The Big Data advantage: What enterprises can learn from Formula 1.

“Every movement in the car is tiny. Getting data out of the little movements to analyse and improve the car is fascinating.”

Mika Häkkinen, Double F1 World Champion

At the Cloud Revolution Summit, double world champion Mika Häkkinen and Haas F1’s CIO Gary Foote shared insights on how data analytics powers decisions for both drivers and teams. Their insight had relevance beyond Formula 1, and here we summarise how their principles of data-driven decision-making can be applied to businesses to boost efficiency and performance.

Prioritisation is vital: who needs to know what, when

Sensors throughout an F1 car generate about 1 million data points per second. Computers and data engineers are evaluating minute variables in real time across reliability, safety and performance.

But the driver only communicates with one person on the radio. When he steps out of the car, he only talks to one or two people. The engineer’s job is to understand the driver and filter out feedback into the team, pulling in the expertise required.

‘Formula 1 learned early on that with such a big flood of data, we have to make sure the right data gets to the right person. There may be 30 to 50 sensors in the gearbox, but the driver doesn’t need to know if the gearbox is under stress. He’s thinking ‘Can I speed up in turn 4?’ We ensure he’s presented with data he needs and not data he doesn’t need. The same holds true throughout the team. The reliability engineer doesn’t need strategy or performance data.’

Gary Foote, CIO of F1’s Haas team

Take a problem/solution-led approach, not a tech-led approach

It’s not just about providing people with the data they need, it’s also about aligning data to solving problems. Gary Foote emphasised that they only collect data to solve problems. ‘Because if you’re tech-led, you’re solving problems that don’t need solving.’

Gary gave an example use case for this. The logistics around the F1 season are complex, expensive and carbon-intensive. How can they reduce the travel pressure on staff, spending and carbon footprint? Haas uses data to optimise the logistics of moving equipment around the world – and has seen a quick and measurable benefit. 

The risks of being tech-led are coming to the fore with AI trends. ‘It’s so easy to be tech-led,’ Gary explained. ‘But we have to focus on problems. AI is in our tool bag as one solution among many, including designers with drawing boards. AI might be a solution and it might not, depending on the constraints and caveats.’

Data and people must work together

Mika Häkkinen noted that when driving a race car, he needed to feel what was going in on every millisecond. And that human expertise plays a vital role in the process of optimising reliability, performance and safety.

Tweaks to the car are tested extensively – via a digital twin and in a wind tunnel before they’re validated to be tested on the track. Not only do they look at the real-time data from the track testing, but they get feedback from the driver. There are situations where the maths looked good but the driver reported an increased sensitivity or inferior drivability, for example. 

They’re constantly working to drive down the delta between the maths and the driver experience – because the real-world performance is where it matters.

This human element becomes an important focus with AI’s rising popularity. Gary explained that they’re starting to see some suppliers offering turnkey solutions for race strategy. And there are risks with that, because people may end up doing the same thing.

Embrace constraints

Processing such high data volumes is expensive – and that’s before you add the compute power for AI into the mix. But Gary sees this constraint as a positive:

‘The cost cap is a technologist’s dream. As engineers, we have constants and have to develop a solution based on those constants. That’s what gets us up in the morning. The cost cap is just another constant, and tech is the answer. Each new constraint means we get to play with new tech solutions.’

And that’s where the meaningful innovations come from. 

Focus on how partners can add value

The Haas business model is to look out to market for expertise. But each relationship is rooted in one overarching question: ‘How can you add value?’

This goes back to the problem/solution-led approach vs the tech-led approach. Gary explained:

‘It will start with a problem. For example, if a senior officer says we’re consuming too much data at the circuit and need to put it cloud, I take up the challenge and look for partners. I’m not looking for 2 petabytes of storage in cloud. I want to say: “I have this problem; how can your tech help me solve it?’ And that’s how we choose our partners.”’