(upbeat music) >> Announcer: Live, from Las Vegas, It's theCube! Covering Informatica World 2018, brought to you by Informatica. >> Welcome back everyone. It's theCube's live coverage of Informatica World 2018, here in Las Vegas. I'm John Furrier, your host and analyst, with Peter Burris, co-host and analyst at Wikibon and still going on theCube. Our next two guests is Gitesh Ghai, C Vice President, General Manager of Data Quality Security and Governance for Informatica, and Smail Haddad who is the Senior IT Director of Data Governance and Data Delivery Architecture at Toyota, company wide, Great to have you on Gitesh. Great to have you on Smail. So we were just talking before coming on camera, before we went on live about the massive role that you have at Toyota with data. You are looking at everything now. You're touching all the data. But it wasn't always like that. >> Smail: Yeah it wasn't always like that... >> Tell us about your journey and your role at Toyota. >> Yeah thank you. So Toyota, again, started business in North America. People know, maybe not, 65 years ago. And we started as a little dealership in North Hollywood. Bringing these Japanese cars. So we grew from that single dealership in North Hollywood to this big company we are today, with almost 25 plants around North America, Canada, US, and Mexico. And almost 2,600 dealerships across nationwide. So what that came with, it came with a big responsibility, in terms of understanding our customer base and trying to be more closer to what the customer needs. So our supply chains, where we produce the vehicles, it really was mostly a push supply chain, where we build a car and we push it to the customer to buy it. The model works very well, all the way to 2008. Where things change and we all understand what happened back in the financial meltdown and the crisis, that was a worldwide crisis. And that was a turning point for Toyota because we start seeing a shift in the demand. The customers becoming more savvy. Demanding for example, more electrical cars, less gas guzzlers vehicles and so on. The marketing department, which was a different company back then, understood that but the production companies, which was producing the vehicles, they didn't have that knowledge. So the journey to bring these two together became really critical after that 2008 crisis. Because what it forced us to do was the vehicles were being produced everyday, the dealers were not able to sell, and we were just stuck in vehicles around the lot. So why the digital disruption was so key for us, is the data was always there. Data always told us the truth. And that's what the facts are. Where we started looking at, back after that, is hey, if we look at the data and the data always predicted that the shift in the market will happen that way. And we should've have throttled down maybe, our production system better. Why we didn't do it that way? We were not looking at the data. Data was available. So what we undertook, under Toyota IS, we said, "Can we bring all this data across all these silos, "into one place?" So we build our big data solution, where the data is coming from various departments and various business lines. And it's being blended together and correlated. What that gives us is really that 360 view of our business, which we were missing. 'Cause we were looking at the business in silo, in pieces. And with that explosion of data, that we were gathering, obviously that brings a lot of questions about where this data, how good it is, if I'm going to make decisions on it, can I trust it? All that was a good takeaway into the business I'm in, which is the Data Governance. It's basically how can we govern this data that we are collecting on a daily basis today? And so my department is leading basically, the North American Governance and Quality across all the business line in North America. So as we are gathering these data points everyday, on a daily basis, even today we are gathering. What made it even, made it go even further in terms of volume, is we started capturing data coming from the cost, on a real time basis. So this is not just sales data where we capture the experience, the sales, and configuration of the vehicles on a daily basis... >> John: That's a lot of data coming in. >> A lot of it, a lot of it. So the volume exploded. With that, the responsibility to put a solution, where people can go quickly, find the right data. So basically, the time to data became so critical. How can we shorten that time to find the right data you want? And understand it, and trust it, and use it? >> John: So last... >> Sorry John, the Toyota story that you're telling us is especially interesting 'cause Toyota is legendary for empirical based management, lean manufacturing, so you have plants and marketing organizations, and sales organizations who, because of the Toyota way, have grown up on the role that data needs to play in their function. And what you're doing is you're saying, "That was great. "But we had to take it to a next level "and organize our data differently so we could look at it "across the entire company." >> Across the entire company. So absolutely, there are four, basically, goals that Toyota is trying to achieve today. One is understanding our customer in a more personalized way. Understand today's demand and hopefully predict tomorrow's demand. The second important pillar, empower our employees and our team members. By the way, Toyota, we call employees team members. And the third one is optimize our operations. And the fourth is transform our product. In order to achieve all these four goals, data is at the middle of all this. Why it's so important, we understand that today, in this day and age of digital disruption. And by the way, the automotive industry is being disrupted. Not our competition right now, Toyota, is no more the GM, and the Ford, the traditional automotive companies. But our new competition is all the technology companies, Google, Apple, Amazon. And you might have heard the news. Everyday, how they are disrupting these segments where you hear about autonomous driving cars and everybody's jumping on it. And behind all that, taking just the autonomous driving cars. The amount of data behind these so you can make the vehicle drive itself and take you from point a to point b in a safe manner and avoid all the road hazards. That needs a huge amount of data that's behind it, and fuels that. We're able to make huge stride. The new story of Data Governance at Toyota, is really, how we can enable that and not being just about compliance and risk management, which is kind of understood, that's part of the job. But we make that seamless. We wanted our business unit to focus more on the core business and goals, versus worrying about, "Am I in compliance, do I need to do this or that?" Try to seize the opportunities and put Toyota in a competitive way so they can compete with all these new disrupters like I said, Google, and the, the Apple of the world. Because what they have in common, those companies, >> John: They're data companies. >> Exactly. Data companies, technology. They understand how to use data. They understand how to analyze data. This is where traditional automotive companies like Toyota, and GM, and Ford, are basically bound to learn about that. >> But Waymo is not a car manufacturer, Uber is not a car manufacturer, they're companies that are providing a transportation service. And the only way that Toyota could provide a transportation service, is if you started organizing your data differently, in service to the idea of providing consumers a better, and businesses, with better transportation services. Whether you call it personal. I don't want to be the typical analyst that kind of goes off and starts renaming things. But that's fundamentally what you're trying to do. Is you're saying, "Our customers are mainly focused "on getting from point a to point b safely. "Let's make sure that we have products and services "that help them get there. "Perhaps through a lot of intermediaries along the way." But is that kind of how you're organizing things? >> Absolutely, so in order to achieve that goal. We wanted to bring the silos. Like I said, the data was always there but it was always built in silos, stored in silos. What we did in the next, last few years, we started breaking all the silos because we started looking at the data as an enterprise assets and no more as just a departmental assets or as a tool to get to a goal. It became the strategic assets for the company. And in order to achieve that, was to really break the silos. Bring it together so we can see across and understand how are business is operating. And hopefully, put the company in a competitive advantage to see the future coming to. >> It must be really frustrating to know that the data was there the whole time. And you're kind of kicking yourself. What did you do? I mean, you brought Informatica in. What's the Informatica connection, Gitesh? Get a word in, come on. With the Informatica connection, these guys. Are you the core supplier? Do you guys, the connective tissue between Toyota's groups? >> It's all about the data, right? It's all about the data. Informatica's role in all of this, it's a great story. Toyota's, Smail's story, is a great story. What Informatica brought to bear for Toyota, it's actually the promise of big data. The promise of big data is bringing together data that hasn't been analyzed together in a new context before. So breaking down these silos and bringing together the data. What's interesting is when you bring it together, you create a data lake. But there's a very big difference between a data lake and a data swamp. Which is why naturally, governance, quality, trustworthiness became a focus area of bringing all of this data together. >> Well last year, talking about data swamp and data lake as our core theme. This year governance and enterprise catalog is a bigger story because you guys easily could've been swamped out because of all this new data coming in, whether it's car telemetry or new data. 'Cause if you had set the table for your intercompany connective tissue, if you will, then you're like, "Oh, hey we're done, wait a minute." >> But Toyota was applying data to the work of manufacturing, to the work of marketing cars. And now you're trying to apply data to the work of providing better transportation. And the only way to think that through is to see how all this data can be reorganized and brought together. And at the same time, you can still, then turn that data around and still apply it for the work of manufacturing, the work of marketing, and the work of selling. >> Gitesh: Absolutely. >> Also I'd add, to be competitive in a new market, they are going to use their, leverage their assets. Not only data but their physical assets. To compete at a new level, a new playing field. >> Smail: Absolutely. >> With data at the center. >> And I think you said it earlier, you have to bring this data together in the lake. But you need an organized view of all the data that's out there, which starts with our data catalog. So the data catalog gives you a sense of what data do you want to bring in the lake and what data, frankly, is noise, doesn't matter? >> Whole 'nother level of operations, whole 'nother level of intelligence. Competitive advantage, competitive strategy. >> Peter: What a job. >> We're data geeks, geeking out here. Great story, I'd like to do a follow up. I think that this is a real big story of not only of digital transformation, digital evolution, digital disruption, digital business, great story... >> You used to be able to do this job in Southern California. >> Yes, absolutely. >> Thanks for bringing Toyota to the table. Thanks for coming on. >> My pleasure. Thank you for having me on. >> The beginning of a journey that's going to continue it's not ending anytime soon. Toyota company, really bringing data into the center of the action. Of course, we're in the center of the action as theCube, bringing you the data from Informatica World, right here, on theCube. More coverage after this short break. I'm John Furrier, Peter Burris. Stay with us, we'll be right back. (upbeat music)

so two points max first off ideas aren't worth a damn ever he's got ideas all right I could give a holy hoot about about ideas I mean I I I got people throw ideas at me all the friggin time you know I don't give a shit I just truly told give a shit right I want actions show me how I'm gonna turn something into an action how am I gonna make something better right and I I want to know ahead of time what that something is am I trying to improve customer attention trying to improve recovery time for an athlete who's got back-to-back games right III I know what I'm trying to do and I want to focus on that where ideas become great and you said it really well max is ideas are something I want to test so but I know what I want to test these of the event what outcome I'm trying to drive so it isn't just it is an ideation for the I eat for the sake of ideation its ideation around the idea that I need to drive an outcome I need to have athletes that are better prepare for the next game who can recover faster who are stronger and can you know it can play through a longer point of the season here we are in March Madness and we know that by the way that the teams that tend to rise to the top are the teams that have gone through a more rigorous schedule played tougher teams right they're better prepared for this and it's really hard for a mid-major team to get better prepared because they're playing a bunch of lollipop teams in their own conference so it's it's ideas really don't excite me ideation does around an environment that allows me to test ideas quickly fail fast in order to find those you know variables or metrics those data sources it just might be better predictors of performance yeah I like the idea of acting quickly failing quickly and learning quickly right you have this loop and what happens is and then I think every strand coach in the world is probably guilty of this is we get an idea and we just apply it you go home you know I think eccentric trainings this great idea and we're going to do an eccentric training block and I just apply it to my athletes and you don't know what the hell happened because you don't have any contextual metrics that you base your test on to actually learn from so you at the day go I think it worked you know they jump high but you're not comparing that to anything right they jump they've been the weight room for three months my god I hope they jump higher I hope they're stronger like I can sit in the weight room probably get stronger for three months and my thought is but let's have context and it's um I call them anchor data points they were always reflecting back on so for example if I have a key performance metric where I want to jump high I'll always track jumping high but then I can apply different interventions eccentric training power training strength training and I can see the stress response of these KPIs so now I've set an environment that we have our charter still there my charter being I'm going to improve my athletic development and that's my goal I'm basing that charter on the KPI of jumping high so key performance indicator of jumping high now I can apply different blocks and interventions with that anchor point over and over again and the example I give is I don't come home and ask my girlfriend how she's doing once every month I ask her every day and that's my anchor point right and I might try different things I might try cookie and I might try making dinner I might do the dishes I might stop forgetting our dates I might actually buy groceries for once well maybe she gets happier then I'll continue to buy groceries maybe I'll remember it's her birthday March 30th I remember that that's my put it on there right and so but the idea is we have in life the way life works we have these modular points where we call anchor points where we were self-reflect and we reflect off of others and we understand our progress in our own life environment based on these anchor points and we progress and we apply different interventions I want this job maybe I'll try having this idea outside of here maybe I'll play in a softball league and we're always reflecting it's not making me happier is that making me feel fulfilled and I don't understand why we don't take what we do every day and like subconsciously and apply it into the sports science world but lava is because it happens unconsciously because that's how our body has learned to evolve we have anchor points I want to survive I want to have kids lots of kids strong kids and I and I die so my kids can have my food and that's what we want as a body right your bison care about anything else and so that's why you walk with a limp after you get hurt you don't want perfect again it's a waste of energy to walk perfect right you can still have kids with a limp I hate to break it to you right we're not running from animals anymore and so we have all these anchor points in life let's apply that same model now and like you said it's like design thinking and actually having that architecture to outline it whether it's in that hypothesis canvas to force us to now consciously do it because we're not just interacting with ourselves now we're interacting with other systems other nodes of information to now have to work together in use in to achieve our company's charter interesting max there's a lot of a lot of key points in there the one that strikes me is measurement John Smail at Procter & Gamble I was there you still I say you are what you measure and you measure what you reward that was his way of saying as an organization that the compensation systems are critical and the story just walked through about what Kelsey right and what you guys are doing and how you increase your your happiness level right now here's the damnest your work I mean that is that is how you're rewarded right if you are rewarded by happiness and so you you learn to measure if you're smart right that you don't miss birthdays that you do dishes you you you help up around the house you do things and when you do those things the happiness meter goes up and when you don't do those things happiness meter goes down and you know because you're you're you're probably pulling not just once a day but as you walk by her throughout the day are on a weekend you're you're constantly knowing right if if you're liking your mom you know when mom's not happy you don't need to be a day to sign this and know mom's not happy and so then you you know you re engineer about okay what did I do wrong that causes unhappiness right and so life is a lot of there's a lot of life lessons that we can learn that we can apply to either our business our operations or sports whatever it might be that your your profession is in about the importance of capturing the right metrics and understanding how those metrics really drive you towards a desired outcome and the rewards you're gonna receive from those outcomes yeah and with those it's the right metrics right that's what not metrics the right metrics if I want to know if someone was happy I wouldn't go look at the weather I wouldn't you know check gas prices especially if I'm curious they're happy with me well maybe they might reflect if they're happy in general if they're happy with me right now I'm contextualizing I'm actually trying to look at I know a little bit more about what I should look at I don't know everything and so you might have metrics that you say you know I know science says this metric is good this metric is good maybe we want to explore of these couple of metrics over here because we think that either aid they're related to one of these metrics or they related to the main outcome itself and that gives you a way to then I have these key and core metrics that's not stacking the deck but it's no one you're gonna get insights out of it and then I have these exploratory metrics over here but you're gonna allow me then to dive and explore elsewhere and if you're a company those can be trade secrets they can be proprietary information if you're a trainer it can be ways to learn how different athletes adapt to make yourself better and again we're talking about a company and we're talking about trainer there's no difference when it comes to trade secrets right trainers keep their trade secrets and companies keep their trade secrets and as we talk about this it's really easy to see how these two environments where they're talking about company athletic development sports science personal training health and wellness are really universally governed by the same concepts because life itself is typically governed by these concepts and when we're playing those kind of home iterations to it you can really begin to quickly learn what's going on and whether or not those metrics that you we're good ARCA and whether or not you can learn new metrics and from that max you raise an interesting question or made a point here that's I might be very different in the sports world than it is in the business world and that is the ability to test and what I mean by that is you know the business world is full of concepts like a bee testing and see both custody and simulations and things like that when you're dealing with athletes individually I would imagine it's really hard to test athlete a with one technique and athlete B with another technique when both these athletes are trying to maximize their performance capabilities in order to maximize you know the money there can they can they can generate how do you deal with that so yes no one wants to get the shitty program yes that's correct yeah for the most part people don't and this I'll take people don't test like that and but here's my solution to us I think being a critic without solutions called being an asshole my solution to that is making it very agile and so we're not going to be able to you know test group a versus group B but what you can do if you're a coach and you have faith in because there are a lot of programs coaches use coaches probably use you know every offseason they might try a new program so there's no real difference in all honesty to try a new program on you know these seven athletes versus and then try a different one that you also trust on these seven athletes and part of that comes from the fact that we have science and evidence to show that both these programs are really good right but there's no one's actually broken down the minutiae of it and so yes you probably could do a and B testing because you have faith in both programs so it's not like either athletes getting the wrong program they're both getting programs that are going to probably elicit an outcome of performing better but who wants to perform the best the second asks the second aspect would be what kind of longitudinal data that you can collect very easily to understand typical progression of athletes for example if you coach and you coach for eight years you'll have you know eight different freshman classes theoretically and you'll begin to understand how a freshman typically progresses to a sophomore in what their key performance indicators typically trend ass and so you can now say okay last year we did this this year we do this I'm gonna see if my freshman class responds differently is this going to give us the perfect answer absolutely not no but without data you're just another person with an opinion that's not my quote I stole that quote but it's true because if we don't try and audit ourselves and try to understand the process of how is someone developing then we're just strictly relying on confirmation bias I mean my program was great you know Pat some guys in the back that jumped higher and we did awesome if we're truly into understanding what's best then we'll actually try and you know measure some of these progress some of this some of these KPIs over time in the example I give and it's unfortunate and fortunate I don't mean anything bad by this either we're on a salary right and so what happens when you're on a salary is no matter really what happens assuming you're doing your job you're gonna keep your job but if you look at a start-up a startup has one option and that's to make money or go out of business right they don't really have the luxury of oh we're just gonna you know hang out and not saying coaches hang up or not we're just gonna you know keep this path we're going on as a coach you know how do I apply a similar model well I start up the bank my startup is you can go from worth zero dollars to worth a hundred you know million two billion dollars in one year at the coach we don't have that same environment because we're not producing something tangible which doesn't always it doesn't have the same capitalistic Drive right the invisible hand pushing us the same way the free market does with you know devices and so we don't always follow the same path that these startups have done yet that same path and same model might provide better insights so max you've hit something I found very interesting confirmation bias if if you don't take the time before you execute a test understand the variables that you're gonna test what happens is if you after the test is over you go back and try to triage what the drivers were that impact and confirmation bias and revisionist history and all these other things that make humans really poor decision-makers get in the way and so but before as a coach I would imagine before as a coach what you'd want to do is is set up ahead of time we're gonna test the following things to see if they have impact by thoroughly like the hypothesis development canvas right they'll really understand against what you're really going to test and then when you've done that test you you will you would have much more confidence in the results of that test versus trying to say wow Jimmy Jimmy jumped two inches higher this year thank God what did he do let's figure out and revision it wasn't what he ate was it where he slept oh he played a lot of video games that must be it he is the video games made him jump higher right so it's I think a lot of sports in particular even more than the business for a lot of sports is based on on heuristics and gut feel it's run by a priesthood of former athletes who are were great because of their own skills and capabilities and it maybe had very little do with her development and I don't want to pick on Michael Jordan but no Michael Jordan was notoriously a poor coach and a poor judge of talent he made some of the most industries when the worst draft choices industry has ever seen and that's because he mistakenly thought that everybody was like him that he revision history about well what made me great were the following thing so I'm gonna look for people like that instead of reversing the course and saying okay let's figure out ahead of time what makes what will make you a better plant player and then trying these tests across a number of different players to figure out okay which of these things actually had impact so sports I think has gotten much better Moneyball sort of opened that people's eyes to it now we're seeing now more and more team who are realizing that that data science is as a discipline it's not something you apply after the fact but in order to really uncover what's the real drivers of performance you have to sit down before you do the test to really understand what it is you're testing because then you can learn from the tests and and let's be honest right learning is a process of exploring and failing and if you don't try and fail enough times if you don't have enough might moments you'll never have any break to a moment and I think what people don't understand is they hear the word fail and assumed oh we did a six-month program and failed nope failure can occur in one day and that's okay right you can use for example I'm going to use this piece of technology as motivation for biofeedback to increase my athletes and tint and the amount of effort they put into the weight room that's right hypothesis you can test that in one day you print out that piece of technology the athletes don't respond well you'd have learned something now okay that technology didn't bring about the motivation I thought why was that you can do reflect and that revision because you had the infrastructure beforehand on maybe notes that you may have taken and scribbled down on your pad or observations from the coaches I am I but you know what the athletes weren't very invested because the technology took too long to set up right it wasn't the technology's fault it was the process of given technology available to act and utilize on so maybe you retest again with it set up beforehand or a piece of technology that's much easier to use and the intent increases so now you say okay it's not the technology's fault it's the application of how we're using the technology at the same time we hear a lot of things like I'm gonna take a little bit of pivot not too far though is in the baseball world you see technology being more used more and more as a tool and it's helping guide immediate actions on the field whether it's not it's a you know spin rates its arm velocities with accelerometers or some sort of measurement they decide to use but that's not necessarily collecting data that's using technology as a performance tool and I think there's a distinction between the two the two are not mutually exclusive you can still use it as a performance tool but that performance data if the infrastructure is not there to store a file and reflect and analyze it's only being used one-sided and so people think oh we're doing sports science we're doing data science because we're collecting data well that's not I can go count ants that's collecting data but that's not you know I don't unless I count ants every day and say oh my game populations decreasing right and kind of a here's a really easy way to think of it in my opinion you have cookies in the fridge right and every day I go and every week will say my mom makes cookies this doesn't happen I wish it did be very cool but I love your mom and we didn't eat cookies every week but in the fridge I go when I count how many cookies there were right and using data I'd say oh twelve cookies if there's any cookies at all I can eat right that's using technology and that moment but doing data Sciences well you know what she's gonna make you know twelve and a couple of days and I have two days left and there's six cookies I can eat three today and three tomorrow because now you're doing prescriptive analytics right because you are prescribing an action based on the information you collected it's based on historical data because you know that every seventh day the cookies are coming no I just take it as I'm using technology as a tool I might only eat one cookie and forever be leaving six cookies on the table right and so there's hid don't want to do that no we don't but we trick ourselves I think we see that not saying baseball does is but I'm saying we've see that in all domains where we use technology we say oh technology good we had someone use technology that's data science no that's not data science that's using technology to help Tripp augment training using data Sciences understand the information that happened during the training process looking at it contextually to them prescribed saying I'm going to do this exercise or this exercise based on the collection and maturation of the information so instead of cookies here I eat one cookie it's a historic Lee I know there's going to be twelve cookies every seven days I have two days left I can eat three cookies now I can hide two and tell my sister Amelia oh there's only one left very weird I don't know who ate data - well let max let me let me let me wrap up with a very interesting challenge that I think all all data scientists face wellmaybe all citizens of data science face and I say did as citizens of data science I mean people who understand how to use the results of data science not necessarily people who are creating the data science and here's here's the challenge that if you if you make your decisions just based on the numbers alone you're likely to end up with suboptimal results and the reason why that happens is because there's lots of outside variables that have huge influence especially when it comes to humans and even machines to a certain extent let me give you an example know baseball is is infatuated with cyber metrics and numbers right everybody is making decisions we're seeing this now in the current offseason you know who was signing contracts and who has given given money and they're using they're using the numbers to show you know how much is that person really worth and and organizations are getting really surgical and their ability to figure out that that person is not worth a you know a six year contract for you know 84 million dollars they're worth a two-year contract for 36 and that's the best way I'm gonna you know pay but minimize my risks and so then the numbers are really drive and allow that but it isn't just the big data that helps to make decisions and in fact I would argue the insights carried from the small data is equally important especially in sports and I think this is a challenge in other parts of the business is the numbers itself the data itself doesn't tell the full story and in particular think about how does an organization leverage the small data the observed data to really help make a better decision so right now in baseball for example in this offseason the teams became infatuated with using numbers to figure out who were they going to offer contracts to how much they were going to pay him for how long and we saw really the contracts in most cases really shrinking and value in size cuz people are using the numbers and comparing that to say always so and so it only got this you're only going to get this and numbers are great but they miss some of the smaller aspects that really differentiate good athletes from great athletes and those are things like fortitude part you know effort resilience these these kind of things that aren't you can't find that in the number so somebody's ability to a closer write who goes out there in the eighth-inning and and just has a shit performance gets beat up all over the place comes back in it still has to lead and and does that person have the guts the fortitude to go back out there after us bad eighth-inning and go do it again who can fight through when they're tired it's late in the game now you've been playing it's a you know 48 minute game you've been playing forty minutes already you've hardly had a break and you're down by two the balls in your hand a three-pointer is gonna win it what are you gonna do my numbers don't measure that it's theirs these these these other metrics out there like fortitude at heart and such that you actually can start to measure they don't show up a numbers where they come from the inside some subject matter experts to say yeah that person has fight and in fact there's one pro team that actually what they do in the minor leagues they actually put their players into situations that are almost no win because they want to see what they're gonna do do they give up or do they fight back and and you know what you again you can't batting average then tell you that if somebody's gonna get up and that you're gonna give up it's a ninth in and you think you've lost you know what I don't want that person out there and so think about in sports how do you complement the data that you can see coming off of devices with the data that experience coach can say that that person's got something extra there they got the fight they have the fortitude they have the resilience when they're down they keep battling they don't give up and you know from experience from from playing and coaching I know from playing and coaching the guy is going to give up you know who they are I don't want them on the court right it made me the best player from a numbers perspective hell if that was the case Carmelo Anthony would be an all-star every time his numbers are always great the guide lacks heart but he doesn't know how to win so think about how as an organization a sporting organization you use the metrics to help give you a baseline but don't forget about the the soft metrics the servable things that you got to tell you that somebody has something special that is an awesome way to bring this together because subject matter experts those are people who have been in the trenches who see it firsthand date is here to augment you in your decisions it's not here to override you it's not here to take your place and so in coaches fear data it's the silliest thing ever because it's giving more ammo to a gunslinger that's all it does right it's not going to win the battle right it's just the bullets you got to still aim it in fire and so when we look at it in regards to performance and athletic development all these numbers they'll never be right ever they'll never be 100% perfect but neither will you and so what we're trying to do is help your decisions with more information that you can process into your brain that you might otherwise not be able to quantify so it's giving that paintbrush not just the color red but given all the colors to you and so now you can make whatever painting you want and you're not constrained by things you can't measure yourself I could add one point max to bill on that data won't make a shitty coach good but it will make a good coach great yeah yeah I couldn't agree more well dad thank you for being on here I really appreciate and for everyone who's listening this is going on prime March Madness time and so to pull away the dean of big data from March Madness who for people listening he made his bracket on the Google cloud using AI and so it only he so I was thanking him to come here and only he would be the one to I guess take I don't say take the fun out of it but try and grid the family bracket for used it all augmented decision-making he possibly can like it the data will make won't make somebody shitty good and I'm still not good Google Cloud couldn't help me I still at the bottom of the family pool it's great to have you in I guess every minute here is worth double being that's March Madness time thanks max for the opportunity it's a fun conversation alright thank you guys for listening really appreciate it and [Music] [Applause] [Music] you