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UBER
How Big Data Is At The Centre Of Uber’s Transportation Business
Background
Uber is a smartphone app-based taxi booking service which connects users who need to get somewhere with drivers willing to give them a ride. The service has been hugely popular. Since being launched to serve San Francisco in 2009, the service has been expanded to many major cities on every continent except for Antarctica, and the company are now valued at $41 billion. The business are rooted firmly in Big Data, and leveraging this data in a more effective way than traditional taxi firms has played a huge part in their success.
What Problem Is Big Data Helping To Solve?
Uber’s entire business model is based on the very Big Data principle of crowdsourcing: anyone with a car who is willing to help someone get to where they want to go can offer to help get them there. This gives greater choice for those who live in areas where there is little public transport, and helps to cut the number of cars on our busy streets by pooling journeys.
How Is Big Data Used In Practice?
Uber store and monitor data on every journey their users take, and use it to determine demand, allocate resources and set fares. The company also carry out in-depth analysis of public transport networks in the cities they serve, so they can focus coverage in poorly served areas and provide links to buses and trains.
Uber hold a vast database of drivers in all of the cities they cover, so when a passenger asks for a ride, they can instantly match you with the most suitable drivers. The company have developed algorithms to monitor traffic conditions and journey times in real time, meaning prices can be adjusted as demand for rides changes, and traffic conditions mean journeys are likely to take longer. This encourages more drivers to get behind the wheel when they are needed – and stay at home when demand is low. The company have applied for a patent on this method of Big Data-informed pricing, which they call “surge pricing”. This is an implementation of “dynamic pricing” – similar to that used by hotel chains and airlines to adjust price to meet demand – although rather than simply increasing prices at weekends or during public holidays it uses predictive modelling to estimate demand in real time.
Data also drives (pardon the pun) the company’s UberPool service, which allows users to find others near to them who, according to Uber’s data, often make similar journeys at similar times so that they can share a ride. According to Uber’s blog, introducing this service became a no-brainer when their data told them the “vast majority of [Uber trips in New York] have a look-a-like trip – a trip that starts near, ends near and is happening around the same time as another trip”. Other initiatives either trialled or due to launch in the future include UberChopper, offering helicopter rides to the wealthy, UberFresh for grocery deliveries and Uber Rush, a package courier service.
Uber rely on a detailed rating system – users can rate drivers, and vice versa – to build up trust and allow both parties to make informed decisions about who they want to share a car with. Drivers in particular have to be very conscious of keeping their standards high, as falling below a certain threshold could result in their not being offered any more work. They have another metric to worry about, too: their “acceptance rate”. This is the number of jobs they accept versus those they decline. Drivers apparently have been told they should aim to keep this above 80%, in order to provide a consistently available service to passengers.
What Were The Results?
Data is at the very heart of everything Uber do, meaning this case is less about short-term results and more about long-term development of a data-driven business model. But it’s fair to say that without their clever use of data the company wouldn’t have grown into the phenomenon they are.
There is a bigger-picture benefit to all this data that goes way beyond changing the way we book taxis or get ourselves to the office each day. Uber CEO Travis Kalanick has claimed that the service will also cut the number of private, owner-operated automobiles on the roads of the world’s most congested cities. For instance, he hopes UberPool alone could help cut traffic on the streets of London by a third. Services like Uber could revolutionize the way we travel around our crowded cities. There are certainly environmental as well as economic reasons why this would be a good thing.
What Data Was Used?
The company use a mixture of internal and external data. For example, Uber calculate fares automatically using GPS, traffic data and the company’s own algorithms, which make adjustments based on the time the journey is likely to take. The company also analyse external data such as public transport routes to plan services.
What Are The Technical Details?
It has proven tricky to get any great detail on Uber’s big data infrastructure, but it appears all their data is collected into a Hadoop data lake and they use Apache Spark and Hadoop to process the data.
Any Challenges That Had To Be Overcome?
The company’s algorithm-based approach to surge pricing has occasionally caused problems at busy times – a Forbes article noted how one five-mile journey on New Year’s Eve 2014 that would normally cost an average of less than $20 ended up costing $122.1 This was because of the number of drivers on the road and the extra time taken to complete the journey. Plenty of people would argue that’s simple economics: it’s normal to pay more for a product or service in times of peak demand (as anyone going away in the school holidays will confirm). But it hasn’t stopped the company coming under fire for their pricing policy.
There have been other controversies – most notably regular taxi drivers claiming it is destroying their livelihoods, and concerns over the lack of regulation of the company’s drivers. Uber’s response to protests by taxi drivers has been to attempt to co-opt them, by adding a new category to their fleet. Their UberTaxi service means you can be picked up by a licensed taxi driver in a registered private hire vehicle.
It’s fair to say there are still some legal hurdles to overcome: the service is currently banned in a handful of jurisdictions, including Brussels and parts of India, and is receiving intense scrutiny in many other parts of the world. There have been several court cases in the US regarding the company’s compliance with regulatory procedures – some of which have been dismissed and some are still ongoing. But, given their popularity, there’s a huge financial incentive for the company to press ahead with their plans to transform private travel.
What Are The Key Learning Points And Takeaways?
Uber demonstrate how even your very business model can be based on Big Data – with outstanding results. And Uber are not alone in this realization. They have competitors offering similar services on a (so far) smaller scale such as Lyft, Sidecar and Haxi. Providing the regulation issues can be overcome, competition among these upstarts is likely to be very fierce. The most successful company is likely to be the one that manages to best use the data available to them to improve the service they provide to customers.
REFERENCES AND FURTHER READING
- Worstall, T. (2015) So Uber and Lyft’s surge pricing worked just perfectly on New Year’s Eve then, http://www.forbes.com/sites/timworstall/2015/01/03/so-uber-and-lyfts-surge-pricing-worked-just-perfectly-on-new-years-eve-then/, accessed 5 January 2016.
Read more about Uber’s plans to revolutionize the way we move aroundour cities at: