Behind the papaya-coloured machines of McLaren sit not just race-winning drivers, Lando Norris and Oscar Piastri, but billions of data points fuelling every decision from concept to chequered flag. In a sport where milliseconds matter, the team’s edge lies in turning complexity into clarity and insight into action.
“We’re swimming in data, and the challenge is turning it into insight,” says Dan Keyworth, director of business technology at McLaren Racing.
From the moment a car is conceived to the final lap of race day, the team’s process is data-first and tech-driven. Computational fluid dynamics (CFD) simulations model aerodynamic performance before parts are built. Results from wind tunnel tests and high-performance computing workloads are triangulated with telemetry and simulator feedback. Throughout this process, data is the common thread.
Over a typical season, McLaren handles more than 11.8 billion data points, runs over 300 million race simulations, manages 80,000 car components (95% of which change yearly), and collects real-time input from 300 telemetry sensors on each car, producing 100,000 parameters.
The complexity demands precision tools, and that’s where analytics platform Alteryx has proven serious.
“McLaren has become extremely data-driven in what they do,” says Andy MacMillan, CEO of Alteryx. “You don’t go out and buy a piece of software that says F1-race-car-management.com. They’ve built a lot of this infrastructure themselves, and we help them make sense of it to make better decisions.”
The technology plays a decisive role across McLaren’s three core pillars: design, build, and race. During design, engineers use Alteryx to cross-correlate CFD, wind tunnel and HPC results. In build, it supports compliance checks and cost-cap tracking. And in race scenarios, the team uses Alteryx to optimise performance, predict failures, and manage part replacements efficiently.
“If our reliability had been 98% instead of 100%, we wouldn’t have won,” says Keyworth. “There was a two-second pit stop with three laps to go, and that 3.4 seconds gained won us the title.”
Keyworth explains that while artificial intelligence isn’t new to F1, the rise of public large language models and generative AI has shifted how teams work.
“We’ve been using traditional machine learning for a long time,” he says. “We run millions of simulations and model countless race weekend scenarios. We’re always trying to teach models and learn from them.”
Initially, McLaren was as wary as others when it came to the technology: “We were cautious by keeping everything in-house,” Keyworth admits. “Now, we’re more open to using external models if the data’s right.”
Plus, just like McLaren needs real-time insights as the cars race, MacMillan sees this shift as part of a broader industry movement. “Very few processes now don’t demand near real-time data,” he says. “Whether you’re on a racetrack or in a boardroom, people expect answers now, not a week-old report.”
He continues: “If you’re in a manufacturing plant and something starts to go wrong, learning about it even one minute earlier could save an hour of downtime. That’s a massive deal.”
What McLaren does, he argues, is symbolic of a larger trend. “The world isn’t as cookie-cutter as large business application software providers would like to believe. Most companies don’t run their business out-of-the-box with legacy enterprise software. They customise, they adapt, and they end up with data everywhere.”
“What we do for McLaren, helping people move quickly, work with distributed data, and automate decisions, that’s universal,” he adds. “You could be in supply chain, accounting, plant floor systems. The need is the same.”
But technology alone isn’t the full story. It’s how the team integrates tools into its culture of speed and adaptability that unlocks its competitive advantage.
“We might call ourselves a racing team,” says Keyworth, “but really, we’re a high-performance people organisation that uses technology to go racing.”
McLaren embedding it into the culture
That sentiment is echoed by Dan Gallo, chief people and sustainability officer. “As a pro sports team, high performance is in our DNA,” says Gallo. “Our work plays out on a public stage in front of hundreds of millions of people every week, which sharpens our focus.”
“You can’t micromanage at that scale; you must trust. That’s easier said than done. We enable it with a flat, non-hierarchical structure that maximises talent.”
Gallo says McLaren’s “no-fear, no-blame” culture is key to innovation. “If we don’t allow for mistakes, people won’t push boundaries,” he says. “And in F1, if you’re not innovating, you’re not standing still – you’re falling behind.”
He adds: “We demand a lot. F1 is high-pressure, high-demand and high-fun. But you have to provide pastoral support too. Otherwise, you risk burnout. Productivity doesn’t come from exhaustion.”
MacMillan believes that culture is vital. “Every car they build is a wonder of innovation,” he says. “That kind of work demands a culture where you acknowledge mistakes without blame, and learn from them quickly.”
The team’s data-first approach now also extends into sustainability. “Our ESG efforts really accelerated over the past four years,” says Gallo. “We knew we had to approach it with commitment and authenticity. There’s been a lot of talk about sportswashing and greenwashing, and that’s not who we are.”
As with racing, the work began with measurement. “We didn’t even know where we were starting from,” he says. By tracking emissions, setting net-zero targets and monitoring KPIs, the team has applied its racing mindset to climate goals.
That mindset extends to inclusion, too. McLaren’s DEI strategy includes efforts like the 60 Scholars Programme to encourage more young women into motorsports and STEM. “We use data to guide every step but then apply our racing mindset to get things done,” says Gallo.
