Alter Everything

MaddieJ · ‎06-21-2022

McLaren Technical Analyst, Jess Tomkins, shares a behind the scenes look at how they've implemented Alteryx throughout different teams to help McLaren race faster in-house, and on the track.

Panelists

Jess Tomkins - LinkedIn, McLaren Racing
Dan Menke - @DanM, LinkedIn

Topics

Alteryx and McLaren Partnership
McLaren Extreme E
- Extreme E Drivers Tanner Foust and Emma Gilmour
Alteryx Auto Insights
Alteryx Inspire 2022
Podcast highlights: How McLaren uses Alteryx to race faster - blog by @DanM

Jess and Dan recording at Inspire 2022 in Denver, Colorado

Transcript

Episode Transcription

JESS 00:01	So for me in my role, it's great because, like I say, I get exposure to all data across McLaren and as well, all the series that we work with. So, for example, in Extreme E, we've got our drivers, Emma Gilmour, and Tanner, and we work closely with them and their race engineers who implement Alteryx, use their data and see what we can do in that series. It's our first year competing in it. So all the data we take in and any projects we do in Alteryx, they're all new, and they're all helpful to the team. So ultimately, again, we're just trying to make the team go faster and bring that extra performance. [Music]
DAN 00:35	Welcome to Alter Everything, a podcast about data science, and analytics culture. I'm Dan Menke, manager of community operations at Alteryx, and I'm a huge F1 fan. I love everything about it from the drivers, the cars, and the data behind the races. Luckily for a fan like me, Alteryx and McLaren have an awesome partnership. So I sat down with Jess Tomkins, technical analysts at McLaren, to talk about Alteryx and how McLaren is using it across the organization.
JESS 01:04	Whether it's spatial mapping, to see if we can find the perfect line, see if we can interpret the Extreme E drivers driving through gates. Is it better to go round? All sorts of dilemmas there.
DAN 01:16	From different departments to analyzing race data and making quick decisions on the go for their drivers, it was so much fun diving into the data with Jess. Let's get started. [Music ends]
JESS 01:30	So I'm Jess Tomkins. I'm a technical analyst at McLaren Racing, so I work with all our technical partners, integrating their software, their hardware, all across McLaren, ultimately to try and make the team, and try to make us race faster.
DAN 01:43	I love that your use case is race faster. I love that. Learning a little bit about you. You took a different kind of path, not even just like into the work world, but into McLaren. Can you kind of talk about how your experience was, after your secondary school, you kind of went to college, and you kind of didn't. Can you kind of explain it? Because it was really interesting to hear a little bit about that.
JESS 02:08	Yeah. So a bit of a unique path, especially in the UK, as well. When I finished my A levels in which that was about 17, you start those, you do those for two years. I picked maths, business and physics. I already knew I wanted to be in the world of data and numbers. Didn't really resonate well with words, so I wasn't really sure what I wanted to do after that. I didn't really want to go to university full time, didn't want to pay for it. I didn't want to be in debt, wasn't really sure. So I went and spoke to my careers advisor at school, and at the time, there was only four types of integrated degree you could do, which is basically an apprenticeship where you work full time every day of the week. They pay you to be there. And then on a Tuesday, it was for me, I'd go to University. So I was in the workplace for four years, every Tuesday, going to University and getting a degree at the same time. So they pay you and they pay for your degree, which is great. And at the end of it, yeah, you get four years real life work experience and also a degree at the end of it. So that was sort of my journey after school and how I managed to get a degree and both the experience at the same time. Following that, I also had a separate love for F1 in the background, and went to my first F1 race just randomly with one of my friends. I went to Barcelona and that's where I made the connection between the two. So my love of data and my love of F1. So, yeah, sort of following that, I applied for a job at McLaren, sort of really randomly for me. My initial thoughts was that I wanted to be a strategist, so I wanted to sit on the pit wall, look at the day to make life decisions. That's where I thought I wanted to go. I applied for the job that I have now currently as a technical analyst. So I really wasn't sure what it entailed at the time, and sort of opened my eyes to the commercial world of F1 and sponsors and data. Which in the end, I'm kind of glad I found this route because you get exposed to a whole different world. You get to go and see all sorts of partners, all sorts of technology, and really work to improve the team by spreading that sponsorship and spreading the technology across the team.
DAN 04:05	And are you working not only when you're looking at the technology piece, you're not just looking at one area of McLaren, right? Like, you're looking at, HR and marketing and stuff like that. Are you looking at all of that for everybody or is it just certain specific departments that you're looking at?
JESS 04:21	Yeah. So it's really great. So in my role, I get to get my hands on everyone's data across all different departments. So, for example, deploying Alteryx across McLaren. I go and speak to people, whether it's in HR, production, quality, the wind tunnel team, I hear their pain points, hear what they're doing with data, and introduce them to a software like Alteryx, and say, this is how it's going to make you go faster. Let's work together on developing your skills. I also support them in that. Alteryx supports them in that. And it's really a great deployment. It's never focused in one area. It's really a team spirit.
DAN 04:52	And so you testing out Alteryx, what did you think? What was your sort of first impressions of that? Because obviously you have a data background in math, and all this stuff, and probably talking about the people you learn SQL and things like that in the program language. When you saw Alteryx, what was your kind of first impressions of that?
JESS 05:10	Yeah. So pretty early on in my role was exposed to Alteryx, which was a number of software partners that we use and we talk to. I think, as I said, it was really early on, I was sort of lost in getting used to McLaren, getting used to a new role, learning everything around me. And as part of that, I got introduced to Alteryx. I think it sounds a bit cheesy, but it was almost calming to me to have a data software that I could relate to. I always understood data and numbers. They don't change. As I'm adapting to McLaren, I can relate to software like Alteryx. So initially had an intro with it, with the Alteryx team, a really short 45 minutes introduction. I think that's really great. And what's unique about Alteryx is you don't need long to see the software and work out what it does. It's self explanatory, it's user friendly. And after that 45 minutes session, you're away, you put your data in and you're off and you're doing whatever you want to do.
DAN 06:03	Yeah, that's great. So these use cases that you're getting from all over the place, are you seeing like, okay, can I do this in Alteryx first? Or are you implementing it into the Department? Or are you doing it both ways?
JESS 06:15	Yeah, so it works in both ways. It's really great for me. I'm like networking with McLaren, talking to individuals across the team. So if I use HR, for example, they'll come to me and they said, here are pain points, this is what we do. And sometimes they don't even realize that they think, this is it, this is how it gets done. It takes me 15 hours and that's it. And I think it's really opening their eyes up and spreading the knowledge of the software we have available. Have you heard of Alteryx? This is what it does. And I think from that moment, even those conversations, it's a 5, 10 minutes conversation. There's a better way of doing this. How can I automate it? And they're really interested in upskilling themselves and learning the software for themselves.
DAN 06:53	So you're not necessarily trying to solve people's problems. You're also trying to talk to them about how Alteryx can almost create opportunities for them to solve things. Maybe they didn't even know they needed solving.
JESS 07:05	Yeah. So I'd love to say I could self-sufficiently build all Alteryx models across McLaren, but it just doesn't work like that. We've got so much data and with such a fast paced team, we need to speed up our processes and streamline wherever we can. So great for me because I get to learn the tool, and use data, and build workflows myself, but also be an enabler so that there's individuals across all departments that know how to use Alteryx as well.
DAN 07:27	Cool. Have you had so far, like, a particular project that you were really excited about using Alteryx and bringing into that department because you knew that this was going to have such a big impact?
JESS 07:39	I think for me, there's some departments that I know, and I hear of, and I'm like, you know what, let me come to you, let me tell you about it. And that was sort of the initial stages when I first got going with Alteryx and McLaren. I think it's great now that it's so far spread across McLaren. The day before I flew out here, I had someone come to me and I said, hey, look, I've got a use case. Can you tell me how to use Alteryx? I want to use it. Really interesting one across the quality of our parts, how we manufacture them, can we predict if the parts are going to fail? How long it takes, and any comments we've made on these parts, can we automate that and identify these things before they happen? So it's really great that we're at that point that the knowledge and the awareness of Alteryx has spread across McLaren, and had people coming to me for their very own use cases every day.
DAN 08:22	That's really cool. So you're working across, like, even the teams, right? So even, like, Extreme-E for Formula One, are you doing with the IndyCar and stuff like that too, or just mainly in the UK teams?
JESS 08:35	Yeah. So for me, in my role, it's great because, like I say, I get exposure to all data across McLaren, and as well, all the series that we work with. So, for example, in Extreme E, we've got our drivers, Emma Gilmour, and Tanner, and we work closely with them and their race engineers to implement Alteryx, use their data and see what we can do in that series. It's our first year competing in it. So all the data we take in and any projects we do in Alteryx, they're all new and they're all helpful to the team. So ultimately, again, we're just trying to make the team go faster and bring the extra performance.
DAN 09:07	So do you think as you guys-- because I know that you've only had one race so far, right? So are you excited to keep getting more and more data as this series goes on and being able to make adjustments to the cars and things like that?
JESS 09:20	Yeah. So it's really great for us to see, as we get the extra layers of data, we progress in the series into the next race, and we pull the data back, and we put it all into Alteryx. It becomes that sort of central source of truth for us, even through, like, data gathering, checking sensors. All of it will now live in Alteryx for Extreme E. So, yeah, definitely really excited to get ahold of more data and see where we can be at the end of the year.
DAN 09:43	That's really cool. Since you've gotten Alteryx, is there something on your bucket list that you're interested in using? Are there tools that you haven't used yet that you know about that you'd be interested in using?
JESS 09:55	Deploying across different use cases means they get access to different tools, whether it's spatial mapping to see if we can find the perfect line, see if we can interpret the Extreme E drivers driving through gates. Is it better to go round? All sorts of dilemmas there. And again, with that becomes using a range of tools. I think we're looking into discovering auto insights and what that can do for McLaren. So really interested to explore that and find a data set to look into.
DAN 10:22	So we asked this a lot of our users, do you have a favorite tool that you like to use, that's like your go to tool to play with in the software?
JESS 10:32	I think the more simplistic tools are just so understated, and I can't emphasize enough the value they bring to McLaren. So sometimes some of our use cases, in the scope of Alteryx and the tools available, they're the more simplistic tools that seem simple to very heavy data users. But for example, yeah, in our HR Department, that simple data manipulation, pulling data in from different sources, manipulating it and saving those workflows, it saves them 15 hours a month. So in the scale of Alteryx, simple tools, but for us, McLaren, it's saving us hours. And as that small and fast paced team, we need those wins.
DAN 11:09	So because there's a tremendous amount of different data that you're working with, right, like you have the HR data that you're talking about, right? Then you have all this race data. How difficult is it for you to jump from data set to data set being completely different, whether one's numbers or it could be people, right? With HR, things like that, how are you able to just jump around like that?
JESS 11:31	I think for me, yeah, it's exploring one use case at a time, getting stuck into it. And like I say, if that enablement, that's really great there. So it's not me taking on the full load of each data set. I'm really enabling the team, so I'm working with them. The workload is shared, and using Alteryx, the tools just, yeah, so user friendly and easily adapted to each type of data set and relates back to at the end of the day, it's data. So just changing the form of it, exploring what you want to do with it, and having a clear idea of how it works, what you want to achieve, that sort of makes it sort of an easier task dealing with different data sets.
DAN 12:06	What sort of analysis are you doing? Say for the Extreme E, you've mentioned the spatial mapping, which is really cool to see, but they have a very crazy variable. It's not like they're on the road. Right? So do you get data like that that's like, oh, the temperature is out, or the track is hot, things like that? Or you take those types of things into consideration for that?
JESS 12:27	Yeah. So it's hard to say we've explored everything. We haven't. There's so much data. We're always thinking, what's a priority for us at this moment in time? Currently with Alteryx and Extreme E, we do lots of sensory checks prior to the race. So before Emma or Tanner can get into the car, we're doing wheel speed checks, steering checks, all sorts of sensor checks, torque on the car to make sure everything's within range, automating that within Alteryx saves the race engineers on the day, loads of time, which is great for us to see. And that's sort of a starting base for Alteryx and Extreme E. We've started exploring what we can do next and, yeah, how we can bring absolute performance to the team. So, yeah, the spatial mapping and finding the sort of perfect fit line, which is very different to F1, like, say, the course is completely interpretable. So you can go 10 meters left, 10 meters right. As long as you pass through the waypoints, you won't get a fault for it. So, yeah, tons of data there to explore. That's one of the big ones for us. We want to give Tanner and Emma some insight into some guidance, really around each track, what would be the best lines to take? And we love to take their feedback as well, and overlay that with the data and give them somewhere to look.
DAN 13:35	Yeah, and compared to Extreme E, compared to Formula One, on the ground, at least, there's a much smaller team there, right? I think it's like one race engineer and three or four mechanics or something like that. So I would imagine Alteryx is really helping you guys to provide that data, because you don't have 100 other people running through and sending data like maybe the Formula One team does.
JESS 14:02	Yeah. So an Extreme E team, I work really closely with their performance engineer, and their lead race engineer, and I make them enablers of Alteryx as well, giving them the skill set, giving them Alteryx licenses, and creating contact within Alteryx as well. They become enablers and they become self-sufficient so they can look at the data themselves. I can support them on that, and as a process as a whole, brings great value to the team.
DAN 14:26	That's awesome. So do you help in some of the debriefs, help with the data, things like that, after the races?
JESS 14:32	So at that point, our enablers should be self-sufficient. So as a race or performance engineer, that's using Alteryx, if I've done my job well, then they should be self sufficient enough to use Alteryx, be in the race debrief and use the data themselves.
DAN 14:45	Nice. So why don't you tell me a little bit about the enablement that you do across the organization?
JESS 14:52	Yeah. So working really closely with Alteryx, and sort of the way we work and the way they work has been the perfect outcome for us. Introducing our users to Alteryx, hitting their pain points, and then working with Alteryx to set up workshops is really a great process. And we streamline that by saying, bring your own data. It's pointless us having a three hour conversation with no data in front of us. So they turn up with their own data. We talk to Alteryx, get a demo, and get sort of a live build on the go, and from that we get a workflow already created. We've already solved half the problem. And then following that enablement, we've got loads of curious users across McLaren. And I guess it sort of allows us to roll that out on a wider scale. From wind tunnel analysis, where some of our employees have got PhD degrees, or down to simple Excel usage, users like that, they can share those workflows and build off their own, build out further and explore data sets in that way.
DAN 15:47	Yeah, it's almost like, to me, going across the org like that, especially because of what you guys are doing, it's almost like passing those things along. Everybody can just keep building off of those things, because it's almost like you're building a car, but you're using the data from each of these departments and people can look at it a certain way and then say, okay, now let's add our piece to this and move across the org until they almost build this giant workflow with everybody's information on it, giving you information for specific things.
JESS 16:18	Yeah. The way we share those workflows across the team has become super powerful. It's removed the aspect of having 100 lines of code saved on someone's desktop, and publishing those workflows, having them easily accessible means as a team we can explore analysis in all different areas. Any user can give it a go from tire pressures to those use cases in Extreme E. Those workflows are published and accessible for everyone in the team.
DAN 16:44	That's really cool. So I was at the Miami Grand Prix a couple of weekends ago, and where I was sitting, every time Lando or Daniel went by, the crowd went absolutely bananas. And it's known that you guys have probably the biggest fan base out of any of the Formula One teams. Are you using Alteryx to kind of propel those fans to get them more information or to bring in more fans?
JESS 17:13	Yes. So we've worked on small scale projects before, looking at social media, doing sentiment analysis in Alteryx, and sort of seeing what our fans think lap by Lap on a race brings super powerful insights to the team. I think looking forward, we'd love to pull our separate internal data sets, whether we're looking at e commerce data or fan data and overlay those in Alteryx with that previous work we've done, and really see the power of what fans think and overlay that with our existing data.
DAN 17:41	Yeah, it was very interesting being at the race like that. I'd never experienced anything that, it was my first Grand Prix. And to see all the people with all the different-- usually when you go to an event, at least events, I've been to a sporting event, it's one team against another team or this. Or there's ten teams or however many teams there are, and people walking around, and-- so the simple fact of, there's Ferrari fans sitting next to us and Mercedes fans sitting by me. But when those two guys came flying by, the crowd would go nuts. So it was pretty wild to see that. And I definitely saw a lot more papaya walking around Miami than any other color, which was really cool to see.
JESS 18:21	Yeah. I think sometimes I forget that I have the privilege of diving into all these datasets. It becomes my daily norm. So for us, actually reaching out and gathering fan sentiment analysis, and gathering fans thoughts is a really powerful aspect for us, and we really look forward to analyzing that data.
DAN 18:37	Were you surprised by that, when you came to McLaren? Did you know that McLaren was that popular?
JESS 18:43	[laughter] I think as a fan of F1, everyone has their favorite teams. For me, I'm not even sure I had one in particular. But coming to such a great team like McLaren and the history and everything you learn whilst there, I think, again, yeah, sometimes I forget that the power that has on the public and F1 fans in general. So it's great to ground yourself sometimes, have another reevaluation of the data, and sort of think, yeah, the privilege we get of working with this team. [Music]
DAN 19:10	Thank you so much for joining us, Jess. We really appreciate you taking the time and talking to us. It was a lot fun.
JESS 19:15	Yeah. It's been a pleasure. Thank you.
DAN 19:18	Thanks. Thanks for listening. For more on Extreme E, Alteryx auto insights, and the Alteryx and McLaren partnership, check out our show notes at community.altreryx.com/podcast. [Music ends] So do you get to go the races?
JESS 19:41	Some of the races. So I went to-- have just been coming back from the Indy car race, at the Speedway just last weekend in [Lorraine?].
DAN 19:49	Yeah, I watched that race.
JESS 19:50	It was amazing.
DAN 19:51	In the rain, the crazy rain? That was--
JESS 19:52	Yeah. First wet race in three years. Yeah.
DAN 19:56	Yeah, I know. I was watching it on TV. So they had Alteryx on the back of one of the cars.
JESS 20:02	Yeah.
DAN 20:04	On Juan Paolo's. And I think he was, like, way back in 20th for most of the race, but this rain came in and it got crazy, and he moved all the way up to 5th or something like that. And then literally, like, on the last lap, I don't remember what happened, but he crashed. But of course, on TV, it was a big Alteryx sign on the guy that crashed, but so what?
JESS 20:28	That's the first thing I noticed. That's the first thing, yeah, I noticed they had all the cameras on the car, the big Alteryx on the back. [laughter]
DAN 20:35	Yeah.

JESS 00:01 So for me in my role, it's great because, like I say, I get exposure to all data across McLaren and as well, all the series that we work with. So, for example, in Extreme E, we've got our drivers, Emma Gilmour, and Tanner, and we work closely with them and their race engineers who implement Alteryx, use their data and see what we can do in that series. It's our first year competing in it. So all the data we take in and any projects we do in Alteryx, they're all new, and they're all helpful to the team. So ultimately, again, we're just trying to make the team go faster and bring that extra performance. [Music] DAN 00:35 Welcome to Alter Everything, a podcast about data science, and analytics culture. I'm Dan Menke, manager of community operations at Alteryx, and I'm a huge F1 fan. I love everything about it from the drivers, the cars, and the data behind the races. Luckily for a fan like me, Alteryx and McLaren have an awesome partnership. So I sat down with Jess Tomkins, technical analysts at McLaren, to talk about Alteryx and how McLaren is using it across the organization. JESS 01:04 Whether it's spatial mapping, to see if we can find the perfect line, see if we can interpret the Extreme E drivers driving through gates. Is it better to go round? All sorts of dilemmas there. DAN 01:16 From different departments to analyzing race data and making quick decisions on the go for their drivers, it was so much fun diving into the data with Jess. Let's get started. [Music ends] JESS 01:30 So I'm Jess Tomkins. I'm a technical analyst at McLaren Racing, so I work with all our technical partners, integrating their software, their hardware, all across McLaren, ultimately to try and make the team, and try to make us race faster. DAN 01:43 I love that your use case is race faster. I love that. Learning a little bit about you. You took a different kind of path, not even just like into the work world, but into McLaren. Can you kind of talk about how your experience was, after your secondary school, you kind of went to college, and you kind of didn't. Can you kind of explain it? Because it was really interesting to hear a little bit about that. JESS 02:08 Yeah. So a bit of a unique path, especially in the UK, as well. When I finished my A levels in which that was about 17, you start those, you do those for two years. I picked maths, business and physics. I already knew I wanted to be in the world of data and numbers. Didn't really resonate well with words, so I wasn't really sure what I wanted to do after that. I didn't really want to go to university full time, didn't want to pay for it. I didn't want to be in debt, wasn't really sure. So I went and spoke to my careers advisor at school, and at the time, there was only four types of integrated degree you could do, which is basically an apprenticeship where you work full time every day of the week. They pay you to be there. And then on a Tuesday, it was for me, I'd go to University. So I was in the workplace for four years, every Tuesday, going to University and getting a degree at the same time. So they pay you and they pay for your degree, which is great. And at the end of it, yeah, you get four years real life work experience and also a degree at the end of it. So that was sort of my journey after school and how I managed to get a degree and both the experience at the same time. Following that, I also had a separate love for F1 in the background, and went to my first F1 race just randomly with one of my friends. I went to Barcelona and that's where I made the connection between the two. So my love of data and my love of F1. So, yeah, sort of following that, I applied for a job at McLaren, sort of really randomly for me. My initial thoughts was that I wanted to be a strategist, so I wanted to sit on the pit wall, look at the day to make life decisions. That's where I thought I wanted to go. I applied for the job that I have now currently as a technical analyst. So I really wasn't sure what it entailed at the time, and sort of opened my eyes to the commercial world of F1 and sponsors and data. Which in the end, I'm kind of glad I found this route because you get exposed to a whole different world. You get to go and see all sorts of partners, all sorts of technology, and really work to improve the team by spreading that sponsorship and spreading the technology across the team. DAN 04:05 And are you working not only when you're looking at the technology piece, you're not just looking at one area of McLaren, right? Like, you're looking at, HR and marketing and stuff like that. Are you looking at all of that for everybody or is it just certain specific departments that you're looking at? JESS 04:21 Yeah. So it's really great. So in my role, I get to get my hands on everyone's data across all different departments. So, for example, deploying Alteryx across McLaren. I go and speak to people, whether it's in HR, production, quality, the wind tunnel team, I hear their pain points, hear what they're doing with data, and introduce them to a software like Alteryx, and say, this is how it's going to make you go faster. Let's work together on developing your skills. I also support them in that. Alteryx supports them in that. And it's really a great deployment. It's never focused in one area. It's really a team spirit. DAN 04:52 And so you testing out Alteryx, what did you think? What was your sort of first impressions of that? Because obviously you have a data background in math, and all this stuff, and probably talking about the people you learn SQL and things like that in the program language. When you saw Alteryx, what was your kind of first impressions of that? JESS 05:10 Yeah. So pretty early on in my role was exposed to Alteryx, which was a number of software partners that we use and we talk to. I think, as I said, it was really early on, I was sort of lost in getting used to McLaren, getting used to a new role, learning everything around me. And as part of that, I got introduced to Alteryx. I think it sounds a bit cheesy, but it was almost calming to me to have a data software that I could relate to. I always understood data and numbers. They don't change. As I'm adapting to McLaren, I can relate to software like Alteryx. So initially had an intro with it, with the Alteryx team, a really short 45 minutes introduction. I think that's really great. And what's unique about Alteryx is you don't need long to see the software and work out what it does. It's self explanatory, it's user friendly. And after that 45 minutes session, you're away, you put your data in and you're off and you're doing whatever you want to do. DAN 06:03 Yeah, that's great. So these use cases that you're getting from all over the place, are you seeing like, okay, can I do this in Alteryx first? Or are you implementing it into the Department? Or are you doing it both ways? JESS 06:15 Yeah, so it works in both ways. It's really great for me. I'm like networking with McLaren, talking to individuals across the team. So if I use HR, for example, they'll come to me and they said, here are pain points, this is what we do. And sometimes they don't even realize that they think, this is it, this is how it gets done. It takes me 15 hours and that's it. And I think it's really opening their eyes up and spreading the knowledge of the software we have available. Have you heard of Alteryx? This is what it does. And I think from that moment, even those conversations, it's a 5, 10 minutes conversation. There's a better way of doing this. How can I automate it? And they're really interested in upskilling themselves and learning the software for themselves. DAN 06:53 So you're not necessarily trying to solve people's problems. You're also trying to talk to them about how Alteryx can almost create opportunities for them to solve things. Maybe they didn't even know they needed solving. JESS 07:05 Yeah. So I'd love to say I could self-sufficiently build all Alteryx models across McLaren, but it just doesn't work like that. We've got so much data and with such a fast paced team, we need to speed up our processes and streamline wherever we can. So great for me because I get to learn the tool, and use data, and build workflows myself, but also be an enabler so that there's individuals across all departments that know how to use Alteryx as well. DAN 07:27 Cool. Have you had so far, like, a particular project that you were really excited about using Alteryx and bringing into that department because you knew that this was going to have such a big impact? JESS 07:39 I think for me, there's some departments that I know, and I hear of, and I'm like, you know what, let me come to you, let me tell you about it. And that was sort of the initial stages when I first got going with Alteryx and McLaren. I think it's great now that it's so far spread across McLaren. The day before I flew out here, I had someone come to me and I said, hey, look, I've got a use case. Can you tell me how to use Alteryx? I want to use it. Really interesting one across the quality of our parts, how we manufacture them, can we predict if the parts are going to fail? How long it takes, and any comments we've made on these parts, can we automate that and identify these things before they happen? So it's really great that we're at that point that the knowledge and the awareness of Alteryx has spread across McLaren, and had people coming to me for their very own use cases every day. DAN 08:22 That's really cool. So you're working across, like, even the teams, right? So even, like, Extreme-E for Formula One, are you doing with the IndyCar and stuff like that too, or just mainly in the UK teams? JESS 08:35 Yeah. So for me, in my role, it's great because, like I say, I get exposure to all data across McLaren, and as well, all the series that we work with. So, for example, in Extreme E, we've got our drivers, Emma Gilmour, and Tanner, and we work closely with them and their race engineers to implement Alteryx, use their data and see what we can do in that series. It's our first year competing in it. So all the data we take in and any projects we do in Alteryx, they're all new and they're all helpful to the team. So ultimately, again, we're just trying to make the team go faster and bring the extra performance. DAN 09:07 So do you think as you guys-- because I know that you've only had one race so far, right? So are you excited to keep getting more and more data as this series goes on and being able to make adjustments to the cars and things like that? JESS 09:20 Yeah. So it's really great for us to see, as we get the extra layers of data, we progress in the series into the next race, and we pull the data back, and we put it all into Alteryx. It becomes that sort of central source of truth for us, even through, like, data gathering, checking sensors. All of it will now live in Alteryx for Extreme E. So, yeah, definitely really excited to get ahold of more data and see where we can be at the end of the year. DAN 09:43 That's really cool. Since you've gotten Alteryx, is there something on your bucket list that you're interested in using? Are there tools that you haven't used yet that you know about that you'd be interested in using? JESS 09:55 Deploying across different use cases means they get access to different tools, whether it's spatial mapping to see if we can find the perfect line, see if we can interpret the Extreme E drivers driving through gates. Is it better to go round? All sorts of dilemmas there. And again, with that becomes using a range of tools. I think we're looking into discovering auto insights and what that can do for McLaren. So really interested to explore that and find a data set to look into. DAN 10:22 So we asked this a lot of our users, do you have a favorite tool that you like to use, that's like your go to tool to play with in the software? JESS 10:32 I think the more simplistic tools are just so understated, and I can't emphasize enough the value they bring to McLaren. So sometimes some of our use cases, in the scope of Alteryx and the tools available, they're the more simplistic tools that seem simple to very heavy data users. But for example, yeah, in our HR Department, that simple data manipulation, pulling data in from different sources, manipulating it and saving those workflows, it saves them 15 hours a month. So in the scale of Alteryx, simple tools, but for us, McLaren, it's saving us hours. And as that small and fast paced team, we need those wins. DAN 11:09 So because there's a tremendous amount of different data that you're working with, right, like you have the HR data that you're talking about, right? Then you have all this race data. How difficult is it for you to jump from data set to data set being completely different, whether one's numbers or it could be people, right? With HR, things like that, how are you able to just jump around like that? JESS 11:31 I think for me, yeah, it's exploring one use case at a time, getting stuck into it. And like I say, if that enablement, that's really great there. So it's not me taking on the full load of each data set. I'm really enabling the team, so I'm working with them. The workload is shared, and using Alteryx, the tools just, yeah, so user friendly and easily adapted to each type of data set and relates back to at the end of the day, it's data. So just changing the form of it, exploring what you want to do with it, and having a clear idea of how it works, what you want to achieve, that sort of makes it sort of an easier task dealing with different data sets. DAN 12:06 What sort of analysis are you doing? Say for the Extreme E, you've mentioned the spatial mapping, which is really cool to see, but they have a very crazy variable. It's not like they're on the road. Right? So do you get data like that that's like, oh, the temperature is out, or the track is hot, things like that? Or you take those types of things into consideration for that? JESS 12:27 Yeah. So it's hard to say we've explored everything. We haven't. There's so much data. We're always thinking, what's a priority for us at this moment in time? Currently with Alteryx and Extreme E, we do lots of sensory checks prior to the race. So before Emma or Tanner can get into the car, we're doing wheel speed checks, steering checks, all sorts of sensor checks, torque on the car to make sure everything's within range, automating that within Alteryx saves the race engineers on the day, loads of time, which is great for us to see. And that's sort of a starting base for Alteryx and Extreme E. We've started exploring what we can do next and, yeah, how we can bring absolute performance to the team. So, yeah, the spatial mapping and finding the sort of perfect fit line, which is very different to F1, like, say, the course is completely interpretable. So you can go 10 meters left, 10 meters right. As long as you pass through the waypoints, you won't get a fault for it. So, yeah, tons of data there to explore. That's one of the big ones for us. We want to give Tanner and Emma some insight into some guidance, really around each track, what would be the best lines to take? And we love to take their feedback as well, and overlay that with the data and give them somewhere to look. DAN 13:35 Yeah, and compared to Extreme E, compared to Formula One, on the ground, at least, there's a much smaller team there, right? I think it's like one race engineer and three or four mechanics or something like that. So I would imagine Alteryx is really helping you guys to provide that data, because you don't have 100 other people running through and sending data like maybe the Formula One team does. JESS 14:02 Yeah. So an Extreme E team, I work really closely with their performance engineer, and their lead race engineer, and I make them enablers of Alteryx as well, giving them the skill set, giving them Alteryx licenses, and creating contact within Alteryx as well. They become enablers and they become self-sufficient so they can look at the data themselves. I can support them on that, and as a process as a whole, brings great value to the team. DAN 14:26 That's awesome. So do you help in some of the debriefs, help with the data, things like that, after the races? JESS 14:32 So at that point, our enablers should be self-sufficient. So as a race or performance engineer, that's using Alteryx, if I've done my job well, then they should be self sufficient enough to use Alteryx, be in the race debrief and use the data themselves. DAN 14:45 Nice. So why don't you tell me a little bit about the enablement that you do across the organization? JESS 14:52 Yeah. So working really closely with Alteryx, and sort of the way we work and the way they work has been the perfect outcome for us. Introducing our users to Alteryx, hitting their pain points, and then working with Alteryx to set up workshops is really a great process. And we streamline that by saying, bring your own data. It's pointless us having a three hour conversation with no data in front of us. So they turn up with their own data. We talk to Alteryx, get a demo, and get sort of a live build on the go, and from that we get a workflow already created. We've already solved half the problem. And then following that enablement, we've got loads of curious users across McLaren. And I guess it sort of allows us to roll that out on a wider scale. From wind tunnel analysis, where some of our employees have got PhD degrees, or down to simple Excel usage, users like that, they can share those workflows and build off their own, build out further and explore data sets in that way. DAN 15:47 Yeah, it's almost like, to me, going across the org like that, especially because of what you guys are doing, it's almost like passing those things along. Everybody can just keep building off of those things, because it's almost like you're building a car, but you're using the data from each of these departments and people can look at it a certain way and then say, okay, now let's add our piece to this and move across the org until they almost build this giant workflow with everybody's information on it, giving you information for specific things. JESS 16:18 Yeah. The way we share those workflows across the team has become super powerful. It's removed the aspect of having 100 lines of code saved on someone's desktop, and publishing those workflows, having them easily accessible means as a team we can explore analysis in all different areas. Any user can give it a go from tire pressures to those use cases in Extreme E. Those workflows are published and accessible for everyone in the team. DAN 16:44 That's really cool. So I was at the Miami Grand Prix a couple of weekends ago, and where I was sitting, every time Lando or Daniel went by, the crowd went absolutely bananas. And it's known that you guys have probably the biggest fan base out of any of the Formula One teams. Are you using Alteryx to kind of propel those fans to get them more information or to bring in more fans? JESS 17:13 Yes. So we've worked on small scale projects before, looking at social media, doing sentiment analysis in Alteryx, and sort of seeing what our fans think lap by Lap on a race brings super powerful insights to the team. I think looking forward, we'd love to pull our separate internal data sets, whether we're looking at e commerce data or fan data and overlay those in Alteryx with that previous work we've done, and really see the power of what fans think and overlay that with our existing data. DAN 17:41 Yeah, it was very interesting being at the race like that. I'd never experienced anything that, it was my first Grand Prix. And to see all the people with all the different-- usually when you go to an event, at least events, I've been to a sporting event, it's one team against another team or this. Or there's ten teams or however many teams there are, and people walking around, and-- so the simple fact of, there's Ferrari fans sitting next to us and Mercedes fans sitting by me. But when those two guys came flying by, the crowd would go nuts. So it was pretty wild to see that. And I definitely saw a lot more papaya walking around Miami than any other color, which was really cool to see. JESS 18:21 Yeah. I think sometimes I forget that I have the privilege of diving into all these datasets. It becomes my daily norm. So for us, actually reaching out and gathering fan sentiment analysis, and gathering fans thoughts is a really powerful aspect for us, and we really look forward to analyzing that data. DAN 18:37 Were you surprised by that, when you came to McLaren? Did you know that McLaren was that popular? JESS 18:43 [laughter] I think as a fan of F1, everyone has their favorite teams. For me, I'm not even sure I had one in particular. But coming to such a great team like McLaren and the history and everything you learn whilst there, I think, again, yeah, sometimes I forget that the power that has on the public and F1 fans in general. So it's great to ground yourself sometimes, have another reevaluation of the data, and sort of think, yeah, the privilege we get of working with this team. [Music] DAN 19:10 Thank you so much for joining us, Jess. We really appreciate you taking the time and talking to us. It was a lot fun. JESS 19:15 Yeah. It's been a pleasure. Thank you. DAN 19:18 Thanks. Thanks for listening. For more on Extreme E, Alteryx auto insights, and the Alteryx and McLaren partnership, check out our show notes at community.altreryx.com/podcast. [Music ends] So do you get to go the races? JESS 19:41 Some of the races. So I went to-- have just been coming back from the Indy car race, at the Speedway just last weekend in [Lorraine?]. DAN 19:49 Yeah, I watched that race. JESS 19:50 It was amazing. DAN 19:51 In the rain, the crazy rain? That was-- JESS 19:52 Yeah. First wet race in three years. Yeah. DAN 19:56 Yeah, I know. I was watching it on TV. So they had Alteryx on the back of one of the cars. JESS 20:02 Yeah. DAN 20:04 On Juan Paolo's. And I think he was, like, way back in 20th for most of the race, but this rain came in and it got crazy, and he moved all the way up to 5th or something like that. And then literally, like, on the last lap, I don't remember what happened, but he crashed. But of course, on TV, it was a big Alteryx sign on the guy that crashed, but so what? JESS 20:28 That's the first thing I noticed. That's the first thing, yeah, I noticed they had all the cameras on the car, the big Alteryx on the back. [laughter] DAN 20:35 Yeah.

This episode was produced by Matt Rotundo (@AlteryxMatt), Mike Cusic (@mikecusic), Dan Menke (@DanM), and Maddie Johannsen (@MaddieJ). Special thanks to @andyuttley for the theme music track, and @mikecusic for our album artwork.

Alter Everything

Episode Guide

108: How McLaren uses Alteryx to race faster

Panelists

Topics

Transcript