Data in Motorsport and Business.

Both a racing team and a business gather huge amounts of data, but the value comes from being able to take this data, transform it into information and use it to make better decisions. The process of collecting and using data follows a very similar process whether it is used in business or motorsport and it can be broken down into four stages: Capture, Clean, Understand and Use.

Capture

Racing cars contain hundreds of sensors that collect a large range of information from all over the car, ranging from tyre pressures to engine temperatures. This data is gathered every single time the car runs, creating a detailed picture of how the car is functioning internally. However, this is not the only way data is gathered. During testing, teams use many other aerodynamic testing methods, such as applying flow-vis paint to the car to see how air is moving over its surfaces. Video footage is also used to analyse the driver’s racing line and how different lines affect lap times, which is combined with feedback from the driver and external data sources such as weather and temperature.

In business data is gathered through similar processes ranging from internal data sources which vary greatly depending upon the sector in which the business operates, such as financial performance, sales figures, customer behaviour and website visits and also external sources such as economic data like inflation, interest rates and fuel prices.

Clean

Once the data has been captured in any environment, it needs to be checked to make sure it can be trusted. Sensors need to be calibrated and engineers need to identify faulty or missing readings. This is vital to the process as there is no point in using inaccurate data to improve the car. This process is also very similar in business, where data is checked for incorrect, missing or duplicate information before it is used.

Understand

In order to understand the data, engineers take large amounts of raw numbers that are difficult to interpret and turn them into information via graphs which make it easier to identify patterns, trends and to compare different laps or drivers. For example, an engineer can compare two laps where the car was running different setups and they may find that one setup produces a faster lap overall while the other is better through a particular section of high-speed corners. This information can then be used to understand why each setup performed differently. This is where external data including driver feedback is critical to understanding. Similarly, in business, data is analysed to identify sales trends and customer behaviour. For example, by identifying which products sell together or which times of day have the highest sales. Data analysis is the process of turning raw data into information.

Use

All of this data is then available to be used to make decisions about how to change the car’s setup, improve its reliability or to develop new components for the car. All of this combines to meet the main goal of developing the best-performing car possible. For example, engineers may find that one setup performs better in high-speed corners while another is better in a straight line. They can then use this information to find the most effective setup for the overall track. On the other hand, in business the data is used to inform decisions about pricing, marketing, staffing and products.

Whether the goal is reducing lap time or improving the performance of a business, the underlying process is very similar: Capture, Clean, Understand and Use.

Author : Anne Morson – Powertrain Team, HV Design at Team Bath Racing Electric
Photos : Danilla Sutton Racing