How can businesses learn from how Formula 1 teams use data analytics to inform strategies?

Formula 1 is one of the most rapidly growing sports in the United States. After decades of being a premier sport in the rest of the world, the US has finally caught on. Now that Formula 1 is in the US spotlight, its growing impact is hard to ignore. Since F1 seems to be on the tip of everyone’s tongues lately, how can businesses learn from the highest level of motorsport?

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F1 is one of, if not the most, data-directed sports worldwide. Anyone can tell that the ability to harness data has exploded recently. Those in the corporate sphere can attest to the push to make strategic decisions using data. As companies have been increasingly able to collect mounds of data, they are now confronted with the task of using that data to inform strategy. We have moved from businesses relying on gut decision-making to a data-driven approach. This transition is where F1 comes in. Formula 1 teams are known for using innovative data analytics to drive strategic decision-making and improve performance. Companies wanting to increase their ability to utilize their data meaningfully can look to the progression of data collection and analytics within Formula 1 over the last few decades.

In the 1980s, Formula 1 teams began installing electronic systems in their cars.[i] Using radios and telemetry generated by these systems, teams could make decisions faster. The sport moved from relying solely on a driver’s decision-making within a race to the team using data to make suggestions and change strategy. By today’s standards, these rudimentary systems were “initially limited to a single lap’s worth of data, and drivers were given a signal to turn on the telemetry when the team needed to collect data.”[ii]However, team members could not use this data in real time. The engineers and drivers had to analyze it after the race.

In the 90s and 2000s, teams could use “streaming data, which was piped back to the garage and then to the factory.”[iii]Streamed data was limited to physical changes within the cars, such as tire sensors. However, this ability was a massive development from teams relying on radio messages from a driver to communicate what was going on. It set the foundation for the current level of data transmission during a race.

In 2023, the amount of data sent in real-time feels light years away from the streaming data of three decades ago, like the change felt in procurement and day-to-day business operations.

Modern F1 cars are equipped with around 300 sensors across three categories: instrumentation, monitoring, and control sensors; these sensors transmit around 1.5 terabytes per car throughout a race weekend.[iv] This data is generated from the start of the first practice session to the end of the race. If each car produces 1.5 TB a weekend, then a team collects 3 TB of data each weekend across both cars. The 2023 calendar has 23 races, which means each team ends up with 69 TB of data throughout the year. The 69 TB of data encompasses around 11.8 billion data points.[v]

As many businesses have started to increase their data collection, they end up with vast swaths of data but often need more resources to do much with it. It’s like buying many groceries but not having the means to cook anything. A vital call-out regarding Formula 1’s use of data is the ability of each team to parse the sheer enormity of the data they receive. Deciphering what is essential and what’s not directly impacts team and car performance.

If you watch a race, you’ll see the team principal and the lead engineers sitting at a row of computers, all showing complex data transmitted by sensors within the car and on the track. For most of us, it’s hard to fathom being able to read that data, understand it, and provide suggestions within a few minutes. However, the weight of producing these analyses and strategies does not lie solely with the people at the race.

During this time, teams are constantly feeding the data back to their analysts and engineers at their respective factories. The people receiving this data analyze it in real-time and provide strategy suggestions.

Sports, like the rest of the world, have come a long way in using technology and the power of data to inform strategies and plays (i.e., football, basketball, etc.). Formula 1 has taken the collection and utilization of data to the pinnacle of the sporting world. If you’re wondering how this can relate to your business operations, look to F1’s principles and practices. If a team’s winning performance is a marriage of instinct and data,[vi] then a successful business strategy is a marriage of experience and informed decision-making.

By adopting some of the same principles and practices, businesses can learn from their approach and stay ahead of the competition. Here are a few ways that companies can adopt Formula 1’s approach to data:

Embrace data-driven decision-making: Formula 1 teams rely heavily on data to make strategic decisions, such as when to pit, what tires to use, and how to adjust the car's settings. Similarly, businesses can use data to inform decision-making across different functions, such as sales, marketing, and operations. By leveraging data analytics tools, companies can gain insights into customer behavior, market trends, and operational efficiency and use this information to make informed decisions.
Foster a culture of experimentation: Formula 1 teams constantly experiment with different approaches to improve performance. They use data to test new ideas and are not afraid to take risks or fail fast. Similarly, businesses can foster a culture of experimentation by encouraging employees to try out new ideas and test them rigorously using data analytics. This can help enterprises to stay ahead of the competition and find innovative solutions to problems.
Invest in talent and technology: Formula 1 teams have some of the best talent and technology in the world. They employ top engineers, data scientists, and drivers and use cutting-edge technology to analyze data and improve performance. Similarly, businesses can invest in talent and technology to enhance their data analytics capabilities. This might include hiring data scientists or investing in data analytics tools and software.
Collaborate across functions: In Formula 1, success requires collaboration across many different functions, from engineering to logistics to driver management. Similarly, businesses can benefit from breaking down silos and encouraging collaboration across various functions. By bringing together employees with different skills and perspectives, companies can generate new ideas and approaches that might otherwise have been impossible.

[i] Formula 1 & Big Data Analytics (medium.com)

[ii] Formula 1 & Big Data Analytics (medium.com)

[iii] Formula 1 & Big Data Analytics (medium.com)

[iv] How Formula 1 Car Sensors Create Data at Every Turn (purestorage.com)

[v] Data Analytics: Managing F1's Digital Gold - Racecar Engineering (racecar-engineering.com)

[vi] How Formula 1 Car Sensors Create Data at Every Turn (purestorage.com)