In Motorsport real-time data is the only option and race teams have been at the cutting edge of data and analytics technology for decades. As car and driver shoot past the pit wall at speeds in excess of 200 mph streaming data, real-time analysis can be the difference between winning and losing. The volume and velocity of data streaming from car and driver is as impressive as the on track action. A Formula 1 car has over 200 on-board sensors that can generate around 400GB of data throughout a race. This data is not limited to track side analysis, data is also streamed to from any race track in the world to engineers based in the team headquarters with less than 300 milliseconds of latency.
In Addition to real-time data from car and driver, additional data sets are added into the mix such as meteorological data feeds, and live competitor time gaps. The analysis of this high volume and velocity ‘Big Data’ brings challenges. This is why artificial intelligence has a long history in Motorsport for data analysis. For example, Formula 1 engine manufacturer Honda use the cognitive and artificial intelligence capabilities of the IBM Watson IoT platform for real-time engine data analytics. This technology enables predictive analysis that can be presented to engineers to act upon. For example, the track wind speed will have an impact on fuel efficiency. From this track position and competitor time gaps are used to predict the optimum lap for a pit stop.
Pit stops are also data driven. Beyond predicting the optimum lap for a pit stop, pit crew performance is optimized by data analysis. The pit crews performance is measured through the collection of biometric data. Through biometric analysis the Williams Formula 1 Team pit crew performance became world beating and hold the current record for the fastest ever pit stop. At the European Grand Prix in Baku Azerbaijan 2016, the pit crew replaced all four wheels on the race car just 1.92 seconds.
In the second part of this post I will look at what organizations can learn from Motorsport, and how this can be applied to data and analytics strategies that support digital transformation. Part 2.
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