Want to figure out how to build a better car? Take a page from Mercedes AMG and companies competing in the Formula One U.S. Grand Prix, which are using real-time analytics to determine how engine design impacts performance.
"Teams that participated in the 2014 Grand Prix race aggregated more than 243 terabytes of data in early November, a little more information than what is stored in the Library of Congress."
Not just any garage
For those who aren't familiar with the sport, the U.S. Grand Prix is a race consisting of single-seat vehicles, which must all abide by the standards and rules set by Formula One. According to Forbes contributor Frank Bi, teams that participated in this year's race aggregated more than 243 terabytes of data in early November, a little more information than what is stored in the Library of Congress.
Where are analysts geting this data from? It is generated by hundreds of smart devices capable of scrutinizing a variety of factors, such as tire pressure and fuel burn efficiency. As if the volume of information wasn't intimidating enough, Bi maintained on-site race engineers are registering and interpreting it in real-time.
How much does velocity matter?
Why analyze streaming data? Consider the situation the Red Bull Racing team encountered during a race in 2012, during which driver Sebastian Vettel was clipped from behind, causing his vehicle to come off-balance. Although the team wasn't optimistic about finishing in third, it snapped into action:
- First, engineers assessed the vehicle's telemetry data.
- A few laps later, the analysts completed simulations and devised a temporary fix, allowing them to rebalance the car.
- Analytics assisted the team in developing a new race strategy, allowing them to collect enough points in the race to win the world championship.
This scenario is a perfect example how real-time qualitative data analysis can help enterprises make strategic, on-the-spot decisions in order to achieve desired outcomes.
"Real-time data analysis has enabled Mercedes AMG to reduce the overhead associated with research and development."
Developing quality vehicles
Mercedes AMG isn't neglecting the capabilities of data analysis platforms synchronized with Internet-connected devices. As opposed to using the technology to win races, ZDNet contributor Christine Donato maintained the company leverages it as a quality assurance solution. Whenever Mercedes AMG tests an engine, sensors pick up anomalies that allude to unusual behavior. The finished intelligence is then sent to the company's development team, whose members figure out a way to redesign the engine so the problems are eliminated.
Real-time data analysis has also enabled the car manufacturer to reduce the overhead associated with research and development. Donato noted Mercedes AMG's testing process is 94 percent faster now than it was before it integrated its analytics solution.
Although the "velocity" component of big data can be intimidating, there are ways in which organizations can use speed to their advantage – they simply need analytics engines capable of performing in tandem with data collection tools.