(upbeat music) >> Welcome back to theCUBE's continuing coverage of Snowflake Summit 22 live from Caesars Forum in Las Vegas. I'm Lisa Martin with Dave Vellante. We've got a couple of guests joining us now. We're going to be talking about financial services. Rinesh Patel joins us, the Global Head of Financial Services for Snowflake, and Jack Berkowitz, Chief Data Officer at ADP. Guys, welcome to the program. >> Thanks, thanks for having us. >> Thanks for having us. >> Talk to us about what's going on in the financial services industry as a whole. Obviously, we've seen so much change in the last couple of years. What does the data experience look like for internal folks and of course, for those end user consumers and clients? >> So, one of the big things happening inside of the financial services industry is overcoming the COVID wait, right? A lot of banks, a lot of institutions like ours had a lot of stuff on-prem. And then the move to the Cloud allows us to have that flexibility to deal with it. And out of that is also all these new capabilities. So the machine learning revolution has really hit the services industry, right? And so it's affecting how our IT teams or our data teams are building applications. Also really affecting what the end consumers get out of them. And so there's all sorts of consumerization of the experience over the past couple of years much faster than we ever expected it to happen. >> Right, we have these expectations as consumers that bleed into our business lives that I can do transactions. It's going to be on the swipe in terms of checking authenticity, fraud detection, et cetera. And of course we don't want things to go back in terms of how brands are serving us. Talk about some of the things that you guys have put in place with Snowflake in the last couple of years, particularly at ADP. >> Yeah, so one of the big things that we've done, is, one of the things that we provide is compensation data. So we issue a thing called the National Employment Report that informs the world as to what's happening in the U.S. economy in terms of workers. And then we have compensation data on top of that. So the thing that we've been able to do with Snowflake is to lower the time that it takes us to process that and get that information out into the fingertips of people. And so people can use it to see what's changed in terms of with the worker changes, how much people are making. And they can get it very, very quickly. And we're able to do that with Snowflake now. Used to take us weeks, now it's in a matter of moments we can get that updated information out to people. >> Interesting. It helps with the talent war and- >> Helps in the talent war, helps people adjust, even where they're going to put supply chain in reaction to where people are migrating. We can have all of that inside of the Snowflake system and available almost instantaneously. >> You guys announced the Financial Data Cloud last year. What was that like? 'Cause I know we had Frank on early, he clearly was driving the verticalization of Snowflake if you will, which is kind of rare for a relatively new software company but what's that been like? Give us the update on where you're at and biggest vertical, right? >> Absolutely, it's been an exciting 12 months. We're a platform, but the journey and the vision is more. We're trying to bring together a fragmented ecosystem across financial services. The aim is really to bring together key customers, key data providers, key solution providers all across the different Clouds that exist to allow them to collaborate with data in a seamless way. To solve industry problems. To solve industry problems like ESG, to solve industry problems like quantitative research. And we're seeing a massive groundswell of customers coming to Snowflake, looking at the Financial Services Data Cloud now to actually solve business problems, business critical problems. That's really driving a lot of change in terms of how they operate, in terms of how they win customers, mitigate risk and so forth. >> Jack, I think, I feel like the only industry that's sometimes more complicated than security, is data. Maybe not, security's still maybe more fragmented- >> Well really the intersection of the two is a nightmare. >> And so as you look out on this ecosystem, how do you as the chief data officer, how do you and your organization, what process do you use to decide, okay, which of the, like a chef, which of these ingredients am I going to put together for my business. >> It's a great question, right? There's been explosion of companies. We kind of look at it in two ways. One is we want to make sure that the software and the data can interoperate because we don't want to be in the business of writing bridge code. So first thing is, is having the ecosystem so that the things are tested and can work together. The other area is, and it's important to us is understanding the risk profile of that company. We process about 20% of the U.S. payroll, another 25% of the taxes. And so there's a risk to us that we have an imperative to protect. So we're looking at those companies are they financed, what's their management team. What's the sales experience like, that's important to us. And so technology and the experience of the company coming together are super important to us. >> What's your purview as a chief data officer, I mean, a lot of CDOs that I know came out of the back office and it was a compliance or data quality. You come out of industry from a technology company. So you're sort of the modern... You're like the modern CDO. >> Thanks. Thanks. >> Dave: What's your role? >> I appreciate that. >> You know what I'm saying though? >> And for a while it was like, oh yeah, compliance. >> So I actually- >> And then all of a sudden, boom, big deal. >> Yeah, I really have two jobs. So I have that job with data governance but a lot of data security. But I also have a product development unit, a massive business in monetization of data or people analytics or these compensation benchmarks or helping people get mortgages. So providing that information, so that people can get their mortgage, or their bank loans, or all this other type of transactional data. *So it's both sides of that equation is my reading inside. >> You're responsible for building data products? >> That's right. >> Directly. >> That's right. I've got a massive team that builds data products. >> Okay. That's somewhat unique in your... >> I think it's where CDOs need to be. So we build data products. We build, and we assist as a hub to allow other business units to build analytics that help them either optimize their cost or increase their sales. And then we help with all that governance and communication, we don't want to divide it up. There's a continuum to it. >> And you're a peer of the CIO and the CISO? >> Yeah, exactly. They're my peers. I actually talk to them almost every day. So I've got the CIO as a peer. >> It's a team. >> I've got the security as a peer and we get things done together. >> Talk about the alignment with business. We've been talking a lot about alignment with the data folks, the business folks, the technical folks to identify the right solutions, to be able to govern data, to monetize it, to create data products. What does that... You mentioned a couple of your cohorts, but on the business side, who are some of those key folks? >> So we're like any other big, big organization. We have lots of different business units. So we work directly with either the operational team or the heads of those business units to divine analytic missions that they'll actually execute. And at the same time, we actually have a business unit that's all around data monetization. And so I work with them every single day. And so these business units will come together. I think the big thing for us is to define value and measure that value as we go. As long as we're measuring that value as we go, then we can continue to see improvements. And so, like I said, sometimes it's bottom line, sometimes it's top line, but we're involved. Data is actually a substrate of the company. It's not a side thing to the company. >> Yeah, you are. >> ADP. >> Yeah but if they say data first but you really are data first. >> Yeah. I mean, our CEO says- >> Data's your product. >> Data's our middle name. And it literally is. >> Well, so what do you do in the Snowflake financial services data Cloud? Are you monetizing? >> Yeah. >> What's the plan? >> Yeah, so we have clients. So part of our data monetization is actually providing aggregate and anonymized information that helps other clients make business decisions. So they'll take it into their analytics. So, supply chain optimization, where should we actually put the warehouses based on the population shifts? And so we're actually using the file distribution capabilities or the information distribution, no longer files, where we use Snowflake to actually be that data cloud for those clients. So the data just pops up for our other clients. >> I think the industry's existed a lot with the physical movement of data. When you physically move data, you also physically move the data management challenges. Where do you store it? How do you map it? How do you concord it? And ultimately data sharing is taking away that friction that exists. So it's easier to be able to make informed decisions with the data at hand across two counterparties. >> Yeah, and there's a benefit to us 'cause it lowers our friction. We can have a conversation and somebody can be... Obviously the contracts have to be signed, but once they get done, somebody's up and running on it within minutes. And where it used to be, as you were saying, the movement of data and loss of control, we never actually lose control of it. We know where it is. >> Or yeah, contracts signed, now you got to go through this long process of making sure everything's cool, or a lot of times it could slow down the sale. >> That's right. >> Let's see how that's going to... Let's do a little advanced work. Now you're working without a contract. Here, you can say, "Hey, we're in the Snowflake data cloud. It's governed, you're a part of the ecosystem." >> Yeah, and the ecosystem we announced, oh gee, I think it's probably almost a year and a half ago, a relationship with ICE, Intercontinental Exchange, where they're actually taking our information and their information and creating a new data product that they in turn sell. So you get this sort of combination. >> Absolutely. The ability to form partnerships and monetize data with your partners vastly increases as a consequence. >> Talk to us about the adoption of the financial services data cloud in the last what, maybe nine months or so, since it was announced? And also in terms of the its value proposition, how does the ADP use case articulate that? >> So, very much so. So in terms of momentum, we're a global organization, as you mentioned, we are verticalized. So we have increasingly more expertise and expertise experience now within financial services that allows us to really engage and accelerate our momentum with the top banks, with the biggest asset managers by AUM, insurance companies, sovereign wealth funds on Snowflake. And obviously those data providers and solution providers that we engage with. So the momentum's really there. We're really moving very, very fast in a great market because we've got great opportunity with the capabilities that we have. I mean, ADP is just one of many use cases that we're working with and collaborations that we're taking to market. So yeah, the opportunity to monetize data and help our partners monetize the data has vastly increased within this space. >> When you think about... Oh go ahead, please. >> Yeah I was just going to say, and from our perspective, as we were getting into this, Snowflake was with us on the journey. And that's been a big deal. >> So when you think about data privacy, governance, et cetera, and public policy, it seems like you have, obviously you got things going on in Europe, and you got California, you have other states, there's increasing in complexity. You guys probably love that. (Dave laughs) More data warehouses, but where are we at with that whole? >> It's a great question. Privacy is... We hold some of the most critical information about people because that's our job to help people get paid. And we respect that as sort of our prime agenda. Part of it deals with the technology. How do you monitor, how do you see, make sure that you comply with all these regulations, but a lot of it has to do with the basic ethics of why you're doing and what you're doing. So we have a data and AI ethics board that meets and reviews our use cases. Make sure not only are we doing things properly to the regulation, but are these the types of products, are these the types of opportunities that we as a company want to stand behind on behalf of the consumers? Our company's been around 75 years. We talk about ourselves as a national asset. We have a trust relationship. We want to ensure that that trust relationship is never violated. >> Are you in a position where you can influence public policy and create more standards or framework. >> We actually are, right. We issue something every month called the National Employment Report. It actually tells you what's happening in the U.S. economy. We also issue it in some overseas countries like France. Because of that, we work a lot with various groups. And we can help shape, either data policy, we're involved in understanding although we don't necessarily want to be out in the front, but we want to learn about what's happening with federal trade commission, EOC, because at the end of the day we serve people, I always joke ADP, it's my grandfather's ADP. Well, it was actually my grandfather's ADP. (Dave laughs) He was a small businessman, and he used a ADP all those years ago. So we want to be part of that conversation because we want to continue to earn that trust every day. >> Well, plus your observation space is pretty wide. >> And you've got context and perspective on that that you can bring. >> We move somewhere between two, two and a half trillion dollars a year through our systems. And so we understand what's happening in the economy. >> What are some of the, oh sorry. >> Can your National Employment Report combined with a little Snowflake magic tell us what the hell's going to happen with this economy? >> It's really interesting you say that. Yeah, we actually can. >> Okay. (panelists laugh) >> I think when you think about the amount of data that we are working with, the types of partners that we're working with, the opportunities are infinite. They really, really are. >> So it's either a magic eight ball or it's a crystal ball, but you have it. >> We think- >> We've just uncovered that here on theCUBE. >> We think we have great partners. We have great data. We have a set of industry problems out there that we're working, collaboration with the community to be able to solve. >> What are some of the upcoming use cases Rinesh, that excite you, that are coming up in financial services- >> Great question. >> That snowflake is just going to knock out of the park. >> So look, I think there's a set of here and now problems that the industry faces, ESG's a good one. If you think about ESG, it means many different things from business ethics, to diversity, to your carbon footprint and every asset manager has to make sure they have now some form of green strategy that reflects the values of their investors. And every bank is looking to put in place sustainable lending to help their corporate customers transition. That's a big data problem. And so we're very much at the center of helping those organizations support those informed investors and help those corporates transition to a more sustainable landscape. >> Let me give you an example on Snowflake, we launched capabilities about diversity benchmarks. The first time in the industry companies can understand for their industry, their size, their location what their diversity profile looks like and their org chart profile looks like to differentiate or at least to understand are they doing the right things inside the business. The ability for banks to understand that and everything else, it's a big deal. And that was built on Snowflake. >> I think it's massive, especially in the context of the question around regulation 'cause we're seeing more and more disclosure agreements come out where regulators are making sure that there's no greenwashing taking place. So when you have really strong sources of data that are standardized, that allow that investment process to ingest that data, it does allow for a better outcome for investors. >> Real data, I mean, that diversity example they don't have to rely on a survey. >> It's not a survey. >> Anecdotes. >> It's coming right out of the transactional systems and it's updated, whenever those paychecks are run, whether it's weekly, whether it's biweekly or monthly, all that information gets updated and it's available. >> So it sounds like ADP is a facilitator of a lot of companies ESG initiatives, at least in part? >> Well, we partner with companies all the time. We have over 900,000 clients and all of them are... We've never spoken to a client who's not concerned about their people. And that's just good business. And so, yeah we're involved in that and we'll see where it goes over time now. >> I think there's tremendous opportunity if you think about the data that the ADP have in terms of diversity, in terms of gender pay gap. Huge, huge opportunity to incorporate that, as I said into the ESG principles and criteria. >> Good, 'cause that definitely is what needs to be addressed. (Lisa laughs) Guys thank you so much for joining Dave and me on the program, talking about Snowflake ADP, what you're doing together, and the massive potential that you're helping unlock with the value of data. We appreciate your insights and your time. >> Thank you for having us. >> Dave: Thanks guys. >> Thank you so much. >> For our guests, and Dave Vellante, I'm Lisa Martin. You're watching theCUBE, live in Las Vegas at Snowflake Summit 22. Dave and I will be right back with our next guest. (upbeat music)

>> Announcer: From around the globe, it's theCUBE with digital coverage of AWS reInvent 2020 sponsored by Intel, AWS and our community partners. >> Hi, and welcome to theCUBE virtual and our coverage of AWS reInvent 2020. I'm your host Rebecca Knight. Joining me is Sachin Dhoot, he is the vice president for data and platform engineering at Ellie Mae. Thank you so much for coming on theCUBE, Sachin. >> Nice to be here. >> So we are talking today about Ellie Mae's journey towards data monetization. Before we begin though, I want you to give our viewers a little bit, tell our viewers a little bit about yourself and your role at Ellie Mae. >> Sure. So I'm the vice president for data and platform engineering at Ellie Mae. A little bit about Ellie Mae before I talk about myself. So Ellie Mae, which is now part of ICE Mortgage Technology, a division of Intercontinental exchange is the leading cloud based loan origination platform for the mortgage industry. Our technology solutions actually enable lenders to originate more loans, lower origination cost and reduce the time to close. Or when ensuring the highest degree of compliance quality and efficiency. Our mission as we call it here internally is to automate everything 'automatable' for the residential mortgage industry. So that's what we do here. And we take great pride in doing that. >> Everything automatable, I love it. >> Yes. And if you have gone through the mortgage process, you'll see the number of papers you have to sign. And so we are on the journey to automate as much as possible in this. So as part of this, my charter here so I'm the vice president of data and platform engineering. Like I said, I lead and I'm responsible for all AWS based platform and data solutions including our highly secure, scalable data platform and the global, literally. Just to give you a magnitude of how much data we are talking about; so currently Ellie Mae in its platform stores data of nearly 50% of all US for mortgages. So that's the scale which we are talking about and I'm responsible for having the AWS based data platform to support that. >> So in terms of the data monetization journey like most innovations, it starts with a problem. What was the problem that you were trying to solve here? >> Yes, that's a great question. So earlier in our initial design what used to happen is the customers had access to their loan origination system and data in it. And the way they had access to the data was writing some customer SDK applications to actually export our data from their production systems. So this had its own share of challenges. Like for example, if I wrote some inefficient queries to export out the data, since they were acting on the same production database it used to slow down their loan origination system. Plus they did not get access to all of their data. And we had heard it loud and clear from our customers that not only did they need access to the data, but they also wanted us to manage their data. They did not want to get into managing the database or schema changes and all of that. Plus we also had such a rich industry data set. We are talking about 50% of all US home mortgages. So they were also very interested in using that data to get actionable insights about the industry, about their competitive advantages and develop some innovative services on top of it. So those were the challenges which we were trying to solve. >> So what was the original architecture like you're describing what sounds like a very poor experience for Ellie Mae and the lenders themselves. It sounds clunky and cumbersome. And then also leaving a lot on the table because as you said, it was a rich dataset. What was the original architecture? >> So the original architecture was not a cloud-based architecture. We were in our own private data center and every customer had their own database to work with. So, and it wasn't great architecture at that time when the technologies had not evolved. And we had a highly successful product as a result of that but when it came to data it was not a very good experience for them. So why did their loan origination system was working great? The access to the data was not to the extent what we wanted. >> So using best-in-class technologies from AWS tell us a little bit about the new product. >> Yes. So, our journey really started when we heard all of the customer's feedback and the requirements. Then we basically went back to the drawing board. We said, yes, we have a highly successful encompass product in the market, but we also want to solve this problem without affecting their experience with the loan origination system. So that was the challenge which we had taken internally. So what we did was we evaluated quite a bit of cloud providers and technology stacks and the parameters which we had put in that time because of the scale of data was, we needed unlimited scalability and reliability of any provider. We needed a secure data storage including the personally identifiable information protection. So as you can imagine, we deal with loan mortgages, I mean the mortgage and we pretty much have so much of PII data as we call it. Security is on the forefront for us. So we needed a cloud provider which could match up with that expectation. We needed.. >> AWS, was it? >> AWS was definitely it and there were some other parameters which also we were able to check because of that highly scalable and performance data Lake. We needed a big data Lake for this, storage compute separation. We also needed ability to seamlessly import data from any applications internal or external, right? And AWS absolutely gave us all of this. And we did evaluate a lot of cloud vendors and AWS came up on the top. So AWS along with persistent technologies actually helped us with this evaluation and the development of the data platform. >> So tell our viewers a little bit now about data connect and what it is for lenders now. >> Yeah. So what we did was as any cloud technology, we first developed a common platform and then we started building data connect solutions on top of it, right? So we created solutions based on the customer's needs. So one solution which we have is what we call as the data connects future products. In this, they can replicate, customers can replicate their data from the cloud, from their private data Lake into their warehouse, or they can access reports and run analytical queries directly on our warehouse which is again in the cloud. So all the solutions that are available depending on the customer's needs but that is all separate from the loan origination system. So we made sure that we are not impacting that existing business while creating this new solutions in the market. And all of these were built on AWS. >> But you also took things a step further and explored what was possible if you aggregated data from all lenders the resulting being insights. Tell our viewers a little bit about insights and what it allows. >> Absolutely. So that was a very cool product which we came up with. So again, because of the rich data set, which we have, right? We are in the position right now to aggregate the data and come up with actionable insights on top of the data. And so we call this product insights. This is our latest offering from Ellie Mae, again based off AWS and the data platform. So this product gives us information about the industry dreams on how the mortgage industry is going in US. It gives the lenders the ability to compare themselves with their peers and with the industry. So they can actually benchmark themselves and decide whether they are doing great, not great, what do they have to change? And this is all in near real time. So this is not like a month old data and all that. So that's the beauty of this product. >> And what are you hearing from customers? Because as you said, that real-time benchmarking and understanding how they're doing relative to their rivals is a game changer. It is and customers are super excited about it. We just launched this few months back and we are seeing amazing adoption for this product. In fact, just not the adoption side of things, we are also seeing so many new use cases and requirements coming from the customer now that they understand we have such a massive data and this data can scale and it's not impacted their business. They just want to add more and more things to it so that it can solve their problem. So it gives a unique opportunity for us where we can monetize more but we can also help solve lenders problems. >> Right. Helping them solve the challenges that they're facing. Talk a little bit more about the primary benefits of the solution, the unlimited scalability, the fact that it's fully managed, the storage compute separation. Tell our viewers a little bit more about the benefits. So the benefits about the solutions are, the customers or lenders don't have to worry about how it is managed. It is all taken care of. They just how to access it when they need it. It is available on demand. It is available 24/7. In this time, this year has been especially very busy for us where the interest rates have dropped and the loan volume and the loan applications have just gone through the roof. But I'm very proud to say that Ellie Mae stack or, all of the data solutions, and in fact, all of our other products, they are able to scale and they have been able to scale to the record volume this year, all because of how we have designed it using the AWS technology stack. So the customers really benefit. They just need to focus on their business. They don't have to worry about underlying infrastructure or how things are going to scale if their volume is going to go up or not or is there any security issues of that? We take care of all of those things and this is all a self provision just web based access for some of our products. So they don't even have to do a lot of customization to get hold of these products. >> So I want to ask what's next for you. You just referenced the fact that Ellie Mae's incredibly busy with record mortgage applications, of course, companies and people around the globe are still grappling with the COVID-19 pandemic. What are some of the big trends you're seeing and what's next for Ellie Mae in the coming coming year? >> We have a exciting and a very rich roadmap coming up. So as I started this interview, I said, Ellie Mae is now part of ICE mortgage technology, which is a Intercontinental exchange division. So as part of this transition, which happened recently, we also have under our umbrella, two companies called MERS and Simplifile, which actually touch so if you take MERS as an example, it touches close to 80% of US loans for home mortgages. So we have such a unique opportunity now to not only expand our data set, make it more rich, and then come up with more additional use cases which are going to help solve customer's problem and also make them competitive in the market. So we have a lot of good opportunity related to data and I feel a lot confident because of the data platform and the technology stack we to use. We will be able to handle all of those things. >> Sachin, tell our viewers a little bit about the partners that are helping you on this data monetization journey. >> So AWS definitely helped us in the initial parts in evaluating the design and the solution architects came in and worked with us. But along with that, I would definitely want to mention Persistent Technologies. They came up with a lot of good design suggestions on how we should develop the data platform and the solutions on top of it. Those insights product, which I talked about is done along with their help. So I'm very happy with the partnership I have with the Persistent Technologies and AWS. >> Excellent, well, Sachin Dhoot, thank you so much for coming on theCUBE. I really appreciate talking to you >> Same here, nice talking to you. >> Stay tuned for more of theCUBE virtual coverage at AWS reInvent. (upbeat music)

>>Live from Miami, Florida. It's the cube covering IBM's data and AI forum brought to you by IBM. >>Welcome back to downtown Miami. Everybody. We're here at the Intercontinental hotel covering the IBM data AI form hashtag data AI forum. My name is Dave Volante and you're watching the cube, the leader in live tech coverage. Ritika gunner is here. She's the vice president of data and AI expert labs and learning at IBM. Ritika, great to have you on. Again, always a pleasure to be here. Dave. I love interviewing you because you're a woman executive that said a lot of different roles at IBM. Um, you know, you've, we've talked about the AI ladder. You're climbing the IBM ladder and so it's, it's, it's, it's awesome to see and I love this topic. It's a topic that's near and dear to the cubes heart, not only women in tech, but women in AI. So great to have you. Thank you. So what's going on with the women in AI program? We're going to, we're going to cover that, but let me start with women in tech. It's an age old problem that we've talked about depending on, you know, what statistic you look at. 15% 17% of, uh, of, of, of the industry comprises women. We do a lot of events. You can see it. Um, let's start there. >>Well, obviously the diversity is not yet there, right? So we talk about women in technology, um, and we just don't have the representation that we need to be able to have. Now when it comes to like artificial intelligence, I think the statistic is 10 to 15% of the workforce today in AI is female. When you think about things like bias and ethicacy, having the diversity in terms of having male and female representation be equal is absolutely essential so that you're creating fair AI, unbiased AI, you're creating trust and transparency, set of capabilities that really have the diversity in backgrounds. >>Well, you work for a company that is as chairman and CEO, that's, that's a, that's a woman. I mean IBM generally, you know, we could see this stuff on the cube because IBM puts women on a, we get a lot of women customers that, that come on >>and not just because we're female, because we're capable. >>Yeah. Well of course. Right. It's just because you're in roles where you're spokespeople and it's natural for spokespeople to come on a forum like this. But, but I have to ask you, with somebody inside of IBM, a company that I could say the test to relative to most, that's pretty well. Do you feel that way or do you feel like even a company like IBM has a long way to go? >>Oh, um, I personally don't feel that way and I've never felt that to be an issue. And if you look at my peers, um, my um, lead for artificial intelligence, Beth Smith, who, you know, a female, a lot of my peers under Rob Thomas, all female. So I have not felt that way in terms of the leadership team that I have. Um, but there is a gap that exists, not necessarily within IBM, but in the community as a whole. And I think it goes back to you want to, you know, when you think about data science and artificial intelligence, you want to be able to see yourself in the community. And while there's only 10 to 15% of females in AI today, that's why IBM has created programs such as women AI that we started in June because we want strong female leaders to be able to see that there are, is great representation of very technical capable females in artificial intelligence that are doing amazing things to be able to transform their organizations and their business model. >>So tell me more about this program. I understand why you started it started in June. What does it entail and what's the evolution of this? >>So we started it in June and the idea was to be able to get some strong female leaders and multiple different organizations that are using AI to be able to change their companies and their business models and really highlight not just the journey that they took, but the types of transformations that they're doing and their organizations. We're going to have one of those events tonight as well, where we have leaders from Harley Davidson in Miami Dade County coming to really talk about not only what was their journey, but what actually brought them to artificial intelligence and what they're doing. And I think Dave, the reason that's so important is you want to be able to understand that those journeys are absolutely approachable. They're doable by any females that are out there. >>Talk about inherent bias. The humans are biased and if you're developing models that are using AI, there's going to be inherent bias in those models. So talk about how to address that and why is it important for more diversity to be injected into those models? >>Well, I think a great example is if you took the data sets that existed even a decade ago, um, for the past 50 years and you created a model that was to be able to predict whether to give loans to certain candidates or not, all things being equal, what would you find more males get these loans than females? The inherent data that exists has bias in it. Even from the history based on what we've had yet, that's not the way we want to be able to do things today. You want to be able to identify that bias and say all things being equal, it is absolutely important that regardless of whether you are a male or a female, you want to be able to give that loan to that person if they have all the other qualities that are there. And that's why being able to not only detect these things but have the diversity and the kinds of backgrounds of people who are building AI who are deploying this AI is absolutely critical. >>So for the past decade, and certainly in the past few years, there's been a light shined on this topic. I think, you know, we were at the Grace Hopper conference when Satya Nadella stuck his foot in his mouth and it said, Hey, it's bad karma for you know, if you feel like you're underpaid to go complain. And the women in the audience like, dude, no way. And he, he did the right thing. He goes, you know what, you're right. You know, any, any backtrack on that? And that was sort of another inflection point. But you talk about the women in, in AI program. I was at a CDO event one time. It was I and I, an IBM or had started the data divas breakfast and I asked, can I go? They go, yeah, you can be the day to dude. Um, which was, so you're seeing a lot of initiatives like this. My question is, are they having the impact that you would expect and that you want to have? >>I think they absolutely are. Again, I mean, I'll go back to, um, I'll give you a little bit of a story. Um, you know, people want to be able to relate and see that they can see themselves in these females leaders. And so we've seen cases now through our events, like at IBM we have a program called grow, which is really about helping our female lead female. Um, technical leaders really understand that they can grow, they can be nurtured, and they have development programs to help them accelerate where they need to be on their technical programs. We've absolutely seen a huge impact from that from a technology perspective. In terms of more females staying in technology wanting to go in the, in those career paths as another story. I'll, I'll give you kind of another kind of point of view. Um, Dave and that is like when you look at where it starts, it starts a lot earlier. >>So I have a young daughter who a year, year and a half ago when I was doing a lot of stuff with Watson, she would ask me, you know, not only what Watson's doing, but she would say, what does that mean for me mom? Like what's my job going to be? And if you think about the changes in technology and cultural shifts, technology and artificial intelligence is going to impact every job, every industry, every role that there is out there. So much so that I believe her job hasn't been invented yet. And so when you think about what's absolutely critical, not only today's youth, but every person out there needs to have a foundational understanding, not only in the three RS that you and I know from when we grew up have reading, writing and arithmetic, we need to have a foundational understanding of what it means to code. And you know, having people feel confident, having young females feel confident that they can not only do that, that they can be technical, that they can understand how artificial intelligence is really gonna impact society. And the world is absolutely critical. And so these types of programs that shed light on that, that help bridge that confidence is game changing. >>Well, you got kids, I >>got kids, I have daughters, you have daughter. Are they receptive to that? So, um, you know, I think they are, but they need to be able to see themselves. So the first time I sent my daughter to a coding camp, she came back and said, not for me mom. I said, why? Because she's like, all the boys, they're coding in their Minecraft area. Not something I can relate to. You need to be able to relate and see something, develop that passion, and then mix yourself in that diverse background where you can see the diversity of backgrounds. When you don't have that diversity and when you can't really see how to progress yourself, it becomes a blocker. So as she started going to grow star programs, which was something in Austin where young girls coded together, it became something that she's really passionate about and now she's Python programming. So that's just an example of yes, you need to be able to have these types of skills. It needs to start early and you need to have types of programs that help enhance that journey. >>Yeah, and I think you're right. I think that that is having an impact. My girls who code obviously as a some does some amazing work. My daughters aren't into it. I try to send them to coder camp too and they don't do it. But here's my theory on that is that coding is changing and, and especially with artificial intelligence and cognitive, we're a software replacing human skills. Creativity is going to become much, much more important. My daughters are way more creative than my sons. I shouldn't say that, but >>I think you just admitted that >>they, but, but in a way they are. I mean they've got amazing creativity, certainly more than I am. And so I see that as a key component of how coding gets done in the future, taking different perspectives and then actually codifying them. Your, your thoughts on that. >>Well there is an element of understanding like the outcomes that you want to generate and the outcomes really is all about technology. How can you imagine the art of the possible with technology? Because technology alone, we all know not useful enough. So understanding what you do with it, just as important. And this is why a lot of people who are really good in artificial intelligence actually come from backgrounds that are philosophy, sociology, economy. Because if you have the culture of curiosity and the ability to be able to learn, you can take the technology aspects, you can take those other aspects and blend them together. So understanding the problem to be solved and really marrying that with the technological aspects of what AI can do. That's how you get outcomes. >>And so we've, we've obviously talking in detail about women in AI and women in tech, but it's, there's data that shows that diversity drives value in so many different ways. And it's not just women, it's people of color, it's people of different economic backgrounds, >>underrepresented minorities. Absolutely. And I think the biggest thing that you can do in an organization is have teams that have that diverse background, whether it be from where they see the underrepresented, where they come from, because those differences in thought are the things that create new ideas that really innovate, that drive, those business transformations that drive the changes in the way that we do things. And so having that difference of opinion, having healthy ways to bring change and to have conflict, absolutely essential for progress to happen. >>So how did you get into the tech business? What was your background? >>So my background was actually, um, a lot in math and science. And both of my parents were engineers. And I have always had this unwavering, um, need to be able to marry business and the technology side and really figure out how you can create the art of the possible. So for me it was actually the creativity piece of it where you could create something from nothing that really drove me to computer science. >>Okay. So, so you're your math, uh, engineer and you ended up in CS, is that right? >>Science. Yeah. >>Okay. So you were coded. Did you ever work as a programmer? >>Absolutely. My, my first years at IBM were all about coding. Um, and so I've always had a career where I've coded and then I've gone to the field and done field work. I've come back and done development and development management, gone back to the field and kind of seen how that was actually working. So personally for me, being able to create and work with clients to understand how they drive value and having that back and forth has been a really delightful part. And the thing that drives me, >>you know, that's actually not an uncommon path for IBM. Ours, predominantly male IBM, or is in the 50 sixties and seventies and even eighties. Who took that path? They started out programming. Um, I just think, trying to think of some examples. I know Omar para, who was the CIO of Aetna international, he started out coding at IBM. Joe Tucci was a programmer at IBM. He became CEO of EMC. It was a very common path for people and you took the same path. That's kind of interesting. Why do you think, um, so many women who maybe maybe start in computer science and coding don't continue on that path? And what was it that sort of allowed you to break through that barrier? >>No, I'm not sure why most women don't stay with it. But for me, I think, um, you know, I, I think that every organization today is going to have to be technical in nature. I mean, just think about it for a moment. Technology impacts every part of every type of organization and the kinds of transformation that happens. So being more technical as leaders and really understanding the technology that allows the kinds of innovations and business for informations is absolutely essential to be able to see progress in a lot of what we're doing. So I think that even general CXOs that you see today have to be more technically acute to be able to do their jobs really well and marry those business outcomes with what it fundamentally means to have the right technology backbone. >>Do you think a woman in the white house would make a difference for young people? I mean, part of me says, yeah, of course it would. Then I say, okay, well some examples you can think about Margaret Thatcher in the UK, Angela Merkel, and in Germany it's still largely male dominated cultures, but I dunno, what do you think? Maybe maybe that in the United States would be sort of the, >>I'm not a political expert, so I wouldn't claim to answer that, but I do think more women in technology, leadership role, CXO leadership roles is absolutely what we need. So, you know, politics aside more women in leadership roles. Absolutely. >>Well, it's not politics is gender. I mean, I'm independent, Republican, Democrat, conservative, liberal, right? Absolutely. Oh yeah. Well, companies, politics. I mean you certainly see women leaders in a, in Congress and, and the like. Um, okay. Uh, last question. So you've got a program going on here. You have a, you have a panel that you're running. Tell us more about. >>Well this afternoon we'll be continuing that from women leaders in AI and we're going to do a panel with a few of our clients that really have transformed their organizations using data and artificial intelligence and they'll talk about like their backgrounds in history. So what does it actually mean to come from? One of, one of the panelists actually from Miami Dade has always come from a technical background and the other panelists really etched in from a non technical background because she had a passion for data and she had a passion for the technology systems. So we're going to go through, um, how these females actually came through to the journey, where they are right now, what they're actually doing with artificial intelligence in their organizations and what the future holds for them. >>I lied. I said, last question. What is, what is success for you? Cause I, I would love to help you achieve that. That objective isn't, is it some metric? Is it awareness? How do you know it when you see it? >>Well, I think it's a journey. Success is not an endpoint. And so for me, I think the biggest thing I've been able to do at IBM is really help organizations help businesses and people progress what they do with technology. There's nothing more gratifying than like when you can see other organizations and then what they can do, not just with your technology, but what you can bring in terms of expertise to make them successful, what you can do to help shape their culture and really transform. To me, that's probably the most gratifying thing. And as long as I can continue to do that and be able to get more acknowledgement of what it means to have the right diversity ingredients to do that, that success >>well Retika congratulations on your success. I mean, you've been an inspiration to a number of people. I remember when I first saw you, you were working in group and you're up on stage and say, wow, this person really knows her stuff. And then you've had a variety of different roles and I'm sure that success is going to continue. So thanks very much for coming on the cube. You're welcome. All right, keep it right there, buddy. We'll be back with our next guest right after this short break, we're here covering the IBM data in a AI form from Miami right back.

>>live from Miami, Florida It's the Q covering. IBM is data in a I forum brought to you by IBM. >>Welcome back to the port of Miami, everybody. We're here at the Intercontinental Hotel. You're watching the Cube? The leader and I live tech covered set. Daubert is here. He's the vice president of data and I and a I and the chief data officer of cloud and cognitive software. And I'd be upset too. Good to see you again. >>Good. See, Dave, thanks for having me >>here. The data in a I form hashtag data. I I It's amazing here. 1700 people. Everybody's gonna hands on appetite for learning. Yeah. What do you see out in the marketplace? You know what's new since we last talked. >>Well, so I think if you look at some of the things that are really need in the marketplace, it's really been around filling the skill shortage. And how do you operationalize and and industrialize? You're a I. And so there's been a real need for things ways to get more productivity out of your data. Scientists not necessarily replace them. But how do you get more productivity? And we just released a few months ago, something called Auto A I, which really is, is probably the only tool out there that automates the end end pipeline automates 80% of the work on the Indian pipeline, but isn't a black box. It actually kicks out code. So your data scientists can then take it, optimize it further and understand it, and really feel more comfortable about it. >>He's got a eye for a eyes. That's >>exactly what is a eye for an eye. >>So how's that work? So you're applying machine intelligence Two data to make? Aye. Aye, more productive pick algorithms. Best fit. >>Yeah, So it does. Basically, you feed it your data and it identifies the features that are important. It does feature engineering for you. It does model selection for you. It does hyper parameter tuning and optimization, and it does deployment and also met monitors for bias. >>So what's the date of scientists do? >>Data scientist takes the code out the back end. And really, there's some tweaks that you know, the model, maybe the auto. Aye, aye. Maybe not. Get it perfect, Um, and really customize it for the business and the needs of the business. that the that the auto A I so they not understand >>the data scientist, then can can he or she can apply it in a way that is unique to their business that essentially becomes their I p. It's not like generic. Aye, aye for everybody. It's it's customized by And that's where data science to complain that I have the time to do this. Wrangling data >>exactly. And it was built in a combination from IBM Research since a great assets at IBM Research plus some cattle masters at work here at IBM that really designed and optimize the algorithm selection and things like that. And then at the keynote today, uh, wonderment Thompson was up there talking, and this is probably one of the most impactful use cases of auto. Aye, aye to date. And it was also, you know, my former team, the data science elite team, was engaged, but wonderment Thompson had this problem where they had, like, 17,000 features in their data sets, and what they wanted to do was they wanted to be able to have a custom solution for their customers. And so every time they get a customer that have to have a data scientist that would sit down and figure out what the right features and how the engineer for this customer. It was an intractable problem for them. You know, the person from wonderment Thompson have prevented presented today said he's been trying to solve this problem for eight years. Auto Way I, plus the data science elite team solve the form in two months, and after that two months, it went right into production. So in this case, oughta way. I isn't doing the whole pipeline. It's helping them identify the features and engineering the features that are important and giving them a head start on the model. >>What's the, uh, what's the acquisition bottle for all the way as a It's a license software product. Is it assassin part >>of Cloudpack for data, and it's available on IBM Cloud. So it's on IBM Cloud. You can use it paper use so you get a license as part of watching studio on IBM Cloud. If you invest in Cloudpack for data, it could be a perpetual license or committed term license, which essentially assassin, >>it's essentially a feature at dawn of Cloudpack for data. >>It's part of Cloudpack per day and you're >>saying it can be usage based. So that's key. >>Consumption based hot pack for data is all consumption based, >>so people want to use a eye for competitive advantage. I said by my open that you know, we're not marching to the cadence of Moore's Law in this industry anymore. It's a combination of data and then cloud for scale. So so people want competitive advantage. You've talked about some things that folks are doing to gain that competitive advantage. But the same time we heard from Rob Thomas that only about 4 to 10% penetration for a I. What? What are the key blockers that you see and how you're knocking them >>down? Well, I think there's. There's a number of key blockers, so one is of access to data, right? Cos have tons of data, but being able to even know what data is, they're being able to pull it all together and being able to do it in a way that is compliant with regulation because you got you can't do a I in a vacuum. You have to do it in the context of ever increasing regulation like GDP R and C, C, P A and all these other regulator privacy regulations that are popping up. So so that's that's really too so access to data and regulation can be blockers. The 2nd 1 or the 3rd 1 is really access to appropriate skills, which we talked a little bit about. Andi, how do you retrain, or how do you up skill, the talent you have? And then how do you actually bring in new talent that can execute what you want on then? Sometimes in some cos it's a lack of strategy with appropriate measurement, right? So what is your A II strategy, and how are you gonna measure success? And you and I have talked about this on Cuban on Cube before, where it's gotta measure your success in dollars and cents right cost savings, net new revenue. That's really all your CFO is care about. That's how you have to be able to measure and monitor your success. >>Yes. Oh, it's so that's that Last one is probably were where most organizations start. Let's prioritize the use cases of the give us the best bang for the buck, and then business guys probably get really excited and say Okay, let's go. But to up to truly operationalize that you gotta worry about these other things. You know, the compliance issues and you gotta have the skill sets. Yeah, it's a scale. >>And sometimes that's actually the first thing you said is sometimes a mistake. So focusing on the one that's got the most bang for the buck is not necessarily the best place to start for a couple of reasons. So one is you may not have the right data. It may not be available. It may not be governed properly. Number one, number two the business that you're building it for, may not be ready to consume it right. They may not be either bought in or the processes need to change so much or something like that, that it's not gonna get used. And you can build the best a I in the world. If it doesn't get used, it creates zero value, right? And so you really want to focus on for the first couple of projects? What are the one that we can deliver the best value, not Sarah, the most value, but the best value in the shortest amount of time and ensure that it gets into production because especially when you're starting off, if you don't show adoption, people are gonna lose interest. >>What are you >>seeing in terms of experimentation now in the customer base? You know, when you talk to buyers and you talk about, you know, you look at the I T. Spending service. People are concerned about tariffs. The trade will hurt the 2020 election. They're being a little bit cautious. But in the last two or three years have been a lot of experimentation going on. And a big part of that is a I and machine learning. What are you seeing in terms of that experimentation turning into actually production project that we can learn from and maybe do some new experiments? >>Yeah, and I think it depends on how you're doing the experiments. There's, I think there's kind of academic experimentation where you have data science, Sistine Data science teams that come work on cool stuff that may or may not have business value and may or may not be implemented right. They just kind of latch on. The business isn't really involved. They latch on, they do projects, and that's I think that's actually bad experimentation if you let it that run your program. The good experimentation is when you start identity having a strategy. You identify the use cases you want to go after and you experiment by leveraging, agile to deliver these methodologies. You deliver value in two weeks prints, and you can start delivering value quickly. You know, in the case of wonderment, Thompson again 88 weeks, four sprints. They got value. That was an experiment, right? That was an experiment because it was done. Agile methodologies using good coding practices using good, you know, kind of design up front practices. They were able to take that and put it right into production. If you're doing experimentation, you have to rewrite your code at the end. And it's a waste of time >>T to your earlier point. The moon shots are oftentimes could be too risky. And if you blow it on a moon shot, it could set you back years. So you got to be careful. Pick your spots, picked ones that maybe representative, but our lower maybe, maybe lower risk. Apply agile methodologies, get a quick return, learn, develop those skills, and then then build up to the moon ship >>or you break that moon shot down its consumable pieces. Right, Because the moon shot may take you two years to get to. But maybe there are sub components of that moon shot that you could deliver in 34 months and you start delivering knows, and you work up to the moon shot. >>I always like to ask the dog food in people. And I said, like that. Call it sipping your own champagne. What do you guys done internally? When we first met, it was and I think, a snowy day in Boston, right at the spark. Some it years ago. And you did a big career switch, and it's obviously working out for you, But But what are some of the things? And you were in part, brought in to help IBM internally as well as Interpol Help IBM really become data driven internally? Yeah. How has that gone? What have you learned? And how are you taking that to customers? >>Yeah, so I was hired three years ago now believe it was that long toe lead. Our internal transformation over the last couple of years, I got I don't want to say distracted there were really important business things I need to focus on, like gpr and helping our customers get up and running with with data science, and I build a data science elite team. So as of a couple months ago, I'm back, you know, almost entirely focused on her internal transformation. And, you know, it's really about making sure that we use data and a I to make appropriate decisions on DSO. Now we have. You know, we have an app on her phone that leverages Cognos analytics, where at any point, Ginny Rometty or Rob Thomas or Arvin Krishna can pull up and look in what we call E P M. Which is enterprise performance management and understand where the business is, right? What what do we do in third quarter, which just wrapped up what was what's the pipeline for fourth quarter? And it's at your fingertips. We're working on revamping our planning cycle. So today planning has been done in Excel. We're leveraging Planning Analytics, which is a great planning and scenario planning tool that with the tip of a button, really let a click of a button really let you understand how your business can perform in the future and what things need to do to get it perform. We're also looking across all of cloud and cognitive software, which data and A I sits in and within each business unit and cloud and cognitive software. The sales teams do a great job of cross sell upsell. But there's a huge opportunity of how do we cross sell up sell across the five different businesses that live inside of cloud and cognitive software. So did an aye aye hybrid cloud integration, IBM Cloud cognitive Applications and IBM Security. There's a lot of potential interplay that our customers do across there and providing a I that helps the sales people understand when they can create more value. Excuse me for our customers. >>It's interesting. This is the 10th year of doing the Cube, and when we first started, it was sort of the beginning of the the big data craze, and a lot of people said, Oh, okay, here's the disruption, crossing the chasm. Innovator's dilemma. All that old stuff going away, all the new stuff coming in. But you mentioned Cognos on mobile, and that's this is the thing we learned is that the key ingredients to data strategies. Comprised the existing systems. Yes. Throw those out. Those of the systems of record that were the single version of the truth, if you will, that people trusted you, go back to trust and all this other stuff built up around it. Which kind of created dissidents. Yeah. And so it sounds like one of the initiatives that you you're an IBM I've been working on is really bringing in the new pieces, modernizing sort of the existing so that you've got sort of consistent data sets that people could work. And one of the >>capabilities that really has enabled this transformation in the last six months for us internally and for our clients inside a cloud pack for data, we have this capability called IBM data virtualization, which we have all these independent sources of truth to stomach, you know? And then we have all these other data sources that may or may not be as trusted, but to be able to bring them together literally. With the click of a button, you drop your data sources in the Aye. Aye, within data. Virtualization actually identifies keys across the different things so you can link your data. You look at it, you check it, and it really enables you to do this at scale. And all you need to do is say, pointed out the data. Here's the I. P. Address of where the data lives, and it will bring that in and help you connect it. >>So you mentioned variances in data quality and consumer of the data has to have trust in that data. Can you use machine intelligence and a I to sort of give you a data confidence meter, if you will. Yeah. So there's two things >>that we use for data confidence. I call it dodging this factor, right. Understanding what the dodging this factor is of the data. So we definitely leverage. Aye. Aye. So a I If you have a date, a dictionary and you have metadata, the I can understand eight equality. And it can also look at what your data stewards do, and it can do some of the remediation of the data quality issues. But we all in Watson Knowledge catalog, which again is an in cloudpack for data. We also have the ability to vote up and vote down data. So as much as the team is using data internally. If there's a data set that had a you know, we had a hive data quality score, but it wasn't really valuable. It'll get voted down, and it will help. When you search for data in the system, it will sort it kind of like you do a search on the Internet and it'll it'll down rank that one, depending on how many down votes they got. >>So it's a wisdom of the crowd type of. >>It's a crowd sourcing combined with the I >>as that, in your experience at all, changed the dynamics of politics within organizations. In other words, I'm sure we've all been a lot of meetings where somebody puts foursome data. And if the most senior person in the room doesn't like the data, it doesn't like the implication he or she will attack the data source, and then the meeting's over and it might not necessarily be the best decision for the organization. So So I think it's maybe >>not the up, voting down voting that does that, but it's things like the E PM tool that I said we have here. You know there is a single source of truth for our finance data. It's on everyone's phone. Who needs access to it? Right? When you have a conversation about how the company or the division or the business unit is performing financially, it comes from E. P M. Whether it's in the Cognos app or whether it's in a dashboard, a separate dashboard and Cognos or is being fed into an aye aye, that we're building. This is the source of truth. Similarly, for product data, our individual products before me it comes from here's so the conversation at the senior senior meetings are no longer your data is different from my data. I don't believe it. You've eliminated that conversation. This is the data. This is the only data. Now you can have a conversation about what's really important >>in adult conversation. Okay, Now what are we going to do? It? It's >>not a bickering about my data versus your data. >>So what's next for you on? You know, you're you've been pulled in a lot of different places again. You started at IBM as an internal transformation change agent. You got pulled into a lot of customer situations because yeah, you know, you're doing so. Sales guys want to drag you along and help facilitate activity with clients. What's new? What's what's next for you. >>So really, you know, I've only been refocused on the internal transformation for a couple months now. So really extending IBM struck our cloud and cognitive software a data and a I strategy and starting to quickly implement some of these products, just like project. So, like, just like I just said, you know, we're starting project without even knowing what the prioritized list is. Intuitively, this one's important. The team's going to start working on it, and one of them is an aye aye project, which is around cross sell upsell that I mentioned across the portfolio and the other one we just got done talking about how in the senior leadership meeting for Claude Incognito software, how do we all work from a Cognos dashboard instead of Excel data data that's been exported put into Excel? The challenge with that is not that people don't trust the data. It's that if there's a question you can't drill down. So if there's a question about an Excel document or a power point that's up there, you will get back next meeting in a month or in two weeks, we'll have an e mail conversation about it. If it's presented in a really live dashboard, you can drill down and you can actually answer questions in real time. The value of that is immense, because now you as a leadership team, you can make a decision at that point and decide what direction you're going to do. Based on data, >>I said last time I have one more questions. You're CDO but you're a polymath on. So my question is, what should people look for in a chief data officer? What sort of the characteristics in the attributes, given your >>experience, that's kind of a loaded question, because there is. There is no good job, single job description for a chief date officer. I think there's a good solid set of skill sets, the fine for a cheap date officer and actually, as part of the chief data officer summits that you you know, you guys attend. We had were having sessions with the chief date officers, kind of defining a curriculum for cheap date officers with our clients so that we can help build the chief. That officer in the future. But if you look a quality so cheap, date officer is also a chief disruption officer. So it needs to be someone who is really good at and really good at driving change and really good at disrupting processes and getting people excited about it changes hard. People don't like change. How do you do? You need someone who can get people excited about change. So that's one thing. On depending on what industry you're in, it's got to be. It could be if you're in financial or heavy regulated industry, you want someone that understands governance. And that's kind of what Gardner and other analysts call a defensive CDO very governance Focus. And then you also have some CDOs, which I I fit into this bucket, which is, um, or offensive CDO, which is how do you create value from data? How do you caught save money? How do you create net new revenue? How do you create new business models, leveraging data and a I? And now there's kind of 1/3 type of CDO emerging, which is CDO not as a cost center but a studio as a p N l. How do you generate revenue for the business directly from your CDO office. >>I like that framework, right? >>I can't take credit for it. That's Gartner. >>Its governance, they call it. We say he called defensive and offensive. And then first time I met Interpol. He said, Look, you start with how does data affect the monetization of my organization? And that means making money or saving money. Seth, thanks so much for coming on. The Cube is great to see you >>again. Thanks for having me >>again. All right, Keep it right to everybody. We'll be back at the IBM data in a I form from Miami. You're watching the Cube?

>> From Boston, Massachusets, it's The Cube. Covering Actifio 2019: Data Driven, Brought to you by Actifio. >> Welcome back to Boston, everybody. We're here at the Intercontinental Hotel at Actifio's Data Driven conference, day one. You're watching The Cube. The leader in on-the-ground tech coverage. My name is is Dave Valante, Stu Minamin is here, so is John Ferrer, my friend Frank Gens is here, he's the Senior Vice President and Chief Analyst at IDC and Head Dot Connector. Frank, welcome to The Cube. >> Well thank you Dave. >> First time. >> First time. >> Newbie. >> Yep. >> You're going to crush it, I know. >> Be gentle. >> You know, you're awesome, I've watched you over the many years, of course, you know, you seem to get competitive, and it's like who gets the best rating? Frank always had the best ratings at the Directions conference. He's blushing but I could- >> I don't know if that's true but I'll accept it. >> I could never beat him, no matter how hard I tried. But you are a phenomenal speaker, you gave a great conversation this morning. I'm sure you drew a lot from your Directions talk, but every year you lay down this, you know, sort of, mini manifesto. You describe it as, you connect the dots, IDC, thousands of analysts. And it's your job to say okay, what does this all mean? Not in the micro, let's up-level a little bit. So, what's happening? You talked today, You know you gave your version of the wave slides. So, where are we in the waves? We are exiting the experimentation phase, and coming in to a new phase that multiplied innovation. I saw AI on there, block-chain, some other technologies. Where are we today? >> Yeah, well I think having mental models of the6 industry or any complex system is pretty important. I mean I've made a career dumbing-down a complex industry into something simple enough that I can understand, so we've done it again now with what we call the third platform. So, ten years ago seeing the whole raft of new technologies at the time were coming in that would become the foundation for the next thirty years of tech, so, that's an old story now. Cloud, mobile, social, big data, obviously IOT technologies coming in, block-chain, and so forth. So we call this general era the third platform, but we noticed a few years ago, well, we're at the threshold of kind of a major scale-up of innovation in this third platform that's very different from the last ten or twelve years, which we called the experimentation stage. Where people were using this stuff, using the cloud, using mobile, big data, to create cool things, but they were doing it in kind of a isolated way. Kind of the traditional, well I'm going to invent something and I may have a few friends help me, whereas, the promise of the cloud has been , well, if you have a lot of developers out on the cloud, that form a community, an ecosystem, think of GitHub, you know, any of the big code repositories, or the ability to have shared service as often Amazon, Cloud, or IBM, or Google, or Microsoft, the promise is there to actually bring to life what Bill Joy said, you know, in the nineties. Which was no matter how smart you are, most of the smart people in the world work for someone else. So the questions always been, well, how do I tap into all those other smart people who don't work for me? So we can feel that where we are in the industry right now is the business model of multiplied innovation or if you prefer, a network of collaborative innovation, being able to build something interesting quickly, using a lot of innovation from other people, and then adding your special sauce. But that's going to take the scale of innovation just up a couple of orders of magnitude. And the pace, of course, that goes with that, is people are innovating much more rapid clip now. So really, the full promise of a cloud-native innovation model, so we kind of feel like we're right here, which means there's lots of big changes around the technologies, around kind of the world of developers and apps, AI is changing, and of course, the industry structure itself. You know the power positions, you know, a lot of vendors have spent a lot of energy trying to protect the power positions of the last thirty years. >> Yeah so we're getting into some of that. So, but you know, everybody talks about digital transformation, and they kind of roll their eyes, like it's a big buzzword, but it's real. It's dataware at a data-driven conference. And data, you know, being at the heart of businesses means that you're seeing businesses transition industries, or traverse industries, you know, Amazon getting into groceries, Apple getting into content, Amazon as well, etcetera, etcetera, etcetera, so, my question is, what's a tech company? I mean, you know, Bennyhoff says that, you know, every company's a sass company, and you're certainly seeing that, and it's got to be great for your business. >> Yeah, yeah absolutely >> Quantifying all those markets, but I mean, the market that you quantify is just it's every company now. Banks, insurance companies, grocers, you know? Everybody is a tech company. >> I think, yeah, that's a hundred percent right. It is that this is the biggest revolution in the economy, you know, for many many decades. Or you might say centuries even. Is yeah, whoever put it, was it Mark Andreson or whoever used to talk about software leading the world, we're in the middle of that. Only, software now is being delivered in the form of digital or cloud services so, you know, every company is a tech company. And of course it really raises the question, well what are tech companies? You know, they need to kind of think back about where does our value add? But it is great. It's when we look at the world of clouds, one of the first things we observed in 2007, 2008 was, well, clouds wasn't just about S3 storage clouds, or salesforce.com's softwares and service. It's a model that can be applied to any industry, any company, any offering. And of course we've seen all these startups whether it's Uber or Netflix or whoever it is, basically digital innovation in every single industry, transforming that industry. So, to me that's the exciting part is if that model of transforming industries through the use of software, through digital technology. In that kind of experimentation stage it was mainly a startup story. All those unicorns. To me the multiplied innovation chapter, it's about- (audio cuts out) finally, you know, the cities, the Procter & Gambles, the Walmarts, the John Deere's, they're finally saying hey, this cloud platform and digital innovation, if we can do that in our industry. >> Yeah, so intrapreneurship is actually, you know, starting to- >> Yeah. >> So you and I have seen a lot of psychos, we watched the you know, the mainframe wave get crushed by the micro-processor based revolution, IDC at the time spent a lot of time looking at that. >> Vacuum tubes. >> Water coolant is back. So but the industry has marched to the cadence of Moore's Law forever. Even Thomas Friedman when he talks about, you know, his stuff and he throws in Moore's Law. But no longer Moore's Law the sort of engine of innovation. There's other factors. So what's the innovation cocktail looking forward over the next ten years? You've talked about cloud, you know, we've talked about AI, what's that, you know, sandwich, the innovation sandwich look like? >> Yeah so to me I think it is the harnessing of all this flood of technologies, again, that are mainly coming off the cloud, and that parade is not stopping. Quantum, you know, lots of other technologies are coming down the pipe. But to me, you know, it is the mixture of number one the cloud, public cloud stacks being able to travel anywhere in the world. So take the cloud on the road. So it's even, I would say, not even just scale, I think of, that's almost like a mount of compute power. Which could happen inside multiple hyperscale data centers. I'm also thinking about scale in terms of the horizontal. >> Bringing that model anywhere. >> Take me out to the edge. >> Wherever your data lives. >> Take me to a Carnival cruise ship, you know, take me to, you know, an apple-powered autonomous car, or take me to a hospital or a retail store. So the public cloud stacks where all the innovation is basically happening in the industry. Jail-breaking that out so it can come, you know it's through Amazon, AWS Outpost, or Ajerstack, or Google Anthos, this movement of the cloud guys, to say we'll take public cloud innovation wherever you need it. That to me is a big part of the cocktail because that's you know, basically the public clouds have been the epicenter of most tech innovation the last three or four years, so, that's very important. I think, you know just quickly, the other piece of the puzzle is the revolution that's happening in the modularity of apps. So the micro services revolution. So, the building of new apps and the refactoring of old apps using containers, using servos technologies, you know, API lifecycle management technologies, and of course, agile development methods. Kind of getting to this kind of iterative sped up deployment model, where people might've deployed new code four times a year, they're now deploying it four times a minute. >> Yeah right. >> So to me that's- and kind of aligned with that is what I was mentioning before, that if you can apply that, kind of, rapid scale, massive volume innovation model and bring others into the party, so now you're part of a cloud-connected community of innovators. And again, that could be around a Github, or could be around a Google or Amazon, or it could be around, you know, Walmart. In a retail world. Or an Amazon in retail. Or it could be around a Proctor & Gamble, or around a Disney, digital entertainment, you know, where they're creating ecosystems of innovators, and so to me, bringing people, you know, so it's not just these technologies that enable rapid, high-volume modular innovation, but it's saying okay now plugging lots of people's brains together is just going to, I think that, here's the- >> And all the data that throws off obviously. >> Throws a ton of data, but, to me the number we use it kind of is the punchline for, well where does multiplied innovation lead? A distributed cloud, this revolution in distributing modular massive scale development, that we think the next five years, we'll see as many new apps developed and deploye6d as we saw developed and deployed in the last forty years. So five years, the next five years, versus the last forty years, and so to me that's, that is the revolution. Because, you know, when that happens that means we're going to start seeing that long tail of used cases that people could never get to, you know, all the highly verticalized used cases are going to be filled, you know we're going to finally a lot of white space has been white for decades, is going to start getting a lot of cool colors and a lot of solutions delivered to them. >> Let's talk about some of the macro stuff, I don't know the exact numbers, but it's probably three trillion, maybe it's four trillion now, big market. You talked today about the market's going two x GDP. >> Yeah. >> For the tech market, that is. Why is it that the tech market is able to grow at a rate faster than GDP? And is there a relationship between GDP and tech growth? >> Yeah, well, I think, we are still, while, you know, we've been in tech, talk about those apps developed the last forty years, we've both been there, so- >> And that includes the iPhone apps, too, so that's actually a pretty impressive number when you think about the last ten years being included in that number. >> Absolutely, but if you think about it, we are still kind of teenagers when you think about that Andreson idea of software eating the world. You know, we're just kind of on the early appetizer, you know, the sorbet is coming to clear our palates before we go to the next course. But we're not even close to the main course. And so I think when you look at the kind of, the percentage of companies and industry process that is digital, that has been highly digitized. We're still early days, so to me, I think that's why. That the kind of the steady state of how much of an industry is kind of process and data flow is based on software. I'll just make up a number, you know, we may be a third of the way to whatever the steady state is. We've got two-thirds of the way to go. So to me, that supports growth of IT investment rising at double the rate of overall. Because it's sucking in and absorbing and transforming big pieces of the existing economy, >> So given the size of the market, given that all companies are tech companies. What are your thoughts on the narrative right now? You're hearing a lot of pressure from, you know, public policy to break up big tech. And we saw, you know you and I were there when Microsoft, and I would argue, they were, you know, breaking the law. Okay, the Department of Justice did the right thing, and they put handcuffs on them. >> Yeah. >> But they never really, you know, went after the whole breakup scenario, and you hear a lot of that, a lot of the vitriol. Do you think that makes sense? To break up big tech and what would the result be? >> You don't think I'm going to step on those land mines, do you? >> Okay well I've got an opinion. >> Alright I'll give you mine then. Alright, since- >> I mean, I'll lay it out there, I just think if you break up big tech the little techs are going to get bigger. It's going to be like AT&T all over again. The other thing I would add is if you want to go after China for, you know, IP theft, okay fine, but why would you attack the AI leaders? Now, if they're breaking the law, that should not be allowed. I'm not for you know, monopolistic, you know, illegal behavior. What are your thoughts? >> Alright, you've convinced me to answer this question. >> We're having a conversation- >> Nothing like a little competitive juice going. You're totally wrong. >> Lay it out for me. >> No, I think, but this has been a recurring pattern, as you were saying, it even goes back further to you know, AT&T and people wanting to connect other people to the chiraphone, and it goes IBM mainframes, opening up to peripherals. Right, it goes back to it. Exactly. It goes back to the wheel. But it's yeah, to me it's a valid question to ask. And I think, you know, part of the story I was telling, that multiplied innovation story, and Bill Joy, Joy's Law is really about platform. Right? And so when you get aggregated portfolio of technical capabilities that allow innovation to happen. Right, so the great thing is, you know, you typically see concentration, consolidation around those platforms. But of course they give life to a lot of competition and growth on top of them. So that to me is the, that's the conundrum, because if you attack the platform, you may send us back into this kind of disaggregated, less creative- so that's the art, is to take the scalpel and figure out well, where are the appropriate boundaries for, you know, putting those walls, where if you're in this part of the industry, you can't be in this. So, to me I think one, at least reasonable way to think about it is, so for example, if you are a major cloud platform player, right, you're providing all of the AI services, the cloud services, the compute services, the block-chain services, that a lot of the sass world is using. That, somebody could argue, well, if you get too strong in the sass world, you then could be in a position to give yourself favorable position from the platform. Because everyone in the sass world is depending on the platform. So somebody might say you can't be in. You know, if you're in the sass position you'll have to separate that from the platform business. But I think to me, so that's a logical way to do it, but I think you also have to ask, well, are people actually abusing? Right, so I- >> I think it's a really good question. >> I don't think it's fair to just say well, theoretically it could be abused. If the abuse is not happening, I don't think you, it's appropriate to prophylactically, it's like go after a crime before it's committed. So I think, the other thing that is happening is, often these monopolies or power positions have been about economic power, pricing power, I think there's another dynamic happening because consumer date, people's data, the Facebook phenomenon, the Twitter and the rest, there's a lot of stuff that's not necessarily about pricing, but that's about kind of social norms and privacy that I think are at work and that we haven't really seen as big a factor, I mean obviously we've had privacy regulation is Europe with GDPR and the rest, obviously in check, but part of that's because of the social platforms, so that's another vector that is coming in. >> Well, you would like to see the government actually say okay, this is the framework, or this is what we think the law should be. I mean, part of it is okay, Facebook they have incentive to appropriate our data and they get, okay, and maybe they're not taking enough responsibility for. But I to date have not seen the evidence as we did with, you know, Microsoft wiping out, you know, Lotus, and Novel, and Word Perfect through bundling and what it did to Netscape with bundling the browser and the price practices that- I don't see that, today, maybe I'm just missing it, but- >> Yeah I think that's going to be all around, you know, online advertising, and all that, to me that's kind of the market- >> Yeah, so Google, some of the Google stuff, that's probably legit, and that's fine, they should stop that. >> But to me the bigger issue is more around privacy.6 You know, it's a social norm, it's societal, it's not an economic factor I think around Facebook and the social platforms, and I think, I don't know what the right answer is, but I think certainly government it's legitimate for those questions to be asked. >> Well maybe GDPR becomes that framework, so, they're trying to give us the hook but, I'm having too much fun. So we're going to- I don't know how closely you follow Facebook, I mean they're obviously big tech, so Facebook has this whole crypto-play, seems like they're using it for driving an ecosystem and making money. As opposed to dealing with the privacy issue. I'd like to see more on the latter than the former, perhaps, but, any thoughts on Facebook and what's going on there with their crypto-play? >> Yeah I don't study them all that much so, I am fascinated when Mark Zuckerberg was saying well now our key business now is about privacy, which I find interesting. It doesn't feel that way necessarily, as a consumer and an observer, but- >> Well you're on Facebook, I'm on Facebook, >> Yeah yeah. >> Okay so how about big IPOs, we're in the tenth year now of this huge, you know, tail-wind for tech. Obviously you have guys like Uber, Lyft going IPO,6 losing tons of money. Stocks actually haven't done that well which is kind of interesting. You saw Zoom, you know, go public, doing very well. Slack is about to go public. So there's really a rush to IPO. Your thoughts on that? Is this sustainable? Or are we kind of coming to the end here? >> Yeah so, I think in part, you know, predicting the stock market waves is a very tough thing to do, but I think one kind of secular trend is going to be relevant for these tech IPOs is what I was mentioning earlier, is that we've now had a ten, twelve year run of basically startups coming in and reinventing industries while the incumbents in the industries are basically sitting on their hands, or sleeping. So to me the next ten years, those startups are going to, not that, I mean we've seen that large companies waking up doesn't necessarily always lead to success but it feels to me like it's going to be a more competitive environment for all those startups Because the incumbents, not all of them, and maybe not even most of them, but some decent portion of them are going to wind up becoming digital giants in their own industry. So to me I think that's a different world the next ten years than the last ten. I do think one important thing, and I think around acquisitions MNA, and we saw it just the last few weeks with Google Looker and we saw Tab Low with Salesforce, is if that, the mega-cloud world of Microsoft, Ajer, and Amazon, Google. That world is clearly consolidating. There's room for three or four global players and that game is almost over. But there's another power position on top of that, which is around where did all the app, business app guys, all the suite guys, SAP, Oracle, Salesforce, Adobe, Microsoft, you name it. Where did they go? And so we see, we think- >> Service Now, now kind of getting big. >> Absolutely, so we're entering a intensive period, and I think again, the Tab Low and Looker is just an example where those companies are all stepping on the gas to become better platforms. So apps as platforms, or app portfolio as platforms, so, much more of a data play, analytics play, buying other pieces of the app portfolio, that they may not have. And basically scaling up to become the business process platforms and ecosystems there. So I think we are just at the beginning of that, so look for a lot of sass companies. >> And I wonder if Amazon could become a platform for developers to actually disrupt those traditional sass guys. It's not obvious to me how those guys get disrupted, and I'm thinking, everybody says oh is Amazon going to get into the app space? Maybe some day if they happen to do a cam expans6ion, But it seems to me that they become a platform fo6r new apps you know, your apps explosion.6 At the edge, obviously, you know, local. >> Well there's no question. I think those appcentric apps is what I'd call that competition up there and versus kind of a mega cloud. There's no question the mega cloud guys. They've already started launching like call center, contact center software, they're creeping up into that world of business apps so I don't think they're going to stop and so I think that that is a reasonable place to look is will they just start trying to create and effect suites and platforms around sass of their own. >> Startups, ecosystems like you were saying. Alright, I got to give you some rapid fire questions here, so, when do you think, or do you think, no, I'm going to say when you think, that owning and driving your own car will become the exception, rather than the norm? Buy into the autonomous vehicles hype? Or- >> I think, to me, that's a ten-year type of horizon. >> Okay, ten plus, alright. When will machines be able to make better diagnosis than than doctors? >> Well, you could argue that in some fields we're almost there, or we're there. So it's all about the scope of issue, right? So if it's reading a radiology, you know, film or image, to look for something right there, we're almost there. But for complex cancers or whatever that's going to take- >> One more dot connecting question. >> Yeah yeah. >> So do you think large retail stores will essentially disappear? >> Oh boy that's a- they certainly won't disappear, but I think they can so witness Apple and Amazon even trying to come in, so it feels that the mix is certainly shifting, right? So it feels to me that the model of retail presence, I think that will still be important. Touch, feel, look, socialize. But it feels like the days of, you know, ten thousand or five thousand store chains, it feels like that's declining in a big way. >> How about big banks? You think they'll lose control of the payment systems? >> I think they're already starting to, yeah, so, I would say that is, and they're trying to get in to compete, so I think that is on its way, no question. I think that horse is out of the barn. >> So cloud, AI, new apps, new innovation cocktails, software eating the world, everybody is a tech company. Frank Gens, great to have you. >> Dave, always great to see you. >> Alright, keep it right there buddy. You're watching The Cube, from Actifio: Data Driven nineteen. We'll be right back right after this short break. (bouncy electronic music)

>> Narrator: Live from Vancouver, Canada, it's theCUBE, covering OpenStack Summit North America 2018, brought to you by Red Hat, the OpenStack Foundation, and its ecosystem partners. >> The sun has come out, but we're still talking about a lot of the cloud here at the OpenStack Summit 2018 in Vancouver. I'm Stu Miniman with my co-host John Troyer. Happy to welcome to the program the 2018 Super User Award winner, George Mihaiescu, who's the senior cloud architect with the Ontario Institute for Cancer Research or OICR. First of all, congratulations. >> Thank you very much for having me. >> And thank you so much for joining us. So cancer research, obviously is, one of the things we talk about is how can technology really help us at a global standpoint, help people. So, tell us a little about the organization first, before we get into the tech of it? >> So OICR is the largest cancer research institution in Canada, and is funded by government of Ontario. Located in Toronto, we support about 1,700 researchers, trainees and clinician staff. It's focused entirely on cancer research, it's located in a hub of cancer research in downtown Toronto, with Princess Margaret Hospital, Sick Kids Hospital, Mount Sinai, very, very powerful research centers, and OICR basically interconnects all these research centers and tries to bring together and to advance cancer research in the province, in Canada and globally. >> That's fantastic George. So with that, sketch out for us a little bit your role, kind of the purview that you have, the scope of what you cover. >> So I was hired four years ago by OICR to build and design cloud environment, based on a research grant that was awarded to a number of principal investigators in Canada to build this cloud computing infrastructure that can be used by cancer researchers to do large-scale analysis. What happens with cancer, because the variety of limitations happening in cancer patients, researchers found that they cannot just analyze a few samples and draw a conclusion, because the conclusion wouldn't be actually valid. So they needed to do large-scale research, and the ICGC, which is International Cancer Genome Consortium, an organization that's made of 17 countries that are donating, collecting and analyzing data from cancer patients, okay, they decided to put together all this data and to align it uniformly using the same algorithm and then analyze it using the same workflows, in order to actually draw conclusion that's valid across multiple data sets. They are focusing on the 50 most common types of cancer that affect most people in this world, and for each type of cancer, at least two countries provide and collect data. So for brain cancer, let's say we have data sets from two countries, for melanoma, for skin, and this basically gives you better confidence that the conclusion you draw is valid, and then the more pieces of the puzzle you throw on the table, the easier to see the big picture that's this cancer. >> You know George, I mean, I'm a former academic, and you know, the more data you get right, the more infrastructure you're going to have to have. I'm just reading off the announcement, 2,600 cores, 18 terabytes of RAM, 7.3 petabytes of storage, right, that's a lot of data, and it's a lot of... accessed by a lot of different researchers. When you came in, was the decision to use OpenStack already made, or did you make that decision, and how was the cloud architected in that way? >> The decision was basically made to use open source. We wanted basically to spend the money on capacity, on hardware, on research and not on licensing and support. >> John: Good use of everybody's tax dollars. >> Exactly, so you cannot do that if you have to spend money for paying licensing, then you probably have only half of the capacity that you could. So that means less large analysis, and longer it takes, and more costly. So Ceph for storing the data sets and OpenStack for infrastructure as a service offering was a no-brainer. My specialty was in OpenStack and Ceph, I started OpenStack seven years ago, so I was hired to design and build, and I had a chance to actually do alignment, and invitation calling for some of the data sets, so I was able to monitor the kind of stress that this workflows put on the system, so when I design it, I knew what is important, and what to focus on. So it's a cloud environment, it's customized for cancer research. We have very good ratio of RAM per CPU, we have very large local discs for the VM, for the virtual machines to be able to download very large data sets. We built it so if one compute node fails, you only impact a few workflows running there, you don't impact single small points of failures. Another tuning that we applied to the system too. >> George, can walk us through a little bit of the stack? What do you use, do you build your own OpenStack, or do you get it from someone? >> So basically, we use community hardware, we just high-density chassis, currently from Super Micro, Ubuntu for the operating system, no licensing there, OpenStack from the VM packages. We focus more on stability, scalability and support costs, internal support costs, because it's just myself and I have a colleague Gerard Baker, who's a cloud engineer, and you have to support all this environment, so we try to focus on the features that are most useful to our users, as well as less strain on our time and support resources. >> I mean that's, let's talk about the scalability right? You said the team is you and a colleague. >> George: Yes. >> But mostly, right. And you know, in the olden days, right, you would be taking care of maybe a handful of machines, and maybe some disk arrays in the lab. Now you're basically servicing an entire infrastructure for all of Canada, right? At how many universities? >> Well basically, it's global, so we have 40 research projects from four continents. So we have from Australia, from Israel, from China, from Europe, US, Canada. So approved cancer researchers that can access the data open up an account with us, and they get a quota, and they start their virtual machines, they download the data sets from the extra API of Ceph to their VMS, and they do analysis and we charge them for the time used, and because the use, everything is open source, and we don't pay any licensing fees, we are able to, and we don't run for profit, we charge them just what it costs us to be able to replenish the hardware when it fails. >> Nice, nice. And these are actually the very large machines, right? Because you have to have huge, thick data sets, you've got big data sets you have to compare all at once. >> Yeah, an average bandwidth of a file that has the normal DNA of the patient, and they need also the tumor DNA from the biopsy, an average whole genome sequence is about 150 gigabytes. So they need at least 300 gigabytes, and depending on the analysis, if they find mutations, then the output is usually five, 10 gigabytes, so much smaller. For other workflows, you have to actually align the data, so you input 150 gigabytes and the output is 150 or a bit more with metadata. And so nevertheless, you need very large storage for the virtual machines, and these are virtual machines that run very hard, in terms of you cannot do CPU over subscription, you cannot do memory over subscription, when you have a workflow that runs for four days, hundred percent CPU. So is different than other web scale environments, where you have website was running at 10%, or you can do 10 to one subscription, and then you go much cheaper or different solutions. Here you have to only provide what you have physically. >> John: That's great. >> George, you've said you participated in the OpenStack community for about seven years now. >> George: Yes. >> What kind of, do you actually contribute code, what pieces are you active in the community? >> Yeah, so I'm not a developer. My background is in networking, system administration and security, but I was involved in OpenStack since the beginning, before it was a foundation. I went to the first OpenStack public conference in Boston seven years ago, at the International Intercontinental Hotel and over time I was involved in discussions from the RAC channel, mailing list support, reporting backs. Even recently we had very interesting packet affected as well. The cloud package that is supposed to resize the disk of the VM as it boots, it was not using more than two terabytes because it was a bug, okay. So we reported this, and Scott Moffat, who's the maintainer of the cloud utils package, worked on the bug, and two days later, we had a fix, and they built a package, it's in the latest cloud Ubuntu image, and that happen, everybody else is going to use the same virtual Ubuntu package, so somebody who now has larger than two terabytes VMs, when they boot, they'll be able to resize and use the entire disk. And that's just an example of how with open source we can achieve things that would take much longer in commercial distribution, where even if you pay, doesn't necessarily mean that the response... >> Sure. Also George, any lessons learned? You've been with us a long time, right, and like Ceph. One thing we noticed today in the keynote, is actually a lot of the storage networking and compute wasn't really talked, those projects were maybe down focused a bit, as they talked about all the connectivity to everything else. So, I mean any lessons, so you... My point is, the infrastructure is stable of OpenStack, but any lessons learned along the journey? >> I think the lessons are that you can definitely build very affordable and useful and scalable infrastructure, but you have to get your expectations right. We only use from the open standard project that we consider are stable enough, so we can support them confidently without spending, like if a project adds 5% value to your offering, but eats 80% of your time debugging and trying to get it working, and doesn't have packages and missing documentation and so on, that's maybe not a good fit for your environment if you don't have the manpower to. And if it's not absolutely needed. Another very important lesson is that you have to really stay up to date, like go to the conferences, read the emails from the mailing list, be active in the community, because the OpenStack meetups in Toronto for 2018, we present there, we talk to other members. In these seven years I read tens of thousands of emails, so I learn from other users experiences, I try to help where I can. You have to be involved with the developers, I know the Ceph core developers, Sage and other people. So, you can't do this just by staying on the side and looking, you have to be involved. >> Good, George what are you looking for next from this community? You talked about the stability, are there pieces that you're hoping reach that maturity threshold for yourselves, or new functionalities that you're looking for down the road? >> I think what we want to provide to our researchers, 'cause they don't run web scale applications, so their needs are a little bit different. We want to add Magnum to our environment, to allow them deploy Kubernetes cluster easily. We want to add Octavia to expose the services, even though they don't run many web services, but you have to find a way to expose them when they run them. Maybe, Trove, database as a service, we'll see if we can deploy it safely and if it's stable enough. Anything that OpenStack comes up with, we basically look, is it useful, is it stable, can you do it, and we try it. >> George, last thing. Your group is the Super User of the Year. Can you just walk us through that journey, what led to the nomination, what does it mean to your team to win? >> I think we are a bit surprised, because we are a very small team, and our scale is not as big as T-Mobile or the other members, but I think it shows that again, for a big company to be able to deploy OpenStack at scale and make it work, it's maybe not very surprising 'cause yes, they have the resources, they have a lot of manpower and a lot of... But for a small institution or organization, or small company to be able to do it, without involving a vendor, without involving extra costs, I think that's the thing that was appreciated by the community and by the OpenStack Foundation, and yeah, we are pretty excited to have won it. >> All right, George, let me give you the final word, as somebody that's been involved with the community for a while. What would you say to people if they're, you know, still maybe looking from the outside or played with it a little bit. What tips would you give? >> I think we are living proof that it can be done, and if you wait until things are perfect, then they will never be, okay. Even Google has services in beta, Amazon has services in beta. You have to install OpenStack, it's much more performant and stable than when I started with OpenStack, where there was just a few projects, but definitely they will get help from the community, and the documentation's much better. Just go and do it, you won't regret it. >> George, as we know, software will eventually work, hardware will eventually fail. >> Absolutely. >> So, George Mihaiescu, congratulations to OICR on the Super User of the Year award, for John Troyer, I'm Stu Miniman, we're getting towards the end of day one of three days of wall to wall coverage here at OpenStack Summit 2018 in Vancouver. Thanks so much for watching theCUBE.

>> Narrator: Live, from San Francisco, it's the Cube, covering Informatica World 2017. Brought to you by Informatica. (techno music) >> Hey, welcome back, everyone. Live here in San Francisco, this is the Cube's exclusive coverage of Informatica World 2017, our third year covering Informatica, and more to come. I'm John Furrier with Silicon Angle, the Cube. My co-host, Peter Burris, Head of Research for Silicon Angle Media, as well as General Manager of Wikibon.com, check out the great research at Wikibon. Some great stuff there on IOT, cloud ping data, great stuff. Of course, go to SiliconAngle.com for all the coverage YouTube.com/SiliconAngle for all the Cube videos. Our next guest is Bruce Chizen, board member of a lot of private companies, also Special Advisor at Informatica. You're on the board of Informatica, no? >> Executive Chair. >> John: Executive Chair of Informatica. Not only as Special Advisor, Executive Chair. Welcome back, good to see you. >> Great to be here. >> You were on last year, great to have you back. What a popular video. Jerry Held was on yesterday. Let's get some Board insights, so first question, when are you going public? (laughing) >> Good one. >> John: Warmed you up, and then, no. I mean the performance is doing well. Give us a quick update. >> Company's doing well. Q4 was a good quarter, Q1 was a good quarter. I think we will be positioned to do something late 2018, early 2019. A lot depends on how the company continues to do. A lot depends on the market. The private equity investors are in no hurry. >> John: Yeah. >> But it's always nice to have that option. >> So it's one of the things we, yeah, great option. Doing well. We heard that also from some of the management. We got O'Neil coming on, we'll press him on some of the performance side, but always had good products out, we talked about it last year. But the industry's going through a massive transformation. You've seen many waves over the years. The waves are hitting. What's your perspective right now? I mean, it's a pretty big wave. You got to get the surfboard out there, there's a set coming in. What's the big wave right now? >> So, data is driving every transformation within every organization. Any company that is not using and taking advantage of data will be left behind. You look at how companies like Amazon and Google and now a lot of our customers like Schwab and Tesla and others, the way they're using data, that will allow them to continue to either be successful in the case of a Schwab, or be a disruptor, like somebody like Tesla. Fortunately for us at Informatica, we are helping to drive that digital transformation. >> One of the things that I always observe, younger than you are, I've only seen a few waves in my day, but in the waves that were the most impactful in terms of creating wealth, and opportunity, and innovation, has had a cool and relevant factor. Meaning, if you go back to the PC days, it was cool and relevant. If you go back to mini computer, cool and relevant. And it goes on and on and on. And certainly internet, cool and relevant. But now, the, you mention Tesla. I'm testing driving one on Friday. My kids are like "Don't buy the Audi, buy the Tesla." This is my kids. So it's a cooler, it's a spaceship, it's cooler than the other cars. >> Bruce: Or an iPhone on wheels. >> Peter: (laughs) Exactly. A computer on wheels. >> So cool and relevant, talk about what is the cool and relevant thing right now. You talk about user experience, that's one. Data's changing it. So how is data being the cool and relevant trend? Point to some things that... >> If you look at what's happening from the chip on up, everything, everything will be intelligent. And I hate to use the term "internet of things," but the reality is everything will have intelligence. And that intelligent information will be able to be taken advantage of because of the scale of the cloud. Which means that any company will be able to take information, data, analyze it on the cloud, and then use it to do something with. And it's happening now. Fortunately, Informatica sits right in the middle of that, because they're the ones who could rationalize that data on behalf of their customers. 'Cause there's going to be a lot of it and somebody needs to govern it, secure it, homogenize it. >> John: You consider them an enabling platform? >> Absolutely, absolutely. I was joking, we just went through a rebranding exercise. And it's kind of cute, new logo, and it's kind of bold and sleek and it shows we'll have a leader, but it's a logo. But there's really around the messaging, we are finally getting across that we are the ones unleashing the power of data. That's what Informatica does. We'd just never really told anybody about it. We're very product focused, not really helping customers understand how uniquely positioned the company was. >> And it's also, you guys have done some things. Let's just go back and look at going private. Brought a new management team, have product chops again, we've talked about that in previous years. Last year in particular. So, okay, you have the wind at your back. Now you got Sally as a CMO, now you got to start being a humble braggart about the cool stuff you're doing. So which is marketing, basically. >> That's correct. >> John: But now, it's digital. >> Yeah. >> So, what's the Board conversation like, you say "Go, go build the brand!" >> So first of all, being private is great. (laughing) Because we get to do things you couldn't do as a public company. We're, a lot of our customers what to buy the products and solutions via subscription, that has huge impact to the P&L, especially in the short term. Cash flow's fine. So the PE guys are going okay, it's great, because we'll come out of this as a better company, and our customers like it because that's the way they want to buy products. So, that helps a lot. The conversation at the Board level has been, "Wow, we're number one in every category in which "we participate in. "Everything from big data to cloud integration "to traditional on-premise, to real-time streaming, "and, and, and data security." >> You're only one of three vendors in the Google general availabilities banner which went out yesterday. We covered that on Silicon Angle. >> We're number one there, we had AWS speak at our conference, we had Azure speak at our conference. All of the cloud guys love Informatica because we are the ones who are uniquely positioned to deal with all this data on behalf of their customers. As a private company, we're able to take advantage of that, spend some extra money on marketing. You know a lot of our customers know about us, but a lot more should know about us. So, part of coming out, having a new logo, having a new digital campaign, changing the website, that costs money. But as a private company, we get to do that. Because the fruits of those efforts will end up occurring a couple of years down the road, which is fine. >> So let me see if I can weave those two thoughts together in what I thought was an interesting way. Given that increasingly a lot of data's going to be in the cloud, and that's where the longer analysis is going to be required, that means a lot of the tools are going to have to be in the cloud. Amazon Marketplace is going to be a place where a lot of tools are going to be chosen. People are going to go into the Amazon Marketplace and see a lot of different options, including some that are free. They may not work as well, but they're free. You guys, what happens with marketing, and what's happening with that kind of a trend, is you need to buy, as customers, to choose tools that are actually going to work to serve or to solve the problem, to do the work that you need them to perform. And so what Sally Jenkins, the CMO, has done, with this new branding, is introduce the process of how do you buy us more customers to choose the right tool to do the right job? Does that make sense to you? >> It makes absolute sense, free is good. But be careful what you ask for. Sometimes you get what you pay for. You're talking about enterprise data. You want it to be governed, you want it to be secure. You want it to be accurate. >> John: Now there's laws coming out where you have to do it. >> You look at GTB... >> Peter: GDBPR. >> GDBPR in Europe, the privacy issues. You look at what's happening with Facebook, or what was reported today with France and how they're not happy with Facebook's privacy behaviors. It's an issue. It's an issue for anybody who does business anywhere, especially if you're a global company and you do business in Europe. You have to worry about corporate governance. Data security, data governance, data security. That's Informatica. The other thing is, while there will be some customers who will say "I'm going to AWS," there will be more customers who will either say "I have some legacy "systems that I'm going to leave on-premise, "and new projects will be in the cloud." Or they're going to say "I'm moving everything to "the cloud, but I don't want to be held hostage "by one cloud provider." And they're going to go with Amazon and Azure and Google and maybe Oracle, and, and, and. And again, because Informatica is Swiss, we're able to provide them with a solution that allows them to accomplish their data needs. >> Well, congratulations on the performance, I want to get that out of the way. But I want to ask a specific question on the historical, holistic picture of Informatica. Going back, what were the key bets that you guys made? 'Cause you guys sit around, and you got the private equity now coming to the table, they have expectations, but at the end of the day you've got to build a business. What were the key bets that is yielding the fruit that we're seeing? >> The number one bet was that the company had great products and a great R&D organization. We believed that, and fortunately, we got it right. Because if you don't have great products and passionate R&D organizations around the world, you can't make up for that. It doesn't make a difference how much you spend on marketing. At least not in the business that we're in. So that was number one bet, and that proved to play out well. The second thing was, this was a company that had done so well for so long that they never needed to change their business processes to behave like a billion, two billion, three billion, four billion dollar company. Many of their business processes were like that of a 200 million dollar company. And that's easier to fix. So things around back end, IT, legal, finance, go-to-market, marketing, sales. >> John: Less of a risk from an investment standpoint. >> That's correct. So that's what we believed, we were right And where we've been spending most of our energy and effort is helping the company, through the new management team, improve their business processes and their go-to-market. >> So we had a critical analysis yesterday during our wrap up session, and one of the comments I made, I want to get your reaction to this, was although impressive, your number one and all these Gartner Magic Quadrant categories, but that's an old scoreboard. If we're really living in digital transformation, those shouldn't really be a tell sign for what the performance of the new KBIs or the new metrics are. And so we were pontificating and analyzing what that would be, still unknown, we're going to see it. But Peter had a good point, he said "At the end "of the day, customer wins." >> Yeah, that was my reaction. It's like at the end of the day, all that matters do the customers.... >> What's the scoreboard look for customer wins? I know you were at the executive summit they had yesterday at the Intercontinental right around the corner. I had a chance to meet some of them at that dinner, some conversation. But I want to get your perspective. What is the vibe of the customers, what are those customer wins, and how does that translate into future growth for Informatica? >> Any customer who is looking at data, data management, strategically, is going with Informatica. >> Mmm hmm. >> There are a number of competitors that we have who try to compete with Informatica at the product level, and they end up doing okay through pricing, through better sales tactics, but when we have the opportunity to speak to the Chief Data Officer, the CIO, the CEO, they go with Informatica. It's the reason why Tesla went with Informatica on their project where they're trying to tie together the auto business with the solar business. Because if they get to know both sets of customers and are able to sync that up, one plus one will be greater than two for them, and that's why they did that deal. Or it's why Amazon has chosen our MDM solution for their sales operations. So you look at leading companies who are able to look at the enterprise level, at the strategic level, they are going with Informatica. That's why we know we're winning. >> So Bruce, give us three sentences, what is strategic data management? >> Strategic data management is being able to take reams and reams of data from all different platforms, traditional legacy, big data, real-time solutions, and data from the cloud and be able to look at it intelligently. Use artificial intelligence and machine learning to be able to analyze that data in a more intelligent way, and then act on it. >> So two questions on that point, I was going to ask about the AI washing going on in the industry. Every event now is like, "Oh my god, AI, we've got AI," but that's not really AI. What is AI, we call it augmented intelligence because you're really augmenting with the data, but even Google IO's got a little neural net throwback to the 80s, but what's your thoughts on how customers should look through the lens of b.s. to say, "Wow, that's the real AI, or the real "augmented intelligence." >> Does it do anything? That's ultimately the question that a Chief Data Officer or CIO or CEO...is something changing because of the artificial intelligence being applied? In the case of Informatica, we announced an AI platform called Clair, "clairvoyant," so artificial intelligence. What is Clair? It allows you to develop solutions like our enterprise information catalog, where an organization has thousands and thousands of databases, it's able to look at the metadata within those databases and then over time keep disclosing more and more data appropriate to the information that you're looking for. So then, if I'm an analyst or a businessperson, a marketing person, a sales person, I can take action on the right set of data. That's true artificial intelligence. >> Bruce, I want to get to one final point as we are winding down here. Again, you've seen many waves. But I want to talk about the companies that are trying to get through the transition of this transformation, Informatica certainly cleared the runway, they've got some things to work on, certainly brand-building. I see that as their air cover in many rising tide will float a lot of boats in the ecosystem. But there are companies where they have been in the infrastructure business and the cloud is one big infrastructure, selling boxes and whatnot. Other companies have traditional software models, download, whatever you want to call it, on-prem licenses, not subscriptions. They're working hard. Your advice to them if you are on their Board, or as a friend, what do you say to them, what do they got to do to get through this? And how should customers look at who's winning and who's losing, in terms of progress? >> The world of enterprise computing is moving to the cloud. Legacy systems will remain for a while. They need to figure out how to take their legacy solutions and make them relevant to the world of cloud computing. And if they can't do that, they should sell their company or get out of business. (laughing) >> And certainly data is the oil, it's the gold, it's the lifeblood of an organization. >> Of any organization. Even at Informatica, internally, we're using our own intelligent data platform to do our own marketing. Sally Jenkins is working closely with our CIO Graeme Thompson on working on solutions where we could help better understand what our customers want and need, so we can provide them with the right solution, leveraging our intelligent data leg. >> Bruce, thanks for coming on the Cube. Really appreciate your insight. Again, you've seen a lot of waves, you've been in the industry a long time, you have great Board presence, as well as other companies. Thanks for sharing the insight, and the data here on the Cube. A lot of insights and analytics being extracted here and sharing it with you. Certainly we're not legacy, we don't need to sell our business, we're doing great. If you haven't, make the transition. Good advice, thanks so much. >> Bruce: Great to be here. >> Bruce Chizen inside the Cube here. I'm John Furrier with Peter Burris. Stay with us for more coverage after this short break. (techno music)

>> Announcer: Live from San Francisco it's the CUBE, covering Informatica World 2017 brought to you by Informatica. >> Welcome back everyone, we're here live in San Francisco for our wrap-up of day one, the CUBE's exclusive coverage of Informatica World 2017. So our flagship program, we go out to the events, and extract the signal and the noise. I'm John Furrier with SiliconANGLE, the CUBE. My co-host this week is Peter Burris, Head of Research at SiliconANGLE Media, also the General Manager of Wikibon.com. Go to Wikibon.com and check out all the great research, great stuff behind the paywall, subscription required but also some free content there as well and our special guest is Neil Raden, who's the new analyst on the Wikibon team. Welcome to the team. You're covering the value of data, and analytics industry veteran, great to have you on the team. Thanks for joining us on our wrap-up here. >> Thank you. >> Welcome Peter, look here, this is kind of a coming-out party for Informatica. We've been following them for multiple years. Some of their top exec has been CUBE alumni for many, many years. I think I admit Wallie's going to be on his eighth year, eighth time on the CUBE, but look behind as you see a new branding. Informatica has a new CMO, and she's got swagger and she's got brand impact. Informatica is now going to start bragging about their products. Although I have some critical analysis of Informatica we'll get to in a second but I've always said they've brought in a team of folks during the going private that have product chops, and they've had an install base, and their goal has been from the beginning let's go private close the curtain, and get stuff where you organize, and really work on the product and install base. Can they pivot? That was my question three years ago. Every year they keep on coming out with not a land grab but an incremental and moving the ball down the field to use your football analogy first and then do it again but starting to get into that horizontally scalable cloud model and with good cloud deals, looking poised, in my opinion, for being a data layer, potentially for making that data fabric on. So to me, I think that's the big story here. So far is some good news around the brand, product increases got AI, augmented intelligence with CLAIRE. Some interesting dynamics, which means the interface the data is changing, not only the underlying value of the data, which we want to get into but Informatica is trying to up level the interface. Your thoughts? >> So I think you're absolutely right, John. I think we've seen three things here. First off, Informatica has always had a pretty decent product kit, well embedded within some really first-rate customers. Number two, they've been talking about the need to accommodate some of the new trends around cloud, and how they're going to move in that direction. They've been talking about it. This is the first year we've really seen it. Number three, they even, when Informatica talks, people have historically not listened as much. Sally Jenkins, the new CMO has gotten an enormous amount of work done in a very, very short period of time. This is a bang so and it's manifesting itself in that people are buzzing about it. >> Yeah. >> It's even if Informatica has a lot to do to really be that enterprise cloud data management leader, a lot left to do but it all starts by presenting themselves in a coherent way. That's something that we're seeing for the first time. >> The value proposition has changed significantly. I was talking about the Amazon stock price. Since 2010, it's just been a skyrocket growth for Amazon across the board. Yeah, I got the retail but AWS certainly has been powering it. Having a good brand behind you is going to really energize the channel, ISV, software developers. Like I always joked about the old days, when I used to work at Hewlett-Packard, the joke was I used to call cold dead fish versus sushi. HP would be so accurate, so engineering-oriented, they would be so accurate around the products. They didn't really have a lot of marketing, and the joke was if they had sushi, they called it cold dead fish because that's what it is. Sushi sounds better but again back to marketing, they need to bring the brand out, and put a message around the shift to value. >> Well think about how important it's going to be to a company that increasingly acknowledges or acknowledges increasingly. Their customers are going to find it in something like the Amazon Marketplace or in Google, or in some other cloud environment. It means that they have to bias the customer to choose Informatica versus a range of tools that may not be as good but some of them are going to be open source to get people to bias at that moment, you got to get your brand out there. You have to get your identity out there. Informatica was not going to be a success when the customers making that decision as they're looking at that Amazon Marketplace just by word-of-mouth. They have to get their name out there. So this is big. The products are still good and making, and improving. Getting to the cloud is a big deal. Delivering on their promises, and now having a marketing platform that allows them to scream a little bit louder from the rafters, about who they are and what they want to be. A lot of it's coming again. >> Day two, we're going to have a lot. All the top execs on, they were in an off-site down the street right here at Intercontinental in San Francisco with Executive Customer Day, but we had SVP on for cloud business. We had the board member, Jerry Hill, who's five decades in the business. I mean he essentially laid it out. Hybrid cloud is here to stay, and it's not going to be an overnight success. It's going to be a transition, and that favors the legacy vendors, who are sharpening their saw, and getting their products in line. Of course we have the Chicago Cubs on, and we took the ring, almost put in my pocket in a very Putin-esque like style. We all know Robert Kraft waltz Super Bowl ring, to the biggest criminal in the world, Putin, but kind of a fun there. But the baseball team highlights the customer journeys. They have customers that love them and stay with them 'cause of the install base. >> Absolutely and Informatica is deeply embedded, and has been, Neil has been on the vanguard. Look, they got a lot of work to do but they've been on the vanguard of tying together the idea of data, data is an asset, tooling. So you can get more value out of the data through analytics. >> That's right, I'll tell you a little story. I mean, I brought Informatica in to one of my clients the first time in 1996. They were pretty much a brand new company. >> Peter: About a year after they started? >> Yeah and what motivated me to think about Informatica as opposed to any other way we were trying to get data into a data warehouse was that they understood metadata. They were the only company that had an active metadata repository. So this is their heritage. I know that Informatica claims they have, I don't know 10,000 customers. I think a significant number of those are not going to be interested in this whole thing. They don't have the budget for it. They don't have the time or the staff or anything but they've got the elite. When you look at the companies that are clients of Informatica, those are the people that would be interested in and spend time and money on this sort of thing. >> Well let me get you guys perspective as analyst because let's turn this into the analysis of what's happening here with Informatica, and compare that to what's happening in the industry. SAP Sapphire is happening right now in Orlando. We got CUBE coverage in our studio in Palo Alto. But Oracle, SAP, these are database guys. They have systems of record. IBM, Amazon is now a new player in that. They have to balance the install base, systems of record of their data. Now granted old techniques for walls, data warehouse, whatever you want to call. It was an old way. Now the new way's to empower developers to actually build and use that data. So the question is how do they get their product from old to new and modernize quickly, and highlight data as value because this is the thing that you guys are both researching heavily, is that data now is going to start to be evaluation discussion. Are we getting the data through the pipes, if you will, into the hands of the developers, into the apps, into the decision-making process in the value chains that are being reconstructed. This is a top conversation that not a lot of people are laying out there with best practices. Your thoughts? >> Well I think the first thing, then I'll start, like the first thing is that that's probably my biggest thing on Informatica here, is that they need to be more of a beacon about what is the new data management. It's more than just the combination of tools. It's more than just getting data out of applications, and getting data out of databases, and freeing it up so they can be applied. The notion of data management is evolving rapidly, and businesses are trying to as they try to use data as an asset is going to require some significant changes to how we think about-- >> Do you think they could put that stake in the ground right now and owned that right now? >> I think somebody has to. I think they need to take a crack at it. >> I think they're weak on the value. I think they're weak on, you know what happens, I always have this idea that you see their layer cakes, and the things that go from left to right but on the right-hand side of the diagram, there are no people right. What happens when you implement all this? How do people use this okay? That's true of everybody in this industry, not just Informatica. So that's one weakness. Last year when I was here, I thought they had a real weakness in governance but with the dike who acts on acquisitions, I think they made a giant step towards that. They've got a lot, they've got a lot of the piece, parts, and then putting them together but I don't think they're addressing what happens next. I call it the Jordan River problem. We wandered around for 40 years. We got to the Jordan River. We can't get across the damn river right. >> Is it a river or lake or an ocean? I mean it's a data river. It's something happening. It's a lake or something. >> I think that's where they are because it's a whole different discipline. >> But is that on Informatica? I mean they're now a smaller company or is that an industry issue? >> No, it's an industry issue but companies like Informatica, if they really want to be a leader as he flings his grad classes around, that's all passionate I have about this. If they want to be a leader or the leader, they have to put a stake in the ground don't they? >> I believe so. >> Okay so what about positives? Neil what's your thoughts on how are they well-positioned in your mind? >> Well, putting together all these different pieces so that they operate together is phenomenal. Moving to, I still don't understand how enterprise software companies move to a subscription model smoothly. That's got to be a real headache but they're moving to that. They've adopted the cloud. They still have the data integration. I mean that's their keystone. It works beautifully, it gets better every year, and that's what attracts people to them. So I think these are all good things. >> And the good news is they're private so they can do that subscription, kind of hide the ball a little bit then come out. But they're not doing bad. I mean they have a spring to their step. >> Here's, I think Neil's absolutely right. Here's what I would add to it. They're executing, they have demonstrated over the past couple of years, certainly that we've been here, and listening to them, they made promises, they've delivered. They've made new promises, they've delivered. Some of these promises have been complex. Some of them have been extremely hard. They've still delivered. That is a real important piece of the story because this notion of data management is changing. Developers are going to want to work with companies that have competent management, that deliver on the promises they're making and Informatica is proving that they're up to that progress. >> I think, I think-- >> Another thing John, they have brilliant people. Everybody I've met here from Informatica is really special. I mean you know maybe they kept the clunkers in the closet somewhere but they've got brilliant motivated people working here. >> John: You've got a ton of experience. >> Peter: We're passionate about this stuff. >> They've got a lot of experience too, and they brought in some new guns the product side we're going to admit has a fantastic product executive and he obviously has that background but I want to shift now to the end user now. They're now living in the world of massive business transformation. Yeah, digital transformation, rah-rah. It's kind of overhyped but what that translates to is business transformation. That's the conversation on all CXOs. >> Peter: Business transformation around data. >> Around data so I want to get your thoughts now vis-a-vis that the customers perspective. I'm looking at Informatica. How do I feel about them? >> Well before I march off this mortal coil, this is what I want to happen. I want to say look computer I want to put together a new pricing model all right. Here are a couple variables I'm interested in. One of my competitors just issued a press release with some new pricing data. Go get that and come back to me with some data. Recommend some data I need to build a model to do this. >> Peter: Give me some updates. >> Yeah. >> That's CLAIRE, I mean that's something to talk about doing some automation machine learning. Peter, do you think that's the nirvana, that is a nice position to be? It's like hey Alexa, play some music, and you know they play a genre for you. So I mean give me some data, pricing data. >> So let's think about the elements that are going to be important as we think about this new notion of data management. Again, I don't think Informatica is too far from this. A new notion of data management suggests number one, that if your business is going to use data differently, you have to introduce some notion of some concept of design. How do I design business around data, and how do I design data around business needs? Part of that problem is going to be being able to go out and capture inventory catalog metadata. No question about it. You're going to need the next generation of data management. It's going to be very metadata focused. Secondly, you need a lot of the tooling that's capable of doing the transformation and creating derivative value out of data. That's something else that they have. The third one and this is a really, really important piece, and we talked about it for example with (faint) and a couple of other people. Data has to move but it has to move not just based on point to point interfaces that are programmed and built but based on patterns of utilization, and in a way that the system recognizes that. That is going to be crucially important, that whole notion of data that's moving in response to what the business needs, and not what the people recognize and do. >> Okay, so that reminds me. I was speaking to someone who is part of their security stuff right and I said well have you considered how data security could benefit analyst as opposed to keeping the company out of trouble? >> Peter: Business analysts? >> Yeah, just couldn't answer the question right. Because I said, so tell me how this works, and she said "Well, if someone has a pattern "of how they work with data, "if suddenly they work out in that pattern, "it's going to send off a signal." I said what's a signal? Are they going to get a skull and crossbones like oh you can't have this data. >> It sends off a policy flag. Okay, hey you're out of your swim lane. It's like get back into your jail cell. I mean it's restrictive, Absolutely restrictive. >> But think about it this way. Don't you want your analysts to be thinking out of the box? Which means on a regular basis, they would be requesting data they don't normally want. >> Here's what I like about Informatica from my perspective. Again, you guys are in under the hood in a deeper way but from my perspective is that what they're doing with the horizontally scalable is interesting, and this is interesting on the metadata side, you mentioned that with SPAN, Google SPAN are now available. They're in Amazon. If they can somehow create that addressable data sets that could be horizontally scalable and freely available, I think that is a winning strategy because most of the vendors in the data take a siloed stack approach. Okay here's our stack, you own it. So I think they're on this genius play of okay we can get this horizontal layer, that is now the lock spec, because now I mean I'm agnostic on cloud. So to me I think that directionally is correct. Where that is when the rubber meets the road, is a whole another story. So your thoughts on that. >> It's very exciting, I hope they pull it off. >> Yeah, I think it's very exciting. So if you think about it-- >> How do they pull it off? >> Let's well, so there's, let's-- >> We're not being shot by the other income with bigger guns. >> Let's think about it a couple of different ways of thinking about this right. On the one hand, you have new ways of thinking about how data is going to be spread in a multi or a hybrid cloud world. So that's happening. Secondly, we're thinking about data control, and a data control plane above that, and they're a bunch of companies that are talking about how you bring control across all these different multi cloud instances. On top of that, now we're talking about some of the analytics and how data gets huge from a metadata standpoint. So this is extremely relevant to where the industry is going to be in five years. Somebody is going to get there. It's best to look for the folks who are skating to that puck. Informatica seems to be skating to that puck. >> All right, I want to ask you guys a question. I want you to tell me if I'm smoking crack or not. When I say this the whole goal of getting data from any database at any given time in less than 100 milliseconds, no matter where it came from, when it came from, IOT included all the stuff that's coming in, I'm an app developer, I want data programmability. Meaning I have an app, and I'm doing some some cognitive, cognition thing and all sudden, Neil you bought something at Nordstrom's from three years ago. It's some database. Yeah, that means let's think about the logic on that query. But that data could be cross connected with other relevant data, your Twitter stuff or whatever you're doing, and pull together, provide some insight for you. Now that sounds like I'm smoking crack to pull that off. Is that possible? Can it be done in the kind of low latency mechanism? >> You know it can be done but I don't think we know enough about the data. There are four types of metadata still leave out deep semantic information. I'm hoping they're going to work on that. I mean I was in here 10 years ago pitching ontologies, and they threw me out. (laughs) But I think that the four types of-- (fast crosstalk) Yeah, I think the four types of metadata are great but they're still generating it mechanically from datasets as opposed to some knowledge about what the data really means. To do what you want to do, I think you need some kind of semantic metadata. >> I agree with that, and you also need semantic information about the underlying network as well. So the idea of a semantic-- >> So a lot more work to do to make that happen. >> A lot more work. So final question-- >> It's probably not going to be 100 milliseconds, 140, 150, maybe 200. >> Yeah, well I mean anything, just getting the data would be a win. Okay final question this is kind of more on the stuff we were talking about in leading to the intro of the work you guys are doing. The valuation concept of data, I mean I say valuation, I could mean financial valuation. How valuable a firm is? Or what is our CFO goes, where's our assets, where's our data assets? So there's a combination of data hygiene, and also in heart surgery, right, if it's the heartbeat of your organization, who the hell's the surgeon? Who's the doctor? When do you do CPR? Who does the hygiene? Who does the amputation? I mean who does, I mean this is like, I mean this is a data nightmare of a reconstruction of a company. The nature of the firm is completely upside down when you start thinking data. Just your reaction to that concept. >> Well, they have a very loyal customer base. So I think that they can get out with this before it's completely cooked, and have some success. Maybe I'm being optimistic. I don't know but I think-- >> John: Valuation of data. >> I think that there is a way of thinking about it is not to value data in a narrow sense but to think about data, what we're calling data dynamics. The idea that data's value is founded in its use. It's not something that has value when it's just sitting there and not being used. >> It's, yeah, it's like that old saw. I don't know how to define pornography but I know it when I see it, right. That was a Supreme Court Justice. I didn't say that. >> John: Looks like teenage sex. The hero things they're having it, pull the notch. >> This goes back to the notion of data management. It's how am I going to use data? How am I going to get value out of data through its use? That suggests a whole different set of principles and practices that are quite different from how we normally value assets. >> Okay, tomorrow we got the top execs coming in. We got the CMO, we got the CEO, we got the EVP of Products. What should we be asking them tomorrow? What should I be opening up the kimono and digging into them on? >> I'll ask him what the roadmap is in terms of getting this implemented in their best customers. Not the software development roadmap. So tell us. (fast crosstalk) How this is going to roll out for you? >> You're going to be here up 'til two o'clock. We'll be there, what are you going to be looking at? >> I'll look for two things. One is I would continuously push on the execution. Are they really executing as reliably as we think they are? 'Cause they're making some big promises this year too. The second one I'd look at is again, that beacon, that touchstone, what is this new data management? What are you really going to be leading? >> I'm still blown away by the conferences I go to, everyone's like what is a new way, new modern era's evolving and it's transforming. We're number one in five magic quadrants. I mean how can you get magic quadrants as the scoreboard if you go into a new definition? So again, our metric KPI on that is customers. What is your customer traction? Show me the proof points. I don't care what magic quadrant you're in. That's an old metric. That's siloed based. That's not reality based, in my opinion. So we will drill them on customers. To me that's the scoreboard. Okay it's the CUBE breaking down day one wrap up here at Informatica World. This is CUBE coverage. I'm John Furrier, Peter Burris, and special guest new Wikibon analyst, Neil Raden, covering the value of data and analytics. See you tomorrow, stay with us for more continuing coverage tomorrow for full day. Be right back.

live from San Francisco California it's the queue at vmworld 2014 brought to you by vmware cisco EMC HP and nutanix now here are your hosts John furrier and Stu minimum okay welcome back around here live in San Francisco for VMware 2014 this is our fifth year with the cube extracting the city from the noise at vmworld always a pleasure and we have the chief marketing officer Robin Matlock here inside the queue of my Coast stupid minute for this segment Robin welcome back to the cube thank you great keynote this morning you opened it up in front of a packed house for Pat Gelsinger and delivered an amazing keynote before we get some icky knows what some of the stats with the show here obviously vmworld it just keeps getting bigger and bigger and bigger every year well you know it's amazing the energy is fantastic here this year we're going strong we have well over twenty two thousand attendees the solutions exchange is packed there's about 250 companies that are they're exhibiting we have all kinds of breakout sessions and content I mean if you just walk around here the energy is just really thrive and the theme is no limit so I got to get some a back story on the theme I'll see no limits breaking through this is the transformation market the sign is just break it was a quick taste of wow how this all came together yeah what's the meaning behind the pictures are they're all on the hall you know it's really fun the themes that every year actually put just tremendous effort into them they can really be stressful but at the end when you land or right when it feels so good this whole notion of concrete you know in breaking through and that there's something on the other side that is truly infinite for us that just really spoke to our business it spoke to what our customers are going through and it truly spoke to the potential of this incredible you know this incredible industry you know i was when i think of the No Limits I think about the space jump the Red Bull I think about some of the things with it within the cloud that developers are doing you know Pat mentioned uber they have no asses of mass evaluation of hurts and to cumbies combined this is the kind of dream that entrepreneurs think about is like this is this inflection point stuff right so is that was that some of the vibe you guys were thinking absolutely and I think when we look at where we are in our journey relative to cloud relative to a software-defined world we're really passionate that you know the customers and the attendees of this conference are very well positioned to truly break through some of the silos that have been holding us back for a long time and we are at Crossroads um you know we believe vehemently that the data center is destined to be software-defined and that many of these attendees are well positioned to take us on that journey so I got to ask you because I see you're involved in the brain trust and all the formulation of the strategy the company and out of how to communicate it's always a challenge when it's like a moving train of innovation but you have some new things going on this year first of all nothing new on strategy it's the same marching orders with with Pats cadence hybrid cloud you know March to that cadence ops ii server defined data center but now AirWatch comes on over the top how did that affect things for you or did it it's just more of more the same so actually they bring in there some of that security and the apps piece of the business did that change some of the thinking and all I know it's an interesting question but I think at the end of the day the three strategic priorities for VMware have been very consistent now for multiple years you know largely under Pat's leadership it's about a software-defined world that's the software-defined data center it's about extending that to the hybrid cloud and it's always been about end-user computing I think the air watch acquisition just took it up a couple notches really the world of mobility we're big advocates and believers that the mobile workforce is exploding but there's a really strong connective value between what's happening at the infrastructure layer and what we can do to enable that mobile workforce so I think it was very consistent with the strategy but I do think the air guac acquisition is changing the game it's certainly producing Pat was giving us a little taste on the cube talk about the steams of the show today we had Pat had bill father's Carl up sure do a little Q&A a little little cube action almost on stage with Bill and what's what's tomorrow did you guys bring it up by thieves share with the folks out here Shey lay the land here what's the what's the contracts for tomorrow so today what we try to do is really telex the expanse of entire story what's going on holistically and you know the Karl part of it was a lot about getting our customers to really talk about what's working for them I think that's really important because we laid out a vision for VMware um you know a couple years ago and it's important to make that tangible and real and I hope the customers were able to bring that to life for people tomorrow is all about the technical under the hood let's get you know inside and really understand how the technologies are delivering against that vision and we're going to go through the whole thing it's going to cover the infrastructure it's going to talk about the hybrid cloud and we're going to talk a lot about mobility well the geeks want under the hood I mean it gets a gig show the end of the day it's very content rich at vmworld as we know it super busy a lot of parties going off as Deb going on certainly the business transactions are happening but it's still a geek show you guys have preserved that here right you know if we ask ourselves every year you know how how and should or shouldn't we evolve vmworld and i tell you we're really resolved at the end of the day this is largely a practitioner show they come for technological information education certifications and we have no desire to take a square pose and put in a round hole I mean it works so well for this audience let's just give this crowd what they need and I want to do more of it year after year yeah and we can always tell how good the conferences are in terms of content based upon how much Twitter activity there is in terms of like if people are just talking a lot on Twitter and not say anything that means it's kind of a boring show when there's not a lot of Twitter activity mostly it's text sessions people have too busy running around between between the events I mean are you guys seeing the sessions packet but we haven't had a chance to go out there what's happening yeah well to be really honest I haven't at a moment to scan too much but from what I'm hearing they are overflowing and frankly they were booked you know even before we showed up today because we do give people the schedule builder and a chance to book their sessions so I know that they are all full we're doing repeats we're trying to get you know more breakouts so people can deal with Wednesday and Thursday as things settle down but all the reports I'm getting so far is that we are pretty much over sold and oversubscribed yeah so buds do you Robin I was just gonna say you know is my fifth year now coming to vmworld it's all we impressive just the passion of the people in the virtualization community it's such a good community everybody gives back I really like what you guys did with the charity event that's going I mean what's a destination give by 25,000 with 250 oh not twenty five thousand two hundred and hundred and fifty thousand dollars that that's fantastic you know I got to talk to the hands-on lab guys today and things were running so smooth and so many people do it because as John said the geeks really love to geek out here I noticed it looked like on the badge it had you know the show spread out beyond just the north south and the West you brought the analysts kind of off to off to a hotel because they don't need to be in the center of all the geeks and everything the show floor is cranking as usual so you know it sounds like you still have the core and just pieces add on to it yeah i mean the core of the program if you were to look at breakout sessions keynotes labs that's going to stay right here in moscone but the reality is we're bursting out of the scenes and we love San Francisco we loved the venue but we have to take advantage of all the hotel space around so we got things at the w we got things at the westin we got things at the marriott we got things at the Intercontinental I mean we're or everywhere frankly but you're right we are having to kind of spread out a little bit so I got to ask you about the 10-year anniversary because that was a pretty epic event and you mentioned you made a comment on stage where'd that world go and i love the Golden Gate Bridge metaphor you put together what's changed for you over the past year it seems to be like it seems like seven years ago internet years it seems like a decade ago almost from last year yeah a lots changed and you share your perspective yeah I think a lot has changed I think on though um to be almost all for the good in my view I think you know VMware had built such a business on kind of one core platform which was compute virtualization and over the last several years we've really broadened our wings right and we are now dealing with networking and storage and security and automation and cloud and mobility and I think the diversity that that brings um from a customer perspective from an ecosystem perspective from our routes to market perspective I mean certainly it is definitely a charge because there's just so much tremendous diversity it also means we got a lot of things to cover so you know I think with that comes a responsibility to make sure our customers can understand all these different diverse you know offerings what's your objective for the show what's your preferred outcomes you can look back and just fast forward to thursday evening friday morning you know you're in a hot tub relaxing maybe it's saturday or monday morning what do you want to have happen what's your ideal outcome for vmworld beyond the fact that i like my feet attached to my body because right now i'm afraid they might fall off but let's say personal attributes aside you know i really hope that these attendees you know 22,000 plus people get on those airplanes fly home and feel like they had one of the most invigorating educational inspirational experiences professionally that they're going to have all year I hope that they got to the content that was relevant for them that they were able to navigate and you know really spend time in the areas of focus for them and I hope that people met dozens and dozens of new people that will only help them broaden their career so I have this little prop I brought because I was attended the VIP event you guys had an amazing event mark injuries since the NBC was broadcasting there Joe Tucci was there and then you know opening up your new facility which could have been around for a while so we've got some new new areas got these hot pens there so I'm going to ask you about the culture and the brand future brand for vmware I mean it's an amazing campus eco-friendly beautiful design high quality is this the brand of VMware that you seeing vision for me and you what's your vision for the brand I mean it's evolving in in real time for the company it is evolving but at the same time I think our brand and what we stand for as a company is also very stable it's great that you came to that event and saw the final unveiling of the last building as we finished it up and certainly it's a beautiful campus and it's green you know it's very you know natural woods and doing all kinds of things to protect the environment I think at the core of VMware there's you know five key values and those values are sustaining the test of time you know it's about innovation it's about community it's about people it's about integrity and it's about our customers and I think really no matter what products and services and solutions we wrap around our company I think we still stand for the same core values and I hope that never changes so I got to ask you out the community I think it's one of those things and you know something to pat about how doctor is implemented community aspect of the open source of their product and made them success you guys have had great community over the years really part of the backbone of vmware versus other companies some don't even have a heartbeat to a community you guys have a great thriving ecosystem how do you maintain that as we get more connected with the crowdsourcing with the Twitter expansion and all the people talking and it's not just forums anymore it's and more it's it's it's a virtual event every day it's like vmworld every day out there how do you handle that what's your vision and how you going to get your arms around that going forward well it's yeah I think it's really critical first of all just like anything whether you're talking about technologies you're talking about engaging with customers you have to evolve you can't use the same techniques that you use last year really to propel you next year so I think it's all about making sure you understand how our customers choosing to engage and then embrace that for us our social channels are really important our communities are really important and we're all about enabling facilitation and engagement and I think we're really that's kind of philosophically how we go about our whole social strategy it's all about enablement so that's a personal question for you to you always loved your eye for you know detail remember the first VMware we did you had pointed out the vmware stickers which ended up being perfect camera location ibly I like her I like this Robin woman she's awesome but what are you excited about now I mean what are you personally motivated upon right now what gets you really excited about the tech industry about what you what you're involved in what's the what's the one thing that get you so excited you know frankly I'm extremely proud to be the CMO of VMware I think there was a great company and I think we're part of something truly meaningful I think there was a time when maybe we weren't going to be as relevant we and by we I don't mean to see him or I mean this this whole thing that maybe we weren't going to be as relevant in the next decade but we collectively as a mystery are making bold moves we're doubling down on software we're pushing the boundaries of the data center we're getting out beyond compute we're going to storage or going to networking we're looking at security we're layering in automation and I think we are really securing our future as an industry that we are relevant and we need a seat at the table a strategic seat at the table and I'm thrilled to be a part and you certainly the global footprint the virtualization has been a great part of enabling that that mindset great to have you on the cube any other tidbits about the show you'd like to share the folks you know I think the main thing is just get involved and try some things that are different push your own personal boundaries explore there's so much content there's so many networking opportunities there's breakouts and I think definitely sampling a little bit of everything and making sure that you go home exhausted and then I'll be happy but certainly is exhausting show but Pat brought up the whole brave concept that's really about bold moves writing that's about that's kind of the whole theme here right yeah I think you know the notion of bravery is in the sense that given that things are changing so rapidly and the world is so dynamic and fluid as a business climate it's going to take some calculated risk you're going to have to really decide where are you partnering where are you betting what kind of steps are you going to take and I think action is key and the one thing it probably isn't going to work is status quo Robin Matlock the chief marketing officer for VMware keynote speech this morning set the table for Pat Gelsinger great jobs at the big picture laid out everything out the holistic vision of VMware continues to thrive thanks for coming down the cube always great to have you it's the Cubist retin from the noise we'll be right back with our next guest after the short break great thanks John you