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The thing that really attracted me to Formula 1 is that its always been about data and technology, says Graeme Hackland, Williams Group IT director and chief information officer of Williams Racing.
Since joining the motorsport racing team in 2014, Hackland has been putting that theory into practice. He is pursuing what he refers to as a data-led digital transformation agenda that helps the organizations designers and engineers create a potential competitive advantage for the teams drivers on race day.
Hackland explains to VentureBeat how Williams F1 is looking to exploit data to make further advances up the grid and how emerging technologies, such as artificial intelligence (AI) and quantum computing, might help in that process.
This interview has been edited for clarity.
VentureBeat: Whats the aim of your data-led transformation process?
Graeme Hackland: Ten years ago, we might have been putting four major package upgrades on the car a year. Were now able to do that much more quickly, and we dont have to wait for big packages of changes. Our digital transformation has been focused on shortening that life cycle. Thats about getting something from a designers brain onto the car as quickly as possible. Test it on a Friday; if its good, it stays. If its not, we refine it, and just keep doing that through the season. And that process has gone really well.
VentureBeat: What kind of data technology are you using to support that process?
Hackland: Some of it is what you would in some industries consider standard data warehousing and business intelligence tools. Some of that is written in-house. At the moment, I dont have a piece of middleware that lies across the whole layer. But thats where we want to head to, so that absolutely everything is feeding into that.
VentureBeat: What would that piece of middleware look like?
Hackland: We originally thought of three main domains: design, manufacturing, and race engineering. And you would have these three bubbles that would all talk to each other. But what weve realized is trying to create data lakes just hasnt worked. It hasnt given us the actual intelligence that we wanted, so we often refer to data puddles. Its much better to have many of these puddles that are well-structured and the data is well understood. And then, through a middleware layer, we can get to the graphical user interfaces.
VentureBeat: What does that layer of information mean for the Williams F1 teams engineers?
Hackland: Were covering everything, from what they look at through to the data structure. And the data structure has been one of our biggest challenges. We relied heavily on Microsoft Excel, and pulling data from all these other sources into Excel was very manual it took too long. So thats the piece of work that weve been doing. Weve not made it public who were working with in that area. Talking publicly about some of the stuff were doing around data and computation, were just not ready yet.
VentureBeat: How do you work out the build vs. buy question?
Hackland: When I got to Williams, we were largely buy-only. We built an in-house capability across three groups: manufacturing, aerodynamics, and race engineering. So they have embedded development groups, and I think thats really important. We considered whether we were going to create a centralized development function. But actually, we feel having them in those three groups is really important. And then as you build those groups, the pendulum swings from buy-only because youve got the capability in-house. The default now is that we will always develop our own if we can. Where theres a genuine competitive advantage, wed develop it ourselves.
VentureBeat: Where might you choose to buy data technologies?
Hackland: Some of the tools that we use trackside are off-the-shelf. Its not all in-house-written, because it doesnt make sense to write your own in some areas. But if you dont write your own applications, youre also accepting that these applications are used by multiple teams. If its a race-engineering application, its probably used across Formula 1 and maybe in other formulas as well. So then you cant customize it and you cant get competitive advantage out of it because everyone else has access to it too. So sometimes well use those as maybe a front end and then well be doing other things in the background. When you start to combine that data with other information, thats when theres a real competitive advantage, and thats where weve put our internal resources.
VentureBeat: What about AI? Is that a technology youre investigating?
Hackland: None of the teams are talking about AI except in passing; theyre just mentioning that AI is being used. None of us want to talk about it yet, and where were applying it. But what weve said publicly is that there are some really interesting challenges that AI can logically be applied to and you get benefits straightaway. So pit stops, the rulebook there are roles that AI can play.
VentureBeat: Can you give me a sense of how AI might be applied in F1?
Hackland: Initially, to augment humans to give engineers more accurate data to work with, or to shortcut their decision-making process so that they can make the right decision more frequently. I felt, even five years ago, that it would be possible that AI could make a pit stop decision without any human intervention. So that is possible, but I dont believe any of the teams will be doing it this year, and we wont. The engineers are not ready, and the humans are not ready to be replaced by AI. So that might take a little bit of time to show them that we can. I think theres still that reluctance to completely hand over the decision-making process, and I can understand that.
VentureBeat: What about other areas of emerging technology?
Hackland: From my perspective, quantum computing is a really exciting opportunity to take computation to a whole new level. And if we can get in there early before the other teams, I think well have a real advantage. There are interesting things happening with some [racing] organizations around that. Once again, were not talking about it publicly, but quantum is completely awesome. I think quantum will take a while. I dont want to be sitting here saying that in the next two years that were going to be developing, designing, and running the car and doing the race analytics on a quantum computer. But a hybrid computer that has quantum elements to it? Absolutely, and within a couple of years. Im really excited about what were doing already.
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