Everyone knows Uber. But dude, they know you at least equally well!
While Uber transports people around the world without owning a car, there is only one fuel that powers Uber: Data. This is the secret key driving growth of the silicon valley start-up revolutionizing the taxi industry. What makes Uber unique is that the data driven insights don’t just stay within its internal dashboards but are implemented real-time into its services to generate an unprecedented user experience for both customers and drivers.1
Wait, what’s the use of knowing my way to work?
Come on, you can do better! Uber uses data in many different ways with two applications standing out.
Starting as soon as you open the app, until you reach your destination, Uber’s routing engine and matching algorithms are working hard. By entering the planned route and time of day, prediction models directly forecast the driving time and allocates the optimal driver through a process called batch-matching.
Through a machine learning algorithm, the models become more accurate in their predictive power with each ride filed. This matching algorithm allows Uber to minimize the number of variables a customer has to enter. In addition to that, they offer lower wait times and a more reliable experience for riders. Drivers, in turn, get more time to earn. 1
The instant implementation of live data allows Uber to effectively operate a dynamic pricing model. Using geo-location coordinates from drivers, street traffic and ride demand data, the so called Geosurge-algorithm compares theoretical ideals with what is actually implemented in the real world to make alterations based on the time of the journey. Using this process, fares are updated in real time based on demand. In addition, this allows prices to be adjusted specifically to different areas in cites, so that some neighborhoods may have surge pricing while others do not. 2
Furthermore, smart machine learning algorithms will take multiple data inputs and predict where the highest demand is going to be. During peak time, drivers receive live data in form of heat maps to compare the demand in different areas.3
This system allows Uber to optimally position drivers ensuring that there is no supply and demand shortage. Doing so, they create the most efficient market and maximize the number of rides it can provide which in turn benefits all parties.1
But that’s billions of data – how do they manage?
That’s right, Uber gives about 15 million rides per day.4 To manage this data flood, they introduced its own Machine Learning platform called Michelangelo which is used to create different models for Uber’s various services.
Michelangelo is an internal ML-as-a-service platform that democratizes and optimizes the scaling of AI, ML and Deep Learning. It enables internal teams to seamlessly build, deploy, and operate machine learning solutions at Uber’s scale. It is designed to cover the end-to-end ML workflow: manage data, train, evaluate, and deploy models, make predictions, and monitor predictions. For the Geeks, visit this page where Michelangelo is presented in detail.5
Boy, this sounds expensive – was it really necessary?
Hell yes! Before Michelangelo was born, Uber’s ML operations faced big challenges such as bad data quality, high data latency, lack of efficiency and scalability, and poor reliability. With its business growing exponentially, the amount of incoming data increased every day.
To realize Michelangelo, new data scientists, analysts and engineers had to be hired and the computing power and its internet bandwidth had to be heavily increased.6,7 There are no exact spending figures available on this, but Ubers financials’ show that R&D spending increased by over 150 million8 over the year prior to implementation in 2017. Although the entire amount was certainly not invested in this project, we expect that quite some money was spent for Uber’s new best buddy.
So, all their problems are solved now?
You have no idea! Even though Uber has managed to successfully process and use the vast amounts of data, they still face major challenges. The most important to mention here are the status of its drivers, tax issues, constitutional issues and of course the rising competition of companies such as Lyft, Didi or Grab (details about challenges).9 In my view, however, Uber remains a highly competitive company with virtually no limits. Consider the diverse offerings such as packaging and food delivery, the upcoming driverless technologies and of course even air taxis which is by the way my favorite idea!
But Jesus! Think about how much data you need to manage for that!