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welcome to the data strategy show my name's samir sharma and i'll be your host for the next 60 minutes my guest today is andrew wright andrew manages the walt disney world's commercial strategy data science team in his 14 years with the company he has also supported marketing and operations analytics prior to joining disney andrew worked in financial services and consulting firms where he learned the value of performance storytelling backed with data he has been selected to present at several disney data and analytics conferences and is often invited to speak to corporate and non-profit organizations about the value of analytics holding an undergraduate degree in foreign affairs as well as an mba and master of applied science andrew enjoys finding non-obvious solutions for businesses biggest decision-making challenges i hope you enjoy this episode and please do like share and leave your feedback thank you for listening welcome andrew wright to the data strategy show it's uh really fantastic to have you here um andrew why didn't you uh just say hello to everybody sure well thanks for having me samir um andrew wright uh working with disney as a as a senior data scientist focusing on commercial endeavors um and i'm super excited to get to talk to you today samir that's great now and and thank you for for being my guest today it's um we talked about this um pre-christmas and we're finally here um so it's it's really great and um you know what what i always like to start with is is andrew you know tell me a little bit about yourself your journey you know through data um obviously at about 30 000 feet uh and then we'll get straight into it yeah sure um so right now i i uh currently i've been 14 years at uh walt disney uh parks um current segment name is disney parks experience and products so we're continuing to to bolt on other businesses to our responsibilities um but i started my uh sort of data career um while i was in college um i was working on a foreign affairs so a political science type of career but i had a sort of a night job where i was doing some data entry for a financial publishing firm and i started to within that particular position um both while i was working part-time and then they offered me a position as i came out of college um learning that data has power and as i've moved through my career there uh and in subsequent positions i've also learned that that storytelling with data or being able to tell a compelling story uh derived from data or defended by data has enormous influence on the people who are running our organizations um running our charities running our philanthropic organizations and people who can do this type of work and do it well are are very rare uh and so i've tried to position myself professionally as always being that person who can bring to the table a compelling story but one that is rooted in empirical data evidence and that's brilliant and i love the fact that you were so so political science you went from political science to data well that's quite a leap um it was yeah you know and unless uh unless you know as a political scientist it's not that difficult to make the leap into data science because it's all about data you know at the same time really isn't it yeah well for me for me it was the fact that i saw people who were way way smarter than i was or i am um not struggling with the foreign service exam and i said well then that's this is just not for me i need to figure out some other path uh and but i was able to find um something that that speaks to me personally professionally something that seems to be um well received by by forward thinking organizations and so i'm really excited to be able to to bring a perspective um that combines sort of the the critical thinking that one finds in something like political science exactly but with uh with with elements with empirical evidence with with data so do you think that uh you know as a political scientist as as having that base and say if i'm you know um i'm i'm you know finishing my degree at university and i actually am studying um data uh analytics or i'm doing some kind of business analytics degree or you know i'm doing computer science what types of things would you suggest to that individual who wants to become a data scientist you know 14 years in your career but starting out very much you know at that level where oh my goodness i really like this thing called data but what do i need to get there right right now the kinds of attributes that you might you know say right now today 14 years on qualify for being a really good data scientist yeah and it's a great question and and i appreciate that because um oftentimes people who are in my position don't get asked that question but at the same time um we're we're trying to find appropriate candidates we're trying to onboard new talent etc we're trying to make things better and so it's always nice to be able to go back and say well if you're in this position right now these are the things that i as a as a hiring manager or someone who wants to work with you would be looking for so so thank you for asking that um i would say that one thing is certainly critical thinking and that's something that that you know when i was in school uh working on political science um we spent a lot of time writing um and and using critical thinking to sort of deconstruct thoughts um or to take an audience from point a to point b to point c so on and so forth as opposed to sort of non-linear thinking and jumping from a to you know c at the very end um so critical thinking is certainly certainly one of those things um that i look for when i'm hiring data science uh the second is the ability to communicate and that that takes many many different forms um and i i don't i don't want to say that there's only one way to communicate that you'd actually be focusing on on one sort of mode um but communication takes uh forms of visualization it takes forms of speaking it takes forms of you know formal presentation ballroom style it takes the form of facilitating conversations and holding q a and critical listening and active listening so i think that's that's another skill that um that we absolutely look for when we're trying to bring somebody into into our organization is um is that communication and their communication style and making sure that it balances out with our um with how we internally communicate yeah yeah but you know that there's there's stories and there's critical thinking and there's the ability to communicate how um how important then you know is it for me to come to to come into the interview come into you know my first day at the job knowing every single r python sql blah blah all of these things how important is that versus these softer skills yeah i i would certainly put them as sort of tertiary um you need to be able to hold an intelligent conversation about a concept um but but by and large the every business i'm you know every business that i've worked in has been materially different in so many ways in terms of how they store their data where they persisted how you access it but there are there are common themes that sort of stretch across every single position that i've ever been in both from when i've been in junior roles to roles like i am now senior when i've worked in finance you know banking when i've worked in real estate and now working with theme parks and entertainment and hospitality and what is sort of common across all of those is critical thinking skills it is communication skills it is the ability to convey your thoughts in a manner that is reset received by your audience and received well it's the ability to influence by not just what you know but how you express what you know and so you know dickering about well should we be using uh linear regression here should we be using a decision tree here should be using some more you know arcane new uh you know new cutting edge algorithm here that that takes up very very very little of our time what we're what we're usually talking about most of the time is what is the business question do we really understand it completely yeah can we provide concrete examples that that solve the business problem that we're trying to solve that's where we start and then we go figure out the algorithms in the wall are we going to use r we're going to use python are we going to use this package are we going to use that yeah so that's that is the tail of the you know the big follow of which is critical thinking communication understanding a business partner working with a business partner in a uh in a in a very um influential way yeah and i think i i love that because i get asked so many times on linkedin um can you tell me what what things i need to learn to become a data analyst or a you know a data scientist and i often you know do talk about the softer skills and i never hear back from that person again because you know i can always learn even you know i can always learn how to program i can always learn that that that aspect of it but actually sitting in front and you know one thing that you mentioned about listening that that ability to listen and really understand and ask the questions at the right time is so key to getting that the right outcome you know and and it and it's something that um uh i'd like to explore in a little bit more with you because you recently uh did a presentation um i think it was it fiesta i think it was the uh djack yeah so it was a disney conference but the title of it was your organization wants analytics but needs insights and in brackets in parentheses you said how to transition to insightful thinking so you know that i just just elaborate on that you know what what what is your what is your hypothesis around what's the what's the thinking behind that because that's a that packs a massive punch um as a title well thank you thanks i i i would do a little stint in marketing so i know how to you know i know a couple of the tricks on how to get eyeballs i need to learn from you i need to learn from you i can tell you how not to do it as well um so so something that i've noticed throughout my own professional career um and i i've i've talked with peers i talk to peers at different levels i've talked to people in academia as well as in you know the more standard industry and one of the things that that that i have just kind of seen throughout the entire trajectory is it can be fairly easy to find technically oriented analytical talent right like it's very easy to check off you have this degree you know this particular programming language you know this particular algorithm but at the end of the day in most cases most businesses the businesses that i work with and the partners that i've worked with that is a means to an end right and the end is what is generally referred to as insight right they in my perspective what what they're going for when they use terms like insight is they want to make decisions better they want to make them smarter they want to minimize risk they want to feel that out of the thousands of things that it could be doing at any particular decision point in a fork in the road if they're taking the best path that's going to yield the best result however that's measured measured in many different ways so it's not about i need to use this particular algorithm or it's i need to know this programming language or i need to you know have sql it's more i need to be able to help this decision maker feel confident in the decisions that they're making and i need to give them sort of the the unvarnished pros and cons to the different decisions that they can make the different paths that they can take and so in my experience that's really what people want when they say they want insights they want incremental knowledge that uh sort of changes or enhances their view of how the world works so that they can make better decisions so that's that's what i think that most organizations are really looking for out of analytics yeah and and i think that that expression you know i want to see the insights and i think what what you're saying is tell me something that i don't already know tell me something that i don't know about my business that is going to be a game changer and that's going to allow me to either pivot or to to think in a different you know certainly from a commercial sense because walt disney is absolutely commercial in that sense and you said it before you know parks you know recreation hospitality the ecosystem that you work in is a really really interesting one because not only are you you know you you're uh you you've got the theme park and you've got the hotel and you've got the whole experience how do you connect all of that together and provide the the insights that you know um that the executive or or the commercial individual needs to drive their part of the business um you know it's it's it must be fascinating it is it is fascinating i mean the the the psychology behind um our guests what we refer to as our customers is our yes the psychology behind our guests and um you know i contrast it with where i was before i came to disney i worked for a large bank and people certainly have very um they may have strong opinions about their financial institutions so insofar as you know they want someone pleasant to speak with when they have a problem they want they want to make sure that their money is safe and secure uh that it's not going to get lost um and in the case of investments they want to make sure that those investments are growing but many of those things are by and large sort of um objective uh objectives that they're they're that they're going for when you think about something like a a brand such as disney whether it be um the content that we put into our movies and into our television shows when you think about um the intellectual property that we have in our parks and the experiences that you know all the wonderful things that people can do in our parks around the world when you think about the programming that we have on television when you think about uh you know disney streaming and so on and so forth yeah it it is massive and so you know people's uh people's objectives what what they value what they want out of their relationship with our brand is is a bit different from a financial institution or a part or a pharmaceutical company or they're they're automotive selections etc and so we obviously want to be good stewards of that but um it's a very different psychology and it takes a number of different themes working in collaboration with each other some on you know the harder purist type of sciences um some of those sciences may be operationally focused so we have operations researchers and industrial engineers okay we have consumer insight teams who are focusing focusing on psychometrics and kind of keeping a pulse uh on on our guest and consumer base and then you've got you know folks like myself who are focused on what is it that can be done to drive the highest and greatest value out of our assets so our cultural property our parks our hotels food and beverage the merchandise that we sell etc so it is a it is a a whole symphony of different teams coming together to think about that problem and and lend their particular voice uh so that again our most senior most executive uh decision makers can think through all the possible possible paths that they could take and come up with the ones that are securing you know the best experience for our guests the best uh return for our shareholders uh the best uh sort of working conditions and experience for our cast members our employees and that's that's hopefully what they're doing every single day so that sounds like a pretty complex ecosystem you've just talked about it is how do you orchestrate that you talked about it being a symphony you know being at the level where you're at you're you know um you're looking at this thing and saying right i may be dealing with transactional data today and i may be dealing with that customer type of data but then suddenly i'm dealing with streaming data and suddenly i'm dreaming with you know i i'm dealing with different types of data coming from every single direction and i've got you know the value piece which i need to deliver because my commercial team are saying we need x y and z but then you've got the engineers you've got the in you know the the the sort of um what did you call them the industrial engineers and the um the sort of more scientific types yeah and then you've got your your data engineers and then you've got your you know data scientists and you've got bi people and how are you orchestrating that so that you're absolutely being able to hit those goals and objectives sure um so we rely very heavily upon our partners in technology um who help produce data platforms for us and and in many cases um they're they're also a consumer of these same products as well these these same sort of technology products as well data products so that um they have a vested interest because they're also they have their own mandates for what it is they're trying to do but um it all sort of starts with the business question what is it we're trying to answer um are we trying to answer operationally focused um types of questions which may have a very different latency than are we trying to make finance decisions are we trying to make marketing decisions um these are all work off of very different cadences so we try to start with uh what is the business need what is the latency associated with that business decision sure um and then kind of work backwards from there um but always always always hand in hand with our technology teams who are are great at delivering um technology products that have the data have the data at the right cadence um and are exposing it to only the people who need to have access to it uh putting those appropriate safety and guardrails on top of them as well um so a lot of it is really driven by their expertise and their partnership and and i know that there are ideal scenarios in every company around how teams work together but what are some of the challenges that you've had to overcome within that and i don't i you know i i'm sure lots of people out there who will be listening to this will have similar challenges so you know it's just trying to piece that together and say well actually this is how we got over it and this is what we did and you know it could be an i.t challenge it could be a data quality challenge it could be a thinking it could be a systemic problem you know how do you kind of deal with those challenges yeah they're they're certainly you just pick a couple of examples yeah no this is a loaded question sorry disney's not immune to that i don't think there's any organization is i mean i think you know the ones that we that we think of when we think of well you know they must have everything solved they must you know have everything buttoned up i'm sure even those organizations have their own moments of of struggle at the very least and disney's no exception to that but uh i think one of the um one of the sort of the the back end of the sword for um all the fantastic developments that have gone on in terms of data creation data collection um algorithmic synthesis of those uh of that data the computing power that we're seeing now compared to just even five years ago 10 years ago um the second edge to that particular sword is that there also seems to be a rise in the culture of well the data will solve my problem my algorithm will solve my problem yeah and one of the things that i talked about in my disney analytics conference uh uh talk was that i personally have never seen data in a database or a specific algorithm that actually created this thing that we call insight right like in insight is the end product of a factory of many different steps some technology oriented some data oriented but but just as many um human and intuition and the cognitive processing that that you know the power that we've got up here um that is as much a a driver of insight than anything else so i've certainly had experiences where someone says well you've got this database you've got this you know degree in statistics you've got this instance of python what more could you need yeah you're in heaven you're in utopia you should just do it like that absolutely so it's like well can't you just build a model too right like that's that's not quite what a model is so it's so so the the um one of the things that we regularly um work with our partners on who are maybe less data savvy um one of those things is just education on what is data what can it do what can it not do what is statistics or machine learning or data science what can it do what can it not do what things are not substitutes for critical thinking what things are not substitutes for good problem definition and and so that is that is a problem that we run into regularly the great thing is that you know disney brings in people who are open um and who uh who want to learn who are hungry for this this type of uh uh of knowledge yeah exactly knowledge thank you thank you and um and so it's it's great working with people and sort of like illuminating them on oh i you know i i thought you could just do this this and this and you know out pops my answer it's gonna be a little more complicated than that yeah people are always super receptive to that sure and you know disney obviously being the the mammoth organization that huge ecosystem that you have um would you be able to touch upon without obviously giving any commercial um commercials away uh you know would you be able to touch on some of the analytics or give us some examples of what you might be doing that's you know really pushing what you want to do with that customer experience and how you're gonna help those guests have a much better time or something else it might be operational it might be you know staffing or something like that yeah absolutely let me see if i can't come with uh with at least one if not uh if not two different examples so i i i think um as as everybody um realizes we're in a very different situation here globally with uh with covid19 uh the impact from that yeah and you know disney parks has not been immune to that whatsoever uh and so something that we're focusing on at walt disney world uh which is the site where where i work and that i support uh down in uh orlando florida is this concept of making sure that our our guests when they visit our parks and our resorts are um are safe um and that that we abide by um federal as well as uh state and local standards for health and safety and hygiene um et cetera and we we have one of the cool things about disney is that just about any job you can think of we've probably got a person doing that and so you know we add more and more so we have you know chief chief medical officers we have physicians on staff who are advising et cetera so you know we listen we listen to them as well and so um i can't get into all the details of it but you can imagine that when you've got these four theme parks that just a year ago it was sort of anybody who buys their ticket can can come to the park and we want them to explore the park and we want them to feel like they're immersed in in in you know whether it be our fantasy land that has a certain theme or eventually it has a certain theme or the you know the world showcase within epcot which has its own sort of theming etc we want them to feel like they're in a different world in a world that's safe and a world where they can kind of leave a lot of um a lot of the outside world outside um it's different today um and so we we need to be respectful not just of the ideals of of disney in terms of our storytelling and the immersiveness and and the wonderful experiences but we also need to make sure that our cast are safe that our guests are safe we are abiding by those various standards and so the process by which we manage our parks now um is very different from from how we thought about the operations of our parks just a year ago so ensuring that we can keep things clean and that we can keep guests at least six feet apart from each other um etc is is is a fascinating undertaking i'm i'm i'm disappointed that that we live in a world where we have to do that type of work yeah but at the same time as a data scientist it's it's just a it's a fascinating new data set um and a new set of problems um for someone like myself or our industrial engineers and our operations researchers um and and many more um help uh help solve so i think people in the parks getting them in there safely keeping them distanced making sure that our cast members are are are also equally uh safe and healthy etc is a fascinating data science problem it must be because you've got a lot of spatial kind of um factors there as well haven't you you know i know that that your guests wear um these types of sensor based um magic wristbands oh magic bands you call them okay yeah we call them magic fans so they um absolutely so that's a i've got one here all right um so they uh they're guests are certainly not required um to to to use or interact with these but uh we find that many of our guests do um they are rfid based uh and um it allows for uh both short range as well as long range um interactions with with physical sets to create you know uh digital sort of moments so you know just at a very from a very um operational perspective um this can be your token to to enter your park so that you don't necessarily have to fish out of your wallet a um you know a physical piece of media uh you know a ticket basically um you know this can this can operate as that same thing for our resort guests when they want to pay for merchandise or pay for their food and beverage or you know just get a you know just get a water etc um this can be linked to their resort uh folio charge account okay um and they can just have this so they're not neces they don't necessarily have to carry around again uh you know a credit card or you know a gift card etc this can this can function as that particular token it's also used um for you know the cooler things the things that i'm i much more like to point out is you know sort of personalized moments and and interactions that are our um views that we have at some of our attractions um for example so when uh when a guest is um is in the line for uh our expedition everest attraction which is a disney's animal kingdom uh it's steamed around everest and you know the abodible snowman or the yeti um when the guests are in queue there are sensors um you know where they are but they're they're they're sensors within the queue infrastructure sure that um that can sense that you know samir has his magic band on and um you know samir has has sort of with his on his own digital online account and is not as an optional um optional convenience uh is uh what we'll do is we'll change some of the signage within the queue to to reflect some here so there may be a digital sign uh promoting a particular fictitious of course um you know product uh you know yeah snow boots or something like that and it will say you know these are these are the warmest snow boots i've ever worn says samir so as guests are going through the queue they get just little kind of easter eggs um that are all predicated on on being able to correctly identify that the guest is there and yeah the guest is so um i can't talk much more about him no no i i'm fascinated by that though but it's i mean look that this is that is hyper personalization that you're talking about yes that is absolutely in the moment um a slightly um you know sort of mino was it minority report when or was it i don't mean but but wait no no no what was the other one where um you know as you walk past the advertising horde oh that was that was minority oh it was yeah and then they sort of pick up on your eyes and suddenly they've got something for you so it's you know and and that's a lot of data that you're absolutely gathering right there the streaming the sensor data and so on so you're are those are those real-time decisions that you're then passing back to somebody as well so from an operational perspective i've got this band and suddenly um i'm you know it could be the eyeball effect as well maybe i'm overdoing it right but um you've got a finite amount of stuff within the kovid related world and suddenly a lot of rides are are backing up people are backing up in queues is that also one of the things use cases that you're using the wristband for to to manage operationally the park so you know as you say the work that the sort of flow of people around it is is easier and effective and people aren't bumping to each other and so on you know that's a pretty big task in itself it is it is and the great thing is that we've we've had people uh we have people who are operating our parks and resorts who've been doing so for for decades so they've got experience doing this under varying different conditions they have experience doing this with various different technologies you know very very minimal technology um versus what we might have today through things like sensor based things like camera based etc and what's great is that um those people are really really good at what they do at managing these parks and what we're finding though um is i'm sort of a latecomer into this because i don't come from an operational background but what's what's great to see is that when you give those uh extremely capable operators additional data like the ones that we're talking about yeah they're able to make much much better decisions and they're able to sort of squeeze much better efficiencies out of uh out of our assets and then on on the other side of this you know not from the operational but more from the the guest experiential perspective we're absolutely able to give uh guests a better experience we're able to today something that we can do that we couldn't do 10 years ago through the implementation of mobile applications through the implementation of a wi-fi network on property through the implementation of sensor-based things like the magic bands is that we can tell guests regardless of where they are technically in the world what are the wait times right now at walt disney world so that i'm not wow you know take taking my family in 100 degree heat yeah across a park which can be a very long distance particularly for you know the little ones and and you know finally finding out oh this line is you know longer than i want to wait right now so with the introduction of um of our mobile publication and my magic plus um we're able to now tell somebody you know don't don't go across the park to see what the line is like this is what it is right now we've also introduced enhancements to our virtual cue type process so we're able to you know keep people out of eq right okay the technology the technology is allowing us to to have a virtual cue and you've probably seen this at you know some of your local restaurants perhaps so it's not a completely new innovation but um our guests certainly appreciate you know i'd rather go spend my time in uh you know maybe in the shop or maybe interacting with a character maybe taking photos etc rather than waiting in this queue that you know every few minutes moves forward every few minutes moves forward again health safety operational efficiency but also giving the guests a great experience keeping them out of lines and and it's through this data it's through the sensor network yeah this technology it's through this partnership that i've talked about that we are able to do these things both for our own assets as well as for the the the customers who are so generous with their time and money by visiting that's fantastic i i mean that must be an exciting job in you know just to be there and seeing it and doing it which is phenomenal you know you mentioned something earlier which i really want to pick up on um and that's storytelling and you know it's a very i think a tough thing to sort of be able to do um certainly you know with data i think it's a very very tough uh uh message to convey and actually i you know you you mentioned it earlier that you've been in marketing so uh you know you you've obviously got a talent to with words and and to convey a message what is the core of of storytelling for you as a data scientist wow that's uh i don't think i've ever been asked that question and i probably haven't put conscious thought to it so you're perfect i'll give you a moment on this one but but i'll uh so the first thing that comes to mind is empathy okay um i think empathy is lacking in a lot of facets of our lives but certainly certainly when it comes to storytelling because why am i telling a story um from from a data perspective usually what i'm trying to do is and i'm trying to help somebody make a decision that they didn't feel um confident comfortable knowledgeable enough to sort of make on their own so i need to have empathy for the problem that they're in the problem that they're trying to solve um and so one of the things uh for example that what you know when i talk to to clients um is i ask them what keeps you up at night um what are the things that you know professionally of course um what what is it that you're uh what is it that you're just really unable to solve what is the question that keeps coming back what is it that you're being measured on no at the end of the year what is your leader going to say you were successful because you accomplished xyz what does your leader care about um and having empathy for the position that we're all humans and they're just trying to solve a problem they're trying to do something a little bit better than they did yesterday they're trying to overcome this this rub that they've just had in their professional life for five 10 15 years if i have empathy then i'm going to probably bring my best self to the solution because i'm not going to uh i'm going to make sure that the solutions that i bring um are tailor-made to the language that they speak in that it's going to be something that is actionable right that that that's a qualifier we always put on our insights sure that would be an actionable insight yep if i tell you that you know left-handed people always behave this way and right-handed people always behave this other way if you can't take any action with it handedness then it's it's of no use so having that empathy for for the position they're in and what their limitations are helps me craft a much better solution for them and having that information also helps me tell the story in such a way that it speaks to them it resonates to them some people i i work with you know they want to know about some of the nitty gritty things you know they want to know you know the the uh the proverbial r-square of this particular oh boy progression that that you know sorry linear regression that you built fine you want to talk in that language totally fine talking with you in that language other people say how do i sell more stuff to more people at a higher price yeah different conversations and so i'm going to tell very different stories when i'm talking with those with with those clients so having empathy and and and taking the time to understand the values that what i refer to oftentimes as the currency of the partner that you're working with uh helps make for a much better story i love that i love that and i've never heard that before around the values or the currency and i understand what you're saying what are their goals where what do they need to achieve at the end of their uh year or month or quarter or whatever it might be um but but absolutely being able to pin what you do from a data perspective and and blend it with their narrative of what they need to go and and and say to others and i think that's what you're sort of you know and and i've i haven't heard it like that said like that before i've heard it very much said yes if you have the powerpoint like this and you you know provide the report like that and blah blah blah but actually this this i love that the values and currency piece i really do so that that brings me on to another point actually um you know there's this big big and um huge uh word within the data industry right now and that's data literacy is that something that has has raised its its its you know hairy head at um at disney and is that something that is talked about as a oh we're really dating illiterate or you know we need to get over this is that something that that exists in that way i will say that that's that's not a specific term that um that we're using right now um that's not to say that we're not representing that same concept uh in a different way yeah um if if i can i'm probably gonna project my own shortcomings on to that particular question but okay one of the uh when i hear a term data literacy um i think what kind of comes to mind is trying to understand the uh the limitations of what we refer to as as data right like data is one of those things information is another one insight is another one that depending on who you're talking to it kind of means something a little bit different sure um so what i prefer to do and maybe this is a way of helping to improve data literacy or or minimize data illiteracy is i try to talk about the data in terms of its effects what it can do what it can't do what i believe it can answer what i feel that it will fall short on and and i think as part of that one of the questions that we we do often ask ourselves um in in terms of the scientific circles that i work in within in the organization is if i have something that maybe i'm not 100 confident in you know there's some videos some error around it or maybe there's a possibility of a misunderstanding of a particular data element what we try to do is ask ourselves if we follow this conclusion will it make things better will it make things worse and so you know it's first that sort of physician do no harm type of thing if if we feel that you know that something may be inaccurate or we may have a misunderstanding and we act off of that do we feel that there is still that we're gonna make things worse because in many cases you can have quote unquote bad data you can have a quote-unquote poor model or you can have a quote-unquote misunderstanding of what's going on but you're you're not really going to hurt anything if you take an action off of that um so we ask ourselves a lot are we going to make things worse by following this conclusion and we're wrong you know so we're thinking about in terms of the data scientists we're talking about our type 1 and our type 2 errors that's what that's what these things are what is that what is the cost of being wrong what is the outcome or what is the the advantage of being right so i think when we when if i were to start to introduce a concept around something called data or literacy it would really focus a lot on on those aspects of data where is it going to help us where is it going to hurt us um yeah so i don't think i gave you a great answer there no no no do you know what i i'm i i'm actually that that's that that's a really good answer because one of the one of the not struggles but one of the things that i'm continuously seeing is that everybody's saying that we're all data illiterate you know 95 percent of finance teams are data illiterate you know um leaders are data illiterate and blah blah blah but actually what you've just said to me is those guys really don't know what they don't know yeah they don't know what what is in that data they don't know what what can happen or what you can do with that data and what you're saying to me is i'm going to explain that to them because i'm the experienced one and i can speak in a way that is going to convey my understanding of the current situation of the data that we have however you know whatever quality basis there is in that data and then i'm going to say well with this we can do x and you can have that outcome and that may well help you as you said or it might you know and and i have to look at the hurt aspect as well so i think that's actually really interesting because do you find that that drives a different conversation with your business partner and what types of what types of feedback do you get from that as a data scientist sure it absolutely does um because when you start to frame it in the concepts of risk and reward people's mindsets often uh well they tend to grasp that a bit more and there is this sort of uh cognitive bias that that almost all humans have which is this like risk aversion right like we much we'd much rather put our eggs in the risk aversion basket than in the reward basket for the most part right and so when you start framing it in terms of risk and reward or what is the consequence of being right here what is the consequence of being wrong here what if we misuse this data or use it in a way that it shouldn't be used um then their their way of thinking tends to sharpen i have found okay and and in many cases granted i'm in i'm in a great position in that where i sit um very few of the decisions that i help support are going to result in somebody actually you know being hurt yeah or you know a safety issue or somebody losing their entire checking account you know those types of things i i get it i work for a theme park right like so so i have a very different set of you know a very different risk profile than someone who may be in the pharmaceutical field or someone should be in the finance field but that aside what we often find lots of times is that the cost of being wrong is minimal and and and we find that we're we're willing to make this decision we're willing to take this um this particular uh recommendation simply because if we don't uh we're really not any worse off than if we do and yeah and we happen to have made a mistake because you're not learning about that are you then and i guess that's the whole part of innovation that you've got to have that level of trust in doing something wrong to learn you know something more about it or or you know about the situation or the model or whatever it might be but you know just to go back to that ecosystem because something touched you know that you touched upon right there and was about innovation disney has always been seen as an innovative company you know from the 1920s when when mickey was born and you know how that came out into to certainly my childhood and my memories how is innovation um captured in the essence of disney and and the and the parks how do you do you have a safe space somewhere to play around and to do that innovation with you know or do you have hackathons do you get together or you know is there a hub somewhere how do you nurture that spirit of innovation yes yes yes and yes all those things and so that's that's one of the reasons why it's it's it's a really cool place to be yeah and i i wouldn't say that there is a cookie cutter you know every team approaches it the same way every segment approaches it the same way but i will say that there is 100 a culture of we want to do things differently um with a set of well-established values and principles from which everything derives so um we have what we refer to as the five keys and that sort of guides guides everything from a from it from a top-level perspective and it's safety courtesy show efficiency and diversity so we work off of those particular uh principles and what's great is that um our our senior leaders um have have come to us and and regularly say the way we're doing things is great for now but that's not necessarily how we want to do things tomorrow what can you do to do things differently tomorrow okay uh josh josh tomorrow who is uh who is the the chairman of our particular segment just just recently uh put out a video for for all of our cats where he really challenged us to think through doing our business differently and i think um i can't remember exactly where i heard it but the definition of innovation to me um and again i'm stealing this from somebody else is is that it's it's changing before you have to and that's that's cool that's what josh's message was to us was let's change our our parks before we have to whatever that means so you know we've been successful here at walt disney world and launching our new park pass uh park inventory management systems again that's that's sort of the underpinning reservation system that's making sure that we're keeping people distanced and we're not overloading the parks etc that's something that we didn't have 12 months ago but we stood that up we stood that up over over the summer in a matter of a few months and that's you know that's just kind of one example of of changing when we had to but we need to start thinking about that in in terms of other aspects of our organization and so the way we encourage that type of thinking is certainly through all the things that you um you just mentioned about creating safe spaces for people to have ideas that challenge the status quo right um and and and making sure that it's not we don't hear responses of but that's not how we've done things so we're not going to accept that right like we've we've been running parks since 1955. sure some some things can stay the same but many things have to change yeah yeah the concept of cues like what we have to have cues should we be having queues things like that oh if you're in england it's good to have a queue yeah absolutely so you know we uh we definitely need to make sure that our uk visitors are in a comfortable exactly personalize it for me please it could be the it could be the next attraction to get into the queue it's just a bunch of british people kind of standing in line just meandering and chatting and you know yeah yeah it's perfect so we we absolutely do things like like um you know we have various innovation and incubator incubation uh type of of programs um as well um the company as a whole um has an incubation program where we invite people to come and showcase what can you do and some great things have come out of that um for example we acquired a company uh that participated and was a finalist in one of those programs bam tech and that is now the underpinning for disney plus our stream oh wow okay so it's not just a matter of sort of performative oh that's cute you built a nice thing yeah it's no we're we're we want to see what you can do push the envelope show us things that we can't do and if we think that there's value in that we'll put real tangible benefits to to this culture of innovation um that just that just happens to be one of the most uh obvious ones yeah sure so you know we're coming to the end of the show and um i i want to ask you one final question um i i can't remember way back when when it was but you know the data scientist was seen as the the most sexiest job of the 21st in you know 21st century yeah where where where do you think the the job of the data scientists is going to be in the next five years maybe 10 years you know you've seen that over time now how you know you've seen how it's changed where do you think it's going that's that's a great question and i um i wish i were prescient i i tend to i tend to always fall short on these types of things you know like i don't uh i don't have a great eye for it but but maybe i could talk a little bit about kind of where i hope it goes it goes absolutely yeah so so i i could take that i i want the data science as as much as i have data science in in my title and i want it to be recognized and and sometimes put on a little bit of a pedestal um look at the end of the day it is problem solving to help make decisions better that's what a data scientist in my in my world um is is meant to do and so i'm hoping that we kind of blend into the fabric and we're not seen as a uh uh as a sort of all-in-one all-encompassing can do it all type of show type of role where it's just you know feed in a question out comes a million dollars in revenue whatever whatever it is you're trying to maximize because that's unrealistic yeah right what what i want to see is uh maybe back to that concept of data literacy is is a is a more general amongst the general partners that we work with a slightly elevated understanding and respect for what data is but as much as that what it is not and then help bring us into their world um where they share with us again what are their pains what are their struggles what's not working well um and we can start to have conversations where it's not i've got a problem make it better sure but we can have a conversation and explain well what is that problem why do you see that as a problem when you say make it better what do you mean by better you know and we can have a a conversation with a common language that allows us on the data science side to say ah i know of some data like that or you know let me go do some exploration on some data like that or i know of some approachable algorithms or solutions or visualizations or whatever it happens to be they don't need to be experts in that but i i would like to see a bit more of an integration where there is not this sort of mysticism about data science and and we're just seen as other people who who just we just want to make things better yeah like yeah that's what i want to do i want my leaders to have the confidence of their decisions i want them to understand the risks and rewards of the different options that are in front of them i want them to feel convinced that we have turned over every important stone that there is um and so i think the data scientist will no longer be this this mythical wizardy thing um and it will just be ah these are smart people who want to want to make things better and want us to want us to be you know more effective um and so that's kind of how the data scientist will be seen and that was a great answer but that's i know i think that was i think it was brilliant because it actually blends it more into a seamless um asset to the business it's it's more ingrained in the business it doesn't necessarily have to be as you said oh my goodness there's that magician over there they're going to make us you know do amazing things and i you know that is completely wrong i think what you've said is is is brilliant because it is about the decision-making or the actions or the outcomes and basically you're just helping them navigate to that point and and and giving them a helping hand so actually it it's it's it's a great answer i i you know i wasn't i wasn't looking for this you know nostradamus you know the data scientists will be you know sitting sitting on a plinth somewhere and and being able to you know uh visualize into the into the future now i i think the practical and pragmatic things of what i like about the answer um and and that's fantastic listen andrew wright it's been an absolute pleasure to have you on the show um just the last bit where can people find you if they want to get in contact with you and ask you to come on to their show or you know just ask you some questions or get a job at disney job it doesn't matter yeah if trust me if if i were in the workforce right now trying to get a job at disney i think i would be uh you know i i'm not sure i would even hire myself but uh but i think um one way that people can reach me is certainly through uh through linkedin yeah andrew wright um i have an m because there are a billion andrew i'm sure yeah so i'm i'm andrew m wright um i am uh certainly at disney uh please feel free to reach out to me on linkedin um i'd be more than happy to connect with you talk with you about your various problems um i i love talking about this type of stuff you can see i will go on at infinitum i've got a few posts out there i'm not super chatty on linkedin i read a lot i'm much more of a lurker i do have a few posts on there particularly focused on what what are insights how to create insights scientific method those are things that i walk every day in my life and so those are the things that i really like to emphasize when people are asking me how do i think about a problem how do i tackle a problem what do i need to be learning i'm going to always come back to those things but please feel free to connect with me um and i'd be happy to talk with you about anything and i think you'll probably talk to them about chicken nuggets as well wouldn't you absolutely absolutely yeah nuggets factor in very heavily in my last speech so yeah which i didn't get to see but you know um i certainly read and saw the pictures of very many chicken nuggets being dipped into sauce so you know absolutely absolutely aren't we all just looking for nuggets like at the end of the day you know you walk into meetings so like just give us the nuggets we want the nuggets so that's absolutely nice and the golden ones well look it's it's been an absolute pleasure and i've i've really enjoyed speaking to you so thank you so much samir i appreciate thank you thank you i really do appreciate it take care have a good day cheers