Formula One racers have a long legacy of leveraging the latest in automotive design and technology. Today, these intelligent, high-performance machines are capable of reaching speeds of more than 200 miles per hour (faster than a commercial jet at takeoff). And increasingly, they are being driven by data.
To understand the role of data analytics, I spoke with McLaren Racing of Britain, which was founded in 1963 by racecar driver Bruce McLaren. “The world of Formula One is a world driven by innovation and data,” said Jessica “Jess” Tomkins, a technical analyst at McLaren Racing.
The sheer numbers bear this out: Formula One cars have 300 sensors producing 100,000 data points, accumulating 1.5 terabytes of data over the course of a race—all of which are analyzed to inform everything from car design to racing strategy. Moreover, by measuring track temperature, tire degradation, aerodynamics, and a vast array of other performance indicators in real time, a racing team can use this data to make adjusts on the fly.
“We’re using data to make all these decisions, before the race and after the race, and future decisions as well,” Tomkins explained.
The reason performance data and analytics are so important is that many aspects of Formula One car design are regulated by the Fédération Internationale de l'Automobile (FIA). That makes data the X factor for every competitor trying to go faster and shatter records. That kind of performance attracts legions of fans to Formula One racing, including younger enthusiasts in the United States. But this article is less about racing and far more about the data behind it.
What makes McLaren an unusual use case is the small scale of its new data analytics team. McLaren Group has about 4,000 employees, with about 800 on its McLaren Racing team, but only three dedicated analysts working across the company. “On a Monday, I could be working on tire degradation and on a Friday helping to produce a better marketing strategy,” Tomkins said.
This differs dramatically from other professional sports teams in which data analysis is strictly separated between the business side and performance side. In other industries, it’s hard to imagine commingling departments, such as operations and customer-facing functions.
Given its small data analytics team, McLaren needs to put predictive modeling into the hands of functional experts. Therefore these professionals, who have expertise in areas such as aerodynamics or engine performance, are among a new breed of professionals known as citizen data scientists.
With no knowledge of or experience in computer coding or designing algorithms, these citizen data scientists need tools and dashboards that put predictive modeling within their reach. Enter Alteryx, a leading analytics workbench company that works with McLaren and other automotive companies, as well as firms in other industries.
Mark Anderson, CEO of Alteryx, believes citizen data scientists are here to stay. “We have been advocating for the better part of 20 years,” he said of the trend.
The goal is not to eliminate data analysts and data scientists but to leverage their expertise across the firm. Given the growing demand for data analysis in every aspect of a business—accounting and finance, supply chain management, sales and marketing, and more—hiring sufficient numbers of data scientists becomes a very expensive proposition. As a result, there is growing demand for software that allows people with functional expertise to transfer data into tools and dashboards to conduct analyses or produce a predictive model. “Companies need to support democratizing access to these data tools,” Anderson said.
