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“What really attracted me to Formula 1 is that it has always been about data and technology,” said Graeme Hackland, Williams Group IT Director and Williams Racing CIO.

Since joining the racing team in 2014, Hackland has put this theory into practice. He’s pursuing what he calls a data-driven digital transformation program that helps the organization’s designers and engineers create a potential competitive advantage for the team’s riders on race day.

Hackland explains to VentureBeat how Williams F1 seeks to leverage data to make further advancements in the grid and how emerging technologies, such as artificial intelligence (AI) and quantum computing, could help in this process.

This interview has been edited for clarity.

VentureBeat: What is the goal of your data-driven transformation process?

Graeme Hackland: Ten years ago we could have put four major package upgrades on the car per year. We are now able to do this much faster, and we don’t have to wait for big bundles of changes. Our digital transformation has focused on shortening this lifecycle. It’s about getting something from a designer’s brain into the car as quickly as possible. Test it on a Friday; if it’s good, it stays. If not, we are refining it and continuing to do so throughout the season. And this process went very well.

VentureBeat: What type of data technology are you using to support this process?

Hackland: Some of them are what you would consider in some industries to be standard data warehousing and business intelligence tools. Some of it is written internally. At the moment, I don’t have middleware that spans the entire layer. But that’s where we want to go, so that absolutely everything feeds into that.

VentureBeat: What would this middleware look like?

Hackland: Originally, we had thought of three main areas: design, manufacturing and racing engineering. And you would have these three bubbles that would all talk to each other. But what we did was try to create data lakes just didn’t work. It didn’t give us the real intelligence we wanted, so we often refer to puddles of data. It’s much better to have a lot of these puddles well structured and the data well understood. And then, thanks to a middleware layer, we can access the graphical user interfaces.

On the middleware layer

VentureBeat: What does this layer of information mean for the engineers of the Williams F1 team?

Hackland: We cover everything from what they examine to the structure of the data. And the data structure has been one of our biggest challenges. We relied heavily on Microsoft Excel, and extracting data from all these other sources into Excel was very manual – taking too long. So that’s the job we did. We have not publicly disclosed who we are working with in this area. Speaking publicly about some of the things we’re doing around data and computation, we’re just not ready yet.

VentureBeat: How do you resolve the issue of building versus buying?

Hackland: When I first came to Williams, we were largely buying only. We have built internal capacity in three groups: manufacturing, aerodynamics and racing engineering. So they have built-in development groups, and I think that’s really important. We wondered if we were going to create a centralized development function. But actually, we think having them in these three groups is really important. And then, as you create these groups, the pendulum goes from buying only because you have the capacity in-house. The default now is we’ll always develop ours if we can. When there is a real competitive advantage, we develop it ourselves.

VentureBeat: Where could you choose to buy data technology from?

Hackland: Some of the tools we use at the edge of the track are ready to use. Not everything is written in-house, as it doesn’t make sense to write your own in some areas. But if you don’t write your own apps, you also agree to those apps being used by multiple teams. If this is a racing engineering application, it is probably used in Formula 1 and perhaps also in other formulas. So you can’t customize it and you can’t gain competitive advantage from it because everyone has access to it as well. So sometimes we might use them as a front end and then do other things in the background. When you start to combine that data with other information, that’s when there is a real competitive advantage, and that’s where we put our internal resources.

On AI and quantum computing applications

VentureBeat: What about AI? Is this a technology that you are studying?

Hackland: Neither team talks about AI except in passing; they just mention that AI is being used. None of us want to talk about it yet, and where we are applying it. But what we’ve said publicly is that there are some really interesting challenges that AI can logically be applied to and you get the benefits immediately. So pit stops, the rulebook – there are roles that AI can play.

VentureBeat: Can you give me an idea of ​​how AI could be applied in F1?

Hackland: Initially, to increase humans – to give engineers more precise data to work with, or to shorten their decision-making process so they can make the right decision more frequently. I sensed, five more years ago, that it would be possible for the AI ​​to be able to make a pit stop decision without any human intervention. So it’s possible, but I don’t believe any of the teams will do it this year, and we won’t. Engineers are not ready, and humans are not ready to be replaced by AI. So it might take a little while to show them that we can. I think there’s always this reluctance to completely cede 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 computing to a whole new level. And if we can get there early before the other teams, I think we will have a real advantage. There are some interesting things going on with some [racing] organizations around that. Again, we’re not talking about it publicly, but the quantum is downright awesome. I think quantum will take some time. I don’t want to sit here and say that over the next couple of years we’re going to develop, design and operate the car and do the race analysis on a quantum computer. But a hybrid computer that contains quantum elements? Absolutely, and within a few years. I am really excited about what we are already doing.

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