>>Really excited about this year's data, citizens with so many of you together. Uh, I'm going to talk today about accessible data, because what good is the data. If you can get it into your hands and shop for it, but you can't understand it. Uh, and I'm here today with, uh, bald, really thrilled to be here with Paul. Paul is an award-winning author on all topics data. I think 20 books with 21st on the way over 300 articles, he's been a frequent speaker. He's an expert in future trends. Uh, he's a VP at cognitive systems, uh, over at IBM teachers' data also, um, at the business school and as a champion of diversity initiatives. Paul, thank you for being here, really the conformance, uh, to the session with you. >>Oh, thanks for having me. It's a privilege. >>So let's get started with, uh, our origins and data poll. Um, and I'll start with a little story of my own. So, uh, I trained as an engineer way back when, uh, and, um, in one of the courses we got as an engineer, it was about databases. So we got the stick thick book of CQL and me being in it for the programming. I was like, well, who needs this stuff? And, uh, I wanted to do my part in terms of making data accessible. So essentially I, I was the only book that I sold on. Uh, obviously I learned some hard lessons, uh, later on, as I did a master's in AI after that, and then joined the database research lab at the university that Libra spun off from. Uh, but Hey, we all learned along the way. And, uh, Paula, I'm really curious. Um, when did you awaken first to data? If you will? >>You know, it's really interesting Stan, because I come from the opposite side, an undergrad in economics, uh, with some, uh, information systems research at the higher level. And so I think I was always attuned to what data could do, but I didn't understand how to get at it and the kinds of nuances around it. So then I started this job, a database company, like 27 years ago, and it started there, but I would say the awakening has never stopped because the data game is always changing. Like I look at these epochs that I've been through data. I was a real relational databases thinking third normal form, and then no SQL databases. And then I watch no SQL be about no don't use SQL, then wait a minute. Not only sequel. And today it's really for the data citizens about wait, no, I need SQL. So, um, I think I'm always waking up in data, so I'll call it a continuum if you will. But that was it. It was trying to figure out the technology behind driving analytics in which I took in school. >>Excellent. And I fully agree with you there. Uh, every couple of years they seem to reinvent new stuff and they want to be able to know SQL models. Let me see. I saw those come and go. Uh, obviously, and I think that's, that's a challenge for most people because in a way, data is a very abstract concepts, um, until you get down in the weeds and then it starts to become really, really messy, uh, until you, you know, from that end button extract a certain insights. Um, and as the next thing I want to talk about with you is that challenging organizations, we're hearing a lot about data, being valuable data, being the new oil data, being the new soil, the new gold, uh, data as an asset is being used as a slogan all over. Uh, people are investing a lot in data over multiple decades. Now there's a lot of new data technologies, always, but still, it seems that organizations fundamentally struggle with getting people access to data. What do you think are some of the key challenges that are underlying the struggles that mud, that organizations seem to face when it comes to data? >>Yeah. Listen, Stan, I'll tell you a lot of people I think are stuck on what I call their data, acumen curves, and you know, data is like a gym membership. If you don't use it, you're not going to get any value on it. And that's what I mean by accurate. And so I like to think that you use the analogy of some mud. There's like three layers that are holding a lot of organizations back at first is just the amount of data. Now, I'm not going to give you some stat about how many times I can go to the moon and back with the data regenerate, but I will give you one. I found interesting stat. The average human being in their lifetime will generate a petabyte of data. How much data is that? If that was my apple music playlist, it would be about 2000 years of nonstop music. >>So that's some kind of playlist. And I think what's happening for the first layer of mud is when I first started writing about data warehousing and analytics, I would be like, go find a needle in the haystack. But now it's really finding a needle in a stack of needles. So much data. So little time that's level one of mine. I think the second thing is people are looking for some kind of magic solution, like Cinderella's glass slipper, and you put it on her. She turns into a princess that's for Disney movies, right? And there's nothing magical about it. It is about skill and acumen and up-skilling. And I think if you're familiar with the duper, you recall the Hadoop craze, that's exactly what happened, right? Like people brought all their data together and everyone was going to be able to access it and give insights. >>And it teams said it was pretty successful, but every line of business I ever talked to said it was a complete failure. And the third layer is governance. That's actually where you're going to find some magic. And the problem in governance is every client I talked to is all about least effort to comply. They don't want to violate GDPR or California consumer protection act or whatever governance overlooks, where they do business and governance. When you don't lead me separate to comply and try not to get fine, but as an accelerant to your analytics, and that gets you out of that third layer of mud. So you start to invoke what I call the wisdom of the crowd. Now imagine taking all these different people with intelligence about the business and giving them access and acumen to hypothesize on thousands of ideas that turn into hundreds, we test and maybe dozens that go to production. So those are three layers that I think every organization is facing. >>Well. Um, I definitely follow on all the days, especially the one where people see governance as a, oh, I have to comply to this, which always hurts me a little bit, honestly, because all good governance is about making things easier while also making sure that they're less riskier. Um, but I do want to touch on that Hadoop thing a little bit, uh, because for me in my a decade or more over at Libra, we saw it come as well as go, let's say around 2015 to 2020 issue. So, and it's still around. Obviously once you put your data in something, it's very hard to make it go away, but I've always felt that had do, you know, it seemed like, oh, now we have a bunch of clusters and a bunch of network engineers. So what, >>Yeah. You know, Stan, I fell for, I wrote the book to do for dummies and it had such great promise. I think the problem is there wasn't enough education on how to extract value out of it. And that's why I say it thinks it's great. They liked clusters and engineers that you just said, but it didn't drive lineup >>Business. Got it. So do you think that the whole paradigm with the clouds that we're now on is going to fundamentally change that or is just an architectural change? >>Yeah. You know, it's, it's a great comment. What you're seeing today now is the movement for the data lake. Maybe a way from repositories, like Hadoop into cloud object stores, right? And then you look at CQL or other interfaces over that not allows me to really scale compute and storage separately, but that's all the technical stuff at the end of the day, whether you're on premise hybrid cloud, into cloud software, as a service, if you don't have the acumen for your entire organization to know how to work with data, get value from data, this whole data citizen thing. Um, you're not going to get the kind of value that goes into your investment, right? And I think that's the key thing that business leaders need to understand is it's not about analytics for kind of science project sakes. It's about analytics to drive. >>Absolutely. We fully agree with that. And I want to touch on that point. You mentioned about the wisdom of the crowds, the concept that I love about, right, and your organization is a big grout full of what we call data citizens. Now, if I remember correctly from the book of the wisdom of the crowds, there's, there's two points that really, you have to take Canada. What is, uh, for the wisdom of the grounds to work, you have to have all the individuals enabled, uh, for them to have access to the right information and to be able to share that information safely kept from the bias from others. Otherwise you're just biasing the outcome. And second, you need to be able to somehow aggregate that wisdom up to a certain decision. Uh, so as Felix mentioned earlier, we all are United by data and it's a data citizen topic. >>I want to touch on with you a little bit, because at Collibra we look at it as anyone who uses data to do their job, right. And 2020 has sort of accelerated digitization. Uh, but apart from that, I've always believed that, uh, you don't have to have data in your title, like a data analyst or a data scientist to be a data citizen. If I take a look at the example inside of Libra, we have product managers and they're trying to figure out which features are most important and how are they used and what patterns of behavior is there. You have a gal managers, and they're always trying to know the most they can about their specific accounts, uh, to be able to serve as them best. So for me, the data citizen is really in its broadest sense. Uh, anyone who uses data to do their job, does that, does that resonate with you? >>Yeah, absolutely. It reminds me of myself. And to be honest in my eyes where I got started from, and I agree, you don't need the word data in your title. What you need to have is curiosity, and that is in your culture and in your being. And, and I think as we look at organizations to transform and take full advantage of their, their data investments, they're going to need great governance. I guarantee you that, but then you're going to have to invest in this data citizen concept. And the first thing I'll tell you is, you know, that kind of acumen, if you will, as a team sport, it's not a departmental sport. So you need to think about what are the upskilling programs of where we can reach across to the technical and the non-technical, you know, lots and lots of businesses rely on Microsoft Excel. >>You have data citizens right there, but then there's other folks who are just flat out curious about stuff. And so now you have to open this up and invest in those people. Like, why are you paying people to think about your business without giving the data? It would be like hiring Tom Brady as a quarterback and telling him not to throw a pass. Right. And I see it all the time. So we kind of limit what we define as data citizen. And that's why I love what you said. You don't need the word data in your title and more so if you don't build the acumen, you don't know how to bring the data together, maybe how to wrangle it, but where did it come from? And where can you fixings? One company I worked with had 17 definitions for a sales individual, 17 definitions, and the talent team and HR couldn't drive to a single definition because they didn't have the data accurate. So when you start thinking of the data citizen, concept it about enabling everybody to shop for data much. Like I would look for a USB cable on Amazon, but also to attach to a business glossary for definition. So we have a common version of what a word means, the lineage of the data who owns it, who did it come from? What did it do? So bring that all together. And, uh, I will tell you companies that invest in the data, citizen concept, outperform companies that don't >>For all of that, I definitely fully agree that there's enough research out there that shows that the ones who are data-driven are capturing the most markets, but also capturing the most growth. So they're capturing the market even faster. And I love what you said, Paul, about, um, uh, the brains, right? You've already paid for the brains you've already invested in. So you may as well leverage them. Um, you may as well recognize and, and enable the data citizens, uh, to get access to the assets that they need to really do their job properly. That's what I want to touch on just a little bit, if, if you're capable, because for me, okay. Getting access to data is one thing, right? And I think you already touched on a few items there, but I'm shopping for data. Now I have it. I have a cul results set in my hands. Let's say, but I'm unable to read and write data. Right? I don't know how to analyze it. I don't know maybe about bias. Uh, maybe I, I, I don't know how to best visualize it. And maybe if I do, maybe I don't know how to craft a compelling persuasion narrative around it to change my bosses decisions. So from your viewpoint, do you think that it's wise for companies to continuously invest in data literacy to continuously upgrade that data citizens? If you will. >>Yeah, absolutely. Forest. I'm going to tell you right now, data literacy years are like dog years stage. So fast, new data types, new sources of data, new ways to get data like API APIs and microservices. But let me take it away from the technical concept for a bit. I want to talk to you about the movie. A star is born. I'm sure most of you have seen it or heard it Bradley Cooper, lady Gaga. So everyone knows the movie. What most people probably don't know is when lady Gaga teamed up with Bradley Cooper to do this movie, she demanded that he sing everything like nothing could be auto-tuned everything line. This is one of the leading actors of Hollywood. They filmed this remake in 42 days and Bradley Cooper spent 18 months on singing lessons. 18 months on a guitar lessons had a voice coach and it's so much and so forth. >>And so I think here's the point. If one of the best actors in the world has to invest three and a half years for 42 days to hit a movie out of the park. Why do we think we don't need a continuous investment in data literacy? Even once you've done your initial training, if you will, over the data, citizen, things are going to change. I don't, you don't. If I, you Stan, if you go to the gym and workout every day for three months, you'll never have to work out for the rest of your life. You would tell me I was ridiculous. So your data literacy is no different. And I will tell you, I have managed thousands of individuals, some of the most technical people around distinguished engineers, fellows, and data literacy comes from curiosity and a culture of never ending learning. That is the number one thing to success. >>And that curiosity, I hire people who are curious, I'll give you one more story. It's about Mozart. And this 21 year old comes to Mozart and he says, Mozart, can you teach me how to compose a symphony? And Mozart looks at this person that says, no, no, you're too young, too young. You compose your fourth symphony when you were 12 and Mozart looks at him and says, yeah, but I didn't go around asking people how to compose a symphony. Right? And so the notion of that story is curiosity. And those people who show up in always want to learn, they're your home run individuals. And they will bring data literacy across the organization. >>I love it. And I'm not going to try and be Mozart, but you know, three and a half years, I think you said two times, 18 months, uh, maybe there's hope for me yet in a singing, you'll be a good singer. Um, Duchy on the, on the, some of the sports references you've made, uh, Paul McGuire, we first connected, uh, I'm not gonna like disclose where you're from, but, uh, I saw he did come up and I know it all sorts of sports that drive to measure everything they can right on the field of the field. So let's imagine that you've done the best analysis, right? You're the most advanced data scientists schooled in the classics, as well as the modernist methods, the best tools you've made a beautiful analysis, beautiful dashboards. And now your coach just wants to put their favorite player on the game, despite what you're building to them. How do you deal with that kind of coaches? >>Yeah. Listen, this is a great question. I think for your data analytics strategy, but also for anyone listening and watching, who wants to just figure out how to drive a career forward? I would give the same advice. So the story you're talking about, indeed hockey, you can figure out where I'm from, but it's around the Ottawa senators, general manager. And he made a quote in an interview and he said, sometimes I want to punch my analytics, people in the head. Now I'm going to tell you, that's not a good culture for analytics. And he goes on to say, they tell me not to play this one player. This one player is very tough. You know, throws four or five hits a game. And he goes, I'd love my analytics people to get hit by bore a wacky and tell me how it feels. That's the player. >>Sure. I'm sure he hits hard, but here's the deal. When he's on the ice, the opposing team gets more shots on goal than the senators do on the opposing team. They score more goals, they lose. And so I think whenever you're trying to convince a movement forward, be it management, be it a project you're trying to fund. I always try to teach something that someone didn't previously know before and make them think, well, I never thought of it that way before. And I think the great opportunity right now, if you're trying to get moving in a data analytics strategy is around this post COVID era. You know, we've seen post COVID now really accelerate, or at least post COVID in certain parts of the world, but accelerate the appetite for digital transformation by about half a decade. Okay. And getting the data within your systems, as you digitize will give you all kinds of types of projects to make people think differently than the way they thought before. >>About data. I call this data exhaust. I'll give you a great example, Uber. I think we're all familiar with Uber. If we all remember back in the days when Uber would offer you search pricing. Okay? So basically you put Uber on your phone, they know everything about you, right? Who are your friends, where you going, uh, even how much batteries on your phone? Well, in a data science paper, I read a long time ago. They recognize that there was a 70% chance that you would accept a surge price. If you had less than 10% of your battery. So 10% of battery on your phone is an example of data exhaust all the lawns that you generate on your digital front end properties. Those are logs. You can take those together and maybe show executive management with data. We can understand why people abandoned their cart at the shipping phase, or what is the amount of shipping, which they abandoned it. When is the signal when our systems are about to go to go down. So, uh, I think that's a tremendous way. And if you look back to the sports, I mean the Atlanta Falcons NFL team, and they monitor their athletes, sleep performance, the Toronto Raptors basketball, they're running AI analytics on people's personalities and everything they tweet and every interview to see if the personality fits. So in sports, I think athletes are the most important commodity, if you will, or asset a yet all these teams are investing in analytics. So I think that's pretty telling, >>Okay, Paul, it looks like we're almost out of time. So in 30 seconds or less, what would you recommend to the data citizens out there? >>Okay. I'm going to give you a four tips in 30 seconds. Number one, remember learning never ends be curious forever. You'll drive your career. Number two, remember companies that invest in analytics and data, citizens outperform those that don't McKinsey says it's about 1.4 times across many KPIs. Number three, stop just collecting the dots and start connecting them with that. You need a strong governance strategy and that's going to help you for the future because the biggest thing in the future is not going to be about analytics, accuracy. It's going to be about analytics, explainability. So accuracy is no longer going to be enough. You're going to have to explain your decisions and finally stay positive and forever test negative. >>Love it. Thank you very much fall. Um, and for all the data seasons is out there. Um, when it comes down to access to data, it's more than just getting your hands on the data. It's also knowing what you can do with it, how you can do that and what you definitely shouldn't be doing with it. Uh, thank you everyone out there and enjoy your learning and interaction with the community. Stay healthy. Bye-bye.

>> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're having a Cube conversation in our Palo Alto studio, the conference season hasn't got to full swing yet, so we can have a little bit more relaxed atmosphere here in the studio and we're really excited, as part of our continuing coverage for the Data Makes Possible sponsored by Western Digital, looking at cool applications, really the impact of data and analytics, ultimately it gets stored usually on a Western Digital hard drive some place, and this is a great segment. Who doesn't like talking about sports, and football, and advanced analytics? And we're really excited, I have John Pollard here, he is the VP of Business Development for Zebra Sports, John, great to see you. >> Jeff, thanks for having me. >> Absolutely, so before we jump into the fun stuff, just a little bit of background on Zebra Sports and Zebra Technologies. >> Okay well, first, Zebra Technologies is a publicly traded company, we started in the late 1960s, and really what we do is we track enterprise assets in industries typically like healthcare, retail, travel and logistics, and transportation. And what we've done is take that heritage and bring that over into the world of sports, starting four years ago with our relationship with the NFL as the official player tracking technology. >> It's such a great story of an old-line company, right? based in Illinois-- >> Yeah, Lincolnshire. >> Outside of Chicago, right? RFID tags, and inventory management, and all this kind of old-school stuff. But then to take that into this really dynamic world, A, of sports, but even more, advanced analytics, which is relatively new. And we've been at it for a few years, but what a great move by the company to go into this space. How did they choose to do that? >> Well it was an opportunity that just came to them through an RFP, the NFL had investigated different technologies to track players including optical and a GPS-based technologies, and now of course with Zebra, our location and technologies are based on RFID. And so we just took the heritage and our capabilities of really working at the edge of enterprises in those traditional industries from transactional moments, to inventory control moments, to analytics at the end, and took that model and ported it over to football, and it's turned out to be a very good relationship for us in a couple of ways. We've matured as a sports business over the four years, we've developed more opportunities to take our solutions, not just in-game but moving them into the practice facilities for NFL teams, but it's also opened up the aperture for other industries to now appreciate how we can track minute types of information, like players moving around on the football field, and translating it into usable information. >> So, for the people that aren't familiar, they can do a little homework. But basically you have a little tag, a little sensor, that goes onto the shoulder pads, right? >> There's two chips. >> Two chips, and from that you can tell where that player is all the time and how they move, how they fast they move, acceleration and all the type of stuff, right? >> Correct, we put two chips inside of the shoulder pads for down linemen, or people who play with their hands on the ground, we put a third chip between the shoulder blades. Those chips communicate with receiver boxes that have been installed across the perimeter or around the perimeter of a stadium, and they blink 12 times per second. And that does tell you who's on the field, where they are on the field, and in proximity to other players on the field. And once the play starts itself, we can see how fast they're going, we can calculate change of direction, acceleration and deceleration metrics, we can also see, as you know with football, interesting information like separation from a wide receiver in defensive back, which is critical when you're evaluating players' capabilities. >> So, this started about four years ago, right? >> Yes, we started our relationship with the league in-game, four years ago. >> Okay, so I'd just love to kind of hear your take on how the evolution of the introduction of this data was received by the league, received by the teams, something they'd never had before, right? Kind of a look and feel and you can look at film, but not to the degree and the tightness of tolerances that you guys are able to deliver. >> Well, like any new technology and information resource, it takes time to first of all determine what you want to do with that information, you have an idea when you start, and then it evolves over time. And so what we started with was tagging the players themselves and during the time, what we've really enjoyed in working with the NFL is that the league has to be very pragmatic and thoughtful when introducing new technologies and information. So they studied and researched the information to determine how much of this information do they share with the clubs, how much do they share with the fans and the media, and then what type of information sharing, what does that mean in terms of impact of the integrity of the game and fair competition. So, for the first two years it was more of a research and testing type of process, and starting in 2016 you started to see more of an acceleration of that data being shared with the clubs. Each club would receive their own data for in-game, and then we would start to see some of that trickle out through the NFL's Next Gen Stats brand banner on their NFL.com site. And so then we start to see more of that and then what I think we've really seen pick up pace certainly in 2017 is more utilization of this information from a media perspective. We're seeing it more integrated into the broadcasts themselves, so you have like kind of a live tracking set of information that keeps you contextually involved in the game. >> Right. And you were involved in advanced analytics before you joined Zebra, so you've been kind of in this advanced stats world for a while. So how did it change when you actually had a real-time sensor on people's bodies? >> Yeah it does feel a bit like Groundhog Day, right? I started more in the stats and advanced analytics when I worked for STATS LLC. In 2007, I developed a piece of software for the New Orleans Saints that they used to track observational statistics to game video. And it was a similar type of experience in starting in 2009 and introducing that to teams where it took about three or four years where teams started to feel like that new information resource was not a nice to have but a need to have, a premium ingredient that they could use for game planning, and then player evaluation, and also the technology could provide them some efficiencies. We're seeing that now with the tracking data. We just returned from the NFL Combine a couple weeks ago, and what I felt in all the conversations that we had with clubs was that there was a high level of appreciation and a lot of interest in how tracking data can help facilitate their traditional scouting and player evaluation processes, the technology itself how can it make the teams more efficient in evaluating players and developing game plans, so there's a lot of excitement. We've kind of hit that tipping point, if I may, where there's general acceptance and excitement about the data and then it's incumbent upon us as a partner with the league and with the teams for our practice clients to teach them how to use the analytics and statistics effectively. >> So I'm just curious, some of the specific data points that you've seen evolve over time and also the uses. I think you were talking about a little bit off camera that originally it was really more the training staff and it was really more kind of the health of the player. Then I would imagine it evolved to now you can actually see what's going on in terms of better analysis, but I would imagine it's going to evolve where coaches are getting that feedback in real-time on a per-play basis and are making in-game adjustments based on this real-time data. >> Well technically that's feasible today but then there's the rules of engagement with the league itself, and so the teams themselves, and the coaches, and the sideline aren't seeing this tracking data live, whether it be in the booth or on the sidelines. Now in a practice environment, that's what teams are using our system for. With inside of three seconds they're seeing real-time information show up about players during practice. Let's take an example, a player during practice who's coming back from injury. You might want to monitor their output during the week as they come back and they make sure that they're ready for the game on a week to week basis. Trainers are now able to see that information and take that over to a position coach or a head coach and make them aware of the performance of the player during practice. And I think sometimes people think with tracking data it's all about managing in the health of the player and making sure they don't overwork. Where really, the antithesis of that is you can actually also identify players who aren't necessarily reaching their maximum output that will help them build throughout the week from peak performance during a game. And so a lot of teams like to say okay, I have a wide receiver, I know their max miles per hour, is, let's use an example, 20.5 miles an hour. He hasn't hit his max yet during the entire week, so let's get him into some drills and some sessions, where he can start hitting that max so that we reduce the potential for injury on game day. >> Right, another area that probably a lot of people would never think is you also put sensors on the refs. So you know not only where the refs are, but are they in the right positions technically and kind of from a best practices to make the calls for the areas that they're trying to cover. >> Right. >> There's got to be, was their a union pushback on this type of stuff? I mean there's got to be some interesting kind of dynamics going on. >> Yeah as far as the referees, I know that referees are tagged and the NFL uses that information and correlates that with the play calls themselves. We're not involved in that process but I know they're utilizing the information. In addition to the referees I should add, we also have a tag in the ball itself. >> [Jeff] That's right. >> 2017 season was the first year that we had every single game had a tagged ball. Now that tagged information in the ball was not shared with the clubs yet, the league is still researching the information, like they did with the players' stuff. A couple years of research, then they decide to distribute that to the teams and the media. So we are tracking a lot of assets, we also have tags in the first down markers and the pylons and I'll just cut to the chase, there are people who will say okay, does that mean you can use these chips and this technology to identify first down marks or when a ball might break the plane for a potential touchdown? Technically you can do that, and that's something the league may be researching, but right now that's not part of our charter with them. >> Right, so I'm just curious about the conversations about the data and the use of the data. 'Cause as you said there's a lot of raw data, and there's kind of governance issues and rules of engagement, and then there's also what types of analytics get applied on top of that data, and then of course also it's about context, what's the context of the analytics? So I wonder if you could speak to the kind of the evolution of that process, what were people looking at when you first introduced this four years ago, and how has it moved over time in terms of adding new analytics on top of that data set? >> That's one of my favorite topics to talk about, when we first started with the league and engaging teams for the practice solution or providing them analytics, they in essence got a large raw data file of XY coordinates, you can imagine (laughs) it was a gigantic hard drive-- >> Even better, XY coordinates. >> And put it into a spreadsheet and go. There was some of that early on and really what we had to do through the power of software, is develop and application platform that would help teams manage and organize this data appropriately, develop the appropriate reports, or interesting reports and analysis. And over the last two or three years I think we've really found our stride at Zebra in providing solutions to go along with the capabilities of the technology itself. So at first it was strength and conditioning coaches, plowing through this information in great detail or analytics staffs, and what we've seen over the last 24 months is director of analytics now, personnel staff, coaches as well, a broadening group of people inside of a football organization start to use this data because the software itself allows them to do so. I'll give an example, instead of just tabular information, and charts and graphs, we now take the data and we can plot them into a play field schematic, which as you know as we talked off camera you're very familiar with football, that just automates the process of what teams do today manually, is develop play cards so they can do self-study and advanced scouting techniques. That's all automated today, and not only that, it's animated because we have the tracking information and we can merge that to game video. So we're just trying to make the tools with the software more functional so everybody in the organization can utilize it beyond strength and conditioning, which is important, but now we're broadening the aperture and appealing to everybody in the organization. >> Do you do, I can just see you can do play development too, if you plug in everybody's speeds and feeds, you have a certain duration of time, you can probably AB test all types of routes, and timing on drops and now you know how hard the guy throws the ball to come up with a pretty wide array of options, I would imagine within the time window. >> Exactly, a couple of examples I could give, when we meet with teams we have every player, let's say on a team and we know all the routes they ran during an entire season. So you can imagine on a visualization tool, you can imagine, it's like a spaghetti chart of different routes and then you start breaking down the scenarios of context like we talked about earlier, it's third down, it's in the red zone, it's receptions. And so that becomes a smaller set of lines that you see on the chart. I'll tell you Jeff, when we start meeting with teams at the Combine and we start showing them their X or a primary receiver, or their slot receiver tendencies visually, they start leaning forward a bit, oh my goodness, we spend way too much time on the same route when we're targeting for touch down passes. Or we're right-handed too much, we have to change that up. That's the most gratifying thing, is that you're taking a picture and you're really illuminating and those coaches who intrinsically know that, but once they see a visual cue, it validates something in their head that either they have to change or evolve something in their game plan or their practice regimen. >> Well, that's what I was going to ask, and you lead right into it is, what are some of the things that get the old-school person or the people that just don't get that, they don't get it, they don't have the time, they don't believe it, or maybe believe it but they don't have the time, they're afraid to understand. What are some of those kind of light bulb moments when they go okay, I get it, as you said, most of the time if they're smart, it's going to be kind of a validation of something they've already felt, but they've never actually had the data in front of them. >> Right, that's exactly right. So that, the first thing is just quantifying, providing a quantifiable empirical set of evidence to support what they intrinsically know as professional evaluators or coaches. So we always say that they data itself and the technology isn't meant to be a silver bullet. It's now a new premium ingredient that can help support the processes that existed in the past and hopefully provide some efficiency. And so that's the first thing, I think the visual, the example I showed about the wide receiver tendencies when they're thrown to in the red zone, that always gets people leaning forward a little bit. Also with running backs, third down in three plus yards, or third down in short situations, and my right-hander to left-hander when I'm on a certain hash. Again the visualization just allows them to really mark something in their head-- >> Just in the phase. >> Where it makes them really understand. Another example that's interesting is players who play on special teams who are also wide receivers, so as we know, linebackers and tight ends tend to be, and quarterbacks tend to be involved in special teams. Well is there an effect when they've covered kick offs and punts, a large amount of those in a game, did that affect them on side a ball play, for instance? Think about Julian Edelman two Superbowls ago, he played 93 snaps against the Atlanta Falcons. and when you look at the route-- >> [Jeff] He played 93 snaps? >> Yeah, between special, because it went into overtime, right? It was an offensive game-- >> And he's on all the-- >> He played a lot of snaps, he played 93 snaps. how does that affect his route integrity? Not only the types and quality of the route, but the depth and speed he gets to those points, those change over time. So this type of information can give the experts just a little bit more information to find that edge. And I have a great mentor of mine, I have to bring him up, Gill Brant, former VP of Personnel to Dallas Cowboys, with Tex Schramm and Tom Landry, he looks at this type of information and he says, what would a team pay for one more victory? >> So as we know, all coaches and professional organizations and college are looking for an edge, and if we can provide that with our technology through efficiencies and some type of support information resource then we're doing our job. >> I just wanted to, before I let you go, just the human factors on that. I mean, football coaches are notoriously crazy workers and, right, you can always watch more films. So now you're adding a whole new category of data and information. How's that being received on their side? Is it, are they going to have to put new staff and resources against this? I mean, there's only so many hours in a day and I can't help but think of the second tier or third tier coaches who are going to be on the hook for going through this. Or can you automate so much of it so it's not necessarily this additional burden that they have to take on? 'Cause I would imagine if the Cowboys are doing it, the Eagles got to do it, the Giants got to do it, and the Washington Redskins got to do it, right? >> Right, right, well each team as you might expect, their cultures are different. And I would say two or three years ago you started to see more teams hire literally by title, director of analytics, or director of football information, instead of sharing that responsibility between two or three people that already existed in the organization. So that staffing I think occurred a couple, two or three years ago or over the last two or three years. This becomes another element for those staffs to work with. But also along that process over the last two or three years is, really, I always try to say in talking to teams and I'll be on the road again here soon talking to clubs after pro days conclude, is forget about staffs and analytics and that idea. Do you want to be information driven, and do you want to be efficient? And that's something everybody can grasp onto, whether you're the strength and conditioning coach, personnel staff or scout, or a position coach, or a head coach, or a coordinator. So we try to be information driven, and then that seems to ease the process of people thinking I have to hire more people. What I really need to do is ask my people that are already in place to maybe be more curious about this information, and if we're going to invest in a resource that can help support them and make them more efficient, make sure we leverage it. And so that's our process that we work with, it varies by team, some teams have large, large expansive staffs. That doesn't necessarily mean, in my opinion the most effective staff is using information. Sometimes it's the organizations that run very lean with a few set of people, but very focused and moving in one direction. >> I love it, data for efficiency, right? In God we trust, everybody else bring data. One of my favorite lines that we hear over and over and over at these shows. >> In fact, I might borrow that next week. >> You could take that one, alright. >> Thank you, Jeff. >> Well John, thanks for taking a few minutes and stopping by and participating in this Western Digital program, because it is all about the data and it is about efficiency, so it's not necessarily trying to kill people with more tools, but help them be better. >> That's what we're trying to do, I appreciate the opportunity and love to talk to you more. >> Absolutely, well hopefully we'll see you again. He's John Pollard, I'm Jeff Frick, you're watching theCUBE from Palo Alto studios, thanks for watching, we'll see you next time. (Upbeat music)