As global sporting juggernauts go, few come close to Formula 1 racing. I attended the Singapore Grand Prix last month and this action-filled series of motor races generates intense competition and excitement wherever it sets up its Grand Prix camp.
Each year billions of dollars are spent on tracks, cars, drivers and support crews. Intent on finishing in first place, teams constantly strive to extract the best possible performance from their cars in each and every race.
Increasingly, one of the most important competitive advantages in this relentless pursuit of excellence is data. By gathering and analysing it, teams are finding new and innovative ways to win.
As an example, the Lotus F1 Team places more than 150 sensors in its cars to monitor every aspect of performance in order to enhance speed. The feedback from these sensors generates on average 25 MB of data during each race lap.
Meanwhile rival team McLaren has turned to SAP’s HANA in-memory database to analyse the vast swathes of data its collects from its cars. Using sophisticated analytical tools, the team can compare performances in multiple races and see how even slight changes to their cars can have an impact on results.
Data is everywhere in F1 with each circuit delivering different driving conditions and multiple variables for each team.
In Singapore, the 5.06-kilometre circuit has 23 corners and the race cars cover a total distance of 308.8 km over 61 laps. In Melbourne, the 5.3 km circuit has just 16 corners, making racing conditions very different. By constantly monitoring every facet of a race track and car, team technicians are continually finding new ways to fine tune operations.
As a result, big data analytics has become a vital part of the job. However, it’s a job made even more challenging because teams are constantly shifting between different geographic locations. Unlike most multi-billion dollar enterprises, F1 is always on the move.
To get the most from analytical processing of large data sets, it’s vital that the data being used is stored in a robust, secure, reliable and highly-connected facility. If not, the real benefit of using sophisticated analytics tools won’t be realised.
Data captured during each F1 race needs to be quickly transmitted to this central data store and be immediately available for analysis. Keeping it in just track-side data storage facilities would limit its value and reduce the team’s ability to put it to best use.
Having a central store also means data from other sources can also be introduced. Weather predictions, track changes and individual driver performance details can be added to the analytics mix, thus creating even more opportunities to find ways to win.
Motor sports in general – and F1 in particular – are evolving at an astounding rate. Because of the strict rules and technical constraints under which each team must compete, finding even the slightest way of gaining an edge can mean the difference between failure and the chequered flag.
Like most industries today, the ability to effectively collect, store and analyse large sets of data will continue to give winning F-1 teams a huge competitive advantage. It’s another example of how enterprises are turning their data centres into a data advantage.