 Live from Las Vegas, Nevada, it's theCUBE covering EMC World 2015 brought to you by EMC, Brocade, and VCE. Welcome back everybody. I'm Jeff Frick. You're watching theCUBE. We're at EMC World 2015, our 60th year, I think, community EMC World. That's really where theCUBE got started. We're always excited to come back. It's bigger and better than ever. Two cubes, just like we had last year. And we're really excited for our very special guest, the Dean of Big Data himself, Bill Schmarzo. Let me get this right. The CTO of Global Services, Big Data Practice. That is right. If the name was any longer, I'd need two cards. Well, welcome as always. I think this is a repeat of a session that we had a year ago. I think we had two for it. I think you have me on every year. I haven't bored you enough yet. You keep bringing me back. So as everyone knows, we love to get customers. We love to get practitioners on theCUBE. And you spend, as we know, all day, Monday through Thursday, not in the flyover country actually, right? You don't fly straight to the other coast. You land, wheels down, you're in the hinterland. So tell us what is going on? Well, the Big Data journey continues to mature. We're seeing that more and more the shift in the focus of Big Data, as far as from a championship perspective from the champion, is definitely moving from IT to the business. The business users are becoming front and center and are the ones in many cases who maybe aren't leading, but they're definitely arm in arm with the CIO helping to drive these things. And is that because they're hearing about it from other people? Is that because there's enough use cases that they can grab onto? Or what is giving the business owner enough information that they can really drive this adoption? So I think, Jeff, there's two things. I think first off, the technology experiment with Big Data, where you brought Hadoop in, you threw some data on it, you hired a couple of data scientists, and you waited for magic to happen, and you waited, and you waited, and no magic happened. I think there was a high degree of disillusionment, because all the press and the analysts and the books are talking about all this Big Data nirvana. And I think what happened is the business people said, okay, let's reset. We're obviously doing something wrong. It must be the approach. It's got to be more than just a technology. And it is more than a technology. I mean, I still scoff at organizations and they say, oh, we're going to do a proof of concept, proof of technology on Big Data. Are you kidding me? Google, Yahoo, Netflix, NSA is spying on us right now using the technology. Technology works. Technology works, right. So it's not a proof of concept challenge. It's a proof of value challenge. And when you get into the proof of value discussion, the business has to be front and center, because who understands the value more than the business? Right, right. And then it becomes the standard kind of early adoption process, right? Find an early win, find something that's easy to go after, where you can demonstrate the technology and the business values. Exactly. We go through a process we call a vision workshop, and it's about a two or three-week engagement process where we go through and we look at use cases, trying to find those use cases that have both high value and high feasibility success in a nine to 12-month window. The nine to 12 months is key because we don't want anything longer than 12 months because, you know, organizations change, people get fired, people get bored. So when we focus on nine to 12 months, it really limits and forces us to focus on things where we can deliver quick wins and then build on that success, like you said. And then what are some of the attributes that you look at when you start that process to get the people in the room thinking about potential projects? I'm sure there's a laundry list of kind of priorities or things that you can decide to really drive you to that successful early adoption. So there's two important things that I think distinguish a successful engagement from a failure. Number one is the business and the IT people actually have to like each other. They actually have to get along, right? If you think about it, the business owns the value termination process. They're the ones who are going to make decisions based on the analytics that we glean out of the data. And IT owns the feasibility. You have the right skills, the right data, the right data governance, all the right things. So first off, it can't be done by business or IT. It has to be done by business and IT. So that's job one. The second thing is we encourage organizations to pick a use case. Don't worry about picking the use case. You can spend a lot of cycles trying to figure out is this the right use case? Is this the right use case? You know, in a certain extent pick one and we'll go from there. Because what's going to happen as you go through that use case, you're going to build expertise in data. You're going to build your data lake. You're going to build your analytic capabilities, your data science team. All these sort of these residual exhausts from the project are going to slowly build out your data science and your big data capabilities. So don't worry about picking the use case. Pick one and get started. So once they get started, what are some of the big surprises that you see over and over and over again? Is it where the data is? Do they even know where the data is? Is it schema? What are some of the kind of consistent themes that you know you're going to get in day three of this process? So it wouldn't be schmarzo without telling a story. Oh, absolutely. So I got this marvelous story to tell. So we're running our vision workshop process and we're working with a gaming company. And the gaming company is focused on how do they create a more predictive customer lifetime value. That's their focus, right? So we're doing a vision workshop process and we have, we must have 12 or 13 executives in the room. And it's free form thinking, ideas all over the place. It's a very creative process. And there's this woman in the back in the corner, way in the back. And of course my job as a facilitator is to bring those people out. And so I say, you know, what ideas, Susan, do you have? She says, well, I got this data source. And she says it's, it's on a PDF. So I'm not sure how usable it is, but it lists all of our, all of our players we have and their lines of credit at our gaming facility as well as all those around us. Now she says this, and it was funny, because the whole room. So this is gaming like Vegas style gaming. This is not a World of Warcraft game. Yeah, this is like, this is gaming game. This is like Casino's, right? And the whole, the whole room goes quiet. And everybody turns and looks at her like, what? We have access to all this line of credit data for all the players for our casino and all the casinos around us. And she says, yeah, but it's on a PDF. And you can see all the data scientists are drooling. We'll scrape it off the PDF. If you're thinking about trying to calculate a customer lifetime value, knowing not only what the line of credit they have with you is, but potentially have other casinos is gold. Right. So what, my takeaway is the best ideas, our best ideas come from our users because they live this business every day. They wrestle with it every day. It's not our data scientists. It's not me. Right, right. It's not our subject. It's the people who live it every day who know this stuff intrinsically. Right. So all our job is to put them into an environment where we can get those ideas out of them. It's interesting because we talk all the time about people processing tech, right? And it's always about people and the biggest barriers to execution are people and the greatest assets to bring to bear are people. Why? I'm curious in this case because we also had the women of the world on earlier today in talking about using diversity as a way to get different points of view so you can explore a wider set of data and a wider solution set that maybe just men or whatever would not have identified. So I'm curious if you can share more light. Was she not senior? Was it some random thing she had? Did it fall off the back of a truck? I mean... So I think what happens when we were born, everybody's young, we're naturally curious, right? We put our hands on the hot stove, right? We stick our head in the dog house, right? We do all these dumb things, right? Yes, I got proof I stick my head in the dog house, right? So wrong kind of dog house. So what happens is people have these sort of creative thinking ideas and they get into a work environment and a lot of times organizations spend too much time telling them what they can't do. Swaddling creativity. When we run these vision workshops, one rule we have in place is no filtering. You can't filter an idea, so you capture everything. And that woman back there had a great idea. Now she didn't know it was great, she didn't think it was even great, but she had an idea. And what happens when you get people in an environment where they can freely share ideas, creativity is contagious. It is contagious and you'll see people all of a sudden start coming up with these great ideas that be honest with you, our data scientists would never figure out because we don't know the business as well as those people do. Right, right. So it's all about putting in place an environment where they're free to share ideas, they're free to make mistakes, they're free to say dumb things and no one's there to criticize them. Right. And then talk about some of these second order effects. Like you said, the exhaust of the process has a lot of value in it beyond just kind of the core, you know, running the train down the track, if you will. Some of the things that people have found and kind of that tangential second iteration of value. What we find is as soon as we open up what we can do, the possibilities, especially after we do the proof of value stage and we're ready to move into operationalization. So we do the vision workshop to find the use cases and quantify them. We do the proof of value to show the ROI and analytical lift and then we operationalize it. Out of the proof of value, we've proven the technology, there's value there, the analytics work, that's where the use cases come out of the wall. Right, we were sitting in the meeting, I was meeting with the executive, the executive management team of this casino and we're doing our presentation update on where we are with things. After it's over, he pulls me inside and says, hey we're running a marketing campaign, do you mind looking at our collateral? Right, like I don't know anything at all about marketing collateral for a casino, but they wanted us our opinion. And then the guy who's the head of vice president player development said, hey, come up here with me, and he takes me to his office and he has all these great ideas about how he wants to leverage all his analytics. And it's when you start freeing people up to think about what they could do to envision the realm of what's possible, the sky is truly the limit. And I tell you Jeff, it's a blast. I love these engagements because you can see, you can literally see people's eyes start to sparkle. Right, let's talk about the naysayers. The gut feel guy, the old guy in the back of the room who's been doing it by gut feel and has been in the industry forever. Talk a bit about do they transform, how does that transformation work to move to a more kind of data based decision making even though we all know that without inferences, without historical reference, without some basis, you really can't get to the true value of the data. So at the risk of tweaking some of my clients, the most dangerous person in the company is what they call the hippo, the highest paid person's opinion. Usually some senior vice president who like you said, Jeff, has always done it this way. This is the way we're going to do it. We're always in this way and they block all ideas. That's the most dangerous person and the viability of a company. Now what we see sometimes, not all of the time, we'll get into a meeting and the data science team will have found a better predictor of performance, something that's going to help in true performance and the hippo comes out and says, no, no, we're going to do it this way. The data scientist is getting pushed down, the business person getting pushed down and then somebody says, hey, let's try them both. When people realize that you can try them both, you can literally see the weight lifted off people's shoulders. They're all going like, yeah, let's try. Let's try them both to see if they work. Now the hippo's put into a box. What's the hippo do then? The hippo's put into a box. We're going to still do your idea. We're going to see if it's actually going to work. Now some hippos, they play the executive order card and they throttle it, but at that point in time, the genie is out of the bottle. That you can actually test both. And then all of a sudden you again open up the creative floodgates of people who have all these creative ideas and how we can improve customer acquisition and retention and predictive maintenance and teacher performance and drive down health care costs and all these ideas that are bottled up in the organization, they just explode. It's so great to have you here, Bill, because you are literally out in the field. When I say every week, Bill leaves on Monday and he comes back on Thursday. Should have worked for a center. That's kind of a travel schedule. But anyway, at Big Data SV, when last we had you on, you told some really compelling stories about emergency rooms and really leveraging big data. This is not about do you get a latte when you walk by Starbucks, it's got the right amount of sweetener. This is life and death decisions moving diagnosis much quicker. Share with us some other stories, because you have the best kind of customer stories and you see a breadth of them as opposed to just one instance with kind of a traditional practitioner. Well, I like the hospital ones because I think there is such a bevy of opportunities inside hospitals to leverage data and analytics to really improve the quality of care and driving down costs. I was just meeting last week with a CIO of a hospital and their number one issue is how do I reduce falls in the hospital? We have a bad... Falls in the hospital. People falling in the hospital, right? Because people fall in the hospital, they get hurt and they want to be able to apply analytics to create literally a likelihood of fall score, a propensity to fall, right? Looking at how old they are, what kind of health conditions they are and if they've got a high probability of they might fall, maybe you put them in a wheelchair, maybe you guide them differently. A very heavy focus on falls because they didn't share with us, but they quantified what it costs the organization from both a financial as well as a PR perspective from people who fall in the hospital. I never had thought about that before as being a major issue. Trust me, as our population is aging, as we're getting more and more fragile people into the system, fall is a real problem. When old people fall, they break their hips. And guess what? You've got to replace them. So we see hospital-inquired infections, how do we do staff infections, how to get people out of the purgatory place when somebody checks into admissions in a hospital. Sometimes, especially in ER, they will sit in the hallway on a gurney waiting to get to a room. Well, guess for your real susceptible to captioned staff infection or other disease. Sit in the hallway. So how do I figure out how do I prioritize getting people out of that purgatory into the rooms and being cared more quickly? They're just full of opportunities to really drive down costs and drive down costs and improve quality of care. Right, right. Well, it's interesting because people love to diss on the medical profession as being really late to the game on innovation and technology and leveraging technology. Do you think within your kind of broad base of experience are they at par? Are they really that far behind? Or do people just not give them credit? A lot of medical folks on the Cube at all these different shows that are implementing new technology and innovative and creative ways. I actually think that the healthcare industry is still far behind. Especially compared to retail and financial services or the gaming industry, they are far behind. But on the other hand, I think the opportunities are even more fruitful. Right. Because not only are they lead to financial benefits, they improve quality of care. They're changing, literally changing people's lives. So, while the healthcare industry may be further behind than others, the number of opportunities that are there are almost boundless. And there's no reason why they can't start today. There's no reason why they have to go through that, oh, I got to build a data warehouse with BI expertise first. Why? Right. Let's just start right away with the data science and analytics. Let's just go right after it. And do you think it's the culture? Do you think it's the finances driven by insurance? I mean, is it just, you know, what is it that's caused them to lag? Is they risk averse? I mean, why do you think that's caused them to lag? And what do you think might kind of open the floodgates a little bit there? So, if you look at the healthcare industry between payers and providers, who has all the money? The payers do. The payers, right? The providers have operating very low margins. Right? So, they've historically had to make, they've had a hard time making technology decisions. Especially ones that don't have a clear, well-defined ROI. So, yeah, I think they've been behind because the hospitals, the providers, just don't have the financial resources. When we come in now, we can show how we can literally use the use case to finance the project. The ROI is so compelling that, you know, we will help them find money. But it's an industry that the payers still have the bulk of the money, and the providers are still operating on very slim margins. Yeah. Okay, so we had you on a while back. You launched your book on, The Cube at Big Data New York City 2013. You've been lazy. Where's the next book? What's going on? Give us a quick update on the dean. We're getting the hook, but you've got to be working on something because I know you like to write books in there. I've held off on announcing this, but I've saved it for The Cube. Yes, I'm in the process of working on a second book. I'm in discussions right now with a couple of publishers. The book will be, if I can retain the rights, it'll be called The Big Data NBA. And one of the themes of the book will be getting business people to think like a data scientist. We're not going to convert business people into data scientists, but we can teach business executives and business users to think like a data scientist so they're better partners with a data scientist. Awesome. I think that's first use, right? So first use, Bill's got it, the dean. The dean of Big Data, Bill Schmarzo. Thanks for stopping by, as usual. I'm Jeff Rick. You're watching The Cube. We're DMC World 2015. We'll be back with our next guest after this short break.