F1 is all about finding the tiniest margins for improvement - both on the race track and with technology. Collecting as much data as possible is crucial, so there is tons of data that needs to be processed, stored, and protected. IONOS has developed a cloud-based solution that makes all of this easier. Gary Foote, CIO of the Haas F1 team, explains the importance of data in the team’s racing strategy and how IONOS Cloud became a key partner to help collect, analyse and secure the vast amount of data the team needs to help continually optimise its performance..
The structure of the Haas F1 team and the role of the CIO
Collection, scalability, and protection of data
The importance of global data accessibility
Who’s more important: data or the driver?
🏎️ #drivedigitalsuccess Take a technical glimpse behind the scenes of modern F1 racing to understand how technology is accelerating motorsport! How is racing connected to Cloud? What technology drives a modern race car? How can sustainability and racing go together? We talk with experts from the Haas F1 team as well as industry experts to answer questions you don’t even know you have yet.
🎙️Guest: Gary Foote – CIO at Haas F1
🎙️Host: Chris Medland — Formula One Expert and Presenter
🎙️Host: Mandy Carter — Cloud Expert & Head of Marketing Ionos UK
00:00 | Introduction
01:41 | Team structure and the role of a CIO at Haas F1
07:50 | Ionos Cloud: global data access
12:13 | Evaluation of success at Haas F1
16:55 | The link between driver and data
24:35 | Cloud & data scalability and protection
32:46 | Will there be more data fed to fans in the future?
☁️ Brought to you by IONOS- First-class cloud and IT infrastructure: https://cloud.ionos.com/
🎧 Produced by digital kompakt – Your expert in first class podcasts: www.digitalkompakt.de
00:00:00: Welcome to drive digital success.
00:00:08: You're behind the scenes podcast about Formula 1 and the technology driving is presented by Chris medland and Mandy Carter Howard bionis.
00:00:26: Hello everyone and welcome to another edition of The Drive digital success podcast from Isles I'm Chris medland and alongside me again is Arnold head of marketing Mandy Carter hello Mum
00:00:35: now we're also going to join today by one of the pubs more unseen but crucial members of the Haas F1 team in chief Information Officer Gary
00:00:44: Gary thank you very much for joining us tonight you're busy man and it's the oxygen that doesn't mean you get time off now just for the listeners can you introduce yourself tell everybody here that wants to know more about data who you are what you do it
00:00:56: yeah of course I say I'm Gary for I'm chief Information Officer at so I've been here since the team started actually my first race in March 26th
00:01:03: previously I was at Mercedes I was there for about 8 years or so I think before that was Braun before that it was wondering couple of the team saying in F1 for quite a while now and I can bought it has to suddenly the operation to put the team together
00:01:17: put the instruction to go work on degressive timeline I'm sorted fairly well documented we got the racing sort of licence in 2014 but we do need to start putting the team together and
00:01:26: late 2015 and obviously had to get the car out.
00:01:29: A nice 2016 was a very aggressive and a growth strategy and I was born in to put together build it up but you know now it's Donna Summer tour that 80 functions of the technical function to get us into a more honest
00:01:42: what does a CIO do on a day-to-day basis in an F1 team is it similar to the same sort of roller in other businesses was it very unique TF1 bit of both actually I mean you know what sort of stripping out the Glamour of Awesome the racing you know where an advanced
00:01:57: company at the end of the day and a great role and all of those organisations
00:02:01: putting that technical status of the liaison between the board and the son of the financial business side of it
00:02:07: you know and then the operation side when you're creating your products in our case our product is our race car and so that's where I made it becomes a little bit you
00:02:15: where you where do we have any data generating sport we have an awful lot of infrastructure with carrier of infrastructure much more than maybe people realise behind the scenes
00:02:24: the car runs on that infrastructure in that data so maybe there's quite a lot of Alliance on there so you know without that technology of the car just doesn't go down the track but how did you get this role yourself in terms of your career path you mention all the teams you work was always a name.
00:02:37: Come to YO18 was it you found your way into it so clear route for someone that's a good question I mean I'm a huge racing fan you know into cars since no single digits and very much still I'm a 4-wheel guy rather than by which is a very controversial.
00:02:51: In in racing
00:02:52: I don't think I deliberately set out to get into Formula 1 I was very keen on going down the it path you know I've been playing with technology since you know programme BBC micros back in my editing
00:03:03: coming out I didn't realise degree at university and I came out of that and I started working by accident really for Cosworth so you know that time in the Road Cardiff sort of area.
00:03:13: But there was a route into F1 that I spy very early on so within 12 months of starting my career that's when I probably got my eyes on F1 but only because I had that tiny first step in in a working for cos we're back then yeah I not work there
00:03:27: I don't know you old still a bit of Hughes Motors Broughton but I don't think I would have been tired into F1 but once I knew there was a way to get in then that was why Focus
00:03:35: and now you're an F1 and you're heading up your present at how does the structure work here you ever seen a specific team below self you better across the whole.
00:03:45: Yeah it is a very unique company
00:03:47: some of the listeners might not realised that you're very different somebody in the F1 teams there up and down the Grace and we much smaller we have very lean headcount and we operate a sort of a model where we choose to outsource as much as they can they say very documented sort of parachute we have with Ferrari where we choose to put your quite a lot of components from
00:04:04: but they're actually quite lots of Life behind the scenes that we also choose to procure4 where where are the teams typically manufactured in-house so that does change
00:04:12: sort of the company has a secret linen company got your child like the ability to colour twisting and more
00:04:19: it was quite a controversial business model so it was first brought out but I think it's becoming a little bit more accepted now but my role within that is actually got three company
00:04:29: Haas F1 team as an entity is made up of a US firm a UK firm Italian firm and then we have different business functions based and different locations my remit overseas all three of those and I have technical teams.
00:04:43: F1 teams and Christina different going to set up and business model how much do enjoyed one you've got how much water does it have three separate companies that you're over to you know it is
00:04:52: building a good
00:04:53: you know does unique challenges that are introduced into this business models United lot of advantages you know and we'll happily took 4 hours about the advantages of brings behind-the-scenes there are some challenges that especially the technique
00:05:05: you know we've got data movement across dinner continents and your big data will generate so much of it and we have people who want eyes on that data and all of those countries and they all wanted at the same time that introduces unique challenges
00:05:19: having no changes what are the many unique aspects for this F1 team that you have to put in place to make that's all set of work I think he is tackling
00:05:28: any sort of challenges is to look at the requirement Yahoo needs what where and when you know once you understood what you need
00:05:36: do then you can start to define the solution that does that so yeah very early on when we were still before we didn't put the car around the track we wanted to understand.
00:05:44: You know who's going to be doing what with what data where do they need it how often do they need it how quickly they need it you know other subsets of data you know we talk a lot in various forms about
00:05:54: performance reliability and safety and their subsets of data that come off the car you know so who needs what and where
00:06:00: you will look at that you know from a safety perspective you know that's real-time data and it's needed no there and then but if it's maybe sort of a retrospective performance data that can maybe come later but then there's business data behind the scenes yeah we often racing car on the car data that's what everybody loves to look at that actually understands that business data another talk cycling boring data
00:06:19: say that but yeah you got finance data you've got
00:06:23: HR data and then you've also got an awful lot of Focus and last 4 years and take protection data security and data integrity so as soon as you start moving data around a global landscape just like any multinational basically challenges in a we're just a much more.
00:06:38: I'm not smaller company that I've got to deal with those challenges
00:06:41: before you get really into the detail of some of the challenges that you face what about for you in terms of your typical working day day today kind of setup it is that something
00:06:49: it is dealing with data across different channels is that one has access to everything I need was a lot more to the roll a bit more to it so talking about the sort of core technology call Function will also got the infrastructure company night could be back at 6 in the UK the US and Italy
00:07:05: you're obviously the racetrack as well that you know that sort of mobile infrastructure the travels around to all the races obviously course running the car but you know behind the scenes there's a lot of other
00:07:14: technological things that we need to look at your data governance is another massive one and no data retention and spend all this money generating tons of data then we have to sort it ready to organise it we need to make sure that we retain we need to not retaining what we don't
00:07:28: yeah one of the things that we want to make sure we do it house is not build up this Legacy said of infrastructure instead of data and his old systems you have to read about these companies are the bog down
00:07:39: buy Legacy infrastructure and Legacy data in a we got an opportunity has got a young team to be able to make sure we put the foundations in early on to make sure that doesn't become a problem does the company match
00:07:49: question to you Mandy is it challenge or 4-in off to work for the team across multiple bases as well as at the racetrack or does it not matter where us access is it.
00:07:58: That's one of the main benefits and reasons for using £1 you can access data anywhere from pretty much any web-enabled device these days
00:08:07: and beyond that cloud enables you to mirror data across different data centres in different regions so you will pick up data from the nearest card instance which mean
00:08:16: latency and basically speed data retrieval fast YouTube.
00:08:20: How much is great if you think about it most large corporations have global presence and multiple regions right so I understand no different we are headquartered in Germany but we have regional offices and data centre
00:08:32: all of the world including the UK where I am and in the US
00:08:36: and now I mean because of Corona this is wrong exponentially so if you think about it we have thousands of employees working across thousands of location to because everybody's home office or living room is now become a remote offers so is probably two holes and really most large organisations out there today
00:08:52: cloud actually makes it much easier.
00:08:55: For multiple working points and how much growing is are not doing with Haas is Gary puts it the company is maturing so is it attractive to be part of a team that is going through such a pro
00:09:04: I mean absolutely that was actually one of the points that made her such an attractive partner for ayanna SAS and they are clearly a Challenger in F1 Circus and while we are a leader in Europe and in Germany on and we are also still grow when you know we are very much in that growth phase
00:09:20: a great part of being a Challenger is the need a work order and with ingenuity and you know it's a green right to think differently interesting things and to try to find that way
00:09:30: breakthrough into the marketplace and be better and do better and these are all principles that I honest and hospital together so it's great with feels like we're both kind of working towards similar goal
00:09:39: on a day-to-day basis we learn a lot from each other right so just in the course of the past year of our relationship we worked on a multitude of projects together which have been implemented like migrating as website over cloud and using virtual machine
00:09:54: in are clouds you fast-track your software testing Cycles in the near quite a few be for your kind of sexual projects in the pipeline for this year
00:10:01: supporting houses thermal analysis tools their vehicle performance analysis tools and hopefully rolling.
00:10:07: Fluid dynamics so some really pretty cool Turkey stuff that will help elevator performance in the end and
00:10:15: you know for us the benefit brushes with each of these products company on both sides but certainly for us and we take all of those learnings and apply them to her other customer so it's not just us as benefiting from these BT products and I'll let you know we're having to elevate other customers as well and service is benefiting from the partner
00:10:31: and Gary you mentioned just now about being a young team and avoiding Legacy infrastructure
00:10:36: is that something Warehouse has an advantage I guess because it's a newest F1 team on the good will you able to look at everything special someone who's having very start and go you know what this is where it doesn't really work on other teams or maybe is a bit
00:10:47: difficult not perfect for our setup but because we knew we can have clean sheet of paper and we can make out what's going to work better.
00:10:52: Yeah 100% yeah that's exactly so what we're able to do is take lessons learnt
00:10:57: I'm going well I put the team together I delivery brought in a mix of people with F1 experience and also people without a delivery wanted that sort of combination of staff and so I brought people in from a couple of the teams and we're able what does your team do well I have my Xperia
00:11:11: Mercedes of absolutely fantastic team they did so much so well I was able to replicate that but I had to
00:11:17: adapt it for the size of our organisation both year in terms of headcount and financially you know that's a team that had much more resources than we had at our disposal and also a lot of history
00:11:26: I think it's the same you do with any sort of challenge and solution you pick out the bits that you need to do when you implement them in the right way but then I also wanted to make sure that I pull his staff from non motorsport teams because I wanted to
00:11:38: how do I do it you know in the public sector where money finances of maybe more heavily scrutinized and valuers is really important Haas F1 is built around the principle of Val.
00:11:48: And we've got budget but with very careful on how we spend it to you know public services doesn't want to look at the manufacturing company so you'll have tried to bring in some some activities from there and say what do other companies that make stuff how do they do it that outside of the really unique
00:12:02: it's got quite a peppered history from a financial perspective you know there's that many days gone money was the thing you fall out last
00:12:10: I think I've changed now every team ever ever see I think every team would agree that money and valuers now got high on the agenda especially the budget can just tell me how do you do a car and it's performance or are there other factors
00:12:27: you know I mean we're a team so when the card as well other drivers do well or you know whether that's in raw performance or reliability then I think that reflects well on me and my team and our objectives but then also behind the I've got wonderful
00:12:41: a business and side as long as the business is functioning people getting paid maybe people are being organised maybe
00:12:48: your business system uptime you know whatever happens to be there's the two sides we've got a win and losses the team together that's usually important in sports but also behind the scenes I've got I've just a business to Prague
00:12:58: you mention when the child is being successful because being successful that focus on I guess what maybe is the fundata the stuff that revolves on stuff is on track in your perspective tonight easy.
00:13:07: Which is more important the data you get all the driving
00:13:10: I say I'm a tech guy and the drivers on in the room so I would say it so it's text first I think it's important the we provide
00:13:18: the drive with the tools then he'll her needs to do the job right so there are drivers that can take you know a top car and get the best out of it but there are drivers that can take a tough are and you know everybody else
00:13:32: yeah it takes both you know the pilot and the craft but I think the crash comes first is our job as a team
00:13:38: to provide the driver with the best possible tool that they can to do their job but then we had it to them and we say do your best.
00:13:45: First stated to the car development side of things especially as a company that split across 3 locations how do you make sure that I sent you those three or interlock and connect
00:13:55: so they can develop the best car possible so I think we understand what day till we put
00:14:03: and what is used for so if we taking data off the car for instance you know there's the day there's having an effect of real-time decision-making primarily at the circuit but we also do bleed out
00:14:13: facilities so that can be your safety performance reliability and now different subsets of data will go to a different years for different staff members and their making real-time decisions you know there'll be some people are looking at the cellar so taking in some of that date
00:14:27: understanding it and then say hey for the next one next week this electric that
00:14:31: any other people looking for a reliability pressures and sensors in real-time and they can say I think the car's got a problem come
00:14:38: let's put it out before something serious happens that's the real-time data you then you got your retrospect today so that we wanted to development you mentioned so that stage
00:14:48: then we're looking at retrospective analysis of the data unit how can we make that car better either in the short-term going to maybe for the next race but also in the longest
00:14:57: you know whether that's the Next Generation next year of a car
00:15:00: I was certainly when it comes to things like your arrow you can't make arrow fundamental arrow changes on event so that data feedback into the design team and then it starts working it's way.
00:15:10: Through that the development process be that from an idea and designers headpaint looking at this data I think I can make the cargo the quicker by do
00:15:19: this and that it goes through their get into the into the winter process into that the cfd and the high-performance computer process you know which sits on the mighty
00:15:27: and that's a big pile of proving out and aerodynamic change before it gets made into a physical part of which has quite a lot of expense the race car so there's a whole spectrum there from real-time my really Ms seeing a piece of information and making a decision
00:15:43: right through to some data having an effect on a previously next-generation car even multi-year generation is it fed saving the real-time data is maybe the more crucial because
00:15:53: you need a bearing then to make certain decisions and that's is there where you have to protect it or send it to make sure.
00:15:59: Me easily understood and accessed I think it's hard to differentiate it's being more crucial has its job to play in terms of real-time data you know it's usually having an effect on that event
00:16:08: so yeah absolutely it's crucial but yeah one could argue that
00:16:12: data that is looked up your retrospectively and a picture built-up could have an effect on a whole generation of a car up and that would affect every race and a season.
00:16:20: I think you can ask each person out on out on the factory Floor that question and you get a different answer the key thing from my side is making sure that the right people have the data that they need
00:16:29: you mentioned protecting it's very much about saying in order to focus somebody's time on what they need to do I need to present them with the data that they need you know that's not just celebrity data you know that might.
00:16:40: Historical data it might be in a mechanical or setup data you know so well cork traditional file data documents.
00:16:47: You know that there's a lot of that flowing around 2 and 5 units.
00:16:51: One engineer might send out a document that shows the seller paid his car and he'll be asking maybe 15-20 30 people for feedback on that that's just as important as the delivery is coming off
00:17:00: what is the driver's seat back in like how much of that counts as data and how much of it is too subjective and you have to focus on a hard day today
00:17:07: it comes of the car the driver is actually really crucial link in
00:17:11: putting together what we see and data to what is happening in real terms that should have collaboration and sort of the delta's between the two rights.
00:17:20: You gotta understand ok we looking at the data and we're seeing this particular thing then we speak to the driver and he said yeah that is what's happening with the car great coral.
00:17:29: Bang on or what more often than not quite honestly as well we say ok the data is saying that you know this is happening to the car.
00:17:36: And the driver says no that's not what I'm feeling and at that point you've got a data correlation issue.
00:17:41: That's where the engineers who wrote you know with years of experience under their belt and ok I know what that correlation issue is and they can use that data to make the right change if we went purely on data I think actually you could run into quarter Spotify.
00:17:55: Because what you're doing there is you're not taking into a car
00:17:59: all those variables that only a human can do and yeah you got the driver is it is a human and it's got all his or her sensors available to them under has the engineer and you've got there.
00:18:08: What is ingredients go into a part gets turned out and out of the back of it becomes Odyssey.
00:18:13: Yeah whether that's the driver needs to take a line I do it differently or an engineer needs to set the car up differently there's a decision made and it's either you know the right decision or restaurant
00:18:21: Somerset you can see my notes about the human
00:18:25: Haas Formula 1 knowing is special in your time of it as it developed as there been more The Reliance on data and that's what feedback and is it an ever shrinking kind of window
00:18:33: at reliance on the human Factor.
00:18:41: Because what we are allowing the human to do is Focus their skills and their time right there's only a finite amount of time
00:18:47: an energy almost that member of the team can put into their jobs
00:18:51: and that can be an IT person through to mechanic to a driver to an engineer so what we do with data in the technology is we're allowing them to focus the bit that they can do
00:19:01: yeah just a bit that the channel is you can't if that makes sense.
00:19:05: Ultimately what I'm able to do is take care of a lot of the work that you roll back 10 years 20 years and engineer would have been doing a particular amount of work if I've taken three quarters that away.
00:19:16: Yeah they don't sit down and drink tea for three quarters that time they Focus their energies on that quarter that's left
00:19:22: and I just really really good job of it so as the data to human workload ratio changes.
00:19:29: All that does it's it just allows the human Factor to get ever better at what they do whether that's a driver or or someone looking at the day.
00:19:37: When we talk about Gathering some of this data where does it all come from is it always sensors on the car and only things controlled by you or are there other data points at the Trap example where do you gather the data from
00:19:49: yeah in terms of job working for talking just out and sort of the racing side of it unless about the business side it comes quite a few places are videos playing going to be part of the landscape in the last 12 years
00:19:59: shifting in the use of video and high resolution video
00:20:03: you know when we get video feed from all over the place so we've got cameras in the garage we got cameras in all of the pitstop gantry so I'll give you a good example where we took that mix of human performance we know we have cameras that look at the top of each person on the pit stop.
00:20:17: After a pit Stop they're able to go back and look at themselves and say did I get that we'll on right and we have performance coaches that will work with the guys and girls on the pit crew to say
00:20:27: here's how you can improve your technique that so yeah a good example of where we using the technology to influence the performance
00:20:34: team members so your video There is obviously cardata we have two types of cars date we got toiletry data that comes over the radio
00:20:41: so that's your story your real-time data and that is typically
00:20:44: I'm generalising heavily here but that's data that the the engineers and the team and looking out to make those real-time decisions is the car safe is absolutely is it going to maintain the liabilities going to get to the end of the credits on going to be quick
00:20:58: is there any reasonable changes we can make
00:21:00: the Newcastle have a subset of data that's the store on the car and you'll often see when they on the TV when a car pulls in your c cable go into the salad
00:21:08: umbilical and that will download a much greater dataset
00:21:13: that's that retrospective data that I should have mentioned wear that will reply lad off we do we obviously have people like the circuit looking at it we actually send that data off around the world and we have people looking
00:21:24: do you know the moment lambs in more sort of medium to long-term decision-making on that on that front.
00:21:30: The crucial part of my face when I think of it is surely it's all well and good getting data taking any of the car and have
00:21:38: set the numbers for Saturday to that says this is what happened
00:21:41: understanding that and come interpreting at quickly surely is actually the key point because if you guys have all the data in the world but can't understand it that must be very true so much data now and we can have it
00:21:53: I mean everyday at request of some sort comes across my desk of a way that we can generate some more data can be very strategic about it so we need to understand the whole landscape you who's using.
00:22:04: Where are we going to store it one of the implications of having other costs involved of having it
00:22:08: yeah I'm making sure that you know if there's a particular sensor that we only want to run up once I caught a second one hurts then that's run at that you don't run at 10000 Hz just because you can you need to make sure that way
00:22:20: yeah even more so maybe a house where we're trying to really maintain that kind of lean agile approach to technology you know it's making sure that we absolute
00:22:29: get the data that we need to do our jobs but we don't get the data that we don't have Alexa what kind of products did any non point of view in terms of then getting update around the world and interpreted and shed with the right
00:22:40: where are honest providers value is in the Cloud Computing the cameras also the main
00:22:45: selling point one of the word of the cloud is it scalability so yeah again because we quite a small team and we keep a very nice head of infrastructure
00:22:54: I want to scale up I want to do something bigger but for a shorter period Of Time And The Clapping and technology works for me because at the moment
00:23:02: if I was to develop a system on Prem let's take it a good example of something we are doing with our loss in terms of a car setup so we have some in-house software in mathematical models of the car and we will typically run a load of patients on them through some mathematics software
00:23:17: give an idea of how I will set the car up for a given track in a given conditions in giving surroundings and.
00:23:24: Parliamentary numbers and that goes to the circuit and and and the guys and girls to use those numbers to help set the car now.
00:23:30: If I want to run a big set of simulations if I run out of credit I have to scale my architecture you know my capital expense to
00:23:38: the peak of that load get outlets A41 of a number 100 computers so I have to 100 now if 80% of that time I'm only using 20 of those that means 80% there that sitting down stairs being
00:23:50: being a neuro being powered when it breaks I'll have to have engineers and then what they doing to go down and fix it then it's going to be depreciated and I've got appreciation costs and so you've got this all this Legacy an old school mod.
00:24:03: Much sort of better for that particular purpose and practicing number of our sort of application.
00:24:08: Is to put it on a scalable platform which is where the car comes in so well I can do it in that particular instances say to the team OK when you're about to run your simulations you scale up your resource in the Cloud
00:24:18: you turn the numbers you can have 1000 computers if that's what you want
00:24:21: and then soon as you're done you turn it off and the costs and somebody else is taking care of the power someone else is taking care of the cooling if it break
00:24:29: I don't care because I just found another computer in the clouds to do it on that clown models suits that kind of peak and trough the workload
00:24:36: the F1 housing use one example but there's many others and Mandy is crap scalability particularly effective in if one partnership was is something that are Nazis across a multitude of business.
00:24:47: Me a core skill ability is hugely important ineffective because due to the incredible amount of data points that collect and continue to collect but scalability is a key benefit of a car
00:24:56: for anyone the whole premise behind cloud is that you as a user don't have to worry about the hard way you need your cloud provider will do with all the physical and it's already there waiting virtually for you when you're ready so if you need resources you simply
00:25:11: Adam from the cloud a nice
00:25:13: side benefit of that is the only pay for what you need when you need it so another sorry benefit but you know these days it's really particularly relevant with the global chip short we have much is making it harder and.
00:25:26: So you revising iconfinder means that the heavy-lifting about cleaning and maintaining a service done for you so your business can still sell quickly without the weight really
00:25:35: just giving on-premise platform
00:25:37: you can't just had a server infrastructure you you also need to integrate it with the existing systems that you know that the os card automatically handles all that for you of course every organization's differ.
00:25:48: And not all will need rapid scalability that that cut off so doesn't apply to everybody but I guess that's another difference between ionos and a pure hosting service where you just cry
00:25:58: off-the-shelf products are technical consultants work directly with our customers such as to understand their needs and find the right Solutions together so that they have
00:26:08: bespoke solution at meet there and business goals really that date will go to the team at the track and I'll use it to setup and things like that when you run the simulation how much.
00:26:18: Is done by the humans off the back of that and how much is this data used to
00:26:22: machine learning why is there like a split where you go ok this is someone going to trust her engineers and people in this is my actually much more than Trust
00:26:30: yeah so does pros and cons so we talk about that sort of machine learning and AI technologies do the key thing at house for us and we're we looking at that technology is to streamline and focus at The Resource that we do have so we need to be taking away repetitive
00:26:47: and shouldn't more calculation based part of their job
00:26:51: and get your and we can get software to do that for us that means again they can focus on what they are or maybe the bits that the technology can't do you've got the human at the end of it is still the last cog at the words
00:27:05: is that in terms of
00:27:07: got into action so you can have all the technology and the software turning away and spinning Out a set of numbers but ultimately we still have a finite number of people that will look at it at that where they put the human Factor
00:27:21: ok the number to telling me to do x but I know that's not quite why I'm going to do X plus a little bit I'm more than what they get it right
00:27:28: that's where I was someone's are still not quite there yet it's.
00:27:32: Is definitely coming out in all the teams and looking at how the human decision-making and that experience Factor can be done through
00:27:40: huge Analytics attached events event you know the 60 ideas of Formula 1 data kicking around you no not necessarily at half but just in the public domain
00:27:49: can feed into your AI or a machine learning algorithm to say I'm starting to spot some patterns here and I can start to reply then so the date is there.
00:27:58: And as we go through that yo consuming video and consuming car data
00:28:02: yeah we're building up a data landscape that technology can really work but at the end of the day I think it's still a human support and you're still have
00:28:11: guys and girls applying their trade and their experience you know that the last
00:28:15: but if the human names at the end of the jail most and the final call for me once all about the slightest time is Martin's that you can't find my new games anywhere and obviously a lot of data can all go into 1000 to the end of the game into the set of 1
00:28:29: is it possible for humans to have to do that or is it more effective for data machines to be able to
00:28:34: I think where the technology is at the moment I still think the human is best at doing that you know the human mind he has incredible and its ability to take care
00:28:44: so many factors and experience being one of them you know you've got that I've got feel but that's a real thing you know that supplied a lot more than maybe people realise that's what technology can't provide can provide loads of inputs into that
00:28:58: I think the human mind is still the right tool to make most of those decisions when it comes down your and that's why it's still motor racing you know the moment I machine race
00:29:07: completely then that's not a sport anymore will be fed say then.
00:29:10: Talking Focus Butler on the track stuff it's what fancy this year practice session qualifying race this year car out on track they really see Behind the Scenes at a factory in the work
00:29:19: hear that when we sit now to record it is a lot of the data that your Gathering then more commonly used here it is all built on data this factory and the Works people doing compared to a maybe Trackside it is that still
00:29:31: direct me to the driver Direct engineering experience that makes that a little bit more the human Factor is tracks I compared to factory
00:29:38: the data is what everybody uses to do their jobs but they are still ultimately doing their jobs if that makes sense to you know whether that's business deity that you know finances i t i t personnel personnel yeah they're using massive sunset to date as we have to do their jobs just like you know the performance data is naive
00:29:57: performance teams to do
00:29:58: someone is a very secretive sport though the factory because this is a space that people don't get to see very often and understandably teams that one other team singing to the factories on know what they're doing how much you have to worry about protecting your data how much is it that you gathered that needs to be kept confidential.
00:30:13: I would
00:30:14: all that actually yeah I data is alright and that can be anything from you not a business data is is it in terms of our business model is very happy protected you know we like our business model like what we do and we think it will take a while to replicate correctly
00:30:28: I would say I'm less concerned about other teams and privacy to other teams you know it's got a respectful sport I think more it it's people using the data for their own personal gain
00:30:39: you know on a global stage you know we're seeing everyday big companies and big teams their data being used for questionable purposes
00:30:46: that probably a bigger cancer yeah obviously don't read other teams differently quite frankly we just protect your data protect the perimeter and
00:30:54: you know that way we know that we're trying to be a security possibly can help protect that data when so much of it is stored in the.
00:31:03: That's a great question when we develop your own cloud platform data security was a major consideration.
00:31:09: Sell-by building at own cloudstack it was much easier to implement them by relying on other services
00:31:15: in fact data security is basically whenever very key Focuses we have more than 20-years of experience in building and running data centres
00:31:23: you know we take all that knowledge and learning and apply all of that in every undertaking so you know from my perspective and from an honest respective relationships are built on trust and our customers.
00:31:33: Trust assets to killer data so that is basically you know the cornerstone of our relationship with our customers
00:31:39: yeah I was going to say that how important is data protection in terms of honest his list of priorities on this very
00:31:46: yeah I mean data protection both in terms of protecting
00:31:50: physical safety in the in the integrity of the data but also obviously in preventing third parties from a legitimate access that's a Dacorum business.
00:31:58: While we have a subsidiary in the USA honest is first and foremost a European company which means that gdpr has become our Bible in terms of
00:32:08: privacy practice and our headquarters in Germany where they are.
00:32:15: Being European company bound to gdpr we are also not subject to the snooping was like the US cloud act and I'm not sure if you're familiar with that but under the US contract the American can basically demand access.
00:32:29: To any data hosted by a US company regardless of whether that is actually hosted in you as long as the company is a US company.
00:32:37: They can take it and you don't even know they're doing it as the end customer that he has to compromise you
00:32:43: you may not even realise it's going on so security it is certainly like I said at the heart of our company and making sure your customers data is protected is it
00:32:51: that probably explains why it's not so easy to fans to know will see everything will get all the data that they would love to have but you'll see aspects of the sport of the team of the data channels you have that could be fed to find a bit more to help you standing in the sport Intu Merry Hill
00:33:04: boost its popularity yeah for sure I mean yeah it was such a long way as well in the last sort of 5 or 7 years in that from the liberty of done a great job in
00:33:14: fans connected because I think that's one of the big attractions for Formula 1 I know many people that will watch the sport with laptop to the side you know getting some some data feeds whether that's from F1 themselves or some other teams.
00:33:26: I think that's going to grow you know lives I've got big plans for the Sporting that and there's devil
00:33:31: huge amounts of data onto the I put my own hat on here is a race van there's loads of data that you love to see you know we look at that the last rates and those last
00:33:40: there was an F1 fan not on the edge of the seat right doesn't matter who you are a fan
00:33:44: I'm not sending either of those teams but I was on the edge of my seat and if you can you serve up you know what's going on you know what's going on with
00:33:52: maxima vs pulse rates for instance you know is there human data that we can start looking at I think that's going to connect people they're going to feel like they're you know and then that feeds into esports
00:34:02: you know again you know you can imagine a world where you can play live and he's sports game and you could be on that last lap with those drivers but in your game well that's all posh
00:34:12: when you say that about heart rate data and things like that stuff he still gather for the team to maybe a drug performance yeah absolutely yeah so driver by electrics and and crew member biometrics is something that we're looking at and working with understanding.
00:34:25: Yeah we need to get everybody that can be your driver or pitstop person may be the most public but actually everybody out there that people
00:34:33: because as soon as you start getting maybe 40 you know that's where you know we talked about that sort of human Element that human decision-making we want to make sure that people are at the able to do their best work
00:34:45: understanding what's going on with people you know has we have got a big well-being programme and again that's you know it's almost of a taboo subject 10 years ago because it was such as a masculine
00:34:55: that's gone now and I think the sports are really great job of being able to you not get staff performing at their best
00:35:02: and loving what they do right as soon as there's somebody like what they're doing and they looked after
00:35:07: then you know they start my phone well and that those metrics know all data is all being captured and all being understood is that my cloud
00:35:14: important to you guys then because this sounds like someone in the new avenues that just almost overnight someone has the idea you know what we should be focusing on that all look into that and suddenly you need any short spell you need more
00:35:25: yeah 100% in fact I can give you a real world example of that just last week and so we had some software would rather than human form as well card format software that we thought would make quite a difference
00:35:35: despairing late stage for the 22 programme but it was very computational heavily now
00:35:41: yeah there's very well documented lead times on semiconductors you tried ringing your car at the moment so.
00:35:47: What's playing some capital tea and bring it down stairs monthly exerciser best what we can do with the cloud and I'll have a go
00:35:53: should I do a system where you can just logon stay up some computer not really 15 to 20 minutes later
00:35:59: I can present that technology to the team mate and a couple of hours later they're running the software yeah that is a game changer I do every team is using that now yet to give that sort of instant reaction so yeah if you've got
00:36:11: the person to come up with a great idea what you want to do you want to give them tools to exercise that idea as quickly as you
00:36:17: so just that all the way back to the very start when you talk about your role and what you do you just sit here waiting for waiting but waiting for people to come to you
00:36:25: I need to go to do this give me the question to do it will give you the tools to do it are you essentially trying to find ways to a different avenues of giving people
00:36:33: that capability yeah that's a really good we're putting it to Jackie
00:36:37: what's the first parts thank you very much for your time really appreciate it and as you say we're looking forward to seeing what happens between 22 and we won't tell her driver's that you think that's more important
00:36:49: thank you thanks for listening
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