>> Voiceover: Live from San Jose in the heart of Silicon Valley, it's The Cube covering DataWorks Summit 2017. Brought to you by: Hortonworks. >> Hey, welcome back to The Cube. 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 guess, the CTO of Dell AMC, Bill Shmarzo. And Cube alumni, clearly sporting the pride. >> Did they win? I don't even remember. I just was-- >> Are we breaking news? (laughter) Bill, it's great to have you back on The Cube. >> The Division III All-American from-- >> Cole College. >> 1947? >> Oh, yeah, yeah, about then. They still had the peach baskets. You make a basket, you have to climb up this ladder and pull it out. >> They're going rogue on me. >> It really slowed the game down a lot. (laughter) >> 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's 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 on the business side. We like to jokingly say they care of the four M's of big data, make me more money. So, when you think about digital transformation and how it might take an organization from where they are today to sort of imbed digital capabilities around data and analytics, it's really about, "How do I make more money?" What processes can I eliminate or reduce? How do I improve my ability to market and reach customers? How do I, ya know-- All the things that are designed to drive value from a value perspective. Let's go back to, ya know, Tom Peters kind of thinking, right? 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, it 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 is of equal value. >> Peter: Absolutely. >> Right? So, what data, what technologies-- Do I need to have Spark? Well, I don't know, what are you trying to do, right? Do I need to have Kafka or Ioda, 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 and infrastructure. >> But, that's what I want to come to. So, increasingly, what business executives, at least the ones who we're talking to all the time, are make me more money. >> Right. >> 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 valuable tomorrow, I can find some other way to not spend money on it, etc. >> Right. >> That's different from the variety, velocity, volume statement which is all about the infrastructure-- >> Amen. >> --and what an IT guy might be worried about. So, I've done a lot of work on data value, you've done a lot of work in data value. We've coincided a couple times. Let's pick that notion up of, ya 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 it was sort of an "aha" moment to me was when I had a conversation with you about Adam Smith. 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? The evaluation of that data comes from putting it into use to make better decisions. If I know then what decision I'm trying to make, now I have a process not only in deciding what data's most valuable but, 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 from this EVD conversation, not only from a data evaluation perspective but also from a risk perspective. Cause you've 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 double entry book keeping with data. What we're really talking about is understand how your business used its 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 understand how you go from point "a" to point "b". >> Correct, yes. And, in fact, the underlying premise behind driving economic value of data, you know people say data is the new oil. Well, that's a BS 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-- You can use data across an endless number of use cases. If you go out and get-- >> Peter: 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. >> Peter: 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 or that activity. >> It has a transactional limitation. >> Peter: Exactly, it's an oar. >> 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 the data to predict what's likely to happen? To prescribe actions and to uncover new monetization opportunities. So, 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 economic value of data? And we realized that using an accounting approach grossly undervalued the data's worth. So, instead of using an accounting, we started with an economics perspective. The multiplier effect, marginal perpetuity to consume, all that kind of stuff that we all forgot about once we got out of college really applies here because now I can use that same data over and over again. And if I apply data science to it to really try to predict, prescribe, and monetize; all of a sudden economic value of your 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 cases. >> They genetically mutate. >> That's exactly right. So, think about-- I think it's right. So, congratulations, we agree. Thank you very much. >> 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 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 same-store sales at a Chipotle, one of my favorite places; if you're trying to increase it by 7.1 percent, that's worth about 191 million dollars. And the use cases that support that like increasing local even marketing or increasing new product introduction effectiveness, increasing customer cross-sale or 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 at a number of use cases, now the valued 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. >> Yeah, and what's also interesting-- >> Increasing returns. >> From an ROI perspective, what you're going to find that as you go down 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 ya 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'd just love to get, kind of the last word, your take on what are you seeing in university life today where these business students are understanding more about analytics? Do you see them as kind of, helping to build the next generation of data scientists since that's really kind of the next leg of the digital transformation? >> So, the premise we have in our class is 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. 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 that knows how to do it, we see exponential impact. We just did a client project around customer attrition. The industry benchmark in customer attrition is it was published, I won't name the company, but they had a 24 percent identification rate. We had a 59 percent. We two X'd the number. Not because our data scientists are smarter or our tools are 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 want to test. And when our data scientists tested them they said, "Oh my gosh, that's a very highly predicted 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, you're never going to understand 100 percent 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 the livestream or you're in person, you can see Bill's keynote. Bill Shmarzo, CTO of Dell AMC, thank you for joining Peter and I. Great to have you on the show. A show where you can talk about the Warriors and Chipotle in one show. I've never seen it done, this is groundbreaking. Fantastic. >> Psycho donuts too. >> And psycho donuts and now I'm hungry. (laughter) Thank you for watching this segment. Again, we are live on day one of the DataWorks Summit in San Francisco for Bill Shmarzo and Peter Burris, my co-host. I am Lisa Martin. Stick around, we will be right back. (music)