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

produced from the cube studios this is strong by science in-depth conversations about science based training sports performance and all things health and wellness here's your host max smart [Music] [Applause] [Music] all right thank you guys tune in today I have the one and only Dean of big data the man the myth the legend bill Schwarz oh also my dad is the CTO of Hitachi van Tara and IOC in analytics he has a very interesting background because he is the well he's known as the Dean of big data but also the king of the court and all things basketball related when it comes to our household and unlike most people in the data world and I want to say most as an umbrella term but a some big bill has an illustrious sports career playing at Coe College the Harvard of the Midwest my alma mater as well but I think having that background of not just being computer science but where you have multiple disciplines involved when it comes to your jazz career you had basketball career you have obviously the career Iran now all that plays a huge role in being able to interpret and take multiple domains and put it into one so thank you for being here dad yeah thanks max that's a great introduction I rep reciate that no it's it's wonderful to have you and for our listeners who are not aware bill is referring him is Bill like my dad but I call my dad the whole time is gonna drive me crazy bill has a mind that thinks not like most so he he sees things he thinks about it not just in terms of the single I guess trajectory that could be taken but the multiple domains that can go so both vertically and horizontally and when we talk about data data is something so commonly brought up in sports so commonly drop in performance and athletic development big data is probably one of the biggest guess catchphrases or hot words or sayings that people have nowadays but doesn't always have a lot of meaning to it because a lot of times we get the word big data and then we don't have action out of big data and bill specialty is not just big data but it's giving action out of big data with that going forward I think a lot of this talk to be talking about how to utilize Big Data how do you guys data in general how to organize it how to put yourself in a situation to get actionable insights and so just to start it off Becky talked a little bit on your background some of the things you've done and how you develop the insights that you have thanks max I have kind of a very nos a deep background but I've been doing data analytics a long time and I was very fortunate one of those you know Forrest Gump moments in life where in the late 1980s I was involved in a project at Procter & Gamble I ran the project where we brought in Walmart's point of sales data for the first time into a what we would now call a data warehouse and for many of this became the launching point of the data warehouse bi marketplace and we can trace the effect the origins of many of the BI players to that project at Procter & Gamble in 87 and 88 and I spent a big chunk of my life just a big believer in business intelligence and data warehousing and trying to amass data together and trying to use that data to report on what's going on and writing insights and I did that for 20 25 years of my life until as you probably remember max I was recruited out Business Objects where I was the vice president of analytic applications I was recruited out of there by Yahoo and Yahoo had a very interesting problem which is they needed to build analytics for their advertisers to help those advertisers to optimize or spend across the Yahoo ad network and what I learned there in fact what I unlearned there was that everything that I had learned about bi and data warehouse and how you constructed data warehouses how you were so schema centric how everything was evolved around tabular data at Yahoo there was an entirely different approach the of my first introduction to Hadoop and the concept of a data Lake that was my first real introduction into data science and how to do predictive analytics and prescriptive analytics and in fact it was it was such a huge change for me that I was I was asked to come back to the TD WI data world Institute right was teaching for many years and I was asked to do a keynote after being at Yahoo for a year or so to share sort of what were the observations what did I learn and I remember I stood up there in front of about 600 people and I started my presentation by saying everything I've taught you the past 20 years is wrong and it was well I didn't get invited back for 10 years so that probably tells you something but it was really about unlearning a lot about what I had learned before and probably max one of the things that was most one of the aha moments for me was bi was very focused on understanding the questions that people were trying to ask an answer davus science is about us to understand the decisions they're trying to take action on questions by their very nature our informative but decisions are actionable and so what we did at Yahoo in order to really drive the help our advertisers optimize your spend across the Yahoo ad network is we focus on identifying the decisions the media planners and buyers and the campaign managers had to make around running a campaign know what what how much money to allocate to what sides how much how many conversions do I want how many impressions do I want so all the decisions we built predictive analytics around so that we can deliver prescriptive actions to these two classes of stakeholders the media planners and buyers and the campaign managers who had no aspirations about being analysts they're trying to be the best digital marketing executives or you know or people they could possibly be they didn't want to be analysts so and that sort of leads me to where I am today and my my teaching my books my blogs everything I do is very much around how do we take data and analytics and help organizations become more effective so everything I've done since then the books I've written the teaching I do with University of San Francisco and next week at the National University of Ireland and Galway and all the clients I work with is really how do we take data and analytics and help organizations become more effective at driving the decisions that optimize their business and their operational models it's really about decisions and how do we leverage data and analytics to drive those decisions so what would how would you define the difference between a question that someone's trying to answer versus a decision but they're trying to be better informed on so here's what I'd put it I call it the Sam test I am and that is it strategic is it actionable is it material and so you can ask questions that are provocative but you might not fast questions that are strategic to the problems you're trying to solve you may not be able to ask questions that are actionable in a sense you know what to do and you don't necessarily ask questions that are material in the sense that the value of that question is greater than the cost of answering that question right and so if I think about the Sam test when I apply it to data science and decisions when I start mining the data so I know what decisions are most important I'm going through a process to identify to validate the value and prioritize those decisions right I understand what decisions are most important now when I start to dig through the data all this structured unstructured data across a number different data sources I'm looking for I'm trying to codify patterns and relationships buried in that data and I'm applying the Sam test is that against those insights is it strategic to the problem I'm trying to solve can I actually act on it and is it material in the sense that it's it's it's more valuable to act than it is to create the action around it so that's the to me that big difference is by their very nature decisions are actually trying to make a decision I'm going to take an action questions by their nature are informative interesting they could be very provocative you know questions have an important role but ultimately questions do not necessarily lead to actions so if I'm a a sport coach I'm writing a professional basketball team some of the decisions I'm trying to make are I'm deciding on what program best develops my players what metrics will help me decide who the best prospect is is that the right way of looking at it yeah so we did an exercise at at USF too to have the students go through an exercise - what question what decisions does Steve Kerr need to make over the next two games he's playing right and we go through an exercise of the identifying especially in game decisions exercise routes oh no how often are you gonna play somebody no how long are they gonna play what are the right combinations what are the kind of offensive plays that you're gonna try to run so there's a know a bunch of decisions that Steve Kerr is coach of the Warriors for example needs to make in the game to not only try to win the game but to also minimize wear and tear on his players and by the way that's a really good point to think about the decisions good decisions are always a conflict of other ideas right win the game while minimizing wear and tear on my players right there's there are there are all the important decisions in life have two three or four different variables that may not be exactly the same which is by this is where data science comes in the data science is going to look across those three or four very other metrics against what you're going to measure success and try to figure out what's the right balance of those given the situation I'm in so if going back to the decision about about playing time well think about all the data you might want to look at in order to optimize that so when's the next game how far are they in this in this in the season where do they currently sit ranking wise how many minutes per game has player X been playing looking over the past few years what's there you know what's their maximum point so there's there's a there's not a lot of decisions that people are trying to make and by the way the beauty of the decisions is the decisions really haven't changed in years right what's changed is not the decisions it's the answers and the answers have changed because we have this great bound of data available to us in game performance health data you know all DNA data all kinds of other data and then we have all these great advanced analytic techniques now neural networks and unstructured supervised machine learning on right all this great technology now that can help us to uncover those relationships and patterns that are buried in the data that we can use to help individualize those decisions one last point there the point there to me at the end when when people talk about Big Data they get fixated on the big part the volume part it's not the volume of big data that I'm going to monetize it's the granularity and what I mean by that is I now have the ability to build very detailed profiles going back to our basketball example I can build a very detailed performance profile on every one of my players so for every one of the players on the Warriors team I can build a very detailed profile it the details out you know what's their optimal playing time you know how much time should they spend before a break on the feet on the on the on the court right what are the right combinations of players in order to generate the most offense or the best defense I can build these very detailed individual profiles and then I can start mission together to find the right combination so when we talk about big it's not the volume it's interesting it's the granularity gotcha and what's interesting from my world is so when you're dealing with marketing and business a lot of that when you're developing whether it be a company that you're trying to find more out about your customers or your startup trying to learn about what product you should develop there's tons of unknowns and a lot of big data from my understanding it can help you better understand some patterns within customers how to market you know in your book you talk about oh we need to increase sales at Chipotle because we understand X Y & Z our current around us now in the sports science world we have our friend called science and science has helped us early identify certain metrics that are very important and correlated to different physiological outcomes so it almost gives us a shortcut because in the big data world especially when you're dealing with the data that you guys are dealing with and trying to understand customer decisions each customer is individual and you're trying to compile all together to find patterns no one's doing science on that right it's not like a lab work where someone is understanding muscle protein synthesis and the amount of nutrients you need to recover from it so in my position I have all these pillars that maybe exist already where I can begin my search there's still a bunch of unknowns with that kind of environment do you take a different approach or do you still go with the I guess large encompassing and collect everything you can and siphon after maybe I'm totally wrong I'll let you take it away no that's it's a it's a good question and what's interesting about that max is that the human body is governed by a series of laws we'll say in each me see ology and the things you've talked about physics they have laws humans as buyers you know shoppers travelers we have propensity x' we don't have laws right I have a propensity that I'm gonna try to fly United because I get easier upgrades but I might fly you know Southwest because of schedule or convenience right I have propensity x' I don't have laws so you have laws that work to your advantage what's interesting about laws that they start going into the world of IOT and this concept called digital twins they're governed by laws of physics I have a compressor or a chiller or an engine and it's got a bunch of components in it that have been engineered together and I can actually apply the laws I can actually run simulations against my digital twins to understand exactly when is something likely to break what's the remaining useful life in that product what's the severity of the the maintenance I need to do on that so the human body unlike the human psyche is governed by laws human behaviors are really hard right and we move the las vegas is built on the fact that human behaviors are so flawed but body mate but bat body physics like the physics that run these devices you can actually build models and one simulation to figure out exactly how you know what's the wear and tear and what's the extensibility of what you can operate in gotcha yeah so that's when from our world you start looking at subsystems and you say okay this is your muscular system this is your autonomic nervous system this is your central nervous system these are ways that we can begin to measure it and then we can wrote a blog on this that's a stress response model where you understand these systems and their inferences for the most part and then you apply a stress and you see how the body responds and even you determine okay well if I know the body I can only respond in a certain number of ways it's either compensatory it's gonna be you know returning to baseline and by the mal adaptation but there's only so many ways when you look at a cell at the individual level that that cell can actually respond and it's the aggregation of all these cellular responses that end up and manifest in a change in a subsystem and that subsystem can be measured inferential II through certain technology that we have but I also think at the same time we make a huge leap and that leap is the word inference right we're making an assumption and sometimes those assumptions are very dangerous and they lead to because that assumptions unknown and we're wrong on it then we kind of sway and missed a little bit on our whole projection so I like the idea of looking at patterns and look at the probabilistic nature of it and I'm actually kind of recently change my view a little bit from my room first I talked about this I was much more hardwired and laws but I think it's a law but maybe a law with some level of variation or standard deviation and it we have guardrails instead so that's kind of how I think about it personally is that something that you say that's on the right track for that or how would you approach it yeah actually there's a lot of similarities max so your description of the human body made up of subsystems when we talk to organizations about things like smart cities or smart malls or smart hospitals a smart city is comprised of a it's made up of a series of subsystems right I've got subsystems regarding water and wastewater traffic safety you know local development things like this look there's a bunch of subsystems that make a city work and each of those subsystems is comprised of a series of decisions or clusters of decisions with equal use cases around what you're trying to optimize so if I'm trying to improve traffic flow if one of my subsystems is practically flow there are a bunch of use cases there about where do I do maintenance where do I expand the roads you know where do I put HOV lanes right so and so you start taking apart the smart city into the subsystems and then know the subsystems are comprised of use cases that puts you into really good position now here's something we did recently with a client who is trying to think about building the theme park of the future and how do we make certain that we really have a holistic view of the use cases that I need to go after it's really easy to identify the use cases within your own four walls but digital transformation in particular happens outside the four walls of an organization and so what we what we're doing is a process where we're building journey maps for all their key stakeholders so you've got a journey map for a customer you have a journey map for operations you have a journey map for partners and such so you you build these journey maps and you start thinking about for example I'm a theme park and at some point in time my guest / customer is going to have a pity they want to go do something you want to go on vacation at that point in time that theme park is competing against not only all the other theme parks but it's competing against major league baseball who's got things it's competing against you know going to the beach in Sanibel Island just hanging around right there they're competing at that point and if they only start engaging the customer when the customers actually contacted them they must a huge part of the market they made you miss a huge chance to influence that person's agenda and so one of the things that think about I don't know how this applies to your space max but as we started thinking about smart entities we use design thinking and customer journey match there's a way to make certain that we're not fooling ourselves by only looking within the four walls of our organization that we're knocking those walls down making them very forest and we're looking at what happens before somebody engages it with us and even afterwards so again going back to the theme park example once they leave the theme park they're probably posting on social media what kind of fun they had or fun they didn't have they're probably making plans for next year they're talking to friends and other things so there's there's a bunch of stuff we're gonna call it afterglow that happens after event that you want to make certain that you're in part of influencing that so again I don't know how when you combined the data science of use cases and decisions with design thinking of journey Maps what that might mean to do that your business but for us in thinking about smart cities it's opened up all kinds of possibilities and most importantly for our customers it's opened up all kinds of new areas where they can create new sources of value so anyone listening to this need to understand that when the word client or customer is used it can be substituted for athlete and what I think is really important is that when we hear you talk about your the the amount of infrastructure you do for an idea when you approach a situation is something that sports science for in my opinion especially across multiple domains it's truly lacking what happens is we get a piece of technology and someone says go do science while you're taking the approach of let's actually think out what we're doing beforehand let's determine our key performance indicators let's understand maybe the journey that this piece of technology is going to take with the athlete or how the athletes going to interact with this piece of technology throughout their four years if you're in the private sector right that afterglow effect might be something that you refer to as a client retention and their ability to come back over and over and spread your own word for you if you're in the sector with student athletes maybe it's those athletes talking highly about your program to help with recruiting and understanding that developing athletes is going to help you know make that college more enticing to go to or that program or that organization but what really stood out was the fact that you have this infrastructure built beforehand and the example I give I spoke with a good number of organizations and teams about data utilization is that if if you're to all of a sudden be dropped in the middle of the woods and someone says go build a cabin now how was it a giant forest I could use as much wood as I want I could just keep chopping down trees until I had something that had with a shelter of some sort right even I could probably do that well if someone said you know what you have three trees to cut down to make a cabin you could become very efficient and you're going to think about each chop in each piece of wood and how it's going to be used and your interaction with that wood and conjunction with that woods interaction with yourself and so when we start looking at athlete development and we're looking at client retention or we're looking at general health and wellness it's not just oh this is a great idea right we want to make the world's greatest theme park and we want to make the world's greatest training facility but what infrastructure and steps you need to take and you said stakeholders so what individuals am i working with am I talking with the physical therapist am i talking with the athletic trainer am I talking with the skill coach how does the skill coach want the data presented to them maybe that's different than how the athletic trainer is going to have a day to present it to them maybe the sport coach doesn't want to see the data unless something a red flag comes up so now you have all these different entities just like how you're talking about developing this customer journey throughout the theme park and making sure that they have a you know an experience that's memorable and causes an afterglow and really gives that experience meaning how can we now take data and apply it in the same way so we get the most value like you said on the granular aspect of data and really turn that into something valuable max you said something really important and one of the things that let me share one of many horror stories that that that comes up in my daily life which is somebody walking up to me and saying hey I got a client here's their data you know go do some science on it like well well what the heck right so when we created this thing called the hypothesis development canvas our sales teams hate it or do the time our data science teams love it because we do all this pre work we just say we make sure we understand the problem we're going after the decision they're trying to make the KPI is it's what you're going to measure success in progress what are they the operational and financial business benefits what are the data sources we want to consider here's something by the way that's it's important that maybe I wish Boeing would have thought more about which is what are the costs of false positives and false negatives right do you really understand where your risks points are and the reason why false positive and false negatives are really important in data science because data size is making predictions and by virtue of making predictions we are never 100% certain that's right or not predictions hath me built on I'm good enough well when is good enough good enough and a lot of that determination as to when is good enough good enough is really around the cost of false positives and false negatives think about a professional athlete like the false the you know the ramifications of overtraining professional athlete like a Kevin Durant or Steph Curry and they're out for the playoffs as huge financial implications them personally and for the organization so you really need to make sure you understand exactly what's the cost of being wrong and so this hypothesis development canvas is we do a lot of this work before we ever put science to the data that yeah it's it's something that's lacking across not just sports science but many fields and what I mean by that is especially you referred to the hypothesis canvas it's a piece of paper that provides a common language right it's you can sit it out before and for listeners who aren't aware a hypothesis canvas is something bill has worked and developed with his team and it's about 13 different squares and boxes and you can manipulate it based on your own profession and what you're diving into but essentially it goes through the infrastructure that you need to have setup in order for this hypothesis or idea or decision to actually be worth a damn and what I mean by that is that so many times and I hate this but I'm gonna go in a little bit of a rant and I apologize that people think oh I get an idea and they think Thomas Edison all son just had an idea and he made a light bulb Thomas Edison's famous for saying you know I did you know make a light bulb I learned was a 9000 ways to not make a light bulb and what I mean by that is he set an environment that allowed for failure and allowed for learning but what happens often people think oh I have an idea they think the idea comes not just you know in a flash because it always doesn't it might come from some research but they also believe that it comes with legs and it comes with the infrastructure supported around it that's kind of the same way that I see a lot of the data aspect going in regards to our field is that we did an idea we immediately implement and we hope it works as opposed to set up a learning environment that allows you to go okay here's what I think might happen here's my hypothesis here's I'm going to apply it and now if I fail because I have the infrastructure pre mapped out I can look at my infrastructure and say you know what that support beam or that individual box itself was the weak link and we made a mistake here but we can go back and fix it