we'll see you in Southern California beautiful Anaheim next year thank you so much baby thank you Jordao free [Applause] ladies and gentlemen this concludes our afternoon keynote breakout sessions will begin in 20 minutes

>> Announcer: Live from New York City it's The Cube, covering IBM data science for all. Brought to you by IBM. (light digital music) >> Welcome back to data science for all. It's a whole new game. And it is a whole new game. >> Dave Vellante, John Walls here. We've got quite a distinguished panel. So it is a new game-- >> Well we're in the game, I'm just happy to be-- (both laugh) Have a swing at the pitch. >> Well let's what we have here. Five distinguished members of our panel. It'll take me a minute to get through the introductions, but believe me they're worth it. Jennifer Shin joins us. Jennifer's the founder of 8 Path Solutions, the director of the data science of Comcast and part of the faculty at UC Berkeley and NYU. Jennifer, nice to have you with us, we appreciate the time. Joe McKendrick an analyst and contributor of Forbes and ZDNet, Joe, thank you for being here at well. Another ZDNetter next to him, Dion Hinchcliffe, who is a vice president and principal analyst of Constellation Research and also contributes to ZDNet. Good to see you, sir. To the back row, but that doesn't mean anything about the quality of the participation here. Bob Hayes with a killer Batman shirt on by the way, which we'll get to explain in just a little bit. He runs the Business over Broadway. And Joe Caserta, who the founder of Caserta Concepts. Welcome to all of you. Thanks for taking the time to be with us. Jennifer, let me just begin with you. Obviously as a practitioner you're very involved in the industry, you're on the academic side as well. We mentioned Berkeley, NYU, steep experience. So I want you to kind of take your foot in both worlds and tell me about data science. I mean where do we stand now from those two perspectives? How have we evolved to where we are? And how would you describe, I guess the state of data science? >> Yeah so I think that's a really interesting question. There's a lot of changes happening. In part because data science has now become much more established, both in the academic side as well as in industry. So now you see some of the bigger problems coming out. People have managed to have data pipelines set up. But now there are these questions about models and accuracy and data integration. So the really cool stuff from the data science standpoint. We get to get really into the details of the data. And I think on the academic side you now see undergraduate programs, not just graduate programs, but undergraduate programs being involved. UC Berkeley just did a big initiative that they're going to offer data science to undergrads. So that's a huge news for the university. So I think there's a lot of interest from the academic side to continue data science as a major, as a field. But I think in industry one of the difficulties you're now having is businesses are now asking that question of ROI, right? What do I actually get in return in the initial years? So I think there's a lot of work to be done and just a lot of opportunity. It's great because people now understand better with data sciences, but I think data sciences have to really think about that seriously and take it seriously and really think about how am I actually getting a return, or adding a value to the business? >> And there's lot to be said is there not, just in terms of increasing the workforce, the acumen, the training that's required now. It's a still relatively new discipline. So is there a shortage issue? Or is there just a great need? Is the opportunity there? I mean how would you look at that? >> Well I always think there's opportunity to be smart. If you can be smarter, you know it's always better. It gives you advantages in the workplace, it gets you an advantage in academia. The question is, can you actually do the work? The work's really hard, right? You have to learn all these different disciplines, you have to be able to technically understand data. Then you have to understand it conceptually. You have to be able to model with it, you have to be able to explain it. There's a lot of aspects that you're not going to pick up overnight. So I think part of it is endurance. Like are people going to feel motivated enough and dedicate enough time to it to get very good at that skill set. And also of course, you know in terms of industry, will there be enough interest in the long term that there will be a financial motivation. For people to keep staying in the field, right? So I think it's definitely a lot of opportunity. But that's always been there. Like I tell people I think of myself as a scientist and data science happens to be my day job. That's just the job title. But if you are a scientist and you work with data you'll always want to work with data. I think that's just an inherent need. It's kind of a compulsion, you just kind of can't help yourself, but dig a little bit deeper, ask the questions, you can't not think about it. So I think that will always exist. Whether or not it's an industry job in the way that we see it today, and like five years from now, or 10 years from now. I think that's something that's up for debate. >> So all of you have watched the evolution of data and how it effects organizations for a number of years now. If you go back to the days when data warehouse was king, we had a lot of promises about 360 degree views of the customer and how we were going to be more anticipatory in terms and more responsive. In many ways the decision support systems and the data warehousing world didn't live up to those promises. They solved other problems for sure. And so everybody was looking for big data to solve those problems. And they've begun to attack many of them. We talked earlier in The Cube today about fraud detection, it's gotten much, much better. Certainly retargeting of advertising has gotten better. But I wonder if you could comment, you know maybe start with Joe. As to the effect that data and data sciences had on organizations in terms of fulfilling that vision of a 360 degree view of customers and anticipating customer needs. >> So. Data warehousing, I wouldn't say failed. But I think it was unfinished in order to achieve what we need done today. At the time I think it did a pretty good job. I think it was the only place where we were able to collect data from all these different systems, have it in a single place for analytics. The big difference between what I think, between data warehousing and data science is data warehouses were primarily made for the consumer to human beings. To be able to have people look through some tool and be able to analyze data manually. That really doesn't work anymore, there's just too much data to do that. So that's why we need to build a science around it so that we can actually have machines actually doing the analytics for us. And I think that's the biggest stride in the evolution over the past couple of years, that now we're actually able to do that, right? It used to be very, you know you go back to when data warehouses started, you had to be a deep technologist in order to be able to collect the data, write the programs to clean the data. But now you're average causal IT person can do that. Right now I think we're back in data science where you have to be a fairly sophisticated programmer, analyst, scientist, statistician, engineer, in order to do what we need to do, in order to make machines actually understand the data. But I think part of the evolution, we're just in the forefront. We're going to see over the next, not even years, within the next year I think a lot of new innovation where the average person within business and definitely the average person within IT will be able to do as easily say, "What are my sales going to be next year?" As easy as it is to say, "What were my sales last year." Where now it's a big deal. Right now in order to do that you have to build some algorithms, you have to be a specialist on predictive analytics. And I think, you know as the tools mature, as people using data matures, and as the technology ecosystem for data matures, it's going to be easier and more accessible. >> So it's still too hard. (laughs) That's something-- >> Joe C.: Today it is yes. >> You've written about and talked about. >> Yeah no question about it. We see this citizen data scientist. You know we talked about the democratization of data science but the way we talk about analytics and warehousing and all the tools we had before, they generated a lot of insights and views on the information, but they didn't really give us the science part. And that's, I think that what's missing is the forming of the hypothesis, the closing of the loop of. We now have use of this data, but are are changing, are we thinking about it strategically? Are we learning from it and then feeding that back into the process. I think that's the big difference between data science and the analytics side. But, you know just like Google made search available to everyone, not just people who had highly specialized indexers or crawlers. Now we can have tools that make these capabilities available to anyone. You know going back to what Joe said I think the key thing is we now have tools that can look at all the data and ask all the questions. 'Cause we can't possibly do it all ourselves. Our organizations are increasingly awash in data. Which is the life blood of our organizations, but we're not using it, you know this a whole concept of dark data. And so I think the concept, or the promise of opening these tools up for everyone to be able to access those insights and activate them, I think that, you know, that's where it's headed. >> This is kind of where the T shirt comes in right? So Bob if you would, so you've got this Batman shirt on. We talked a little bit about it earlier, but it plays right into what Dion's talking about. About tools and, I don't want to spoil it, but you go ahead (laughs) and tell me about it. >> Right, so. Batman is a super hero, but he doesn't have any supernatural powers, right? He can't fly on his own, he can't become invisible on his own. But the thing is he has the utility belt and he has these tools he can use to help him solve problems. For example he as the bat ring when he's confronted with a building that he wants to get over, right? So he pulls it out and uses that. So as data professionals we have all these tools now that these vendors are making. We have IBM SPSS, we have data science experience. IMB Watson that these data pros can now use it as part of their utility belt and solve problems that they're confronted with. So if you''re ever confronted with like a Churn problem and you have somebody who has access to that data they can put that into IBM Watson, ask a question and it'll tell you what's the key driver of Churn. So it's not that you have to be a superhuman to be a data scientist, but these tools will help you solve certain problems and help your business go forward. >> Joe McKendrick, do you have a comment? >> Does that make the Batmobile the Watson? (everyone laughs) Analogy? >> I was just going to add that, you know all of the billionaires in the world today and none of them decided to become Batman yet. It's very disappointing. >> Yeah. (Joe laughs) >> Go ahead Joe. >> And I just want to add some thoughts to our discussion about what happened with data warehousing. I think it's important to point out as well that data warehousing, as it existed, was fairly successful but for larger companies. Data warehousing is a very expensive proposition it remains a expensive proposition. Something that's in the domain of the Fortune 500. But today's economy is based on a very entrepreneurial model. The Fortune 500s are out there of course it's ever shifting. But you have a lot of smaller companies a lot of people with start ups. You have people within divisions of larger companies that want to innovate and not be tied to the corporate balance sheet. They want to be able to go through, they want to innovate and experiment without having to go through finance and the finance department. So there's all these open source tools available. There's cloud resources as well as open source tools. Hadoop of course being a prime example where you can work with the data and experiment with the data and practice data science at a very low cost. >> Dion mentioned the C word, citizen data scientist last year at the panel. We had a conversation about that. And the data scientists on the panel generally were like, "Stop." Okay, we're not all of a sudden going to turn everybody into data scientists however, what we want to do is get people thinking about data, more focused on data, becoming a data driven organization. I mean as a data scientist I wonder if you could comment on that. >> Well I think so the other side of that is, you know there are also many people who maybe didn't, you know follow through with science, 'cause it's also expensive. A PhD takes a lot of time. And you know if you don't get funding it's a lot of money. And for very little security if you think about how hard it is to get a teaching job that's going to give you enough of a pay off to pay that back. Right, the time that you took off, the investment that you made. So I think the other side of that is by making data more accessible, you allow people who could have been great in science, have an opportunity to be great data scientists. And so I think for me the idea of citizen data scientist, that's where the opportunity is. I think in terms of democratizing data and making it available for everyone, I feel as though it's something similar to the way we didn't really know what KPIs were, maybe 20 years ago. People didn't use it as readily, didn't teach it in schools. I think maybe 10, 20 years from now, some of the things that we're building today from data science, hopefully more people will understand how to use these tools. They'll have a better understanding of working with data and what that means, and just data literacy right? Just being able to use these tools and be able to understand what data's saying and actually what it's not saying. Which is the thing that most people don't think about. But you can also say that data doesn't say anything. There's a lot of noise in it. There's too much noise to be able to say that there is a result. So I think that's the other side of it. So yeah I guess in terms for me, in terms of data a serious data scientist, I think it's a great idea to have that, right? But at the same time of course everyone kind of emphasized you don't want everyone out there going, "I can be a data scientist without education, "without statistics, without math," without understanding of how to implement the process. I've seen a lot of companies implement the same sort of process from 10, 20 years ago just on Hadoop instead of SQL. Right and it's very inefficient. And the only difference is that you can build more tables wrong than they could before. (everyone laughs) Which is I guess >> For less. it's an accomplishment and for less, it's cheaper, yeah. >> It is cheaper. >> Otherwise we're like I'm not a data scientist but I did stay at a Holiday Inn Express last night, right? >> Yeah. (panelists laugh) And there's like a little bit of pride that like they used 2,000, you know they used 2,000 computers to do it. Like a little bit of pride about that, but you know of course maybe not a great way to go. I think 20 years we couldn't do that, right? One computer was already an accomplishment to have that resource. So I think you have to think about the fact that if you're doing it wrong, you're going to just make that mistake bigger, which his also the other side of working with data. >> Sure, Bob. >> Yeah I have a comment about that. I've never liked the term citizen data scientist or citizen scientist. I get the point of it and I think employees within companies can help in the data analytics problem by maybe being a data collector or something. I mean I would never have just somebody become a scientist based on a few classes here she takes. It's like saying like, "Oh I'm going to be a citizen lawyer." And so you come to me with your legal problems, or a citizen surgeon. Like you need training to be good at something. You can't just be good at something just 'cause you want to be. >> John: Joe you wanted to say something too on that. >> Since we're in New York City I'd like to use the analogy of a real scientist versus a data scientist. So real scientist requires tools, right? And the tools are not new, like microscopes and a laboratory and a clean room. And these tools have evolved over years and years, and since we're in New York we could walk within a 10 block radius and buy any of those tools. It doesn't make us a scientist because we use those tools. I think with data, you know making, making the tools evolve and become easier to use, you know like Bob was saying, it doesn't make you a better data scientist, it just makes the data more accessible. You know we can go buy a microscope, we can go buy Hadoop, we can buy any kind of tool in a data ecosystem, but it doesn't really make you a scientist. I'm very involved in the NYU data science program and the Columbia data science program, like these kids are brilliant. You know these kids are not someone who is, you know just trying to run a day to day job, you know in corporate America. I think the people who are running the day to day job in corporate America are going to be the recipients of data science. Just like people who take drugs, right? As a result of a smart data scientist coming up with a formula that can help people, I think we're going to make it easier to distribute the data that can help people with all the new tools. But it doesn't really make it, you know the access to the data and tools available doesn't really make you a better data scientist. Without, like Bob was saying, without better training and education. >> So how-- I'm sorry, how do you then, if it's not for everybody, but yet I'm the user at the end of the day at my company and I've got these reams of data before me, how do you make it make better sense to me then? So that's where machine learning comes in or artificial intelligence and all this stuff. So how at the end of the day, Dion? How do you make it relevant and usable, actionable to somebody who might not be as practiced as you would like? >> I agree with Joe that many of us will be the recipients of data science. Just like you had to be a computer science at one point to develop programs for a computer, now we can get the programs. You don't need to be a computer scientist to get a lot of value out of our IT systems. The same thing's going to happen with data science. There's far more demand for data science than there ever could be produced by, you know having an ivory tower filled with data scientists. Which we need those guys, too, don't get me wrong. But we need to have, productize it and make it available in packages such that it can be consumed. The outputs and even some of the inputs can be provided by mere mortals, whether that's machine learning or artificial intelligence or bots that go off and run the hypotheses and select the algorithms maybe with some human help. We have to productize it. This is a constant of data scientist of service, which is becoming a thing now. It's, "I need this, I need this capability at scale. "I need it fast and I need it cheap." The commoditization of data science is going to happen. >> That goes back to what I was saying about, the recipient also of data science is also machines, right? Because I think the other thing that's happening now in the evolution of data is that, you know the data is, it's so tightly coupled. Back when you were talking about data warehousing you have all the business transactions then you take the data out of those systems, you put them in a warehouse for analysis, right? Maybe they'll make a decision to change that system at some point. Now the analytics platform and the business application is very tightly coupled. They become dependent upon one another. So you know people who are using the applications are now be able to take advantage of the insights of data analytics and data science, just through the app. Which never really existed before. >> I have one comment on that. You were talking about how do you get the end user more involved, well like we said earlier data science is not easy, right? As an end user, I encourage you to take a stats course, just a basic stats course, understanding what a mean is, variability, regression analysis, just basic stuff. So you as an end user can get more, or glean more insight from the reports that you're given, right? If you go to France and don't know French, then people can speak really slowly to you in French, you're not going to get it. You need to understand the language of data to get value from the technology we have available to us. >> Incidentally French is one of the languages that you have the option of learning if you're a mathematicians. So math PhDs are required to learn a second language. France being the country of algebra, that's one of the languages you could actually learn. Anyway tangent. But going back to the point. So statistics courses, definitely encourage it. I teach statistics. And one of the things that I'm finding as I go through the process of teaching it I'm actually bringing in my experience. And by bringing in my experience I'm actually kind of making the students think about the data differently. So the other thing people don't think about is the fact that like statisticians typically were expected to do, you know, just basic sort of tasks. In a sense that they're knowledge is specialized, right? But the day to day operations was they ran some data, you know they ran a test on some data, looked at the results, interpret the results based on what they were taught in school. They didn't develop that model a lot of times they just understand what the tests were saying, especially in the medical field. So when you when think about things like, we have words like population, census. Which is when you take data from every single, you have every single data point versus a sample, which is a subset. It's a very different story now that we're collecting faster than it used to be. It used to be the idea that you could collect information from everyone. Like it happens once every 10 years, we built that in. But nowadays you know, you know here about Facebook, for instance, I think they claimed earlier this year that their data was more accurate than the census data. So now there are these claims being made about which data source is more accurate. And I think the other side of this is now statisticians are expected to know data in a different way than they were before. So it's not just changing as a field in data science, but I think the sciences that are using data are also changing their fields as well. >> Dave: So is sampling dead? >> Well no, because-- >> Should it be? (laughs) >> Well if you're sampling wrong, yes. That's really the question. >> Okay. You know it's been said that the data doesn't lie, people do. Organizations are very political. Oftentimes you know, lies, damned lies and statistics, Benjamin Israeli. Are you seeing a change in the way in which organizations are using data in the context of the politics. So, some strong P&L manager say gets data and crafts it in a way that he or she can advance their agenda. Or they'll maybe attack a data set that is, probably should drive them in a different direction, but might be antithetical to their agenda. Are you seeing data, you know we talked about democratizing data, are you seeing that reduce the politics inside of organizations? >> So you know we've always used data to tell stories at the top level of an organization that's what it's all about. And I still see very much that no matter how much data science or, the access to the truth through looking at the numbers that story telling is still the political filter through which all that data still passes, right? But it's the advent of things like Block Chain, more and more corporate records and corporate information is going to end up in these open and shared repositories where there is not alternate truth. It'll come back to whoever tells the best stories at the end of the day. So I still see the organizations are very political. We are seeing now more open data though. Open data initiatives are a big thing, both in government and in the private sector. It is having an effect, but it's slow and steady. So that's what I see. >> Um, um, go ahead. >> I was just going to say as well. Ultimately I think data driven decision making is a great thing. And it's especially useful at the lower tiers of the organization where you have the routine day to day's decisions that could be automated through machine learning and deep learning. The algorithms can be improved on a constant basis. On the upper levels, you know that's why you pay executives the big bucks in the upper levels to make the strategic decisions. And data can help them, but ultimately, data, IT, technology alone will not create new markets, it will not drive new businesses, it's up to human beings to do that. The technology is the tool to help them make those decisions. But creating businesses, growing businesses, is very much a human activity. And that's something I don't see ever getting replaced. Technology might replace many other parts of the organization, but not that part. >> I tend to be a foolish optimist when it comes to this stuff. >> You do. (laughs) >> I do believe that data will make the world better. I do believe that data doesn't lie people lie. You know I think as we start, I'm already seeing trends in industries, all different industries where, you know conventional wisdom is starting to get trumped by analytics. You know I think it's still up to the human being today to ignore the facts and go with what they think in their gut and sometimes they win, sometimes they lose. But generally if they lose the data will tell them that they should have gone the other way. I think as we start relying more on data and trusting data through artificial intelligence, as we start making our lives a little bit easier, as we start using smart cars for safety, before replacement of humans. AS we start, you know, using data really and analytics and data science really as the bumpers, instead of the vehicle, eventually we're going to start to trust it as the vehicle itself. And then it's going to make lying a little bit harder. >> Okay, so great, excellent. Optimism, I love it. (John laughs) So I'm going to play devil's advocate here a little bit. There's a couple elephant in the room topics that I want to, to explore a little bit. >> Here it comes. >> There was an article today in Wired. And it was called, Why AI is Still Waiting for It's Ethics Transplant. And, I will just read a little segment from there. It says, new ethical frameworks for AI need to move beyond individual responsibility to hold powerful industrial, government and military interests accountable as they design and employ AI. When tech giants build AI products, too often user consent, privacy and transparency are overlooked in favor of frictionless functionality that supports profit driven business models based on aggregate data profiles. This is from Kate Crawford and Meredith Whittaker who founded AI Now. And they're calling for sort of, almost clinical trials on AI, if I could use that analogy. Before you go to market you've got to test the human impact, the social impact. Thoughts. >> And also have the ability for a human to intervene at some point in the process. This goes way back. Is everybody familiar with the name Stanislav Petrov? He's the Soviet officer who back in 1983, it was in the control room, I guess somewhere outside of Moscow in the control room, which detected a nuclear missile attack against the Soviet Union coming out of the United States. Ordinarily I think if this was an entirely AI driven process we wouldn't be sitting here right now talking about it. But this gentlemen looked at what was going on on the screen and, I'm sure he's accountable to his authorities in the Soviet Union. He probably got in a lot of trouble for this, but he decided to ignore the signals, ignore the data coming out of, from the Soviet satellites. And as it turned out, of course he was right. The Soviet satellites were seeing glints of the sun and they were interpreting those glints as missile launches. And I think that's a great example why, you know every situation of course doesn't mean the end of the world, (laughs) it was in this case. But it's a great example why there needs to be a human component, a human ability for human intervention at some point in the process. >> So other thoughts. I mean organizations are driving AI hard for profit. Best minds of our generation are trying to figure out how to get people to click on ads. Jeff Hammerbacher is famous for saying it. >> You can use data for a lot of things, data analytics, you can solve, you can cure cancer. You can make customers click on more ads. It depends on what you're goal is. But, there are ethical considerations we need to think about. When we have data that will have a racial bias against blacks and have them have higher prison sentences or so forth or worse credit scores, so forth. That has an impact on a broad group of people. And as a society we need to address that. And as scientists we need to consider how are we going to fix that problem? Cathy O'Neil in her book, Weapons of Math Destruction, excellent book, I highly recommend that your listeners read that book. And she talks about these issues about if AI, if algorithms have a widespread impact, if they adversely impact protected group. And I forget the last criteria, but like we need to really think about these things as a people, as a country. >> So always think the idea of ethics is interesting. So I had this conversation come up a lot of times when I talk to data scientists. I think as a concept, right as an idea, yes you want things to be ethical. The question I always pose to them is, "Well in the business setting "how are you actually going to do this?" 'Cause I find the most difficult thing working as a data scientist, is to be able to make the day to day decision of when someone says, "I don't like that number," how do you actually get around that. If that's the right data to be showing someone or if that's accurate. And say the business decides, "Well we don't like that number." Many people feel pressured to then change the data, change, or change what the data shows. So I think being able to educate people to be able to find ways to say what the data is saying, but not going past some line where it's a lie, where it's unethical. 'Cause you can also say what data doesn't say. You don't always have to say what the data does say. You can leave it as, "Here's what we do know, "but here's what we don't know." There's a don't know part that many people will omit when they talk about data. So I think, you know especially when it comes to things like AI it's tricky, right? Because I always tell people I don't know everyone thinks AI's going to be so amazing. I started an industry by fixing problems with computers that people didn't realize computers had. For instance when you have a system, a lot of bugs, we all have bug reports that we've probably submitted. I mean really it's no where near the point where it's going to start dominating our lives and taking over all the jobs. Because frankly it's not that advanced. It's still run by people, still fixed by people, still managed by people. I think with ethics, you know a lot of it has to do with the regulations, what the laws say. That's really going to be what's involved in terms of what people are willing to do. A lot of businesses, they want to make money. If there's no rules that says they can't do certain things to make money, then there's no restriction. I think the other thing to think about is we as consumers, like everyday in our lives, we shouldn't separate the idea of data as a business. We think of it as a business person, from our day to day consumer lives. Meaning, yes I work with data. Incidentally I also always opt out of my credit card, you know when they send you that information, they make you actually mail them, like old school mail, snail mail like a document that says, okay I don't want to be part of this data collection process. Which I always do. It's a little bit more work, but I go through that step of doing that. Now if more people did that, perhaps companies would feel more incentivized to pay you for your data, or give you more control of your data. Or at least you know, if a company's going to collect information, I'd want you to be certain processes in place to ensure that it doesn't just get sold, right? For instance if a start up gets acquired what happens with that data they have on you? You agree to give it to start up. But I mean what are the rules on that? So I think we have to really think about the ethics from not just, you know, someone who's going to implement something but as consumers what control we have for our own data. 'Cause that's going to directly impact what businesses can do with our data. >> You know you mentioned data collection. So slightly on that subject. All these great new capabilities we have coming. We talked about what's going to happen with media in the future and what 5G technology's going to do to mobile and these great bandwidth opportunities. The internet of things and the internet of everywhere. And all these great inputs, right? Do we have an arms race like are we keeping up with the capabilities to make sense of all the new data that's going to be coming in? And how do those things square up in this? Because the potential is fantastic, right? But are we keeping up with the ability to make it make sense and to put it to use, Joe? >> So I think data ingestion and data integration is probably one of the biggest challenges. I think, especially as the world is starting to become more dependent on data. I think you know, just because we're dependent on numbers we've come up with GAAP, which is generally accepted accounting principles that can be audited and proven whether it's true or false. I think in our lifetime we will see something similar to that we will we have formal checks and balances of data that we use that can be audited. Getting back to you know what Dave was saying earlier about, I personally would trust a machine that was programmed to do the right thing, than to trust a politician or some leader that may have their own agenda. And I think the other thing about machines is that they are auditable. You know you can look at the code and see exactly what it's doing and how it's doing it. Human beings not so much. So I think getting to the truth, even if the truth isn't the answer that we want, I think is a positive thing. It's something that we can't do today that once we start relying on machines to do we'll be able to get there. >> Yeah I was just going to add that we live in exponential times. And the challenge is that the way that we're structured traditionally as organizations is not allowing us to absorb advances exponentially, it's linear at best. Everyone talks about change management and how are we going to do digital transformation. Evidence shows that technology's forcing the leaders and the laggards apart. There's a few leading organizations that are eating the world and they seem to be somehow rolling out new things. I don't know how Amazon rolls out all this stuff. There's all this artificial intelligence and the IOT devices, Alexa, natural language processing and that's just a fraction, it's just a tip of what they're releasing. So it just shows that there are some organizations that have path found the way. Most of the Fortune 500 from the year 2000 are gone already, right? The disruption is happening. And so we are trying, have to find someway to adopt these new capabilities and deploy them effectively or the writing is on the wall. I spent a lot of time exploring this topic, how are we going to get there and all of us have a lot of hard work is the short answer. >> I read that there's going to be more data, or it was predicted, more data created in this year than in the past, I think it was five, 5,000 years. >> Forever. (laughs) >> And that to mix the statistics that we're analyzing currently less than 1% of the data. To taking those numbers and hear what you're all saying it's like, we're not keeping up, it seems like we're, it's not even linear. I mean that gap is just going to grow and grow and grow. How do we close that? >> There's a guy out there named Chris Dancy, he's known as the human cyborg. He has 700 hundred sensors all over his body. And his theory is that data's not new, having access to the data is new. You know we've always had a blood pressure, we've always had a sugar level. But we were never able to actually capture it in real time before. So now that we can capture and harness it, now we can be smarter about it. So I think that being able to use this information is really incredible like, this is something that over our lifetime we've never had and now we can do it. Which hence the big explosion in data. But I think how we use it and have it governed I think is the challenge right now. It's kind of cowboys and indians out there right now. And without proper governance and without rigorous regulation I think we are going to have some bumps in the road along the way. >> The data's in the oil is the question how are we actually going to operationalize around it? >> Or find it. Go ahead. >> I will say the other side of it is, so if you think about information, we always have the same amount of information right? What we choose to record however, is a different story. Now if you want wanted to know things about the Olympics, but you decide to collect information every day for years instead of just the Olympic year, yes you have a lot of data, but did you need all of that data? For that question about the Olympics, you don't need to collect data during years there are no Olympics, right? Unless of course you're comparing it relative. But I think that's another thing to think about. Just 'cause you collect more data does not mean that data will produce more statistically significant results, it does not mean it'll improve your model. You can be collecting data about your shoe size trying to get information about your hair. I mean it really does depend on what you're trying to measure, what your goals are, and what the data's going to be used for. If you don't factor the real world context into it, then yeah you can collect data, you know an infinite amount of data, but you'll never process it. Because you have no question to ask you're not looking to model anything. There is no universal truth about everything, that just doesn't exist out there. >> I think she's spot on. It comes down to what kind of questions are you trying to ask of your data? You can have one given database that has 100 variables in it, right? And you can ask it five different questions, all valid questions and that data may have those variables that'll tell you what's the best predictor of Churn, what's the best predictor of cancer treatment outcome. And if you can ask the right question of the data you have then that'll give you some insight. Just data for data's sake, that's just hype. We have a lot of data but it may not lead to anything if we don't ask it the right questions. >> Joe. >> I agree but I just want to add one thing. This is where the science in data science comes in. Scientists often will look at data that's already been in existence for years, weather forecasts, weather data, climate change data for example that go back to data charts and so forth going back centuries if that data is available. And they reformat, they reconfigure it, they get new uses out of it. And the potential I see with the data we're collecting is it may not be of use to us today, because we haven't thought of ways to use it, but maybe 10, 20, even 100 years from now someone's going to think of a way to leverage the data, to look at it in new ways and to come up with new ideas. That's just my thought on the science aspect. >> Knowing what you know about data science, why did Facebook miss Russia and the fake news trend? They came out and admitted it. You know, we miss it, why? Could they have, is it because they were focused elsewhere? Could they have solved that problem? (crosstalk) >> It's what you said which is are you asking the right questions and if you're not looking for that problem in exactly the way that it occurred you might not be able to find it. >> I thought the ads were paid in rubles. Shouldn't that be your first clue (panelists laugh) that something's amiss? >> You know red flag, so to speak. >> Yes. >> I mean Bitcoin maybe it could have hidden it. >> Bob: Right, exactly. >> I would think too that what happened last year is actually was the end of an age of optimism. I'll bring up the Soviet Union again, (chuckles). It collapsed back in 1991, 1990, 1991, Russia was reborn in. And think there was a general feeling of optimism in the '90s through the 2000s that Russia is now being well integrated into the world economy as other nations all over the globe, all continents are being integrated into the global economy thanks to technology. And technology is lifting entire continents out of poverty and ensuring more connectedness for people. Across Africa, India, Asia, we're seeing those economies that very different countries than 20 years ago and that extended into Russia as well. Russia is part of the global economy. We're able to communicate as a global, a global network. I think as a result we kind of overlook the dark side that occurred. >> John: Joe? >> Again, the foolish optimist here. But I think that... It shouldn't be the question like how did we miss it? It's do we have the ability now to catch it? And I think without data science without machine learning, without being able to train machines to look for patterns that involve corruption or result in corruption, I think we'd be out of luck. But now we have those tools. And now hopefully, optimistically, by the next election we'll be able to detect these things before they become public. >> It's a loaded question because my premise was Facebook had the ability and the tools and the knowledge and the data science expertise if in fact they wanted to solve that problem, but they were focused on other problems, which is how do I get people to click on ads? >> Right they had the ability to train the machines, but they were giving the machines the wrong training. >> Looking under the wrong rock. >> (laughs) That's right. >> It is easy to play armchair quarterback. Another topic I wanted to ask the panel about is, IBM Watson. You guys spend time in the Valley, I spend time in the Valley. People in the Valley poo-poo Watson. Ah, Google, Facebook, Amazon they've got the best AI. Watson, and some of that's fair criticism. Watson's a heavy lift, very services oriented, you just got to apply it in a very focused. At the same time Google's trying to get you to click on Ads, as is Facebook, Amazon's trying to get you to buy stuff. IBM's trying to solve cancer. Your thoughts on that sort of juxtaposition of the different AI suppliers and there may be others. Oh, nobody wants to touch this one, come on. I told you elephant in the room questions. >> Well I mean you're looking at two different, very different types of organizations. One which is really spent decades in applying technology to business and these other companies are ones that are primarily into the consumer, right? When we talk about things like IBM Watson you're looking at a very different type of solution. You used to be able to buy IT and once you installed it you pretty much could get it to work and store your records or you know, do whatever it is you needed it to do. But these types of tools, like Watson actually tries to learn your business. And it needs to spend time doing that watching the data and having its models tuned. And so you don't get the results right away. And I think that's been kind of the challenge that organizations like IBM has had. Like this is a different type of technology solution, one that has to actually learn first before it can provide value. And so I think you know you have organizations like IBM that are much better at applying technology to business, and then they have the further hurdle of having to try to apply these tools that work in very different ways. There's education too on the side of the buyer. >> I'd have to say that you know I think there's plenty of businesses out there also trying to solve very significant, meaningful problems. You know with Microsoft AI and Google AI and IBM Watson, I think it's not really the tool that matters, like we were saying earlier. A fool with a tool is still a fool. And regardless of who the manufacturer of that tool is. And I think you know having, a thoughtful, intelligent, trained, educated data scientist using any of these tools can be equally effective. >> So do you not see core AI competence and I left out Microsoft, as a strategic advantage for these companies? Is it going to be so ubiquitous and available that virtually anybody can apply it? Or is all the investment in R&D and AI going to pay off for these guys? >> Yeah, so I think there's different levels of AI, right? So there's AI where you can actually improve the model. I remember when I was invited when Watson was kind of first out by IBM to a private, sort of presentation. And my question was, "Okay, so when do I get "to access the corpus?" The corpus being sort of the foundation of NLP, which is natural language processing. So it's what you use as almost like a dictionary. Like how you're actually going to measure things, or things up. And they said, "Oh you can't." "What do you mean I can't?" It's like, "We do that." "So you're telling me as a data scientist "you're expecting me to rely on the fact "that you did it better than me and I should rely on that." I think over the years after that IBM started opening it up and offering different ways of being able to access the corpus and work with that data. But I remember at the first Watson hackathon there was only two corpus available. It was either the travel or medicine. There was no other foundational data available. So I think one of the difficulties was, you know IBM being a little bit more on the forefront of it they kind of had that burden of having to develop these systems and learning kind of the hard way that if you don't have the right models and you don't have the right data and you don't have the right access, that's going to be a huge limiter. I think with things like medical, medical information that's an extremely difficult data to start with. Partly because you know anything that you do find or don't find, the impact is significant. If I'm looking at things like what people clicked on the impact of using that data wrong, it's minimal. You might lose some money. If you do that with healthcare data, if you do that with medical data, people may die, like this is a much more difficult data set to start with. So I think from a scientific standpoint it's great to have any information about a new technology, new process. That's the nice that is that IBM's obviously invested in it and collected information. I think the difficulty there though is just 'cause you have it you can't solve everything. And if feel like from someone who works in technology, I think in general when you appeal to developers you try not to market. And with Watson it's very heavily marketed, which tends to turn off people who are more from the technical side. Because I think they don't like it when it's gimmicky in part because they do the opposite of that. They're always trying to build up the technical components of it. They don't like it when you're trying to convince them that you're selling them something when you could just give them the specs and look at it. So it could be something as simple as communication. But I do think it is valuable to have had a company who leads on the forefront of that and try to do so we can actually learn from what IBM has learned from this process. >> But you're an optimist. (John laughs) All right, good. >> Just one more thought. >> Joe go ahead first. >> Joe: I want to see how Alexa or Siri do on Jeopardy. (panelists laugh) >> All right. Going to go around a final thought, give you a second. Let's just think about like your 12 month crystal ball. In terms of either challenges that need to be met in the near term or opportunities you think will be realized. 12, 18 month horizon. Bob you've got the microphone headed up, so I'll let you lead off and let's just go around. >> I think a big challenge for business, for society is getting people educated on data and analytics. There's a study that was just released I think last month by Service Now, I think, or some vendor, or Click. They found that only 17% of the employees in Europe have the ability to use data in their job. Think about that. >> 17. >> 17. Less than 20%. So these people don't have the ability to understand or use data intelligently to improve their work performance. That says a lot about the state we're in today. And that's Europe. It's probably a lot worse in the United States. So that's a big challenge I think. To educate the masses. >> John: Joe. >> I think we probably have a better chance of improving technology over training people. I think using data needs to be iPhone easy. And I think, you know which means that a lot of innovation is in the years to come. I do think that a keyboard is going to be a thing of the past for the average user. We are going to start using voice a lot more. I think augmented reality is going to be things that becomes a real reality. Where we can hold our phone in front of an object and it will have an overlay of prices where it's available, if it's a person. I think that we will see within an organization holding a camera up to someone and being able to see what is their salary, what sales did they do last year, some key performance indicators. I hope that we are beyond the days of everyone around the world walking around like this and we start actually becoming more social as human beings through augmented reality. I think, it has to happen. I think we're going through kind of foolish times at the moment in order to get to the greater good. And I think the greater good is using technology in a very, very smart way. Which means that you shouldn't have to be, sorry to contradict, but maybe it's good to counterpoint. I don't think you need to have a PhD in SQL to use data. Like I think that's 1990. I think as we evolve it's going to become easier for the average person. Which means people like the brain trust here needs to get smarter and start innovating. I think the innovation around data is really at the tip of the iceberg, we're going to see a lot more of it in the years to come. >> Dion why don't you go ahead, then we'll come down the line here. >> Yeah so I think over that time frame two things are likely to happen. One is somebody's going to crack the consumerization of machine learning and AI, such that it really is available to the masses and we can do much more advanced things than we could. We see the industries tend to reach an inflection point and then there's an explosion. No one's quite cracked the code on how to really bring this to everyone, but somebody will. And that could happen in that time frame. And then the other thing that I think that almost has to happen is that the forces for openness, open data, data sharing, open data initiatives things like Block Chain are going to run headlong into data protection, data privacy, customer privacy laws and regulations that have to come down and protect us. Because the industry's not doing it, the government is stepping in and it's going to re-silo a lot of our data. It's going to make it recede and make it less accessible, making data science harder for a lot of the most meaningful types of activities. Patient data for example is already all locked down. We could do so much more with it, but health start ups are really constrained about what they can do. 'Cause they can't access the data. We can't even access our own health care records, right? So I think that's the challenge is we have to have that battle next to be able to go and take the next step. >> Well I see, with the growth of data a lot of it's coming through IOT, internet of things. I think that's a big source. And we're going to see a lot of innovation. A new types of Ubers or Air BnBs. Uber's so 2013 though, right? We're going to see new companies with new ideas, new innovations, they're going to be looking at the ways this data can be leveraged all this big data. Or data coming in from the IOT can be leveraged. You know there's some examples out there. There's a company for example that is outfitting tools, putting sensors in the tools. Industrial sites can therefore track where the tools are at any given time. This is an expensive, time consuming process, constantly loosing tools, trying to locate tools. Assessing whether the tool's being applied to the production line or the right tool is at the right torque and so forth. With the sensors implanted in these tools, it's now possible to be more efficient. And there's going to be innovations like that. Maybe small start up type things or smaller innovations. We're going to see a lot of new ideas and new types of approaches to handling all this data. There's going to be new business ideas. The next Uber, we may be hearing about it a year from now whatever that may be. And that Uber is going to be applying data, probably IOT type data in some, new innovative way. >> Jennifer, final word. >> Yeah so I think with data, you know it's interesting, right, for one thing I think on of the things that's made data more available and just people we open to the idea, has been start ups. But what's interesting about this is a lot of start ups have been acquired. And a lot of people at start ups that got acquired now these people work at bigger corporations. Which was the way it was maybe 10 years ago, data wasn't available and open, companies kept it very proprietary, you had to sign NDAs. It was like within the last 10 years that open source all of that initiatives became much more popular, much more open, a acceptable sort of way to look at data. I think that what I'm kind of interested in seeing is what people do within the corporate environment. Right, 'cause they have resources. They have funding that start ups don't have. And they have backing, right? Presumably if you're acquired you went in at a higher title in the corporate structure whereas if you had started there you probably wouldn't be at that title at that point. So I think you have an opportunity where people who have done innovative things and have proven that they can build really cool stuff, can now be in that corporate environment. I think part of it's going to be whether or not they can really adjust to sort of the corporate, you know the corporate landscape, the politics of it or the bureaucracy. I think every organization has that. Being able to navigate that is a difficult thing in part 'cause it's a human skill set, it's a people skill, it's a soft skill. It's not the same thing as just being able to code something and sell it. So you know it's going to really come down to people. I think if people can figure out for instance, what people want to buy, what people think, in general that's where the money comes from. You know you make money 'cause someone gave you money. So if you can find a way to look at a data or even look at technology and understand what people are doing, aren't doing, what they're happy about, unhappy about, there's always opportunity in collecting the data in that way and being able to leverage that. So you build cooler things, and offer things that haven't been thought of yet. So it's a very interesting time I think with the corporate resources available if you can do that. You know who knows what we'll have in like a year. >> I'll add one. >> Please. >> The majority of companies in the S&P 500 have a market cap that's greater than their revenue. The reason is 'cause they have IP related to data that's of value. But most of those companies, most companies, the vast majority of companies don't have any way to measure the value of that data. There's no GAAP accounting standard. So they don't understand the value contribution of their data in terms of how it helps them monetize. Not the data itself necessarily, but how it contributes to the monetization of the company. And I think that's a big gap. If you don't understand the value of the data that means you don't understand how to refine it, if data is the new oil and how to protect it and so forth and secure it. So that to me is a big gap that needs to get closed before we can actually say we live in a data driven world. >> So you're saying I've got an asset, I don't know if it's worth this or this. And they're missing that great opportunity. >> So devolve to what I know best. >> Great discussion. Really, really enjoyed the, the time as flown by. Joe if you get that augmented reality thing to work on the salary, point it toward that guy not this guy, okay? (everyone laughs) It's much more impressive if you point it over there. But Joe thank you, Dion, Joe and Jennifer and Batman. We appreciate and Bob Hayes, thanks for being with us. >> Thanks you guys. >> Really enjoyed >> Great stuff. >> the conversation. >> And a reminder coming up a the top of the hour, six o'clock Eastern time, IBMgo.com featuring the live keynote which is being set up just about 50 feet from us right now. Nick Silver is one of the headliners there, John Thomas is well, or rather Rob Thomas. John Thomas we had on earlier on The Cube. But a panel discussion as well coming up at six o'clock on IBMgo.com, six to 7:15. Be sure to join that live stream. That's it from The Cube. We certainly appreciate the time. Glad to have you along here in New York. And until the next time, take care. (bright digital music)