In 2016, Dimension Data became the official technology partner for the Tour de France, the world’s largest and most prestigious cycling race. In support of their goal to revolutionize the viewing experience of billions of cycling fans across the globe, Dimension Data needed to analyze thousands of data points per second, from nearly 200 riders, across 21 gruelling days of cycling.
They chose to partner with IBM and leverage their streaming analytics platform, InfoSphere Streams. The joint team developed a winning solution that provides advanced analysis of rider data, calculating dynamic race position ranking and grouping of riders.
What changed things was putting GPS sensors on every bicycle and installing a sophisticated relay system that transmitted data to central servers, and then to apps, Web sites and broadcasters. The racers and their bikes were data-collection points on the Internet of Things. The goal was nothing less than transforming the Tour fan experience.
One of the key ingredients in this setup was IBM Streams technology, which gathers data from many sources in real time and organizes it so insights can be drawn from it. In the future, thanks to a new but related IBM technology, Quarks, cycling fans, broadcasters and team strategists could gain much deeper insights during the Tour.
Quarks is an open source project that makes it makes it easier for developers to create IoT applications to analyze data on the edge of their networks.
I spoke to Chris Howard, Big Data Technical Leader at IBM Asia Pacific, and Nagui Halim, IBM Fellow, about the collaboration.
RW: What lead to the collaboration with Dimension Data?
Chris: Dimension Data actually won the partnership rights with Amaury Sports Organization (ASO), the company that own the Tour de France, to provide their technology and their analytics platform for the next five years. Really, what they were looking to do was revolutionize the viewing experience for spectators of the Tour de France and really bring it into the 21st century – or even into the 22nd century. And the reason for doing that is that if you look at the way data is both captured, processed and analyzed historically with the Tour de France it’s extremely manual.
It literally relied on people with stopwatches and radios and timing boards radioing back to base about which particular rider has passed which particular milestone. It was very, very cumbersome and quite old-worldly in nature. And looking at how do they group the riders, so if you look at the pack of riders that may spread out over 30 kilometers or more over 5 or 6 hours of the race, how they group the riders was also manual, it was somebody literally hanging off the back of a motorcycle or sticking their head out of the sunroof of a chase vehicle and someone would (check) the race numbers on the shirts and say “Well, there’s 15 people on that group, there are 12 people in that group, oh, and one of them’s just changed groups.”
That would be radioed back and the graphics team would then produce all of their updates and that would then get spread out for updates. So, if you were unlucky enough not to be part of one of those groups, as a rider, there’s no real visibility of who you are and where you are – from a television standpoint, you may as well not exist and no one would know you were even part of the Tour De France or what progress you were making at this stage.
In March last year, we began investigating how we could use IBM’s Streams to analyze a lot of this data, given that they were looking to essentially instrumentalize every single bicycle. Every bike would be intrumented with a GPS receiver which had to ability to relay to a local motorbike or race vehicle that would then manage the signal up to an aircraft and the signal would end up being a mobile data sensor that was stop at the finish line so you could then use that data to do interesting things.
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