 Live from San Jose in the heart of Silicon Valley, it's theCUBE, covering DataWorks Summit 2017, brought to you by Hortonworks. Hey, welcome back to theCUBE. We are live on day one of the DataWorks Summit in the heart of Silicon Valley. I'm Lisa Martin with my co-host Peter Burris. Not only is this day one of the DataWorks Summit, this is the day after the Golden State Warriors won the NBA Championship. Please welcome our next guest, the CTO of Dell AMC, Bill Schmarzo, and CUBE alumni, clearly sporting the pride. Did they win? I don't even remember. Are we breaking news? Bill, it's great to have you back on theCUBE. And Division Three All-American from what? College. Yeah, yeah, but then they still had the peach baskets. You make a pass, you have to climb up and splatter and pull it out of there. They're going rogue on me. They really slowed the game down a lot. All right, so, and before we started, they were analyzing the game. It was actually really interesting. But kick things off, Bill, as the volume and the variety and the velocity of data are changing, organizations know there's a tremendous amount of transformational value in this data. How is Dell AMC helping enterprises extract and maximize that as the economic value of data is changing? So, the thing that we find is most relevant is most of our customers don't give a hoot about the three V's of big data, especially in the business side. We like to jokingly say they care about the four M's of big data, make me more money. So, when you think about digital transformation and how am I taking an organization from where they are today to sort of embed digital capabilities around data and analytics, it's really around how do I make more money? You know, what processes can I eliminate or reduce? How do I improve my ability to market and reach customers? How do I, you know, all the things that are designed to drive value from a value perspective. Let's go back to, you know, Tom Peters kind of thinking, right, and I guess Michael Porter, right, his value creation processes. So, we find that when we have a conversation around the business, and what the business is trying to accomplish, that provides the framework around which to have this digital transformation conversation. So, well, Bill, it's interesting. The volume, velocity, variety, three V's really say something about the value of the infrastructure. So, you have to have infrastructure in place where you can get more volume, you can move faster, and you can handle more variety. But, fundamentally, it is still a statement about the underlying value of the infrastructure and the tooling associated with the data. True, but one of the things that changes is not all data's of equal value. Absolutely. Right, so what data, what technology is that, do I need to have Spark? Oh, I don't know, what are you trying to do? Right, do I need to have Kafka or Liota? Right, do I need to have these things? Well, if I don't know what I'm trying to do, then I don't have a way to value the data, and I don't have a way to figure out and prioritize my investment in the infrastructure. But that's what I want to come to, so that increasingly, what business executives, at least ones that we're talking to all the time, are, you know, make me more money, but it really is, what is the value of my data? And how do I start pricing data, and how do I start thinking about investing so that today's data can be valuable tomorrow, or the data that's not going to be valued tomorrow, I can find some other way to, you know, not spend money on it, et cetera. That's different from the variety of velocity, volume statement, which is all about the infrastructure, and what an IT guy might be worried about. So talk, I've done a lot of work on data value, you've done a lot of work on data value, we've coincided a couple times, let's pick that notion up of, you know, digital transformation is all about what you do with your data. So what are you seeing in your clients as they start thinking this through? Well, I think one of the first times that sort of the aha moment to me was when I had a conversation with you about Adam Smith, and the difference between value in exchange versus value in use. A lot of people when they think about monetization, how do I monetize my data, are thinking about value in exchange. What is my data worth to somebody else? Well, most people's data isn't worth anything to anybody else. And the way that you can really drive value is not data in exchange or value in exchange, but it's value in use. How am I using that data to make better decisions regarding customer acquisition and customer retention and predictive maintenance and quality of care and all the other oodles of decisions organizations are making, it's the valuation of that data comes from putting it into use to make better decisions. If I know then what decisions I'm trying to make, now I have a process, not only deciding what data is most valuable, but as you said earlier, what data is not important, but may have liability issues with it, right? Do I keep a data set around that might be valuable, but if it falls into the wrong hands through cyber security sort of things, do I actually open myself up to all kinds of liabilities? And so organizations are rushing this EVD conversation, not only from a data valuation perspective, but also from a risk perspective, because you got to balance those two aspects. But this is not a pure, this is not really doing an accounting in a traditional accounting sense. We're not doing a double entry bookkeeping with data. What we're really talking about is understand how your business used this data, number one, today. Understand how you think you want your business to be able to use data, to become a more digital corporation, and then understand how you go from point A to point B. Correct, yes. And in fact, the underlying premise behind driving economic value of data, people say data is the new oil, well that's a BF statement, because it really misses the point. The point is, imagine if you had a barrel of oil, a single barrel of oil that can be used across an infinite number of vehicles and it never depleted. That's what data is, right? Explain that. You're right, but explain it. So what it means is that data has, you can use data across an endless number of use cases. If you go out and get it. At the same time. At the same time, you pay for it once, you put it in the data lake once, and then I can use it for customer acquisition and retention and upsell and cross-sell and fraud and all these other use cases, right? So it never wears out, it never depletes. So I can use it. And what organizations struggle with, if you look at data from an accounting perspective, accounting tends to value assets based on what you paid for it. And how you can apply them uniquely to a particular activity. A machine can be applied to this activity and it's either that activity or that activity. A building can be applied to that activity or that activity. A person's time to that activity. It has a transactional limitation. Exactly, it's an or. It's a trans, yeah, so what happens now is instead of looking at it from an accounting perspective, let's look at it from an economics and a data science perspective. That is, what can I do with the data? What can I do as far as using that data to predict what's likely to happen, to prescribe actions, and to uncover new monetization opportunities? So really, the entire approach of looking at it from an accounting perspective, we just completed that research at the University of San Francisco, where we looked at, how do you determine the economic value of data? And we realized that using an accounting approach grossly undervalued what data is worth. So instead of using an accounting, we started with an economics perspective. The multiplier effect. Marginal propensity to consume, all that kind of stuff that we all forgot about when we got out of college, really applies here because now I can use that same data over and over again, and if I apply a data science to it to really try to predict, prescribe, and monetize, all of a sudden the economic value of data just explodes. Precisely because of your connecting a source of data, which has a particular utilization to another source of data that has a particular utilization, and you can combine them, create new utilizations that might in and of itself be even more valuable than either of the original... They genetically mutate. That's exactly right. So think about, at least, I think it's right. So congratulations, we agree. Which is rare, yeah, which is rare. So now let's talk about this notion of as we move forward with data value, how does an organization have to start translating some of these new ways of thinking about the value of data into investments in data so that you have the data where you want it, when you want it, and in the form that you need it? That's the heart of why you do this, right? If I know what the value of my data is, then I can make decisions regarding what data am I going to try to protect, enhance, what data am I going to get rid of, right? And put on cold storage, for example. And so we came up with a methodology for how we tie the value of data back to use cases. Everything we do is use case-based. So if you're trying to increase the same store sales at Chipotle, one of my favorite places, if you're trying to increase it by 7.1%, that's worth about $191 million, right? And the use cases that support that, like increasing local event marketing, increasing new product introduction effectiveness, increasing customer cross-seller upsell. If you start breaking those use cases down, you can start tying financial value to those use cases. And if I know what data sets, what three, five, seven data sets are required to help solve that problem, I now have a basis against which I can start attaching value to data. And as I look across that across a number of use cases, now the data start, the value data starts to increment. It grows exponentially, not exponentially, but it does increment, right? And it gets more and more. It's non-linear, it's super linear. Yes, yeah. And what's also interesting. Increasing returns. From an ROI perspective, what you're going to find that as you go down on these use cases, the financial value of that use case may not be really high, but when the denominator of your ROI calculation starts approaching zero because I'm reusing data at zero cost, I can reuse data at zero cost. When the denominator starts going to zero, you know what happens to your ROI? In infinity, it explodes. Last question, Bill. You mentioned the University of San Francisco, and you've been there a while teaching business students how to embrace analytics. One of the things that was talked about this morning in the keynote was Hortonworks dedication to the open source community from the beginning. And they kind of talked about there with kids in college these days, they have access to this open source software that's free. I just love to get kind of the last word, your take on what are you seeing in the university life today where these business students are understanding more about analytics, but also do you see them as kind of helping to build the next generation of data scientists? That's really kind of the next leg of the digital transformation. So the premise we have in our class is that we probably can't turn business people into data scientists. In fact, we don't think that's valuable. What we want to do is teach them how to think like a data scientist. And what happens if we can get the business stakeholders to understand what's possible with data and analytics and then you couple them with a data scientist and knows how to do it, we see exponential impact. We had, we did see a client project around customer attrition. The industry benchmark and customer attrition is, it was published, I won't name the company, but they had a 24% identification rate. We had a 59%. We 2xed the number. Not because our data scientists were smarter, our tools were smarter, but because our approach was to leverage and teach the business people how to think like a data scientist. And they were able to identify variables and metrics they wanted to test. And when our data scientists test them, we said, oh my gosh, that's a very highly produced variable. And trust what they said. And trust what they said, right? So how do you build trust? On the data science side, you fail. You test, you fail, you test, you fail. And you don't, you never, you're never going to understand 100% accuracy. But have you failed enough times that you feel comfortable and confident that the model is good enough? Well, what a great spirit of innovation that you're helping to bring there. Your keynote we should mention is tomorrow. That's right. So you can, if you're watching a live stream or you're here in person, you can see Bill's keynote. Bill Schmarzo, CTO of Dell EMC. Thank you for joining Peter and I. Great to have you on the show. Where a show where you can talk about the Warriors and Chipotle in one show, I've never seen it done. This is groundbreaking. Fantastic. And psychodonats too. And psychodonats. Now I'm hungry. Thank you for watching this segment. Again, we are live on day one of the DataWorks Summit in San Francisco for Bill Schmarzo and Peter Burris, my co-host, I am Lisa Martin. Stick around. We will be right back.