 From Sand Hill Road to the heart of Silicon Valley, it's theCUBE, presenting the People First Network, insights from entrepreneurs and tech leaders. Hello, and welcome to this special CUBE conversation. We're here in Sand Hill Road at Mayfield Fund. This is theCUBE co-creation of the People First Network content series. I'm John Furrier, host of theCUBE. Our next guest is Yubi Kuchar, who's the data-centric digital transformation strategist at GameStop, a variety of stints in the industry going and cutting edge problems around data, Washington Post, ComScore, among others, gets your own practice. From Washington DC, thanks for joining us. Thank you, thanks for hosting me. This is an awesome conversation. We just talked before, we came on camera about data and the roles you've had over your career have been very interesting, and this seems to be the theme for some of the innovators that I've been interviewing and running the People First is, they see an advantage with technology and they help companies, they grow companies and they assist. You did a lot of different things, most notably that I recognized was the Washington Post, which is on the mainstream conversations now as a rebooted media company with a storied historic experience from the Graham family. Jeff Bezos purchased them for a song, with my opinion, and now growing still, with a monetization, with subscriber base growing. I think they're number one in subscribers. I don't believe, I believe so. Interesting time for media and data. You've been there for how many years were you at Washington Post? I spent about 13 years in the corporate office. So the Washington Post company was a conglomerate, it owned a lot of businesses, not very well known to have owned Kaplan, the education company. We owned Slate, we owned Newsweek, we owned TV stations and now they're into buying all kinds of stuff. So I was involved with a lot of varied businesses, but obviously we were in the same building with the Washington Post and I had front row seat to see the digital transformation of the media industry and how we responded. Yeah, I want to dig into that because I think that illustrates kind of what's happening now, we're seeing with cloud computing, obviously cloud 1.0, the rise of Amazon, public cloud. Clearly, Czech done that, a lot of companies startups go there, why would you provision a data center if you're a startup, you're crazy. But at some point you're going to have a data center now hybrid, clouds important. DevOps, the application development market, building your own stack is shifting now, it seems like the old days, but upside down, it's flipped around where applications are in charge, data is critical for the application, infrastructure is now elastic. Unlike the old days of here's your infrastructure, you're limited to what you could run on it based on the infrastructure. Right. What's your thoughts on that? My thoughts are that I'm a very, as my title suggests, data centric person. So I think about everything data first. We were in a time when cloud first is becoming old and we are now moving into data first because what's happening in the marketplace is the ability, the capability of data analytics has reached a point where prediction in any aspect of a business has become really inexpensive. So empowering employees with prediction machines, whether you call them bots or you call them analytics or you call them machine learning or AI has become really inexpensive. And so I'm thinking more of applications which are built data out instead of data in, which is you build process and capture data and then you decide, oh, maybe I should build some reporting, that's what we used to do. Now you need to start with what's the data I have got? What's the data I need? What's the data I can get? We were just talking about everybody needs the data monetization strategy. People don't realize how much asset is sitting in their data and where to monetize it and how to use it. It's interesting, you know, I mean, I got my computer science degree in the 80s and one of the tracks I got a degree in was database. And let's just say that my main one was operating system. Database was kind of a throwaway at that time. It wasn't considered a big field. Database wasn't sexy at all, it was like database. Now, if you're a database, if you're a data guru, you're a rockstar. The world has changed, but also databases are changing. It used to be one centralized database rules the world. Oracle made a lot of money with that, bought all their competitors. Now you have open source came into the realm. So the world of data is also limited by where the data is stored, how the data is retrieved, how the data moves around the network. This is a new dynamic. How do you look at that? Because again, lagging in business is a lot to do with the data. Whether it's in an application, that's one thing, but also having data available, not necessarily in real time, but if I'm going to work on something, I want the data set handy, which means I can download it or maybe get real time. What's your thoughts on data as an element in all that moving around? So I think what you're talking about is still data analytics. How do I get insights about my business? How do I make decisions using data in a better way? What flexibility do I need? So you talk about open source, you think about MongoDB and those kind of databases. They give you a lot of flexibility. You can develop interesting insights very quickly, but I think that is still very much thinking about data in an old school kind of way. I think what's happening now is we're teaching algorithms with data. So data is actually the software, right? So you get an open source algorithm in Google and everybody else is happy to open source their algorithms if they're all available for free. But the asset is now the data, which means how you train your algorithm with your data and then now moving towards deploying it on the edge, right? Which is you take an algorithm, you train it, then you deploy it on the edge in an IOT kind of environment, and now you're doing decision-making, whether it's self-driving cars, and those are great examples, but I think it's going down into very interesting spaces in the enterprise, which is so we have to all think about software differently because actually data is the software. That's an interesting take on it. I love that. I mean, I wrote a blog post in 2007 when we first started playing with the, you're looking at the network effects on social media and those platforms was, I wrote a post was kind of data's the new development kit. Development kit was what people did back then. They had a development kit and they would download stuff and then code, but the idea was is that data has to be part of the runtime and the compilation of as software acts, data needs to be, you know, resident, not just here's a database access it, pull it out, use it, present it, where data is much more of a key ingredient into the development. Is that kind of what you're getting at? Yes, and I think we're moving from the age of arithmetic based machines, which is, you know, we put arithmetic onto chips and we then made general purpose chips which were used to solve a huge amount of problems in the world. We're talking about now prediction machines on a chip. So you think about algorithms that are trained using data which are going to be available on chips. And now you can do very interesting algorithmic work right on the edge devices. And so I think a lot of businesses and I've seen that recently at GameStop, I think business leaders have a hard time understanding the change because we have moved from process centric, process automation, how can I do it better? How can I be more productive? How can I make better decisions? We have trained our business partners on that kind of thinking. And now we're starting to say, no, no, no, we've got something that's going to help you make those decisions. It's interesting, you mentioned GameStop, obviously well-known, you know, Mike Sons are all gamers. I used to be a gamer back before I had kids, but can't keep up anymore. You had to be on that for so long. But GameStop was a retail giant in gaming, okay, when they had physical displays. But now with online, they're under pressure. And I had interviewed, again, at an Amazon event, Best Buy, CIO, and he says, we don't compete with price anymore. They want to buy from Amazon, no problem, but our store traffic is off the charts. We personalized 50,000 emails a day. So personalization became their strategy. It was a data strategy. This is a user experience, not a purchase decision. Is this how you guys are thinking about at GameStop? I think retail, if you look at the segment per se, personalization, Amazon obviously led the way, but it's obvious that personalization is key to attract the customer. If I don't know what games you play, or if I don't know what video you watched a little while ago about which game, then I'm not offering you the product that you're most prone or looking for or what you want to buy. And I think that's why personalization is key. I think that's the biggest thing. And data drives that. And data drives that. Data drives that, and for personalization, if you look at retail, there's customer information. You need to know the customer. You need to know, understand the customer preferences. But then there's the product, and you need to marry the two. And that's where personalization comes into play. So I'll get your thoughts. You have obviously a great perspective on how tech has been built working on some real cutting edge, clear view on what the future looks like. Totally agree with you, by the way, on the data. There's kind of an old guard, new guard, kind of two sides of the street. The winners and the losers behind it look at, I think, the old guard. If they don't innovate and become fresh and new and adopt the modern things that need to attract the new expectations and new experiences from their customers are going to die. That being said, what is the success formula? Because some people might say, hey, I'm data driven. I'm doing it. Look at me. I'm data. Well, not really. How do you tell if someone's really data driven or data centric? What's the difference? Is there a tell sign? I think when you say the old guard, right? You're talking about companies that have large assets that have been very successful in a business model that maybe they even innovated. Like GameStop came up with pre-owned games. And for the longest of times, we've made a huge amount of revenue and profit from that segment of our business. So yes, that's becoming old now. And but I think the most important thing for large enterprises at least to battle the new upstarts is to develop strategies which are leveraging the new technologies but are building on their existing capability. And that's what I drive at GameStop. And also the startups too, that we're here at Adventure Capital from where at Mayfield Fund doing this program, startups want to come in and take a big market down or come in on a narrow entry and get a position and then eat away at an incumbent. They could do it fast if they have it if they're data centric. And I think its speed is what you're talking about. I think the biggest challenge large companies have is an ability to play the field at the speed of the new upstarts and the firms that Mayfield and others are investing in. That's a big challenge because you see this, you see an opportunity but I saw that at the Washington Post. You know, everybody went to meetings and said yes, we need to be digital. And but you know, they went back to their desk and they had to print a paper, right? And so yes, so we'll be digital tomorrow, right? And that's very hard because finally the paper had to come out. Let's take us through the journey. You were the CTO, VP of Technology, Graham Holdings, Washington Post, they sold at the Jeff Bezos, well-documented historic moment. But what a storied company, Washington Post, local paper, was the movie about it. You know, all the historic things they've done from a reporting and journalism standpoint. We admire that. Then, you know, they hit the media business starts changing, gets bloated, not making any money, online classifiers are dying, search engine marketing's growing, they have to adjust. You were there, what was the big, take us through that journey. I think that the transformation was occurring really fast. The new opportunities were coming up fast. We were one of the first companies to set up a website. But we were not allowed to use the brand on the website because, you know, there was a lot of concern in the newsroom that we are going to use or put the brand on this misunderstood, nearly misunderstood opportunity. So I think it started there. This is classic old guard mentality. Yes, and it continued down because, you know, people had seen downturns. It's not like media companies hadn't been through downturns. They had because the market crashes and we have a recession and, you know, there's a downturn. But it always came back, right? But this was a wave. I mean, the thing is downturns are economic and this business that happens there, advertisers, consumption changes. This was a shift in their user base based upon a technology wave. And they didn't see it coming. And they hadn't ever experienced it. So they were experiencing it as it was happening. And I think it's very hard to respond to a transformation of that kind in a very old story. As a leader, how did you handle that? Tips and example of what you did, how you make your mark, how do you get them to move? What were some of the things that were notable moments? I think the main thing that happened there was that we spun out Washington Post.com. So it became an independent business. It was actually running across the river. It moved out of the corporate offices. It went to a separate place. And they were given a- Like Steve Jobs and the Macintosh team, they go in a separate building. And we were given, you know, I was the CTO of the dot-com for some time while we were turning over our CTO there. And we were given a lot of flexibility. We were not held accountable to the same level. We used the, obviously, we used the news. You're running a fast boost. Yes, we had a lot of flexibility and we were doing things differently. We were giving away the content in some way. On the online side, there was no paywall. We started with a paywall, but advertising kind of was so much more lucrative in the beginning that the paywall was shut down. And so I think we experimented a lot. And I think where we missed and a lot of large companies miss is that you need to leave your existing business behind and scale your new business. And I think that's very hard to do, which is, okay, we're going to, it's happening at GameStop. You know, we're no longer completely have a control of the market where we're the primary source of where you talked about your kids, where they go to get their games. They can get the games online. And I think that's a shift. People are afraid to let go because they're so used to operating their business. And now it has to pivot to a new operating model and grow two different dynamics, growth, operation, operating and growing. Not all managers have that growth mindset. And I think there's also an experienced thing. So most people who are in these businesses who've been running these businesses very successfully have not been watching what's happening in technology. And so the technology team comes out and says, look, let me show you what we can do. I think there has to be this open and very candid discussion around how we are going to transform this technology. How would you talk about your peers out there? Your peers and other CIOs, even CSIS on the security side have been dealing with the same suppliers over again. In fact, on the security side, the supplier base is getting larger. There's more tools coming out. Who wants another tool? So platform tool, these are big decisions being made around companies. If you want to be data-centric, you want to be a data-centric model, you got to understand platforms, not just buying tools. If you buy a hammer, I think we'll have a nail. And then you have so many hammers, you want versions. So platform discussions come in. What's your thoughts on this? Because this is the cutting edge topic we've been talking about with a lot of senior engineering leaders around platform 2.0 kind of thing, not like a classic platform. Right. I think that each organization has to leverage or build their own stack on top of commodity platforms. You talked about AWS or Azure or whatever cloud you use and you take all their platform capability and the services that they offer. But then on top of that, you structure your own platform with your vertical capabilities which become your differentiators which is what you take to market. You enable those for all your product lines so that now you are building capability which is a layer on top of and the commodity platforms will continue to bite into your platform because they will start offering capabilities that earlier. I remember, I started at this company called Brassering Recruitment Automation, one of the first software as a service companies. And we bought a little company and the CTO there had built a web server. You know, it was called, it was his name. It was called Barrett's Engine, right? And so- It's probably a patchy with something on the code around it. So, you know, in those days we used to build our own web servers. But now today you can't even find an engineer who would build a web server. I mean the LAMP stack and these notions of, you know just simple web 1.0, building blocks of change. We've been calling it Cloud 2.0. I want to get your thoughts on this because one of the things I've been riffing on lately is this, I remember Mark Andreessen wrote the famous article in the Wall Street Journal, software's eating the world, which I agree with in general, no debate there. But also the 10X engineer, you go into any forum online, talk about 10X engineers, you get five different opinions, meaning a 10X engineer is an engineer who can do 10 times more work than an old school, old classical engineer. I bring this up because the notion of full stack developer used to be a real premium. But what you're talking about here with Cloud is a horizontally scalable commodity layer with differentiation at the application level. That's not full stack, that's half stack. So the world's kind of changing. If you're going to be data centric, the control plane is data. The software that's domain specific is on top. That's what you're essentially laying out. That's what I'm talking about. But I think that also what I'm beginning to find and we've been working on a couple of projects is you put the data scientists in the same room with engineers who write code, write software, and it's fascinating to see them communicate and collaborate. They do not talk the same language at all. What's it like? Give us some mental picture. Right. So data scientists. Are they throwing rocks at each other? Well, nearly because the data scientists come from the math side of the house. They're very math oriented. They're very algorithm oriented. Mathematical algorithms. Whereas software engineers are much more logic oriented and they're thinking about scalability and a whole lot of other things. And if you think about data scientists develops an algorithm, it rarely scales. Right? You have to actually then hand it to an engineer to rewrite it in a scalable format. I want to ask you a question on that. This is why I got you an awesome guest. Thanks for your insights here. I'm going to take a detour on the machine learning. Machine learning really is what AI is about. AI is really nothing more than just, they love AI. I guess people excited about computer science which is great. I mean, my kids talk about AI. They don't talk about IoT, which is good that AI does that. But it's really machine learning. So there's two schools of thought of machine. I call it the Berkeley School on one end, not Berkeley per se, but Berkeley talks about math. Machine learning, math, math, math. And then you have other schools of thought that are on cognition. That machine learning should be more cognitive, less math driven, spectrum of full math, full cognition, and everything in between. What's your thoughts on the relationship between math and cognition? Yeah, so I, you know, it's interesting. I, you know, you get gray hair and you kind of move up the stack. And I'm much more business focused, right? These are tools. You know, you can get passionate about either school of thought, but I think that what that does is you lose sight of what the business needs. And I think it's most important to start with, what are we here trying to do? And what is the best tool? What is the approach that we should utilize to meet that need? Like the other day, we were looking at product data from GameStop. And we know that the quality of data should be better. But we found a simple algorithm that we could utilize to create product affinity. Now, whether it's cognition or math, it doesn't matter. The outcome is the outcome. The outcome is the outcome. They're not usually exclusive. I mean, it's a good conversation to debate, but it really gets to your point of does it really matter as long as it's accurate and the data drives that? And this is where I think data is interesting. If you look at folks who are thinking about data and back to the cloud as an example, it's only good as what you can get access to. And cybersecurity, the transparency issue around sharing data becomes a big thing. Having access to the data is super important. How do you view that as CIOs and start to think about they're re-architecting their organizations for these digital transformations? Is there a school of thought there? Yes, so I think data is now getting consolidated. For the longest time, we were building data warehouses, departmental data warehouses. You can go do your own analytics and just take your data and add whatever else you want to do. And so the part of data that's interesting to you becomes much more clean, much more reliable, but the rest, you don't care much about. I think given the new technologies that are available and the opportunity of the data, data is coming back together and it's being put into a single place. You know what I mean? Well, that's certainly a honeypot for a hacker. But we'll get to that in a second. If you and I were doing a startup, we said, hey, let's, you know, we got a great idea. We're going to build something. How would we want to think about the data in terms of having data be a competitive advantage, being native into the architecture of the system, obviously we'd use cloud unless we need some scale on premise for privacy reasons or whatever. But we would, you know, how would we go to market and we have an app, it's apps defined, great use case, but I want to have extensibility around the data. I don't want to foreclose any future options. How should we think about our data strategy? Yes, so there was a very interesting conversation I had just a month ago with a friend of mine who was working at a startup in New York and they're going to build a solution, take it to market and he said, I want to try it only in a small market and learn from it and he's going very old school focus groups, analytics, analysis and I sat down, we sat at Grand Central Station and we talked about how today he should be thinking about capturing the data and letting the data tell him what's working and what's not working instead of trying to find focus groups and find, you know, very small data points to make big decisions. He should actually utilize the target, the POC market to capture data and get ready for scale because if you want to go national after having run a test in part of New York, then you need to already have built the data capability to scale that business in today's... Is it a SaaS business? It's a service and... So instrument it, just watch the data. And yes, but he's not thinking like that because most business people are still thinking the old way and, you know, if you look at Uber and others, you know, they have gone global at such a rapid pace because they're very data centric, right? And they scale with data and they don't scale with just, let's go to that market and then let's try... Yeah, ship off and get the data, then think of it as part of the life cycle of development. Don't think of it as like the old school craft launch it and then see how it goes and watch it fail or succeed and know six months later what happened. And if you go data centric, then you can turn the R&D crank really fast, learn, test and learn, test and learn, test and learn at a very rapid pace, right? That changes the game. And I think people are beginning to realize that data needs to be thought about as the application and the service is being developed because the data will help scale the service really fast. Data comes into applications and love your outline of data is the new software. Right. It's better than the new oil, which has been said before. But data comes into the app. You also mentioned that the app throws off data. We know that humans have data exhausts we do all the time. Facebook made billions of dollars on our exhaust and our data. The role of data in and out of the application, the IO of the application is a new concept. You brought that up, I like that and I see that happening. How should we capture that data? This used to be log files, now you got observability. All kinds of new words kind of coming into this cloud equation. How should people think about this? I think that has to be part of the design of your applications, right? Because data is the application and you need to design the application with data in mind and that needs to be thought of upfront and not later. Yuvie, what's next for you? We're here in San Hill Road, VC firm. They're doing a lot of investments. They've got a great project with GameStop. You advising startups, what's going on in your world? Yes, so I'm totally focused as you probably are beginning to sense on the opportunity that data is enabling, especially in the enterprise. I'm very interested in helping business understand how to leverage data because this is another major shift that's occurring in the marketplace. Opportunities have opened up. Prediction is becoming cheap and at scale and I think any business runs on their capability to predict what is the shirt I should buy, how many I should buy, what color should I buy. I think data is going to drive that prediction at scale. This is a legit way that everyone should pay attention to. All businesses, not just one category. All businesses, everything because prediction is becoming cheap and automated and granular. That means you need to be able to not just, you need to empower your people with low level prediction that comes out of the machine. Data is the new software. Yvie, thanks so much for great insight. This is theCUBE conversation. I'm John Furrier here at San Hill Road, the Mayfield Fund for the People First Network series. Thanks for watching. Thank you.